title,citations_google_scholar,conference,forks,issues,lastModified,repo_url,stars,title_google_scholar,url_google_scholar,watchers,year
Squeeze-and-Excitation Networks,21250,cvpr,825,15,2023-06-03 02:02:44.143000,https://github.com/hujie-frank/SENet,3151,Squeeze-and-excitation networks,"https://scholar.google.com/scholar?cluster=11424287065250598243&hl=en&as_sdt=0,33",82,2018
Context Encoding for Semantic Segmentation,1213,cvpr,453,147,2023-06-03 02:02:44.344000,https://github.com/zhanghang1989/PyTorch-Encoding,1998,Context encoding for semantic segmentation,"https://scholar.google.com/scholar?cluster=11819343174629820664&hl=en&as_sdt=0,5",43,2018
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer,120,cvpr,50,5,2023-06-03 02:02:44.544000,https://github.com/MVIG-SJTU/WSHP,298,Weakly and semi supervised human body part parsing via pose-guided knowledge transfer,"https://scholar.google.com/scholar?cluster=5411440861626628225&hl=en&as_sdt=0,33",19,2018
Relation Networks for Object Detection,1170,cvpr,191,15,2023-06-03 02:02:44.743000,https://github.com/msracver/Relation-Networks-for-Object-Detection,1082,Relation networks for object detection,"https://scholar.google.com/scholar?cluster=6312471696354344603&hl=en&as_sdt=0,33",34,2018
Link and Code: Fast Indexing With Graphs and Compact Regression Codes,39,cvpr,2977,305,2023-06-03 02:02:44.942000,https://github.com/facebookresearch/faiss,22187,Link and code: Fast indexing with graphs and compact regression codes,"https://scholar.google.com/scholar?cluster=14984005711302229427&hl=en&as_sdt=0,33",449,2018
Neural Baby Talk,465,cvpr,122,31,2023-06-03 02:02:45.142000,https://github.com/jiasenlu/NeuralBabyTalk,525,Neural baby talk,"https://scholar.google.com/scholar?cluster=9034308427911290096&hl=en&as_sdt=0,44",18,2018
Textbook Question Answering Under Instructor Guidance With Memory Networks,17,cvpr,0,1,2023-06-03 02:02:45.342000,https://github.com/freerailway/igmn,8,Textbook question answering under instructor guidance with memory networks,"https://scholar.google.com/scholar?cluster=10345622211722999040&hl=en&as_sdt=0,33",2,2018
Single View Stereo Matching,196,cvpr,60,14,2023-06-03 02:02:45.541000,https://github.com/lawy623/SVS,283,Single view stereo matching,"https://scholar.google.com/scholar?cluster=3110406571941339913&hl=en&as_sdt=0,32",13,2018
Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments,946,cvpr,119,40,2023-06-03 02:02:45.741000,https://github.com/peteanderson80/Matterport3DSimulator,377,Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,"https://scholar.google.com/scholar?cluster=11715302607690282214&hl=en&as_sdt=0,33",19,2018
Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading,44,cvpr,21,1,2023-06-03 02:02:45.942000,https://github.com/BjoernHaefner/DepthSRfromShading,50,Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading,"https://scholar.google.com/scholar?cluster=18357062275262437656&hl=en&as_sdt=0,41",11,2018
CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization,176,cvpr,28,4,2023-06-03 02:02:46.141000,https://github.com/david-husx/crossview_localisation,73,Cvm-net: Cross-view matching network for image-based ground-to-aerial geo-localization,"https://scholar.google.com/scholar?cluster=7906333180197319428&hl=en&as_sdt=0,33",6,2018
Wasserstein Introspective Neural Networks,52,cvpr,20,0,2023-06-03 02:02:46.341000,https://github.com/kjunelee/WINN,60,Wasserstein introspective neural networks,"https://scholar.google.com/scholar?cluster=7419455746270333750&hl=en&as_sdt=0,5",5,2018
Taskonomy: Disentangling Task Transfer Learning,1048,cvpr,145,25,2023-06-03 02:02:46.541000,https://github.com/StanfordVL/taskonomy,806,Taskonomy: Disentangling task transfer learning,"https://scholar.google.com/scholar?cluster=14005457704431675372&hl=en&as_sdt=0,33",32,2018
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation,1597,cvpr,147,27,2023-06-03 02:02:46.740000,https://github.com/mil-tokyo/MCD_DA,503,Maximum classifier discrepancy for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=16796743387010769346&hl=en&as_sdt=0,11",20,2018
Unsupervised Feature Learning via Non-Parametric Instance Discrimination,2687,cvpr,131,19,2023-06-03 02:02:46.940000,https://github.com/zhirongw/lemniscate.pytorch,709,Unsupervised feature learning via non-parametric instance discrimination,"https://scholar.google.com/scholar?cluster=18199728308463029716&hl=en&as_sdt=0,33",13,2018
Alive Caricature From 2D to 3D,38,cvpr,11,1,2023-06-03 02:02:47.140000,https://github.com/QianyiWu/Caricature-Data,59,Alive caricature from 2d to 3d,"https://scholar.google.com/scholar?cluster=10599805237435207750&hl=en&as_sdt=0,36",4,2018
Nonlinear 3D Face Morphable Model,337,cvpr,124,43,2023-06-03 02:02:47.341000,https://github.com/tranluan/Nonlinear_Face_3DMM,626,Nonlinear 3d face morphable model,"https://scholar.google.com/scholar?cluster=16917443177432685099&hl=en&as_sdt=0,44",36,2018
Actor and Observer: Joint Modeling of First and Third-Person Videos,120,cvpr,9,10,2023-06-03 02:02:47.540000,https://github.com/gsig/actor-observer,70,Actor and observer: Joint modeling of first and third-person videos,"https://scholar.google.com/scholar?cluster=10236946423035602005&hl=en&as_sdt=0,5",6,2018
Density Adaptive Point Set Registration,45,cvpr,14,5,2023-06-03 02:02:47.746000,https://github.com/felja633/DARE,63,Density adaptive point set registration,"https://scholar.google.com/scholar?cluster=2753522151223136403&hl=en&as_sdt=0,5",8,2018
Fast and Accurate Online Video Object Segmentation via Tracking Parts,267,cvpr,28,2,2023-06-03 02:02:47.946000,https://github.com/JingchunCheng/FAVOS,180,Fast and accurate online video object segmentation via tracking parts,"https://scholar.google.com/scholar?cluster=8220914409241455873&hl=en&as_sdt=0,47",14,2018
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry With Deep Feature Reconstruction,594,cvpr,67,5,2023-06-03 02:02:48.145000,https://github.com/Huangying-Zhan/Depth-VO-Feat,335,Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction,"https://scholar.google.com/scholar?cluster=9329635045900538034&hl=en&as_sdt=0,5",13,2018
Tangent Convolutions for Dense Prediction in 3D,487,cvpr,25,3,2023-06-03 02:02:48.346000,https://github.com/tatarchm/tangent_conv,118,Tangent convolutions for dense prediction in 3d,"https://scholar.google.com/scholar?cluster=493491956826244384&hl=en&as_sdt=0,25",16,2018
Style Aggregated Network for Facial Landmark Detection,329,cvpr,180,14,2023-06-03 02:02:48.546000,https://github.com/D-X-Y/SAN,899,Style aggregated network for facial landmark detection,"https://scholar.google.com/scholar?cluster=1805110380044896880&hl=en&as_sdt=0,33",34,2018
RayNet: Learning Volumetric 3D Reconstruction With Ray Potentials,78,cvpr,17,6,2023-06-03 02:02:48.746000,https://github.com/paschalidoud/raynet,71,Raynet: Learning volumetric 3d reconstruction with ray potentials,"https://scholar.google.com/scholar?cluster=8899382585377763451&hl=en&as_sdt=0,5",12,2018
Learning to Adapt Structured Output Space for Semantic Segmentation,1270,cvpr,202,27,2023-06-03 02:02:48.946000,https://github.com/wasidennis/AdaptSegNet,807,Learning to adapt structured output space for semantic segmentation,"https://scholar.google.com/scholar?cluster=3871902646877787670&hl=en&as_sdt=0,39",20,2018
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation,286,cvpr,18,0,2023-06-03 02:02:49.145000,https://github.com/danxuhk/StructuredAttentionDepthEstimation,108,Structured attention guided convolutional neural fields for monocular depth estimation,"https://scholar.google.com/scholar?cluster=12491875579978899487&hl=en&as_sdt=0,47",8,2018
Extreme 3D Face Reconstruction: Seeing Through Occlusions,176,cvpr,178,13,2023-06-03 02:02:49.345000,https://github.com/anhttran/extreme_3d_faces,741,Extreme 3d face reconstruction: Seeing through occlusions,"https://scholar.google.com/scholar?cluster=5737169588965246749&hl=en&as_sdt=0,33",51,2018
VITON: An Image-Based Virtual Try-On Network,429,cvpr,124,21,2023-06-03 02:02:49.544000,https://github.com/xthan/VITON,438,Viton: An image-based virtual try-on network,"https://scholar.google.com/scholar?cluster=4604722267799491636&hl=en&as_sdt=0,33",21,2018
Glimpse Clouds: Human Activity Recognition From Unstructured Feature Points,142,cvpr,10,6,2023-06-03 02:02:49.744000,https://github.com/fabienbaradel/glimpse_clouds,30,Glimpse clouds: Human activity recognition from unstructured feature points,"https://scholar.google.com/scholar?cluster=7849340999484253764&hl=en&as_sdt=0,10",5,2018
Finding Beans in Burgers: Deep Semantic-Visual Embedding With Localization,107,cvpr,19,6,2023-06-03 02:02:49.944000,https://github.com/technicolor-research/dsve-loc,58,Finding beans in burgers: Deep semantic-visual embedding with localization,"https://scholar.google.com/scholar?cluster=9521343984071570663&hl=en&as_sdt=0,31",7,2018
Multi-Content GAN for Few-Shot Font Style Transfer,301,cvpr,128,16,2023-06-03 02:02:50.143000,https://github.com/azadis/MC-GAN,428,Multi-content gan for few-shot font style transfer,"https://scholar.google.com/scholar?cluster=8640883336869257369&hl=en&as_sdt=0,33",22,2018
Context-Aware Deep Feature Compression for High-Speed Visual Tracking,236,cvpr,3,1,2023-06-03 02:02:50.343000,https://github.com/jongwon20000/TRACA,12,Context-aware deep feature compression for high-speed visual tracking,"https://scholar.google.com/scholar?cluster=571172096804857192&hl=en&as_sdt=0,39",1,2018
Correlation Tracking via Joint Discrimination and Reliability Learning,187,cvpr,9,3,2023-06-03 02:02:50.542000,https://github.com/cswaynecool/DRT,24,Correlation tracking via joint discrimination and reliability learning,"https://scholar.google.com/scholar?cluster=1575208581023691189&hl=en&as_sdt=0,43",3,2018
Future Person Localization in First-Person Videos,163,cvpr,16,6,2023-06-03 02:02:50.741000,https://github.com/takumayagi/fpl,57,Future person localization in first-person videos,"https://scholar.google.com/scholar?cluster=12809311731040909987&hl=en&as_sdt=0,33",8,2018
Show Me a Story: Towards Coherent Neural Story Illustration,31,cvpr,2,0,2023-06-03 02:02:50.940000,https://github.com/Hareesh-Ravi/Show-Me-A-Story,8,Show me a story: Towards coherent neural story illustration,"https://scholar.google.com/scholar?cluster=7308999289037993317&hl=en&as_sdt=0,33",3,2018
Who Let the Dogs Out? Modeling Dog Behavior From Visual Data,59,cvpr,11,0,2023-06-03 02:02:51.140000,https://github.com/ehsanik/dogTorch,66,Who let the dogs out? modeling dog behavior from visual data,"https://scholar.google.com/scholar?cluster=5907444303842448183&hl=en&as_sdt=0,14",5,2018
Mining on Manifolds: Metric Learning Without Labels,121,cvpr,8,0,2023-06-03 02:02:51.339000,https://github.com/gtolias/mom,34,Mining on manifolds: Metric learning without labels,"https://scholar.google.com/scholar?cluster=13423863468293341196&hl=en&as_sdt=0,43",3,2018
Deep Texture Manifold for Ground Terrain Recognition,117,cvpr,15,5,2023-06-03 02:02:51.539000,https://github.com/jiaxue1993/Deep-Encoding-Pooling-Network-DEP-,48,Deep texture manifold for ground terrain recognition,"https://scholar.google.com/scholar?cluster=7515181483454142602&hl=en&as_sdt=0,33",2,2018
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank,281,cvpr,27,0,2023-06-03 02:02:51.739000,https://github.com/xialeiliu/CrowdCountingCVPR18,101,Leveraging unlabeled data for crowd counting by learning to rank,"https://scholar.google.com/scholar?cluster=15969046811114831794&hl=en&as_sdt=0,22",12,2018
IQA: Visual Question Answering in Interactive Environments,367,cvpr,29,4,2023-06-03 02:02:51.938000,https://github.com/danielgordon10/thor-iqa-cvpr-2018,115,Iqa: Visual question answering in interactive environments,"https://scholar.google.com/scholar?cluster=16877717073782366284&hl=en&as_sdt=0,33",6,2018
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric,5295,cvpr,466,44,2023-06-03 02:02:52.138000,https://github.com/richzhang/PerceptualSimilarity,2870,The unreasonable effectiveness of deep features as a perceptual metric,"https://scholar.google.com/scholar?cluster=14149575231067904672&hl=en&as_sdt=0,11",50,2018
Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform,739,cvpr,102,17,2023-06-03 02:02:52.337000,https://github.com/xinntao/SFTGAN,529,Recovering realistic texture in image super-resolution by deep spatial feature transform,"https://scholar.google.com/scholar?cluster=842104437082826960&hl=en&as_sdt=0,10",22,2018
Learning to Parse Wireframes in Images of Man-Made Environments,133,cvpr,37,19,2023-06-03 02:02:52.538000,https://github.com/huangkuns/wireframe,180,Learning to parse wireframes in images of man-made environments,"https://scholar.google.com/scholar?cluster=11403942593493139051&hl=en&as_sdt=0,38",9,2018
Grounding Referring Expressions in Images by Variational Context,167,cvpr,4,2,2023-06-03 02:02:52.737000,https://github.com/yuleiniu/vc,28,Grounding referring expressions in images by variational context,"https://scholar.google.com/scholar?cluster=14943545970405420327&hl=en&as_sdt=0,10",5,2018
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning,801,cvpr,39,3,2023-06-03 02:02:52.937000,https://github.com/arunmallya/packnet,220,Packnet: Adding multiple tasks to a single network by iterative pruning,"https://scholar.google.com/scholar?cluster=3391923480766796153&hl=en&as_sdt=0,33",9,2018
Single-Shot Refinement Neural Network for Object Detection,1430,cvpr,397,4,2023-06-03 02:02:53.136000,https://github.com/sfzhang15/RefineDet,1437,Single-shot refinement neural network for object detection,"https://scholar.google.com/scholar?cluster=2942593974177604568&hl=en&as_sdt=0,3",66,2018
Neural Sign Language Translation,407,cvpr,42,19,2023-06-03 02:02:53.336000,https://github.com/neccam/nslt,134,Neural sign language translation,"https://scholar.google.com/scholar?cluster=2296982368739717596&hl=en&as_sdt=0,33",10,2018
Non-Local Neural Networks,8204,cvpr,335,39,2023-06-03 02:02:53.535000,https://github.com/facebookresearch/video-nonlocal-net,1932,Non-local neural networks,"https://scholar.google.com/scholar?cluster=10047890866676489078&hl=en&as_sdt=0,10",73,2018
LAMV: Learning to Align and Match Videos With Kernelized Temporal Layers,38,cvpr,16,7,2023-06-03 02:02:53.734000,https://github.com/facebookresearch/videoalignment,127,LAMV: Learning to align and match videos with kernelized temporal layers,"https://scholar.google.com/scholar?cluster=17917158544788310143&hl=en&as_sdt=0,44",12,2018
Fast and Accurate Single Image Super-Resolution via Information Distillation Network,660,cvpr,27,1,2023-06-03 02:02:53.933000,https://github.com/Zheng222/IDN-Caffe,115,Fast and accurate single image super-resolution via information distillation network,"https://scholar.google.com/scholar?cluster=8046970097927078874&hl=en&as_sdt=0,21",5,2018
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval,365,cvpr,50,11,2023-06-03 02:02:54.134000,https://github.com/lelan-li/SSAH,160,Self-supervised adversarial hashing networks for cross-modal retrieval,"https://scholar.google.com/scholar?cluster=3463045162726683481&hl=en&as_sdt=0,33",2,2018
NAG: Network for Adversary Generation,148,cvpr,10,1,2023-06-03 02:02:54.334000,https://github.com/val-iisc/nag,30,Nag: Network for adversary generation,"https://scholar.google.com/scholar?cluster=3931174507658557691&hl=en&as_sdt=0,40",5,2018
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification,228,cvpr,2,2,2023-06-03 02:02:54.534000,https://github.com/longxiang92/Flash-MNIST,16,Attention clusters: Purely attention based local feature integration for video classification,"https://scholar.google.com/scholar?cluster=2790056115661546897&hl=en&as_sdt=0,39",0,2018
A Two-Step Disentanglement Method,65,cvpr,7,2,2023-06-03 02:02:54.733000,https://github.com/naamahadad/A-Two-Step-Disentanglement-Method,20,A two-step disentanglement method,"https://scholar.google.com/scholar?cluster=17956824225475130504&hl=en&as_sdt=0,33",2,2018
Decorrelated Batch Normalization,172,cvpr,9,2,2023-06-03 02:02:54.932000,https://github.com/umich-vl/DecorrelatedBN,77,Decorrelated batch normalization,"https://scholar.google.com/scholar?cluster=1350163161054747576&hl=en&as_sdt=0,44",5,2018
SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks,138,cvpr,6,3,2023-06-03 02:02:55.131000,https://github.com/julianfaraone/SYQ,32,Syq: Learning symmetric quantization for efficient deep neural networks,"https://scholar.google.com/scholar?cluster=715328766505784785&hl=en&as_sdt=0,33",4,2018
ClcNet: Improving the Efficiency of Convolutional Neural Network Using Channel Local Convolutions,10,cvpr,0,0,2023-06-03 02:02:55.331000,https://github.com/dqzhang17/clcnet.torch,5,Clcnet: Improving the efficiency of convolutional neural network using channel local convolutions,"https://scholar.google.com/scholar?cluster=5898385564503550326&hl=en&as_sdt=0,33",1,2018
Dynamic Few-Shot Visual Learning Without Forgetting,1038,cvpr,113,15,2023-06-03 02:02:55.531000,https://github.com/gidariss/FewShotWithoutForgetting,505,Dynamic few-shot visual learning without forgetting,"https://scholar.google.com/scholar?cluster=9404727509298354192&hl=en&as_sdt=0,5",17,2018
Unsupervised Cross-Dataset Person Re-Identification by Transfer Learning of Spatial-Temporal Patterns,189,cvpr,95,6,2023-06-03 02:02:55.730000,https://github.com/ahangchen/TFusion,305,Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns,"https://scholar.google.com/scholar?cluster=17916644735518880120&hl=en&as_sdt=0,5",13,2018
SplineCNN: Fast Geometric Deep Learning With Continuous B-Spline Kernels,420,cvpr,3251,796,2023-06-03 02:02:55.930000,https://github.com/rusty1s/pytorch_geometric,17762,Splinecnn: Fast geometric deep learning with continuous b-spline kernels,"https://scholar.google.com/scholar?cluster=15661096994661097727&hl=en&as_sdt=0,44",255,2018
On the Robustness of Semantic Segmentation Models to Adversarial Attacks,262,cvpr,34,0,2023-06-03 02:02:56.130000,https://github.com/hmph/adversarial-attacks,93,On the robustness of semantic segmentation models to adversarial attacks,"https://scholar.google.com/scholar?cluster=16709136699457218640&hl=en&as_sdt=0,47",4,2018
Conditional Probability Models for Deep Image Compression,440,cvpr,55,3,2023-06-03 02:02:56.330000,https://github.com/fab-jul/imgcomp-cvpr,175,Conditional probability models for deep image compression,"https://scholar.google.com/scholar?cluster=1142257343807759555&hl=en&as_sdt=0,47",13,2018
Feedback-Prop: Convolutional Neural Network Inference Under Partial Evidence,10,cvpr,4,1,2023-06-03 02:02:56.530000,https://github.com/uvavision/feedbackprop,13,Feedback-prop: Convolutional neural network inference under partial evidence,"https://scholar.google.com/scholar?cluster=6455871249036885657&hl=en&as_sdt=0,5",5,2018
Deep Diffeomorphic Transformer Networks,38,cvpr,7,2,2023-06-03 02:02:56.729000,https://github.com/SkafteNicki/ddtn,51,Deep diffeomorphic transformer networks,"https://scholar.google.com/scholar?cluster=10889902313214190390&hl=en&as_sdt=0,47",7,2018
The Lovász-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks,661,cvpr,263,17,2023-06-03 02:02:56.929000,https://github.com/bermanmaxim/LovaszSoftmax,1336,The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,"https://scholar.google.com/scholar?cluster=13704152839598748624&hl=en&as_sdt=0,5",32,2018
Regularizing RNNs for Caption Generation by Reconstructing the Past With the Present,99,cvpr,22,4,2023-06-03 02:02:57.129000,https://github.com/chenxinpeng/ARNet,97,Regularizing rnns for caption generation by reconstructing the past with the present,"https://scholar.google.com/scholar?cluster=7665949997968365444&hl=en&as_sdt=0,5",4,2018
Frustum PointNets for 3D Object Detection From RGB-D Data,2144,cvpr,529,87,2023-06-03 02:02:57.328000,https://github.com/charlesq34/frustum-pointnets,1485,Frustum pointnets for 3d object detection from rgb-d data,"https://scholar.google.com/scholar?cluster=4885970701564432144&hl=en&as_sdt=0,5",46,2018
Generative Adversarial Perturbations,298,cvpr,22,3,2023-06-03 02:02:57.528000,https://github.com/OmidPoursaeed/Generative_Adversarial_Perturbations,114,Generative adversarial perturbations,"https://scholar.google.com/scholar?cluster=14065454435917122629&hl=en&as_sdt=0,14",5,2018
Learning Strict Identity Mappings in Deep Residual Networks,44,cvpr,1850,10,2023-06-03 02:02:57.727000,https://github.com/ppwwyyxx/tensorpack,6277,Learning strict identity mappings in deep residual networks,"https://scholar.google.com/scholar?cluster=16983153151878497830&hl=en&as_sdt=0,5",201,2018
Geometric Robustness of Deep Networks: Analysis and Improvement,132,cvpr,0,0,2023-06-03 02:02:57.927000,https://github.com/moosavism/ManiFool,3,Geometric robustness of deep networks: analysis and improvement,"https://scholar.google.com/scholar?cluster=17237058907276803718&hl=en&as_sdt=0,5",1,2018
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization,263,cvpr,55,5,2023-06-03 02:02:58.127000,https://github.com/jiangtaoxie/fast-MPN-COV,256,Towards faster training of global covariance pooling networks by iterative matrix square root normalization,"https://scholar.google.com/scholar?cluster=17395559231482729917&hl=en&as_sdt=0,44",13,2018
Recurrent Scene Parsing With Perspective Understanding in the Loop,110,cvpr,7,4,2023-06-03 02:02:58.326000,https://github.com/aimerykong/Recurrent-Scene-Parsing-with-Perspective-Understanding-in-the-loop,37,Recurrent scene parsing with perspective understanding in the loop,"https://scholar.google.com/scholar?cluster=17329955787132612464&hl=en&as_sdt=0,33",5,2018
Resource Aware Person Re-Identification Across Multiple Resolutions,267,cvpr,14,5,2023-06-03 02:02:58.526000,https://github.com/mileyan/DARENet,63,Resource aware person re-identification across multiple resolutions,"https://scholar.google.com/scholar?cluster=15130580374831521830&hl=en&as_sdt=0,48",4,2018
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition,360,cvpr,69,6,2023-06-03 02:02:58.725000,https://github.com/mikacuy/pointnetvlad,313,Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition,"https://scholar.google.com/scholar?cluster=11092893691585597959&hl=en&as_sdt=0,6",16,2018
Pointwise Convolutional Neural Networks,497,cvpr,26,8,2023-06-03 02:02:58.925000,https://github.com/hkust-vgd/pointwise,128,Pointwise convolutional neural networks,"https://scholar.google.com/scholar?cluster=2469352137681828239&hl=en&as_sdt=0,30",11,2018
Large-Scale Point Cloud Semantic Segmentation With Superpoint Graphs,1069,cvpr,211,59,2023-06-03 02:02:59.124000,https://github.com/loicland/superpoint_graph,682,Large-scale point cloud semantic segmentation with superpoint graphs,"https://scholar.google.com/scholar?cluster=8028725506123126280&hl=en&as_sdt=0,33",30,2018
R-FCN-3000 at 30fps: Decoupling Detection and Classification,93,cvpr,449,116,2023-06-03 02:02:59.323000,https://github.com/MahyarNajibi/SNIPER,2675,R-fcn-3000 at 30fps: Decoupling detection and classification,"https://scholar.google.com/scholar?cluster=12610408670018801079&hl=en&as_sdt=0,34",84,2018
Scale-Recurrent Network for Deep Image Deblurring,990,cvpr,185,29,2023-06-03 02:02:59.523000,https://github.com/jiangsutx/SRN-Deblur,673,Scale-recurrent network for deep image deblurring,"https://scholar.google.com/scholar?cluster=9767326897374302436&hl=en&as_sdt=0,5",23,2018
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks,1345,cvpr,503,137,2023-06-03 02:02:59.722000,https://github.com/KupynOrest/DeblurGAN,2316,Deblurgan: Blind motion deblurring using conditional adversarial networks,"https://scholar.google.com/scholar?cluster=5089744073478166070&hl=en&as_sdt=0,5",60,2018
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping,106,cvpr,39,2,2023-06-03 02:02:59.922000,https://github.com/wuhuikai/TF-A2RL,175,A2-RL: Aesthetics aware reinforcement learning for image cropping,"https://scholar.google.com/scholar?cluster=13340683651444641061&hl=en&as_sdt=0,5",12,2018
Arbitrary Style Transfer With Deep Feature Reshuffle,155,cvpr,8,3,2023-06-03 02:03:00.122000,https://github.com/msracver/Style-Feature-Reshuffle,42,Arbitrary style transfer with deep feature reshuffle,"https://scholar.google.com/scholar?cluster=11719204420277283592&hl=en&as_sdt=0,5",7,2018
Learning Less Is More - 6D Camera Localization via 3D Surface Regression,317,cvpr,46,0,2023-06-03 02:03:00.322000,https://github.com/vislearn/LessMore,240,Learning less is more-6d camera localization via 3d surface regression,"https://scholar.google.com/scholar?cluster=11804576689209781173&hl=en&as_sdt=0,33",15,2018
Mask-Guided Contrastive Attention Model for Person Re-Identification,581,cvpr,22,5,2023-06-03 02:03:00.522000,https://github.com/developfeng/MGCAM,89,Mask-guided contrastive attention model for person re-identification,"https://scholar.google.com/scholar?cluster=10435630685951917541&hl=en&as_sdt=0,11",5,2018
COCO-Stuff: Thing and Stuff Classes in Context,984,cvpr,146,0,2023-06-03 02:03:00.721000,https://github.com/nightrome/cocostuff,747,Coco-stuff: Thing and stuff classes in context,"https://scholar.google.com/scholar?cluster=6845361642027556320&hl=en&as_sdt=0,5",19,2018
Visual Feature Attribution Using Wasserstein GANs,135,cvpr,10,6,2023-06-03 02:03:00.921000,https://github.com/baumgach/vagan-code,51,Visual feature attribution using wasserstein gans,"https://scholar.google.com/scholar?cluster=4863009192322574764&hl=en&as_sdt=0,5",8,2018
Multi-Scale Location-Aware Kernel Representation for Object Detection,86,cvpr,24,7,2023-06-03 02:03:01.121000,https://github.com/Hwang64/MLKP,108,Multi-scale location-aware kernel representation for object detection,"https://scholar.google.com/scholar?cluster=2919288544075184668&hl=en&as_sdt=0,5",6,2018
Min-Entropy Latent Model for Weakly Supervised Object Detection,217,cvpr,7,1,2023-06-03 02:03:01.320000,https://github.com/WinFrand/MELM,36,Min-entropy latent model for weakly supervised object detection,"https://scholar.google.com/scholar?cluster=12196799827841565864&hl=en&as_sdt=0,33",2,2018
Unsupervised Training for 3D Morphable Model Regression,288,cvpr,117,0,2023-06-03 02:03:01.521000,https://github.com/google/tf_mesh_renderer,485,Unsupervised training for 3d morphable model regression,"https://scholar.google.com/scholar?cluster=4735286085521456884&hl=en&as_sdt=0,44",34,2018
MAttNet: Modular Attention Network for Referring Expression Comprehension,581,cvpr,74,20,2023-06-03 02:03:01.721000,https://github.com/lichengunc/MAttNet,284,Mattnet: Modular attention network for referring expression comprehension,"https://scholar.google.com/scholar?cluster=13069017082141468333&hl=en&as_sdt=0,33",8,2018
Video Based Reconstruction of 3D People Models,351,cvpr,147,1,2023-06-03 02:03:01.921000,https://github.com/thmoa/videoavatars,600,Video based reconstruction of 3d people models,"https://scholar.google.com/scholar?cluster=2077709938273462084&hl=en&as_sdt=0,5",35,2018
Multi-Cue Correlation Filters for Robust Visual Tracking,396,cvpr,15,2,2023-06-03 02:03:02.121000,https://github.com/594422814/MCCT,43,Multi-cue correlation filters for robust visual tracking,"https://scholar.google.com/scholar?cluster=10195225925386567515&hl=en&as_sdt=0,5",6,2018
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking,524,cvpr,12,5,2023-06-03 02:03:02.320000,https://github.com/foolwood/RASNet,76,Learning attentions: residual attentional siamese network for high performance online visual tracking,"https://scholar.google.com/scholar?cluster=13442164500664539732&hl=en&as_sdt=0,26",26,2018
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition,310,cvpr,44,0,2023-06-03 02:03:02.520000,https://github.com/kevin-ssy/Optical-Flow-Guided-Feature,193,Optical flow guided feature: A fast and robust motion representation for video action recognition,"https://scholar.google.com/scholar?cluster=1614018678326125147&hl=en&as_sdt=0,5",14,2018
Revisiting Video Saliency: A Large-Scale Benchmark and a New Model,242,cvpr,25,4,2023-06-03 02:03:02.719000,https://github.com/wenguanwang/DHF1K,131,Revisiting video saliency: A large-scale benchmark and a new model,"https://scholar.google.com/scholar?cluster=12383728254270518882&hl=en&as_sdt=0,33",11,2018
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking,742,cvpr,39,13,2023-06-03 02:03:02.919000,https://github.com/lifeng9472/STRCF,141,Learning spatial-temporal regularized correlation filters for visual tracking,"https://scholar.google.com/scholar?cluster=3477617967498244668&hl=en&as_sdt=0,36",8,2018
Multimodal Visual Concept Learning With Weakly Supervised Techniques,8,cvpr,2,0,2023-06-03 02:03:03.119000,https://github.com/gbouritsas/cvpr18_multimodal_weakly_supervised_learning,2,Multimodal visual concept learning with weakly supervised techniques,"https://scholar.google.com/scholar?cluster=4907691631173710779&hl=en&as_sdt=0,6",1,2018
Appearance-and-Relation Networks for Video Classification,349,cvpr,58,1,2023-06-03 02:03:03.318000,https://github.com/wanglimin/ARTNet,204,Appearance-and-relation networks for video classification,"https://scholar.google.com/scholar?cluster=18079198362259325313&hl=en&as_sdt=0,34",16,2018
Excitation Backprop for RNNs,54,cvpr,2,0,2023-06-03 02:03:03.518000,https://github.com/sbargal/Caffe-ExcitationBP-RNNs,15,Excitation backprop for RNNs,"https://scholar.google.com/scholar?cluster=4789269652313774623&hl=en&as_sdt=0,33",2,2018
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning,195,cvpr,75,3,2023-06-03 02:03:03.719000,https://github.com/davidmascharka/tbd-nets,352,Transparency by design: Closing the gap between performance and interpretability in visual reasoning,"https://scholar.google.com/scholar?cluster=1353061912650405734&hl=en&as_sdt=0,33",15,2018
A PID Controller Approach for Stochastic Optimization of Deep Networks,93,cvpr,56,8,2023-06-03 02:03:03.918000,https://github.com/tensorboy/PIDOptimizer,173,A PID controller approach for stochastic optimization of deep networks,"https://scholar.google.com/scholar?cluster=17450325485562246088&hl=en&as_sdt=0,5",15,2018
Learning Pixel-Level Semantic Affinity With Image-Level Supervision for Weakly Supervised Semantic Segmentation,594,cvpr,61,17,2023-06-03 02:03:04.119000,https://github.com/jiwoon-ahn/psa,346,Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=13437232166306950122&hl=en&as_sdt=0,10",10,2018
Deflecting Adversarial Attacks With Pixel Deflection,305,cvpr,21,3,2023-06-03 02:03:04.318000,https://github.com/iamaaditya/pixel-deflection,67,Deflecting adversarial attacks with pixel deflection,"https://scholar.google.com/scholar?cluster=12321589763437686771&hl=en&as_sdt=0,38",4,2018
Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation,432,cvpr,73,4,2023-06-03 02:03:04.518000,https://github.com/naoto0804/cross-domain-detection,396,Cross-domain weakly-supervised object detection through progressive domain adaptation,"https://scholar.google.com/scholar?cluster=6318150909176455782&hl=en&as_sdt=0,43",11,2018
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews From Unsupervised Viewpoints,458,cvpr,24,8,2023-06-03 02:03:04.718000,https://github.com/kanezaki/rotationnet,120,Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints,"https://scholar.google.com/scholar?cluster=3856056923662439293&hl=en&as_sdt=0,5",8,2018
SemStyle: Learning to Generate Stylised Image Captions Using Unaligned Text,115,cvpr,12,7,2023-06-03 02:03:04.917000,https://github.com/computationalmedia/semstyle,63,Semstyle: Learning to generate stylised image captions using unaligned text,"https://scholar.google.com/scholar?cluster=4328398835989809262&hl=en&as_sdt=0,18",11,2018
An End-to-End TextSpotter With Explicit Alignment and Attention,213,cvpr,112,18,2023-06-03 02:03:05.117000,https://github.com/tonghe90/textspotter,324,An end-to-end textspotter with explicit alignment and attention,"https://scholar.google.com/scholar?cluster=18084699074907307988&hl=en&as_sdt=0,33",20,2018
MoCoGAN: Decomposing Motion and Content for Video Generation,951,cvpr,113,9,2023-06-03 02:03:05.317000,https://github.com/sergeytulyakov/mocogan,535,Mocogan: Decomposing motion and content for video generation,"https://scholar.google.com/scholar?cluster=5549182152929176563&hl=en&as_sdt=0,33",27,2018
Learning Descriptor Networks for 3D Shape Synthesis and Analysis,126,cvpr,8,3,2023-06-03 02:03:05.517000,https://github.com/jianwen-xie/3DDescriptorNet,33,Learning descriptor networks for 3d shape synthesis and analysis,"https://scholar.google.com/scholar?cluster=536863175475660528&hl=en&as_sdt=0,3",3,2018
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data,13,cvpr,0,0,2023-06-03 02:03:05.717000,https://github.com/ArghyaPal/Adversarial-Data-Programming,1,Adversarial data programming: Using gans to relax the bottleneck of curated labeled data,"https://scholar.google.com/scholar?cluster=6926480641310170970&hl=en&as_sdt=0,19",1,2018
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation From a Single Depth Map,403,cvpr,68,5,2023-06-03 02:03:05.917000,https://github.com/mks0601/V2V-PoseNet_RELEASE,349,V2v-posenet: Voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map,"https://scholar.google.com/scholar?cluster=11037099377007218251&hl=en&as_sdt=0,5",18,2018
Iterative Learning With Open-Set Noisy Labels,261,cvpr,0,1,2023-06-03 02:03:06.117000,https://github.com/YisenWang/ONL,9,Iterative learning with open-set noisy labels,"https://scholar.google.com/scholar?cluster=17508278419210766419&hl=en&as_sdt=0,44",2,2018
SBNet: Sparse Blocks Network for Fast Inference,190,cvpr,90,4,2023-06-03 02:03:06.320000,https://github.com/uber/sbnet,430,Sbnet: Sparse blocks network for fast inference,"https://scholar.google.com/scholar?cluster=4427306398598179551&hl=en&as_sdt=0,33",21,2018
Language-Based Image Editing With Recurrent Attentive Models,91,cvpr,2,3,2023-06-03 02:03:06.520000,https://github.com/Jianbo-Lab/LBIE,6,Language-based image editing with recurrent attentive models,"https://scholar.google.com/scholar?cluster=18051777584011321259&hl=en&as_sdt=0,36",2,2018
Learning to Promote Saliency Detectors,61,cvpr,8,1,2023-06-03 02:03:06.721000,https://github.com/zengxianyu/lps,35,Learning to promote saliency detectors,"https://scholar.google.com/scholar?cluster=377805752772553101&hl=en&as_sdt=0,31",5,2018
Dense 3D Regression for Hand Pose Estimation,166,cvpr,34,21,2023-06-03 02:03:06.921000,https://github.com/melonwan/denseReg,179,Dense 3d regression for hand pose estimation,"https://scholar.google.com/scholar?cluster=8182022194391219912&hl=en&as_sdt=0,31",16,2018
Net2Vec: Quantifying and Explaining How Concepts Are Encoded by Filters in Deep Neural Networks,200,cvpr,3,0,2023-06-03 02:03:07.120000,https://github.com/ruthcfong/net2vec,31,Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks,"https://scholar.google.com/scholar?cluster=5946143100705962819&hl=en&as_sdt=0,33",6,2018
Camera Style Adaptation for Person Re-Identification,614,cvpr,76,9,2023-06-03 02:03:07.320000,https://github.com/zhunzhong07/CamStyle,279,Camera style adaptation for person re-identification,"https://scholar.google.com/scholar?cluster=8214559104303782322&hl=en&as_sdt=0,47",8,2018
A Neural Multi-Sequence Alignment TeCHnique (NeuMATCH),19,cvpr,4,0,2023-06-03 02:03:07.519000,https://github.com/pelindogan/NeuMATCH,16,A neural multi-sequence alignment technique (neumatch),"https://scholar.google.com/scholar?cluster=15334581548691379629&hl=en&as_sdt=0,33",2,2018
Path Aggregation Network for Instance Segmentation,4328,cvpr,278,52,2023-06-03 02:03:07.723000,https://github.com/ShuLiu1993/PANet,1295,Path aggregation network for instance segmentation,"https://scholar.google.com/scholar?cluster=8407059638791168431&hl=en&as_sdt=0,14",28,2018
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence,374,cvpr,10,5,2023-06-03 02:03:07.923000,https://github.com/Seth-Park/MultimodalExplanations,47,Multimodal explanations: Justifying decisions and pointing to the evidence,"https://scholar.google.com/scholar?cluster=6065410565479708036&hl=en&as_sdt=0,5",6,2018
LSTM Pose Machines,126,cvpr,41,8,2023-06-03 02:03:08.123000,https://github.com/lawy623/LSTM_Pose_Machines,273,Lstm pose machines,"https://scholar.google.com/scholar?cluster=5712699183271385332&hl=en&as_sdt=0,39",23,2018
Salient Object Detection Driven by Fixation Prediction,198,cvpr,6,2,2023-06-03 02:03:08.323000,https://github.com/wenguanwang/ASNet,42,Salient object detection driven by fixation prediction,"https://scholar.google.com/scholar?cluster=2801091682611332670&hl=en&as_sdt=0,1",5,2018
High-Resolution Image Synthesis and Semantic Manipulation With Conditional GANs,3638,cvpr,1346,227,2023-06-03 02:03:08.523000,https://github.com/NVIDIA/pix2pixHD,6196,High-resolution image synthesis and semantic manipulation with conditional gans,"https://scholar.google.com/scholar?cluster=8637738140607437341&hl=en&as_sdt=0,10",172,2018
Convolutional Sequence to Sequence Model for Human Dynamics,241,cvpr,20,14,2023-06-03 02:03:08.723000,https://github.com/chaneyddtt/Convolutional-Sequence-to-Sequence-Model-for-Human-Dynamics,81,Convolutional sequence to sequence model for human dynamics,"https://scholar.google.com/scholar?cluster=145333264460540785&hl=en&as_sdt=0,33",5,2018
Semi-Parametric Image Synthesis,153,cvpr,40,4,2023-06-03 02:03:08.923000,https://github.com/xjqicuhk/SIMS,269,Semi-parametric image synthesis,"https://scholar.google.com/scholar?cluster=15898753167236393863&hl=en&as_sdt=0,43",25,2018
BlockDrop: Dynamic Inference Paths in Residual Networks,448,cvpr,38,2,2023-06-03 02:03:09.122000,https://github.com/Tushar-N/blockdrop,137,Blockdrop: Dynamic inference paths in residual networks,"https://scholar.google.com/scholar?cluster=13596312469142174239&hl=en&as_sdt=0,34",8,2018
Interpretable Convolutional Neural Networks,730,cvpr,52,5,2023-06-03 02:03:09.322000,https://github.com/zqs1022/interpretableCNN,215,Interpretable convolutional neural networks,"https://scholar.google.com/scholar?cluster=17034472567859664305&hl=en&as_sdt=0,5",9,2018
A Variational U-Net for Conditional Appearance and Shape Generation,416,cvpr,96,12,2023-06-03 02:03:09.522000,https://github.com/CompVis/vunet,483,A variational u-net for conditional appearance and shape generation,"https://scholar.google.com/scholar?cluster=11078035773738033687&hl=en&as_sdt=0,5",23,2018
Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser,745,cvpr,64,11,2023-06-03 02:03:09.721000,https://github.com/lfz/Guided-Denoise,213,Defense against adversarial attacks using high-level representation guided denoiser,"https://scholar.google.com/scholar?cluster=8705389398028374086&hl=en&as_sdt=0,32",5,2018
Learning Deep Structured Active Contours End-to-End,147,cvpr,24,10,2023-06-03 02:03:09.921000,https://github.com/dmarcosg/DSAC,80,Learning deep structured active contours end-to-end,"https://scholar.google.com/scholar?cluster=1193297421461090891&hl=en&as_sdt=0,5",3,2018
Learning Latent Super-Events to Detect Multiple Activities in Videos,90,cvpr,34,6,2023-06-03 02:03:10.121000,https://github.com/piergiaj/super-events-cvpr18,122,Learning latent super-events to detect multiple activities in videos,"https://scholar.google.com/scholar?cluster=7952995141891673880&hl=en&as_sdt=0,11",8,2018
Deep Learning Under Privileged Information Using Heteroscedastic Dropout,82,cvpr,14,2,2023-06-03 02:03:10.321000,https://github.com/johnwlambert/dlupi-heteroscedastic-dropout,36,Deep learning under privileged information using heteroscedastic dropout,"https://scholar.google.com/scholar?cluster=12603369642103835644&hl=en&as_sdt=0,47",3,2018
PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference,198,cvpr,21,9,2023-06-03 02:03:10.521000,https://github.com/prashnani/PerceptualImageError,174,Pieapp: Perceptual image-error assessment through pairwise preference,"https://scholar.google.com/scholar?cluster=8138503949958601505&hl=en&as_sdt=0,5",2,2018
Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning,251,cvpr,5,2,2023-06-03 02:03:10.720000,https://github.com/xinmei9322/SNTG,41,Smooth neighbors on teacher graphs for semi-supervised learning,"https://scholar.google.com/scholar?cluster=2406002490632333318&hl=en&as_sdt=0,31",2,2018
Fast End-to-End Trainable Guided Filter,191,cvpr,148,0,2023-06-03 02:03:10.920000,https://github.com/wuhuikai/DeepGuidedFilter,788,Fast end-to-end trainable guided filter,"https://scholar.google.com/scholar?cluster=6557028345263040826&hl=en&as_sdt=0,50",32,2018
When Will You Do What? - Anticipating Temporal Occurrences of Activities,150,cvpr,20,1,2023-06-03 02:03:11.120000,https://github.com/yabufarha/anticipating-activities,69,When will you do what?-anticipating temporal occurrences of activities,"https://scholar.google.com/scholar?cluster=2962936283769065548&hl=en&as_sdt=0,4",5,2018
"PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume",1984,cvpr,349,58,2023-06-03 02:03:11.320000,https://github.com/NVlabs/PWC-Net,1500,"Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume","https://scholar.google.com/scholar?cluster=5637139753418325919&hl=en&as_sdt=0,33",46,2018
Crowd Counting With Deep Negative Correlation Learning,268,cvpr,21,10,2023-06-03 02:03:11.520000,https://github.com/shizenglin/Deep-NCL,54,Crowd counting with deep negative correlation learning,"https://scholar.google.com/scholar?cluster=11202985326678769571&hl=en&as_sdt=0,33",8,2018
Learning Spatial-Aware Regressions for Visual Tracking,228,cvpr,6,3,2023-06-03 02:03:11.720000,https://github.com/cswaynecool/LSART,15,Learning spatial-aware regressions for visual tracking,"https://scholar.google.com/scholar?cluster=6829855268105267653&hl=en&as_sdt=0,6",1,2018
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation,655,cvpr,103,0,2023-06-03 02:03:11.919000,https://github.com/twhui/LiteFlowNet,553,Liteflownet: A lightweight convolutional neural network for optical flow estimation,"https://scholar.google.com/scholar?cluster=2536526206253919253&hl=en&as_sdt=0,10",23,2018
Pyramid Stereo Matching Network,1298,cvpr,408,160,2023-06-03 02:03:12.119000,https://github.com/JiaRenChang/PSMNet,1274,Pyramid stereo matching network,"https://scholar.google.com/scholar?cluster=6542126250928371536&hl=en&as_sdt=0,1",33,2018
Recurrent Pixel Embedding for Instance Grouping,164,cvpr,21,5,2023-06-03 02:03:12.319000,https://github.com/aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping,142,Recurrent pixel embedding for instance grouping,"https://scholar.google.com/scholar?cluster=2298316817080377557&hl=en&as_sdt=0,10",10,2018
CleanNet: Transfer Learning for Scalable Image Classifier Training With Label Noise,397,cvpr,28,3,2023-06-03 02:03:12.519000,https://github.com/kuanghuei/clean-net,85,Cleannet: Transfer learning for scalable image classifier training with label noise,"https://scholar.google.com/scholar?cluster=16677004174186912203&hl=en&as_sdt=0,37",6,2018
Independently Recurrent Neural Network (IndRNN): Building a Longer and Deeper RNN,748,cvpr,26,1,2023-06-03 02:03:12.720000,https://github.com/Sunnydreamrain/IndRNN_Theano_Lasagne,65,Independently recurrent neural network (indrnn): Building a longer and deeper rnn,"https://scholar.google.com/scholar?cluster=5784188603612259222&hl=en&as_sdt=0,15",8,2018
Mix and Match Networks: Encoder-Decoder Alignment for Zero-Pair Image Translation,33,cvpr,5,2,2023-06-03 02:03:12.920000,https://github.com/yaxingwang/Mix-and-match-networks,21,Mix and match networks: encoder-decoder alignment for zero-pair image translation,"https://scholar.google.com/scholar?cluster=8929857447752153415&hl=en&as_sdt=0,31",4,2018
"ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM",129,cvpr,230,27,2023-06-03 02:03:13.124000,https://github.com/baidu/ICE-BA,665,"Ice-ba: Incremental, consistent and efficient bundle adjustment for visual-inertial slam","https://scholar.google.com/scholar?cluster=8671494492906580575&hl=en&as_sdt=0,33",69,2018
"GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose",1051,cvpr,182,6,2023-06-03 02:03:13.324000,https://github.com/yzcjtr/GeoNet,705,"Geonet: Unsupervised learning of dense depth, optical flow and camera pose","https://scholar.google.com/scholar?cluster=1957429302279516892&hl=en&as_sdt=0,44",35,2018
Between-Class Learning for Image Classification,207,cvpr,20,2,2023-06-03 02:03:13.524000,https://github.com/mil-tokyo/bc_learning_image,61,Between-class learning for image classification,"https://scholar.google.com/scholar?cluster=3081558640601116808&hl=en&as_sdt=0,33",17,2018
Gibson Env: Real-World Perception for Embodied Agents,588,cvpr,142,45,2023-06-03 02:03:13.724000,https://github.com/StanfordVL/GibsonEnv,773,Gibson env: Real-world perception for embodied agents,"https://scholar.google.com/scholar?cluster=4664950294258475912&hl=en&as_sdt=0,34",31,2018
Radially-Distorted Conjugate Translations,37,cvpr,7,3,2023-06-03 02:03:13.925000,https://github.com/prittjam/repeats,13,Radially-distorted conjugate translations,"https://scholar.google.com/scholar?cluster=3359150264756483345&hl=en&as_sdt=0,33",3,2018
Deep Ordinal Regression Network for Monocular Depth Estimation,1435,cvpr,105,27,2023-06-03 02:03:14.125000,https://github.com/hufu6371/DORN,459,Deep ordinal regression network for monocular depth estimation,"https://scholar.google.com/scholar?cluster=14501834367390829598&hl=en&as_sdt=0,28",19,2018
Generative Image Inpainting With Contextual Attention,2102,cvpr,761,60,2023-06-03 02:03:14.325000,https://github.com/JiahuiYu/generative_inpainting,3018,Generative image inpainting with contextual attention,"https://scholar.google.com/scholar?cluster=11541279513087894235&hl=en&as_sdt=0,23",76,2018
Detail-Preserving Pooling in Deep Networks,115,cvpr,23,10,2023-06-03 02:03:14.525000,https://github.com/visinf/dpp,113,Detail-preserving pooling in deep networks,"https://scholar.google.com/scholar?cluster=5703076471782110981&hl=en&as_sdt=0,31",9,2018
LayoutNet: Reconstructing the 3D Room Layout From a Single RGB Image,228,cvpr,33,10,2023-06-03 02:03:14.726000,https://github.com/zouchuhang/LayoutNetv2,188,Layoutnet: Reconstructing the 3d room layout from a single rgb image,"https://scholar.google.com/scholar?cluster=2583895775464722589&hl=en&as_sdt=0,33",13,2018
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation,60,cvpr,13,9,2023-06-03 02:03:14.925000,https://github.com/kbatsos/CBMV,53,CBMV: A coalesced bidirectional matching volume for disparity estimation,"https://scholar.google.com/scholar?cluster=14846125074255444911&hl=en&as_sdt=0,33",9,2018
Convolutional Image Captioning,368,cvpr,38,15,2023-06-03 02:03:15.125000,https://github.com/aditya12agd5/convcap,128,Convolutional image captioning,"https://scholar.google.com/scholar?cluster=9745346638332045201&hl=en&as_sdt=0,33",6,2018
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains,48,cvpr,2,2,2023-06-03 02:03:15.325000,https://github.com/Artifineuro/zole,23,Zoom and learn: Generalizing deep stereo matching to novel domains,"https://scholar.google.com/scholar?cluster=10212198475260872815&hl=en&as_sdt=0,44",1,2018
Learning Intelligent Dialogs for Bounding Box Annotation,42,cvpr,9,1,2023-06-03 02:03:15.525000,https://github.com/google/intelligent_annotation_dialogs,28,Learning intelligent dialogs for bounding box annotation,"https://scholar.google.com/scholar?cluster=14057255053273314230&hl=en&as_sdt=0,33",9,2018
PointGrid: A Deep Network for 3D Shape Understanding,317,cvpr,20,10,2023-06-03 02:03:15.725000,https://github.com/trucleduc/PointGrid,59,Pointgrid: A deep network for 3d shape understanding,"https://scholar.google.com/scholar?cluster=5793294535318142928&hl=en&as_sdt=0,33",6,2018
Look at Boundary: A Boundary-Aware Face Alignment Algorithm,416,cvpr,274,34,2023-06-03 02:03:15.925000,https://github.com/wywu/LAB,997,Look at boundary: A boundary-aware face alignment algorithm,"https://scholar.google.com/scholar?cluster=16870653029527986322&hl=en&as_sdt=0,5",70,2018
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs,319,cvpr,181,54,2023-06-03 02:03:16.126000,https://github.com/mapillary/inplace_abn,1276,In-place activated batchnorm for memory-optimized training of dnns,"https://scholar.google.com/scholar?cluster=5642068797752856080&hl=en&as_sdt=0,19",39,2018
3D Semantic Segmentation With Submanifold Sparse Convolutional Networks,1100,cvpr,315,52,2023-06-03 02:03:16.326000,https://github.com/facebookresearch/SparseConvNet,1848,3d semantic segmentation with submanifold sparse convolutional networks,"https://scholar.google.com/scholar?cluster=7438723884655241525&hl=en&as_sdt=0,6",45,2018
DVQA: Understanding Data Visualizations via Question Answering,152,cvpr,1,1,2023-06-03 02:03:16.526000,https://github.com/kushalkafle/DVQA_dataset,26,Dvqa: Understanding data visualizations via question answering,"https://scholar.google.com/scholar?cluster=14643397421272652205&hl=en&as_sdt=0,33",4,2018
TOM-Net: Learning Transparent Object Matting From a Single Image,63,cvpr,13,0,2023-06-03 02:03:16.726000,https://github.com/guanyingc/TOM-Net,86,Tom-net: Learning transparent object matting from a single image,"https://scholar.google.com/scholar?cluster=15606383417990887187&hl=en&as_sdt=0,40",9,2018
An Unsupervised Learning Model for Deformable Medical Image Registration,596,cvpr,14,0,2023-06-03 02:03:16.927000,https://github.com/balakg/voxelmorph,50,An unsupervised learning model for deformable medical image registration,"https://scholar.google.com/scholar?cluster=12326446542960778734&hl=en&as_sdt=0,39",15,2018
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation,150,cvpr,59,17,2023-06-03 02:03:17.127000,https://github.com/adalca/neuron,286,Anatomical priors in convolutional networks for unsupervised biomedical segmentation,"https://scholar.google.com/scholar?cluster=16777184933272965833&hl=en&as_sdt=0,34",21,2018
Social GAN: Socially Acceptable Trajectories With Generative Adversarial Networks,1519,cvpr,250,59,2023-06-03 02:03:17.327000,https://github.com/agrimgupta92/sgan,734,Social gan: Socially acceptable trajectories with generative adversarial networks,"https://scholar.google.com/scholar?cluster=129172247046928959&hl=en&as_sdt=0,25",18,2018
Deep Group-Shuffling Random Walk for Person Re-Identification,160,cvpr,29,1,2023-06-03 02:03:17.527000,https://github.com/YantaoShen/kpm_rw_person_reid,103,Deep group-shuffling random walk for person re-identification,"https://scholar.google.com/scholar?cluster=9330144390873119070&hl=en&as_sdt=0,37",6,2018
Real-Time Rotation-Invariant Face Detection With Progressive Calibration Networks,113,cvpr,301,27,2023-06-03 02:03:17.728000,https://github.com/Rock-100/FaceKit,1071,Real-time rotation-invariant face detection with progressive calibration networks,"https://scholar.google.com/scholar?cluster=13534144308727796881&hl=en&as_sdt=0,32",71,2018
Learning to Evaluate Image Captioning,123,cvpr,11,4,2023-06-03 02:03:17.929000,https://github.com/richardaecn/cvpr18-caption-eval,81,Learning to evaluate image captioning,"https://scholar.google.com/scholar?cluster=6231020248740057140&hl=en&as_sdt=0,31",9,2018
SO-Net: Self-Organizing Network for Point Cloud Analysis,866,cvpr,81,9,2023-06-03 02:03:18.130000,https://github.com/lijx10/SO-Net,333,So-net: Self-organizing network for point cloud analysis,"https://scholar.google.com/scholar?cluster=16985384514000508037&hl=en&as_sdt=0,47",14,2018
Neural Motifs: Scene Graph Parsing With Global Context,787,cvpr,116,40,2023-06-03 02:03:18.331000,https://github.com/rowanz/neural-motifs,494,Neural motifs: Scene graph parsing with global context,"https://scholar.google.com/scholar?cluster=503182414007319468&hl=en&as_sdt=0,43",18,2018
SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis,267,cvpr,32,10,2023-06-03 02:03:18.530000,https://github.com/wchen342/SketchyGAN,124,Sketchygan: Towards diverse and realistic sketch to image synthesis,"https://scholar.google.com/scholar?cluster=3882701906354721873&hl=en&as_sdt=0,47",6,2018
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing,289,cvpr,73,4,2023-06-03 02:03:18.731000,https://github.com/chenhsuanlin/spatial-transformer-GAN,330,St-gan: Spatial transformer generative adversarial networks for image compositing,"https://scholar.google.com/scholar?cluster=13423942867154123344&hl=en&as_sdt=0,33",13,2018
A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos,28,cvpr,7,2,2023-06-03 02:03:18.931000,https://github.com/verlab/SemanticFastForward_CVPR_2018,8,A weighted sparse sampling and smoothing frame transition approach for semantic fast-forward first-person videos,"https://scholar.google.com/scholar?cluster=13523099890582023655&hl=en&as_sdt=0,32",9,2018
Deep Layer Aggregation,1181,cvpr,87,12,2023-06-03 02:03:19.131000,https://github.com/ucbdrive/dla,414,Deep layer aggregation,"https://scholar.google.com/scholar?cluster=12605188017174616564&hl=en&as_sdt=0,33",13,2018
Human-Centric Indoor Scene Synthesis Using Stochastic Grammar,139,cvpr,15,1,2023-06-03 02:03:19.331000,https://github.com/SiyuanQi/human-centric-scene-synthesis,80,Human-centric indoor scene synthesis using stochastic grammar,"https://scholar.google.com/scholar?cluster=14155708423777654581&hl=en&as_sdt=0,5",5,2018
Convolutional Neural Networks With Alternately Updated Clique,140,cvpr,77,9,2023-06-03 02:03:19.531000,https://github.com/iboing/CliqueNet,329,Convolutional neural networks with alternately updated clique,"https://scholar.google.com/scholar?cluster=6622253267851873985&hl=en&as_sdt=0,10",19,2018
Residual Dense Network for Image Super-Resolution,3009,cvpr,107,17,2023-06-03 02:03:19.732000,https://github.com/yulunzhang/RDN,505,Residual dense network for image super-resolution,"https://scholar.google.com/scholar?cluster=10362327168909609533&hl=en&as_sdt=0,33",16,2018
FSRNet: End-to-End Learning Face Super-Resolution With Facial Priors,432,cvpr,54,11,2023-06-03 02:03:19.932000,https://github.com/tyshiwo/FSRNet,254,Fsrnet: End-to-end learning face super-resolution with facial priors,"https://scholar.google.com/scholar?cluster=11045837059104237071&hl=en&as_sdt=0,21",20,2018
Surface Networks,84,cvpr,20,6,2023-06-03 02:03:20.132000,https://github.com/jiangzhongshi/SurfaceNetworks,87,Surface networks,"https://scholar.google.com/scholar?cluster=4701841533359128721&hl=en&as_sdt=0,5",8,2018
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation,488,cvpr,61,30,2023-06-03 02:03:20.332000,https://github.com/laughtervv/SGPN,253,Sgpn: Similarity group proposal network for 3d point cloud instance segmentation,"https://scholar.google.com/scholar?cluster=10828175727380616387&hl=en&as_sdt=0,5",9,2018
PlaneNet: Piece-Wise Planar Reconstruction From a Single RGB Image,187,cvpr,82,20,2023-06-03 02:03:20.532000,https://github.com/art-programmer/PlaneNet,381,Planenet: Piece-wise planar reconstruction from a single rgb image,"https://scholar.google.com/scholar?cluster=15607486127971815360&hl=en&as_sdt=0,33",18,2018
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering,4124,cvpr,385,65,2023-06-03 02:03:20.732000,https://github.com/peteanderson80/bottom-up-attention,1337,Bottom-up and top-down attention for image captioning and visual question answering,"https://scholar.google.com/scholar?cluster=7383633913245131178&hl=en&as_sdt=0,18",25,2018
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis,315,cvpr,15,2,2023-06-03 02:03:20.932000,https://github.com/nitika-verma/FeaStNet,58,Feastnet: Feature-steered graph convolutions for 3d shape analysis,"https://scholar.google.com/scholar?cluster=18220966840925909139&hl=en&as_sdt=0,33",9,2018
Geometry-Aware Learning of Maps for Camera Localization,327,cvpr,78,9,2023-06-03 02:03:21.131000,https://github.com/NVlabs/geomapnet,330,Geometry-aware learning of maps for camera localization,"https://scholar.google.com/scholar?cluster=15361942550307476955&hl=en&as_sdt=0,33",27,2018
Recurrent Slice Networks for 3D Segmentation of Point Clouds,423,cvpr,40,13,2023-06-03 02:03:21.331000,https://github.com/qianguih/RSNet,112,Recurrent slice networks for 3d segmentation of point clouds,"https://scholar.google.com/scholar?cluster=15745594784158675393&hl=en&as_sdt=0,33",9,2018
Focal Visual-Text Attention for Visual Question Answering,103,cvpr,15,0,2023-06-03 02:03:21.531000,https://github.com/JunweiLiang/FVTA_MemexQA,33,Focal visual-text attention for visual question answering,"https://scholar.google.com/scholar?cluster=8603088268635789565&hl=en&as_sdt=0,14",4,2018
SeGAN: Segmenting and Generating the Invisible,130,cvpr,12,0,2023-06-03 02:03:21.731000,https://github.com/ehsanik/SeGAN,61,Segan: Segmenting and generating the invisible,"https://scholar.google.com/scholar?cluster=145087875116009054&hl=en&as_sdt=0,39",5,2018
Cascade R-CNN: Delving Into High Quality Object Detection,3901,cvpr,300,49,2023-06-03 02:03:21.931000,https://github.com/zhaoweicai/cascade-rcnn,1014,Cascade r-cnn: Delving into high quality object detection,"https://scholar.google.com/scholar?cluster=90386599069203250&hl=en&as_sdt=0,33",41,2018
Photographic Text-to-Image Synthesis With a Hierarchically-Nested Adversarial Network,319,cvpr,36,1,2023-06-03 02:03:22.132000,https://github.com/ypxie/HDGan,149,Photographic text-to-image synthesis with a hierarchically-nested adversarial network,"https://scholar.google.com/scholar?cluster=15980568541820197143&hl=en&as_sdt=0,5",11,2018
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions,792,cvpr,136,11,2023-06-03 02:03:22.332000,https://github.com/ShichenLiu/CondenseNet,692,Condensenet: An efficient densenet using learned group convolutions,"https://scholar.google.com/scholar?cluster=962038121370501229&hl=en&as_sdt=0,33",23,2018
Inferring Light Fields From Shadows,65,cvpr,2,0,2023-06-03 02:03:22.531000,https://github.com/mbaradad/shadow2lightfield,3,Inferring light fields from shadows,"https://scholar.google.com/scholar?cluster=537944593784033046&hl=en&as_sdt=0,33",1,2018
PU-Net: Point Cloud Upsampling Network,452,cvpr,81,20,2023-06-03 02:03:22.731000,https://github.com/yulequan/PU-Net,314,Pu-net: Point cloud upsampling network,"https://scholar.google.com/scholar?cluster=13363120035792906943&hl=en&as_sdt=0,21",6,2018
Real-Time Monocular Depth Estimation Using Synthetic Data With Domain Adaptation via Image Style Transfer,255,cvpr,36,0,2023-06-03 02:03:22.932000,https://github.com/atapour/monocularDepth-Inference,141,Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer,"https://scholar.google.com/scholar?cluster=13567427763844087834&hl=en&as_sdt=0,18",11,2018
DeepMVS: Learning Multi-View Stereopsis,391,cvpr,85,9,2023-06-03 02:03:23.133000,https://github.com/phuang17/DeepMVS,322,Deepmvs: Learning multi-view stereopsis,"https://scholar.google.com/scholar?cluster=3902531294143230272&hl=en&as_sdt=0,33",23,2018
"Efficient, Sparse Representation of Manifold Distance Matrices for Classical Scaling",2,cvpr,4,0,2023-06-03 02:03:23.333000,https://github.com/alexhuth/BHA,7,"Efficient, sparse representation of manifold distance matrices for classical scaling","https://scholar.google.com/scholar?cluster=4797402609750056863&hl=en&as_sdt=0,33",4,2018
Motion Segmentation by Exploiting Complementary Geometric Models,35,cvpr,1,0,2023-06-03 02:03:23.533000,https://github.com/alex-xun-xu/MultiViewMoSeg,10,Motion segmentation by exploiting complementary geometric models,"https://scholar.google.com/scholar?cluster=11763896756712542552&hl=en&as_sdt=0,44",1,2018
AMNet: Memorability Estimation With Attention,56,cvpr,11,6,2023-06-03 02:03:23.733000,https://github.com/ok1zjf/amnet,43,Amnet: Memorability estimation with attention,"https://scholar.google.com/scholar?cluster=11101962702948448138&hl=en&as_sdt=0,33",5,2018
Deep Marching Cubes: Learning Explicit Surface Representations,198,cvpr,33,5,2023-06-03 02:03:23.933000,https://github.com/yiyiliao/deep_marching_cubes,240,Deep marching cubes: Learning explicit surface representations,"https://scholar.google.com/scholar?cluster=11921517846424504884&hl=en&as_sdt=0,33",9,2018
A Closer Look at Spatiotemporal Convolutions for Action Recognition,2480,cvpr,162,28,2023-06-03 02:03:24.133000,https://github.com/facebookresearch/R2Plus1D,1020,A closer look at spatiotemporal convolutions for action recognition,"https://scholar.google.com/scholar?cluster=9524036545693727210&hl=en&as_sdt=0,33",111,2018
SurfConv: Bridging 3D and 2D Convolution for RGBD Images,22,cvpr,6,0,2023-06-03 02:03:24.333000,https://github.com/chuhang/SurfConv,53,Surfconv: Bridging 3d and 2d convolution for rgbd images,"https://scholar.google.com/scholar?cluster=7050444684028736511&hl=en&as_sdt=0,33",4,2018
Efficient Video Object Segmentation via Network Modulation,344,cvpr,24,5,2023-06-03 02:03:24.532000,https://github.com/linjieyangsc/video_seg,152,Efficient video object segmentation via network modulation,"https://scholar.google.com/scholar?cluster=1976126252599424631&hl=en&as_sdt=0,33",7,2018
Weakly-Supervised Action Segmentation With Iterative Soft Boundary Assignment,170,cvpr,12,1,2023-06-03 02:03:24.732000,https://github.com/ld-ing/TCFPN-ISBA,40,Weakly-supervised action segmentation with iterative soft boundary assignment,"https://scholar.google.com/scholar?cluster=8585781658332698642&hl=en&as_sdt=0,44",4,2018
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?,1749,cvpr,911,153,2023-06-03 02:03:24.932000,https://github.com/kenshohara/3D-ResNets-PyTorch,3611,Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?,"https://scholar.google.com/scholar?cluster=4579944187863414163&hl=en&as_sdt=0,22",59,2018
PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection,759,cvpr,16,0,2023-06-03 02:03:25.132000,https://github.com/nian-liu/PiCANet,34,Picanet: Learning pixel-wise contextual attention for saliency detection,"https://scholar.google.com/scholar?cluster=10105062679755015166&hl=en&as_sdt=0,5",5,2018
What Do Deep Networks Like to See?,36,cvpr,3,0,2023-06-03 02:03:25.333000,https://github.com/spalaciob/s2snets-reconstruction,14,What do deep networks like to see?,"https://scholar.google.com/scholar?cluster=5407091813991175051&hl=en&as_sdt=0,33",2,2018
Multi-Frame Quality Enhancement for Compressed Video,194,cvpr,21,2,2023-06-03 02:03:25.532000,https://github.com/ryangBUAA/MFQE,92,Multi-frame quality enhancement for compressed video,"https://scholar.google.com/scholar?cluster=13441933018643229678&hl=en&as_sdt=0,5",6,2018
Active Fixation Control to Predict Saccade Sequences,41,cvpr,1,1,2023-06-03 02:03:25.732000,https://github.com/TsotsosLab/STAR-FC,16,Active fixation control to predict saccade sequences,"https://scholar.google.com/scholar?cluster=5313953482186601173&hl=en&as_sdt=0,14",8,2018
Latent RANSAC,30,cvpr,10,0,2023-06-03 02:03:25.932000,https://github.com/rlit/LatentRANSAC,23,Latent ransac,"https://scholar.google.com/scholar?cluster=16432006796453756987&hl=en&as_sdt=0,47",8,2018
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation,476,cvpr,50,11,2023-06-03 02:03:26.133000,https://github.com/yhjo09/VSR-DUF,217,Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation,"https://scholar.google.com/scholar?cluster=14547343431451944570&hl=en&as_sdt=0,1",10,2018
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations,860,cvpr,78,19,2023-06-03 02:03:26.333000,https://github.com/cszn/SRMD,403,Learning a single convolutional super-resolution network for multiple degradations,"https://scholar.google.com/scholar?cluster=12748399699451058322&hl=en&as_sdt=0,5",13,2018
FFNet: Video Fast-Forwarding via Reinforcement Learning,53,cvpr,4,1,2023-06-03 02:03:26.534000,https://github.com/shuyueL/FFNet,20,Ffnet: Video fast-forwarding via reinforcement learning,"https://scholar.google.com/scholar?cluster=12600528794155457319&hl=en&as_sdt=0,34",1,2018
Domain Adaptive Faster R-CNN for Object Detection in the Wild,1042,cvpr,69,13,2023-06-03 02:03:26.734000,https://github.com/yuhuayc/da-faster-rcnn,320,Domain adaptive faster r-cnn for object detection in the wild,"https://scholar.google.com/scholar?cluster=2169991804006218555&hl=en&as_sdt=0,5",11,2018
Low-Shot Learning With Large-Scale Diffusion,116,cvpr,5,0,2023-06-03 02:03:26.935000,https://github.com/facebookresearch/low-shot-with-diffusion,47,Low-shot learning with large-scale diffusion,"https://scholar.google.com/scholar?cluster=18181091707667282501&hl=en&as_sdt=0,5",7,2018
Referring Relationships,94,cvpr,81,10,2023-06-03 02:03:27.136000,https://github.com/StanfordVL/ReferringRelationships,262,Referring relationships,"https://scholar.google.com/scholar?cluster=6942937541363789656&hl=en&as_sdt=0,33",19,2018
Adversarially Learned One-Class Classifier for Novelty Detection,617,cvpr,77,0,2023-06-03 02:03:27.336000,https://github.com/khalooei/ALOCC-CVPR2018,204,Adversarially learned one-class classifier for novelty detection,"https://scholar.google.com/scholar?cluster=13603058643518336613&hl=en&as_sdt=0,48",16,2018
Improving Object Localization With Fitness NMS and Bounded IoU Loss,189,cvpr,29,4,2023-06-03 02:03:27.537000,https://github.com/lachlants/denet,112,Improving object localization with fitness nms and bounded iou loss,"https://scholar.google.com/scholar?cluster=10163124065967812010&hl=en&as_sdt=0,10",11,2018
End-to-End Deep Kronecker-Product Matching for Person Re-Identification,148,cvpr,29,1,2023-06-03 02:03:27.739000,https://github.com/YantaoShen/kpm_rw_person_reid,103,End-to-end deep kronecker-product matching for person re-identification,"https://scholar.google.com/scholar?cluster=4096787433309580582&hl=en&as_sdt=0,5",6,2018
Deformable GANs for Pose-Based Human Image Generation,443,cvpr,81,21,2023-06-03 02:03:27.940000,https://github.com/AliaksandrSiarohin/pose-gan,373,Deformable gans for pose-based human image generation,"https://scholar.google.com/scholar?cluster=3043558815618961635&hl=en&as_sdt=0,5",18,2018
Deep Reinforcement Learning of Region Proposal Networks for Object Detection,73,cvpr,19,0,2023-06-03 02:03:28.139000,https://github.com/aleksispi/drl-rpn-tf,72,Deep reinforcement learning of region proposal networks for object detection,"https://scholar.google.com/scholar?cluster=5027202690407581149&hl=en&as_sdt=0,5",6,2018
Discriminability Objective for Training Descriptive Captions,189,cvpr,22,7,2023-06-03 02:03:28.340000,https://github.com/ruotianluo/DiscCaptioning,110,Discriminability objective for training descriptive captions,"https://scholar.google.com/scholar?cluster=4573642077602787213&hl=en&as_sdt=0,10",6,2018
Robust Classification With Convolutional Prototype Learning,227,cvpr,32,4,2023-06-03 02:03:28.540000,https://github.com/YangHM/Convolutional-Prototype-Learning,117,Robust classification with convolutional prototype learning,"https://scholar.google.com/scholar?cluster=15165539618467322604&hl=en&as_sdt=0,44",7,2018
Generative Modeling Using the Sliced Wasserstein Distance,190,cvpr,3,2,2023-06-03 02:03:28.740000,https://github.com/ishansd/swg,33,Generative modeling using the sliced wasserstein distance,"https://scholar.google.com/scholar?cluster=16060361472863218540&hl=en&as_sdt=0,44",4,2018
Learning Time/Memory-Efficient Deep Architectures With Budgeted Super Networks,96,cvpr,5,0,2023-06-03 02:03:28.941000,https://github.com/TomVeniat/bsn,26,Learning time/memory-efficient deep architectures with budgeted super networks,"https://scholar.google.com/scholar?cluster=5514870632955712695&hl=en&as_sdt=0,33",3,2018
Cross-View Image Synthesis Using Conditional GANs,153,cvpr,11,8,2023-06-03 02:03:29.141000,https://github.com/kregmi/cross-view-image-synthesis,50,Cross-view image synthesis using conditional gans,"https://scholar.google.com/scholar?cluster=15120435124755343968&hl=en&as_sdt=0,34",4,2018
Weakly-Supervised Semantic Segmentation Network With Deep Seeded Region Growing,516,cvpr,36,15,2023-06-03 02:03:29.340000,https://github.com/speedinghzl/DSRG,245,Weakly-supervised semantic segmentation network with deep seeded region growing,"https://scholar.google.com/scholar?cluster=11401463134094123362&hl=en&as_sdt=0,39",12,2018
Deep Spatial Feature Reconstruction for Partial Person Re-Identification: Alignment-Free Approach,265,cvpr,31,9,2023-06-03 02:03:29.541000,https://github.com/lingxiao-he/Partial-Person-ReID,160,Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach,"https://scholar.google.com/scholar?cluster=16383414441740542927&hl=en&as_sdt=0,5",7,2018
Cascaded Pyramid Network for Multi-Person Pose Estimation,1245,cvpr,201,43,2023-06-03 02:03:29.743000,https://github.com/chenyilun95/tf-cpn,788,Cascaded pyramid network for multi-person pose estimation,"https://scholar.google.com/scholar?cluster=5670760275903596839&hl=en&as_sdt=0,33",27,2018
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal,324,cvpr,31,6,2023-06-03 02:17:46.966000,https://github.com/Clarifai/few-shot-ctm,152,Finding task-relevant features for few-shot learning by category traversal,"https://scholar.google.com/scholar?cluster=7690321196923369761&hl=en&as_sdt=0,5",25,2019
Edge-Labeling Graph Neural Network for Few-Shot Learning,442,cvpr,65,21,2023-06-03 02:17:47.165000,https://github.com/khy0809/fewshot-egnn,260,Edge-labeling graph neural network for few-shot learning,"https://scholar.google.com/scholar?cluster=8574108187034418609&hl=en&as_sdt=0,36",6,2019
Learning Video Representations From Correspondence Proposals,66,cvpr,12,1,2023-06-03 02:17:47.364000,https://github.com/xingyul/cpnet,93,Learning video representations from correspondence proposals,"https://scholar.google.com/scholar?cluster=7665702673430883055&hl=en&as_sdt=0,34",1,2019
Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology,57,cvpr,188,17,2023-06-03 02:17:47.564000,https://github.com/rsummers11/CADLab,409,Holistic and comprehensive annotation of clinically significant findings on diverse CT images: learning from radiology reports and label ontology,"https://scholar.google.com/scholar?cluster=6878217630382772571&hl=en&as_sdt=0,6",29,2019
Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning,251,cvpr,21,12,2023-06-03 02:17:47.764000,https://github.com/gidariss/wDAE_GNN_FewShot,148,Generating classification weights with gnn denoising autoencoders for few-shot learning,"https://scholar.google.com/scholar?cluster=13700723211966973086&hl=en&as_sdt=0,5",13,2019
Kervolutional Neural Networks,74,cvpr,3,4,2023-06-03 02:17:47.964000,https://github.com/wang-chen/kervolution,37,Kervolutional neural networks,"https://scholar.google.com/scholar?cluster=11581113643161028532&hl=en&as_sdt=0,33",7,2019
Sphere Generative Adversarial Network Based on Geometric Moment Matching,38,cvpr,4,1,2023-06-03 02:17:48.164000,https://github.com/pswkiki/SphereGAN,14,Sphere generative adversarial network based on geometric moment matching,"https://scholar.google.com/scholar?cluster=7334963438871902572&hl=en&as_sdt=0,33",0,2019
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation,399,cvpr,93,10,2023-06-03 02:17:48.363000,https://github.com/xamyzhao/brainstorm,389,Data augmentation using learned transformations for one-shot medical image segmentation,"https://scholar.google.com/scholar?cluster=15710438805315912771&hl=en&as_sdt=0,6",12,2019
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem,409,cvpr,21,1,2023-06-03 02:17:48.563000,https://github.com/max-andr/relu_networks_overconfident,181,Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem,"https://scholar.google.com/scholar?cluster=1495531200128833945&hl=en&as_sdt=0,1",6,2019
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks,505,cvpr,22,4,2023-06-03 02:17:48.763000,https://github.com/dongyp13/Translation-Invariant-Attacks,124,Evading defenses to transferable adversarial examples by translation-invariant attacks,"https://scholar.google.com/scholar?cluster=165426210106722967&hl=en&as_sdt=0,33",3,2019
Hardness-Aware Deep Metric Learning,176,cvpr,29,6,2023-06-03 02:17:48.963000,https://github.com/wzzheng/HDML,149,Hardness-aware deep metric learning,"https://scholar.google.com/scholar?cluster=8375662549135355127&hl=en&as_sdt=0,33",4,2019
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation,969,cvpr,46274,1204,2023-06-03 02:17:49.162000,https://github.com/tensorflow/models,75883,Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation,"https://scholar.google.com/scholar?cluster=548023770660590636&hl=en&as_sdt=0,14",2774,2019
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration,956,cvpr,107,12,2023-06-03 02:17:49.362000,https://github.com/he-y/filter-pruning-geometric-median,477,Filter pruning via geometric median for deep convolutional neural networks acceleration,"https://scholar.google.com/scholar?cluster=3978996322124428221&hl=en&as_sdt=0,22",7,2019
Content Authentication for Neural Imaging Pipelines: End-To-End Optimization of Photo Provenance in Complex Distribution Channels,13,cvpr,30,4,2023-06-03 02:17:49.561000,https://github.com/pkorus/neural-imaging,136,Content authentication for neural imaging pipelines: End-to-end optimization of photo provenance in complex distribution channels,"https://scholar.google.com/scholar?cluster=11213241954859682918&hl=en&as_sdt=0,5",11,2019
DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds,60,cvpr,46,0,2023-06-03 02:17:49.761000,https://github.com/ai4ce/DeepMapping,190,DeepMapping: Unsupervised map estimation from multiple point clouds,"https://scholar.google.com/scholar?cluster=9988954344653969501&hl=en&as_sdt=0,11",21,2019
3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans,359,cvpr,75,13,2023-06-03 02:17:49.961000,https://github.com/Sekunde/3D-SIS,360,3d-sis: 3d semantic instance segmentation of rgb-d scans,"https://scholar.google.com/scholar?cluster=18028162509439004162&hl=en&as_sdt=0,45",21,2019
Image Deformation Meta-Networks for One-Shot Learning,206,cvpr,7,3,2023-06-03 02:17:50.161000,https://github.com/tankche1/IDeMe-Net,49,Image deformation meta-networks for one-shot learning,"https://scholar.google.com/scholar?cluster=8511386292870768575&hl=en&as_sdt=0,1",3,2019
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching,554,cvpr,136,30,2023-06-03 02:17:50.361000,https://github.com/feihuzhang/GANet,507,Ga-net: Guided aggregation net for end-to-end stereo matching,"https://scholar.google.com/scholar?cluster=731989336931762383&hl=en&as_sdt=0,49",26,2019
Real-Time Self-Adaptive Deep Stereo,233,cvpr,73,19,2023-06-03 02:17:50.561000,https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stereo,405,Real-time self-adaptive deep stereo,"https://scholar.google.com/scholar?cluster=18225618983635239070&hl=en&as_sdt=0,43",26,2019
Occupancy Networks: Learning 3D Reconstruction in Function Space,1770,cvpr,254,75,2023-06-03 02:17:50.761000,https://github.com/LMescheder/Occupancy-Networks,1197,Occupancy networks: Learning 3d reconstruction in function space,"https://scholar.google.com/scholar?cluster=14064961512731216993&hl=en&as_sdt=0,33",30,2019
Detailed Human Shape Estimation From a Single Image by Hierarchical Mesh Deformation,120,cvpr,47,4,2023-06-03 02:17:50.961000,https://github.com/zhuhao-nju/hmd,265,Detailed human shape estimation from a single image by hierarchical mesh deformation,"https://scholar.google.com/scholar?cluster=1439227329003947447&hl=en&as_sdt=0,33",15,2019
Self-Calibrating Deep Photometric Stereo Networks,116,cvpr,29,0,2023-06-03 02:17:51.161000,https://github.com/guanyingc/SDPS-Net,155,Self-calibrating deep photometric stereo networks,"https://scholar.google.com/scholar?cluster=3649234197372500195&hl=en&as_sdt=0,5",11,2019
Argoverse: 3D Tracking and Forecasting With Rich Maps,865,cvpr,211,49,2023-06-03 02:17:51.361000,https://github.com/argoai/argoverse-api,720,Argoverse: 3d tracking and forecasting with rich maps,"https://scholar.google.com/scholar?cluster=17363497644081299357&hl=en&as_sdt=0,44",28,2019
Timeception for Complex Action Recognition,193,cvpr,35,5,2023-06-03 02:17:51.562000,https://github.com/noureldien/timeception,158,Timeception for complex action recognition,"https://scholar.google.com/scholar?cluster=1175087663521850723&hl=en&as_sdt=0,11",8,2019
Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion,35,cvpr,17,1,2023-06-03 02:17:51.762000,https://github.com/zhenpeiyang/RelativePose,147,Extreme relative pose estimation for rgb-d scans via scene completion,"https://scholar.google.com/scholar?cluster=9221764684170544668&hl=en&as_sdt=0,33",7,2019
Long-Term Feature Banks for Detailed Video Understanding,436,cvpr,67,15,2023-06-03 02:17:51.962000,https://github.com/facebookresearch/video-long-term-feature-banks,364,Long-term feature banks for detailed video understanding,"https://scholar.google.com/scholar?cluster=12076206268409002970&hl=en&as_sdt=0,48",11,2019
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition,199,cvpr,40,7,2023-06-03 02:17:52.162000,https://github.com/xpwu95/IP102,143,Ip102: A large-scale benchmark dataset for insect pest recognition,"https://scholar.google.com/scholar?cluster=4487446333535453318&hl=en&as_sdt=0,44",9,2019
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment,84,cvpr,14,0,2023-06-03 02:17:52.362000,https://github.com/ParitoshParmar/MTL-AQA,42,What and how well you performed? a multitask learning approach to action quality assessment,"https://scholar.google.com/scholar?cluster=16672989544412031563&hl=en&as_sdt=0,21",4,2019
MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation,46,cvpr,9,1,2023-06-03 02:17:52.563000,https://github.com/shuangjiexu/MHP-VOS,62,Mhp-vos: Multiple hypotheses propagation for video object segmentation,"https://scholar.google.com/scholar?cluster=14226363239100634228&hl=en&as_sdt=0,33",6,2019
SelFlow: Self-Supervised Learning of Optical Flow,305,cvpr,63,6,2023-06-03 02:17:52.762000,https://github.com/ppliuboy/SelFlow,387,Selflow: Self-supervised learning of optical flow,"https://scholar.google.com/scholar?cluster=14077594709771456352&hl=en&as_sdt=0,21",14,2019
UPSNet: A Unified Panoptic Segmentation Network,367,cvpr,118,76,2023-06-03 02:17:52.962000,https://github.com/uber-research/UPSNet,625,Upsnet: A unified panoptic segmentation network,"https://scholar.google.com/scholar?cluster=18301475211197676940&hl=en&as_sdt=0,5",28,2019
2.5D Visual Sound,163,cvpr,17,2,2023-06-03 02:17:53.162000,https://github.com/facebookresearch/FAIR-Play,84,2.5 d visual sound,"https://scholar.google.com/scholar?cluster=15955658488872503923&hl=en&as_sdt=0,33",9,2019
Taking a Deeper Look at the Inverse Compositional Algorithm,48,cvpr,29,2,2023-06-03 02:17:53.362000,https://github.com/lvzhaoyang/DeeperInverseCompositionalAlgorithm,152,Taking a deeper look at the inverse compositional algorithm,"https://scholar.google.com/scholar?cluster=16780531933965862286&hl=en&as_sdt=0,5",15,2019
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields,205,cvpr,34,2,2023-06-03 02:17:53.562000,https://github.com/pqhieu/JSIS3D,168,Jsis3d: Joint semantic-instance segmentation of 3d point clouds with multi-task pointwise networks and multi-value conditional random fields,"https://scholar.google.com/scholar?cluster=16927809821668290492&hl=en&as_sdt=0,5",7,2019
Deeper and Wider Siamese Networks for Real-Time Visual Tracking,820,cvpr,178,19,2023-06-03 02:17:53.761000,https://github.com/researchmm/SiamDW,737,Deeper and wider siamese networks for real-time visual tracking,"https://scholar.google.com/scholar?cluster=17696051770837517858&hl=en&as_sdt=0,33",24,2019
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth,248,cvpr,31,8,2023-06-03 02:17:53.961000,https://github.com/davyneven/SpatialEmbeddings,209,Instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth,"https://scholar.google.com/scholar?cluster=14151595211459682440&hl=en&as_sdt=0,5",15,2019
Parsing R-CNN for Instance-Level Human Analysis,103,cvpr,36,25,2023-06-03 02:17:54.160000,https://github.com/soeaver/Parsing-R-CNN,283,Parsing r-cnn for instance-level human analysis,"https://scholar.google.com/scholar?cluster=5570486802749692020&hl=en&as_sdt=0,33",24,2019
Semantic Correlation Promoted Shape-Variant Context for Segmentation,164,cvpr,0,0,2023-06-03 02:17:54.360000,https://github.com/henghuiding/SVC,0,Semantic correlation promoted shape-variant context for segmentation,"https://scholar.google.com/scholar?cluster=16130229518023869149&hl=en&as_sdt=0,33",0,2019
Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification,311,cvpr,4,1,2023-06-03 02:17:54.561000,https://github.com/YifanSun-ReID/VPM-reID,28,Perceive where to focus: Learning visibility-aware part-level features for partial person re-identification,"https://scholar.google.com/scholar?cluster=4047481609137728387&hl=en&as_sdt=0,33",1,2019
Relation-Shape Convolutional Neural Network for Point Cloud Analysis,727,cvpr,74,20,2023-06-03 02:17:54.760000,https://github.com/Yochengliu/Relation-Shape-CNN,402,Relation-shape convolutional neural network for point cloud analysis,"https://scholar.google.com/scholar?cluster=11483419455001652603&hl=en&as_sdt=0,1",21,2019
Meta-Transfer Learning for Few-Shot Learning,976,cvpr,140,38,2023-06-03 02:17:54.960000,https://github.com/yaoyao-liu/meta-transfer-learning,661,Meta-transfer learning for few-shot learning,"https://scholar.google.com/scholar?cluster=16670100435832438519&hl=en&as_sdt=0,33",24,2019
ATOM: Accurate Tracking by Overlap Maximization,971,cvpr,577,55,2023-06-03 02:17:55.160000,https://github.com/visionml/pytracking,2782,Atom: Accurate tracking by overlap maximization,"https://scholar.google.com/scholar?cluster=963225988420087018&hl=en&as_sdt=0,5",90,2019
Visual Tracking via Adaptive Spatially-Regularized Correlation Filters,366,cvpr,23,9,2023-06-03 02:17:55.361000,https://github.com/Daikenan/ASRCF,99,Visual tracking via adaptive spatially-regularized correlation filters,"https://scholar.google.com/scholar?cluster=1936083344177917118&hl=en&as_sdt=0,5",7,2019
BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames,40,cvpr,19,5,2023-06-03 02:17:55.560000,https://github.com/griffbr/BubbleNets,100,Bubblenets: Learning to select the guidance frame in video object segmentation by deep sorting frames,"https://scholar.google.com/scholar?cluster=1778787356825965594&hl=en&as_sdt=0,26",7,2019
Deep RNN Framework for Visual Sequential Applications,41,cvpr,13,0,2023-06-03 02:17:55.760000,https://github.com/BoPang1996/Deep-RNN-Framework,45,Deep rnn framework for visual sequential applications,"https://scholar.google.com/scholar?cluster=13952108559320750928&hl=en&as_sdt=0,1",6,2019
Deep Tree Learning for Zero-Shot Face Anti-Spoofing,207,cvpr,54,19,2023-06-03 02:17:55.961000,https://github.com/yaojieliu/CVPR2019-DeepTreeLearningForZeroShotFaceAntispoofing,180,Deep tree learning for zero-shot face anti-spoofing,"https://scholar.google.com/scholar?cluster=4809478454905690384&hl=en&as_sdt=0,32",13,2019
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images,104,cvpr,78,7,2023-06-03 02:17:56.161000,https://github.com/chenwydj/ultra_high_resolution_segmentation,312,Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images,"https://scholar.google.com/scholar?cluster=9353770986207663680&hl=en&as_sdt=0,25",12,2019
Graph-Based Global Reasoning Networks,416,cvpr,48,8,2023-06-03 02:17:56.361000,https://github.com/facebookresearch/GloRe,193,Graph-based global reasoning networks,"https://scholar.google.com/scholar?cluster=7036510083148234312&hl=en&as_sdt=0,5",9,2019
ArcFace: Additive Angular Margin Loss for Deep Face Recognition,4654,cvpr,4361,952,2023-06-03 02:17:56.561000,https://github.com/deepinsight/insightface,15442,Arcface: Additive angular margin loss for deep face recognition,"https://scholar.google.com/scholar?cluster=14066082468781799933&hl=en&as_sdt=0,21",476,2019
Efficient Parameter-Free Clustering Using First Neighbor Relations,133,cvpr,53,0,2023-06-03 02:17:56.761000,https://github.com/ssarfraz/FINCH-CLustering,277,Efficient parameter-free clustering using first neighbor relations,"https://scholar.google.com/scholar?cluster=5006622334338007650&hl=en&as_sdt=0,5",22,2019
Reversible GANs for Memory-Efficient Image-To-Image Translation,41,cvpr,10,10,2023-06-03 02:17:56.962000,https://github.com/tychovdo/RevGAN,79,Reversible gans for memory-efficient image-to-image translation,"https://scholar.google.com/scholar?cluster=15019867665042081663&hl=en&as_sdt=0,33",10,2019
Latent Space Autoregression for Novelty Detection,386,cvpr,60,9,2023-06-03 02:17:57.162000,https://github.com/aimagelab/novelty-detection,185,Latent space autoregression for novelty detection,"https://scholar.google.com/scholar?cluster=18196632214960310707&hl=en&as_sdt=0,14",11,2019
Feature Denoising for Improving Adversarial Robustness,784,cvpr,86,2,2023-06-03 02:17:57.362000,https://github.com/facebookresearch/ImageNet-Adversarial-Training,664,Feature denoising for improving adversarial robustness,"https://scholar.google.com/scholar?cluster=961363392550158116&hl=en&as_sdt=0,39",20,2019
Selective Kernel Networks,1592,cvpr,105,6,2023-06-03 02:17:57.562000,https://github.com/implus/SKNet,542,Selective kernel networks,"https://scholar.google.com/scholar?cluster=11785614849903023316&hl=en&as_sdt=0,5",9,2019
FlowNet3D: Learning Scene Flow in 3D Point Clouds,354,cvpr,84,18,2023-06-03 02:17:57.762000,https://github.com/xingyul/flownet3d,339,Flownet3d: Learning scene flow in 3d point clouds,"https://scholar.google.com/scholar?cluster=15188873080391320732&hl=en&as_sdt=0,47",13,2019
Bag of Tricks for Image Classification with Convolutional Neural Networks,1246,cvpr,1198,60,2023-06-03 02:17:57.961000,https://github.com/dmlc/gluon-cv,5561,Bag of tricks for image classification with convolutional neural networks,"https://scholar.google.com/scholar?cluster=8341554460743296519&hl=en&as_sdt=0,33",154,2019
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack,228,cvpr,16,2,2023-06-03 02:17:58.163000,https://github.com/elliothe/CVPR_2019_PNI,38,Parametric noise injection: Trainable randomness to improve deep neural network robustness against adversarial attack,"https://scholar.google.com/scholar?cluster=3553606349914507364&hl=en&as_sdt=0,6",2,2019
Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects,276,cvpr,16,0,2023-06-03 02:17:58.362000,https://github.com/airalcorn2/strike-with-a-pose,75,Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects,"https://scholar.google.com/scholar?cluster=16755141960750698067&hl=en&as_sdt=0,11",5,2019
SparseFool: A Few Pixels Make a Big Difference,152,cvpr,11,0,2023-06-03 02:17:58.562000,https://github.com/LTS4/SparseFool,48,Sparsefool: a few pixels make a big difference,"https://scholar.google.com/scholar?cluster=6158227118856345030&hl=en&as_sdt=0,44",4,2019
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification,572,cvpr,61,8,2023-06-03 02:17:58.762000,https://github.com/zhunzhong07/ECN,297,Invariance matters: Exemplar memory for domain adaptive person re-identification,"https://scholar.google.com/scholar?cluster=1740171451548448125&hl=en&as_sdt=0,5",11,2019
Dissecting Person Re-Identification From the Viewpoint of Viewpoint,180,cvpr,15,1,2023-06-03 02:17:58.971000,https://github.com/sxzrt/Dissecting-Person-Re-ID-from-the-Viewpoint-of-Viewpoint,117,Dissecting person re-identification from the viewpoint of viewpoint,"https://scholar.google.com/scholar?cluster=6413659946848887161&hl=en&as_sdt=0,1",8,2019
Instance-Level Meta Normalization,25,cvpr,3,0,2023-06-03 02:17:59.173000,https://github.com/Gasoonjia/ILM-Norm,17,Instance-level meta normalization,"https://scholar.google.com/scholar?cluster=17437121449575767985&hl=en&as_sdt=0,5",2,2019
Iterative Normalization: Beyond Standardization Towards Efficient Whitening,93,cvpr,4,0,2023-06-03 02:17:59.373000,https://github.com/huangleiBuaa/IterNorm,22,Iterative normalization: Beyond standardization towards efficient whitening,"https://scholar.google.com/scholar?cluster=17943009712133460569&hl=en&as_sdt=0,5",4,2019
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells,150,cvpr,25,2,2023-06-03 02:17:59.574000,https://github.com/drsleep/nas-segm-pytorch,143,Fast neural architecture search of compact semantic segmentation models via auxiliary cells,"https://scholar.google.com/scholar?cluster=8910584954060361446&hl=en&as_sdt=0,5",8,2019
Generating 3D Adversarial Point Clouds,214,cvpr,26,3,2023-06-03 02:17:59.774000,https://github.com/xiangchong1/3d-adv-pc,92,Generating 3d adversarial point clouds,"https://scholar.google.com/scholar?cluster=10039699469817980264&hl=en&as_sdt=0,5",6,2019
Partial Order Pruning: For Best Speed/Accuracy Trade-Off in Neural Architecture Search,128,cvpr,25,1,2023-06-03 02:17:59.974000,https://github.com/lixincn2015/Partial-Order-Pruning,147,Partial order pruning: for best speed/accuracy trade-off in neural architecture search,"https://scholar.google.com/scholar?cluster=8555049247914418659&hl=en&as_sdt=0,5",6,2019
Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics,204,cvpr,38,7,2023-06-03 02:18:00.175000,https://github.com/Yunbo426/MIM,137,Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics,"https://scholar.google.com/scholar?cluster=15070346920682817473&hl=en&as_sdt=0,5",5,2019
Distilling Object Detectors With Fine-Grained Feature Imitation,264,cvpr,70,22,2023-06-03 02:18:00.375000,https://github.com/twangnh/Distilling-Object-Detectors,402,Distilling object detectors with fine-grained feature imitation,"https://scholar.google.com/scholar?cluster=5085359464416711778&hl=en&as_sdt=0,11",10,2019
Adapting Object Detectors via Selective Cross-Domain Alignment,294,cvpr,15,4,2023-06-03 02:18:00.576000,https://github.com/xinge008/SCDA,78,Adapting object detectors via selective cross-domain alignment,"https://scholar.google.com/scholar?cluster=7497629776340245603&hl=en&as_sdt=0,20",2,2019
Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure,162,cvpr,12,2,2023-06-03 02:18:00.775000,https://github.com/ShawnDing1994/Centripetal-SGD,59,Centripetal sgd for pruning very deep convolutional networks with complicated structure,"https://scholar.google.com/scholar?cluster=17050571465031021795&hl=en&as_sdt=0,5",5,2019
Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation,110,cvpr,5,2,2023-06-03 02:18:00.976000,https://github.com/shenyunhang/WS-JDS,19,Cyclic guidance for weakly supervised joint detection and segmentation,"https://scholar.google.com/scholar?cluster=764160567722431856&hl=en&as_sdt=0,5",1,2019
"ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network",369,cvpr,83,9,2023-06-03 02:18:01.176000,https://github.com/sacmehta/EdgeNets,394,"Espnetv2: A light-weight, power efficient, and general purpose convolutional neural network","https://scholar.google.com/scholar?cluster=16963777558588378530&hl=en&as_sdt=0,33",21,2019
Hybrid Task Cascade for Instance Segmentation,1036,cvpr,8690,798,2023-06-03 02:18:01.378000,https://github.com/open-mmlab/mmdetection,24397,Hybrid task cascade for instance segmentation,"https://scholar.google.com/scholar?cluster=1410244110601279407&hl=en&as_sdt=0,33",372,2019
Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization,26,cvpr,10,0,2023-06-03 02:18:01.578000,https://github.com/junbaoZHUO/UODTN,43,Unsupervised open domain recognition by semantic discrepancy minimization,"https://scholar.google.com/scholar?cluster=16433699674820383702&hl=en&as_sdt=0,33",3,2019
Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition,335,cvpr,40,22,2023-06-03 02:18:01.778000,https://github.com/researchmm/tasn,214,Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition,"https://scholar.google.com/scholar?cluster=17579017795365184505&hl=en&as_sdt=0,33",7,2019
PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud,1704,cvpr,411,141,2023-06-03 02:18:01.978000,https://github.com/sshaoshuai/PointRCNN,1595,Pointrcnn: 3d object proposal generation and detection from point cloud,"https://scholar.google.com/scholar?cluster=7703744261736010068&hl=en&as_sdt=0,33",33,2019
Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning,610,cvpr,76,13,2023-06-03 02:18:02.178000,https://github.com/MalongTech/research-ms-loss,462,Multi-similarity loss with general pair weighting for deep metric learning,"https://scholar.google.com/scholar?cluster=16955065946481735055&hl=en&as_sdt=0,33",23,2019
Class-Balanced Loss Based on Effective Number of Samples,1585,cvpr,67,18,2023-06-03 02:18:02.378000,https://github.com/richardaecn/class-balanced-loss,564,Class-balanced loss based on effective number of samples,"https://scholar.google.com/scholar?cluster=2548366253851952984&hl=en&as_sdt=0,5",20,2019
Generative Dual Adversarial Network for Generalized Zero-Shot Learning,216,cvpr,11,4,2023-06-03 02:18:02.578000,https://github.com/stevehuanghe/GDAN,53,Generative dual adversarial network for generalized zero-shot learning,"https://scholar.google.com/scholar?cluster=196772291159062355&hl=en&as_sdt=0,33",1,2019
Learning to Learn From Noisy Labeled Data,302,cvpr,29,8,2023-06-03 02:18:02.778000,https://github.com/LiJunnan1992/MLNT,116,Learning to learn from noisy labeled data,"https://scholar.google.com/scholar?cluster=357917848207300571&hl=en&as_sdt=0,5",6,2019
Query-Guided End-To-End Person Search,120,cvpr,6,3,2023-06-03 02:18:02.978000,https://github.com/munjalbharti/Query-guided-End-to-End-Person-Search,68,Query-guided end-to-end person search,"https://scholar.google.com/scholar?cluster=1165933786153172260&hl=en&as_sdt=0,31",6,2019
Libra R-CNN: Towards Balanced Learning for Object Detection,1162,cvpr,8690,798,2023-06-03 02:18:03.179000,https://github.com/open-mmlab/mmdetection,24397,Libra r-cnn: Towards balanced learning for object detection,"https://scholar.google.com/scholar?cluster=13559955005752027345&hl=en&as_sdt=0,39",372,2019
Label Propagation for Deep Semi-Supervised Learning,537,cvpr,19,2,2023-06-03 02:18:03.379000,https://github.com/ahmetius/LP-DeepSSL,102,Label propagation for deep semi-supervised learning,"https://scholar.google.com/scholar?cluster=9368555603387579120&hl=en&as_sdt=0,33",7,2019
Deep Global Generalized Gaussian Networks,14,cvpr,1,0,2023-06-03 02:18:03.579000,https://github.com/csqlwang/3G-Net,6,Deep global generalized gaussian networks,"https://scholar.google.com/scholar?cluster=9845683876306953514&hl=en&as_sdt=0,10",3,2019
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-Based Image Retrieval,110,cvpr,22,4,2023-06-03 02:18:03.778000,https://github.com/AnjanDutta/sem-pcyc,106,Semantically tied paired cycle consistency for zero-shot sketch-based image retrieval,"https://scholar.google.com/scholar?cluster=12348799218521490025&hl=en&as_sdt=0,5",11,2019
Bottom-Up Object Detection by Grouping Extreme and Center Points,884,cvpr,171,31,2023-06-03 02:18:03.978000,https://github.com/xingyizhou/ExtremeNet,1023,Bottom-up object detection by grouping extreme and center points,"https://scholar.google.com/scholar?cluster=18336064849612757553&hl=en&as_sdt=0,33",26,2019
Context-Aware Crowd Counting,512,cvpr,46,2,2023-06-03 02:18:04.179000,https://github.com/weizheliu/Context-Aware-Crowd-Counting,145,Context-aware crowd counting,"https://scholar.google.com/scholar?cluster=5927348553478395733&hl=en&as_sdt=0,33",4,2019
Shape Robust Text Detection With Progressive Scale Expansion Network,466,cvpr,345,101,2023-06-03 02:18:04.379000,https://github.com/whai362/PSENet,1145,Shape robust text detection with progressive scale expansion network,"https://scholar.google.com/scholar?cluster=18151448532266277965&hl=en&as_sdt=0,33",56,2019
Detect-To-Retrieve: Efficient Regional Aggregation for Image Search,116,cvpr,46274,1204,2023-06-03 02:18:04.578000,https://github.com/tensorflow/models,75883,Detect-to-retrieve: Efficient regional aggregation for image search,"https://scholar.google.com/scholar?cluster=14581235657428193058&hl=en&as_sdt=0,33",2774,2019
Dual Encoding for Zero-Example Video Retrieval,239,cvpr,33,17,2023-06-03 02:18:04.779000,https://github.com/danieljf24/dual_encoding,154,Dual encoding for zero-example video retrieval,"https://scholar.google.com/scholar?cluster=16428040265188562035&hl=en&as_sdt=0,33",7,2019
Towards Accurate One-Stage Object Detection With AP-Loss,121,cvpr,26,6,2023-06-03 02:18:04.979000,https://github.com/cccorn/AP-loss,168,Towards accurate one-stage object detection with ap-loss,"https://scholar.google.com/scholar?cluster=4378389006630715202&hl=en&as_sdt=0,43",7,2019
Destruction and Construction Learning for Fine-Grained Image Recognition,366,cvpr,155,33,2023-06-03 02:18:05.179000,https://github.com/JDAI-CV/DCL,560,Destruction and construction learning for fine-grained image recognition,"https://scholar.google.com/scholar?cluster=15023579179489900756&hl=en&as_sdt=0,33",18,2019
Panoptic Segmentation,1087,cvpr,182,41,2023-06-03 02:18:05.380000,https://github.com/cocodataset/panopticapi,355,Panoptic segmentation,"https://scholar.google.com/scholar?cluster=5074178277454044798&hl=en&as_sdt=0,44",11,2019
High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection,383,cvpr,194,71,2023-06-03 02:18:05.580000,https://github.com/liuwei16/CSP,743,High-level semantic feature detection: A new perspective for pedestrian detection,"https://scholar.google.com/scholar?cluster=17881481840442344449&hl=en&as_sdt=0,33",28,2019
Explore-Exploit Graph Traversal for Image Retrieval,34,cvpr,10,1,2023-06-03 02:18:05.780000,https://github.com/layer6ai-labs/egt,31,Explore-exploit graph traversal for image retrieval,"https://scholar.google.com/scholar?cluster=4153424755614235693&hl=en&as_sdt=0,33",8,2019
Ranked List Loss for Deep Metric Learning,232,cvpr,8,4,2023-06-03 02:18:05.980000,https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DML,61,Ranked list loss for deep metric learning,"https://scholar.google.com/scholar?cluster=12233728284002453426&hl=en&as_sdt=0,21",5,2019
Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction,65,cvpr,25,3,2023-06-03 02:18:06.180000,https://github.com/chenhsuanlin/photometric-mesh-optim,205,Photometric mesh optimization for video-aligned 3d object reconstruction,"https://scholar.google.com/scholar?cluster=4471815922858483977&hl=en&as_sdt=0,31",18,2019
Guided Stereo Matching,67,cvpr,21,2,2023-06-03 02:18:06.380000,https://github.com/mattpoggi/guided-stereo,106,Guided stereo matching,"https://scholar.google.com/scholar?cluster=15108739186090417114&hl=en&as_sdt=0,10",6,2019
Precise Detection in Densely Packed Scenes,138,cvpr,172,16,2023-06-03 02:18:06.579000,https://github.com/eg4000/SKU110K_CVPR19,724,Precise detection in densely packed scenes,"https://scholar.google.com/scholar?cluster=9843821758940633840&hl=en&as_sdt=0,5",42,2019
Variational Prototyping-Encoder: One-Shot Learning With Prototypical Images,58,cvpr,14,7,2023-06-03 02:18:06.779000,https://github.com/mibastro/VPE,52,Variational prototyping-encoder: One-shot learning with prototypical images,"https://scholar.google.com/scholar?cluster=12933387554347957331&hl=en&as_sdt=0,5",3,2019
Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss,161,cvpr,19,2,2023-06-03 02:18:06.980000,https://github.com/roysubhankar/dwt-domain-adaptation,62,Unsupervised domain adaptation using feature-whitening and consensus loss,"https://scholar.google.com/scholar?cluster=15139839283686829540&hl=en&as_sdt=0,5",5,2019
FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation,368,cvpr,46274,1204,2023-06-03 02:18:07.184000,https://github.com/tensorflow/models,75883,Feelvos: Fast end-to-end embedding learning for video object segmentation,"https://scholar.google.com/scholar?cluster=4014880912704688403&hl=en&as_sdt=0,5",2774,2019
Fast Interactive Object Annotation With Curve-GCN,213,cvpr,132,10,2023-06-03 02:18:07.384000,https://github.com/fidler-lab/curve-gcn,835,Fast interactive object annotation with curve-gcn,"https://scholar.google.com/scholar?cluster=7580296482516718366&hl=en&as_sdt=0,5",39,2019
Single-Image Piece-Wise Planar 3D Reconstruction via Associative Embedding,77,cvpr,82,10,2023-06-03 02:18:07.585000,https://github.com/svip-lab/PlanarReconstruction,333,Single-image piece-wise planar 3d reconstruction via associative embedding,"https://scholar.google.com/scholar?cluster=1605501962554659722&hl=en&as_sdt=0,5",22,2019
RVOS: End-To-End Recurrent Network for Video Object Segmentation,205,cvpr,53,24,2023-06-03 02:18:07.786000,https://github.com/imatge-upc/rvos,274,Rvos: End-to-end recurrent network for video object segmentation,"https://scholar.google.com/scholar?cluster=6872099416296198308&hl=en&as_sdt=0,31",17,2019
3DN: 3D Deformation Network,108,cvpr,16,8,2023-06-03 02:18:07.986000,https://github.com/laughtervv/3DN,99,3dn: 3d deformation network,"https://scholar.google.com/scholar?cluster=5142067875695068873&hl=en&as_sdt=0,44",10,2019
HorizonNet: Learning Room Layout With 1D Representation and Pano Stretch Data Augmentation,135,cvpr,77,25,2023-06-03 02:18:08.186000,https://github.com/sunset1995/HorizonNet,280,Horizonnet: Learning room layout with 1d representation and pano stretch data augmentation,"https://scholar.google.com/scholar?cluster=17660019163818717413&hl=en&as_sdt=0,44",21,2019
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation,77,cvpr,7,3,2023-06-03 02:18:08.386000,https://github.com/maunzzz/cross-season-segmentation,40,A cross-season correspondence dataset for robust semantic segmentation,"https://scholar.google.com/scholar?cluster=4745433650118523256&hl=en&as_sdt=0,36",4,2019
Scene Parsing via Integrated Classification Model and Variance-Based Regularization,10,cvpr,0,0,2023-06-03 02:18:08.586000,https://github.com/shihengcan/ICM-matcaffe,11,Scene parsing via integrated classification model and variance-based regularization,"https://scholar.google.com/scholar?cluster=2535925350992241145&hl=en&as_sdt=0,37",2,2019
ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features,296,cvpr,66,12,2023-06-03 02:18:08.786000,https://github.com/ISICV/ManTraNet,202,Mantra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features,"https://scholar.google.com/scholar?cluster=874511673684313682&hl=en&as_sdt=0,43",10,2019
On Zero-Shot Recognition of Generic Objects,15,cvpr,2,0,2023-06-03 02:18:08.986000,https://github.com/TristHas/GOZ,16,On zero-shot recognition of generic objects,"https://scholar.google.com/scholar?cluster=1899006673146116471&hl=en&as_sdt=0,5",3,2019
Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry,263,cvpr,95,18,2023-06-03 02:18:09.187000,https://github.com/mkocabas/EpipolarPose,560,Self-supervised learning of 3d human pose using multi-view geometry,"https://scholar.google.com/scholar?cluster=7066571099476763696&hl=en&as_sdt=0,33",24,2019
"DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images",323,cvpr,329,50,2023-06-03 02:18:09.387000,https://github.com/switchablenorms/DeepFashion2,1919,"Deepfashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images","https://scholar.google.com/scholar?cluster=14082985904236985683&hl=en&as_sdt=0,5",93,2019
REPAIR: Removing Representation Bias by Dataset Resampling,214,cvpr,11,3,2023-06-03 02:18:09.587000,https://github.com/JerryYLi/Dataset-REPAIR,53,Repair: Removing representation bias by dataset resampling,"https://scholar.google.com/scholar?cluster=14115376234602548755&hl=en&as_sdt=0,5",5,2019
Label Efficient Semi-Supervised Learning via Graph Filtering,160,cvpr,14,1,2023-06-03 02:18:09.787000,https://github.com/liqimai/Efficient-SSL,76,Label efficient semi-supervised learning via graph filtering,"https://scholar.google.com/scholar?cluster=6165593368616188043&hl=en&as_sdt=0,15",5,2019
Linkage Based Face Clustering via Graph Convolution Network,180,cvpr,87,28,2023-06-03 02:18:09.987000,https://github.com/Zhongdao/gcn_clustering,349,Linkage based face clustering via graph convolution network,"https://scholar.google.com/scholar?cluster=1863142488099065715&hl=en&as_sdt=0,7",10,2019
Tightness-Aware Evaluation Protocol for Scene Text Detection,33,cvpr,45,3,2023-06-03 02:18:10.188000,https://github.com/Yuliang-Liu/TIoU-metric,209,Tightness-aware evaluation protocol for scene text detection,"https://scholar.google.com/scholar?cluster=1090233099780103403&hl=en&as_sdt=0,5",7,2019
Creative Flow+ Dataset,18,cvpr,7,1,2023-06-03 02:18:10.387000,https://github.com/creativefloworg/creativeflow,58,Creative flow+ dataset,"https://scholar.google.com/scholar?cluster=11073842256461154770&hl=en&as_sdt=0,36",6,2019
PointConv: Deep Convolutional Networks on 3D Point Clouds,1230,cvpr,98,22,2023-06-03 02:18:10.587000,https://github.com/DylanWusee/pointconv,470,Pointconv: Deep convolutional networks on 3d point clouds,"https://scholar.google.com/scholar?cluster=11585958970100977527&hl=en&as_sdt=0,44",11,2019
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction,288,cvpr,62,0,2023-06-03 02:18:10.788000,https://github.com/barisgecer/ganfit,581,Ganfit: Generative adversarial network fitting for high fidelity 3d face reconstruction,"https://scholar.google.com/scholar?cluster=18439915194255667733&hl=en&as_sdt=0,5",50,2019
A Neurobiological Evaluation Metric for Neural Network Model Search,9,cvpr,5,0,2023-06-03 02:18:10.988000,https://github.com/CVRL/human-model-similarity,10,A neurobiological evaluation metric for neural network model search,"https://scholar.google.com/scholar?cluster=14287587135308208787&hl=en&as_sdt=0,5",7,2019
Learning to Reconstruct People in Clothing From a Single RGB Camera,261,cvpr,79,1,2023-06-03 02:18:11.188000,https://github.com/thmoa/octopus,236,Learning to reconstruct people in clothing from a single RGB camera,"https://scholar.google.com/scholar?cluster=6111852910175872533&hl=en&as_sdt=0,3",16,2019
Learning to Adapt for Stereo,64,cvpr,11,4,2023-06-03 02:18:11.388000,https://github.com/CVLAB-Unibo/Learning2AdaptForStereo,77,Learning to adapt for stereo,"https://scholar.google.com/scholar?cluster=2575889421686296942&hl=en&as_sdt=0,10",18,2019
3D Appearance Super-Resolution With Deep Learning,33,cvpr,19,6,2023-06-03 02:18:11.588000,https://github.com/ofsoundof/3D_Appearance_SR,81,3D appearance super-resolution with deep learning,"https://scholar.google.com/scholar?cluster=15618858737431977599&hl=en&as_sdt=0,33",5,2019
A Perceptual Prediction Framework for Self Supervised Event Segmentation,56,cvpr,1,0,2023-06-03 02:18:11.788000,https://github.com/CVPRUSFTampa/EventSegmentation,8,A perceptual prediction framework for self supervised event segmentation,"https://scholar.google.com/scholar?cluster=395493901031327715&hl=en&as_sdt=0,5",2,2019
Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes,16,cvpr,47,1,2023-06-03 02:18:11.988000,https://github.com/ziquan111/RobustPCLReconstruction,129,Robust point cloud based reconstruction of large-scale outdoor scenes,"https://scholar.google.com/scholar?cluster=17457359442789895622&hl=en&as_sdt=0,34",22,2019
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection,281,cvpr,40,16,2023-06-03 02:18:12.188000,https://github.com/jx-zhong-for-academic-purpose/GCN-Anomaly-Detection,208,Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection,"https://scholar.google.com/scholar?cluster=9753609236570455793&hl=en&as_sdt=0,5",14,2019
Recurrent MVSNet for High-Resolution Multi-View Stereo Depth Inference,351,cvpr,299,69,2023-06-03 02:18:12.388000,https://github.com/YoYo000/MVSNet,1191,Recurrent mvsnet for high-resolution multi-view stereo depth inference,"https://scholar.google.com/scholar?cluster=4238059022746390832&hl=en&as_sdt=0,31",44,2019
"Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on N-Spheres",54,cvpr,16,0,2023-06-03 02:18:12.588000,https://github.com/leoshine/Spherical_Regression,140,"Spherical regression: Learning viewpoints, surface normals and 3d rotations on n-spheres","https://scholar.google.com/scholar?cluster=12003786389855599350&hl=en&as_sdt=0,5",11,2019
The Perfect Match: 3D Point Cloud Matching With Smoothed Densities,325,cvpr,92,4,2023-06-03 02:18:12.788000,https://github.com/zgojcic/3DSmoothNet,405,The perfect match: 3d point cloud matching with smoothed densities,"https://scholar.google.com/scholar?cluster=7009988007929913159&hl=en&as_sdt=0,10",16,2019
Unsupervised Deep Tracking,354,cvpr,24,3,2023-06-03 02:18:12.988000,https://github.com/594422814/UDT,154,Unsupervised deep tracking,"https://scholar.google.com/scholar?cluster=10867085456971449174&hl=en&as_sdt=0,5",5,2019
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation,151,cvpr,23,10,2023-06-03 02:18:13.187000,https://github.com/sshan-zhao/GASDA,133,Geometry-aware symmetric domain adaptation for monocular depth estimation,"https://scholar.google.com/scholar?cluster=7374792956146356394&hl=en&as_sdt=0,31",13,2019
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers,56,cvpr,24,7,2023-06-03 02:18:13.388000,https://github.com/zhen-he/tracking-by-animation,122,Tracking by animation: Unsupervised learning of multi-object attentive trackers,"https://scholar.google.com/scholar?cluster=13050152165706034610&hl=en&as_sdt=0,18",7,2019
PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing,538,cvpr,19,12,2023-06-03 02:18:13.588000,https://github.com/hszhao/PointWeb,177,Pointweb: Enhancing local neighborhood features for point cloud processing,"https://scholar.google.com/scholar?cluster=984130581400842453&hl=en&as_sdt=0,5",3,2019
Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge,185,cvpr,20,1,2023-06-03 02:18:13.788000,https://github.com/fabiotosi92/monoResMatch-Tensorflow,118,Learning monocular depth estimation infusing traditional stereo knowledge,"https://scholar.google.com/scholar?cluster=2961342571768820762&hl=en&as_sdt=0,5",6,2019
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception,59,cvpr,9,1,2023-06-03 02:18:13.988000,https://github.com/mengyuest/SIGNet,20,Signet: Semantic instance aided unsupervised 3d geometry perception,"https://scholar.google.com/scholar?cluster=9357944003335228407&hl=en&as_sdt=0,33",7,2019
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints,734,cvpr,25,0,2023-06-03 02:18:14.189000,https://github.com/coolsunxu/sophie,44,Sophie: An attentive gan for predicting paths compliant to social and physical constraints,"https://scholar.google.com/scholar?cluster=8569269025593933345&hl=en&as_sdt=0,10",2,2019
Leveraging Shape Completion for 3D Siamese Tracking,116,cvpr,27,2,2023-06-03 02:18:14.389000,https://github.com/SilvioGiancola/ShapeCompletion3DTracking,112,Leveraging shape completion for 3d siamese tracking,"https://scholar.google.com/scholar?cluster=9608282867855392423&hl=en&as_sdt=0,11",7,2019
Learning 3D Human Dynamics From Video,394,cvpr,87,10,2023-06-03 02:18:14.589000,https://github.com/akanazawa/human_dynamics,605,Learning 3d human dynamics from video,"https://scholar.google.com/scholar?cluster=825392298300522171&hl=en&as_sdt=0,5",25,2019
Unsupervised Face Normalization With Extreme Pose and Expression in the Wild,82,cvpr,29,10,2023-06-03 02:18:14.789000,https://github.com/mx54039q/fnm,122,Unsupervised face normalization with extreme pose and expression in the wild,"https://scholar.google.com/scholar?cluster=604172293447148911&hl=en&as_sdt=0,33",4,2019
Spatiotemporal CNN for Video Object Segmentation,85,cvpr,19,6,2023-06-03 02:18:14.988000,https://github.com/longyin880815/STCNN,149,Spatiotemporal CNN for video object segmentation,"https://scholar.google.com/scholar?cluster=10827546287580568374&hl=en&as_sdt=0,1",9,2019
Lending Orientation to Neural Networks for Cross-View Geo-Localization,115,cvpr,18,3,2023-06-03 02:18:15.188000,https://github.com/Liumouliu/OriCNN,74,Lending orientation to neural networks for cross-view geo-localization,"https://scholar.google.com/scholar?cluster=2657855986027249738&hl=en&as_sdt=0,33",5,2019
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction,72,cvpr,5,1,2023-06-03 02:18:15.388000,https://github.com/alexklwong/adareg-monodispnet,24,Bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction,"https://scholar.google.com/scholar?cluster=17912807222442486761&hl=en&as_sdt=0,15",4,2019
Generating Multiple Hypotheses for 3D Human Pose Estimation With Mixture Density Network,155,cvpr,9,13,2023-06-03 02:18:15.589000,https://github.com/chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network,92,Generating multiple hypotheses for 3d human pose estimation with mixture density network,"https://scholar.google.com/scholar?cluster=2014886798638153110&hl=en&as_sdt=0,33",7,2019
CrossInfoNet: Multi-Task Information Sharing Based Hand Pose Estimation,72,cvpr,26,8,2023-06-03 02:18:15.789000,https://github.com/dumyy/handpose,128,Crossinfonet: Multi-task information sharing based hand pose estimation,"https://scholar.google.com/scholar?cluster=8584161236933735663&hl=en&as_sdt=0,33",8,2019
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis,457,cvpr,66,4,2023-06-03 02:18:15.990000,https://github.com/HelenMao/MSGAN,404,Mode seeking generative adversarial networks for diverse image synthesis,"https://scholar.google.com/scholar?cluster=4321964936044860868&hl=en&as_sdt=0,5",12,2019
Pluralistic Image Completion,402,cvpr,144,9,2023-06-03 02:18:16.190000,https://github.com/lyndonzheng/Pluralistic-Inpainting,633,Pluralistic image completion,"https://scholar.google.com/scholar?cluster=1587792053076409427&hl=en&as_sdt=0,44",18,2019
Dance With Flow: Two-In-One Stream Action Detection,86,cvpr,9,3,2023-06-03 02:18:16.390000,https://github.com/jiaozizhao/Two-in-One-ActionDetection,31,Dance with flow: Two-in-one stream action detection,"https://scholar.google.com/scholar?cluster=9926061313509022213&hl=en&as_sdt=0,48",1,2019
Attention-Aware Multi-Stroke Style Transfer,123,cvpr,14,0,2023-06-03 02:18:16.590000,https://github.com/JianqiangRen/AAMS,77,Attention-aware multi-stroke style transfer,"https://scholar.google.com/scholar?cluster=13939843994671944521&hl=en&as_sdt=0,33",5,2019
Representation Flow for Action Recognition,154,cvpr,60,5,2023-06-03 02:18:16.791000,https://github.com/piergiaj/representation-flow-cvpr19,253,Representation flow for action recognition,"https://scholar.google.com/scholar?cluster=12306061084535496096&hl=en&as_sdt=0,5",8,2019
Peeking Into the Future: Predicting Future Person Activities and Locations in Videos,333,cvpr,98,2,2023-06-03 02:18:16.992000,https://github.com/google/next-prediction,336,Peeking into the future: Predicting future person activities and locations in videos,"https://scholar.google.com/scholar?cluster=11429514602467603267&hl=en&as_sdt=0,5",19,2019
LSTA: Long Short-Term Attention for Egocentric Action Recognition,145,cvpr,8,3,2023-06-03 02:18:17.192000,https://github.com/swathikirans/LSTA,34,Lsta: Long short-term attention for egocentric action recognition,"https://scholar.google.com/scholar?cluster=17064955558118197026&hl=en&as_sdt=0,16",4,2019
Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting,362,cvpr,76,23,2023-06-03 02:18:17.391000,https://github.com/researchmm/PEN-Net-for-Inpainting,326,Learning pyramid-context encoder network for high-quality image inpainting,"https://scholar.google.com/scholar?cluster=14650766853064741481&hl=en&as_sdt=0,33",13,2019
Learning Actor Relation Graphs for Group Activity Recognition,198,cvpr,37,15,2023-06-03 02:18:17.591000,https://github.com/wjchaoGit/Group-Activity-Recognition,163,Learning actor relation graphs for group activity recognition,"https://scholar.google.com/scholar?cluster=16815703044108262915&hl=en&as_sdt=0,5",8,2019
Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation,228,cvpr,30,2,2023-06-03 02:18:17.792000,https://github.com/visinf/irr,183,Iterative residual refinement for joint optical flow and occlusion estimation,"https://scholar.google.com/scholar?cluster=3156347272675228552&hl=en&as_sdt=0,44",9,2019
Out-Of-Distribution Detection for Generalized Zero-Shot Action Recognition,119,cvpr,12,5,2023-06-03 02:18:17.991000,https://github.com/naraysa/gzsl-od,51,Out-of-distribution detection for generalized zero-shot action recognition,"https://scholar.google.com/scholar?cluster=10382614447629534528&hl=en&as_sdt=0,25",5,2019
Light Field Messaging With Deep Photographic Steganography,85,cvpr,6,4,2023-06-03 02:18:18.191000,https://github.com/mathski/LFM,31,Light field messaging with deep photographic steganography,"https://scholar.google.com/scholar?cluster=2436205911377935623&hl=en&as_sdt=0,33",3,2019
ROI Pooled Correlation Filters for Visual Tracking,85,cvpr,1,0,2023-06-03 02:18:18.391000,https://github.com/rumsyx/RPCF,9,Roi pooled correlation filters for visual tracking,"https://scholar.google.com/scholar?cluster=9895033358928965295&hl=en&as_sdt=0,33",3,2019
Deep Video Inpainting,163,cvpr,91,15,2023-06-03 02:18:18.591000,https://github.com/mcahny/Deep-Video-Inpainting,473,Deep video inpainting,"https://scholar.google.com/scholar?cluster=13347552587483542208&hl=en&as_sdt=0,5",14,2019
Style Transfer by Relaxed Optimal Transport and Self-Similarity,173,cvpr,37,3,2023-06-03 02:18:18.791000,https://github.com/nkolkin13/STROTSS,298,Style transfer by relaxed optimal transport and self-similarity,"https://scholar.google.com/scholar?cluster=15865606145273998674&hl=en&as_sdt=0,11",13,2019
Learning Attraction Field Representation for Robust Line Segment Detection,88,cvpr,63,14,2023-06-03 02:18:18.991000,https://github.com/cherubicXN/afm_cvpr2019,276,Learning attraction field representation for robust line segment detection,"https://scholar.google.com/scholar?cluster=13279452849364535956&hl=en&as_sdt=0,46",16,2019
Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-To-Image Translation,99,cvpr,6,1,2023-06-03 02:18:19.191000,https://github.com/aimagelab/art2real,75,Art2real: Unfolding the reality of artworks via semantically-aware image-to-image translation,"https://scholar.google.com/scholar?cluster=282005273333774515&hl=en&as_sdt=0,47",10,2019
"Capture, Learning, and Synthesis of 3D Speaking Styles",163,cvpr,255,32,2023-06-03 02:18:19.390000,https://github.com/TimoBolkart/voca,972,"Capture, learning, and synthesis of 3D speaking styles","https://scholar.google.com/scholar?cluster=12568957979354957566&hl=en&as_sdt=0,5",38,2019
Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds Using Convolutional Neural Networks,67,cvpr,12,0,2023-06-03 02:18:19.591000,https://github.com/sitzikbs/Nesti-Net,41,Nesti-net: Normal estimation for unstructured 3d point clouds using convolutional neural networks,"https://scholar.google.com/scholar?cluster=12906540584052048324&hl=en&as_sdt=0,33",3,2019
Camera Lens Super-Resolution,121,cvpr,29,8,2023-06-03 02:18:19.792000,https://github.com/ngchc/CameraSR,166,Camera lens super-resolution,"https://scholar.google.com/scholar?cluster=2417165424694352312&hl=en&as_sdt=0,11",8,2019
Deep Geometric Prior for Surface Reconstruction,140,cvpr,15,4,2023-06-03 02:18:19.991000,https://github.com/fwilliams/deep-geometric-prior,110,Deep geometric prior for surface reconstruction,"https://scholar.google.com/scholar?cluster=15060411641721328639&hl=en&as_sdt=0,32",6,2019
Frame-Consistent Recurrent Video Deraining With Dual-Level Flow,85,cvpr,4,4,2023-06-03 02:18:20.192000,https://github.com/flyywh/Dual-FLow-Video-Deraining-CVPR-2019,26,Frame-consistent recurrent video deraining with dual-level flow,"https://scholar.google.com/scholar?cluster=7898668195452947142&hl=en&as_sdt=0,5",4,2019
Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels,320,cvpr,214,13,2023-06-03 02:18:20.393000,https://github.com/cszn/DPSR,822,Deep plug-and-play super-resolution for arbitrary blur kernels,"https://scholar.google.com/scholar?cluster=6289475413772253545&hl=en&as_sdt=0,33",26,2019
Learning Implicit Fields for Generative Shape Modeling,1031,cvpr,40,0,2023-06-03 02:18:20.593000,https://github.com/czq142857/implicit-decoder,372,Learning implicit fields for generative shape modeling,"https://scholar.google.com/scholar?cluster=17379873456343060326&hl=en&as_sdt=0,33",16,2019
Reliable and Efficient Image Cropping: A Grid Anchor Based Approach,53,cvpr,14,4,2023-06-03 02:18:20.793000,https://github.com/HuiZeng/Grid-Anchor-based-Image-Cropping,107,Reliable and efficient image cropping: A grid anchor based approach,"https://scholar.google.com/scholar?cluster=5839731668715144881&hl=en&as_sdt=0,10",7,2019
Viewport Proposal CNN for 360deg Video Quality Assessment,18,cvpr,7,1,2023-06-03 02:18:20.993000,https://github.com/Archer-Tatsu/V-CNN,31,Viewport proposal CNN for 360deg video quality assessment,"https://scholar.google.com/scholar?cluster=7315647537879856949&hl=en&as_sdt=0,33",3,2019
Toward Convolutional Blind Denoising of Real Photographs,793,cvpr,94,30,2023-06-03 02:18:21.194000,https://github.com/GuoShi28/CBDNet,464,Toward convolutional blind denoising of real photographs,"https://scholar.google.com/scholar?cluster=10808191985805196679&hl=en&as_sdt=0,19",13,2019
Patch-Based Progressive 3D Point Set Upsampling,212,cvpr,9,4,2023-06-03 02:18:21.393000,https://github.com/yifita/3PU_pytorch,72,Patch-based progressive 3d point set upsampling,"https://scholar.google.com/scholar?cluster=4861900077338199153&hl=en&as_sdt=0,5",3,2019
Beyond Gradient Descent for Regularized Segmentation Losses,28,cvpr,2,1,2023-06-03 02:18:21.594000,https://github.com/dmitrii-marin/adm-seg,11,Beyond gradient descent for regularized segmentation losses,"https://scholar.google.com/scholar?cluster=2336732449299048628&hl=en&as_sdt=0,47",2,2019
Towards Real Scene Super-Resolution With Raw Images,105,cvpr,5,2,2023-06-03 02:18:21.794000,https://github.com/xuxy09/RawSR,37,Towards real scene super-resolution with raw images,"https://scholar.google.com/scholar?cluster=16601900080142594548&hl=en&as_sdt=0,48",5,2019
MAGSAC: Marginalizing Sample Consensus,182,cvpr,51,14,2023-06-03 02:18:21.994000,https://github.com/danini/magsac,325,MAGSAC: marginalizing sample consensus,"https://scholar.google.com/scholar?cluster=17569749096229295310&hl=en&as_sdt=0,5",20,2019
Understanding and Visualizing Deep Visual Saliency Models,39,cvpr,1,0,2023-06-03 02:18:22.194000,https://github.com/SenHe/uavdvsm,14,Understanding and visualizing deep visual saliency models,"https://scholar.google.com/scholar?cluster=6009796744507889522&hl=en&as_sdt=0,5",1,2019
Unsupervised Domain-Specific Deblurring via Disentangled Representations,132,cvpr,29,12,2023-06-03 02:18:22.394000,https://github.com/ustclby/Unsupervised-Domain-Specific-Deblurring,102,Unsupervised domain-specific deblurring via disentangled representations,"https://scholar.google.com/scholar?cluster=17376435678265014007&hl=en&as_sdt=0,10",4,2019
Searching for a Robust Neural Architecture in Four GPU Hours,545,cvpr,279,13,2023-06-03 02:18:22.593000,https://github.com/D-X-Y/NAS-Projects,1493,Searching for a robust neural architecture in four gpu hours,"https://scholar.google.com/scholar?cluster=4879261064205398932&hl=en&as_sdt=0,14",46,2019
Adaptively Connected Neural Networks,53,cvpr,29,3,2023-06-03 02:18:22.794000,https://github.com/wanggrun/Adaptively-Connected-Neural-Networks,143,Adaptively connected neural networks,"https://scholar.google.com/scholar?cluster=17110143364820542317&hl=en&as_sdt=0,10",6,2019
Hierarchical Discrete Distribution Decomposition for Match Density Estimation,210,cvpr,31,5,2023-06-03 02:18:22.994000,https://github.com/ucbdrive/hd3,201,Hierarchical discrete distribution decomposition for match density estimation,"https://scholar.google.com/scholar?cluster=8538662755168714441&hl=en&as_sdt=0,5",15,2019
FOCNet: A Fractional Optimal Control Network for Image Denoising,105,cvpr,3,5,2023-06-03 02:18:23.194000,https://github.com/hsijiaxidian/FOCNet,24,Focnet: A fractional optimal control network for image denoising,"https://scholar.google.com/scholar?cluster=14736089093178266892&hl=en&as_sdt=0,5",4,2019
TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning,115,cvpr,9,4,2023-06-03 02:18:23.394000,https://github.com/ucbdrive/tafe-net,51,Tafe-net: Task-aware feature embeddings for low shot learning,"https://scholar.google.com/scholar?cluster=6103977558582312967&hl=en&as_sdt=0,22",11,2019
Multi-Source Weak Supervision for Saliency Detection,151,cvpr,11,4,2023-06-03 02:18:23.595000,https://github.com/zengxianyu/mws,53,Multi-source weak supervision for saliency detection,"https://scholar.google.com/scholar?cluster=4072873847492058736&hl=en&as_sdt=0,36",3,2019
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples,199,cvpr,25,23,2023-06-03 02:18:23.795000,https://github.com/jiaxiaojunQAQ/Comdefend,102,Comdefend: An efficient image compression model to defend adversarial examples,"https://scholar.google.com/scholar?cluster=4385418290985322655&hl=en&as_sdt=0,5",1,2019
End-To-End Multi-Task Learning With Attention,686,cvpr,105,0,2023-06-03 02:18:23.995000,https://github.com/lorenmt/mtan,582,End-to-end multi-task learning with attention,"https://scholar.google.com/scholar?cluster=13455553033244468283&hl=en&as_sdt=0,22",10,2019
Superquadrics Revisited: Learning 3D Shape Parsing Beyond Cuboids,132,cvpr,27,6,2023-06-03 02:18:24.196000,https://github.com/paschalidoud/superquadric_parsing,103,Superquadrics revisited: Learning 3d shape parsing beyond cuboids,"https://scholar.google.com/scholar?cluster=16382881621569269566&hl=en&as_sdt=0,5",7,2019
Self-Supervised Learning via Conditional Motion Propagation,38,cvpr,19,3,2023-06-03 02:18:24.396000,https://github.com/XiaohangZhan/conditional-motion-propagation,129,Self-supervised learning via conditional motion propagation,"https://scholar.google.com/scholar?cluster=18041914909773758029&hl=en&as_sdt=0,44",9,2019
Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence,73,cvpr,21,7,2023-06-03 02:18:24.596000,https://github.com/lelimite4444/BridgeDepthFlow,116,Bridging stereo matching and optical flow via spatiotemporal correspondence,"https://scholar.google.com/scholar?cluster=16596578988150565557&hl=en&as_sdt=0,5",6,2019
All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation,220,cvpr,24,4,2023-06-03 02:18:24.796000,https://github.com/a514514772/DISE-Domain-Invariant-Structure-Extraction,140,All about structure: Adapting structural information across domains for boosting semantic segmentation,"https://scholar.google.com/scholar?cluster=10808988174565164614&hl=en&as_sdt=0,38",4,2019
Deep Surface Normal Estimation With Hierarchical RGB-D Fusion,49,cvpr,12,2,2023-06-03 02:18:24.997000,https://github.com/jzengust/RGBD2Normal,64,Deep surface normal estimation with hierarchical RGB-D fusion,"https://scholar.google.com/scholar?cluster=3067890846890303637&hl=en&as_sdt=0,5",1,2019
Revisiting Self-Supervised Visual Representation Learning,689,cvpr,41,1,2023-06-03 02:18:25.199000,https://github.com/google/revisiting-self-supervised,349,Revisiting self-supervised visual representation learning,"https://scholar.google.com/scholar?cluster=10439362362794734109&hl=en&as_sdt=0,4",21,2019
Knowledge-Embedded Routing Network for Scene Graph Generation,301,cvpr,36,7,2023-06-03 02:18:25.399000,https://github.com/yuweihao/KERN,112,Knowledge-embedded routing network for scene graph generation,"https://scholar.google.com/scholar?cluster=2500759860994948425&hl=en&as_sdt=0,15",8,2019
Video Relationship Reasoning Using Gated Spatio-Temporal Energy Graph,91,cvpr,26,1,2023-06-03 02:18:25.599000,https://github.com/yaohungt/GSTEG_CVPR_2019,150,Video relationship reasoning using gated spatio-temporal energy graph,"https://scholar.google.com/scholar?cluster=4319729862936279404&hl=en&as_sdt=0,5",5,2019
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation,11,cvpr,2,0,2023-06-03 02:18:25.800000,https://github.com/jspenmar/SAND_features,31,Scale-adaptive neural dense features: Learning via hierarchical context aggregation,"https://scholar.google.com/scholar?cluster=5547049271392391186&hl=en&as_sdt=0,22",3,2019
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature,429,cvpr,40,2,2023-06-03 02:18:25.999000,https://github.com/mangye16/Unsupervised_Embedding_Learning,190,Unsupervised embedding learning via invariant and spreading instance feature,"https://scholar.google.com/scholar?cluster=1072488172525419971&hl=en&as_sdt=0,37",7,2019
AOGNets: Compositional Grammatical Architectures for Deep Learning,20,cvpr,13,2,2023-06-03 02:18:26.200000,https://github.com/iVMCL/AOGNets,63,Aognets: Compositional grammatical architectures for deep learning,"https://scholar.google.com/scholar?cluster=15106712660226432053&hl=en&as_sdt=0,41",5,2019
MUREL: Multimodal Relational Reasoning for Visual Question Answering,277,cvpr,25,17,2023-06-03 02:18:26.400000,https://github.com/Cadene/murel.bootstrap.pytorch,187,Murel: Multimodal relational reasoning for visual question answering,"https://scholar.google.com/scholar?cluster=1186094337208548043&hl=en&as_sdt=0,36",11,2019
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering,205,cvpr,14,5,2023-06-03 02:18:26.600000,https://github.com/fanchenyou/HME-VideoQA,53,Heterogeneous memory enhanced multimodal attention model for video question answering,"https://scholar.google.com/scholar?cluster=7770828835187830172&hl=en&as_sdt=0,38",6,2019
Answer Them All! Toward Universal Visual Question Answering Models,79,cvpr,6,1,2023-06-03 02:18:26.799000,https://github.com/erobic/ramen,16,Answer them all! toward universal visual question answering models,"https://scholar.google.com/scholar?cluster=6525039695803054929&hl=en&as_sdt=0,14",5,2019
Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning,78,cvpr,13,16,2023-06-03 02:18:26.999000,https://github.com/Dong-JinKim/DenseRelationalCaptioning,66,Dense relational captioning: Triple-stream networks for relationship-based captioning,"https://scholar.google.com/scholar?cluster=8776743934311277227&hl=en&as_sdt=0,39",3,2019
Factor Graph Attention,101,cvpr,4,0,2023-06-03 02:18:27.199000,https://github.com/idansc/fga,28,Factor graph attention,"https://scholar.google.com/scholar?cluster=2910740179552415672&hl=en&as_sdt=0,33",4,2019
Deep Modular Co-Attention Networks for Visual Question Answering,623,cvpr,86,2,2023-06-03 02:18:27.399000,https://github.com/MILVLG/mcan-vqa,392,Deep modular co-attention networks for visual question answering,"https://scholar.google.com/scholar?cluster=11029813876310873400&hl=en&as_sdt=0,31",6,2019
Complete the Look: Scene-Based Complementary Product Recommendation,67,cvpr,14,3,2023-06-03 02:18:27.599000,https://github.com/kang205/STL-Dataset,52,Complete the look: Scene-based complementary product recommendation,"https://scholar.google.com/scholar?cluster=590701942495364123&hl=en&as_sdt=0,14",8,2019
Multi-Target Embodied Question Answering,70,cvpr,64,17,2023-06-03 02:18:27.799000,https://github.com/facebookresearch/EmbodiedQA,271,Multi-target embodied question answering,"https://scholar.google.com/scholar?cluster=15952250835614561913&hl=en&as_sdt=0,10",21,2019
StoryGAN: A Sequential Conditional GAN for Story Visualization,136,cvpr,58,23,2023-06-03 02:18:27.999000,https://github.com/yitong91/StoryGAN,229,Storygan: A sequential conditional gan for story visualization,"https://scholar.google.com/scholar?cluster=9067526876528752401&hl=en&as_sdt=0,44",18,2019
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion,56,cvpr,1,3,2023-06-03 02:18:28.200000,https://github.com/XuelianCheng/LidarStereoNet,45,Noise-aware unsupervised deep lidar-stereo fusion,"https://scholar.google.com/scholar?cluster=4886019654152519526&hl=en&as_sdt=0,5",7,2019
Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model,156,cvpr,11,14,2023-06-03 02:18:28.400000,https://github.com/smilell/AG-CNN,62,Attention based glaucoma detection: a large-scale database and CNN model,"https://scholar.google.com/scholar?cluster=10478894233333708958&hl=en&as_sdt=0,5",4,2019
SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images,154,cvpr,22,10,2023-06-03 02:18:28.600000,https://github.com/MeioJane/SIXray,102,Sixray: A large-scale security inspection x-ray benchmark for prohibited item discovery in overlapping images,"https://scholar.google.com/scholar?cluster=987808359488933242&hl=en&as_sdt=0,33",5,2019
Modularized Textual Grounding for Counterfactual Resilience,29,cvpr,0,1,2023-06-03 02:18:28.800000,https://github.com/jacobswan1/MTG-pytorch,11,Modularized textual grounding for counterfactual resilience,"https://scholar.google.com/scholar?cluster=10665938420960804361&hl=en&as_sdt=0,44",3,2019
Connecting Touch and Vision via Cross-Modal Prediction,80,cvpr,13,1,2023-06-03 02:18:29,https://github.com/YunzhuLi/VisGel,57,Connecting touch and vision via cross-modal prediction,"https://scholar.google.com/scholar?cluster=17326564895972374001&hl=en&as_sdt=0,5",3,2019
Unsupervised Person Re-Identification by Soft Multilabel Learning,385,cvpr,81,5,2023-06-03 02:18:29.200000,https://github.com/KovenYu/MAR,310,Unsupervised person re-identification by soft multilabel learning,"https://scholar.google.com/scholar?cluster=2081679959675411798&hl=en&as_sdt=0,39",10,2019
X2CT-GAN: Reconstructing CT From Biplanar X-Rays With Generative Adversarial Networks,118,cvpr,37,17,2023-06-03 02:18:29.401000,https://github.com/kylekma/X2CT,98,X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks,"https://scholar.google.com/scholar?cluster=8622362370215216578&hl=en&as_sdt=0,5",7,2019
Panoptic Feature Pyramid Networks,928,cvpr,6780,383,2023-06-03 02:18:29.601000,https://github.com/facebookresearch/detectron2,25048,Panoptic feature pyramid networks,"https://scholar.google.com/scholar?cluster=4764353839421452842&hl=en&as_sdt=0,33",368,2019
Practical Full Resolution Learned Lossless Image Compression,163,cvpr,57,12,2023-06-03 02:18:29.801000,https://github.com/fab-jul/L3C-PyTorch,366,Practical full resolution learned lossless image compression,"https://scholar.google.com/scholar?cluster=6227324072432517519&hl=en&as_sdt=0,33",9,2019
Mask Scoring R-CNN,840,cvpr,388,68,2023-06-03 02:18:30.001000,https://github.com/zjhuang22/maskscoring_rcnn,1877,Mask scoring r-cnn,"https://scholar.google.com/scholar?cluster=4161750651251815162&hl=en&as_sdt=0,5",43,2019
Image-To-Image Translation via Group-Wise Deep Whitening-And-Coloring Transformation,114,cvpr,11,6,2023-06-03 02:18:30.202000,https://github.com/WonwoongCho/GDWCT,138,Image-to-image translation via group-wise deep whitening-and-coloring transformation,"https://scholar.google.com/scholar?cluster=5074418672366588381&hl=en&as_sdt=0,33",8,2019
Zero-Shot Task Transfer,35,cvpr,1,0,2023-06-03 02:18:30.403000,https://github.com/ArghyaPal/Zero-shot-task-transfer,7,Zero-shot task transfer,"https://scholar.google.com/scholar?cluster=9450511399353552575&hl=en&as_sdt=0,33",3,2019
Meta-Learning With Differentiable Convex Optimization,1080,cvpr,94,11,2023-06-03 02:18:30.604000,https://github.com/kjunelee/MetaOptNet,496,Meta-learning with differentiable convex optimization,"https://scholar.google.com/scholar?cluster=17021041287722537056&hl=en&as_sdt=0,38",9,2019
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection,205,cvpr,15,5,2023-06-03 02:18:30.804000,https://github.com/Winfrand/C-MIL,62,C-mil: Continuation multiple instance learning for weakly supervised object detection,"https://scholar.google.com/scholar?cluster=627329786836963865&hl=en&as_sdt=0,38",3,2019
Tangent-Normal Adversarial Regularization for Semi-Supervised Learning,30,cvpr,0,0,2023-06-03 02:18:31.004000,https://github.com/uuujf/TNAR,7,Tangent-normal adversarial regularization for semi-supervised learning,"https://scholar.google.com/scholar?cluster=9145773952325087137&hl=en&as_sdt=0,33",2,2019
Auto-Encoding Scene Graphs for Image Captioning,613,cvpr,50,36,2023-06-03 02:18:31.203000,https://github.com/yangxuntu/SGAE,205,Auto-encoding scene graphs for image captioning,"https://scholar.google.com/scholar?cluster=8920795868500143528&hl=en&as_sdt=0,33",4,2019
Locating Objects Without Bounding Boxes,87,cvpr,50,22,2023-06-03 02:18:31.403000,https://github.com/javiribera/locating-objects-without-bboxes,237,Locating objects without bounding boxes,"https://scholar.google.com/scholar?cluster=11365548428780437537&hl=en&as_sdt=0,10",11,2019
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation,409,cvpr,57,5,2023-06-03 02:18:31.604000,https://github.com/machine-perception-robotics-group/attention_branch_network,251,Attention branch network: Learning of attention mechanism for visual explanation,"https://scholar.google.com/scholar?cluster=9517594115832739918&hl=en&as_sdt=0,11",16,2019
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery,126,cvpr,42,6,2023-06-03 02:18:31.804000,https://github.com/kkanshul/finegan,274,Finegan: Unsupervised hierarchical disentanglement for fine-grained object generation and discovery,"https://scholar.google.com/scholar?cluster=3504805931057936629&hl=en&as_sdt=0,33",15,2019
ELASTIC: Improving CNNs With Dynamic Scaling Policies,61,cvpr,10,1,2023-06-03 02:18:32.004000,https://github.com/allenai/elastic,90,Elastic: Improving cnns with dynamic scaling policies,"https://scholar.google.com/scholar?cluster=3620870050981559433&hl=en&as_sdt=0,44",118,2019
DeepCaps: Going Deeper With Capsule Networks,216,cvpr,48,5,2023-06-03 02:18:32.204000,https://github.com/brjathu/deepcaps,143,Deepcaps: Going deeper with capsule networks,"https://scholar.google.com/scholar?cluster=16889828886556749914&hl=en&as_sdt=0,22",7,2019
ScratchDet: Training Single-Shot Object Detectors From Scratch,151,cvpr,38,14,2023-06-03 02:18:32.413000,https://github.com/KimSoybean/ScratchDet,229,ScratchDet: Training single-shot object detectors from scratch,"https://scholar.google.com/scholar?cluster=14320721725653826711&hl=en&as_sdt=0,36",26,2019
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search,1187,cvpr,142,20,2023-06-03 02:18:32.613000,https://github.com/facebookresearch/mobile-vision,851,Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search,"https://scholar.google.com/scholar?cluster=524206379279542606&hl=en&as_sdt=0,33",46,2019
Curls & Whey: Boosting Black-Box Adversarial Attacks,115,cvpr,21,3,2023-06-03 02:18:32.813000,https://github.com/walegahaha/Curls-Whey,59,Curls & whey: Boosting black-box adversarial attacks,"https://scholar.google.com/scholar?cluster=6887259885026760388&hl=en&as_sdt=0,5",4,2019
Aggregation Cross-Entropy for Sequence Recognition,94,cvpr,64,19,2023-06-03 02:18:33.026000,https://github.com/summerlvsong/Aggregation-Cross-Entropy,295,Aggregation cross-entropy for sequence recognition,"https://scholar.google.com/scholar?cluster=15608585135826790706&hl=en&as_sdt=0,44",18,2019
WarpGAN: Automatic Caricature Generation,69,cvpr,61,4,2023-06-03 02:18:33.236000,https://github.com/seasonSH/WarpGAN,253,Warpgan: Automatic caricature generation,"https://scholar.google.com/scholar?cluster=3752178522291653390&hl=en&as_sdt=0,10",8,2019
Few-Shot Learning With Localization in Realistic Settings,119,cvpr,17,0,2023-06-03 02:18:33.437000,https://github.com/daviswer/fewshotlocal,46,Few-shot learning with localization in realistic settings,"https://scholar.google.com/scholar?cluster=4508829922363730236&hl=en&as_sdt=0,5",4,2019
Semantic Image Synthesis With Spatially-Adaptive Normalization,2195,cvpr,991,99,2023-06-03 02:18:33.636000,https://github.com/NVlabs/SPADE,7373,Semantic image synthesis with spatially-adaptive normalization,"https://scholar.google.com/scholar?cluster=12479535951654162053&hl=en&as_sdt=0,34",284,2019
Progressive Pose Attention Transfer for Person Image Generation,285,cvpr,164,40,2023-06-03 02:18:33.836000,https://github.com/tengteng95/Pose-Transfer,698,Progressive pose attention transfer for person image generation,"https://scholar.google.com/scholar?cluster=8533086213886595616&hl=en&as_sdt=0,33",24,2019
Unsupervised Person Image Generation With Semantic Parsing Transformation,117,cvpr,23,7,2023-06-03 02:18:34.037000,https://github.com/SijieSong/person_generation_spt,109,Unsupervised person image generation with semantic parsing transformation,"https://scholar.google.com/scholar?cluster=4604069298545419754&hl=en&as_sdt=0,33",11,2019
Unified Visual-Semantic Embeddings: Bridging Vision and Language With Structured Meaning Representations,124,cvpr,47,9,2023-06-03 02:18:34.255000,https://github.com/vacancy/SceneGraphParser,402,Unified visual-semantic embeddings: Bridging vision and language with structured meaning representations,"https://scholar.google.com/scholar?cluster=1466334913806805421&hl=en&as_sdt=0,3",6,2019
Animating Arbitrary Objects via Deep Motion Transfer,252,cvpr,81,21,2023-06-03 02:18:34.455000,https://github.com/AliaksandrSiarohin/monkey-net,426,Animating arbitrary objects via deep motion transfer,"https://scholar.google.com/scholar?cluster=17230951107215753208&hl=en&as_sdt=0,33",15,2019
Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in a Triadic Interaction,67,cvpr,8,1,2023-06-03 02:18:34.655000,https://github.com/CMU-Perceptual-Computing-Lab/ssp,28,Towards social artificial intelligence: Nonverbal social signal prediction in a triadic interaction,"https://scholar.google.com/scholar?cluster=12550284678910113227&hl=en&as_sdt=0,5",6,2019
Reasoning Visual Dialogs With Structural and Partial Observations,100,cvpr,7,1,2023-06-03 02:18:34.856000,https://github.com/zilongzheng/visdial-gnn,41,Reasoning visual dialogs with structural and partial observations,"https://scholar.google.com/scholar?cluster=6813756487317255348&hl=en&as_sdt=0,23",4,2019
Recursive Visual Attention in Visual Dialog,107,cvpr,12,4,2023-06-03 02:18:35.056000,https://github.com/yuleiniu/rva,64,Recursive visual attention in visual dialog,"https://scholar.google.com/scholar?cluster=498788163580831631&hl=en&as_sdt=0,5",2,2019
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering,767,cvpr,125,15,2023-06-03 02:18:35.257000,https://github.com/stanfordnlp/mac-network,483,Gqa: A new dataset for real-world visual reasoning and compositional question answering,"https://scholar.google.com/scholar?cluster=266091827435565866&hl=en&as_sdt=0,44",32,2019
Self-Supervised Representation Learning From Videos for Facial Action Unit Detection,85,cvpr,28,8,2023-06-03 02:18:35.457000,https://github.com/mysee1989/TCAE,149,Self-supervised representation learning from videos for facial action unit detection,"https://scholar.google.com/scholar?cluster=14803969931076095921&hl=en&as_sdt=0,33",6,2019
Label-Noise Robust Generative Adversarial Networks,62,cvpr,12,1,2023-06-03 02:18:35.656000,https://github.com/takuhirok/rGAN,94,Label-noise robust generative adversarial networks,"https://scholar.google.com/scholar?cluster=832067215173341045&hl=en&as_sdt=0,5",3,2019
Text2Scene: Generating Compositional Scenes From Textual Descriptions,71,cvpr,26,3,2023-06-03 02:18:35.856000,https://github.com/uvavision/Text2Scene,109,Text2scene: Generating compositional scenes from textual descriptions,"https://scholar.google.com/scholar?cluster=14695771730532263499&hl=en&as_sdt=0,39",9,2019
DLOW: Domain Flow for Adaptation and Generalization,285,cvpr,8,1,2023-06-03 02:18:36.058000,https://github.com/ETHRuiGong/DLOW,65,Dlow: Domain flow for adaptation and generalization,"https://scholar.google.com/scholar?cluster=7502453122978248132&hl=en&as_sdt=0,34",10,2019
The Regretful Agent: Heuristic-Aided Navigation Through Progress Estimation,157,cvpr,24,15,2023-06-03 02:18:36.258000,https://github.com/chihyaoma/regretful-agent,123,The regretful agent: Heuristic-aided navigation through progress estimation,"https://scholar.google.com/scholar?cluster=17362239336630276321&hl=en&as_sdt=0,5",3,2019
Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation,138,cvpr,10,2,2023-06-03 02:18:36.458000,https://github.com/Kelym/FAST,58,Tactical rewind: Self-correction via backtracking in vision-and-language navigation,"https://scholar.google.com/scholar?cluster=12923057112531290596&hl=en&as_sdt=0,39",5,2019
Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation,597,cvpr,48,1,2023-06-03 02:18:36.657000,https://github.com/RoyalVane/CLAN,281,Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation,"https://scholar.google.com/scholar?cluster=12296797088344323285&hl=en&as_sdt=0,5",12,2019
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning,171,cvpr,54,19,2023-06-03 02:18:36.857000,https://github.com/allenai/savn,176,Learning to learn how to learn: Self-adaptive visual navigation using meta-learning,"https://scholar.google.com/scholar?cluster=15373556339863734503&hl=en&as_sdt=0,39",13,2019
"Expressive Body Capture: 3D Hands, Face, and Body From a Single Image",826,cvpr,304,60,2023-06-03 02:18:37.058000,https://github.com/vchoutas/smplify-x,1376,"Expressive body capture: 3d hands, face, and body from a single image","https://scholar.google.com/scholar?cluster=18089618953278262954&hl=en&as_sdt=0,43",62,2019
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation,943,cvpr,76,14,2023-06-03 02:18:37.258000,https://github.com/valeoai/ADVENT,349,Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,"https://scholar.google.com/scholar?cluster=4799517383796609013&hl=en&as_sdt=0,10",11,2019
ContextDesc: Local Descriptor Augmentation With Cross-Modality Context,198,cvpr,35,11,2023-06-03 02:18:37.458000,https://github.com/lzx551402/contextdesc,221,Contextdesc: Local descriptor augmentation with cross-modality context,"https://scholar.google.com/scholar?cluster=2024070806793077537&hl=en&as_sdt=0,5",18,2019
Acoustic Non-Line-Of-Sight Imaging,94,cvpr,20,2,2023-06-03 02:18:37.658000,https://github.com/computational-imaging/AcousticNLOS,51,Acoustic non-line-of-sight imaging,"https://scholar.google.com/scholar?cluster=12363469736987930524&hl=en&as_sdt=0,23",9,2019
DVC: An End-To-End Deep Video Compression Framework,405,cvpr,71,0,2023-06-03 02:18:37.859000,https://github.com/GuoLusjtu/DVC,347,Dvc: An end-to-end deep video compression framework,"https://scholar.google.com/scholar?cluster=14025636173094719670&hl=en&as_sdt=0,5",13,2019
AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations Rather Than Data,201,cvpr,28,6,2023-06-03 02:18:38.058000,https://github.com/maple-research-lab/AET,105,Aet vs. aed: Unsupervised representation learning by auto-encoding transformations rather than data,"https://scholar.google.com/scholar?cluster=4610038868574024475&hl=en&as_sdt=0,34",9,2019
End-To-End Projector Photometric Compensation,24,cvpr,18,2,2023-06-03 02:18:38.258000,https://github.com/BingyaoHuang/CompenNet,36,End-to-end projector photometric compensation,"https://scholar.google.com/scholar?cluster=11667961356677288620&hl=en&as_sdt=0,22",6,2019
Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach,75,cvpr,4,1,2023-06-03 02:18:38.458000,https://github.com/human-analysis/MaxEnt-ARL,25,Mitigating information leakage in image representations: A maximum entropy approach,"https://scholar.google.com/scholar?cluster=9808367443005287731&hl=en&as_sdt=0,31",5,2019
Bringing Alive Blurred Moments,69,cvpr,3,2,2023-06-03 02:18:38.657000,https://github.com/anshulbshah/Blurred-Image-to-Video,15,Bringing alive blurred moments,"https://scholar.google.com/scholar?cluster=11054301810786965041&hl=en&as_sdt=0,23",6,2019
Underexposed Photo Enhancement Using Deep Illumination Estimation,574,cvpr,97,36,2023-06-03 02:18:38.858000,https://github.com/wangruixing/DeepUPE,523,Underexposed photo enhancement using deep illumination estimation,"https://scholar.google.com/scholar?cluster=13016623403221821571&hl=en&as_sdt=0,10",24,2019
Blind Visual Motif Removal From a Single Image,18,cvpr,11,6,2023-06-03 02:18:39.057000,https://github.com/amirhertz/visual_motif_removal,36,Blind visual motif removal from a single image,"https://scholar.google.com/scholar?cluster=8406837963286920455&hl=en&as_sdt=0,43",1,2019
Path-Invariant Map Networks,17,cvpr,8,0,2023-06-03 02:18:39.257000,https://github.com/zaiweizhang/path_invariance_map_network,81,Path-invariant map networks,"https://scholar.google.com/scholar?cluster=9505386108948456577&hl=en&as_sdt=0,5",3,2019
Non-Local Meets Global: An Integrated Paradigm for Hyperspectral Denoising,107,cvpr,14,0,2023-06-03 02:18:39.458000,https://github.com/quanmingyao/NGMeet,28,Non-local meets global: An integrated paradigm for hyperspectral denoising,"https://scholar.google.com/scholar?cluster=14516251846200014876&hl=en&as_sdt=0,5",3,2019
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation,459,cvpr,68,23,2023-06-03 02:18:39.658000,https://github.com/hughw19/NOCS_CVPR2019,367,Normalized object coordinate space for category-level 6d object pose and size estimation,"https://scholar.google.com/scholar?cluster=18202882499219400168&hl=en&as_sdt=0,32",15,2019
MAP Inference via Block-Coordinate Frank-Wolfe Algorithm,18,cvpr,23,9,2023-06-03 02:18:39.858000,https://github.com/LPMP/LPMP,63,MAP inference via block-coordinate Frank-Wolfe algorithm,"https://scholar.google.com/scholar?cluster=8115651436723804587&hl=en&as_sdt=0,5",6,2019
Bidirectional Learning for Domain Adaptation of Semantic Segmentation,540,cvpr,31,22,2023-06-03 02:18:40.058000,https://github.com/liyunsheng13/BDL,220,Bidirectional learning for domain adaptation of semantic segmentation,"https://scholar.google.com/scholar?cluster=8308872950577213620&hl=en&as_sdt=0,34",11,2019
Pixel-Adaptive Convolutional Neural Networks,235,cvpr,83,18,2023-06-03 02:18:40.258000,https://github.com/NVlabs/pacnet,491,Pixel-adaptive convolutional neural networks,"https://scholar.google.com/scholar?cluster=16269301846334705331&hl=en&as_sdt=0,5",25,2019
Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification With the COncrete DEfect BRidge IMage Dataset,83,cvpr,15,0,2023-06-03 02:18:40.458000,https://github.com/MrtnMndt/meta-learning-CODEBRIM,52,Meta-learning convolutional neural architectures for multi-target concrete defect classification with the concrete defect bridge image dataset,"https://scholar.google.com/scholar?cluster=3021254096612461995&hl=en&as_sdt=0,31",5,2019
Improving Transferability of Adversarial Examples With Input Diversity,720,cvpr,38,1,2023-06-03 02:18:40.659000,https://github.com/cihangxie/DI-2-FGSM,156,Improving transferability of adversarial examples with input diversity,"https://scholar.google.com/scholar?cluster=13496165357897467185&hl=en&as_sdt=0,5",8,2019
Disentangling Adversarial Robustness and Generalization,218,cvpr,2,0,2023-06-03 02:18:40.864000,https://github.com/davidstutz/cvpr2019-adversarial-robustness,13,Disentangling adversarial robustness and generalization,"https://scholar.google.com/scholar?cluster=6632921217714390019&hl=en&as_sdt=0,36",4,2019
Deeply-Supervised Knowledge Synergy,51,cvpr,8,2,2023-06-03 02:18:41.063000,https://github.com/sundw2014/DKS,61,Deeply-supervised knowledge synergy,"https://scholar.google.com/scholar?cluster=191697563235867163&hl=en&as_sdt=0,5",4,2019
Defending Against Adversarial Attacks by Randomized Diversification,34,cvpr,5,1,2023-06-03 02:18:41.265000,https://github.com/taranO/defending-adversarial-attacks-by-RD,10,Defending against adversarial attacks by randomized diversification,"https://scholar.google.com/scholar?cluster=10013247887032866983&hl=en&as_sdt=0,23",3,2019
Learning to Sample,694,cvpr,20,0,2023-06-03 02:18:41.464000,https://github.com/orendv/learning_to_sample,161,Learning about learning,"https://scholar.google.com/scholar?cluster=4684783748672510811&hl=en&as_sdt=0,10",5,2019
Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration,167,cvpr,25,1,2023-06-03 02:18:41.665000,https://github.com/liu-vis/DualResidualNetworks,146,Dual residual networks leveraging the potential of paired operations for image restoration,"https://scholar.google.com/scholar?cluster=15061267745225364999&hl=en&as_sdt=0,47",8,2019
Probabilistic End-To-End Noise Correction for Learning With Noisy Labels,338,cvpr,19,8,2023-06-03 02:18:41.866000,https://github.com/yikun2019/PENCIL,135,Probabilistic end-to-end noise correction for learning with noisy labels,"https://scholar.google.com/scholar?cluster=5695947996345321960&hl=en&as_sdt=0,33",4,2019
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning,457,cvpr,16,5,2023-06-03 02:18:42.067000,https://github.com/ShaohuiLin/GAL,55,Towards optimal structured cnn pruning via generative adversarial learning,"https://scholar.google.com/scholar?cluster=15336900714703245928&hl=en&as_sdt=0,37",5,2019
Importance Estimation for Neural Network Pruning,616,cvpr,67,6,2023-06-03 02:18:42.266000,https://github.com/NVlabs/Taylor_pruning,277,Importance estimation for neural network pruning,"https://scholar.google.com/scholar?cluster=11394834411572433707&hl=en&as_sdt=0,33",17,2019
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression,125,cvpr,20,6,2023-06-03 02:18:42.467000,https://github.com/yuchaoli/KSE,44,Exploiting kernel sparsity and entropy for interpretable CNN compression,"https://scholar.google.com/scholar?cluster=6803569899494633158&hl=en&as_sdt=0,11",4,2019
MnasNet: Platform-Aware Neural Architecture Search for Mobile,2650,cvpr,1788,293,2023-06-03 02:18:42.668000,https://github.com/tensorflow/tpu,5123,Mnasnet: Platform-aware neural architecture search for mobile,"https://scholar.google.com/scholar?cluster=1725364759924402840&hl=en&as_sdt=0,5",371,2019
PPGNet: Learning Point-Pair Graph for Line Segment Detection,69,cvpr,35,12,2023-06-03 02:18:42.868000,https://github.com/svip-lab/PPGNet,171,Ppgnet: Learning point-pair graph for line segment detection,"https://scholar.google.com/scholar?cluster=11647354298146206932&hl=en&as_sdt=0,18",13,2019
AIRD: Adversarial Learning Framework for Image Repurposing Detection,17,cvpr,0,0,2023-06-03 02:18:43.068000,https://github.com/isi-vista/AIRD-Datasets,7,Aird: Adversarial learning framework for image repurposing detection,"https://scholar.google.com/scholar?cluster=16794184916238918283&hl=en&as_sdt=0,21",6,2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations,32,cvpr,1,0,2023-06-03 02:18:43.269000,https://github.com/asgsaeid/KernelizedManifoldMapping,3,A kernelized manifold mapping to diminish the effect of adversarial perturbations,"https://scholar.google.com/scholar?cluster=15831432352909732709&hl=en&as_sdt=0,38",4,2019
Trust Region Based Adversarial Attack on Neural Networks,55,cvpr,5,3,2023-06-03 02:18:43.470000,https://github.com/amirgholami/trattack,18,Trust region based adversarial attack on neural networks,"https://scholar.google.com/scholar?cluster=14904648874198126567&hl=en&as_sdt=0,5",3,2019
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction,154,cvpr,8,2,2023-06-03 02:18:43.670000,https://github.com/lmb-freiburg/Multimodal-Future-Prediction,46,Overcoming limitations of mixture density networks: A sampling and fitting framework for multimodal future prediction,"https://scholar.google.com/scholar?cluster=967422240947883765&hl=en&as_sdt=0,21",6,2019
Model-Blind Video Denoising via Frame-To-Frame Training,75,cvpr,12,0,2023-06-03 02:18:43.870000,https://github.com/tehret/blind-denoising,40,Model-blind video denoising via frame-to-frame training,"https://scholar.google.com/scholar?cluster=14973639611844679865&hl=en&as_sdt=0,33",6,2019
Learning Metrics From Teachers: Compact Networks for Image Embedding,105,cvpr,15,0,2023-06-03 02:18:44.070000,https://github.com/yulu0724/EmbeddingDistillation,75,Learning metrics from teachers: Compact networks for image embedding,"https://scholar.google.com/scholar?cluster=1501115334715421268&hl=en&as_sdt=0,43",2,2019
PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet,548,cvpr,90,14,2023-06-03 02:18:44.270000,https://github.com/hmgoforth/PointNetLK,388,Pointnetlk: Robust & efficient point cloud registration using pointnet,"https://scholar.google.com/scholar?cluster=17451718299457291321&hl=en&as_sdt=0,33",13,2019
Regularizing Activation Distribution for Training Binarized Deep Networks,109,cvpr,6,4,2023-06-03 02:18:44.470000,https://github.com/ruizhoud/DistributionLoss,29,Regularizing activation distribution for training binarized deep networks,"https://scholar.google.com/scholar?cluster=11777713405317274160&hl=en&as_sdt=0,10",4,2019
Additive Adversarial Learning for Unbiased Authentication,16,cvpr,1,3,2023-06-03 02:18:44.670000,https://github.com/langlrsw/AAL-unbiased-authentication,15,Additive adversarial learning for unbiased authentication,"https://scholar.google.com/scholar?cluster=16746985292159239847&hl=en&as_sdt=0,5",4,2019
Region Proposal by Guided Anchoring,564,cvpr,8690,798,2023-06-03 02:18:44.870000,https://github.com/open-mmlab/mmdetection,24397,Region proposal by guided anchoring,"https://scholar.google.com/scholar?cluster=1265529334352106882&hl=en&as_sdt=0,5",372,2019
Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy,114,cvpr,3,0,2023-06-03 02:18:45.070000,https://github.com/tiangeluo/fsl-hierarchy,30,Large-scale few-shot learning: Knowledge transfer with class hierarchy,"https://scholar.google.com/scholar?cluster=13016121814093526910&hl=en&as_sdt=0,10",1,2019
All You Need Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification,81,cvpr,4,1,2023-06-03 02:18:45.271000,https://github.com/hikvision-research/SparseShiftLayer,13,All you need is a few shifts: Designing efficient convolutional neural networks for image classification,"https://scholar.google.com/scholar?cluster=2946826539064409447&hl=en&as_sdt=0,18",3,2019
Towards Visual Feature Translation,15,cvpr,1,0,2023-06-03 02:18:45.471000,https://github.com/hujiecpp/VisualFeatureTranslation,22,Towards visual feature translation,"https://scholar.google.com/scholar?cluster=7863218348376961763&hl=en&as_sdt=0,31",4,2019
Rethinking Knowledge Graph Propagation for Zero-Shot Learning,298,cvpr,57,1,2023-06-03 02:18:45.672000,https://github.com/cyvius96/adgpm,308,Rethinking knowledge graph propagation for zero-shot learning,"https://scholar.google.com/scholar?cluster=1266179195913592313&hl=en&as_sdt=0,10",11,2019
Global Second-Order Pooling Convolutional Networks,285,cvpr,27,5,2023-06-03 02:18:45.872000,https://github.com/ZilinGao/Global-Second-order-Pooling-Convolutional-Networks,118,Global second-order pooling convolutional networks,"https://scholar.google.com/scholar?cluster=10164378327622616568&hl=en&as_sdt=0,5",3,2019
"NetTailor: Tuning the Architecture, Not Just the Weights",27,cvpr,11,4,2023-06-03 02:18:46.102000,https://github.com/pedro-morgado/nettailor,50,"Nettailor: Tuning the architecture, not just the weights","https://scholar.google.com/scholar?cluster=11402744282307001341&hl=en&as_sdt=0,39",4,2019
Data-Driven Neuron Allocation for Scale Aggregation Networks,29,cvpr,6,3,2023-06-03 02:18:46.303000,https://github.com/Eli-YiLi/ScaleNet,52,Data-driven neuron allocation for scale aggregation networks,"https://scholar.google.com/scholar?cluster=17356089364794181641&hl=en&as_sdt=0,5",2,2019
Learning Unsupervised Video Object Segmentation Through Visual Attention,205,cvpr,34,2,2023-06-03 02:18:46.503000,https://github.com/wenguanwang/AGS,209,Learning unsupervised video object segmentation through visual attention,"https://scholar.google.com/scholar?cluster=9207884552683066038&hl=en&as_sdt=0,5",4,2019
Quantization Networks,249,cvpr,31,7,2023-06-03 02:18:46.703000,https://github.com/aliyun/alibabacloud-quantization-networks,111,Quantization networks,"https://scholar.google.com/scholar?cluster=1471130295334849664&hl=en&as_sdt=0,33",12,2019
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks,1028,cvpr,293,151,2023-06-03 02:18:46.903000,https://github.com/StanfordVL/MinkowskiEngine,1989,4d spatio-temporal convnets: Minkowski convolutional neural networks,"https://scholar.google.com/scholar?cluster=14826656072199783781&hl=en&as_sdt=0,14",40,2019
Deep Transfer Learning for Multiple Class Novelty Detection,95,cvpr,2,0,2023-06-03 02:18:47.103000,https://github.com/PramuPerera/TransferLearningNovelty,21,Deep transfer learning for multiple class novelty detection,"https://scholar.google.com/scholar?cluster=17769888134044652832&hl=en&as_sdt=0,33",3,2019
Pyramid Feature Attention Network for Saliency Detection,566,cvpr,93,22,2023-06-03 02:18:47.303000,https://github.com/CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection,299,Pyramid feature attention network for saliency detection,"https://scholar.google.com/scholar?cluster=10723156253527482274&hl=en&as_sdt=0,5",7,2019
QATM: Quality-Aware Template Matching for Deep Learning,49,cvpr,33,6,2023-06-03 02:18:47.504000,https://github.com/cplusx/QATM,133,QATM: Quality-aware template matching for deep learning,"https://scholar.google.com/scholar?cluster=16340394657434221909&hl=en&as_sdt=0,33",3,2019
Learning Cross-Modal Embeddings With Adversarial Networks for Cooking Recipes and Food Images,113,cvpr,7,0,2023-06-03 02:18:47.704000,https://github.com/LARC-CMU-SMU/ACME,24,Learning cross-modal embeddings with adversarial networks for cooking recipes and food images,"https://scholar.google.com/scholar?cluster=4792190748791935595&hl=en&as_sdt=0,43",8,2019
Weakly Supervised Video Moment Retrieval From Text Queries,137,cvpr,9,2,2023-06-03 02:18:47.904000,https://github.com/niluthpol/weak_supervised_video_moment,42,Weakly supervised video moment retrieval from text queries,"https://scholar.google.com/scholar?cluster=5317279962974333981&hl=en&as_sdt=0,18",4,2019
Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation,187,cvpr,1,0,2023-06-03 02:18:48.106000,https://github.com/developfeng/BCM,5,Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=3721020246019694319&hl=en&as_sdt=0,33",1,2019
Dual Attention Network for Scene Segmentation,4286,cvpr,486,60,2023-06-03 02:18:48.306000,https://github.com/junfu1115/DANet,2245,Dual attention network for scene segmentation,"https://scholar.google.com/scholar?cluster=7464044873387137971&hl=en&as_sdt=0,5",36,2019
InverseRenderNet: Learning Single Image Inverse Rendering,114,cvpr,22,5,2023-06-03 02:18:48.507000,https://github.com/YeeU/InverseRenderNet,147,Inverserendernet: Learning single image inverse rendering,"https://scholar.google.com/scholar?cluster=15916518281958584722&hl=en&as_sdt=0,33",5,2019
Leveraging the Invariant Side of Generative Zero-Shot Learning,270,cvpr,21,7,2023-06-03 02:18:48.707000,https://github.com/lijin118/LisGAN,68,Leveraging the invariant side of generative zero-shot learning,"https://scholar.google.com/scholar?cluster=11098893954539769447&hl=en&as_sdt=0,5",4,2019
A-CNN: Annularly Convolutional Neural Networks on Point Clouds,245,cvpr,15,5,2023-06-03 02:18:48.908000,https://github.com/artemkomarichev/a-cnn,45,A-cnn: Annularly convolutional neural networks on point clouds,"https://scholar.google.com/scholar?cluster=7893535900133819945&hl=en&as_sdt=0,10",4,2019
Local Features and Visual Words Emerge in Activations,59,cvpr,0,0,2023-06-03 02:18:49.108000,https://github.com/osimeoni/DSM,0,Local features and visual words emerge in activations,"https://scholar.google.com/scholar?cluster=8007077270735204964&hl=en&as_sdt=0,33",0,2019
NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction,186,cvpr,6,0,2023-06-03 02:18:49.309000,https://github.com/ethanygao/NDDR-CNN,51,Nddr-cnn: Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction,"https://scholar.google.com/scholar?cluster=9969333786129871278&hl=en&as_sdt=0,33",4,2019
Point Cloud Oversegmentation With Graph-Structured Deep Metric Learning,146,cvpr,211,59,2023-06-03 02:18:49.509000,https://github.com/loicland/superpoint_graph,682,Point cloud oversegmentation with graph-structured deep metric learning,"https://scholar.google.com/scholar?cluster=1283486066827167731&hl=en&as_sdt=0,44",30,2019
Graphonomy: Universal Human Parsing via Graph Transfer Learning,140,cvpr,60,12,2023-06-03 02:18:49.709000,https://github.com/Gaoyiminggithub/Graphonomy,257,Graphonomy: Universal human parsing via graph transfer learning,"https://scholar.google.com/scholar?cluster=7535892148180299763&hl=en&as_sdt=0,5",11,2019
A Late Fusion CNN for Digital Matting,118,cvpr,30,8,2023-06-03 02:18:49.910000,https://github.com/yunkezhang/FusionMatting,177,A late fusion cnn for digital matting,"https://scholar.google.com/scholar?cluster=17877709160871365365&hl=en&as_sdt=0,22",11,2019
BASNet: Boundary-Aware Salient Object Detection,999,cvpr,246,19,2023-06-03 02:18:50.110000,https://github.com/NathanUA/BASNet,1278,Basnet: Boundary-aware salient object detection,"https://scholar.google.com/scholar?cluster=16840207246500229949&hl=en&as_sdt=0,5",36,2019
A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy Images,123,cvpr,10,2,2023-06-03 02:18:50.310000,https://github.com/bmmi/denoising-fluorescence,96,A poisson-gaussian denoising dataset with real fluorescence microscopy images,"https://scholar.google.com/scholar?cluster=9197039696220908597&hl=en&as_sdt=0,33",6,2019
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-Scale Point Clouds,154,cvpr,8,1,2023-06-03 02:18:50.511000,https://github.com/laoreja/HPLFlowNet,92,Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds,"https://scholar.google.com/scholar?cluster=8706139963298776453&hl=en&as_sdt=0,5",8,2019
Object Instance Annotation With Deep Extreme Level Set Evolution,70,cvpr,7,2,2023-06-03 02:18:50.711000,https://github.com/fidler-lab/delse,66,Object instance annotation with deep extreme level set evolution,"https://scholar.google.com/scholar?cluster=6230835967165176038&hl=en&as_sdt=0,44",7,2019
Group-Wise Correlation Stereo Network,390,cvpr,57,11,2023-06-03 02:18:50.911000,https://github.com/xy-guo/GwcNet,284,Group-wise correlation stereo network,"https://scholar.google.com/scholar?cluster=9104090749693468615&hl=en&as_sdt=0,5",15,2019
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning,44,cvpr,5,0,2023-06-03 02:18:51.112000,https://github.com/mahdihosseini/ADP,21,Atlas of digital pathology: A generalized hierarchical histological tissue type-annotated database for deep learning,"https://scholar.google.com/scholar?cluster=12090617679524628386&hl=en&as_sdt=0,39",2,2019
Understanding the Limitations of CNN-Based Absolute Camera Pose Regression,296,cvpr,2,0,2023-06-03 02:18:51.312000,https://github.com/tsattler/understanding_apr,41,Understanding the limitations of cnn-based absolute camera pose regression,"https://scholar.google.com/scholar?cluster=11724607917672549353&hl=en&as_sdt=0,40",8,2019
Robustness of 3D Deep Learning in an Adversarial Setting,65,cvpr,3,0,2023-06-03 02:18:51.513000,https://github.com/matthewwicker/IterativeSalienceOcclusion,12,Robustness of 3d deep learning in an adversarial setting,"https://scholar.google.com/scholar?cluster=14424902463693380029&hl=en&as_sdt=0,5",3,2019
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach,39,cvpr,8,0,2023-06-03 02:18:51.713000,https://github.com/atapour/temporal-depth-segmentation,19,Veritatem dies aperit-temporally consistent depth prediction enabled by a multi-task geometric and semantic scene understanding approach,"https://scholar.google.com/scholar?cluster=1887746925120136124&hl=en&as_sdt=0,5",5,2019
Segmentation-Driven 6D Object Pose Estimation,268,cvpr,36,9,2023-06-03 02:18:51.913000,https://github.com/cvlab-epfl/segmentation-driven-pose,162,Segmentation-driven 6d object pose estimation,"https://scholar.google.com/scholar?cluster=3400614724967065250&hl=en&as_sdt=0,5",10,2019
Exploiting Temporal Context for 3D Human Pose Estimation in the Wild,196,cvpr,27,4,2023-06-03 02:18:52.114000,https://github.com/deepmind/Temporal-3D-Pose-Kinetics,215,Exploiting temporal context for 3D human pose estimation in the wild,"https://scholar.google.com/scholar?cluster=6750649035284089973&hl=en&as_sdt=0,9",15,2019
Learning Non-Volumetric Depth Fusion Using Successive Reprojections,30,cvpr,15,2,2023-06-03 02:18:52.315000,https://github.com/simon-donne/defusr,64,Learning non-volumetric depth fusion using successive reprojections,"https://scholar.google.com/scholar?cluster=13474964124884393440&hl=en&as_sdt=0,5",15,2019
Stereo R-CNN Based 3D Object Detection for Autonomous Driving,458,cvpr,173,40,2023-06-03 02:18:52.515000,https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN,664,Stereo r-cnn based 3d object detection for autonomous driving,"https://scholar.google.com/scholar?cluster=734325547238762341&hl=en&as_sdt=0,47",26,2019
Semantic Graph Convolutional Networks for 3D Human Pose Regression,405,cvpr,76,34,2023-06-03 02:18:52.716000,https://github.com/garyzhao/SemGCN,418,Semantic graph convolutional networks for 3d human pose regression,"https://scholar.google.com/scholar?cluster=13468445676292565719&hl=en&as_sdt=0,5",11,2019
Noise-Tolerant Paradigm for Training Face Recognition CNNs,67,cvpr,22,8,2023-06-03 02:18:52.916000,https://github.com/huangyangyu/NoiseFace,135,Noise-tolerant paradigm for training face recognition CNNs,"https://scholar.google.com/scholar?cluster=13262743614963926246&hl=en&as_sdt=0,36",13,2019
LAEO-Net: Revisiting People Looking at Each Other in Videos,43,cvpr,0,3,2023-06-03 02:18:53.118000,https://github.com/AVAuco/laeonet,13,Laeo-net: revisiting people looking at each other in videos,"https://scholar.google.com/scholar?cluster=1708110166108655427&hl=en&as_sdt=0,33",5,2019
Learning Individual Styles of Conversational Gesture,219,cvpr,35,9,2023-06-03 02:18:53.318000,https://github.com/amirbar/speech2gesture,304,Learning individual styles of conversational gesture,"https://scholar.google.com/scholar?cluster=493752244490743824&hl=en&as_sdt=0,5",28,2019
Disentangled Representation Learning for 3D Face Shape,94,cvpr,36,7,2023-06-03 02:18:53.518000,https://github.com/zihangJiang/DR-Learning-for-3D-Face,224,Disentangled representation learning for 3D face shape,"https://scholar.google.com/scholar?cluster=823415651565938207&hl=en&as_sdt=0,39",13,2019
Mixed Effects Neural Networks (MeNets) With Applications to Gaze Estimation,71,cvpr,7,1,2023-06-03 02:18:53.718000,https://github.com/vsingh-group/MeNets,22,Mixed effects neural networks (menets) with applications to gaze estimation,"https://scholar.google.com/scholar?cluster=12925880536709622206&hl=en&as_sdt=0,33",2,2019
3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training,790,cvpr,720,161,2023-06-03 02:18:53.919000,https://github.com/facebookresearch/VideoPose3D,3301,3d human pose estimation in video with temporal convolutions and semi-supervised training,"https://scholar.google.com/scholar?cluster=14626724142096779446&hl=en&as_sdt=0,5",104,2019
Cross-Task Weakly Supervised Learning From Instructional Videos,153,cvpr,8,3,2023-06-03 02:18:54.119000,https://github.com/DmZhukov/CrossTask,74,Cross-task weakly supervised learning from instructional videos,"https://scholar.google.com/scholar?cluster=5979889052991630691&hl=en&as_sdt=0,5",5,2019
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos,225,cvpr,46,10,2023-06-03 02:18:54.320000,https://github.com/RomeroBarata/skeleton_based_anomaly_detection,119,Learning regularity in skeleton trajectories for anomaly detection in videos,"https://scholar.google.com/scholar?cluster=7847471061498684691&hl=en&as_sdt=0,10",9,2019
PoseFix: Model-Agnostic General Human Pose Refinement Network,152,cvpr,64,11,2023-06-03 02:18:54.519000,https://github.com/mks0601/PoseFix_RELEASE,324,Posefix: Model-agnostic general human pose refinement network,"https://scholar.google.com/scholar?cluster=10118721486245606094&hl=en&as_sdt=0,5",11,2019
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation,448,cvpr,52,13,2023-06-03 02:18:54.719000,https://github.com/yabufarha/ms-tcn,176,Ms-tcn: Multi-stage temporal convolutional network for action segmentation,"https://scholar.google.com/scholar?cluster=12665241567525792413&hl=en&as_sdt=0,5",8,2019
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition,806,cvpr,69,38,2023-06-03 02:18:54.920000,https://github.com/limaosen0/AS-GCN,268,Actional-structural graph convolutional networks for skeleton-based action recognition,"https://scholar.google.com/scholar?cluster=13969616101896512412&hl=en&as_sdt=0,5",13,2019
Unsupervised Learning of Action Classes With Continuous Temporal Embedding,78,cvpr,15,1,2023-06-03 02:18:55.120000,https://github.com/annusha/unsup_temp_embed,63,Unsupervised learning of action classes with continuous temporal embedding,"https://scholar.google.com/scholar?cluster=11120366599222415918&hl=en&as_sdt=0,41",6,2019
"See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks",415,cvpr,61,16,2023-06-03 02:18:55.321000,https://github.com/carrierlxk/COSNet,316,"See more, know more: Unsupervised video object segmentation with co-attention siamese networks","https://scholar.google.com/scholar?cluster=9063656729582652774&hl=en&as_sdt=0,44",11,2019
Patch-Based Discriminative Feature Learning for Unsupervised Person Re-Identification,215,cvpr,16,19,2023-06-03 02:18:55.521000,https://github.com/QizeYang/PAUL,92,Patch-based discriminative feature learning for unsupervised person re-identification,"https://scholar.google.com/scholar?cluster=4596838632003958635&hl=en&as_sdt=0,41",4,2019
The Pros and Cons: Rank-Aware Temporal Attention for Skill Determination in Long Videos,75,cvpr,4,5,2023-06-03 02:18:55.722000,https://github.com/hazeld/rank-aware-attention-network,23,The pros and cons: Rank-aware temporal attention for skill determination in long videos,"https://scholar.google.com/scholar?cluster=17234333327696971635&hl=en&as_sdt=0,5",4,2019
Collaborative Spatiotemporal Feature Learning for Video Action Recognition,106,cvpr,0,0,2023-06-03 02:18:55.922000,https://github.com/hikvision-research/cost,4,Collaborative spatiotemporal feature learning for video action recognition,"https://scholar.google.com/scholar?cluster=12686718892180468834&hl=en&as_sdt=0,36",2,2019
A Neural Temporal Model for Human Motion Prediction,111,cvpr,3,3,2023-06-03 02:18:56.122000,https://github.com/cr7anand/neural_temporal_models,22,A neural temporal model for human motion prediction,"https://scholar.google.com/scholar?cluster=17417189942381769994&hl=en&as_sdt=0,5",4,2019
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing,275,cvpr,83,4,2023-06-03 02:18:56.323000,https://github.com/csmliu/STGAN,408,Stgan: A unified selective transfer network for arbitrary image attribute editing,"https://scholar.google.com/scholar?cluster=1257277387687196331&hl=en&as_sdt=0,31",15,2019
Self-Supervised GANs via Auxiliary Rotation Loss,287,cvpr,323,16,2023-06-03 02:18:56.523000,https://github.com/google/compare_gan,1812,Self-supervised gans via auxiliary rotation loss,"https://scholar.google.com/scholar?cluster=8764250716042636166&hl=en&as_sdt=0,5",52,2019
Depth-Aware Video Frame Interpolation,418,cvpr,831,75,2023-06-03 02:18:56.723000,https://github.com/baowenbo/DAIN,7864,Depth-aware video frame interpolation,"https://scholar.google.com/scholar?cluster=6846312087428048842&hl=en&as_sdt=0,23",193,2019
Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture,39,cvpr,15,1,2023-06-03 02:18:56.924000,https://github.com/ningyu1991/TextureMixer,99,Texture mixer: A network for controllable synthesis and interpolation of texture,"https://scholar.google.com/scholar?cluster=17888404665835444210&hl=en&as_sdt=0,33",5,2019
Deep Flow-Guided Video Inpainting,194,cvpr,440,58,2023-06-03 02:18:57.126000,https://github.com/nbei/Deep-Flow-Guided-Video-Inpainting,2189,Deep flow-guided video inpainting,"https://scholar.google.com/scholar?cluster=12480445715275812500&hl=en&as_sdt=0,7",73,2019
Video Generation From Single Semantic Label Map,86,cvpr,12,2,2023-06-03 02:18:57.327000,https://github.com/junting/seg2vid,138,Video generation from single semantic label map,"https://scholar.google.com/scholar?cluster=6492810652297849370&hl=en&as_sdt=0,38",9,2019
Fully Automatic Video Colorization With Self-Regularization and Diversity,72,cvpr,38,2,2023-06-03 02:18:57.527000,https://github.com/ChenyangLEI/Fully-Automatic-Video-Colorization-with-Self-Regularization-and-Diversity,179,Fully automatic video colorization with self-regularization and diversity,"https://scholar.google.com/scholar?cluster=10806903677244432963&hl=en&as_sdt=0,33",11,2019
Image Super-Resolution by Neural Texture Transfer,254,cvpr,97,22,2023-06-03 02:18:57.727000,https://github.com/ZZUTK/SRNTT,419,Image super-resolution by neural texture transfer,"https://scholar.google.com/scholar?cluster=11593547226705820278&hl=en&as_sdt=0,5",8,2019
Quasi-Unsupervised Color Constancy,55,cvpr,9,7,2023-06-03 02:18:57.927000,https://github.com/claudio-unipv/quasi-unsupervised-cc,35,Quasi-unsupervised color constancy,"https://scholar.google.com/scholar?cluster=12158440535721243618&hl=en&as_sdt=0,5",2,2019
"Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation",502,cvpr,63,11,2023-06-03 02:18:58.128000,https://github.com/anuragranj/cc,441,"Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation","https://scholar.google.com/scholar?cluster=8899288224036536376&hl=en&as_sdt=0,31",39,2019
Learning Parallax Attention for Stereo Image Super-Resolution,213,cvpr,68,7,2023-06-03 02:18:58.328000,https://github.com/LongguangWang/PASSRnet,297,Learning parallax attention for stereo image super-resolution,"https://scholar.google.com/scholar?cluster=15910287610868166333&hl=en&as_sdt=0,36",10,2019
Deep Exemplar-Based Video Colorization,172,cvpr,65,12,2023-06-03 02:18:58.528000,https://github.com/zhangmozhe/video-colorization,283,Deep exemplar-based video colorization,"https://scholar.google.com/scholar?cluster=14469408272096670689&hl=en&as_sdt=0,10",12,2019
Bi-Directional Cascade Network for Perceptual Edge Detection,232,cvpr,68,37,2023-06-03 02:18:58.728000,https://github.com/pkuCactus/BDCN,320,Bi-directional cascade network for perceptual edge detection,"https://scholar.google.com/scholar?cluster=16576139094299758794&hl=en&as_sdt=0,10",9,2019
Scalable Convolutional Neural Network for Image Compressed Sensing,97,cvpr,5,3,2023-06-03 02:18:58.928000,https://github.com/wzhshi/SCSNet,18,Scalable convolutional neural network for image compressed sensing,"https://scholar.google.com/scholar?cluster=9609715805517520381&hl=en&as_sdt=0,33",2,2019
Single Image Deraining: A Comprehensive Benchmark Analysis,228,cvpr,24,6,2023-06-03 02:18:59.132000,https://github.com/lsy17096535/Single-Image-Deraining,144,Single image deraining: A comprehensive benchmark analysis,"https://scholar.google.com/scholar?cluster=17766883545473352036&hl=en&as_sdt=0,39",7,2019
Learning Transformation Synchronization,47,cvpr,8,0,2023-06-03 02:18:59.333000,https://github.com/xiangruhuang/Learning2Sync,42,Learning transformation synchronization,"https://scholar.google.com/scholar?cluster=8470657027462507918&hl=en&as_sdt=0,5",3,2019
Convolutional Neural Networks Can Be Deceived by Visual Illusions,39,cvpr,1,0,2023-06-03 02:18:59.533000,https://github.com/alviur/convnets_vs_vi,2,Convolutional neural networks can be deceived by visual illusions,"https://scholar.google.com/scholar?cluster=2134801593039769309&hl=en&as_sdt=0,23",2,2019
Events-To-Video: Bringing Modern Computer Vision to Event Cameras,259,cvpr,561,0,2023-06-03 02:18:59.734000,https://github.com/uzh-rpg/event-based_vision_resources,2240,Events-to-video: Bringing modern computer vision to event cameras,"https://scholar.google.com/scholar?cluster=4733379566599962041&hl=en&as_sdt=0,10",167,2019
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features,459,cvpr,150,6,2023-06-03 02:18:59.934000,https://github.com/mihaidusmanu/d2-net,689,D2-net: A trainable cnn for joint description and detection of local features,"https://scholar.google.com/scholar?cluster=2793189340183067127&hl=en&as_sdt=0,5",29,2019
Feedback Network for Image Super-Resolution,716,cvpr,129,29,2023-06-03 02:19:00.134000,https://github.com/Paper99/SRFBN_CVPR19,540,Feedback network for image super-resolution,"https://scholar.google.com/scholar?cluster=632407814303638032&hl=en&as_sdt=0,32",9,2019
Learning to Extract Flawless Slow Motion From Blurry Videos,49,cvpr,4,4,2023-06-03 02:19:00.334000,https://github.com/MeiguangJin/slow-motion,26,Learning to extract flawless slow motion from blurry videos,"https://scholar.google.com/scholar?cluster=5629916241744511325&hl=en&as_sdt=0,5",5,2019
Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination,111,cvpr,20,3,2023-06-03 02:19:00.535000,https://github.com/JWSoh/NatSR,118,Natural and realistic single image super-resolution with explicit natural manifold discrimination,"https://scholar.google.com/scholar?cluster=6468542381924015655&hl=en&as_sdt=0,14",9,2019
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection,734,cvpr,67,11,2023-06-03 02:19:00.735000,https://github.com/wuzhe71/CPD,262,Cascaded partial decoder for fast and accurate salient object detection,"https://scholar.google.com/scholar?cluster=5170808131115363004&hl=en&as_sdt=0,33",10,2019
Fast Single Image Reflection Suppression via Convex Optimization,43,cvpr,10,0,2023-06-03 02:19:00.935000,https://github.com/yyhz76/reflectSuppress,53,Fast single image reflection suppression via convex optimization,"https://scholar.google.com/scholar?cluster=14060773775024760460&hl=en&as_sdt=0,5",3,2019
A Mutual Learning Method for Salient Object Detection With Intertwined Multi-Supervision,226,cvpr,0,0,2023-06-03 02:19:01.137000,https://github.com/JosephineRabbit/MLMSNet,2,A mutual learning method for salient object detection with intertwined multi-supervision,"https://scholar.google.com/scholar?cluster=15923500132072869043&hl=en&as_sdt=0,21",1,2019
Representation Similarity Analysis for Efficient Task Taxonomy & Transfer Learning,100,cvpr,6,9,2023-06-03 02:19:01.337000,https://github.com/kshitijd20/RSA-CVPR19-release,24,Representation similarity analysis for efficient task taxonomy & transfer learning,"https://scholar.google.com/scholar?cluster=11083483928699627897&hl=en&as_sdt=0,36",6,2019
Progressive Image Deraining Networks: A Better and Simpler Baseline,578,cvpr,52,23,2023-06-03 02:19:01.538000,https://github.com/csdwren/PReNet,200,Progressive image deraining networks: A better and simpler baseline,"https://scholar.google.com/scholar?cluster=11537285553279008829&hl=en&as_sdt=0,34",7,2019
Object Counting and Instance Segmentation With Image-Level Supervision,99,cvpr,42,6,2023-06-03 02:19:01.738000,https://github.com/GuoleiSun/CountSeg,155,Object counting and instance segmentation with image-level supervision,"https://scholar.google.com/scholar?cluster=13538740343173106848&hl=en&as_sdt=0,5",5,2019
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud,270,cvpr,20,3,2023-06-03 02:19:01.940000,https://github.com/ericyi/GSPN,89,Gspn: Generative shape proposal network for 3d instance segmentation in point cloud,"https://scholar.google.com/scholar?cluster=197770165510054862&hl=en&as_sdt=0,3",7,2019
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements,115,cvpr,51,5,2023-06-03 02:19:02.140000,https://github.com/Vandermode/ERRNet,219,Single image reflection removal exploiting misaligned training data and network enhancements,"https://scholar.google.com/scholar?cluster=14306355940650189000&hl=en&as_sdt=0,5",7,2019
Compressing Convolutional Neural Networks via Factorized Convolutional Filters,96,cvpr,4,2,2023-06-03 02:19:02.341000,https://github.com/wubaoyuan/CNN-FCF-CVPR-2019,24,Compressing convolutional neural networks via factorized convolutional filters,"https://scholar.google.com/scholar?cluster=482598369462314362&hl=en&as_sdt=0,44",2,2019
A Local Block Coordinate Descent Algorithm for the CSC Model,50,cvpr,16,0,2023-06-03 02:19:02.542000,https://github.com/EvZissel/LoBCoD,29,A local block coordinate descent algorithm for the CSC model,"https://scholar.google.com/scholar?cluster=7478959742684637648&hl=en&as_sdt=0,47",2,2019
PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval,147,cvpr,12,1,2023-06-03 02:19:02.742000,https://github.com/XLechter/PCAN,70,PCAN: 3D attention map learning using contextual information for point cloud based retrieval,"https://scholar.google.com/scholar?cluster=9016390861743795252&hl=en&as_sdt=0,5",2,2019
Discovering Fair Representations in the Data Domain,114,cvpr,0,2,2023-06-03 02:19:02.943000,https://github.com/predictive-analytics-lab/Data-Domain-Fairness,4,Discovering fair representations in the data domain,"https://scholar.google.com/scholar?cluster=7406811945738630066&hl=en&as_sdt=0,47",2,2019
Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics,201,cvpr,10,3,2023-06-03 02:19:03.145000,https://github.com/laura-wang/video_repres_mas,63,Self-supervised spatio-temporal representation learning for videos by predicting motion and appearance statistics,"https://scholar.google.com/scholar?cluster=4125071649338402327&hl=en&as_sdt=0,22",7,2019
Actor-Critic Instance Segmentation,13,cvpr,4,2,2023-06-03 02:19:03.348000,https://github.com/visinf/acis,17,Actor-critic instance segmentation,"https://scholar.google.com/scholar?cluster=10107918392054632169&hl=en&as_sdt=0,5",6,2019
"Good News, Everyone! Context Driven Entity-Aware Captioning for News Images",109,cvpr,18,10,2023-06-03 02:19:03.548000,https://github.com/furkanbiten/GoodNews,116,"Good news, everyone! context driven entity-aware captioning for news images","https://scholar.google.com/scholar?cluster=3470684972319721114&hl=en&as_sdt=0,27",8,2019
Multi-Level Multimodal Common Semantic Space for Image-Phrase Grounding,63,cvpr,10,1,2023-06-03 02:19:03.749000,https://github.com/hassanhub/MultiGrounding,30,Multi-level multimodal common semantic space for image-phrase grounding,"https://scholar.google.com/scholar?cluster=6461806921919353461&hl=en&as_sdt=0,5",3,2019
Residual Regression With Semantic Prior for Crowd Counting,106,cvpr,5,2,2023-06-03 02:19:03.950000,https://github.com/jia-wan/ResidualRegression-pytorch,16,Residual regression with semantic prior for crowd counting,"https://scholar.google.com/scholar?cluster=10737601878160272620&hl=en&as_sdt=0,6",2,2019
Informative Object Annotations: Tell Me Something I Don't Know,3,cvpr,0,0,2023-06-03 02:19:04.151000,https://github.com/liorbracha/iota,0,Informative Object Annotations: Tell Me Something I Don't Know,"https://scholar.google.com/scholar?cluster=5005547084014933732&hl=en&as_sdt=0,5",1,2019
Adversarial Structure Matching for Structured Prediction Tasks,16,cvpr,2,0,2023-06-03 02:19:04.351000,https://github.com/twke18/Adversarial_Structure_Matching,11,Adversarial structure matching for structured prediction tasks,"https://scholar.google.com/scholar?cluster=15898235672453930518&hl=en&as_sdt=0,10",5,2019
Unsupervised Image Matching and Object Discovery as Optimization,60,cvpr,0,0,2023-06-03 02:19:04.552000,https://github.com/huyvvo/OSD,13,Unsupervised image matching and object discovery as optimization,"https://scholar.google.com/scholar?cluster=5692088936488589654&hl=en&as_sdt=0,33",1,2019
Vision-Based Navigation With Language-Based Assistance via Imitation Learning With Indirect Intervention,94,cvpr,14,0,2023-06-03 02:19:04.752000,https://github.com/debadeepta/vnla,60,Vision-based navigation with language-based assistance via imitation learning with indirect intervention,"https://scholar.google.com/scholar?cluster=8625024317902722147&hl=en&as_sdt=0,33",9,2019
"Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions",155,cvpr,59,13,2023-06-03 02:19:04.953000,https://github.com/aimagelab/show-control-and-tell,278,"Show, control and tell: A framework for generating controllable and grounded captions","https://scholar.google.com/scholar?cluster=395364837651406081&hl=en&as_sdt=0,5",10,2019
TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments,282,cvpr,11,0,2023-06-03 02:19:05.154000,https://github.com/lil-lab/touchdown,78,Touchdown: Natural language navigation and spatial reasoning in visual street environments,"https://scholar.google.com/scholar?cluster=2750266339730477598&hl=en&as_sdt=0,33",14,2019
Towards VQA Models That Can Read,326,cvpr,926,137,2023-06-03 02:19:05.355000,https://github.com/facebookresearch/pythia,5236,Towards vqa models that can read,"https://scholar.google.com/scholar?cluster=17041161243010218128&hl=en&as_sdt=0,39",117,2019
Associatively Segmenting Instances and Semantics in Point Clouds,209,cvpr,64,15,2023-06-03 02:19:05.556000,https://github.com/WXinlong/ASIS,243,Associatively segmenting instances and semantics in point clouds,"https://scholar.google.com/scholar?cluster=13739004401786447102&hl=en&as_sdt=0,33",3,2019
End-To-End Learned Random Walker for Seeded Image Segmentation,18,cvpr,2,0,2023-06-03 02:19:05.757000,https://github.com/hci-unihd/pytorch-LearnedRandomWalker,26,End-to-end learned random walker for seeded image segmentation,"https://scholar.google.com/scholar?cluster=14205871461075910768&hl=en&as_sdt=0,5",3,2019
Efficient Neural Network Compression,92,cvpr,1,0,2023-06-03 02:19:05.957000,https://github.com/Hyeji-Kim/ENC,22,Efficient neural network compression,"https://scholar.google.com/scholar?cluster=2440997436241799897&hl=en&as_sdt=0,5",2,2019
Explainable and Explicit Visual Reasoning Over Scene Graphs,187,cvpr,18,4,2023-06-03 02:19:06.168000,https://github.com/shijx12/XNM-Net,95,Explainable and explicit visual reasoning over scene graphs,"https://scholar.google.com/scholar?cluster=8517395712319798436&hl=en&as_sdt=0,5",3,2019
Transfer Learning via Unsupervised Task Discovery for Visual Question Answering,16,cvpr,4,0,2023-06-03 02:19:06.369000,https://github.com/HyeonwooNoh/vqa_task_discovery,32,Transfer learning via unsupervised task discovery for visual question answering,"https://scholar.google.com/scholar?cluster=9442012245217169205&hl=en&as_sdt=0,43",4,2019
Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining,194,cvpr,6,1,2023-06-03 02:19:06.569000,https://github.com/rajeevyasarla/UMRL--using-Cycle-Spinning,21,Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining,"https://scholar.google.com/scholar?cluster=14146589120712972884&hl=en&as_sdt=0,5",2,2019
Dynamics Are Important for the Recognition of Equine Pain in Video,20,cvpr,8,1,2023-06-03 02:19:06.770000,https://github.com/sofiabroome/painface-recognition,24,Dynamics are important for the recognition of equine pain in video,"https://scholar.google.com/scholar?cluster=2332358808596779162&hl=en&as_sdt=0,33",5,2019
Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving,829,cvpr,206,23,2023-06-03 02:19:06.970000,https://github.com/mileyan/pseudo_lidar,908,Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving,"https://scholar.google.com/scholar?cluster=10762721569852637212&hl=en&as_sdt=0,1",45,2019
Metric Learning for Image Registration,80,cvpr,29,1,2023-06-03 02:19:07.170000,https://github.com/uncbiag/registration,245,Metric learning for image registration,"https://scholar.google.com/scholar?cluster=5466190255647225576&hl=en&as_sdt=0,3",16,2019
PointPillars: Fast Encoders for Object Detection From Point Clouds,2074,cvpr,232,1,2023-06-03 02:19:07.371000,https://github.com/nutonomy/second.pytorch,888,Pointpillars: Fast encoders for object detection from point clouds,"https://scholar.google.com/scholar?cluster=2548197935459779982&hl=en&as_sdt=0,5",62,2019
Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data,75,cvpr,22,3,2023-06-03 02:19:07.571000,https://github.com/calebrob6/land-cover,82,Large scale high-resolution land cover mapping with multi-resolution data,"https://scholar.google.com/scholar?cluster=485676681711658934&hl=en&as_sdt=0,5",4,2019
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training,352,cvpr,9,2,2023-06-03 02:19:07.772000,https://github.com/TencentYoutuResearch/PersonReID-Pyramid,21,Pyramidal person re-identification via multi-loss dynamic training,"https://scholar.google.com/scholar?cluster=4672402664853334509&hl=en&as_sdt=0,47",4,2019
Dual Super-Resolution Learning for Semantic Segmentation,120,cvpr,7,2,2023-06-03 02:42:43.513000,https://github.com/wanglixilinx/DSRL,106,Dual super-resolution learning for semantic segmentation,"https://scholar.google.com/scholar?cluster=9626575908232283019&hl=en&as_sdt=0,33",21,2020
Deep Unfolding Network for Image Super-Resolution,375,cvpr,114,10,2023-06-03 02:42:43.714000,https://github.com/cszn/USRNet,775,Deep unfolding network for image super-resolution,"https://scholar.google.com/scholar?cluster=15871370420804183123&hl=en&as_sdt=0,5",37,2020
Unsupervised Learning for Intrinsic Image Decomposition From a Single Image,76,cvpr,15,6,2023-06-03 02:42:43.932000,https://github.com/DreamtaleCore/USI3D,74,Unsupervised learning for intrinsic image decomposition from a single image,"https://scholar.google.com/scholar?cluster=12246217488878001072&hl=en&as_sdt=0,32",3,2020
Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference,102,cvpr,14,12,2023-06-03 02:42:44.134000,https://github.com/thomasverelst/dynconv,120,Dynamic convolutions: Exploiting spatial sparsity for faster inference,"https://scholar.google.com/scholar?cluster=7569188649826421763&hl=en&as_sdt=0,5",4,2020
IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous Driving,46,cvpr,15,0,2023-06-03 02:42:44.334000,https://github.com/swords123/IDA-3D,72,Ida-3d: Instance-depth-aware 3d object detection from stereo vision for autonomous driving,"https://scholar.google.com/scholar?cluster=2719754974248434502&hl=en&as_sdt=0,36",6,2020
Forward and Backward Information Retention for Accurate Binary Neural Networks,229,cvpr,35,7,2023-06-03 02:42:44.534000,https://github.com/htqin/IR-Net,162,Forward and backward information retention for accurate binary neural networks,"https://scholar.google.com/scholar?cluster=17180565643358827499&hl=en&as_sdt=0,33",5,2020
KeypointNet: A Large-Scale 3D Keypoint Dataset Aggregated From Numerous Human Annotations,41,cvpr,14,2,2023-06-03 02:42:44.734000,https://github.com/qq456cvb/KeypointNet,124,Keypointnet: A large-scale 3d keypoint dataset aggregated from numerous human annotations,"https://scholar.google.com/scholar?cluster=11800283893745484191&hl=en&as_sdt=0,36",8,2020
Cooling-Shrinking Attack: Blinding the Tracker With Imperceptible Noises,56,cvpr,6,6,2023-06-03 02:42:44.935000,https://github.com/MasterBin-IIAU/CSA,54,Cooling-shrinking attack: Blinding the tracker with imperceptible noises,"https://scholar.google.com/scholar?cluster=18361255063345420357&hl=en&as_sdt=0,48",4,2020
Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection,1019,cvpr,161,20,2023-06-03 02:42:45.136000,https://github.com/sfzhang15/ATSS,1038,Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,"https://scholar.google.com/scholar?cluster=11324600873272743514&hl=en&as_sdt=0,5",25,2020
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution,130,cvpr,168,17,2023-06-03 02:42:45.336000,https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020,852,Zooming slow-mo: Fast and accurate one-stage space-time video super-resolution,"https://scholar.google.com/scholar?cluster=3907379195879536163&hl=en&as_sdt=0,31",30,2020
Structure Aware Single-Stage 3D Object Detection From Point Cloud,383,cvpr,105,57,2023-06-03 02:42:45.536000,https://github.com/skyhehe123/SA-SSD,471,Structure aware single-stage 3d object detection from point cloud,"https://scholar.google.com/scholar?cluster=4441951083249138696&hl=en&as_sdt=0,5",20,2020
A Local-to-Global Approach to Multi-Modal Movie Scene Segmentation,86,cvpr,40,13,2023-06-03 02:42:45.736000,https://github.com/AnyiRao/SceneSeg,191,A local-to-global approach to multi-modal movie scene segmentation,"https://scholar.google.com/scholar?cluster=592994252756538794&hl=en&as_sdt=0,5",8,2020
Video to Events: Recycling Video Datasets for Event Cameras,112,cvpr,67,27,2023-06-03 02:42:45.937000,https://github.com/uzh-rpg/rpg_vid2e,233,Video to events: Recycling video datasets for event cameras,"https://scholar.google.com/scholar?cluster=14107615774727791268&hl=en&as_sdt=0,14",23,2020
Unsupervised Learning From Video With Deep Neural Embeddings,53,cvpr,15,9,2023-06-03 02:42:46.142000,https://github.com/neuroailab/VIE,79,Unsupervised learning from video with deep neural embeddings,"https://scholar.google.com/scholar?cluster=3746976281713793865&hl=en&as_sdt=0,41",5,2020
"Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects",36,cvpr,12,4,2023-06-03 02:42:46.343000,https://github.com/ehsanik/touchTorch,63,"Use the force, luke! learning to predict physical forces by simulating effects","https://scholar.google.com/scholar?cluster=3630174886469941969&hl=en&as_sdt=0,33",2,2020
A Unified Optimization Framework for Low-Rank Inducing Penalties,4,cvpr,0,0,2023-06-03 02:42:46.544000,https://github.com/marcusvaltonen/UnifiedFramework,0,A unified optimization framework for low-rank inducing penalties,"https://scholar.google.com/scholar?cluster=7694607094687908138&hl=en&as_sdt=0,5",2,2020
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks,15,cvpr,1,0,2023-06-03 02:42:46.744000,https://github.com/eugenelet/NeuralScale,22,Neuralscale: Efficient scaling of neurons for resource-constrained deep neural networks,"https://scholar.google.com/scholar?cluster=16108079615293456149&hl=en&as_sdt=0,19",5,2020
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo,175,cvpr,33,18,2023-06-03 02:42:46.945000,https://github.com/JiayuYANG/CVP-MVSNet,220,Cost volume pyramid based depth inference for multi-view stereo,"https://scholar.google.com/scholar?cluster=7347427476190421012&hl=en&as_sdt=0,24",11,2020
Rethinking the Route Towards Weakly Supervised Object Localization,66,cvpr,19,0,2023-06-03 02:42:47.146000,https://github.com/tzzcl/PSOL,65,Rethinking the route towards weakly supervised object localization,"https://scholar.google.com/scholar?cluster=16844557112615644004&hl=en&as_sdt=0,33",4,2020
Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance Normalization,138,cvpr,14,10,2023-06-03 02:42:47.346000,https://github.com/terryoo/AINDNet,73,Transfer learning from synthetic to real-noise denoising with adaptive instance normalization,"https://scholar.google.com/scholar?cluster=15871516082637294149&hl=en&as_sdt=0,6",6,2020
Learning for Video Compression With Hierarchical Quality and Recurrent Enhancement,137,cvpr,17,0,2023-06-03 02:42:47.547000,https://github.com/RenYang-home/HLVC,119,Learning for video compression with hierarchical quality and recurrent enhancement,"https://scholar.google.com/scholar?cluster=14996736321329218278&hl=en&as_sdt=0,5",12,2020
Sub-Frame Appearance and 6D Pose Estimation of Fast Moving Objects,11,cvpr,4,0,2023-06-03 02:42:47.748000,https://github.com/rozumden/deblatting_python,20,Sub-frame appearance and 6d pose estimation of fast moving objects,"https://scholar.google.com/scholar?cluster=15148426920849576029&hl=en&as_sdt=0,5",2,2020
Violin: A Large-Scale Dataset for Video-and-Language Inference,51,cvpr,13,6,2023-06-03 02:42:47.948000,https://github.com/jimmy646/violin,147,Violin: A large-scale dataset for video-and-language inference,"https://scholar.google.com/scholar?cluster=5273353532207843404&hl=en&as_sdt=0,5",11,2020
Local Context Normalization: Revisiting Local Normalization,28,cvpr,2,4,2023-06-03 02:42:48.149000,https://github.com/anthonymlortiz/lcn,18,Local context normalization: Revisiting local normalization,"https://scholar.google.com/scholar?cluster=16204558090215526984&hl=en&as_sdt=0,5",3,2020
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching,43,cvpr,0,4,2023-06-03 02:42:48.349000,https://github.com/ppliuboy/Flow2Stereo,54,Flow2stereo: Effective self-supervised learning of optical flow and stereo matching,"https://scholar.google.com/scholar?cluster=13345498430401992330&hl=en&as_sdt=0,16",13,2020
Tangent Images for Mitigating Spherical Distortion,66,cvpr,14,5,2023-06-03 02:42:48.549000,https://github.com/meder411/Tangent-Images,61,Tangent images for mitigating spherical distortion,"https://scholar.google.com/scholar?cluster=7413500507564109350&hl=en&as_sdt=0,31",8,2020
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation,140,cvpr,12,1,2023-06-03 02:42:48.749000,https://github.com/Ha0Tang/LGGAN,134,Local class-specific and global image-level generative adversarial networks for semantic-guided scene generation,"https://scholar.google.com/scholar?cluster=15314679110337952068&hl=en&as_sdt=0,10",11,2020
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior,108,cvpr,49,11,2023-06-03 02:42:48.950000,https://github.com/csbhr/CDVD-TSP,242,Cascaded deep video deblurring using temporal sharpness prior,"https://scholar.google.com/scholar?cluster=65906966357560872&hl=en&as_sdt=0,33",8,2020
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation,105,cvpr,33,2,2023-06-03 02:42:49.150000,https://github.com/valeoai/xmuda,165,xmuda: Cross-modal unsupervised domain adaptation for 3d semantic segmentation,"https://scholar.google.com/scholar?cluster=875082267491511443&hl=en&as_sdt=0,5",14,2020
Deep Facial Non-Rigid Multi-View Stereo,47,cvpr,26,7,2023-06-03 02:42:49.351000,https://github.com/zqbai-jeremy/dfnrmvs,151,Deep facial non-rigid multi-view stereo,"https://scholar.google.com/scholar?cluster=14710284482588255933&hl=en&as_sdt=0,33",11,2020
SmallBigNet: Integrating Core and Contextual Views for Video Classification,76,cvpr,2,3,2023-06-03 02:42:49.552000,https://github.com/xhl-video/SmallBigNet,38,Smallbignet: Integrating core and contextual views for video classification,"https://scholar.google.com/scholar?cluster=16616613355339499180&hl=en&as_sdt=0,10",7,2020
Deep Shutter Unrolling Network,29,cvpr,5,7,2023-06-03 02:42:49.752000,https://github.com/ethliup/DeepUnrollNet,38,Deep shutter unrolling network,"https://scholar.google.com/scholar?cluster=8907904738398714401&hl=en&as_sdt=0,18",2,2020
Learning Geocentric Object Pose in Oblique Monocular Images,16,cvpr,10,0,2023-06-03 02:42:49.952000,https://github.com/pubgeo/monocular-geocentric-pose,45,Learning geocentric object pose in oblique monocular images,"https://scholar.google.com/scholar?cluster=8090417501108406268&hl=en&as_sdt=0,5",8,2020
DPGN: Distribution Propagation Graph Network for Few-Shot Learning,187,cvpr,38,18,2023-06-03 02:42:50.153000,https://github.com/megvii-research/DPGN,165,Dpgn: Distribution propagation graph network for few-shot learning,"https://scholar.google.com/scholar?cluster=5187741069496682229&hl=en&as_sdt=0,22",12,2020
Towards Learning Structure via Consensus for Face Segmentation and Parsing,13,cvpr,10,1,2023-06-03 02:42:50.353000,https://github.com/isi-vista/structure_via_consensus,55,Towards learning structure via consensus for face segmentation and parsing,"https://scholar.google.com/scholar?cluster=948869340895967812&hl=en&as_sdt=0,41",6,2020
Cross-Domain Document Object Detection: Benchmark Suite and Method,32,cvpr,4,7,2023-06-03 02:42:50.554000,https://github.com/kailigo/cddod,40,Cross-domain document object detection: Benchmark suite and method,"https://scholar.google.com/scholar?cluster=11273124199360326736&hl=en&as_sdt=0,11",8,2020
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform,144,cvpr,195,201,2023-06-03 02:42:50.755000,https://github.com/allenai/ai2thor,825,Robothor: An open simulation-to-real embodied ai platform,"https://scholar.google.com/scholar?cluster=12852310333821230905&hl=en&as_sdt=0,33",68,2020
On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Location,190,cvpr,3,1,2023-06-03 02:42:50.955000,https://github.com/oskyhn/CNNs-Without-Borders,69,On translation invariance in cnns: Convolutional layers can exploit absolute spatial location,"https://scholar.google.com/scholar?cluster=13756215989357813725&hl=en&as_sdt=0,47",11,2020
Style Normalization and Restitution for Generalizable Person Re-Identification,235,cvpr,9,9,2023-06-03 02:42:51.159000,https://github.com/microsoft/SNR,60,Style normalization and restitution for generalizable person re-identification,"https://scholar.google.com/scholar?cluster=16896347985638693525&hl=en&as_sdt=0,5",3,2020
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis,103,cvpr,11,1,2023-06-03 02:42:51.360000,https://github.com/autonomousvision/controllable_image_synthesis,66,Towards unsupervised learning of generative models for 3d controllable image synthesis,"https://scholar.google.com/scholar?cluster=1217881837248907783&hl=en&as_sdt=0,5",17,2020
Training Noise-Robust Deep Neural Networks via Meta-Learning,49,cvpr,2,0,2023-06-03 02:42:51.560000,https://github.com/ZhenWang-PhD/Training-Noise-Robust-Deep-Neural-Networks-via-Meta-Learning,19,Training noise-robust deep neural networks via meta-learning,"https://scholar.google.com/scholar?cluster=4267319052292614392&hl=en&as_sdt=0,5",1,2020
Exploit Clues From Views: Self-Supervised and Regularized Learning for Multiview Object Recognition,10,cvpr,7,0,2023-06-03 02:42:51.761000,https://github.com/chihhuiho/VISPE,12,Exploit clues from views: Self-supervised and regularized learning for multiview object recognition,"https://scholar.google.com/scholar?cluster=3529830865988272132&hl=en&as_sdt=0,14",4,2020
SampleNet: Differentiable Point Cloud Sampling,114,cvpr,40,0,2023-06-03 02:42:51.969000,https://github.com/itailang/SampleNet,322,Samplenet: Differentiable point cloud sampling,"https://scholar.google.com/scholar?cluster=4879612133923428164&hl=en&as_sdt=0,18",8,2020
Towards Transferable Targeted Attack,57,cvpr,0,0,2023-06-03 02:42:52.170000,https://github.com/TiJoy/Towards-Transferable-Targeted-Attack,10,Towards transferable targeted attack,"https://scholar.google.com/scholar?cluster=12435512203496950441&hl=en&as_sdt=0,4",2,2020
Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes,80,cvpr,30,14,2023-06-03 02:42:52.370000,https://github.com/cao-cong/RViDeNet,165,Supervised raw video denoising with a benchmark dataset on dynamic scenes,"https://scholar.google.com/scholar?cluster=668215183292873797&hl=en&as_sdt=0,5",13,2020
Online Deep Clustering for Unsupervised Representation Learning,155,cvpr,409,38,2023-06-03 02:42:52.570000,https://github.com/open-mmlab/OpenSelfSup,2762,Online deep clustering for unsupervised representation learning,"https://scholar.google.com/scholar?cluster=6265301829446145393&hl=en&as_sdt=0,14",41,2020
Graph Embedded Pose Clustering for Anomaly Detection,110,cvpr,16,11,2023-06-03 02:42:52.770000,https://github.com/amirmk89/gepc,68,Graph embedded pose clustering for anomaly detection,"https://scholar.google.com/scholar?cluster=7277677910039981714&hl=en&as_sdt=0,33",7,2020
FDA: Fourier Domain Adaptation for Semantic Segmentation,518,cvpr,67,19,2023-06-03 02:42:52.970000,https://github.com/YanchaoYang/FDA,395,Fda: Fourier domain adaptation for semantic segmentation,"https://scholar.google.com/scholar?cluster=6939572614497399848&hl=en&as_sdt=0,5",7,2020
Graph Structured Network for Image-Text Matching,142,cvpr,26,18,2023-06-03 02:42:53.170000,https://github.com/CrossmodalGroup/GSMN,151,Graph structured network for image-text matching,"https://scholar.google.com/scholar?cluster=944787583936341960&hl=en&as_sdt=0,33",8,2020
SynSin: End-to-End View Synthesis From a Single Image,255,cvpr,91,14,2023-06-03 02:42:53.370000,https://github.com/facebookresearch/synsin,613,Synsin: End-to-end view synthesis from a single image,"https://scholar.google.com/scholar?cluster=8005682821434060108&hl=en&as_sdt=0,33",27,2020
Instance Segmentation of Biological Images Using Harmonic Embeddings,34,cvpr,11,3,2023-06-03 02:42:53.571000,https://github.com/kulikovv/harmonic,52,Instance segmentation of biological images using harmonic embeddings,"https://scholar.google.com/scholar?cluster=10346597625950322783&hl=en&as_sdt=0,33",7,2020
HOPE-Net: A Graph-Based Model for Hand-Object Pose Estimation,137,cvpr,54,16,2023-06-03 02:42:53.771000,https://github.com/bardiadoosti/HOPE,252,Hope-net: A graph-based model for hand-object pose estimation,"https://scholar.google.com/scholar?cluster=17486249697962556113&hl=en&as_sdt=0,5",17,2020
Rethinking Zero-Shot Video Classification: End-to-End Training for Realistic Applications,96,cvpr,22,6,2023-06-03 02:42:53.971000,https://github.com/bbrattoli/ZeroShotVideoClassification,140,Rethinking zero-shot video classification: End-to-end training for realistic applications,"https://scholar.google.com/scholar?cluster=5690120467042779129&hl=en&as_sdt=0,5",10,2020
What You See is What You Get: Exploiting Visibility for 3D Object Detection,105,cvpr,16,2,2023-06-03 02:42:54.171000,https://github.com/peiyunh/wysiwyg,104,What you see is what you get: Exploiting visibility for 3d object detection,"https://scholar.google.com/scholar?cluster=11892744876522525600&hl=en&as_sdt=0,11",11,2020
A Multigrid Method for Efficiently Training Video Models,93,cvpr,1143,348,2023-06-03 02:42:54.372000,https://github.com/facebookresearch/SlowFast,5685,A multigrid method for efficiently training video models,"https://scholar.google.com/scholar?cluster=2801918111473713808&hl=en&as_sdt=0,39",97,2020
Visual Grounding in Video for Unsupervised Word Translation,44,cvpr,8,1,2023-06-03 02:42:54.572000,https://github.com/gsig/visual-grounding,40,Visual grounding in video for unsupervised word translation,"https://scholar.google.com/scholar?cluster=15024758484722184628&hl=en&as_sdt=0,5",8,2020
Deep 3D Portrait From a Single Image,38,cvpr,68,6,2023-06-03 02:42:54.773000,https://github.com/sicxu/Deep3dPortrait,342,Deep 3d portrait from a single image,"https://scholar.google.com/scholar?cluster=3806982893174696653&hl=en&as_sdt=0,47",26,2020
Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis,70,cvpr,135,17,2023-06-03 02:42:54.973000,https://github.com/Rudrabha/Lip2Wav,615,Learning individual speaking styles for accurate lip to speech synthesis,"https://scholar.google.com/scholar?cluster=13502787711988666633&hl=en&as_sdt=0,2",27,2020
PPDM: Parallel Point Detection and Matching for Real-Time Human-Object Interaction Detection,174,cvpr,43,6,2023-06-03 02:42:55.174000,https://github.com/YueLiao/PPDM,198,Ppdm: Parallel point detection and matching for real-time human-object interaction detection,"https://scholar.google.com/scholar?cluster=6282076644108174284&hl=en&as_sdt=0,33",11,2020
Adversarial Latent Autoencoders,198,cvpr,557,36,2023-06-03 02:42:55.374000,https://github.com/podgorskiy/ALAE,3419,Adversarial latent autoencoders,"https://scholar.google.com/scholar?cluster=4723740871272276219&hl=en&as_sdt=0,47",88,2020
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models,372,cvpr,1468,58,2023-06-03 02:42:55.574000,https://github.com/adamian98/pulse,7322,Pulse: Self-supervised photo upsampling via latent space exploration of generative models,"https://scholar.google.com/scholar?cluster=15370347097554476132&hl=en&as_sdt=0,39",225,2020
MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction,76,cvpr,13,2,2023-06-03 02:42:55.775000,https://github.com/Marchetz/MANTRA-CVPR20,62,Mantra: Memory augmented networks for multiple trajectory prediction,"https://scholar.google.com/scholar?cluster=4210995734400618554&hl=en&as_sdt=0,5",3,2020
Counterfactual Samples Synthesizing for Robust Visual Question Answering,218,cvpr,18,8,2023-06-03 02:42:55.975000,https://github.com/yanxinzju/CSS-VQA,72,Counterfactual samples synthesizing for robust visual question answering,"https://scholar.google.com/scholar?cluster=11059606861371208469&hl=en&as_sdt=0,34",2,2020
Inter-Region Affinity Distillation for Road Marking Segmentation,79,cvpr,20,15,2023-06-03 02:42:56.175000,https://github.com/cardwing/Codes-for-IntRA-KD,106,Inter-region affinity distillation for road marking segmentation,"https://scholar.google.com/scholar?cluster=9537126389588928700&hl=en&as_sdt=0,14",5,2020
GhostNet: More Features From Cheap Operations,1429,cvpr,648,59,2023-06-03 02:42:56.376000,https://github.com/huawei-noah/ghostnet,3301,Ghostnet: More features from cheap operations,"https://scholar.google.com/scholar?cluster=9871260588333635525&hl=en&as_sdt=0,34",47,2020
A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection,171,cvpr,6,0,2023-06-03 02:42:56.577000,https://github.com/OIPLab-DUT/CVPR2020-A2dele,39,A2dele: Adaptive and attentive depth distiller for efficient RGB-D salient object detection,"https://scholar.google.com/scholar?cluster=16876628898908447383&hl=en&as_sdt=0,5",2,2020
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization,159,cvpr,15,9,2023-06-03 02:42:56.777000,https://github.com/YueJiang-nj/CVPR2020-SDFDiff,234,Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization,"https://scholar.google.com/scholar?cluster=14066639183053244029&hl=en&as_sdt=0,5",10,2020
Self2Self With Dropout: Learning Self-Supervised Denoising From Single Image,196,cvpr,18,6,2023-06-03 02:42:56.978000,https://github.com/scut-mingqinchen/self2self,131,Self2self with dropout: Learning self-supervised denoising from single image,"https://scholar.google.com/scholar?cluster=15841357425275703413&hl=en&as_sdt=0,5",7,2020
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition,78,cvpr,87,4,2023-06-03 02:42:57.178000,https://github.com/Canjie-Luo/Text-Image-Augmentation,449,Learn to augment: Joint data augmentation and network optimization for text recognition,"https://scholar.google.com/scholar?cluster=6720665817789430503&hl=en&as_sdt=0,10",19,2020
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image,19,cvpr,3,4,2023-06-03 02:42:57.378000,https://github.com/guoyu-niubility/SVIN,25,A spatiotemporal volumetric interpolation network for 4d dynamic medical image,"https://scholar.google.com/scholar?cluster=17469973808184263862&hl=en&as_sdt=0,6",2,2020
Neural Pose Transfer by Spatially Adaptive Instance Normalization,41,cvpr,19,8,2023-06-03 02:42:57.578000,https://github.com/jiashunwang/Neural-Pose-Transfer,137,Neural pose transfer by spatially adaptive instance normalization,"https://scholar.google.com/scholar?cluster=5895408154818468020&hl=en&as_sdt=0,31",8,2020
PointGMM: A Neural GMM Network for Point Clouds,52,cvpr,8,1,2023-06-03 02:42:57.779000,https://github.com/amirhertz/pointgmm,45,Pointgmm: A neural gmm network for point clouds,"https://scholar.google.com/scholar?cluster=12395944022757933812&hl=en&as_sdt=0,33",3,2020
Where Am I Looking At? Joint Location and Orientation Estimation by Cross-View Matching,88,cvpr,6,6,2023-06-03 02:42:57.982000,https://github.com/shiyujiao/cross_view_localization_DSM,46,Where am i looking at? joint location and orientation estimation by cross-view matching,"https://scholar.google.com/scholar?cluster=2592788529518726950&hl=en&as_sdt=0,33",4,2020
RoutedFusion: Learning Real-Time Depth Map Fusion,55,cvpr,18,4,2023-06-03 02:42:58.192000,https://github.com/weders/RoutedFusion,125,Routedfusion: Learning real-time depth map fusion,"https://scholar.google.com/scholar?cluster=2964830636646330816&hl=en&as_sdt=0,5",12,2020
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels,131,cvpr,8,8,2023-06-03 02:42:58.392000,https://github.com/alex-xun-xu/WeakSupPointCloudSeg,98,Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels,"https://scholar.google.com/scholar?cluster=15233469619337801358&hl=en&as_sdt=0,14",9,2020
Towards Large Yet Imperceptible Adversarial Image Perturbations With Perceptual Color Distance,107,cvpr,9,1,2023-06-03 02:42:58.593000,https://github.com/ZhengyuZhao/PerC-Adversarial,50,Towards large yet imperceptible adversarial image perturbations with perceptual color distance,"https://scholar.google.com/scholar?cluster=9997585199758312284&hl=en&as_sdt=0,33",3,2020
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective,188,cvpr,4,2,2023-06-03 02:42:58.793000,https://github.com/abdullahjamal/Longtail_DA,22,Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective,"https://scholar.google.com/scholar?cluster=2255644322777336647&hl=en&as_sdt=0,33",3,2020
Gated Channel Transformation for Visual Recognition,125,cvpr,26,0,2023-06-03 02:42:58.993000,https://github.com/z-x-yang/GCT,116,Gated channel transformation for visual recognition,"https://scholar.google.com/scholar?cluster=14632106519933169181&hl=en&as_sdt=0,5",6,2020
Softmax Splatting for Video Frame Interpolation,220,cvpr,57,2,2023-06-03 02:42:59.193000,https://github.com/sniklaus/softmax-splatting,368,Softmax splatting for video frame interpolation,"https://scholar.google.com/scholar?cluster=5412503042993292983&hl=en&as_sdt=0,47",17,2020
Can Deep Learning Recognize Subtle Human Activities?,9,cvpr,2,0,2023-06-03 02:42:59.392000,https://github.com/kreimanlab/DeepLearning-vs-HighLevelVision,11,Can deep learning recognize subtle human activities?,"https://scholar.google.com/scholar?cluster=2871468803034414570&hl=en&as_sdt=0,33",3,2020
BiDet: An Efficient Binarized Object Detector,48,cvpr,33,5,2023-06-03 02:42:59.593000,https://github.com/ZiweiWangTHU/BiDet,167,Bidet: An efficient binarized object detector,"https://scholar.google.com/scholar?cluster=3965514242665564617&hl=en&as_sdt=0,5",9,2020
A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection,81,cvpr,10,31,2023-06-03 02:42:59.793000,https://github.com/eraserNut/MTMT,81,A multi-task mean teacher for semi-supervised shadow detection,"https://scholar.google.com/scholar?cluster=13302143660272490203&hl=en&as_sdt=0,33",7,2020
Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization,312,cvpr,18,2,2023-06-03 02:42:59.993000,https://github.com/hongxin001/JoCoR,116,Combating noisy labels by agreement: A joint training method with co-regularization,"https://scholar.google.com/scholar?cluster=8672051314340970870&hl=en&as_sdt=0,11",3,2020
Searching for Actions on the Hyperbole,31,cvpr,3,3,2023-06-03 02:43:00.193000,https://github.com/Tenglon/hyperbolic_action,12,Searching for actions on the hyperbole,"https://scholar.google.com/scholar?cluster=17402068943209033206&hl=en&as_sdt=0,40",2,2020
Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution,297,cvpr,99,0,2023-06-03 02:43:00.393000,https://github.com/guoyongcs/DRN,420,Closed-loop matters: Dual regression networks for single image super-resolution,"https://scholar.google.com/scholar?cluster=18061404949489143219&hl=en&as_sdt=0,45",18,2020
Superpixel Segmentation With Fully Convolutional Networks,138,cvpr,79,0,2023-06-03 02:43:00.594000,https://github.com/fuy34/superpixel_fcn,337,Superpixel segmentation with fully convolutional networks,"https://scholar.google.com/scholar?cluster=17254136433445243811&hl=en&as_sdt=0,15",6,2020
SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans,102,cvpr,23,3,2023-06-03 02:43:00.795000,https://github.com/angeladai/sgnn,170,Sg-nn: Sparse generative neural networks for self-supervised scene completion of rgb-d scans,"https://scholar.google.com/scholar?cluster=4891876313591120438&hl=en&as_sdt=0,23",13,2020
ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection,154,cvpr,49,17,2023-06-03 02:43:00.995000,https://github.com/wangyuxin87/ContourNet,214,Contournet: Taking a further step toward accurate arbitrary-shaped scene text detection,"https://scholar.google.com/scholar?cluster=10407924981028323116&hl=en&as_sdt=0,10",14,2020
Wavelet Integrated CNNs for Noise-Robust Image Classification,98,cvpr,15,12,2023-06-03 02:43:01.195000,https://github.com/LiQiufu/WaveCNet,88,Wavelet integrated CNNs for noise-robust image classification,"https://scholar.google.com/scholar?cluster=5545254318212756783&hl=en&as_sdt=0,11",3,2020
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End,79,cvpr,14,2,2023-06-03 02:43:01.396000,https://github.com/abdo-eldesokey/pncnn,82,Uncertainty-aware cnns for depth completion: Uncertainty from beginning to end,"https://scholar.google.com/scholar?cluster=13541424700079169022&hl=en&as_sdt=0,5",2,2020
Deep Snake for Real-Time Instance Segmentation,231,cvpr,226,0,2023-06-03 02:43:01.596000,https://github.com/zju3dv/snake,1107,Deep snake for real-time instance segmentation,"https://scholar.google.com/scholar?cluster=11877838671373541982&hl=en&as_sdt=0,22",51,2020
Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax,195,cvpr,63,21,2023-06-03 02:43:01.797000,https://github.com/FishYuLi/BalancedGroupSoftmax,346,Overcoming classifier imbalance for long-tail object detection with balanced group softmax,"https://scholar.google.com/scholar?cluster=241324425955768124&hl=en&as_sdt=0,47",12,2020
Camera Trace Erasing,16,cvpr,3,1,2023-06-03 02:43:01.998000,https://github.com/ngchc/CameraTE,8,Camera trace erasing,"https://scholar.google.com/scholar?cluster=2544710194731719584&hl=en&as_sdt=0,11",5,2020
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering,196,cvpr,0,0,2023-06-03 02:43:02.198000,https://github.com/huitangtang/SRDC-CVPR2020,1,Unsupervised domain adaptation via structurally regularized deep clustering,"https://scholar.google.com/scholar?cluster=6363086439618954977&hl=en&as_sdt=0,33",1,2020
Symmetry and Group in Attribute-Object Compositions,68,cvpr,5,1,2023-06-03 02:43:02.399000,https://github.com/DirtyHarryLYL/SymNet,47,Symmetry and group in attribute-object compositions,"https://scholar.google.com/scholar?cluster=16870815556752021056&hl=en&as_sdt=0,21",7,2020
AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation,146,cvpr,25,6,2023-06-03 02:43:02.599000,https://github.com/HyeongminLEE/AdaCoF-pytorch,161,Adacof: Adaptive collaboration of flows for video frame interpolation,"https://scholar.google.com/scholar?cluster=10771538884046797245&hl=en&as_sdt=0,5",9,2020
Blurry Video Frame Interpolation,70,cvpr,24,1,2023-06-03 02:43:02.799000,https://github.com/laomao0/BIN,198,Blurry video frame interpolation,"https://scholar.google.com/scholar?cluster=12096278443404837492&hl=en&as_sdt=0,9",9,2020
Learning to Learn Single Domain Generalization,260,cvpr,26,11,2023-06-03 02:43:03,https://github.com/joffery/M-ADA,129,Learning to learn single domain generalization,"https://scholar.google.com/scholar?cluster=14587848551854785624&hl=en&as_sdt=0,5",11,2020
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation,484,cvpr,262,72,2023-06-03 02:43:03.200000,https://github.com/HRNet/Higher-HRNet-Human-Pose-Estimation,1188,Higherhrnet: Scale-aware representation learning for bottom-up human pose estimation,"https://scholar.google.com/scholar?cluster=15368170985283079245&hl=en&as_sdt=0,36",47,2020
CNN-Generated Images Are Surprisingly Easy to Spot... for Now,550,cvpr,150,15,2023-06-03 02:43:03.404000,https://github.com/PeterWang512/CNNDetection,683,CNN-generated images are surprisingly easy to spot... for now,"https://scholar.google.com/scholar?cluster=6504477120744697567&hl=en&as_sdt=0,36",22,2020
One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples,24,cvpr,5,1,2023-06-03 02:43:03.606000,https://github.com/a554b554/DefenseByAttack,6,One man's trash is another man's treasure: Resisting adversarial examples by adversarial examples,"https://scholar.google.com/scholar?cluster=17905691031782291050&hl=en&as_sdt=0,44",2,2020
Image Processing Using Multi-Code GAN Prior,244,cvpr,44,12,2023-06-03 02:43:03.807000,https://github.com/genforce/mganprior,288,Image processing using multi-code gan prior,"https://scholar.google.com/scholar?cluster=10065538952107776043&hl=en&as_sdt=0,5",14,2020
Rethinking Performance Estimation in Neural Architecture Search,22,cvpr,21,3,2023-06-03 02:43:04.008000,https://github.com/zhengxiawu/rethinking_performance_estimation_in_NAS,164,Rethinking performance estimation in neural architecture search,"https://scholar.google.com/scholar?cluster=15122321645117188815&hl=en&as_sdt=0,5",4,2020
ColorFool: Semantic Adversarial Colorization,84,cvpr,10,2,2023-06-03 02:43:04.208000,https://github.com/smartcameras/ColorFool,48,Colorfool: Semantic adversarial colorization,"https://scholar.google.com/scholar?cluster=9584849271056226945&hl=en&as_sdt=0,5",3,2020
Bi3D: Stereo Depth Estimation via Binary Classifications,62,cvpr,25,7,2023-06-03 02:43:04.408000,https://github.com/NVlabs/Bi3D,150,Bi3d: Stereo depth estimation via binary classifications,"https://scholar.google.com/scholar?cluster=11709717789111168184&hl=en&as_sdt=0,33",26,2020
Probability Weighted Compact Feature for Domain Adaptive Retrieval,28,cvpr,9,1,2023-06-03 02:43:04.609000,https://github.com/fuxianghuang1/PWCF,20,Probability weighted compact feature for domain adaptive retrieval,"https://scholar.google.com/scholar?cluster=5336430328985289253&hl=en&as_sdt=0,33",5,2020
Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion,64,cvpr,24,9,2023-06-03 02:43:04.809000,https://github.com/AdamKortylewski/CompositionalNets,104,Compositional convolutional neural networks: A deep architecture with innate robustness to partial occlusion,"https://scholar.google.com/scholar?cluster=6915140898194313831&hl=en&as_sdt=0,44",2,2020
Norm-Aware Embedding for Efficient Person Search,83,cvpr,14,5,2023-06-03 02:43:05.010000,https://github.com/DeanChan/NAE4PS,80,Norm-aware embedding for efficient person search,"https://scholar.google.com/scholar?cluster=8630034257442391060&hl=en&as_sdt=0,14",3,2020
Syntax-Aware Action Targeting for Video Captioning,125,cvpr,22,5,2023-06-03 02:43:05.210000,https://github.com/SydCaption/SAAT,60,Syntax-aware action targeting for video captioning,"https://scholar.google.com/scholar?cluster=9591332838374549550&hl=en&as_sdt=0,5",6,2020
Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction,207,cvpr,27,12,2023-06-03 02:43:05.410000,https://github.com/limaosen0/DMGNN,122,Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction,"https://scholar.google.com/scholar?cluster=4881783104692242403&hl=en&as_sdt=0,36",7,2020
"Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes From a Single Image",134,cvpr,47,22,2023-06-03 02:43:05.611000,https://github.com/yinyunie/Total3DUnderstanding,363,"Total3dunderstanding: Joint layout, object pose and mesh reconstruction for indoor scenes from a single image","https://scholar.google.com/scholar?cluster=11056192555834324480&hl=en&as_sdt=0,39",12,2020
GPS-Net: Graph Property Sensing Network for Scene Graph Generation,149,cvpr,8,0,2023-06-03 02:43:05.811000,https://github.com/taksau/GPS-Net,56,Gps-net: Graph property sensing network for scene graph generation,"https://scholar.google.com/scholar?cluster=6322890861074553647&hl=en&as_sdt=0,15",5,2020
Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination,60,cvpr,5,2,2023-06-03 02:43:06.011000,https://github.com/pratulsrinivasan/lighthouse,69,Lighthouse: Predicting lighting volumes for spatially-coherent illumination,"https://scholar.google.com/scholar?cluster=11152875383538691690&hl=en&as_sdt=0,25",5,2020
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification,188,cvpr,18,1,2023-06-03 02:43:06.212000,https://github.com/bismex/HiCMD,74,Hi-CMD: Hierarchical cross-modality disentanglement for visible-infrared person re-identification,"https://scholar.google.com/scholar?cluster=11531598637713869801&hl=en&as_sdt=0,43",4,2020
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes,33,cvpr,16,4,2023-06-03 02:43:06.412000,https://github.com/lzqsd/TransparentShapeReconstruction,108,Through the looking glass: Neural 3d reconstruction of transparent shapes,"https://scholar.google.com/scholar?cluster=4451887168503458192&hl=en&as_sdt=0,5",10,2020
Hyperbolic Visual Embedding Learning for Zero-Shot Recognition,90,cvpr,8,4,2023-06-03 02:43:06.612000,https://github.com/ShaoTengLiu/Hyperbolic_ZSL,65,Hyperbolic visual embedding learning for zero-shot recognition,"https://scholar.google.com/scholar?cluster=3675190322231427239&hl=en&as_sdt=0,23",6,2020
Clean-Label Backdoor Attacks on Video Recognition Models,170,cvpr,3,1,2023-06-03 02:43:06.814000,https://github.com/ShihaoZhaoZSH/Video-Backdoor-Attack,35,Clean-label backdoor attacks on video recognition models,"https://scholar.google.com/scholar?cluster=12989936269315644810&hl=en&as_sdt=0,39",3,2020
RPM-Net: Robust Point Matching Using Learned Features,257,cvpr,55,18,2023-06-03 02:43:07.015000,https://github.com/yewzijian/RPMNet,261,Rpm-net: Robust point matching using learned features,"https://scholar.google.com/scholar?cluster=1833782385864625577&hl=en&as_sdt=0,21",15,2020
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation,171,cvpr,109,1,2023-06-03 02:43:07.215000,https://github.com/m-tassano/fastdvdnet,464,Fastdvdnet: Towards real-time deep video denoising without flow estimation,"https://scholar.google.com/scholar?cluster=17981991911827164634&hl=en&as_sdt=0,47",11,2020
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration,201,cvpr,16,15,2023-06-03 02:43:07.416000,https://github.com/Juliachang/SC-CAM,171,Weakly-supervised semantic segmentation via sub-category exploration,"https://scholar.google.com/scholar?cluster=3841587906806965072&hl=en&as_sdt=0,5",15,2020
Understanding Human Hands in Contact at Internet Scale,152,cvpr,49,13,2023-06-03 02:43:07.617000,https://github.com/ddshan/hand_object_detector,179,Understanding human hands in contact at internet scale,"https://scholar.google.com/scholar?cluster=7440647218724115290&hl=en&as_sdt=0,5",3,2020
Normalizing Flows With Multi-Scale Autoregressive Priors,7,cvpr,3,0,2023-06-03 02:43:07.818000,https://github.com/visinf/mar-scf,12,Normalizing flows with multi-scale autoregressive priors,"https://scholar.google.com/scholar?cluster=13193500579133703150&hl=en&as_sdt=0,44",2,2020
Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation,423,cvpr,89,14,2023-06-03 02:43:08.018000,https://github.com/YudeWang/SEAM,476,Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=14106391617252066537&hl=en&as_sdt=0,10",9,2020
Dynamic Neural Relational Inference,33,cvpr,18,3,2023-06-03 02:43:08.218000,https://github.com/cgraber/cvpr_dNRI,55,Dynamic neural relational inference,"https://scholar.google.com/scholar?cluster=16082332557255209772&hl=en&as_sdt=0,34",3,2020
TBT: Targeted Neural Network Attack With Bit Trojan,149,cvpr,3,0,2023-06-03 02:43:08.418000,https://github.com/adnansirajrakin/TBT-2020,12,Tbt: Targeted neural network attack with bit trojan,"https://scholar.google.com/scholar?cluster=17783112366316447654&hl=en&as_sdt=0,5",2,2020
Central Similarity Quantization for Efficient Image and Video Retrieval,184,cvpr,45,25,2023-06-03 02:43:08.619000,https://github.com/yuanli2333/Hadamard-Matrix-for-hashing,213,Central similarity quantization for efficient image and video retrieval,"https://scholar.google.com/scholar?cluster=849092170265612535&hl=en&as_sdt=0,16",9,2020
Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning,47,cvpr,9,0,2023-06-03 02:43:08.819000,https://github.com/clovaai/embedding-expansion,70,Embedding expansion: Augmentation in embedding space for deep metric learning,"https://scholar.google.com/scholar?cluster=2367694731347282983&hl=en&as_sdt=0,33",6,2020
End-to-End Learning of Visual Representations From Uncurated Instructional Videos,530,cvpr,29,7,2023-06-03 02:43:09.019000,https://github.com/antoine77340/MIL-NCE_HowTo100M,194,End-to-end learning of visual representations from uncurated instructional videos,"https://scholar.google.com/scholar?cluster=17582844079259155589&hl=en&as_sdt=0,5",10,2020
"OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold",67,cvpr,39,10,2023-06-03 02:43:09.220000,https://github.com/IntuitionMachines/OrigamiNet,136,"OrigamiNet: weakly-supervised, segmentation-free, one-step, full page text recognition by learning to unfold","https://scholar.google.com/scholar?cluster=15845525342391314185&hl=en&as_sdt=0,47",14,2020
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics,717,cvpr,38,5,2023-06-03 02:43:09.420000,https://github.com/danmohaha/celeb-deepfakeforensics,206,Celeb-df: A large-scale challenging dataset for deepfake forensics,"https://scholar.google.com/scholar?cluster=7508089290324359409&hl=en&as_sdt=0,47",5,2020
Suppressing Uncertainties for Large-Scale Facial Expression Recognition,384,cvpr,96,63,2023-06-03 02:43:09.620000,https://github.com/kaiwang960112/Self-Cure-Network,394,Suppressing uncertainties for large-scale facial expression recognition,"https://scholar.google.com/scholar?cluster=1497632452874253304&hl=en&as_sdt=0,47",10,2020
Improved Few-Shot Visual Classification,158,cvpr,15,0,2023-06-03 02:43:09.821000,https://github.com/plai-group/simple-cnaps,44,Improved few-shot visual classification,"https://scholar.google.com/scholar?cluster=10179566061976639313&hl=en&as_sdt=0,33",8,2020
Towards Backward-Compatible Representation Learning,62,cvpr,9,0,2023-06-03 02:43:10.021000,https://github.com/YantaoShen/openBCT,55,Towards backward-compatible representation learning,"https://scholar.google.com/scholar?cluster=11261409184426923278&hl=en&as_sdt=0,34",7,2020
Visual Chirality,23,cvpr,14,0,2023-06-03 02:43:10.222000,https://github.com/linzhiqiu/digital_chirality,89,Visual chirality,"https://scholar.google.com/scholar?cluster=7394456161620427993&hl=en&as_sdt=0,23",3,2020
Neural Architecture Search for Lightweight Non-Local Networks,54,cvpr,16,0,2023-06-03 02:43:10.423000,https://github.com/LiYingwei/AutoNL,105,Neural architecture search for lightweight non-local networks,"https://scholar.google.com/scholar?cluster=12383596481862216698&hl=en&as_sdt=0,43",7,2020
Multi-Domain Learning for Accurate and Few-Shot Color Constancy,43,cvpr,2,3,2023-06-03 02:43:10.623000,https://github.com/msxiaojin/MDLCC,13,Multi-domain learning for accurate and few-shot color constancy,"https://scholar.google.com/scholar?cluster=6271057423741121743&hl=en&as_sdt=0,5",3,2020
UniPose: Unified Human Pose Estimation in Single Images and Videos,138,cvpr,41,15,2023-06-03 02:43:10.824000,https://github.com/bmartacho/UniPose,188,Unipose: Unified human pose estimation in single images and videos,"https://scholar.google.com/scholar?cluster=10578191787955309147&hl=en&as_sdt=0,33",10,2020
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm,158,cvpr,14,4,2023-06-03 02:43:11.024000,https://github.com/xaggi/OGNet,77,Old is gold: Redefining the adversarially learned one-class classifier training paradigm,"https://scholar.google.com/scholar?cluster=11192774920568187037&hl=en&as_sdt=0,36",9,2020
AdversarialNAS: Adversarial Neural Architecture Search for GANs,63,cvpr,12,1,2023-06-03 02:43:11.224000,https://github.com/chengaopro/AdversarialNAS,70,Adversarialnas: Adversarial neural architecture search for gans,"https://scholar.google.com/scholar?cluster=9918291536670516118&hl=en&as_sdt=0,44",12,2020
DSNAS: Direct Neural Architecture Search Without Parameter Retraining,103,cvpr,24,3,2023-06-03 02:43:11.433000,https://github.com/SNAS-Series/SNAS-Series,134,Dsnas: Direct neural architecture search without parameter retraining,"https://scholar.google.com/scholar?cluster=7757218143839179820&hl=en&as_sdt=0,39",5,2020
G2L-Net: Global to Local Network for Real-Time 6D Pose Estimation With Embedding Vector Features,59,cvpr,14,9,2023-06-03 02:43:11.633000,https://github.com/DC1991/G2L_Net,108,G2l-net: Global to local network for real-time 6d pose estimation with embedding vector features,"https://scholar.google.com/scholar?cluster=17831261992133196157&hl=en&as_sdt=0,5",11,2020
SESS: Self-Ensembling Semi-Supervised 3D Object Detection,88,cvpr,30,2,2023-06-03 02:43:11.833000,https://github.com/Na-Z/sess,128,Sess: Self-ensembling semi-supervised 3d object detection,"https://scholar.google.com/scholar?cluster=9564727493626498670&hl=en&as_sdt=0,33",7,2020
DoveNet: Deep Image Harmonization via Domain Verification,118,cvpr,96,0,2023-06-03 02:43:12.033000,https://github.com/bcmi/Image_Harmonization_Datasets,694,Dovenet: Deep image harmonization via domain verification,"https://scholar.google.com/scholar?cluster=18199597418789914925&hl=en&as_sdt=0,5",14,2020
"Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation",424,cvpr,117,14,2023-06-03 02:43:12.234000,https://github.com/bowenc0221/panoptic-deeplab,552,"Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation","https://scholar.google.com/scholar?cluster=18246305387780116611&hl=en&as_sdt=0,47",20,2020
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning,115,cvpr,13,0,2023-06-03 02:43:12.434000,https://github.com/vcg-uvic/acne,49,Acne: Attentive context normalization for robust permutation-equivariant learning,"https://scholar.google.com/scholar?cluster=17062235868798470161&hl=en&as_sdt=0,5",13,2020
DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration,54,cvpr,20,0,2023-06-03 02:43:12.635000,https://github.com/jw4hv/deepflash,82,Deepflash: An efficient network for learning-based medical image registration,"https://scholar.google.com/scholar?cluster=4528085594004647024&hl=en&as_sdt=0,5",4,2020
Distribution-Aware Coordinate Representation for Human Pose Estimation,313,cvpr,899,204,2023-06-03 02:43:12.835000,https://github.com/leoxiaobin/deep-high-resolution-net.pytorch,4043,Distribution-aware coordinate representation for human pose estimation,"https://scholar.google.com/scholar?cluster=9532214621767113733&hl=en&as_sdt=0,36",78,2020
Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction,106,cvpr,34,12,2023-06-03 02:43:13.035000,https://github.com/lrxjason/Attention3DHumanPose,143,Attention mechanism exploits temporal contexts: Real-time 3d human pose reconstruction,"https://scholar.google.com/scholar?cluster=10723447341108778673&hl=en&as_sdt=0,11",11,2020
MaskFlownet: Asymmetric Feature Matching With Learnable Occlusion Mask,160,cvpr,74,12,2023-06-03 02:43:13.235000,https://github.com/microsoft/MaskFlownet,355,Maskflownet: Asymmetric feature matching with learnable occlusion mask,"https://scholar.google.com/scholar?cluster=13358090819819933445&hl=en&as_sdt=0,5",8,2020
Diverse Image Generation via Self-Conditioned GANs,78,cvpr,19,3,2023-06-03 02:43:13.435000,https://github.com/stevliu/self-conditioned-gan,149,Diverse image generation via self-conditioned gans,"https://scholar.google.com/scholar?cluster=7416192416492647206&hl=en&as_sdt=0,41",6,2020
Context-Aware and Scale-Insensitive Temporal Repetition Counting,21,cvpr,5,6,2023-06-03 02:43:13.636000,https://github.com/Xiaodomgdomg/Deep-Temporal-Repetition-Counting,43,Context-aware and scale-insensitive temporal repetition counting,"https://scholar.google.com/scholar?cluster=8283458455147015844&hl=en&as_sdt=0,5",4,2020
3FabRec: Fast Few-Shot Face Alignment by Reconstruction,58,cvpr,19,2,2023-06-03 02:43:13.837000,https://github.com/browatbn2/3FabRec,123,3fabrec: Fast few-shot face alignment by reconstruction,"https://scholar.google.com/scholar?cluster=4960059173283160994&hl=en&as_sdt=0,5",8,2020
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability,21,cvpr,9,2,2023-06-03 02:43:14.037000,https://github.com/zju-vipa/DEPARA,39,Depara: Deep attribution graph for deep knowledge transferability,"https://scholar.google.com/scholar?cluster=8083980828241930895&hl=en&as_sdt=0,5",5,2020
Video Panoptic Segmentation,116,cvpr,55,13,2023-06-03 02:43:14.237000,https://github.com/mcahny/vps,293,Video panoptic segmentation,"https://scholar.google.com/scholar?cluster=17377067048222299228&hl=en&as_sdt=0,10",14,2020
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition,558,cvpr,94,2,2023-06-03 02:43:14.438000,https://github.com/kenziyuliu/ms-g3d,380,Disentangling and unifying graph convolutions for skeleton-based action recognition,"https://scholar.google.com/scholar?cluster=14188108076981434930&hl=en&as_sdt=0,5",16,2020
Bodies at Rest: 3D Human Pose and Shape Estimation From a Pressure Image Using Synthetic Data,39,cvpr,6,1,2023-06-03 02:43:14.639000,https://github.com/Healthcare-Robotics/bodies-at-rest,57,Bodies at rest: 3d human pose and shape estimation from a pressure image using synthetic data,"https://scholar.google.com/scholar?cluster=17853257753584764515&hl=en&as_sdt=0,5",7,2020
Category-Level Articulated Object Pose Estimation,127,cvpr,15,28,2023-06-03 02:43:14.839000,https://github.com/dragonlong/articulated-pose,102,Category-level articulated object pose estimation,"https://scholar.google.com/scholar?cluster=17839565446689433965&hl=en&as_sdt=0,34",7,2020
Cars Can't Fly Up in the Sky: Improving Urban-Scene Segmentation via Height-Driven Attention Networks,154,cvpr,38,3,2023-06-03 02:43:15.040000,https://github.com/shachoi/HANet,209,Cars can't fly up in the sky: Improving urban-scene segmentation via height-driven attention networks,"https://scholar.google.com/scholar?cluster=11925654838550651404&hl=en&as_sdt=0,44",11,2020
ImVoteNet: Boosting 3D Object Detection in Point Clouds With Image Votes,212,cvpr,25,8,2023-06-03 02:43:15.239000,https://github.com/facebookresearch/imvotenet,104,Imvotenet: Boosting 3d object detection in point clouds with image votes,"https://scholar.google.com/scholar?cluster=4943660161491158315&hl=en&as_sdt=0,33",8,2020
Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation,70,cvpr,26,3,2023-06-03 02:43:15.439000,https://github.com/fabbrimatteo/LoCO,131,Compressed volumetric heatmaps for multi-person 3d pose estimation,"https://scholar.google.com/scholar?cluster=11113820627927885466&hl=en&as_sdt=0,41",6,2020
ZeroQ: A Novel Zero Shot Quantization Framework,272,cvpr,50,17,2023-06-03 02:43:15.640000,https://github.com/amirgholami/ZeroQ,240,Zeroq: A novel zero shot quantization framework,"https://scholar.google.com/scholar?cluster=7591323665902876723&hl=en&as_sdt=0,39",17,2020
Orthogonal Convolutional Neural Networks,140,cvpr,13,4,2023-06-03 02:43:15.840000,https://github.com/samaonline/Orthogonal-Convolutional-Neural-Networks,95,Orthogonal convolutional neural networks,"https://scholar.google.com/scholar?cluster=3710519923763274746&hl=en&as_sdt=0,8",5,2020
Unsupervised Adaptation Learning for Hyperspectral Imagery Super-Resolution,76,cvpr,9,4,2023-06-03 02:43:16.041000,https://github.com/JiangtaoNie/UAL,27,Unsupervised adaptation learning for hyperspectral imagery super-resolution,"https://scholar.google.com/scholar?cluster=9047547779090001710&hl=en&as_sdt=0,5",2,2020
Video Playback Rate Perception for Self-Supervised Spatio-Temporal Representation Learning,152,cvpr,10,3,2023-06-03 02:43:16.242000,https://github.com/yuanyao366/PRP,39,Video playback rate perception for self-supervised spatio-temporal representation learning,"https://scholar.google.com/scholar?cluster=9700994684284956285&hl=en&as_sdt=0,44",1,2020
Just Go With the Flow: Self-Supervised Scene Flow Estimation,111,cvpr,5,26,2023-06-03 02:43:16.442000,https://github.com/HimangiM/Just-Go-with-the-Flow-Self-Supervised-Scene-Flow-Estimation,51,Just go with the flow: Self-supervised scene flow estimation,"https://scholar.google.com/scholar?cluster=9069838352389120398&hl=en&as_sdt=0,5",4,2020
StarGAN v2: Diverse Image Synthesis for Multiple Domains,1103,cvpr,629,96,2023-06-03 02:43:16.643000,https://github.com/clovaai/stargan-v2,3214,Stargan v2: Diverse image synthesis for multiple domains,"https://scholar.google.com/scholar?cluster=8553822083669968040&hl=en&as_sdt=0,34",82,2020
Gradually Vanishing Bridge for Adversarial Domain Adaptation,204,cvpr,9,6,2023-06-03 02:43:16.843000,https://github.com/cuishuhao/GVB,75,Gradually vanishing bridge for adversarial domain adaptation,"https://scholar.google.com/scholar?cluster=3008116530605957509&hl=en&as_sdt=0,48",5,2020
Multi-Path Region Mining for Weakly Supervised 3D Semantic Segmentation on Point Clouds,85,cvpr,4,8,2023-06-03 02:43:17.044000,https://github.com/plusmultiply/mprm,39,Multi-path region mining for weakly supervised 3D semantic segmentation on point clouds,"https://scholar.google.com/scholar?cluster=910022059005740712&hl=en&as_sdt=0,5",2,2020
Global Texture Enhancement for Fake Face Detection in the Wild,154,cvpr,2,0,2023-06-03 02:43:17.245000,https://github.com/liuzhengzhe/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild,5,Global texture enhancement for fake face detection in the wild,"https://scholar.google.com/scholar?cluster=3231131997984740300&hl=en&as_sdt=0,5",2,2020
Towards Inheritable Models for Open-Set Domain Adaptation,86,cvpr,0,0,2023-06-03 02:43:17.445000,https://github.com/val-iisc/inheritune,9,Towards inheritable models for open-set domain adaptation,"https://scholar.google.com/scholar?cluster=14243323555575752250&hl=en&as_sdt=0,5",15,2020
Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation,135,cvpr,24,7,2023-06-03 02:43:17.645000,https://github.com/luogen1996/MCN,127,Multi-task collaborative network for joint referring expression comprehension and segmentation,"https://scholar.google.com/scholar?cluster=5698096580926132002&hl=en&as_sdt=0,5",6,2020
Learning Depth-Guided Convolutions for Monocular 3D Object Detection,193,cvpr,58,16,2023-06-03 02:43:17.846000,https://github.com/dingmyu/D4LCN,303,Learning depth-guided convolutions for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=3992022189090691047&hl=en&as_sdt=0,5",9,2020
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models,56,cvpr,17,6,2023-06-03 02:43:18.047000,https://github.com/giannisdaras/ylg,133,Your local GAN: Designing two dimensional local attention mechanisms for generative models,"https://scholar.google.com/scholar?cluster=6691643820203733990&hl=en&as_sdt=0,3",6,2020
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation,122,cvpr,42,6,2023-06-03 02:43:18.248000,https://github.com/alpc91/NICE-GAN-pytorch,218,Reusing discriminators for encoding: Towards unsupervised image-to-image translation,"https://scholar.google.com/scholar?cluster=9474673267066460633&hl=en&as_sdt=0,5",6,2020
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds,94,cvpr,17,3,2023-06-03 02:43:18.449000,https://github.com/raoyongming/PointGLR,110,Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds,"https://scholar.google.com/scholar?cluster=14032550008017369503&hl=en&as_sdt=0,39",9,2020
Generalizing Hand Segmentation in Egocentric Videos With Uncertainty-Guided Model Adaptation,34,cvpr,7,4,2023-06-03 02:43:18.649000,https://github.com/cai-mj/UMA,31,Generalizing hand segmentation in egocentric videos with uncertainty-guided model adaptation,"https://scholar.google.com/scholar?cluster=14854195948022072622&hl=en&as_sdt=0,1",5,2020
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition,37,cvpr,7,0,2023-06-03 02:43:18.849000,https://github.com/Tsingularity/PoseNorm_Fewshot,40,Revisiting pose-normalization for fine-grained few-shot recognition,"https://scholar.google.com/scholar?cluster=14058402549089475889&hl=en&as_sdt=0,15",4,2020
Polarized Reflection Removal With Perfect Alignment in the Wild,55,cvpr,7,0,2023-06-03 02:43:19.049000,https://github.com/ChenyangLEI/CVPR2020-Polarized-Reflection-Removal-with-Perfect-Alignment,79,Polarized reflection removal with perfect alignment in the wild,"https://scholar.google.com/scholar?cluster=794202256015190214&hl=en&as_sdt=0,22",6,2020
Weakly-Supervised Salient Object Detection via Scribble Annotations,187,cvpr,21,11,2023-06-03 02:43:19.250000,https://github.com/JingZhang617/Scribble_Saliency,135,Weakly-supervised salient object detection via scribble annotations,"https://scholar.google.com/scholar?cluster=12565319032125907987&hl=en&as_sdt=0,5",6,2020
Blur Aware Calibration of Multi-Focus Plenoptic Camera,5,cvpr,1,0,2023-06-03 02:43:19.451000,https://github.com/comsee-research/compote,4,Blur aware calibration of multi-focus plenoptic camera,"https://scholar.google.com/scholar?cluster=3942639939193657522&hl=en&as_sdt=0,5",1,2020
ASLFeat: Learning Local Features of Accurate Shape and Localization,186,cvpr,32,14,2023-06-03 02:43:19.652000,https://github.com/lzx551402/ASLFeat,279,Aslfeat: Learning local features of accurate shape and localization,"https://scholar.google.com/scholar?cluster=12601324162617502372&hl=en&as_sdt=0,48",11,2020
Correspondence Networks With Adaptive Neighbourhood Consensus,57,cvpr,5,5,2023-06-03 02:43:19.852000,https://github.com/ActiveVisionLab/ANCNet,22,Correspondence networks with adaptive neighbourhood consensus,"https://scholar.google.com/scholar?cluster=12728206029677467282&hl=en&as_sdt=0,34",5,2020
Video Super-Resolution With Temporal Group Attention,120,cvpr,14,12,2023-06-03 02:43:20.052000,https://github.com/junpan19/VSR_TGA,122,Video super-resolution with temporal group attention,"https://scholar.google.com/scholar?cluster=7357555178809882671&hl=en&as_sdt=0,5",18,2020
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping,201,cvpr,97,3,2023-06-03 02:43:20.253000,https://github.com/graspnet/graspnet-baseline,250,Graspnet-1billion: A large-scale benchmark for general object grasping,"https://scholar.google.com/scholar?cluster=3928408788697064992&hl=en&as_sdt=0,22",7,2020
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition,325,cvpr,74,26,2023-06-03 02:43:20.454000,https://github.com/HuangYG123/CurricularFace,487,Curricularface: adaptive curriculum learning loss for deep face recognition,"https://scholar.google.com/scholar?cluster=17543857641780685133&hl=en&as_sdt=0,44",22,2020
Meshed-Memory Transformer for Image Captioning,671,cvpr,135,41,2023-06-03 02:43:20.654000,https://github.com/aimagelab/meshed-memory-transformer,450,Meshed-memory transformer for image captioning,"https://scholar.google.com/scholar?cluster=13804320630812586637&hl=en&as_sdt=0,33",14,2020
HRank: Filter Pruning Using High-Rank Feature Map,561,cvpr,46,24,2023-06-03 02:43:20.855000,https://github.com/lmbxmu/HRank,232,Hrank: Filter pruning using high-rank feature map,"https://scholar.google.com/scholar?cluster=497278027006601892&hl=en&as_sdt=0,5",12,2020
On the Detection of Digital Face Manipulation,322,cvpr,24,10,2023-06-03 02:43:21.055000,https://github.com/JStehouwer/FFD_CVPR2020,94,On the detection of digital face manipulation,"https://scholar.google.com/scholar?cluster=10353666099603521621&hl=en&as_sdt=0,33",3,2020
HCNAF: Hyper-Conditioned Neural Autoregressive Flow and its Application for Probabilistic Occupancy Map Forecasting,15,cvpr,2,1,2023-06-03 02:43:21.263000,https://github.com/gsoh/HCNAF,13,Hcnaf: Hyper-conditioned neural autoregressive flow and its application for probabilistic occupancy map forecasting,"https://scholar.google.com/scholar?cluster=15718044274423790528&hl=en&as_sdt=0,33",3,2020
AANet: Adaptive Aggregation Network for Efficient Stereo Matching,308,cvpr,96,3,2023-06-03 02:43:21.464000,https://github.com/haofeixu/aanet,467,Aanet: Adaptive aggregation network for efficient stereo matching,"https://scholar.google.com/scholar?cluster=13511423890414176773&hl=en&as_sdt=0,31",14,2020
Unbiased Scene Graph Generation From Biased Training,424,cvpr,216,108,2023-06-03 02:43:21.664000,https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch,907,Unbiased scene graph generation from biased training,"https://scholar.google.com/scholar?cluster=6204976209446651450&hl=en&as_sdt=0,3",14,2020
Analyzing and Improving the Image Quality of StyleGAN,3784,cvpr,2493,26,2023-06-03 02:43:21.865000,https://github.com/NVlabs/stylegan2,10430,Analyzing and improving the image quality of stylegan,"https://scholar.google.com/scholar?cluster=17911154894679089420&hl=en&as_sdt=0,33",370,2020
JA-POLS: A Moving-Camera Background Model via Joint Alignment and Partially-Overlapping Local Subspaces,3,cvpr,1,1,2023-06-03 02:43:22.065000,https://github.com/BGU-CS-VIL/JA-POLS,17,JA-POLS: a moving-camera background model via joint alignment and partially-overlapping local subspaces,"https://scholar.google.com/scholar?cluster=9131716502132904159&hl=en&as_sdt=0,44",6,2020
Proxy Anchor Loss for Deep Metric Learning,272,cvpr,60,2,2023-06-03 02:43:22.266000,https://github.com/tjddus9597/Proxy-Anchor-CVPR2020,288,Proxy anchor loss for deep metric learning,"https://scholar.google.com/scholar?cluster=2276525475394220717&hl=en&as_sdt=0,5",9,2020
Learning to Dress 3D People in Generative Clothing,204,cvpr,40,4,2023-06-03 02:43:22.466000,https://github.com/QianliM/CAPE,276,Learning to dress 3d people in generative clothing,"https://scholar.google.com/scholar?cluster=1344377148672721303&hl=en&as_sdt=0,47",13,2020
Mnemonics Training: Multi-Class Incremental Learning Without Forgetting,222,cvpr,67,27,2023-06-03 02:43:22.667000,https://github.com/yaoyao-liu/mnemonics,420,Mnemonics training: Multi-class incremental learning without forgetting,"https://scholar.google.com/scholar?cluster=10477002989404719589&hl=en&as_sdt=0,48",13,2020
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs,139,cvpr,6,2,2023-06-03 02:43:22.868000,https://github.com/UMBCvision/Universal-Litmus-Patterns,39,Universal litmus patterns: Revealing backdoor attacks in cnns,"https://scholar.google.com/scholar?cluster=17667068421427055111&hl=en&as_sdt=0,5",4,2020
Orderless Recurrent Models for Multi-Label Classification,66,cvpr,11,5,2023-06-03 02:43:23.069000,https://github.com/voyazici/orderless-rnn-classification,41,Orderless recurrent models for multi-label classification,"https://scholar.google.com/scholar?cluster=8167430018987015934&hl=en&as_sdt=0,18",3,2020
Extreme Relative Pose Network Under Hybrid Representations,23,cvpr,4,1,2023-06-03 02:43:23.269000,https://github.com/SimingYan/Hybrid_Relative_Pose,23,Extreme relative pose network under hybrid representations,"https://scholar.google.com/scholar?cluster=6832963235795817967&hl=en&as_sdt=0,39",4,2020
Learning When and Where to Zoom With Deep Reinforcement Learning,54,cvpr,8,3,2023-06-03 02:43:23.470000,https://github.com/ermongroup/PatchDrop,61,Learning when and where to zoom with deep reinforcement learning,"https://scholar.google.com/scholar?cluster=15538408380292510208&hl=en&as_sdt=0,18",8,2020
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline,170,cvpr,86,28,2023-06-03 02:43:23.675000,https://github.com/alex04072000/SingleHDR,480,Single-image HDR reconstruction by learning to reverse the camera pipeline,"https://scholar.google.com/scholar?cluster=6892932677303968289&hl=en&as_sdt=0,5",20,2020
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing,145,cvpr,59,19,2023-06-03 02:43:23.877000,https://github.com/clks-wzz/FAS-SGTD,222,Deep spatial gradient and temporal depth learning for face anti-spoofing,"https://scholar.google.com/scholar?cluster=17425442018601677108&hl=en&as_sdt=0,5",11,2020
Densely Connected Search Space for More Flexible Neural Architecture Search,106,cvpr,48,5,2023-06-03 02:43:24.077000,https://github.com/JaminFong/DenseNAS,292,Densely connected search space for more flexible neural architecture search,"https://scholar.google.com/scholar?cluster=11388886948472789017&hl=en&as_sdt=0,5",9,2020
Learning to Observe: Approximating Human Perceptual Thresholds for Detection of Suprathreshold Image Transformations,5,cvpr,1,9,2023-06-03 02:43:24.278000,https://github.com/dmt-lab/learning-to-observe,2,Learning to observe: Approximating human perceptual thresholds for detection of suprathreshold image transformations,"https://scholar.google.com/scholar?cluster=40488271229020218&hl=en&as_sdt=0,5",3,2020
Instance-Aware Image Colorization,159,cvpr,138,31,2023-06-03 02:43:24.481000,https://github.com/ericsujw/InstColorization,668,Instance-aware image colorization,"https://scholar.google.com/scholar?cluster=10980582552564282229&hl=en&as_sdt=0,18",24,2020
BlendedMVS: A Large-Scale Dataset for Generalized Multi-View Stereo Networks,179,cvpr,39,18,2023-06-03 02:43:24.681000,https://github.com/YoYo000/BlendedMVS,430,Blendedmvs: A large-scale dataset for generalized multi-view stereo networks,"https://scholar.google.com/scholar?cluster=15858776872498696637&hl=en&as_sdt=0,47",27,2020
Zero-Assignment Constraint for Graph Matching With Outliers,15,cvpr,3,7,2023-06-03 02:43:24.883000,https://github.com/wangfudong/ZAC_GM,12,Zero-assignment constraint for graph matching with outliers,"https://scholar.google.com/scholar?cluster=4499518257198248706&hl=en&as_sdt=0,5",3,2020
STEFANN: Scene Text Editor Using Font Adaptive Neural Network,33,cvpr,40,8,2023-06-03 02:43:25.084000,https://github.com/prasunroy/stefann,230,STEFANN: scene text editor using font adaptive neural network,"https://scholar.google.com/scholar?cluster=15462430618860092685&hl=en&as_sdt=0,23",11,2020
"AvatarMe: Realistically Renderable 3D Facial Reconstruction ""In-the-Wild""",116,cvpr,10,0,2023-06-03 02:43:25.284000,https://github.com/lattas/avatarme,124,"AvatarMe: Realistically Renderable 3D Facial Reconstruction"" in-the-wild""","https://scholar.google.com/scholar?cluster=7483829475057164721&hl=en&as_sdt=0,33",10,2020
F-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation,122,cvpr,90,5,2023-06-03 02:43:25.485000,https://github.com/saic-vul/fbrs_interactive_segmentation,541,f-brs: Rethinking backpropagating refinement for interactive segmentation,"https://scholar.google.com/scholar?cluster=5366626130215217097&hl=en&as_sdt=0,5",15,2020
Generalized Product Quantization Network for Semi-Supervised Image Retrieval,32,cvpr,11,1,2023-06-03 02:43:25.685000,https://github.com/youngkyunJang/GPQ,64,Generalized product quantization network for semi-supervised image retrieval,"https://scholar.google.com/scholar?cluster=661030767477922706&hl=en&as_sdt=0,5",3,2020
Extremely Dense Point Correspondences Using a Learned Feature Descriptor,33,cvpr,10,1,2023-06-03 02:43:25.886000,https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch,70,Extremely dense point correspondences using a learned feature descriptor,"https://scholar.google.com/scholar?cluster=17939981704094745202&hl=en&as_sdt=0,5",8,2020
Local-Global Video-Text Interactions for Temporal Grounding,160,cvpr,19,12,2023-06-03 02:43:26.096000,https://github.com/JonghwanMun/LGI4temporalgrounding,115,Local-global video-text interactions for temporal grounding,"https://scholar.google.com/scholar?cluster=9701700386474032090&hl=en&as_sdt=0,5",4,2020
Gate-Shift Networks for Video Action Recognition,133,cvpr,17,14,2023-06-03 02:43:26.297000,https://github.com/swathikirans/GSM,149,Gate-shift networks for video action recognition,"https://scholar.google.com/scholar?cluster=17859780477041675049&hl=en&as_sdt=0,5",8,2020
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task,58,cvpr,10,2,2023-06-03 02:43:26.498000,https://github.com/aritra0593/Reinforced-Feature-Points,63,Reinforced feature points: Optimizing feature detection and description for a high-level task,"https://scholar.google.com/scholar?cluster=15988601049233038345&hl=en&as_sdt=0,33",2,2020
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps,112,cvpr,24,0,2023-06-03 02:43:26.699000,https://github.com/pxiangwu/MotionNet,162,Motionnet: Joint perception and motion prediction for autonomous driving based on bird's eye view maps,"https://scholar.google.com/scholar?cluster=8105858893779164301&hl=en&as_sdt=0,20",15,2020
Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation,116,cvpr,49,22,2023-06-03 02:43:26.900000,https://github.com/lliuz/ARFlow,232,Learning by analogy: Reliable supervision from transformations for unsupervised optical flow estimation,"https://scholar.google.com/scholar?cluster=3875015729190234590&hl=en&as_sdt=0,33",6,2020
Towards Better Generalization: Joint Depth-Pose Learning Without PoseNet,123,cvpr,41,22,2023-06-03 02:43:27.100000,https://github.com/B1ueber2y/TrianFlow,248,Towards better generalization: Joint depth-pose learning without posenet,"https://scholar.google.com/scholar?cluster=3593641350876916040&hl=en&as_sdt=0,47",10,2020
D2Det: Towards High Quality Object Detection and Instance Segmentation,140,cvpr,85,35,2023-06-03 02:43:27.301000,https://github.com/JialeCao001/D2Det,291,D2det: Towards high quality object detection and instance segmentation,"https://scholar.google.com/scholar?cluster=15371804291623005317&hl=en&as_sdt=0,33",12,2020
Quasi-Newton Solver for Robust Non-Rigid Registration,28,cvpr,44,7,2023-06-03 02:43:27.501000,https://github.com/Juyong/Fast_RNRR,219,Quasi-newton solver for robust non-rigid registration,"https://scholar.google.com/scholar?cluster=17224988699653097008&hl=en&as_sdt=0,5",11,2020
Rethinking Classification and Localization for Object Detection,380,cvpr,10,6,2023-06-03 02:43:27.702000,https://github.com/wuyuebupt/doubleheadsrcnn,69,Rethinking classification and localization for object detection,"https://scholar.google.com/scholar?cluster=5261265231167224269&hl=en&as_sdt=0,1",2,2020
Weakly-Supervised Action Localization by Generative Attention Modeling,128,cvpr,21,13,2023-06-03 02:43:27.902000,https://github.com/bfshi/DGAM-Weakly-Supervised-Action-Localization,135,Weakly-supervised action localization by generative attention modeling,"https://scholar.google.com/scholar?cluster=11940490568711834866&hl=en&as_sdt=0,5",5,2020
Multi-Scale Progressive Fusion Network for Single Image Deraining,356,cvpr,32,19,2023-06-03 02:43:28.103000,https://github.com/kuihua/MSPFN,138,Multi-scale progressive fusion network for single image deraining,"https://scholar.google.com/scholar?cluster=16872700392789229768&hl=en&as_sdt=0,5",9,2020
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data,54,cvpr,15,0,2023-06-03 02:43:28.304000,https://github.com/tommy-qichang/AsynDGAN,37,Synthetic learning: Learn from distributed asynchronized discriminator gan without sharing medical image data,"https://scholar.google.com/scholar?cluster=17792693692322986186&hl=en&as_sdt=0,33",4,2020
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects From a Single RGB Image,77,cvpr,7,3,2023-06-03 02:43:28.506000,https://github.com/paschalidoud/hierarchical_primitives,85,Learning unsupervised hierarchical part decomposition of 3d objects from a single rgb image,"https://scholar.google.com/scholar?cluster=8614634556901055278&hl=en&as_sdt=0,5",14,2020
Light Field Spatial Super-Resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization,97,cvpr,9,2,2023-06-03 02:43:28.707000,https://github.com/jingjin25/LFSSR-ATO,44,Light field spatial super-resolution via deep combinatorial geometry embedding and structural consistency regularization,"https://scholar.google.com/scholar?cluster=8730527407456817975&hl=en&as_sdt=0,23",4,2020
Label Decoupling Framework for Salient Object Detection,191,cvpr,16,9,2023-06-03 02:43:28.907000,https://github.com/weijun88/LDF,101,Label decoupling framework for salient object detection,"https://scholar.google.com/scholar?cluster=11889271221051428016&hl=en&as_sdt=0,5",5,2020
Auto-Encoding Twin-Bottleneck Hashing,88,cvpr,17,4,2023-06-03 02:43:29.108000,https://github.com/ymcidence/TBH,84,Auto-encoding twin-bottleneck hashing,"https://scholar.google.com/scholar?cluster=4685118711249456149&hl=en&as_sdt=0,23",5,2020
Recurrent Feature Reasoning for Image Inpainting,257,cvpr,76,25,2023-06-03 02:43:29.308000,https://github.com/jingyuanli001/RFR-Inpainting,319,Recurrent feature reasoning for image inpainting,"https://scholar.google.com/scholar?cluster=2684440541588567552&hl=en&as_sdt=0,5",11,2020
Learning to Super Resolve Intensity Images From Events,47,cvpr,11,0,2023-06-03 02:43:29.509000,https://github.com/gistvision/e2sri,47,Learning to super resolve intensity images from events,"https://scholar.google.com/scholar?cluster=17220003184725413481&hl=en&as_sdt=0,23",6,2020
Harmonizing Transferability and Discriminability for Adapting Object Detectors,172,cvpr,23,22,2023-06-03 02:43:29.709000,https://github.com/chaoqichen/HTCN,112,Harmonizing transferability and discriminability for adapting object detectors,"https://scholar.google.com/scholar?cluster=10593080449633610254&hl=en&as_sdt=0,23",3,2020
Look-Into-Object: Self-Supervised Structure Modeling for Object Recognition,53,cvpr,21,6,2023-06-03 02:43:29.909000,https://github.com/JDAI-CV/LIO,108,Look-into-object: Self-supervised structure modeling for object recognition,"https://scholar.google.com/scholar?cluster=8508821443655139887&hl=en&as_sdt=0,48",11,2020
FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction,161,cvpr,87,18,2023-06-03 02:43:30.110000,https://github.com/zhuhao-nju/facescape,715,Facescape: a large-scale high quality 3d face dataset and detailed riggable 3d face prediction,"https://scholar.google.com/scholar?cluster=14954985719512682305&hl=en&as_sdt=0,34",35,2020
Inferring Attention Shift Ranks of Objects for Image Saliency,25,cvpr,4,6,2023-06-03 02:43:30.310000,https://github.com/SirisAvishek/Attention_Shift_Ranks,24,Inferring attention shift ranks of objects for image saliency,"https://scholar.google.com/scholar?cluster=17403422873244373547&hl=en&as_sdt=0,31",7,2020
Cross-View Tracking for Multi-Human 3D Pose Estimation at Over 100 FPS,58,cvpr,16,8,2023-06-03 02:43:30.511000,https://github.com/longcw/crossview_3d_pose_tracking,123,Cross-view tracking for multi-human 3d pose estimation at over 100 fps,"https://scholar.google.com/scholar?cluster=1132668862084573724&hl=en&as_sdt=0,19",12,2020
PADS: Policy-Adapted Sampling for Visual Similarity Learning,43,cvpr,9,0,2023-06-03 02:43:30.712000,https://github.com/Confusezius/CVPR2020_PADS,58,Pads: Policy-adapted sampling for visual similarity learning,"https://scholar.google.com/scholar?cluster=14756706924647760022&hl=en&as_sdt=0,31",6,2020
Exploring Categorical Regularization for Domain Adaptive Object Detection,205,cvpr,25,15,2023-06-03 02:43:30.912000,https://github.com/Megvii-Nanjing/CR-DA-DET,114,Exploring categorical regularization for domain adaptive object detection,"https://scholar.google.com/scholar?cluster=12709529115468914461&hl=en&as_sdt=0,3",12,2020
Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery,130,cvpr,20,1,2023-06-03 02:43:31.114000,https://github.com/Z-Zheng/FarSeg,98,Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery,"https://scholar.google.com/scholar?cluster=4517467735811473630&hl=en&as_sdt=0,10",2,2020
Effectively Unbiased FID and Inception Score and Where to Find Them,84,cvpr,4,2,2023-06-03 02:43:31.314000,https://github.com/mchong6/FID_IS_infinity,38,Effectively unbiased fid and inception score and where to find them,"https://scholar.google.com/scholar?cluster=985387797474089148&hl=en&as_sdt=0,5",6,2020
Controllable Orthogonalization in Training DNNs,34,cvpr,9,1,2023-06-03 02:43:31.517000,https://github.com/huangleiBuaa/ONI,20,Controllable orthogonalization in training dnns,"https://scholar.google.com/scholar?cluster=18180974355493865351&hl=en&as_sdt=0,48",5,2020
Discovering Human Interactions With Novel Objects via Zero-Shot Learning,31,cvpr,6,8,2023-06-03 02:43:31.718000,https://github.com/scwangdyd/zero_shot_hoi,38,Discovering human interactions with novel objects via zero-shot learning,"https://scholar.google.com/scholar?cluster=8518789012755093875&hl=en&as_sdt=0,5",2,2020
Towards Discriminability and Diversity: Batch Nuclear-Norm Maximization Under Label Insufficient Situations,218,cvpr,30,12,2023-06-03 02:43:31.919000,https://github.com/cuishuhao/BNM,248,Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations,"https://scholar.google.com/scholar?cluster=17259672897391511955&hl=en&as_sdt=0,5",10,2020
Visual Commonsense R-CNN,183,cvpr,58,11,2023-06-03 02:43:32.138000,https://github.com/Wangt-CN/VC-R-CNN,335,Visual commonsense r-cnn,"https://scholar.google.com/scholar?cluster=6886229776034162585&hl=en&as_sdt=0,26",14,2020
4D Association Graph for Realtime Multi-Person Motion Capture Using Multiple Video Cameras,54,cvpr,30,16,2023-06-03 02:43:32.338000,https://github.com/zhangyux15/4d_association,162,4D association graph for realtime multi-person motion capture using multiple video cameras,"https://scholar.google.com/scholar?cluster=13217754809285729497&hl=en&as_sdt=0,33",14,2020
Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules,408,cvpr,32,7,2023-06-03 02:43:32.539000,https://github.com/ZhengxueCheng/Learned-Image-Compression-with-GMM-and-Attention,143,Learned image compression with discretized gaussian mixture likelihoods and attention modules,"https://scholar.google.com/scholar?cluster=2321426178797084426&hl=en&as_sdt=0,10",4,2020
UNAS: Differentiable Architecture Search Meets Reinforcement Learning,27,cvpr,6,2,2023-06-03 02:43:32.739000,https://github.com/NVlabs/unas,54,Unas: Differentiable architecture search meets reinforcement learning,"https://scholar.google.com/scholar?cluster=12269377839123865738&hl=en&as_sdt=0,44",14,2020
Meshlet Priors for 3D Mesh Reconstruction,38,cvpr,2,3,2023-06-03 02:43:32.942000,https://github.com/NVlabs/meshlets,42,Meshlet priors for 3d mesh reconstruction,"https://scholar.google.com/scholar?cluster=3179640149370686547&hl=en&as_sdt=0,33",22,2020
"Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking",66,cvpr,29,3,2023-06-03 02:43:33.145000,https://github.com/whj363636/Adversarial-attack-on-Person-ReID-With-Deep-Mis-Ranking,95,"Transferable, controllable, and inconspicuous adversarial attacks on person re-identification with deep mis-ranking","https://scholar.google.com/scholar?cluster=490422468766489983&hl=en&as_sdt=0,33",5,2020
Semantic Drift Compensation for Class-Incremental Learning,176,cvpr,34,9,2023-06-03 02:43:33.346000,https://github.com/yulu0724/SDC-IL,110,Semantic drift compensation for class-incremental learning,"https://scholar.google.com/scholar?cluster=12272441645660000606&hl=en&as_sdt=0,5",3,2020
More Grounded Image Captioning by Distilling Image-Text Matching Model,108,cvpr,7,5,2023-06-03 02:43:33.547000,https://github.com/YuanEZhou/Grounded-Image-Captioning,59,More grounded image captioning by distilling image-text matching model,"https://scholar.google.com/scholar?cluster=9107893426978040607&hl=en&as_sdt=0,10",5,2020
X-Linear Attention Networks for Image Captioning,401,cvpr,54,4,2023-06-03 02:43:33.747000,https://github.com/Panda-Peter/image-captioning,254,X-linear attention networks for image captioning,"https://scholar.google.com/scholar?cluster=9240812457903545794&hl=en&as_sdt=0,5",4,2020
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection,248,cvpr,14,0,2023-06-03 02:43:33.948000,https://github.com/kerenfu/JLDCF,68,JL-DCF: Joint learning and densely-cooperative fusion framework for RGB-D salient object detection,"https://scholar.google.com/scholar?cluster=12168762254626534005&hl=en&as_sdt=0,42",6,2020
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection,143,cvpr,35,6,2023-06-03 02:43:34.148000,https://github.com/mileyan/pseudo-LiDAR_e2e,177,End-to-end pseudo-lidar for image-based 3d object detection,"https://scholar.google.com/scholar?cluster=3540971209064778234&hl=en&as_sdt=0,5",14,2020
You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions,48,cvpr,7,4,2023-06-03 02:43:34.349000,https://github.com/facebookresearch/you2me,48,You2me: Inferring body pose in egocentric video via first and second person interactions,"https://scholar.google.com/scholar?cluster=7563071385807951312&hl=en&as_sdt=0,23",9,2020
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks,329,cvpr,28,1,2023-06-03 02:43:34.550000,https://github.com/HaohanWang/HFC,220,High-frequency component helps explain the generalization of convolutional neural networks,"https://scholar.google.com/scholar?cluster=8901539838953056872&hl=en&as_sdt=0,36",4,2020
ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network,232,cvpr,636,291,2023-06-03 02:43:34.752000,https://github.com/aim-uofa/AdelaiDet,3150,Abcnet: Real-time scene text spotting with adaptive bezier-curve network,"https://scholar.google.com/scholar?cluster=4041385490106103036&hl=en&as_sdt=0,5",85,2020
Attention-Based Context Aware Reasoning for Situation Recognition,9,cvpr,4,0,2023-06-03 02:43:34.952000,https://github.com/thilinicooray/context-aware-reasoning-for-sr,18,Attention-based context aware reasoning for situation recognition,"https://scholar.google.com/scholar?cluster=8136341880060783104&hl=en&as_sdt=0,1",2,2020
Rotate-and-Render: Unsupervised Photorealistic Face Rotation From Single-View Images,91,cvpr,112,32,2023-06-03 02:43:35.152000,https://github.com/Hangz-nju-cuhk/Rotate-and-Render,445,Rotate-and-render: Unsupervised photorealistic face rotation from single-view images,"https://scholar.google.com/scholar?cluster=2882155676302627088&hl=en&as_sdt=0,39",16,2020
FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation,59,cvpr,13,0,2023-06-03 02:43:35.353000,https://github.com/CM-BF/FeatureFlow,152,Featureflow: Robust video interpolation via structure-to-texture generation,"https://scholar.google.com/scholar?cluster=17611134410867592389&hl=en&as_sdt=0,33",5,2020
Scene-Adaptive Video Frame Interpolation via Meta-Learning,42,cvpr,13,4,2023-06-03 02:43:35.564000,https://github.com/myungsub/meta-interpolation,77,Scene-adaptive video frame interpolation via meta-learning,"https://scholar.google.com/scholar?cluster=2749309242157904592&hl=en&as_sdt=0,5",12,2020
Background Matting: The World Is Your Green Screen,138,cvpr,662,44,2023-06-03 02:43:35.765000,https://github.com/senguptaumd/Background-Matting,4680,Background matting: The world is your green screen,"https://scholar.google.com/scholar?cluster=11725748042291384781&hl=en&as_sdt=0,11",145,2020
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation,30,cvpr,10,2,2023-06-03 02:43:35.965000,https://github.com/koutilya40192/SharinGAN,25,Sharingan: Combining synthetic and real data for unsupervised geometry estimation,"https://scholar.google.com/scholar?cluster=2052042029863774000&hl=en&as_sdt=0,5",2,2020
Explainable Object-Induced Action Decision for Autonomous Vehicles,46,cvpr,8,2,2023-06-03 02:43:36.165000,https://github.com/Twizwei/bddoia_project,21,Explainable object-induced action decision for autonomous vehicles,"https://scholar.google.com/scholar?cluster=6298956570811899330&hl=en&as_sdt=0,14",3,2020
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation From a Blackbox Model,44,cvpr,0,0,2023-06-03 02:43:36.366000,https://github.com/dwang181/active-mixup,21,Neural networks are more productive teachers than human raters: Active mixup for data-efficient knowledge distillation from a blackbox model,"https://scholar.google.com/scholar?cluster=13638644832048062910&hl=en&as_sdt=0,34",5,2020
Deep Residual Flow for Out of Distribution Detection,64,cvpr,4,0,2023-06-03 02:43:36.568000,https://github.com/EvZissel/Residual-Flow,26,Deep residual flow for out of distribution detection,"https://scholar.google.com/scholar?cluster=14899605525797830999&hl=en&as_sdt=0,5",2,2020
Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions,188,cvpr,25,5,2023-06-03 02:43:36.769000,https://github.com/cc-hpc-itwm/UpConv,123,Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions,"https://scholar.google.com/scholar?cluster=18044048085015580055&hl=en&as_sdt=0,5",6,2020
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition,287,cvpr,50,22,2023-06-03 02:43:36.970000,https://github.com/microsoft/SGN,158,Semantics-guided neural networks for efficient skeleton-based human action recognition,"https://scholar.google.com/scholar?cluster=2966382181393438868&hl=en&as_sdt=0,33",10,2020
The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation,127,cvpr,49,10,2023-06-03 02:43:37.171000,https://github.com/HuangJunJie2017/UDP-Pose,287,The devil is in the details: Delving into unbiased data processing for human pose estimation,"https://scholar.google.com/scholar?cluster=18010745089857323264&hl=en&as_sdt=0,5",10,2020
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition,172,cvpr,45,27,2023-06-03 02:43:37.372000,https://github.com/Pay20Y/SEED,163,Seed: Semantics enhanced encoder-decoder framework for scene text recognition,"https://scholar.google.com/scholar?cluster=13345330189856644630&hl=en&as_sdt=0,47",11,2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization,93,cvpr,8,0,2023-06-03 02:43:37.572000,https://github.com/Saehyung-Lee/cifar10_challenge,12,Adversarial vertex mixup: Toward better adversarially robust generalization,"https://scholar.google.com/scholar?cluster=434632984174982270&hl=en&as_sdt=0,6",1,2020
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-Wise Transformations,39,cvpr,18,6,2023-06-03 02:43:37.774000,https://github.com/gyshgx868/graph-ter,56,Graphter: Unsupervised learning of graph transformation equivariant representations via auto-encoding node-wise transformations,"https://scholar.google.com/scholar?cluster=1198484586266735161&hl=en&as_sdt=0,33",7,2020
iTAML: An Incremental Task-Agnostic Meta-learning Approach,127,cvpr,14,5,2023-06-03 02:43:37.976000,https://github.com/brjathu/iTAML,91,itaml: An incremental task-agnostic meta-learning approach,"https://scholar.google.com/scholar?cluster=13903317510243634709&hl=en&as_sdt=0,50",3,2020
Single Image Reflection Removal Through Cascaded Refinement,88,cvpr,17,7,2023-06-03 02:43:38.177000,https://github.com/JHL-HUST/IBCLN,103,Single image reflection removal through cascaded refinement,"https://scholar.google.com/scholar?cluster=15885579593007132929&hl=en&as_sdt=0,21",8,2020
GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction,74,cvpr,345,12,2023-06-03 02:43:38.379000,https://github.com/facebookresearch/fastMRI,1101,GrappaNet: Combining parallel imaging with deep learning for multi-coil MRI reconstruction,"https://scholar.google.com/scholar?cluster=15085105229863616742&hl=en&as_sdt=0,5",77,2020
Distilled Semantics for Comprehensive Scene Understanding from Videos,55,cvpr,12,5,2023-06-03 02:43:38.585000,https://github.com/CVLAB-Unibo/omeganet,57,Distilled semantics for comprehensive scene understanding from videos,"https://scholar.google.com/scholar?cluster=12147029063592298350&hl=en&as_sdt=0,39",13,2020
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions,246,cvpr,142,20,2023-06-03 02:43:38.786000,https://github.com/facebookresearch/mobile-vision,852,Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions,"https://scholar.google.com/scholar?cluster=14734050071484694995&hl=en&as_sdt=0,33",46,2020
VOLDOR: Visual Odometry From Log-Logistic Dense Optical Flow Residuals,33,cvpr,48,3,2023-06-03 02:43:38.987000,https://github.com/htkseason/VOLDOR,364,Voldor: Visual odometry from log-logistic dense optical flow residuals,"https://scholar.google.com/scholar?cluster=1784635091325992885&hl=en&as_sdt=0,5",17,2020
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds,96,cvpr,33,17,2023-06-03 02:43:39.188000,https://github.com/HaozheQi/P2B,175,P2b: Point-to-box network for 3d object tracking in point clouds,"https://scholar.google.com/scholar?cluster=735697306593238715&hl=en&as_sdt=0,38",7,2020
Scale-Equalizing Pyramid Convolution for Object Detection,90,cvpr,56,4,2023-06-03 02:43:39.388000,https://github.com/jshilong/SEPC,326,Scale-equalizing pyramid convolution for object detection,"https://scholar.google.com/scholar?cluster=443944663207581625&hl=en&as_sdt=0,5",13,2020
Learning Selective Self-Mutual Attention for RGB-D Saliency Detection,204,cvpr,8,6,2023-06-03 02:43:39.590000,https://github.com/nnizhang/S2MA,60,Learning selective self-mutual attention for RGB-D saliency detection,"https://scholar.google.com/scholar?cluster=14128598538897215193&hl=en&as_sdt=0,21",3,2020
Learning to Transfer Texture From Clothing Images to 3D Humans,80,cvpr,57,37,2023-06-03 02:43:39.791000,https://github.com/aymenmir1/pix2surf,294,Learning to transfer texture from clothing images to 3d humans,"https://scholar.google.com/scholar?cluster=12771096843818017968&hl=en&as_sdt=0,5",5,2020
Semi-Supervised Learning for Few-Shot Image-to-Image Translation,40,cvpr,5,2,2023-06-03 02:43:39.991000,https://github.com/yaxingwang/SEMIT,49,Semi-supervised learning for few-shot image-to-image translation,"https://scholar.google.com/scholar?cluster=2129382269487229002&hl=en&as_sdt=0,5",10,2020
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning,189,cvpr,87,14,2023-06-03 02:43:40.193000,https://github.com/microsoft/DisentangledFaceGAN,570,Disentangled and controllable face image generation via 3d imitative-contrastive learning,"https://scholar.google.com/scholar?cluster=15496471340661576046&hl=en&as_sdt=0,5",37,2020
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning,42,cvpr,2,1,2023-06-03 02:43:40.393000,https://github.com/jspenmar/DeFeat-Net,23,Defeat-net: General monocular depth via simultaneous unsupervised representation learning,"https://scholar.google.com/scholar?cluster=10482583096470600686&hl=en&as_sdt=0,44",2,2020
Multi-Scale Interactive Network for Salient Object Detection,445,cvpr,28,2,2023-06-03 02:43:40.593000,https://github.com/lartpang/MINet,223,Multi-scale interactive network for salient object detection,"https://scholar.google.com/scholar?cluster=1179192240058937367&hl=en&as_sdt=0,10",10,2020
Regularizing Class-Wise Predictions via Self-Knowledge Distillation,198,cvpr,22,4,2023-06-03 02:43:40.794000,https://github.com/alinlab/cs-kd,93,Regularizing class-wise predictions via self-knowledge distillation,"https://scholar.google.com/scholar?cluster=11350076008765834853&hl=en&as_sdt=0,5",6,2020
Universal Source-Free Domain Adaptation,206,cvpr,0,0,2023-06-03 02:43:40.995000,https://github.com/val-iisc/usfda,14,Universal source-free domain adaptation,"https://scholar.google.com/scholar?cluster=13396021133130094693&hl=en&as_sdt=0,33",12,2020
Meta-Transfer Learning for Zero-Shot Super-Resolution,235,cvpr,59,29,2023-06-03 02:43:41.195000,https://github.com/JWSoh/MZSR,254,Meta-transfer learning for zero-shot super-resolution,"https://scholar.google.com/scholar?cluster=4901353720515503142&hl=en&as_sdt=0,5",4,2020
A Model-Driven Deep Neural Network for Single Image Rain Removal,210,cvpr,21,8,2023-06-03 02:43:41.397000,https://github.com/hongwang01/RCDNet,156,A model-driven deep neural network for single image rain removal,"https://scholar.google.com/scholar?cluster=11451290027329888829&hl=en&as_sdt=0,41",7,2020
Learning to Manipulate Individual Objects in an Image,32,cvpr,0,0,2023-06-03 02:43:41.598000,https://github.com/ChenYutongTHU/Learning-to-manipulate-individual-objects-in-an-image-Implementation,12,Learning to manipulate individual objects in an image,"https://scholar.google.com/scholar?cluster=8469380198303178771&hl=en&as_sdt=0,33",4,2020
High-Dimensional Convolutional Networks for Geometric Pattern Recognition,32,cvpr,4,1,2023-06-03 02:43:41.799000,https://github.com/chrischoy/HighDimConvNets,34,High-dimensional convolutional networks for geometric pattern recognition,"https://scholar.google.com/scholar?cluster=4842349631480755356&hl=en&as_sdt=0,5",14,2020
3D Photography Using Context-Aware Layered Depth Inpainting,185,cvpr,1097,98,2023-06-03 02:43:42,https://github.com/vt-vl-lab/3d-photo-inpainting,6511,3d photography using context-aware layered depth inpainting,"https://scholar.google.com/scholar?cluster=13896011424140202640&hl=en&as_sdt=0,10",147,2020
AOWS: Adaptive and Optimal Network Width Search With Latency Constraints,31,cvpr,4,0,2023-06-03 02:43:42.201000,https://github.com/bermanmaxim/AOWS,34,Aows: Adaptive and optimal network width search with latency constraints,"https://scholar.google.com/scholar?cluster=10309035712406235754&hl=en&as_sdt=0,5",5,2020
"APQ: Joint Search for Network Architecture, Pruning and Quantization Policy",150,cvpr,33,6,2023-06-03 02:43:42.401000,https://github.com/mit-han-lab/apq,141,"Apq: Joint search for network architecture, pruning and quantization policy","https://scholar.google.com/scholar?cluster=1374069721944449122&hl=en&as_sdt=0,14",11,2020
Grid-GCN for Fast and Scalable Point Cloud Learning,190,cvpr,24,10,2023-06-03 02:43:42.602000,https://github.com/Xharlie/Grid-GCN,177,Grid-gcn for fast and scalable point cloud learning,"https://scholar.google.com/scholar?cluster=531285662278739329&hl=en&as_sdt=0,5",11,2020
Attentive Weights Generation for Few Shot Learning via Information Maximization,88,cvpr,1,1,2023-06-03 02:43:42.803000,https://github.com/Yiluan/AWGIM,15,Attentive weights generation for few shot learning via information maximization,"https://scholar.google.com/scholar?cluster=9022059591117885883&hl=en&as_sdt=0,48",2,2020
KFNet: Learning Temporal Camera Relocalization Using Kalman Filtering,51,cvpr,28,3,2023-06-03 02:43:43.004000,https://github.com/zlthinker/KFNet,204,Kfnet: Learning temporal camera relocalization using kalman filtering,"https://scholar.google.com/scholar?cluster=653147639240971150&hl=en&as_sdt=0,5",8,2020
Semantic Correspondence as an Optimal Transport Problem,90,cvpr,10,4,2023-06-03 02:43:43.204000,https://github.com/csyanbin/SCOT,95,Semantic correspondence as an optimal transport problem,"https://scholar.google.com/scholar?cluster=200912752079271114&hl=en&as_sdt=0,47",1,2020
SuperGlue: Learning Feature Matching With Graph Neural Networks,1068,cvpr,547,40,2023-06-03 02:43:43.405000,https://github.com/magicleap/SuperGluePretrainedNetwork,2486,Superglue: Learning feature matching with graph neural networks,"https://scholar.google.com/scholar?cluster=15521088276096645515&hl=en&as_sdt=0,5",58,2020
Improving Confidence Estimates for Unfamiliar Examples,35,cvpr,1,0,2023-06-03 02:43:43.606000,https://github.com/lizhitwo/ConfidenceEstimates,11,Improving confidence estimates for unfamiliar examples,"https://scholar.google.com/scholar?cluster=12998595608002202111&hl=en&as_sdt=0,39",2,2020
How Useful Is Self-Supervised Pretraining for Visual Tasks?,91,cvpr,3,1,2023-06-03 02:43:43.806000,https://github.com/princeton-vl/selfstudy,59,How useful is self-supervised pretraining for visual tasks?,"https://scholar.google.com/scholar?cluster=464561574781045149&hl=en&as_sdt=0,10",10,2020
Learning to Segment the Tail,62,cvpr,6,1,2023-06-03 02:43:44.006000,https://github.com/JoyHuYY1412/LST_LVIS,46,Learning to segment the tail,"https://scholar.google.com/scholar?cluster=14793983145216247407&hl=en&as_sdt=0,5",3,2020
Action Segmentation With Joint Self-Supervised Temporal Domain Adaptation,91,cvpr,22,0,2023-06-03 02:43:44.207000,https://github.com/cmhungsteve/SSTDA,151,Action segmentation with joint self-supervised temporal domain adaptation,"https://scholar.google.com/scholar?cluster=1572542229157505218&hl=en&as_sdt=0,5",10,2020
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks,385,cvpr,66,17,2023-06-03 02:43:44.408000,https://github.com/askforalfred/alfred,241,Alfred: A benchmark for interpreting grounded instructions for everyday tasks,"https://scholar.google.com/scholar?cluster=10541831682119160249&hl=en&as_sdt=0,36",16,2020
MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices,49,cvpr,46274,1204,2023-06-03 02:43:44.608000,https://github.com/tensorflow/models,75883,Mnasfpn: Learning latency-aware pyramid architecture for object detection on mobile devices,"https://scholar.google.com/scholar?cluster=15212100434147300168&hl=en&as_sdt=0,10",2774,2020
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes,140,cvpr,39,2,2023-06-03 02:43:44.815000,https://github.com/rajeevyasarla/Syn2Real,130,Syn2real transfer learning for image deraining using gaussian processes,"https://scholar.google.com/scholar?cluster=13979737242954461405&hl=en&as_sdt=0,5",3,2020
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression,164,cvpr,13,4,2023-06-03 02:43:45.016000,https://github.com/ofsoundof/group_sparsity,55,Group sparsity: The hinge between filter pruning and decomposition for network compression,"https://scholar.google.com/scholar?cluster=185943989367242161&hl=en&as_sdt=0,33",5,2020
On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks,48,cvpr,12,7,2023-06-03 02:43:45.217000,https://github.com/skywalker6174/3d-isometry-robust,22,On isometry robustness of deep 3d point cloud models under adversarial attacks,"https://scholar.google.com/scholar?cluster=17634312630840810623&hl=en&as_sdt=0,33",3,2020
P-nets: Deep Polynomial Neural Networks,51,cvpr,31,5,2023-06-03 02:43:45.418000,https://github.com/grigorisg9gr/polynomial_nets,144,P-nets: Deep polynomial neural networks,"https://scholar.google.com/scholar?cluster=10054840496985054567&hl=en&as_sdt=0,26",6,2020
Straight to the Point: Fast-Forwarding Videos via Reinforcement Learning Using Textual Data,6,cvpr,1,0,2023-06-03 02:43:45.619000,https://github.com/verlab/StraightToThePoint_CVPR_2020,8,Straight to the point: Fast-forwarding videos via reinforcement learning using textual data,"https://scholar.google.com/scholar?cluster=4421532250936590050&hl=en&as_sdt=0,33",4,2020
Self-Supervised Viewpoint Learning From Image Collections,37,cvpr,28,2,2023-06-03 02:43:45.820000,https://github.com/NVlabs/SSV,215,Self-supervised viewpoint learning from image collections,"https://scholar.google.com/scholar?cluster=546931561469949809&hl=en&as_sdt=0,8",22,2020
G3AN: Disentangling Appearance and Motion for Video Generation,59,cvpr,8,4,2023-06-03 02:43:46.020000,https://github.com/wyhsirius/g3an-project,36,G3AN: Disentangling appearance and motion for video generation,"https://scholar.google.com/scholar?cluster=1852334605769980573&hl=en&as_sdt=0,33",6,2020
SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings,10,cvpr,8,1,2023-06-03 02:43:46.222000,https://github.com/ai4ce/SPARE3D,48,Spare3d: A dataset for spatial reasoning on three-view line drawings,"https://scholar.google.com/scholar?cluster=6953548346558587780&hl=en&as_sdt=0,5",7,2020
On the Uncertainty of Self-Supervised Monocular Depth Estimation,152,cvpr,24,7,2023-06-03 02:43:46.423000,https://github.com/mattpoggi/mono-uncertainty,215,On the uncertainty of self-supervised monocular depth estimation,"https://scholar.google.com/scholar?cluster=17564970690624367035&hl=en&as_sdt=0,5",8,2020
DeepFaceFlow: In-the-Wild Dense 3D Facial Motion Estimation,8,cvpr,4,3,2023-06-03 02:43:46.623000,https://github.com/mrkoujan/DeepFaceFlow,80,DeepFaceFlow: in-the-wild dense 3D facial motion estimation,"https://scholar.google.com/scholar?cluster=2315487414431800146&hl=en&as_sdt=0,5",21,2020
StegaStamp: Invisible Hyperlinks in Physical Photographs,166,cvpr,164,25,2023-06-03 02:43:46.824000,https://github.com/tancik/StegaStamp,556,Stegastamp: Invisible hyperlinks in physical photographs,"https://scholar.google.com/scholar?cluster=9141986600005221060&hl=en&as_sdt=0,33",15,2020
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model,202,cvpr,22,9,2023-06-03 02:43:47.025000,https://github.com/BoPang1996/TubeTK,138,Tubetk: Adopting tubes to track multi-object in a one-step training model,"https://scholar.google.com/scholar?cluster=9897780157833606863&hl=en&as_sdt=0,10",9,2020
"Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection",160,cvpr,45,10,2023-06-03 02:43:47.226000,https://github.com/NVlabs/wetectron,348,"Instance-aware, context-focused, and memory-efficient weakly supervised object detection","https://scholar.google.com/scholar?cluster=13727446774856898528&hl=en&as_sdt=0,33",31,2020
Anisotropic Convolutional Networks for 3D Semantic Scene Completion,37,cvpr,14,5,2023-06-03 02:43:47.427000,https://github.com/waterljwant/SSC,81,Anisotropic convolutional networks for 3d semantic scene completion,"https://scholar.google.com/scholar?cluster=12549089821990650739&hl=en&as_sdt=0,23",5,2020
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation,175,cvpr,13,7,2023-06-03 02:43:47.628000,https://github.com/SHI-Labs/Unsupervised-Domain-Adaptation-with-Differential-Treatment,81,Differential treatment for stuff and things: A simple unsupervised domain adaptation method for semantic segmentation,"https://scholar.google.com/scholar?cluster=15596513768387323912&hl=en&as_sdt=0,5",8,2020
Cascaded Refinement Network for Point Cloud Completion,178,cvpr,2,1,2023-06-03 02:43:47.829000,https://github.com/xiaogangw/cascaded-point-completion,18,Cascaded refinement network for point cloud completion,"https://scholar.google.com/scholar?cluster=15354810067135987183&hl=en&as_sdt=0,5",1,2020
Video Object Grounding Using Semantic Roles in Language Description,36,cvpr,7,1,2023-06-03 02:43:48.029000,https://github.com/TheShadow29/vognet-pytorch,67,Video object grounding using semantic roles in language description,"https://scholar.google.com/scholar?cluster=11262259591850878257&hl=en&as_sdt=0,5",4,2020
Rotation Equivariant Graph Convolutional Network for Spherical Image Classification,28,cvpr,0,1,2023-06-03 02:43:48.230000,https://github.com/QinYang12/SGCN,14,Rotation equivariant graph convolutional network for spherical image classification,"https://scholar.google.com/scholar?cluster=6340965602733917547&hl=en&as_sdt=0,5",6,2020
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks,178,cvpr,59,3,2023-06-03 02:43:48.431000,https://github.com/akanimax/msg-stylegan-tf,258,Msg-gan: Multi-scale gradients for generative adversarial networks,"https://scholar.google.com/scholar?cluster=17634072045755762910&hl=en&as_sdt=0,44",14,2020
DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing,209,cvpr,26,5,2023-06-03 02:43:48.632000,https://github.com/B1ueber2y/DIST-Renderer,209,Dist: Rendering deep implicit signed distance function with differentiable sphere tracing,"https://scholar.google.com/scholar?cluster=1091979672567186905&hl=en&as_sdt=0,5",12,2020
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation,94,cvpr,34,5,2023-06-03 02:43:48.833000,https://github.com/zju3dv/disprcnn,202,Disp r-cnn: Stereo 3d object detection via shape prior guided instance disparity estimation,"https://scholar.google.com/scholar?cluster=9023656501136172103&hl=en&as_sdt=0,5",12,2020
Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection,247,cvpr,10,9,2023-06-03 02:43:49.033000,https://github.com/moothes/ITSD-pytorch,67,Interactive two-stream decoder for accurate and fast saliency detection,"https://scholar.google.com/scholar?cluster=2698893050538436365&hl=en&as_sdt=0,5",4,2020
Episode-Based Prototype Generating Network for Zero-Shot Learning,128,cvpr,6,4,2023-06-03 02:43:49.235000,https://github.com/yunlongyu/EPGN,24,Episode-based prototype generating network for zero-shot learning,"https://scholar.google.com/scholar?cluster=15377338687069834231&hl=en&as_sdt=0,31",2,2020
ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation,109,cvpr,25,4,2023-06-03 02:43:49.436000,https://github.com/nihalsid/ViewAL,132,Viewal: Active learning with viewpoint entropy for semantic segmentation,"https://scholar.google.com/scholar?cluster=13436330028069882717&hl=en&as_sdt=0,44",8,2020
A U-Net Based Discriminator for Generative Adversarial Networks,215,cvpr,53,8,2023-06-03 02:43:49.637000,https://github.com/boschresearch/unetgan,326,A u-net based discriminator for generative adversarial networks,"https://scholar.google.com/scholar?cluster=6120539919740151426&hl=en&as_sdt=0,37",8,2020
Semi-Supervised Semantic Segmentation With Cross-Consistency Training,414,cvpr,54,1,2023-06-03 02:43:49.838000,https://github.com/yassouali/CCT,357,Semi-supervised semantic segmentation with cross-consistency training,"https://scholar.google.com/scholar?cluster=16388515887267992352&hl=en&as_sdt=0,5",10,2020
Diversified Arbitrary Style Transfer via Deep Feature Perturbation,74,cvpr,8,0,2023-06-03 02:43:50.039000,https://github.com/EndyWon/Deep-Feature-Perturbation,35,Diversified arbitrary style transfer via deep feature perturbation,"https://scholar.google.com/scholar?cluster=7181013307985111414&hl=en&as_sdt=0,5",5,2020
GaitPart: Temporal Part-Based Model for Gait Recognition,211,cvpr,106,9,2023-06-03 02:43:50.240000,https://github.com/shiqiyu/opengait,422,Gaitpart: Temporal part-based model for gait recognition,"https://scholar.google.com/scholar?cluster=16505851961146284687&hl=en&as_sdt=0,5",14,2020
Adversarial Examples Improve Image Recognition,417,cvpr,1788,293,2023-06-03 02:43:50.441000,https://github.com/tensorflow/tpu,5123,Adversarial examples improve image recognition,"https://scholar.google.com/scholar?cluster=12540097159832752079&hl=en&as_sdt=0,5",371,2020
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack,57,cvpr,9,0,2023-06-03 02:43:50.641000,https://github.com/elliothe/BFA,27,Defending and harnessing the bit-flip based adversarial weight attack,"https://scholar.google.com/scholar?cluster=4166950612750375922&hl=en&as_sdt=0,39",2,2020
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution,405,cvpr,63,19,2023-06-03 02:43:50.842000,https://github.com/YapengTian/TDAN-VSR-CVPR-2020,388,Tdan: Temporally-deformable alignment network for video super-resolution,"https://scholar.google.com/scholar?cluster=9134144164731399402&hl=en&as_sdt=0,5",12,2020
"LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood",121,cvpr,2,1,2023-06-03 02:43:51.043000,https://github.com/abhi1kumar/LUVLi,27,"Luvli face alignment: Estimating landmarks' location, uncertainty, and visibility likelihood","https://scholar.google.com/scholar?cluster=10496106229213355393&hl=en&as_sdt=0,50",3,2020
Resolution Adaptive Networks for Efficient Inference,128,cvpr,28,0,2023-06-03 02:43:51.245000,https://github.com/yangle15/RANet-pytorch,139,Resolution adaptive networks for efficient inference,"https://scholar.google.com/scholar?cluster=521101430028285536&hl=en&as_sdt=0,33",5,2020
PointAugment: An Auto-Augmentation Framework for Point Cloud Classification,107,cvpr,27,9,2023-06-03 02:43:51.445000,https://github.com/liruihui/PointAugment,188,Pointaugment: an auto-augmentation framework for point cloud classification,"https://scholar.google.com/scholar?cluster=14167102715363724840&hl=en&as_sdt=0,5",15,2020
Normal Assisted Stereo Depth Estimation,57,cvpr,20,7,2023-06-03 02:43:51.646000,https://github.com/udaykusupati/Normal-Assisted-Stereo,98,Normal assisted stereo depth estimation,"https://scholar.google.com/scholar?cluster=1339319511524156641&hl=en&as_sdt=0,11",5,2020
Siamese Box Adaptive Network for Visual Tracking,550,cvpr,48,30,2023-06-03 02:43:51.847000,https://github.com/hqucv/siamban,243,Siamese box adaptive network for visual tracking,"https://scholar.google.com/scholar?cluster=9040151999224305592&hl=en&as_sdt=0,5",7,2020
EfficientDet: Scalable and Efficient Object Detection,3680,cvpr,1452,146,2023-06-03 02:43:52.048000,https://github.com/google/automl,5884,Efficientdet: Scalable and efficient object detection,"https://scholar.google.com/scholar?cluster=16138254679061222132&hl=en&as_sdt=0,39",158,2020
"Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image",139,cvpr,32,8,2023-06-03 02:43:52.249000,https://github.com/lzqsd/InverseRenderingOfIndoorScene,236,"Inverse rendering for complex indoor scenes: Shape, spatially-varying lighting and svbrdf from a single image","https://scholar.google.com/scholar?cluster=15619834795097106737&hl=en&as_sdt=0,19",14,2020
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer,80,cvpr,15,0,2023-06-03 02:43:52.451000,https://github.com/UCMerced-ML/LC-model-compression,47,Low-rank compression of neural nets: Learning the rank of each layer,"https://scholar.google.com/scholar?cluster=11332208454238811935&hl=en&as_sdt=0,31",6,2020
Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS,115,cvpr,7284,1013,2023-06-03 02:43:52.651000,https://github.com/google-research/google-research,29540,Can weight sharing outperform random architecture search? an investigation with tunas,"https://scholar.google.com/scholar?cluster=10742561815901242531&hl=en&as_sdt=0,5",727,2020
There and Back Again: Revisiting Backpropagation Saliency Methods,101,cvpr,3,0,2023-06-03 02:43:52.852000,https://github.com/srebuffi/revisiting_saliency,49,There and back again: Revisiting backpropagation saliency methods,"https://scholar.google.com/scholar?cluster=5600358576607487078&hl=en&as_sdt=0,10",4,2020
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network,187,cvpr,679,61,2023-06-03 02:43:53.053000,https://github.com/facebookarchive/fb.resnet.torch,2210,Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network,"https://scholar.google.com/scholar?cluster=10751549547661791339&hl=en&as_sdt=0,33",121,2020
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting,62,cvpr,4,1,2023-06-03 02:43:53.254000,https://github.com/dliu5812/PDAM,15,Unsupervised instance segmentation in microscopy images via panoptic domain adaptation and task re-weighting,"https://scholar.google.com/scholar?cluster=4954223067594419011&hl=en&as_sdt=0,41",2,2020
Learning Meta Face Recognition in Unseen Domains,121,cvpr,18,4,2023-06-03 02:43:53.454000,https://github.com/cleardusk/MFR,147,Learning meta face recognition in unseen domains,"https://scholar.google.com/scholar?cluster=4359786759676260995&hl=en&as_sdt=0,14",20,2020
Heterogeneous Knowledge Distillation Using Information Flow Modeling,92,cvpr,6,0,2023-06-03 02:43:53.655000,https://github.com/passalis/pkth,22,Heterogeneous knowledge distillation using information flow modeling,"https://scholar.google.com/scholar?cluster=10960912660981244645&hl=en&as_sdt=0,5",2,2020
An Adaptive Neural Network for Unsupervised Mosaic Consistency Analysis in Image Forensics,25,cvpr,3,2,2023-06-03 02:43:53.856000,https://github.com/qbammey/adaptive_cfa_forensics,25,An adaptive neural network for unsupervised mosaic consistency analysis in image forensics,"https://scholar.google.com/scholar?cluster=123983947089796389&hl=en&as_sdt=0,24",1,2020
CARS: Continuous Evolution for Efficient Neural Architecture Search,194,cvpr,20,4,2023-06-03 02:43:54.057000,https://github.com/huawei-noah/CARS,102,Cars: Continuous evolution for efficient neural architecture search,"https://scholar.google.com/scholar?cluster=10174262153417334156&hl=en&as_sdt=0,5",10,2020
State-Aware Tracker for Real-Time Video Object Segmentation,81,cvpr,178,19,2023-06-03 02:43:54.258000,https://github.com/MegviiDetection/video_analyst,778,State-aware tracker for real-time video object segmentation,"https://scholar.google.com/scholar?cluster=2093076341053017829&hl=en&as_sdt=0,48",30,2020
Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation,309,cvpr,79,10,2023-06-03 02:43:54.458000,https://github.com/neccam/slt,178,Sign language transformers: Joint end-to-end sign language recognition and translation,"https://scholar.google.com/scholar?cluster=3781725358102616609&hl=en&as_sdt=0,31",12,2020
DualSDF: Semantic Shape Manipulation Using a Two-Level Representation,82,cvpr,18,3,2023-06-03 02:43:54.660000,https://github.com/zekunhao1995/DualSDF,126,Dualsdf: Semantic shape manipulation using a two-level representation,"https://scholar.google.com/scholar?cluster=8918146941403724904&hl=en&as_sdt=0,6",13,2020
A Context-Aware Loss Function for Action Spotting in Soccer Videos,64,cvpr,7,0,2023-06-03 02:43:54.860000,https://github.com/cioppaanthony/context-aware-loss,26,A context-aware loss function for action spotting in soccer videos,"https://scholar.google.com/scholar?cluster=7407375010675176273&hl=en&as_sdt=0,5",5,2020
The Edge of Depth: Explicit Constraints Between Segmentation and Depth,83,cvpr,11,11,2023-06-03 02:43:55.061000,https://github.com/TWJianNuo/EdgeDepth-Release,88,The edge of depth: Explicit constraints between segmentation and depth,"https://scholar.google.com/scholar?cluster=6230826594737072707&hl=en&as_sdt=0,22",6,2020
ReSprop: Reuse Sparsified Backpropagation,12,cvpr,1,1,2023-06-03 02:43:55.263000,https://github.com/negargoli/ReSprop,13,Resprop: Reuse sparsified backpropagation,"https://scholar.google.com/scholar?cluster=15598805521506665571&hl=en&as_sdt=0,5",3,2020
Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation,113,cvpr,62,9,2023-06-03 02:43:55.463000,https://github.com/Maclory/Deep-Iterative-Collaboration,286,Deep face super-resolution with iterative collaboration between attentive recovery and landmark estimation,"https://scholar.google.com/scholar?cluster=14954402977731707377&hl=en&as_sdt=0,36",14,2020
Learning a Unified Sample Weighting Network for Object Detection,35,cvpr,13,1,2023-06-03 02:43:55.665000,https://github.com/caiqi/sample-weighting-network,87,Learning a unified sample weighting network for object detection,"https://scholar.google.com/scholar?cluster=6164165735538464051&hl=en&as_sdt=0,10",4,2020
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network,238,cvpr,43,31,2023-06-03 02:43:55.866000,https://github.com/SSL92/hyperIQA,272,Blindly assess image quality in the wild guided by a self-adaptive hyper network,"https://scholar.google.com/scholar?cluster=81969699158088231&hl=en&as_sdt=0,44",8,2020
Bundle Adjustment on a Graph Processor,35,cvpr,12,1,2023-06-03 02:43:56.067000,https://github.com/joeaortiz/gbp,66,Bundle adjustment on a graph processor,"https://scholar.google.com/scholar?cluster=10217492741597882999&hl=en&as_sdt=0,5",5,2020
Multi-View Neural Human Rendering,68,cvpr,9,3,2023-06-03 02:43:56.275000,https://github.com/wuminye/NHR,85,Multi-view neural human rendering,"https://scholar.google.com/scholar?cluster=10314501776894360592&hl=en&as_sdt=0,34",8,2020
Learning Fast and Robust Target Models for Video Object Segmentation,105,cvpr,25,7,2023-06-03 02:43:56.477000,https://github.com/andr345/frtm-vos,119,Learning fast and robust target models for video object segmentation,"https://scholar.google.com/scholar?cluster=11207612305703571917&hl=en&as_sdt=0,5",9,2020
Adaptive Loss-Aware Quantization for Multi-Bit Networks,34,cvpr,6,0,2023-06-03 02:43:56.678000,https://github.com/zqu1992/ALQ,12,Adaptive loss-aware quantization for multi-bit networks,"https://scholar.google.com/scholar?cluster=16159327957430149358&hl=en&as_sdt=0,33",2,2020
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation,747,cvpr,322,55,2023-06-03 02:43:56.878000,https://github.com/switchablenorms/CelebAMask-HQ,1813,Maskgan: Towards diverse and interactive facial image manipulation,"https://scholar.google.com/scholar?cluster=11254718545246006006&hl=en&as_sdt=0,47",46,2020
Learning Memory-Guided Normality for Anomaly Detection,424,cvpr,74,34,2023-06-03 02:43:57.080000,https://github.com/cvlab-yonsei/MNAD,282,Learning memory-guided normality for anomaly detection,"https://scholar.google.com/scholar?cluster=956898906864347055&hl=en&as_sdt=0,15",12,2020
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection,131,cvpr,21,7,2023-06-03 02:43:57.281000,https://github.com/NUAAXQ/MLCVNet,112,Mlcvnet: Multi-level context votenet for 3d object detection,"https://scholar.google.com/scholar?cluster=14781375345905617472&hl=en&as_sdt=0,39",10,2020
Learning Human-Object Interaction Detection Using Interaction Points,165,cvpr,8,9,2023-06-03 02:43:57.482000,https://github.com/vaesl/IP-Net,62,Learning human-object interaction detection using interaction points,"https://scholar.google.com/scholar?cluster=13827556198416012643&hl=en&as_sdt=0,11",7,2020
A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning,60,cvpr,6,2,2023-06-03 02:43:57.683000,https://github.com/hbdat/cvpr20_LESA,28,A shared multi-attention framework for multi-label zero-shot learning,"https://scholar.google.com/scholar?cluster=7359095780115295946&hl=en&as_sdt=0,31",1,2020
Cross-Domain Detection via Graph-Induced Prototype Alignment,156,cvpr,26,19,2023-06-03 02:43:57.883000,https://github.com/ChrisAllenMing/GPA-detection,137,Cross-domain detection via graph-induced prototype alignment,"https://scholar.google.com/scholar?cluster=9977969504391211037&hl=en&as_sdt=0,44",5,2020
PatchVAE: Learning Local Latent Codes for Recognition,12,cvpr,3,0,2023-06-03 02:43:58.084000,https://github.com/kampta/PatchVAE,12,Patchvae: Learning local latent codes for recognition,"https://scholar.google.com/scholar?cluster=10744669028405220428&hl=en&as_sdt=0,11",4,2020
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation,152,cvpr,46,12,2023-06-03 02:43:58.286000,https://github.com/HKUSTCV/FSS-1000,261,Fss-1000: A 1000-class dataset for few-shot segmentation,"https://scholar.google.com/scholar?cluster=12936364313646977622&hl=en&as_sdt=0,14",9,2020
Sparse Layered Graphs for Multi-Object Segmentation,10,cvpr,2,0,2023-06-03 02:43:58.487000,https://github.com/Skielex/slgbuilder,10,Sparse layered graphs for multi-object segmentation,"https://scholar.google.com/scholar?cluster=11639383520162863993&hl=en&as_sdt=0,10",3,2020
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers,54,cvpr,8,1,2023-06-03 02:43:58.689000,https://github.com/shadyabh/Correction-Filter,75,Correction filter for single image super-resolution: Robustifying off-the-shelf deep super-resolvers,"https://scholar.google.com/scholar?cluster=16558424903454447853&hl=en&as_sdt=0,5",4,2020
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy,109,cvpr,64,10,2023-06-03 02:43:58.890000,https://github.com/clovaai/cutblur,356,Rethinking data augmentation for image super-resolution: A comprehensive analysis and a new strategy,"https://scholar.google.com/scholar?cluster=16893001131009571410&hl=en&as_sdt=0,10",10,2020
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning,188,cvpr,13,0,2023-06-03 02:43:59.090000,https://github.com/TAMU-VITA/Adv-SS-Pretraining,84,Adversarial robustness: From self-supervised pre-training to fine-tuning,"https://scholar.google.com/scholar?cluster=2685889870924243279&hl=en&as_sdt=0,1",13,2020
Evade Deep Image Retrieval by Stashing Private Images in the Hash Space,14,cvpr,4,1,2023-06-03 02:43:59.291000,https://github.com/sugarruy/hashstash,16,Evade deep image retrieval by stashing private images in the hash space,"https://scholar.google.com/scholar?cluster=2766915326915312003&hl=en&as_sdt=0,5",4,2020
Efficient Adversarial Training With Transferable Adversarial Examples,96,cvpr,3,1,2023-06-03 02:43:59.492000,https://github.com/hzzheng93/ATTA,27,Efficient adversarial training with transferable adversarial examples,"https://scholar.google.com/scholar?cluster=10116870602000967496&hl=en&as_sdt=0,5",3,2020
Adversarial Texture Optimization From RGB-D Scans,33,cvpr,19,4,2023-06-03 02:43:59.692000,https://github.com/hjwdzh/AdversarialTexture,163,Adversarial texture optimization from rgb-d scans,"https://scholar.google.com/scholar?cluster=11576262765951327420&hl=en&as_sdt=0,44",8,2020
"EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning",81,cvpr,2,6,2023-06-03 02:43:59.893000,https://github.com/wl082013/ESIM_dataset,21,"Eventsr: From asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning","https://scholar.google.com/scholar?cluster=10954799606774079335&hl=en&as_sdt=0,36",3,2020
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization,490,cvpr,1301,70,2023-06-03 02:44:00.093000,https://github.com/facebookresearch/pifuhd,8945,Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization,"https://scholar.google.com/scholar?cluster=16275007157366291386&hl=en&as_sdt=0,5",266,2020
Quaternion Product Units for Deep Learning on 3D Rotation Groups,15,cvpr,10,1,2023-06-03 02:44:00.294000,https://github.com/IICNELAB/qpu_code,16,Quaternion product units for deep learning on 3d rotation groups,"https://scholar.google.com/scholar?cluster=8054638911070751421&hl=en&as_sdt=0,5",3,2020
3D Human Mesh Regression With Dense Correspondence,73,cvpr,22,6,2023-06-03 02:44:00.495000,https://github.com/zengwang430521/DecoMR,154,3d human mesh regression with dense correspondence,"https://scholar.google.com/scholar?cluster=5377351887090632981&hl=en&as_sdt=0,11",13,2020
Generating Accurate Pseudo-Labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations,30,cvpr,3,1,2023-06-03 02:44:00.696000,https://github.com/lokhande-vishnu/DeepHermites,8,Generating accurate pseudo-labels in semi-supervised learning and avoiding overconfident predictions via hermite polynomial activations,"https://scholar.google.com/scholar?cluster=17979825889785008433&hl=en&as_sdt=0,5",3,2020
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation,112,cvpr,5,0,2023-06-03 02:44:00.897000,https://github.com/chaoyanghe/MiLeNAS,34,Milenas: Efficient neural architecture search via mixed-level reformulation,"https://scholar.google.com/scholar?cluster=12625516063246584324&hl=en&as_sdt=0,21",4,2020
Optimizing Rank-Based Metrics With Blackbox Differentiation,64,cvpr,35,3,2023-06-03 02:44:01.097000,https://github.com/martius-lab/blackbox-backprop,317,Optimizing rank-based metrics with blackbox differentiation,"https://scholar.google.com/scholar?cluster=2799751332391317366&hl=en&as_sdt=0,33",15,2020
G-TAD: Sub-Graph Localization for Temporal Action Detection,328,cvpr,43,16,2023-06-03 02:44:01.298000,https://github.com/Frostinassiky/gtad,213,G-tad: Sub-graph localization for temporal action detection,"https://scholar.google.com/scholar?cluster=1780881352175640648&hl=en&as_sdt=0,5",7,2020
Revisiting Knowledge Distillation via Label Smoothing Regularization,375,cvpr,67,19,2023-06-03 02:44:01.505000,https://github.com/yuanli2333/Teacher-free-Knowledge-Distillation,527,Revisiting knowledge distillation via label smoothing regularization,"https://scholar.google.com/scholar?cluster=5502369757820696222&hl=en&as_sdt=0,5",11,2020
Learning Saliency Propagation for Semi-Supervised Instance Segmentation,28,cvpr,14,4,2023-06-03 02:44:01.706000,https://github.com/ucbdrive/ShapeProp,66,Learning saliency propagation for semi-supervised instance segmentation,"https://scholar.google.com/scholar?cluster=6199731630177168377&hl=en&as_sdt=0,48",8,2020
Rethinking Differentiable Search for Mixed-Precision Neural Networks,68,cvpr,11,1,2023-06-03 02:44:01.907000,https://github.com/zhaoweicai/EdMIPS,50,Rethinking differentiable search for mixed-precision neural networks,"https://scholar.google.com/scholar?cluster=5254475888489897434&hl=en&as_sdt=0,5",4,2020
MUXConv: Information Multiplexing in Convolutional Neural Networks,51,cvpr,9,3,2023-06-03 02:44:02.108000,https://github.com/human-analysis/MUXConv,49,MUXConv: Information multiplexing in convolutional neural networks,"https://scholar.google.com/scholar?cluster=17564910799442188996&hl=en&as_sdt=0,33",6,2020
SAM: The Sensitivity of Attribution Methods to Hyperparameters,63,cvpr,4,15,2023-06-03 02:44:02.309000,https://github.com/anguyen8/sam,23,Sam: The sensitivity of attribution methods to hyperparameters,"https://scholar.google.com/scholar?cluster=7414406041821135002&hl=en&as_sdt=0,21",5,2020
Structure-Guided Ranking Loss for Single Image Depth Prediction,115,cvpr,16,0,2023-06-03 02:44:02.511000,https://github.com/KexianHust/Structure-Guided-Ranking-Loss,166,Structure-guided ranking loss for single image depth prediction,"https://scholar.google.com/scholar?cluster=5448057750815052914&hl=en&as_sdt=0,5",18,2020
RGBD-Dog: Predicting Canine Pose from RGBD Sensors,47,cvpr,9,3,2023-06-03 02:44:02.711000,https://github.com/CAMERA-Bath/RGBD-Dog,67,Rgbd-dog: Predicting canine pose from rgbd sensors,"https://scholar.google.com/scholar?cluster=15122996766672244993&hl=en&as_sdt=0,36",10,2020
Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction,38,cvpr,17,0,2023-06-03 02:44:02.913000,https://github.com/zhangfuyang/Conv-MPN,58,Conv-mpn: Convolutional message passing neural network for structured outdoor architecture reconstruction,"https://scholar.google.com/scholar?cluster=6212304619705946777&hl=en&as_sdt=0,32",6,2020
Transform and Tell: Entity-Aware News Image Captioning,70,cvpr,16,42,2023-06-03 02:44:03.114000,https://github.com/alasdairtran/transform-and-tell,84,Transform and tell: Entity-aware news image captioning,"https://scholar.google.com/scholar?cluster=6182572831401296024&hl=en&as_sdt=0,5",7,2020
Novel Object Viewpoint Estimation Through Reconstruction Alignment,14,cvpr,2,1,2023-06-03 02:44:03.316000,https://github.com/mbanani/novelviewpoints,24,Novel object viewpoint estimation through reconstruction alignment,"https://scholar.google.com/scholar?cluster=1379678199160520280&hl=en&as_sdt=0,11",6,2020
MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning,55,cvpr,15,7,2023-06-03 02:44:03.516000,https://github.com/bhpfelix/MTLNAS,87,Mtl-nas: Task-agnostic neural architecture search towards general-purpose multi-task learning,"https://scholar.google.com/scholar?cluster=7724792473692841645&hl=en&as_sdt=0,33",4,2020
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion,343,cvpr,73,10,2023-06-03 02:44:03.717000,https://github.com/NVlabs/DeepInversion,435,Dreaming to distill: Data-free knowledge transfer via deepinversion,"https://scholar.google.com/scholar?cluster=17947318923388038070&hl=en&as_sdt=0,22",24,2020
12-in-1: Multi-Task Vision and Language Representation Learning,392,cvpr,173,68,2023-06-03 02:44:03.917000,https://github.com/facebookresearch/vilbert-multi-task,737,12-in-1: Multi-task vision and language representation learning,"https://scholar.google.com/scholar?cluster=17276757515931533114&hl=en&as_sdt=0,10",18,2020
Spherical Space Domain Adaptation With Robust Pseudo-Label Loss,107,cvpr,8,2,2023-06-03 02:44:04.119000,https://github.com/XJTU-XGU/RSDA,46,Spherical space domain adaptation with robust pseudo-label loss,"https://scholar.google.com/scholar?cluster=15723344580851800354&hl=en&as_sdt=0,34",2,2020
Disentangling Physical Dynamics From Unknown Factors for Unsupervised Video Prediction,163,cvpr,42,24,2023-06-03 02:44:04.320000,https://github.com/vincent-leguen/PhyDNet,151,Disentangling physical dynamics from unknown factors for unsupervised video prediction,"https://scholar.google.com/scholar?cluster=3187614519519145968&hl=en&as_sdt=0,5",5,2020
MPM: Joint Representation of Motion and Position Map for Cell Tracking,25,cvpr,10,5,2023-06-03 02:44:04.522000,https://github.com/JunyaHayashida/MPM,39,MPM: Joint representation of motion and position map for cell tracking,"https://scholar.google.com/scholar?cluster=5585726339120198281&hl=en&as_sdt=0,5",5,2020
AdderNet: Do We Really Need Multiplications in Deep Learning?,173,cvpr,187,10,2023-06-03 02:44:04.722000,https://github.com/huawei-noah/AdderNet,928,AdderNet: Do we really need multiplications in deep learning?,"https://scholar.google.com/scholar?cluster=1315923257821143156&hl=en&as_sdt=0,5",27,2020
Adaptive Interaction Modeling via Graph Operations Search,4,cvpr,4,1,2023-06-03 02:44:04.923000,https://github.com/lihaoxin05/graph-operations-search,6,Adaptive interaction modeling via graph operations search,"https://scholar.google.com/scholar?cluster=9691540853165665997&hl=en&as_sdt=0,5",5,2020
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking,462,cvpr,60,63,2023-06-03 02:44:05.124000,https://github.com/ohhhyeahhh/SiamCAR,295,SiamCAR: Siamese fully convolutional classification and regression for visual tracking,"https://scholar.google.com/scholar?cluster=12328462444653843486&hl=en&as_sdt=0,33",9,2020
Differentiable Volumetric Rendering: Learning Implicit 3D Representations Without 3D Supervision,611,cvpr,94,10,2023-06-03 02:44:05.325000,https://github.com/autonomousvision/differentiable_volumetric_rendering,729,Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision,"https://scholar.google.com/scholar?cluster=16450344488388654177&hl=en&as_sdt=0,31",30,2020
Deep Image Spatial Transformation for Person Image Generation,129,cvpr,87,30,2023-06-03 02:44:05.526000,https://github.com/RenYurui/Global-Flow-Local-Attention,515,Deep image spatial transformation for person image generation,"https://scholar.google.com/scholar?cluster=10864013708930309356&hl=en&as_sdt=0,5",21,2020
BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition,523,cvpr,97,20,2023-06-03 02:44:05.728000,https://github.com/Megvii-Nanjing/BBN,636,Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition,"https://scholar.google.com/scholar?cluster=2739380849236064029&hl=en&as_sdt=0,33",13,2020
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching,391,cvpr,92,23,2023-06-03 02:44:05.929000,https://github.com/alibaba/cascade-stereo,391,Cascade cost volume for high-resolution multi-view stereo and stereo matching,"https://scholar.google.com/scholar?cluster=9695190812968922429&hl=en&as_sdt=0,33",17,2020
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement,130,cvpr,33,13,2023-06-03 02:44:06.130000,https://github.com/svip-lab/FastMVSNet,224,Fast-mvsnet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement,"https://scholar.google.com/scholar?cluster=12192964191212481924&hl=en&as_sdt=0,26",12,2020
CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection,141,cvpr,47,10,2023-06-03 02:44:06.332000,https://github.com/KiveeDong/CentripetalNet,223,Centripetalnet: Pursuing high-quality keypoint pairs for object detection,"https://scholar.google.com/scholar?cluster=13900810289030899435&hl=en&as_sdt=0,26",18,2020
Cascaded Human-Object Interaction Recognition,99,cvpr,12,9,2023-06-03 02:44:06.532000,https://github.com/tfzhou/C-HOI,82,Cascaded human-object interaction recognition,"https://scholar.google.com/scholar?cluster=10048896405579556840&hl=en&as_sdt=0,33",6,2020
Mixture Dense Regression for Object Detection and Human Pose Estimation,46,cvpr,6,0,2023-06-03 02:44:06.733000,https://github.com/alivaramesh/MixtureDenseRegression,42,Mixture dense regression for object detection and human pose estimation,"https://scholar.google.com/scholar?cluster=5003169344396550385&hl=en&as_sdt=0,5",5,2020
Holistically-Attracted Wireframe Parsing,65,cvpr,39,18,2023-06-03 02:44:06.934000,https://github.com/cherubicXN/hawp,215,Holistically-attracted wireframe parsing,"https://scholar.google.com/scholar?cluster=15871210373020514810&hl=en&as_sdt=0,47",10,2020
"Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance",17,cvpr,0,1,2023-06-03 02:44:07.136000,https://github.com/jspenmar/DejaVu_Features,9,"Same features, different day: Weakly supervised feature learning for seasonal invariance","https://scholar.google.com/scholar?cluster=13048297637119443031&hl=en&as_sdt=0,26",1,2020
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing,360,cvpr,58,13,2023-06-03 02:44:07.336000,https://github.com/Andrew-Qibin/SPNet,355,Strip pooling: Rethinking spatial pooling for scene parsing,"https://scholar.google.com/scholar?cluster=16538960727323461264&hl=en&as_sdt=0,5",15,2020
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion,332,cvpr,55,8,2023-06-03 02:44:07.537000,https://github.com/jchibane/if-net,283,Implicit functions in feature space for 3d shape reconstruction and completion,"https://scholar.google.com/scholar?cluster=11768232612646772794&hl=en&as_sdt=0,5",16,2020
"Detection in Crowded Scenes: One Proposal, Multiple Predictions",136,cvpr,43,5,2023-06-03 02:44:07.738000,https://github.com/megvii-model/CrowdDetection,262,"Detection in crowded scenes: One proposal, multiple predictions","https://scholar.google.com/scholar?cluster=9218039804576410988&hl=en&as_sdt=0,5",19,2020
Background Data Resampling for Outlier-Aware Classification,37,cvpr,1,0,2023-06-03 02:44:07.938000,https://github.com/JerryYLi/bg-resample-ood,13,Background data resampling for outlier-aware classification,"https://scholar.google.com/scholar?cluster=10437181063059310257&hl=en&as_sdt=0,33",2,2020
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus,37,cvpr,11,2,2023-06-03 02:44:08.139000,https://github.com/fkluger/consac,68,Consac: Robust multi-model fitting by conditional sample consensus,"https://scholar.google.com/scholar?cluster=967274997783584946&hl=en&as_sdt=0,5",4,2020
Prime Sample Attention in Object Detection,174,cvpr,8690,798,2023-06-03 02:44:08.340000,https://github.com/open-mmlab/mmdetection,24399,Prime sample attention in object detection,"https://scholar.google.com/scholar?cluster=6048989535795009016&hl=en&as_sdt=0,5",372,2020
Deep Global Registration,297,cvpr,78,24,2023-06-03 02:44:08.541000,https://github.com/chrischoy/DeepGlobalRegistration,387,Deep global registration,"https://scholar.google.com/scholar?cluster=3957673094669575297&hl=en&as_sdt=0,5",17,2020
CycleISP: Real Image Restoration via Improved Data Synthesis,217,cvpr,69,18,2023-06-03 02:44:08.742000,https://github.com/swz30/CycleISP,424,Cycleisp: Real image restoration via improved data synthesis,"https://scholar.google.com/scholar?cluster=1474804572170916004&hl=en&as_sdt=0,5",14,2020
A Neural Rendering Framework for Free-Viewpoint Relighting,39,cvpr,24,3,2023-06-03 02:44:08.943000,https://github.com/LansburyCH/relightable-nr,111,A neural rendering framework for free-viewpoint relighting,"https://scholar.google.com/scholar?cluster=10863703419409180355&hl=en&as_sdt=0,5",7,2020
Attribution in Scale and Space,45,cvpr,185,10,2023-06-03 02:44:09.144000,https://github.com/PAIR-code/saliency,880,Attribution in scale and space,"https://scholar.google.com/scholar?cluster=7442498288685739469&hl=en&as_sdt=0,33",25,2020
Enhancing Generic Segmentation With Learned Region Representations,4,cvpr,2,4,2023-06-03 02:44:09.346000,https://github.com/oranshayer/BRRF,12,Enhancing generic segmentation with learned region representations,"https://scholar.google.com/scholar?cluster=3896983794037035396&hl=en&as_sdt=0,5",4,2020
Neural Cages for Detail-Preserving 3D Deformations,91,cvpr,15,7,2023-06-03 02:44:09.547000,https://github.com/yifita/deep_cage,142,Neural cages for detail-preserving 3d deformations,"https://scholar.google.com/scholar?cluster=6261969056096986815&hl=en&as_sdt=0,5",6,2020
Probabilistic Regression for Visual Tracking,386,cvpr,577,55,2023-06-03 02:44:09.749000,https://github.com/visionml/pytracking,2782,Probabilistic regression for visual tracking,"https://scholar.google.com/scholar?cluster=6281152651769068818&hl=en&as_sdt=0,29",90,2020
What's Hidden in a Randomly Weighted Neural Network?,218,cvpr,46,2,2023-06-03 02:44:09.950000,https://github.com/allenai/hidden-networks,163,What's hidden in a randomly weighted neural network?,"https://scholar.google.com/scholar?cluster=6624809615226595906&hl=en&as_sdt=0,33",10,2020
3DRegNet: A Deep Neural Network for 3D Point Registration,139,cvpr,4,0,2023-06-03 02:44:10.151000,https://github.com/3DVisionISR/3DRegNet,16,3dregnet: A deep neural network for 3d point registration,"https://scholar.google.com/scholar?cluster=3816272348608544416&hl=en&as_sdt=0,36",1,2020
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation,221,cvpr,12,4,2023-06-03 02:44:10.352000,https://github.com/MyeongJin-Kim/Learning-Texture-Invariant-Representation,104,Learning texture invariant representation for domain adaptation of semantic segmentation,"https://scholar.google.com/scholar?cluster=15670126703790116454&hl=en&as_sdt=0,33",4,2020
SCT: Set Constrained Temporal Transformer for Set Supervised Action Segmentation,51,cvpr,4,3,2023-06-03 02:44:10.553000,https://github.com/MohsenFayyaz89/SCT,35,Sct: Set constrained temporal transformer for set supervised action segmentation,"https://scholar.google.com/scholar?cluster=1378334690484097467&hl=en&as_sdt=0,10",6,2020
SEAN: Image Synthesis With Semantic Region-Adaptive Normalization,421,cvpr,95,35,2023-06-03 02:44:10.754000,https://github.com/ZPdesu/SEAN,602,Sean: Image synthesis with semantic region-adaptive normalization,"https://scholar.google.com/scholar?cluster=2104429242148966186&hl=en&as_sdt=0,33",15,2020
VQA With No Questions-Answers Training,8,cvpr,0,1,2023-06-03 02:44:10.954000,https://github.com/benyv/uncord,1,Vqa with no questions-answers training,"https://scholar.google.com/scholar?cluster=16069231097021534730&hl=en&as_sdt=0,5",3,2020
Neural Blind Deconvolution Using Deep Priors,197,cvpr,62,14,2023-06-03 02:44:11.175000,https://github.com/csdwren/SelfDeblur,293,Neural blind deconvolution using deep priors,"https://scholar.google.com/scholar?cluster=6899142045391649307&hl=en&as_sdt=0,33",15,2020
Detailed 2D-3D Joint Representation for Human-Object Interaction,102,cvpr,14,8,2023-06-03 02:44:11.377000,https://github.com/DirtyHarryLYL/DJ-RN,96,Detailed 2d-3d joint representation for human-object interaction,"https://scholar.google.com/scholar?cluster=11861118822179618604&hl=en&as_sdt=0,5",11,2020
Local Non-Rigid Structure-From-Motion From Diffeomorphic Mappings,9,cvpr,3,1,2023-06-03 02:44:11.578000,https://github.com/cvlab-epfl/diff-nrsfm,17,Local non-rigid structure-from-motion from diffeomorphic mappings,"https://scholar.google.com/scholar?cluster=11064244025488128133&hl=en&as_sdt=0,19",5,2020
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions,508,cvpr,80,1,2023-06-03 02:44:11.779000,https://github.com/Sha-Lab/FEAT,366,Few-shot learning via embedding adaptation with set-to-set functions,"https://scholar.google.com/scholar?cluster=9786897578293679610&hl=en&as_sdt=0,7",12,2020
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks,2247,cvpr,182,26,2023-06-03 02:44:11.989000,https://github.com/BangguWu/ECANet,1028,ECA-Net: Efficient channel attention for deep convolutional neural networks,"https://scholar.google.com/scholar?cluster=13737649200143445048&hl=en&as_sdt=0,5",14,2020
Deep Semantic Clustering by Partition Confidence Maximisation,101,cvpr,17,1,2023-06-03 02:44:12.196000,https://github.com/Raymond-sci/PICA,53,Deep semantic clustering by partition confidence maximisation,"https://scholar.google.com/scholar?cluster=6078940806271466041&hl=en&as_sdt=0,5",1,2020
A Transductive Approach for Video Object Segmentation,97,cvpr,30,10,2023-06-03 02:44:12.398000,https://github.com/microsoft/transductive-vos.pytorch,149,A transductive approach for video object segmentation,"https://scholar.google.com/scholar?cluster=534821762532266868&hl=en&as_sdt=0,5",9,2020
DR Loss: Improving Object Detection by Distributional Ranking,76,cvpr,14,0,2023-06-03 02:44:12.599000,https://github.com/idstcv/DR_loss,109,Dr loss: Improving object detection by distributional ranking,"https://scholar.google.com/scholar?cluster=18326797209723533866&hl=en&as_sdt=0,5",8,2020
MAST: A Memory-Augmented Self-Supervised Tracker,137,cvpr,31,15,2023-06-03 02:44:12.799000,https://github.com/zlai0/MAST,273,Mast: A memory-augmented self-supervised tracker,"https://scholar.google.com/scholar?cluster=1908673153164515114&hl=en&as_sdt=0,11",25,2020
Neural Head Reenactment with Latent Pose Descriptors,78,cvpr,31,5,2023-06-03 02:44:13,https://github.com/shrubb/latent-pose-reenactment,152,Neural head reenactment with latent pose descriptors,"https://scholar.google.com/scholar?cluster=11120247729778519421&hl=en&as_sdt=0,5",5,2020
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting,228,cvpr,69,8,2023-06-03 02:44:13.201000,https://github.com/Atlas200dk/sample-imageinpainting-HiFill,364,Contextual residual aggregation for ultra high-resolution image inpainting,"https://scholar.google.com/scholar?cluster=8936335924604323718&hl=en&as_sdt=0,5",17,2020
SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification,22,cvpr,1,1,2023-06-03 02:44:13.403000,https://github.com/cradleai/LKS-Dataset,12,Sos: Selective objective switch for rapid immunofluorescence whole slide image classification,"https://scholar.google.com/scholar?cluster=10809390358237486911&hl=en&as_sdt=0,5",4,2020
Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification,101,cvpr,23,2,2023-06-03 02:44:13.603000,https://github.com/daodaofr/hypergraph_reid,67,Learning multi-granular hypergraphs for video-based person re-identification,"https://scholar.google.com/scholar?cluster=6478543107852893556&hl=en&as_sdt=0,11",5,2020
Hierarchical Human Parsing With Typed Part-Relation Reasoning,77,cvpr,12,13,2023-06-03 02:44:13.804000,https://github.com/hlzhu09/Hierarchical-Human-Parsing,72,Hierarchical human parsing with typed part-relation reasoning,"https://scholar.google.com/scholar?cluster=17781925622971932642&hl=en&as_sdt=0,33",6,2020
Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem,23,cvpr,3,4,2023-06-03 02:44:14.005000,https://github.com/mattdl/DUA,12,Unsupervised model personalization while preserving privacy and scalability: An open problem,"https://scholar.google.com/scholar?cluster=7018250340528729671&hl=en&as_sdt=0,33",2,2020
High-Resolution Daytime Translation Without Domain Labels,92,cvpr,89,8,2023-06-03 02:44:14.206000,https://github.com/saic-mdal/HiDT,636,High-resolution daytime translation without domain labels,"https://scholar.google.com/scholar?cluster=15814694608739833766&hl=en&as_sdt=0,47",99,2020
Learning From Noisy Anchors for One-Stage Object Detection,76,cvpr,1,0,2023-06-03 02:44:14.408000,https://github.com/henrylee2570/NoisyAnchor,23,Learning from noisy anchors for one-stage object detection,"https://scholar.google.com/scholar?cluster=8421028904073023623&hl=en&as_sdt=0,22",2,2020
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes,32,cvpr,2,0,2023-06-03 02:44:14.609000,https://github.com/val-iisc/BPFC,21,Towards achieving adversarial robustness by enforcing feature consistency across bit planes,"https://scholar.google.com/scholar?cluster=10791013047041036720&hl=en&as_sdt=0,44",14,2020
Non-Adversarial Video Synthesis With Learned Priors,23,cvpr,0,1,2023-06-03 02:44:14.810000,https://github.com/abhishekaich27/Navsynth,4,Non-adversarial video synthesis with learned priors,"https://scholar.google.com/scholar?cluster=2635339579718874365&hl=en&as_sdt=0,39",3,2020
Deep Homography Estimation for Dynamic Scenes,79,cvpr,11,4,2023-06-03 02:44:15.011000,https://github.com/lcmhoang/hmg-dynamics,43,Deep homography estimation for dynamic scenes,"https://scholar.google.com/scholar?cluster=17448649820923859538&hl=en&as_sdt=0,5",9,2020
Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints,1,cvpr,1,0,2023-06-03 02:44:15.212000,https://github.com/EmbodiedVision/cosection,3,Where does it end?-reasoning about hidden surfaces by object intersection constraints,"https://scholar.google.com/scholar?cluster=14780516586087573485&hl=en&as_sdt=0,34",3,2020
Fast MSER,1,cvpr,5,2,2023-06-03 02:44:15.412000,https://github.com/mmmn143/fast-mser,17,Fast MSER,"https://scholar.google.com/scholar?cluster=1426168970821321107&hl=en&as_sdt=0,5",3,2020
Epipolar Transformers,101,cvpr,35,4,2023-06-03 02:44:15.613000,https://github.com/yihui-he/epipolar-transformers,383,Epipolar transformers,"https://scholar.google.com/scholar?cluster=14521805966318682246&hl=en&as_sdt=0,5",22,2020
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar,79,cvpr,3,2,2023-06-03 02:44:15.815000,https://github.com/princeton-computational-imaging/doppler_nlos,13,Seeing around street corners: Non-line-of-sight detection and tracking in-the-wild using doppler radar,"https://scholar.google.com/scholar?cluster=18077408996955912252&hl=en&as_sdt=0,15",2,2020
"Correlating Edge, Pose With Parsing",43,cvpr,18,7,2023-06-03 02:44:16.016000,https://github.com/ziwei-zh/CorrPM,104,"Correlating edge, pose with parsing","https://scholar.google.com/scholar?cluster=1249196707410768460&hl=en&as_sdt=0,48",5,2020
Weakly Supervised Visual Semantic Parsing,43,cvpr,6,21,2023-06-03 02:44:16.217000,https://github.com/alirezazareian/vspnet,35,Weakly supervised visual semantic parsing,"https://scholar.google.com/scholar?cluster=5224934209806605662&hl=en&as_sdt=0,5",5,2020
Bringing Old Photos Back to Life,132,cvpr,1641,83,2023-06-03 02:44:16.418000,https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life,12360,Bringing old photos back to life,"https://scholar.google.com/scholar?cluster=6924315392770430435&hl=en&as_sdt=0,23",273,2020
Enhanced Blind Face Restoration With Multi-Exemplar Images and Adaptive Spatial Feature Fusion,47,cvpr,9,0,2023-06-03 02:44:16.619000,https://github.com/csxmli2016/ASFFNet,90,Enhanced blind face restoration with multi-exemplar images and adaptive spatial feature fusion,"https://scholar.google.com/scholar?cluster=18272265346622228958&hl=en&as_sdt=0,5",21,2020
BachGAN: High-Resolution Image Synthesis From Salient Object Layout,38,cvpr,11,3,2023-06-03 02:44:16.819000,https://github.com/Cold-Winter/BachGAN,60,Bachgan: High-resolution image synthesis from salient object layout,"https://scholar.google.com/scholar?cluster=2484771831244859100&hl=en&as_sdt=0,39",4,2020
Multi-Scale Boosted Dehazing Network With Dense Feature Fusion,420,cvpr,65,27,2023-06-03 02:44:17.021000,https://github.com/BookerDeWitt/MSBDN-DFF,281,Multi-scale boosted dehazing network with dense feature fusion,"https://scholar.google.com/scholar?cluster=8683783233230065640&hl=en&as_sdt=0,47",7,2020
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather,201,cvpr,51,14,2023-06-03 02:44:17.223000,https://github.com/princeton-computational-imaging/SeeingThroughFog,211,Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather,"https://scholar.google.com/scholar?cluster=18130577232349887859&hl=en&as_sdt=0,5",13,2020
Globally Optimal Contrast Maximisation for Event-Based Motion Estimation,39,cvpr,561,0,2023-06-03 02:44:17.423000,https://github.com/uzh-rpg/event-based_vision_resources,2240,Globally optimal contrast maximisation for event-based motion estimation,"https://scholar.google.com/scholar?cluster=4354602170292397697&hl=en&as_sdt=0,5",167,2020
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation,112,cvpr,74,14,2023-06-03 02:44:17.624000,https://github.com/mseg-dataset/mseg-semantic,438,MSeg: A composite dataset for multi-domain semantic segmentation,"https://scholar.google.com/scholar?cluster=2323571480313097229&hl=en&as_sdt=0,31",15,2020
Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images Using Graph Convolutional Networks,85,cvpr,76,23,2023-06-03 02:44:17.825000,https://github.com/FuxiCV/3D-Face-GCNs,366,Towards high-fidelity 3d face reconstruction from in-the-wild images using graph convolutional networks,"https://scholar.google.com/scholar?cluster=2122730487174277154&hl=en&as_sdt=0,5",29,2020
Learning Invariant Representation for Unsupervised Image Restoration,54,cvpr,22,11,2023-06-03 02:44:18.026000,https://github.com/Wenchao-Du/LIR-for-Unsupervised-IR,89,Learning invariant representation for unsupervised image restoration,"https://scholar.google.com/scholar?cluster=13371140850855858695&hl=en&as_sdt=0,33",7,2020
DMCP: Differentiable Markov Channel Pruning for Neural Networks,126,cvpr,21,4,2023-06-03 02:44:18.227000,https://github.com/Zx55/dmcp,117,Dmcp: Differentiable markov channel pruning for neural networks,"https://scholar.google.com/scholar?cluster=15309133374809291742&hl=en&as_sdt=0,5",7,2020
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation,211,cvpr,14,2,2023-06-03 02:44:18.429000,https://github.com/princetonvisualai/DomainBiasMitigation,63,Towards fairness in visual recognition: Effective strategies for bias mitigation,"https://scholar.google.com/scholar?cluster=10189745679391469109&hl=en&as_sdt=0,41",7,2020
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio,9,cvpr,2,3,2023-06-03 02:44:18.630000,https://github.com/danczs/NetworkAdjustment,13,Network adjustment: Channel search guided by FLOPs utilization ratio,"https://scholar.google.com/scholar?cluster=5597241930740675157&hl=en&as_sdt=0,5",3,2020
Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation,142,cvpr,4,3,2023-06-03 02:44:18.831000,https://github.com/js-fan/ICD,72,Learning integral objects with intra-class discriminator for weakly-supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=10142530448531137253&hl=en&as_sdt=0,10",5,2020
Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation,24,cvpr,2,1,2023-06-03 02:44:19.031000,https://github.com/yeshaokai/Calibrator-Domain-Adaptation,10,Light-weight calibrator: a separable component for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=16926159104807511055&hl=en&as_sdt=0,33",6,2020
Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction,44,cvpr,3,0,2023-06-03 02:44:19.233000,https://github.com/anonymous0120/dr,18,Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction,"https://scholar.google.com/scholar?cluster=16861420040062582539&hl=en&as_sdt=0,47",3,2020
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning,158,cvpr,6,8,2023-06-03 02:44:19.434000,https://github.com/xinshuoweng/GNN3DMOT,78,Gnn3dmot: Graph neural network for 3d multi-object tracking with 2d-3d multi-feature learning,"https://scholar.google.com/scholar?cluster=2873818380563144640&hl=en&as_sdt=0,5",31,2020
Searching Central Difference Convolutional Networks for Face Anti-Spoofing,256,cvpr,172,5,2023-06-03 02:44:19.636000,https://github.com/ZitongYu/CDCN,514,Searching central difference convolutional networks for face anti-spoofing,"https://scholar.google.com/scholar?cluster=1829978747982388328&hl=en&as_sdt=0,5",15,2020
BSP-Net: Generating Compact Meshes via Binary Space Partitioning,199,cvpr,23,0,2023-06-03 02:44:19.837000,https://github.com/czq142857/BSP-NET-original,173,Bsp-net: Generating compact meshes via binary space partitioning,"https://scholar.google.com/scholar?cluster=18390590308749024682&hl=en&as_sdt=0,5",9,2020
Two Causal Principles for Improving Visual Dialog,109,cvpr,3,0,2023-06-03 02:44:20.038000,https://github.com/simpleshinobu/visdial-principles,33,Two causal principles for improving visual dialog,"https://scholar.google.com/scholar?cluster=9841217633248992434&hl=en&as_sdt=0,31",2,2020
Relation-Aware Global Attention for Person Re-Identification,401,cvpr,64,27,2023-06-03 02:44:20.239000,https://github.com/microsoft/Relation-Aware-Global-Attention-Networks,302,Relation-aware global attention for person re-identification,"https://scholar.google.com/scholar?cluster=15864607000188383728&hl=en&as_sdt=0,39",5,2020
Monocular Real-Time Hand Shape and Motion Capture Using Multi-Modal Data,147,cvpr,163,4,2023-06-03 02:44:20.440000,https://github.com/CalciferZh/minimal-hand,866,Monocular real-time hand shape and motion capture using multi-modal data,"https://scholar.google.com/scholar?cluster=15702388084583446253&hl=en&as_sdt=0,5",38,2020
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles,142,cvpr,18,3,2023-06-03 02:44:20.640000,https://github.com/RjDuan/AdvCam-Hide-Adv-with-Natural-Styles,76,Adversarial camouflage: Hiding physical-world attacks with natural styles,"https://scholar.google.com/scholar?cluster=17515584599994156419&hl=en&as_sdt=0,10",3,2020
Fast Template Matching and Update for Video Object Tracking and Segmentation,47,cvpr,5,0,2023-06-03 02:44:20.841000,https://github.com/insomnia94/FTMU,20,Fast template matching and update for video object tracking and segmentation,"https://scholar.google.com/scholar?cluster=3709180539925267975&hl=en&as_sdt=0,36",2,2020
3DV: 3D Dynamic Voxel for Action Recognition in Depth Video,54,cvpr,3,5,2023-06-03 02:44:21.043000,https://github.com/3huo/3DV-Action,58,3dv: 3d dynamic voxel for action recognition in depth video,"https://scholar.google.com/scholar?cluster=3131571121082566851&hl=en&as_sdt=0,5",9,2020
What It Thinks Is Important Is Important: Robustness Transfers Through Input Gradients,40,cvpr,0,3,2023-06-03 02:44:21.244000,https://github.com/alvinchangw/IGAM_CVPR2020,16,What it thinks is important is important: Robustness transfers through input gradients,"https://scholar.google.com/scholar?cluster=2959696626352091650&hl=en&as_sdt=0,33",3,2020
Deep Parametric Shape Predictions Using Distance Fields,42,cvpr,11,1,2023-06-03 02:44:21.446000,https://github.com/dmsm/DeepParametricShapes,50,Deep parametric shape predictions using distance fields,"https://scholar.google.com/scholar?cluster=5781070693880125016&hl=en&as_sdt=0,33",7,2020
Weakly Supervised Discriminative Feature Learning With State Information for Person Identification,26,cvpr,8,3,2023-06-03 02:44:21.647000,https://github.com/KovenYu/state-information,58,Weakly supervised discriminative feature learning with state information for person identification,"https://scholar.google.com/scholar?cluster=761891001136222127&hl=en&as_sdt=0,5",7,2020
M2m: Imbalanced Classification via Major-to-Minor Translation,145,cvpr,12,1,2023-06-03 02:44:21.848000,https://github.com/alinlab/M2m,82,M2m: Imbalanced classification via major-to-minor translation,"https://scholar.google.com/scholar?cluster=5083788860746797575&hl=en&as_sdt=0,5",6,2020
DSGN: Deep Stereo Geometry Network for 3D Object Detection,147,cvpr,48,7,2023-06-03 02:44:22.049000,https://github.com/chenyilun95/DSGN,307,Dsgn: Deep stereo geometry network for 3d object detection,"https://scholar.google.com/scholar?cluster=15382283662612597207&hl=en&as_sdt=0,5",23,2020
Adaptive Subspaces for Few-Shot Learning,320,cvpr,18,3,2023-06-03 02:44:22.250000,https://github.com/chrysts/dsn_fewshot,83,Adaptive subspaces for few-shot learning,"https://scholar.google.com/scholar?cluster=6709917758415861321&hl=en&as_sdt=0,5",6,2020
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation,64,cvpr,177,11,2023-06-03 02:44:22.452000,https://github.com/Yuheng-Li/MixNMatch,919,Mixnmatch: Multifactor disentanglement and encoding for conditional image generation,"https://scholar.google.com/scholar?cluster=14097606698351716973&hl=en&as_sdt=0,43",58,2020
Predicting Semantic Map Representations From Images Using Pyramid Occupancy Networks,132,cvpr,57,16,2023-06-03 02:44:22.652000,https://github.com/tom-roddick/mono-semantic-maps,253,Predicting semantic map representations from images using pyramid occupancy networks,"https://scholar.google.com/scholar?cluster=11157030601692626647&hl=en&as_sdt=0,5",20,2020
Parsing-Based View-Aware Embedding Network for Vehicle Re-Identification,142,cvpr,24,32,2023-06-03 02:44:22.853000,https://github.com/silverbulletmdc/PVEN,92,Parsing-based view-aware embedding network for vehicle re-identification,"https://scholar.google.com/scholar?cluster=1756278829660523711&hl=en&as_sdt=0,22",9,2020
Dynamic Graph Message Passing Networks,108,cvpr,4,1,2023-06-03 02:44:23.054000,https://github.com/fudan-zvg/dgmn2,21,Dynamic graph message passing networks,"https://scholar.google.com/scholar?cluster=4303557091610097560&hl=en&as_sdt=0,33",3,2020
Evaluating Weakly Supervised Object Localization Methods Right,158,cvpr,56,11,2023-06-03 02:44:23.255000,https://github.com/clovaai/wsolevaluation,314,Evaluating weakly supervised object localization methods right,"https://scholar.google.com/scholar?cluster=14823117010585920954&hl=en&as_sdt=0,14",15,2020
Stochastic Classifiers for Unsupervised Domain Adaptation,119,cvpr,3,0,2023-06-03 02:44:23.456000,https://github.com/zhiheLu/STAR_Stochastic_Classifiers_for_UDA,21,Stochastic classifiers for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=2558021056858878610&hl=en&as_sdt=0,5",2,2020
AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization,265,cvpr,21,6,2023-06-03 02:44:23.657000,https://github.com/vision4robotics/AutoTrack,91,AutoTrack: Towards high-performance visual tracking for UAV with automatic spatio-temporal regularization,"https://scholar.google.com/scholar?cluster=12540468291839351517&hl=en&as_sdt=0,14",7,2020
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks,114,cvpr,22,11,2023-06-03 02:44:23.858000,https://github.com/dvl-tum/ciagan,65,Ciagan: Conditional identity anonymization generative adversarial networks,"https://scholar.google.com/scholar?cluster=16067170614104475076&hl=en&as_sdt=0,5",5,2020
DaST: Data-Free Substitute Training for Adversarial Attacks,97,cvpr,26,2,2023-06-03 02:44:24.059000,https://github.com/zhoumingyi/DaST,107,Dast: Data-free substitute training for adversarial attacks,"https://scholar.google.com/scholar?cluster=5988684575644720763&hl=en&as_sdt=0,5",5,2020
A Morphable Face Albedo Model,42,cvpr,33,2,2023-06-03 02:44:24.260000,https://github.com/waps101/AlbedoMM,218,A morphable face albedo model,"https://scholar.google.com/scholar?cluster=16437944822002654179&hl=en&as_sdt=0,11",11,2020
ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture,21,cvpr,2,1,2023-06-03 02:44:24.460000,https://github.com/senakicir/ActiveMoCap,20,Activemocap: Optimized viewpoint selection for active human motion capture,"https://scholar.google.com/scholar?cluster=3412526141858417261&hl=en&as_sdt=0,10",8,2020
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets,74,cvpr,22,3,2023-06-03 02:44:24.661000,https://github.com/zeiss-microscopy/BSConv,126,Rethinking depthwise separable convolutions: How intra-kernel correlations lead to improved mobilenets,"https://scholar.google.com/scholar?cluster=398559284763885074&hl=en&as_sdt=0,24",6,2020
Vision-Dialog Navigation by Exploring Cross-Modal Memory,38,cvpr,3,5,2023-06-03 02:44:24.863000,https://github.com/yeezhu/CMN.pytorch,19,Vision-dialog navigation by exploring cross-modal memory,"https://scholar.google.com/scholar?cluster=678153147767271352&hl=en&as_sdt=0,36",4,2020
Noise Robust Generative Adversarial Networks,26,cvpr,13,2,2023-06-03 02:44:25.064000,https://github.com/takuhirok/NR-GAN,60,Noise robust generative adversarial networks,"https://scholar.google.com/scholar?cluster=4094388698161781290&hl=en&as_sdt=0,5",3,2020
A Sparse Resultant Based Method for Efficient Minimal Solvers,10,cvpr,2,1,2023-06-03 02:44:25.265000,https://github.com/snehalbhayani/aut_gen_sparse_res_solver,4,A sparse resultant based method for efficient minimal solvers,"https://scholar.google.com/scholar?cluster=16322494935522000934&hl=en&as_sdt=0,5",2,2020
Differentiable Adaptive Computation Time for Visual Reasoning,14,cvpr,3,0,2023-06-03 02:44:25.467000,https://github.com/ceyzaguirre4/DACT-MAC,14,Differentiable adaptive computation time for visual reasoning,"https://scholar.google.com/scholar?cluster=11306709913679582369&hl=en&as_sdt=0,5",4,2020
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection,254,cvpr,68,3,2023-06-03 02:44:25.668000,https://github.com/EndlessSora/DeeperForensics-1.0,489,Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection,"https://scholar.google.com/scholar?cluster=9997678139358274550&hl=en&as_sdt=0,10",36,2020
Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving,51,cvpr,6,1,2023-06-03 02:44:25.870000,https://github.com/autonomousvision/data_aggregation,33,Exploring data aggregation in policy learning for vision-based urban autonomous driving,"https://scholar.google.com/scholar?cluster=10048772985494640887&hl=en&as_sdt=0,43",8,2020
Shape Reconstruction by Learning Differentiable Surface Representations,33,cvpr,6,7,2023-06-03 02:44:26.071000,https://github.com/bednarikjan/differential_surface_representation,21,Shape reconstruction by learning differentiable surface representations,"https://scholar.google.com/scholar?cluster=16485905991969114815&hl=en&as_sdt=0,5",3,2020
Breaking the Cycle - Colleagues Are All You Need,75,cvpr,32,3,2023-06-03 02:44:26.272000,https://github.com/Onr/Council-GAN,260,Breaking the cycle-colleagues are all you need,"https://scholar.google.com/scholar?cluster=8013153262168906656&hl=en&as_sdt=0,33",8,2020
Temporal Pyramid Network for Action Recognition,292,cvpr,57,14,2023-06-03 02:44:26.473000,https://github.com/decisionforce/TPN,381,Temporal pyramid network for action recognition,"https://scholar.google.com/scholar?cluster=17700791289500480350&hl=en&as_sdt=0,5",16,2020
Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks,71,cvpr,5,1,2023-06-03 02:44:26.674000,https://github.com/fiveai/making-better-mistakes,60,Making better mistakes: Leveraging class hierarchies with deep networks,"https://scholar.google.com/scholar?cluster=12658314218813959058&hl=en&as_sdt=0,6",10,2020
A Characteristic Function Approach to Deep Implicit Generative Modeling,24,cvpr,6,0,2023-06-03 02:44:26.875000,https://github.com/crslab/OCFGAN,13,A characteristic function approach to deep implicit generative modeling,"https://scholar.google.com/scholar?cluster=9590461487236584070&hl=en&as_sdt=0,5",4,2020
Bayesian Adversarial Human Motion Synthesis,24,cvpr,2,2,2023-06-03 02:44:27.076000,https://github.com/rort1989/BH-HSMM,10,Bayesian adversarial human motion synthesis,"https://scholar.google.com/scholar?cluster=3976126790868555567&hl=en&as_sdt=0,39",4,2020
A Unified Object Motion and Affinity Model for Online Multi-Object Tracking,83,cvpr,14,6,2023-06-03 02:44:27.277000,https://github.com/yinjunbo/UMA-MOT,89,A unified object motion and affinity model for online multi-object tracking,"https://scholar.google.com/scholar?cluster=5947981595077920840&hl=en&as_sdt=0,5",15,2020
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection,145,cvpr,58,12,2023-06-03 02:44:27.478000,https://github.com/GXYM/DRRG,251,Deep relational reasoning graph network for arbitrary shape text detection,"https://scholar.google.com/scholar?cluster=12199054519078728891&hl=en&as_sdt=0,11",11,2020
Learning to Forget for Meta-Learning,70,cvpr,6,1,2023-06-03 02:44:27.679000,https://github.com/baiksung/L2F,30,Learning to forget for meta-learning,"https://scholar.google.com/scholar?cluster=7941198110470516466&hl=en&as_sdt=0,5",3,2020
A Self-supervised Approach for Adversarial Robustness,134,cvpr,16,0,2023-06-03 02:44:27.879000,https://github.com/Muzammal-Naseer/NRP,72,A self-supervised approach for adversarial robustness,"https://scholar.google.com/scholar?cluster=9329721027382118239&hl=en&as_sdt=0,44",4,2020
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View With a Reachability Prior,25,cvpr,6,3,2023-06-03 02:44:28.080000,https://github.com/lmb-freiburg/FLN-EPN-RPN,28,Multimodal future localization and emergence prediction for objects in egocentric view with a reachability prior,"https://scholar.google.com/scholar?cluster=1762591900453621197&hl=en&as_sdt=0,5",7,2020
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation,281,cvpr,74,24,2023-06-03 02:44:28.281000,https://github.com/edwardzhou130/PolarSeg,327,Polarnet: An improved grid representation for online lidar point clouds semantic segmentation,"https://scholar.google.com/scholar?cluster=4631427403053304409&hl=en&as_sdt=0,10",14,2020
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement,124,cvpr,90,1,2023-06-03 02:44:28.482000,https://github.com/hkchengrex/CascadePSP,714,Cascadepsp: Toward class-agnostic and very high-resolution segmentation via global and local refinement,"https://scholar.google.com/scholar?cluster=6392003581134189535&hl=en&as_sdt=0,31",15,2020
Meta-Learning of Neural Architectures for Few-Shot Learning,108,cvpr,6,1,2023-06-03 02:44:28.683000,https://github.com/boschresearch/metanas,14,Meta-learning of neural architectures for few-shot learning,"https://scholar.google.com/scholar?cluster=17906366543837771279&hl=en&as_sdt=0,5",3,2020
HybridPose: 6D Object Pose Estimation Under Hybrid Representations,187,cvpr,66,38,2023-06-03 02:44:28.883000,https://github.com/chensong1995/HybridPose,371,Hybridpose: 6d object pose estimation under hybrid representations,"https://scholar.google.com/scholar?cluster=3057712227864122267&hl=en&as_sdt=0,34",16,2020
Distilling Knowledge From Graph Convolutional Networks,165,cvpr,8,2,2023-06-03 02:44:29.085000,https://github.com/ihollywhy/DistillGCN.PyTorch,40,Distilling knowledge from graph convolutional networks,"https://scholar.google.com/scholar?cluster=6849351348645594567&hl=en&as_sdt=0,5",1,2020
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks,261,cvpr,6,2,2023-06-03 02:44:29.286000,https://github.com/AI-secure/GMI-Attack,14,The secret revealer: Generative model-inversion attacks against deep neural networks,"https://scholar.google.com/scholar?cluster=17078326569400271767&hl=en&as_sdt=0,33",2,2020
Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining,276,cvpr,46,23,2023-06-03 02:44:29.488000,https://github.com/SHI-Labs/Cross-Scale-Non-Local-Attention,389,Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining,"https://scholar.google.com/scholar?cluster=11182964685373054043&hl=en&as_sdt=0,5",16,2020
Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence,115,cvpr,7,4,2023-06-03 02:44:29.688000,https://github.com/LIX-shape-analysis/GeomFmaps,48,Deep geometric functional maps: Robust feature learning for shape correspondence,"https://scholar.google.com/scholar?cluster=6948059964604489925&hl=en&as_sdt=0,5",6,2020
Designing Network Design Spaces,1037,cvpr,240,24,2023-06-03 02:44:29.889000,https://github.com/facebookresearch/pycls,2054,Designing network design spaces,"https://scholar.google.com/scholar?cluster=1436458088266741112&hl=en&as_sdt=0,31",60,2020
Ego-Topo: Environment Affordances From Egocentric Video,73,cvpr,3,1,2023-06-03 02:44:30.090000,https://github.com/facebookresearch/ego-topo,26,Ego-topo: Environment affordances from egocentric video,"https://scholar.google.com/scholar?cluster=5331619265150019338&hl=en&as_sdt=0,33",7,2020
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector,433,cvpr,47,55,2023-06-03 02:44:30.292000,https://github.com/fanq15/FewX,314,Few-shot object detection with attention-RPN and multi-relation detector,"https://scholar.google.com/scholar?cluster=10133572337251446204&hl=en&as_sdt=0,32",14,2020
Editing in Style: Uncovering the Local Semantics of GANs,204,cvpr,15,4,2023-06-03 02:44:30.493000,https://github.com/IVRL/GANLocalEditing,142,Editing in style: Uncovering the local semantics of gans,"https://scholar.google.com/scholar?cluster=6200969521976099940&hl=en&as_sdt=0,5",5,2020
PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation,299,cvpr,101,26,2023-06-03 02:44:30.693000,https://github.com/ethnhe/PVN3D,422,Pvn3d: A deep point-wise 3d keypoints voting network for 6dof pose estimation,"https://scholar.google.com/scholar?cluster=7432941682090070970&hl=en&as_sdt=0,33",20,2020
A Graduated Filter Method for Large Scale Robust Estimation,4,cvpr,2,0,2023-06-03 02:44:30.894000,https://github.com/intellhave/ASKER,16,A graduated filter method for large scale robust estimation,"https://scholar.google.com/scholar?cluster=11054634057468269095&hl=en&as_sdt=0,5",2,2020
Domain-Aware Visual Bias Eliminating for Generalized Zero-Shot Learning,123,cvpr,2,4,2023-06-03 02:44:31.095000,https://github.com/mboboGO/DVBE,40,Domain-aware visual bias eliminating for generalized zero-shot learning,"https://scholar.google.com/scholar?cluster=17212047460800688269&hl=en&as_sdt=0,5",3,2020
Height and Uprightness Invariance for 3D Prediction From a Single View,3,cvpr,3,1,2023-06-03 02:44:31.297000,https://github.com/mbaradad/im2pcl,8,Height and uprightness invariance for 3d prediction from a single view,"https://scholar.google.com/scholar?cluster=6069115121400584246&hl=en&as_sdt=0,10",5,2020
VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions,150,cvpr,21,5,2023-06-03 02:44:31.498000,https://github.com/ASMIftekhar/VSGNet,97,Vsgnet: Spatial attention network for detecting human object interactions using graph convolutions,"https://scholar.google.com/scholar?cluster=12125960982809080831&hl=en&as_sdt=0,5",12,2020
Projection & Probability-Driven Black-Box Attack,27,cvpr,2,3,2023-06-03 02:44:31.699000,https://github.com/theFool32/PPBA,13,Projection & probability-driven black-box attack,"https://scholar.google.com/scholar?cluster=12975032074953519528&hl=en&as_sdt=0,5",3,2020
Learning Better Lossless Compression Using Lossy Compression,51,cvpr,9,2,2023-06-03 02:44:31.900000,https://github.com/fab-jul/RC-PyTorch,45,Learning better lossless compression using lossy compression,"https://scholar.google.com/scholar?cluster=6500897799231953693&hl=en&as_sdt=0,2",6,2020
Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives,22,cvpr,19,2,2023-06-03 02:44:32.101000,https://github.com/d-li14/dhm,85,Dynamic hierarchical mimicking towards consistent optimization objectives,"https://scholar.google.com/scholar?cluster=4527803130408839442&hl=en&as_sdt=0,33",8,2020
Deep Iterative Surface Normal Estimation,31,cvpr,4,4,2023-06-03 02:44:32.303000,https://github.com/nnaisense/deep-iterative-surface-normal-estimation,27,Deep iterative surface normal estimation,"https://scholar.google.com/scholar?cluster=17138079772193977333&hl=en&as_sdt=0,5",5,2020
Deblurring by Realistic Blurring,213,cvpr,12,0,2023-06-03 02:44:32.505000,https://github.com/HDCVLab/Deblurring-by-Realistic-Blurring,49,Deblurring by realistic blurring,"https://scholar.google.com/scholar?cluster=5892850021726448686&hl=en&as_sdt=0,5",1,2020
BiFuse: Monocular 360 Depth Estimation via Bi-Projection Fusion,110,cvpr,29,17,2023-06-03 02:44:32.706000,https://github.com/Yeh-yu-hsuan/BiFuse,159,Bifuse: Monocular 360 depth estimation via bi-projection fusion,"https://scholar.google.com/scholar?cluster=3724608691256197367&hl=en&as_sdt=0,5",8,2020
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion,68,cvpr,51,15,2023-06-03 02:44:32.907000,https://github.com/wkentaro/morefusion,209,Morefusion: Multi-object reasoning for 6d pose estimation from volumetric fusion,"https://scholar.google.com/scholar?cluster=12133155819369787007&hl=en&as_sdt=0,14",21,2020
Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging,128,cvpr,21,0,2023-06-03 02:44:33.109000,https://github.com/liuyang12/PnP-SCI,40,Plug-and-play algorithms for large-scale snapshot compressive imaging,"https://scholar.google.com/scholar?cluster=635704366161862904&hl=en&as_sdt=0,39",4,2020
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation,15,cvpr,37,7,2023-06-03 02:44:33.310000,https://github.com/JianqiangWan/Super-BPD,193,Super-BPD: Super boundary-to-pixel direction for fast image segmentation,"https://scholar.google.com/scholar?cluster=17108816326157491572&hl=en&as_sdt=0,49",6,2020
Visual Reaction: Learning to Play Catch With Your Drone,12,cvpr,3,1,2023-06-03 02:44:33.511000,https://github.com/KuoHaoZeng/Visual_Reaction,10,Visual reaction: Learning to play catch with your drone,"https://scholar.google.com/scholar?cluster=3568490935721651270&hl=en&as_sdt=0,47",2,2020
Single-Stage Semantic Segmentation From Image Labels,136,cvpr,45,6,2023-06-03 02:44:33.711000,https://github.com/visinf/1-stage-wseg,363,Single-stage semantic segmentation from image labels,"https://scholar.google.com/scholar?cluster=14008306007503312979&hl=en&as_sdt=0,33",21,2020
Learning to See Through Obstructions,44,cvpr,183,14,2023-06-03 02:44:33.912000,https://github.com/alex04072000/ObstructionRemoval,984,Learning to see through obstructions,"https://scholar.google.com/scholar?cluster=1342716409014243031&hl=en&as_sdt=0,1",47,2020
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features,207,cvpr,35,6,2023-06-03 02:44:34.112000,https://github.com/XuyangBai/D3Feat,236,D3feat: Joint learning of dense detection and description of 3d local features,"https://scholar.google.com/scholar?cluster=8766956035257707158&hl=en&as_sdt=0,11",8,2020
Front2Back: Single View 3D Shape Reconstruction via Front to Back Prediction,34,cvpr,0,1,2023-06-03 02:44:34.313000,https://github.com/rozentill/Front2Back,6,Front2back: Single view 3d shape reconstruction via front to back prediction,"https://scholar.google.com/scholar?cluster=10182733411944684144&hl=en&as_sdt=0,16",4,2020
Cross-Batch Memory for Embedding Learning,184,cvpr,39,13,2023-06-03 02:44:34.515000,https://github.com/MalongTech/research-xbm,280,Cross-batch memory for embedding learning,"https://scholar.google.com/scholar?cluster=6637868233984280023&hl=en&as_sdt=0,39",9,2020
IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval,212,cvpr,29,2,2023-06-03 02:44:34.715000,https://github.com/HuiChen24/IMRAM,83,Imram: Iterative matching with recurrent attention memory for cross-modal image-text retrieval,"https://scholar.google.com/scholar?cluster=5242365097575219545&hl=en&as_sdt=0,23",1,2020
Satellite Image Time Series Classification With Pixel-Set Encoders and Temporal Self-Attention,97,cvpr,35,2,2023-06-03 02:44:34.921000,https://github.com/VSainteuf/pytorch-psetae,141,Satellite image time series classification with pixel-set encoders and temporal self-attention,"https://scholar.google.com/scholar?cluster=11396621858163202989&hl=en&as_sdt=0,10",3,2020
"Show, Edit and Tell: A Framework for Editing Image Captions",51,cvpr,13,0,2023-06-03 02:44:35.122000,https://github.com/fawazsammani/show-edit-tell,77,"Show, edit and tell: a framework for editing image captions","https://scholar.google.com/scholar?cluster=7783895758823170058&hl=en&as_sdt=0,44",3,2020
ARShadowGAN: Shadow Generative Adversarial Network for Augmented Reality in Single Light Scenes,58,cvpr,13,3,2023-06-03 02:44:35.323000,https://github.com/ldq9526/ARShadowGAN,53,Arshadowgan: Shadow generative adversarial network for augmented reality in single light scenes,"https://scholar.google.com/scholar?cluster=9090118632543903687&hl=en&as_sdt=0,5",8,2020
"Train in Germany, Test in the USA: Making 3D Object Detectors Generalize",88,cvpr,13,9,2023-06-03 02:44:35.525000,https://github.com/cxy1997/3D_adapt_auto_driving,114,"Train in germany, test in the usa: Making 3d object detectors generalize","https://scholar.google.com/scholar?cluster=11207545328500921809&hl=en&as_sdt=0,5",14,2020
Self-Learning Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence,47,cvpr,11,6,2023-06-03 02:44:35.726000,https://github.com/flyywh/CVPR-2020-Self-Rain-Removal,46,Self-learning video rain streak removal: When cyclic consistency meets temporal correspondence,"https://scholar.google.com/scholar?cluster=17794942426853779059&hl=en&as_sdt=0,5",3,2020
Going Deeper With Lean Point Networks,23,cvpr,9,9,2023-06-03 02:44:35.928000,https://github.com/erictuanle/GoingDeeperwPointNetworks,60,Going deeper with lean point networks,"https://scholar.google.com/scholar?cluster=12165344201622069206&hl=en&as_sdt=0,44",9,2020
CARP: Compression Through Adaptive Recursive Partitioning for Multi-Dimensional Images,1,cvpr,1,0,2023-06-03 02:44:36.130000,https://github.com/xylimeng/CARP,4,CARP: Compression through adaptive recursive partitioning for multi-dimensional images,"https://scholar.google.com/scholar?cluster=17912611468134676639&hl=en&as_sdt=0,22",3,2020
Semantic Image Manipulation Using Scene Graphs,75,cvpr,15,3,2023-06-03 02:44:36.331000,https://github.com/he-dhamo/simsg,53,Semantic image manipulation using scene graphs,"https://scholar.google.com/scholar?cluster=6895435036117557713&hl=en&as_sdt=0,5",4,2020
Memory Enhanced Global-Local Aggregation for Video Object Detection,215,cvpr,112,64,2023-06-03 02:44:36.533000,https://github.com/Scalsol/mega.pytorch,539,Memory enhanced global-local aggregation for video object detection,"https://scholar.google.com/scholar?cluster=2075183739030592763&hl=en&as_sdt=0,5",18,2020
How to Train Your Deep Multi-Object Tracker,175,cvpr,82,4,2023-06-03 02:44:36.733000,https://github.com/yihongXU/deepMOT,487,How to train your deep multi-object tracker,"https://scholar.google.com/scholar?cluster=8670462462759705121&hl=en&as_sdt=0,5",14,2020
From Image Collections to Point Clouds With Self-Supervised Shape and Pose Networks,20,cvpr,2,1,2023-06-03 02:44:36.935000,https://github.com/val-iisc/ssl_3d_recon,26,From image collections to point clouds with self-supervised shape and pose networks,"https://scholar.google.com/scholar?cluster=8872702247915090622&hl=en&as_sdt=0,5",7,2020
Cascaded Deep Monocular 3D Human Pose Estimation With Evolutionary Training Data,123,cvpr,42,4,2023-06-03 02:44:37.135000,https://github.com/Nicholasli1995/EvoSkeleton,306,Cascaded deep monocular 3d human pose estimation with evolutionary training data,"https://scholar.google.com/scholar?cluster=6560357466398755666&hl=en&as_sdt=0,36",12,2020
Distilling Cross-Task Knowledge via Relationship Matching,27,cvpr,8,1,2023-06-03 02:44:37.336000,https://github.com/njulus/ReFilled,47,Distilling cross-task knowledge via relationship matching,"https://scholar.google.com/scholar?cluster=9618992685652379911&hl=en&as_sdt=0,41",2,2020
Predicting Goal-Directed Human Attention Using Inverse Reinforcement Learning,60,cvpr,19,1,2023-06-03 02:44:37.538000,https://github.com/cvlab-stonybrook/Scanpath_Prediction,76,Predicting goal-directed human attention using inverse reinforcement learning,"https://scholar.google.com/scholar?cluster=8166141151783626827&hl=en&as_sdt=0,33",6,2020
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors,88,cvpr,7,4,2023-06-03 02:44:37.738000,https://github.com/giladcohen/NNIF_adv_defense,27,Detecting adversarial samples using influence functions and nearest neighbors,"https://scholar.google.com/scholar?cluster=2252690017384501015&hl=en&as_sdt=0,10",3,2020
Perceptual Quality Assessment of Smartphone Photography,156,cvpr,34,6,2023-06-03 02:44:37.940000,https://github.com/h4nwei/SPAQ,126,Perceptual quality assessment of smartphone photography,"https://scholar.google.com/scholar?cluster=16242416187240613044&hl=en&as_sdt=0,33",5,2020
LiDAR-Based Online 3D Video Object Detection With Graph-Based Message Passing and Spatiotemporal Transformer Attention,114,cvpr,7,15,2023-06-03 02:44:38.141000,https://github.com/yinjunbo/3DVID,71,Lidar-based online 3d video object detection with graph-based message passing and spatiotemporal transformer attention,"https://scholar.google.com/scholar?cluster=1106004144690237377&hl=en&as_sdt=0,10",20,2020
When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks,124,cvpr,16,10,2023-06-03 02:44:38.342000,https://github.com/gmh14/RobNets,120,When nas meets robustness: In search of robust architectures against adversarial attacks,"https://scholar.google.com/scholar?cluster=7847094734431264839&hl=en&as_sdt=0,31",7,2020
Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks,134,cvpr,31,1,2023-06-03 02:44:38.543000,https://github.com/cwmok/Fast-Symmetric-Diffeomorphic-Image-Registration-with-Convolutional-Neural-Networks,112,Fast symmetric diffeomorphic image registration with convolutional neural networks,"https://scholar.google.com/scholar?cluster=5270707781102159135&hl=en&as_sdt=0,5",3,2020
Deep White-Balance Editing,73,cvpr,57,4,2023-06-03 02:44:38.744000,https://github.com/mahmoudnafifi/Deep_White_Balance,412,Deep white-balance editing,"https://scholar.google.com/scholar?cluster=14477825371349896973&hl=en&as_sdt=0,5",22,2020
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection,92,cvpr,25,6,2023-06-03 02:44:38.945000,https://github.com/ggjy/HitDet.pytorch,111,Hit-detector: Hierarchical trinity architecture search for object detection,"https://scholar.google.com/scholar?cluster=13554213228832152540&hl=en&as_sdt=0,26",10,2020
Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis,121,cvpr,13,3,2023-06-03 02:44:39.146000,https://github.com/j1a0m0e4sNTU/3dgcn,97,Convolution in the cloud: Learning deformable kernels in 3d graph convolution networks for point cloud analysis,"https://scholar.google.com/scholar?cluster=9373437675159417256&hl=en&as_sdt=0,5",3,2020
Upgrading Optical Flow to 3D Scene Flow Through Optical Expansion,49,cvpr,26,6,2023-06-03 02:44:39.348000,https://github.com/gengshan-y/expansion,154,Upgrading optical flow to 3d scene flow through optical expansion,"https://scholar.google.com/scholar?cluster=9007645461661941786&hl=en&as_sdt=0,5",7,2020
Collaborative Distillation for Ultra-Resolution Universal Style Transfer,69,cvpr,23,2,2023-06-03 02:44:39.549000,https://github.com/mingsun-tse/collaborative-distillation,180,Collaborative distillation for ultra-resolution universal style transfer,"https://scholar.google.com/scholar?cluster=14849554643871656949&hl=en&as_sdt=0,34",12,2020
GAN Compression: Efficient Architectures for Interactive Conditional GANs,164,cvpr,151,9,2023-06-03 02:44:39.750000,https://github.com/mit-han-lab/gan-compression,1049,Gan compression: Efficient architectures for interactive conditional gans,"https://scholar.google.com/scholar?cluster=4661770039879488594&hl=en&as_sdt=0,5",33,2020
Equalization Loss for Long-Tailed Object Recognition,281,cvpr,17,4,2023-06-03 02:44:39.951000,https://github.com/tztztztztz/eql.detectron2,199,Equalization loss for long-tailed object recognition,"https://scholar.google.com/scholar?cluster=15099630745162536399&hl=en&as_sdt=0,5",9,2020
M-LVC: Multiple Frames Prediction for Learned Video Compression,111,cvpr,14,3,2023-06-03 02:44:40.152000,https://github.com/JianpingLin/M-LVC_CVPR2020,65,M-LVC: Multiple frames prediction for learned video compression,"https://scholar.google.com/scholar?cluster=5201045318110504191&hl=en&as_sdt=0,14",8,2020
Hierarchical Conditional Relation Networks for Video Question Answering,169,cvpr,24,3,2023-06-03 02:44:40.354000,https://github.com/thaolmk54/hcrn-videoqa,120,Hierarchical conditional relation networks for video question answering,"https://scholar.google.com/scholar?cluster=15820917450735928278&hl=en&as_sdt=0,5",7,2020
Fusing Wearable IMUs With Multi-View Images for Human Pose Estimation: A Geometric Approach,45,cvpr,15,7,2023-06-03 02:44:40.556000,https://github.com/CHUNYUWANG/imu-human-pose-pytorch,84,Fusing wearable imus with multi-view images for human pose estimation: A geometric approach,"https://scholar.google.com/scholar?cluster=6381641994204290216&hl=en&as_sdt=0,5",7,2020
PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer,98,cvpr,128,9,2023-06-03 02:44:40.757000,https://github.com/wtjiang98/PSGAN,637,Psgan: Pose and expression robust spatial-aware gan for customizable makeup transfer,"https://scholar.google.com/scholar?cluster=12665927355852020368&hl=en&as_sdt=0,5",33,2020
Single-Stage 6D Object Pose Estimation,139,cvpr,14,6,2023-06-03 02:44:40.958000,https://github.com/cvlab-epfl/single-stage-pose,93,Single-stage 6d object pose estimation,"https://scholar.google.com/scholar?cluster=16555159401268215436&hl=en&as_sdt=0,5",13,2020
McFlow: Monte Carlo Flow Models for Data Imputation,28,cvpr,3,2,2023-06-03 02:44:41.159000,https://github.com/trevor-richardson/MCFlow,13,Mcflow: Monte carlo flow models for data imputation,"https://scholar.google.com/scholar?cluster=17601369502336234807&hl=en&as_sdt=0,5",5,2020
A Physics-Based Noise Formation Model for Extreme Low-Light Raw Denoising,126,cvpr,57,13,2023-06-03 02:44:41.360000,https://github.com/Vandermode/NoiseModel,389,A physics-based noise formation model for extreme low-light raw denoising,"https://scholar.google.com/scholar?cluster=10876763221187077280&hl=en&as_sdt=0,22",13,2020
ManiGAN: Text-Guided Image Manipulation,194,cvpr,24,14,2023-06-03 02:44:41.561000,https://github.com/mrlibw/ManiGAN,140,Manigan: Text-guided image manipulation,"https://scholar.google.com/scholar?cluster=17129748820213352788&hl=en&as_sdt=0,5",6,2020
Putting Visual Object Recognition in Context,36,cvpr,4,0,2023-06-03 02:44:41.762000,https://github.com/kreimanlab/Put-In-Context,16,Putting visual object recognition in context,"https://scholar.google.com/scholar?cluster=6207193649298787857&hl=en&as_sdt=0,21",4,2020
Learning a Reinforced Agent for Flexible Exposure Bracketing Selection,13,cvpr,2,2,2023-06-03 02:44:41.964000,https://github.com/wzhouxiff/EBSNetMEFNet,12,Learning a reinforced agent for flexible exposure bracketing selection,"https://scholar.google.com/scholar?cluster=10473923077690252281&hl=en&as_sdt=0,36",1,2020
Multi-Path Learning for Object Pose Estimation Across Domains,68,cvpr,92,2,2023-06-03 02:44:42.166000,https://github.com/DLR-RM/AugmentedAutoencoder,315,Multi-path learning for object pose estimation across domains,"https://scholar.google.com/scholar?cluster=291620479892302465&hl=en&as_sdt=0,44",15,2020
Focus on Defocus: Bridging the Synthetic to Real Domain Gap for Depth Estimation,46,cvpr,5,7,2023-06-03 02:44:42.367000,https://github.com/dvl-tum/defocus-net,57,Focus on defocus: bridging the synthetic to real domain gap for depth estimation,"https://scholar.google.com/scholar?cluster=6765952716872104702&hl=en&as_sdt=0,5",5,2020
Detecting Attended Visual Targets in Video,61,cvpr,40,8,2023-06-03 02:44:42.568000,https://github.com/ejcgt/attention-target-detection,132,Detecting attended visual targets in video,"https://scholar.google.com/scholar?cluster=16308519208891875188&hl=en&as_sdt=0,33",17,2020
Instance Credibility Inference for Few-Shot Learning,135,cvpr,23,0,2023-06-03 02:44:42.768000,https://github.com/Yikai-Wang/ICI-FSL,79,Instance credibility inference for few-shot learning,"https://scholar.google.com/scholar?cluster=10869277062815166118&hl=en&as_sdt=0,14",5,2020
Defending Against Universal Attacks Through Selective Feature Regeneration,26,cvpr,2,0,2023-06-03 02:44:42.970000,https://github.com/tsborkar/Selective-feature-regeneration,7,Defending against universal attacks through selective feature regeneration,"https://scholar.google.com/scholar?cluster=13715224286626519534&hl=en&as_sdt=0,33",3,2020
GeoDA: A Geometric Framework for Black-Box Adversarial Attacks,67,cvpr,5,5,2023-06-03 02:44:43.170000,https://github.com/thisisalirah/GeoDA,29,Geoda: a geometric framework for black-box adversarial attacks,"https://scholar.google.com/scholar?cluster=11847537038453915350&hl=en&as_sdt=0,14",3,2020
Semantically Multi-Modal Image Synthesis,67,cvpr,52,12,2023-06-03 02:44:43.372000,https://github.com/Seanseattle/SMIS,316,Semantically multi-modal image synthesis,"https://scholar.google.com/scholar?cluster=8730739568243330540&hl=en&as_sdt=0,5",25,2020
Detail-recovery Image Deraining via Context Aggregation Networks,136,cvpr,7,9,2023-06-03 02:44:43.578000,https://github.com/Dengsgithub/DRD-Net,53,Detail-recovery image deraining via context aggregation networks,"https://scholar.google.com/scholar?cluster=8325357835629249582&hl=en&as_sdt=0,33",4,2020
DUNIT: Detection-Based Unsupervised Image-to-Image Translation,55,cvpr,2,6,2023-06-03 02:44:43.779000,https://github.com/IVRL/DUNIT,24,Dunit: Detection-based unsupervised image-to-image translation,"https://scholar.google.com/scholar?cluster=8258186601059420692&hl=en&as_sdt=0,5",1,2020
Dynamic Refinement Network for Oriented and Densely Packed Object Detection,188,cvpr,42,16,2023-06-03 02:44:43.981000,https://github.com/Anymake/DRN_CVPR2020,311,Dynamic refinement network for oriented and densely packed object detection,"https://scholar.google.com/scholar?cluster=7225349818402414566&hl=en&as_sdt=0,5",27,2020
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder,16,cvpr,4,1,2023-06-03 02:44:44.193000,https://github.com/GuanlinLee/FPD-for-Adversarial-Robustness,11,Enhancing intrinsic adversarial robustness via feature pyramid decoder,"https://scholar.google.com/scholar?cluster=16436897262784217662&hl=en&as_sdt=0,5",3,2020
Two-Shot Spatially-Varying BRDF and Shape Estimation,52,cvpr,11,5,2023-06-03 02:44:44.394000,https://github.com/NVlabs/two-shot-brdf-shape,65,Two-shot spatially-varying brdf and shape estimation,"https://scholar.google.com/scholar?cluster=7142289612345703062&hl=en&as_sdt=0,5",10,2020
High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification,286,cvpr,40,4,2023-06-03 02:44:44.596000,https://github.com/wangguanan/HOReID,191,High-order information matters: Learning relation and topology for occluded person re-identification,"https://scholar.google.com/scholar?cluster=16210404718674640994&hl=en&as_sdt=0,14",10,2020
Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields,49,cvpr,8,2,2023-06-03 02:44:44.797000,https://github.com/dulucas/Displacement_Field,109,Predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields,"https://scholar.google.com/scholar?cluster=10196939260877666789&hl=en&as_sdt=0,44",5,2020
Self-Supervised Monocular Scene Flow Estimation,82,cvpr,47,1,2023-06-03 02:44:45.007000,https://github.com/visinf/self-mono-sf,228,Self-supervised monocular scene flow estimation,"https://scholar.google.com/scholar?cluster=3323438378050011990&hl=en&as_sdt=0,5",13,2020
End-to-End Optimization of Scene Layout,34,cvpr,7,0,2023-06-03 02:44:45.210000,https://github.com/aluo-x/3D_SLN,44,End-to-end optimization of scene layout,"https://scholar.google.com/scholar?cluster=4350577753643452386&hl=en&as_sdt=0,14",6,2020
End-to-End Model-Free Reinforcement Learning for Urban Driving Using Implicit Affordances,114,cvpr,14,0,2023-06-03 02:44:45.410000,https://github.com/valeoai/LearningByCheating,49,End-to-end model-free reinforcement learning for urban driving using implicit affordances,"https://scholar.google.com/scholar?cluster=11829769000091826320&hl=en&as_sdt=0,33",1,2020
Action Modifiers: Learning From Adverbs in Instructional Videos,19,cvpr,1,1,2023-06-03 02:44:45.611000,https://github.com/hazeld/action-modifiers,21,Action modifiers: Learning from adverbs in instructional videos,"https://scholar.google.com/scholar?cluster=11599934655228160130&hl=en&as_sdt=0,5",4,2020
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement,728,cvpr,162,40,2023-06-03 02:44:45.813000,https://github.com/Li-Chongyi/Zero-DCE,584,Zero-reference deep curve estimation for low-light image enhancement,"https://scholar.google.com/scholar?cluster=8238952017907762802&hl=en&as_sdt=0,5",19,2020
Uncertainty Based Camera Model Selection,6,cvpr,4,0,2023-06-03 02:44:46.017000,https://github.com/michalpolic/unc_model_selection,10,Uncertainty based camera model selection,"https://scholar.google.com/scholar?cluster=6850727292805615179&hl=en&as_sdt=0,5",3,2020
What Deep CNNs Benefit From Global Covariance Pooling: An Optimization Perspective,13,cvpr,7,0,2023-06-03 02:44:46.219000,https://github.com/ZhangLi-CS/GCP_Optimization,27,What deep CNNs benefit from global covariance pooling: An optimization perspective,"https://scholar.google.com/scholar?cluster=4827649605982928590&hl=en&as_sdt=0,5",4,2020
FPConv: Learning Local Flattening for Point Convolution,118,cvpr,16,5,2023-06-03 02:44:46.420000,https://github.com/lyqun/FPConv,131,Fpconv: Learning local flattening for point convolution,"https://scholar.google.com/scholar?cluster=2514080387916985290&hl=en&as_sdt=0,33",8,2020
Learning a Neural 3D Texture Space From 2D Exemplars,64,cvpr,21,8,2023-06-03 02:44:46.621000,https://github.com/henzler/neuraltexture,99,Learning a neural 3d texture space from 2d exemplars,"https://scholar.google.com/scholar?cluster=15356935227776957169&hl=en&as_sdt=0,5",6,2020
View-GCN: View-Based Graph Convolutional Network for 3D Shape Analysis,163,cvpr,12,6,2023-06-03 02:44:46.822000,https://github.com/weixmath/view-GCN,55,View-gcn: View-based graph convolutional network for 3d shape analysis,"https://scholar.google.com/scholar?cluster=10326802092363152455&hl=en&as_sdt=0,23",4,2020
Structure-Preserving Super Resolution With Gradient Guidance,249,cvpr,83,4,2023-06-03 02:44:47.023000,https://github.com/Maclory/SPSR,414,Structure-preserving super resolution with gradient guidance,"https://scholar.google.com/scholar?cluster=17985509801212582192&hl=en&as_sdt=0,5",12,2020
Universal Weighting Metric Learning for Cross-Modal Matching,70,cvpr,6,2,2023-06-03 02:44:47.223000,https://github.com/wayne980/PolyLoss,19,Universal weighting metric learning for cross-modal matching,"https://scholar.google.com/scholar?cluster=4046794674216561893&hl=en&as_sdt=0,5",3,2020
Footprints and Free Space From a Single Color Image,17,cvpr,23,3,2023-06-03 02:44:47.425000,https://github.com/nianticlabs/footprints,210,Footprints and free space from a single color image,"https://scholar.google.com/scholar?cluster=16295345840538214342&hl=en&as_sdt=0,33",32,2020
Neural Contours: Learning to Draw Lines From 3D Shapes,25,cvpr,13,0,2023-06-03 02:44:47.626000,https://github.com/DifanLiu/NeuralContours,96,Neural contours: Learning to draw lines from 3d shapes,"https://scholar.google.com/scholar?cluster=7830236461158719219&hl=en&as_sdt=0,33",6,2020
Towards Efficient Model Compression via Learned Global Ranking,147,cvpr,13,2,2023-06-03 02:44:47.826000,https://github.com/cmu-enyac/LeGR,107,Towards efficient model compression via learned global ranking,"https://scholar.google.com/scholar?cluster=8264517854632465016&hl=en&as_sdt=0,5",8,2020
An Efficient PointLSTM for Point Clouds Based Gesture Recognition,74,cvpr,19,12,2023-06-03 02:44:48.027000,https://github.com/Blueprintf/pointlstm-gesture-recognition-pytorch,106,An efficient pointlstm for point clouds based gesture recognition,"https://scholar.google.com/scholar?cluster=12824072546514812552&hl=en&as_sdt=0,33",2,2020
Learning From Synthetic Animals,80,cvpr,13,1,2023-06-03 02:44:48.228000,https://github.com/JitengMu/Learning-from-Synthetic-Animals,59,Learning from synthetic animals,"https://scholar.google.com/scholar?cluster=16170292280887164654&hl=en&as_sdt=0,44",2,2020
Learning Multiview 3D Point Cloud Registration,139,cvpr,54,13,2023-06-03 02:44:48.430000,https://github.com/zgojcic/3D_multiview_reg,301,Learning multiview 3d point cloud registration,"https://scholar.google.com/scholar?cluster=5261058929771499202&hl=en&as_sdt=0,5",31,2020
SCOUT: Self-Aware Discriminant Counterfactual Explanations,53,cvpr,5,1,2023-06-03 02:44:48.631000,https://github.com/peiwang062/SCOUT,9,Scout: Self-aware discriminant counterfactual explanations,"https://scholar.google.com/scholar?cluster=2584618861112081961&hl=en&as_sdt=0,10",2,2020
3D Part Guided Image Editing for Fine-Grained Object Understanding,11,cvpr,8,2,2023-06-03 02:44:48.832000,https://github.com/zongdai/EditingForDNN,19,3D part guided image editing for fine-grained object understanding,"https://scholar.google.com/scholar?cluster=10936023318017521668&hl=en&as_sdt=0,23",6,2020
Phase Consistent Ecological Domain Adaptation,84,cvpr,4,2,2023-06-03 02:44:49.033000,https://github.com/donglao/PCEDA,32,Phase consistent ecological domain adaptation,"https://scholar.google.com/scholar?cluster=17332305502361880280&hl=en&as_sdt=0,23",8,2020
EPOS: Estimating 6D Pose of Objects With Symmetries,166,cvpr,11,2,2023-06-03 02:44:49.234000,https://github.com/thodan/epos,66,Epos: Estimating 6d pose of objects with symmetries,"https://scholar.google.com/scholar?cluster=17403580001604201560&hl=en&as_sdt=0,18",9,2020
Single-Side Domain Generalization for Face Anti-Spoofing,148,cvpr,33,14,2023-06-03 02:44:49.436000,https://github.com/taylover-pei/SSDG-CVPR2020,193,Single-side domain generalization for face anti-spoofing,"https://scholar.google.com/scholar?cluster=16640075466988190139&hl=en&as_sdt=0,48",2,2020
Optical Non-Line-of-Sight Physics-Based 3D Human Pose Estimation,49,cvpr,5,2,2023-06-03 02:44:49.637000,https://github.com/marikoisogawa/OpticalNLOSPose,14,Optical non-line-of-sight physics-based 3d human pose estimation,"https://scholar.google.com/scholar?cluster=806070811754323447&hl=en&as_sdt=0,31",1,2020
IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning,30,cvpr,21,4,2023-06-03 02:44:49.838000,https://github.com/intra3d2019/IntrA,112,Intra: 3d intracranial aneurysm dataset for deep learning,"https://scholar.google.com/scholar?cluster=8654024479108162705&hl=en&as_sdt=0,5",6,2020
Future Video Synthesis With Object Motion Prediction,77,cvpr,4,0,2023-06-03 02:44:50.039000,https://github.com/YueWuHKUST/FutureVideoSynthesis,48,Future video synthesis with object motion prediction,"https://scholar.google.com/scholar?cluster=14647851897458086191&hl=en&as_sdt=0,44",1,2020
X3D: Expanding Architectures for Efficient Video Recognition,624,cvpr,1143,348,2023-06-03 02:44:50.241000,https://github.com/facebookresearch/SlowFast,5685,X3d: Expanding architectures for efficient video recognition,"https://scholar.google.com/scholar?cluster=5426206565542427464&hl=en&as_sdt=0,5",97,2020
Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision,300,cvpr,34,1,2023-06-03 02:44:50.443000,https://github.com/feipan664/IntraDA,261,Unsupervised intra-domain adaptation for semantic segmentation through self-supervision,"https://scholar.google.com/scholar?cluster=12195211217185550534&hl=en&as_sdt=0,33",3,2020
Collaborative Motion Prediction via Neural Motion Message Passing,58,cvpr,14,2,2023-06-03 02:44:50.644000,https://github.com/PhyllisH/NMMP,91,Collaborative motion prediction via neural motion message passing,"https://scholar.google.com/scholar?cluster=12178755193012890116&hl=en&as_sdt=0,33",6,2020
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization,68,cvpr,31,3,2023-06-03 02:44:50.845000,https://github.com/BoChenYS/BPnP,276,End-to-end learnable geometric vision by backpropagating pnp optimization,"https://scholar.google.com/scholar?cluster=8507990282725391495&hl=en&as_sdt=0,33",14,2020
Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction,68,cvpr,2,9,2023-06-03 02:44:51.046000,https://github.com/Bei-Jin/STMFANet,32,Exploring spatial-temporal multi-frequency analysis for high-fidelity and temporal-consistency video prediction,"https://scholar.google.com/scholar?cluster=6502524212340839240&hl=en&as_sdt=0,31",3,2020
Learning Texture Transformer Network for Image Super-Resolution,537,cvpr,116,2,2023-06-03 02:44:51.248000,https://github.com/researchmm/TTSR,716,Learning texture transformer network for image super-resolution,"https://scholar.google.com/scholar?cluster=10878914222672812804&hl=en&as_sdt=0,15",13,2020
PhraseCut: Language-Based Image Segmentation in the Wild,37,cvpr,9,0,2023-06-03 02:44:51.449000,https://github.com/ChenyunWu/PhraseCutDataset,75,Phrasecut: Language-based image segmentation in the wild,"https://scholar.google.com/scholar?cluster=5180455052566322504&hl=en&as_sdt=0,10",7,2020
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud,501,cvpr,108,44,2023-06-03 02:44:51.650000,https://github.com/WeijingShi/Point-GNN,465,Point-gnn: Graph neural network for 3d object detection in a point cloud,"https://scholar.google.com/scholar?cluster=3987152815255001944&hl=en&as_sdt=0,5",21,2020
Distribution-Induced Bidirectional Generative Adversarial Network for Graph Representation Learning,28,cvpr,0,1,2023-06-03 02:44:51.852000,https://github.com/SsGood/DBGAN,20,Distribution-induced bidirectional generative adversarial network for graph representation learning,"https://scholar.google.com/scholar?cluster=16746058838823538205&hl=en&as_sdt=0,5",2,2020
How Much Time Do You Have? Modeling Multi-Duration Saliency,27,cvpr,2,1,2023-06-03 02:44:52.053000,https://github.com/diviz-mit/multiduration-saliency,10,How much time do you have? modeling multi-duration saliency,"https://scholar.google.com/scholar?cluster=5437020792139861684&hl=en&as_sdt=0,5",4,2020
PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames,56,cvpr,6,5,2023-06-03 02:44:52.254000,https://github.com/msraig/pfcnn,18,PFCNN: Convolutional neural networks on 3D surfaces using parallel frames,"https://scholar.google.com/scholar?cluster=12608300723111883888&hl=en&as_sdt=0,5",5,2020
Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval,202,cvpr,126,6,2023-06-03 02:44:52.455000,https://github.com/cvdfoundation/google-landmark,624,Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval,"https://scholar.google.com/scholar?cluster=1649178492675700021&hl=en&as_sdt=0,31",27,2020
Deformable Siamese Attention Networks for Visual Object Tracking,271,cvpr,7,7,2023-06-03 02:44:52.656000,https://github.com/msight-tech/research-siamattn,37,Deformable siamese attention networks for visual object tracking,"https://scholar.google.com/scholar?cluster=1847198933733803218&hl=en&as_sdt=0,5",4,2020
Lightweight Photometric Stereo for Facial Details Recovery,22,cvpr,33,2,2023-06-03 02:44:52.857000,https://github.com/Juyong/FacePSNet,172,Lightweight photometric stereo for facial details recovery,"https://scholar.google.com/scholar?cluster=10672163415987577860&hl=en&as_sdt=0,39",14,2020
Learning Video Object Segmentation From Unlabeled Videos,123,cvpr,12,4,2023-06-03 02:44:53.059000,https://github.com/carrierlxk/MuG,95,Learning video object segmentation from unlabeled videos,"https://scholar.google.com/scholar?cluster=16543814832290560057&hl=en&as_sdt=0,5",15,2020
Pose-Guided Visible Part Matching for Occluded Person ReID,150,cvpr,26,9,2023-06-03 02:44:53.260000,https://github.com/hh23333/PVPM,108,Pose-guided visible part matching for occluded person ReID,"https://scholar.google.com/scholar?cluster=4053237082736280023&hl=en&as_sdt=0,41",6,2020
SAL: Sign Agnostic Learning of Shapes From Raw Data,322,cvpr,8,2,2023-06-03 02:44:53.462000,https://github.com/matanatz/SAL,85,Sal: Sign agnostic learning of shapes from raw data,"https://scholar.google.com/scholar?cluster=14565706118773980850&hl=en&as_sdt=0,33",4,2020
ScopeFlow: Dynamic Scene Scoping for Optical Flow,48,cvpr,9,12,2023-06-03 02:44:53.663000,https://github.com/avirambh/ScopeFlow,92,Scopeflow: Dynamic scene scoping for optical flow,"https://scholar.google.com/scholar?cluster=6785305377119480402&hl=en&as_sdt=0,5",7,2020
Probabilistic Pixel-Adaptive Refinement Networks,19,cvpr,8,0,2023-06-03 02:44:53.866000,https://github.com/visinf/ppac_refinement,76,Probabilistic pixel-adaptive refinement networks,"https://scholar.google.com/scholar?cluster=9018436345334389789&hl=en&as_sdt=0,7",5,2020
WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning,38,cvpr,5,2,2023-06-03 02:44:54.067000,https://github.com/maple-research-lab/WCP,20,Wcp: Worst-case perturbations for semi-supervised deep learning,"https://scholar.google.com/scholar?cluster=3920590861770050206&hl=en&as_sdt=0,5",6,2020
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction,461,cvpr,130,3,2023-06-03 02:44:54.268000,https://github.com/abduallahmohamed/Social-STGCNN,395,Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,"https://scholar.google.com/scholar?cluster=15501716893108943391&hl=en&as_sdt=0,48",14,2020
Filter Grafting for Deep Neural Networks,39,cvpr,4,0,2023-06-03 02:44:54.469000,https://github.com/TencentYoutuResearch/Ensemble-Grafting,13,Filter grafting for deep neural networks,"https://scholar.google.com/scholar?cluster=1970916840131715062&hl=en&as_sdt=0,5",3,2020
Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild,237,cvpr,192,21,2023-06-03 02:44:54.670000,https://github.com/elliottwu/unsup3d,1151,Unsupervised learning of probably symmetric deformable 3d objects from images in the wild,"https://scholar.google.com/scholar?cluster=12180649884525084861&hl=en&as_sdt=0,5",34,2020
Referring Image Segmentation via Cross-Modal Progressive Comprehension,92,cvpr,17,5,2023-06-03 02:44:54.871000,https://github.com/spyflying/CMPC-Refseg,58,Referring image segmentation via cross-modal progressive comprehension,"https://scholar.google.com/scholar?cluster=9048652189395362675&hl=en&as_sdt=0,10",6,2020
SAPIEN: A SimulAted Part-Based Interactive ENvironment,231,cvpr,25,14,2023-06-03 02:44:55.072000,https://github.com/haosulab/SAPIEN-Release,223,Sapien: A simulated part-based interactive environment,"https://scholar.google.com/scholar?cluster=12972187517195741238&hl=en&as_sdt=0,11",12,2020
STAViS: Spatio-Temporal AudioVisual Saliency Network,51,cvpr,7,2,2023-06-03 02:44:55.273000,https://github.com/atsiami/STAViS,36,Stavis: Spatio-temporal audiovisual saliency network,"https://scholar.google.com/scholar?cluster=12986130942954530236&hl=en&as_sdt=0,33",4,2020
PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling,381,cvpr,31,17,2023-06-03 02:44:55.474000,https://github.com/yanx27/PointASNL,232,Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling,"https://scholar.google.com/scholar?cluster=12635046361182606238&hl=en&as_sdt=0,33",10,2020
Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation,161,cvpr,36,4,2023-06-03 02:44:55.676000,https://github.com/changlin31/DNA,221,Block-wisely supervised neural architecture search with knowledge distillation,"https://scholar.google.com/scholar?cluster=17578858583799080531&hl=en&as_sdt=0,5",13,2020
Unsupervised Learning of Intrinsic Structural Representation Points,42,cvpr,15,8,2023-06-03 02:44:55.877000,https://github.com/NolenChen/3DStructurePoints,71,Unsupervised learning of intrinsic structural representation points,"https://scholar.google.com/scholar?cluster=15273418012728728856&hl=en&as_sdt=0,15",7,2020
Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision,45,cvpr,6,0,2023-06-03 02:44:56.090000,https://github.com/gudovskiy/al-fk-self-supervision,29,Deep active learning for biased datasets via fisher kernel self-supervision,"https://scholar.google.com/scholar?cluster=3968455994372368436&hl=en&as_sdt=0,44",2,2020
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection,1094,cvpr,1116,48,2023-06-03 02:44:56.292000,https://github.com/open-mmlab/OpenPCDet,3624,Pv-rcnn: Point-voxel feature set abstraction for 3d object detection,"https://scholar.google.com/scholar?cluster=7760022802456783234&hl=en&as_sdt=0,44",73,2020
DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes,70,cvpr,15,6,2023-06-03 02:44:56.493000,https://github.com/VisualComputingInstitute/dcm-net,106,Dualconvmesh-net: Joint geodesic and euclidean convolutions on 3d meshes,"https://scholar.google.com/scholar?cluster=603126275204072750&hl=en&as_sdt=0,33",11,2020
An Investigation Into the Stochasticity of Batch Whitening,21,cvpr,1,0,2023-06-03 02:44:56.694000,https://github.com/huangleiBuaa/StochasticityBW,8,An investigation into the stochasticity of batch whitening,"https://scholar.google.com/scholar?cluster=10161807881020630814&hl=en&as_sdt=0,44",6,2020
VIBE: Video Inference for Human Body Pose and Shape Estimation,628,cvpr,525,115,2023-06-03 02:44:56.895000,https://github.com/mkocabas/VIBE,2614,Vibe: Video inference for human body pose and shape estimation,"https://scholar.google.com/scholar?cluster=17176071884903916917&hl=en&as_sdt=0,32",67,2020
Graph-Structured Referring Expression Reasoning in the Wild,54,cvpr,15,10,2023-06-03 02:44:57.097000,https://github.com/sibeiyang/sgmn,114,Graph-structured referring expression reasoning in the wild,"https://scholar.google.com/scholar?cluster=17681710446145407741&hl=en&as_sdt=0,47",9,2020
Feature-Metric Registration: A Fast Semi-Supervised Approach for Robust Point Cloud Registration Without Correspondences,142,cvpr,15,7,2023-06-03 02:44:57.298000,https://github.com/XiaoshuiHuang/fmr,115,Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences,"https://scholar.google.com/scholar?cluster=10259529966293747361&hl=en&as_sdt=0,5",6,2020
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction,119,cvpr,62,1,2023-06-03 02:44:57.499000,https://github.com/JunweiLiang/Multiverse,227,The garden of forking paths: Towards multi-future trajectory prediction,"https://scholar.google.com/scholar?cluster=5055296385060977989&hl=en&as_sdt=0,5",10,2020
PolarMask: Single Shot Instance Segmentation With Polar Representation,458,cvpr,156,28,2023-06-03 02:44:57.701000,https://github.com/xieenze/PolarMask,853,Polarmask: Single shot instance segmentation with polar representation,"https://scholar.google.com/scholar?cluster=15407313785451367942&hl=en&as_sdt=0,21",36,2020
Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training,172,cvpr,13,10,2023-06-03 02:44:57.902000,https://github.com/weituo12321/PREVALENT,73,Towards learning a generic agent for vision-and-language navigation via pre-training,"https://scholar.google.com/scholar?cluster=9681104275129231918&hl=en&as_sdt=0,5",6,2020
Active Speakers in Context,53,cvpr,12,7,2023-06-03 02:44:58.103000,https://github.com/fuankarion/active-speakers-context,52,Active speakers in context,"https://scholar.google.com/scholar?cluster=7788879475196732804&hl=en&as_sdt=0,44",7,2020
nuScenes: A Multimodal Dataset for Autonomous Driving,2844,cvpr,556,5,2023-06-03 02:44:58.304000,https://github.com/nutonomy/nuscenes-devkit,1760,nuscenes: A multimodal dataset for autonomous driving,"https://scholar.google.com/scholar?cluster=5001044621269851763&hl=en&as_sdt=0,5",57,2020
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds,1019,cvpr,305,168,2023-06-03 02:44:58.506000,https://github.com/QingyongHu/RandLA-Net,1104,Randla-net: Efficient semantic segmentation of large-scale point clouds,"https://scholar.google.com/scholar?cluster=9271488589585712222&hl=en&as_sdt=0,5",29,2020
Learning to Structure an Image With Few Colors,9,cvpr,10,0,2023-06-03 02:44:58.706000,https://github.com/hou-yz/color_distillation,43,Learning to structure an image with few colors,"https://scholar.google.com/scholar?cluster=16744267671465422085&hl=en&as_sdt=0,5",4,2020
Discriminative Multi-Modality Speech Recognition,68,cvpr,4,3,2023-06-03 02:44:58.907000,https://github.com/JackSyu/Discriminative-Multi-modality-Speech-Recognition,22,Discriminative multi-modality speech recognition,"https://scholar.google.com/scholar?cluster=6034956681907996576&hl=en&as_sdt=0,5",2,2020
Improving Convolutional Networks With Self-Calibrated Convolutions,256,cvpr,68,13,2023-06-03 02:44:59.108000,https://github.com/MCG-NKU/SCNet,342,Improving convolutional networks with self-calibrated convolutions,"https://scholar.google.com/scholar?cluster=13875948799153819939&hl=en&as_sdt=0,5",8,2020
CenterMask: Real-Time Anchor-Free Instance Segmentation,416,cvpr,127,30,2023-06-03 02:44:59.310000,https://github.com/youngwanLEE/CenterMask,744,Centermask: Real-time anchor-free instance segmentation,"https://scholar.google.com/scholar?cluster=9190599054932808738&hl=en&as_sdt=0,33",28,2020
Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences,64,cvpr,2,3,2023-06-03 02:44:59.511000,https://github.com/Guaranteer/VidSTG-Dataset,40,Where does it exist: Spatio-temporal video grounding for multi-form sentences,"https://scholar.google.com/scholar?cluster=11128628025238757345&hl=en&as_sdt=0,31",3,2020
Autolabeling 3D Objects With Differentiable Rendering of SDF Shape Priors,67,cvpr,18,4,2023-06-03 02:44:59.711000,https://github.com/TRI-ML/sdflabel,146,Autolabeling 3d objects with differentiable rendering of sdf shape priors,"https://scholar.google.com/scholar?cluster=10828754482259678272&hl=en&as_sdt=0,38",20,2020
Over-the-Air Adversarial Flickering Attacks Against Video Recognition Networks,32,cvpr,5,23,2023-06-03 13:15:34.423000,https://github.com/anonymous-p/Flickering_Adversarial_Video,10,Over-the-air adversarial flickering attacks against video recognition networks,"https://scholar.google.com/scholar?cluster=1971192817021827614&hl=en&as_sdt=0,5",2,2021
Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network,24,cvpr,13,0,2023-06-03 13:15:34.616000,https://github.com/jnjaby/DISCNet,80,Removing diffraction image artifacts in under-display camera via dynamic skip connection network,"https://scholar.google.com/scholar?cluster=3983776603530738938&hl=en&as_sdt=0,11",4,2021
Pose Recognition With Cascade Transformers,111,cvpr,29,4,2023-06-03 13:15:34.808000,https://github.com/mlpc-ucsd/PRTR,137,Pose recognition with cascade transformers,"https://scholar.google.com/scholar?cluster=14887553364347629414&hl=en&as_sdt=0,44",13,2021
Learning Optical Flow From a Few Matches,37,cvpr,20,3,2023-06-03 13:15:34.999000,https://github.com/zacjiang/scv,162,Learning optical flow from a few matches,"https://scholar.google.com/scholar?cluster=7258193922631074097&hl=en&as_sdt=0,33",5,2021
Invertible Denoising Network: A Light Solution for Real Noise Removal,89,cvpr,30,14,2023-06-03 13:15:35.191000,https://github.com/Yang-Liu1082/InvDN,166,Invertible denoising network: A light solution for real noise removal,"https://scholar.google.com/scholar?cluster=14037959648355873882&hl=en&as_sdt=0,33",6,2021
Body Meshes as Points,42,cvpr,8,4,2023-06-03 13:15:35.382000,https://github.com/jfzhang95/BMP,87,Body meshes as points,"https://scholar.google.com/scholar?cluster=3346955857454005555&hl=en&as_sdt=0,5",13,2021
DeepI2P: Image-to-Point Cloud Registration via Deep Classification,26,cvpr,27,12,2023-06-03 13:15:35.573000,https://github.com/lijx10/DeepI2P,155,DeepI2P: Image-to-point cloud registration via deep classification,"https://scholar.google.com/scholar?cluster=7774915630757621107&hl=en&as_sdt=0,5",7,2021
Contrastive Learning for Compact Single Image Dehazing,267,cvpr,41,29,2023-06-03 13:15:35.764000,https://github.com/GlassyWu/AECR-Net,281,Contrastive learning for compact single image dehazing,"https://scholar.google.com/scholar?cluster=17020400068555293189&hl=en&as_sdt=0,11",11,2021
Image Inpainting With External-Internal Learning and Monochromic Bottleneck,39,cvpr,17,2,2023-06-03 13:15:35.956000,https://github.com/Tengfei-Wang/external-internal-inpainting,102,Image inpainting with external-internal learning and monochromic bottleneck,"https://scholar.google.com/scholar?cluster=14224572370234853034&hl=en&as_sdt=0,5",7,2021
Human-Like Controllable Image Captioning With Verb-Specific Semantic Roles,47,cvpr,5,5,2023-06-03 13:15:36.147000,https://github.com/mad-red/VSR-guided-CIC,34,Human-like controllable image captioning with verb-specific semantic roles,"https://scholar.google.com/scholar?cluster=12597473402434340086&hl=en&as_sdt=0,5",3,2021
Camouflaged Object Segmentation With Distraction Mining,134,cvpr,18,2,2023-06-03 13:15:36.338000,https://github.com/Mhaiyang/CVPR2021_PFNet,42,Camouflaged object segmentation with distraction mining,"https://scholar.google.com/scholar?cluster=7630580972527672421&hl=en&as_sdt=0,5",3,2021
Counterfactual Zero-Shot and Open-Set Visual Recognition,111,cvpr,26,11,2023-06-03 13:15:36.530000,https://github.com/yue-zhongqi/gcm-cf,144,Counterfactual zero-shot and open-set visual recognition,"https://scholar.google.com/scholar?cluster=10738089830275739569&hl=en&as_sdt=0,10",4,2021
Enhancing the Transferability of Adversarial Attacks Through Variance Tuning,122,cvpr,17,1,2023-06-03 13:15:36.722000,https://github.com/JHL-HUST/VT,63,Enhancing the transferability of adversarial attacks through variance tuning,"https://scholar.google.com/scholar?cluster=11385140174337165659&hl=en&as_sdt=0,10",2,2021
Patch2Pix: Epipolar-Guided Pixel-Level Correspondences,85,cvpr,23,6,2023-06-03 13:15:36.912000,https://github.com/GrumpyZhou/patch2pix,219,Patch2pix: Epipolar-guided pixel-level correspondences,"https://scholar.google.com/scholar?cluster=13802913128122909727&hl=en&as_sdt=0,14",11,2021
In the Light of Feature Distributions: Moment Matching for Neural Style Transfer,33,cvpr,9,1,2023-06-03 13:15:37.104000,https://github.com/D1noFuzi/cmd_styletransfer,52,In the light of feature distributions: moment matching for neural style transfer,"https://scholar.google.com/scholar?cluster=7113714540707615178&hl=en&as_sdt=0,5",2,2021
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition,171,cvpr,66,5,2023-06-03 13:15:37.295000,https://github.com/QVPR/Patch-NetVLAD,410,Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition,"https://scholar.google.com/scholar?cluster=4050802902199111696&hl=en&as_sdt=0,44",21,2021
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification,46,cvpr,3,2,2023-06-03 13:15:37.486000,https://github.com/blue-blue272/BiCnet-TKS,37,Bicnet-tks: Learning efficient spatial-temporal representation for video person re-identification,"https://scholar.google.com/scholar?cluster=16686470908254801103&hl=en&as_sdt=0,23",1,2021
Harmonious Semantic Line Detection via Maximal Weight Clique Selection,5,cvpr,0,4,2023-06-03 13:15:37.677000,https://github.com/dongkwonjin/Semantic-Line-MWCS,16,Harmonious semantic line detection via maximal weight clique selection,"https://scholar.google.com/scholar?cluster=7226046490518620730&hl=en&as_sdt=0,5",1,2021
Holistic 3D Scene Understanding From a Single Image With Implicit Representation,37,cvpr,26,7,2023-06-03 13:15:37.869000,https://github.com/chengzhag/Implicit3DUnderstanding,163,Holistic 3d scene understanding from a single image with implicit representation,"https://scholar.google.com/scholar?cluster=11414993971770780395&hl=en&as_sdt=0,44",6,2021
Progressive Temporal Feature Alignment Network for Video Inpainting,29,cvpr,11,9,2023-06-03 13:15:38.060000,https://github.com/MaureenZOU/TSAM,83,Progressive temporal feature alignment network for video inpainting,"https://scholar.google.com/scholar?cluster=8405470385786131986&hl=en&as_sdt=0,36",4,2021
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization,34,cvpr,5,1,2023-06-03 13:15:38.252000,https://github.com/huangjh-pub/multibody-sync,54,Multibodysync: Multi-body segmentation and motion estimation via 3d scan synchronization,"https://scholar.google.com/scholar?cluster=15305667257897007287&hl=en&as_sdt=0,5",4,2021
Dual Attention Guided Gaze Target Detection in the Wild,31,cvpr,1,4,2023-06-03 13:15:38.443000,https://github.com/Crystal2333/DAM,16,Dual attention guided gaze target detection in the wild,"https://scholar.google.com/scholar?cluster=10601375019192102383&hl=en&as_sdt=0,5",9,2021
Zero-Shot Instance Segmentation,29,cvpr,13,3,2023-06-03 13:15:38.635000,https://github.com/zhengye1995/Zero-shot-Instance-Segmentation,91,Zero-shot instance segmentation,"https://scholar.google.com/scholar?cluster=5231595860077685933&hl=en&as_sdt=0,5",3,2021
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms,61,cvpr,26,9,2023-06-03 13:15:38.832000,https://github.com/mahmoudnafifi/HistoGAN,229,Histogan: Controlling colors of gan-generated and real images via color histograms,"https://scholar.google.com/scholar?cluster=4600507092960456581&hl=en&as_sdt=0,26",20,2021
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling,80,cvpr,18,0,2023-06-03 13:15:39.042000,https://github.com/jiwei0921/MRNet,84,Learning calibrated medical image segmentation via multi-rater agreement modeling,"https://scholar.google.com/scholar?cluster=5434628992931585830&hl=en&as_sdt=0,33",2,2021
Global Transport for Fluid Reconstruction With Learned Self-Supervision,8,cvpr,4,1,2023-06-03 13:15:39.233000,https://github.com/tum-pbs/Global-Flow-Transport,17,Global transport for fluid reconstruction with learned self-supervision,"https://scholar.google.com/scholar?cluster=8005056883694230577&hl=en&as_sdt=0,21",2,2021
Look Before You Leap: Learning Landmark Features for One-Stage Visual Grounding,33,cvpr,9,2,2023-06-03 13:15:39.425000,https://github.com/svip-lab/LBYLNet,46,Look before you leap: Learning landmark features for one-stage visual grounding,"https://scholar.google.com/scholar?cluster=12785412923589559702&hl=en&as_sdt=0,11",3,2021
Probabilistic Model Distillation for Semantic Correspondence,15,cvpr,3,0,2023-06-03 13:15:39.616000,https://github.com/fanyang587/PMD,8,Probabilistic model distillation for semantic correspondence,"https://scholar.google.com/scholar?cluster=7338013440094189551&hl=en&as_sdt=0,5",2,2021
Pulsar: Efficient Sphere-Based Neural Rendering,59,cvpr,1138,188,2023-06-03 13:15:39.808000,https://github.com/facebookresearch/pytorch3d,7340,Pulsar: Efficient sphere-based neural rendering,"https://scholar.google.com/scholar?cluster=12850046466040235522&hl=en&as_sdt=0,14",139,2021
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction,36,cvpr,27,7,2023-06-03 13:15:40,https://github.com/yinyunie/RfDNet,178,Rfd-net: Point scene understanding by semantic instance reconstruction,"https://scholar.google.com/scholar?cluster=10783127167522609962&hl=en&as_sdt=0,33",7,2021
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network,18,cvpr,1,1,2023-06-03 13:15:40.191000,https://github.com/xdxuyang/Self-Supervised-Semantic-Alignment-for-Graph-Convolution-Network,10,SelfSAGCN: Self-supervised semantic alignment for graph convolution network,"https://scholar.google.com/scholar?cluster=13594420274118500841&hl=en&as_sdt=0,24",1,2021
"Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification",42,cvpr,6,0,2023-06-03 13:15:40.382000,https://github.com/fei960922/GPointNet,28,"Generative pointnet: Deep energy-based learning on unordered point sets for 3d generation, reconstruction and classification","https://scholar.google.com/scholar?cluster=3673274025271862236&hl=en&as_sdt=0,5",5,2021
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization,118,cvpr,31,25,2023-06-03 13:15:40.574000,https://github.com/TencentYoutuResearch/ActionDetection-AFSD,159,Learning salient boundary feature for anchor-free temporal action localization,"https://scholar.google.com/scholar?cluster=8767327019590446489&hl=en&as_sdt=0,14",4,2021
VideoMoCo: Contrastive Video Representation Learning With Temporally Adversarial Examples,154,cvpr,17,9,2023-06-03 13:15:40.766000,https://github.com/tinapan-pt/VideoMoCo,129,Videomoco: Contrastive video representation learning with temporally adversarial examples,"https://scholar.google.com/scholar?cluster=11857838950674173680&hl=en&as_sdt=0,33",11,2021
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect,37,cvpr,1,0,2023-06-03 13:15:40.958000,https://github.com/EarlMadSec/invis-perturbations,8,Invisible perturbations: Physical adversarial examples exploiting the rolling shutter effect,"https://scholar.google.com/scholar?cluster=7374063426812464061&hl=en&as_sdt=0,5",2,2021
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation,55,cvpr,10,11,2023-06-03 13:15:41.150000,https://github.com/chaneyddtt/UDA-Animal-Pose,91,From synthetic to real: Unsupervised domain adaptation for animal pose estimation,"https://scholar.google.com/scholar?cluster=8687446482620476382&hl=en&as_sdt=0,47",5,2021
PointDSC: Robust Point Cloud Registration Using Deep Spatial Consistency,105,cvpr,30,5,2023-06-03 13:15:41.341000,https://github.com/XuyangBai/PointDSC,167,Pointdsc: Robust point cloud registration using deep spatial consistency,"https://scholar.google.com/scholar?cluster=16308411012723071862&hl=en&as_sdt=0,5",7,2021
Wasserstein Barycenter for Multi-Source Domain Adaptation,15,cvpr,5,0,2023-06-03 13:15:41.532000,https://github.com/eddardd/WBTransport,18,Wasserstein barycenter for multi-source domain adaptation,"https://scholar.google.com/scholar?cluster=17028916271918927829&hl=en&as_sdt=0,5",2,2021
Mask Guided Matting via Progressive Refinement Network,61,cvpr,41,24,2023-06-03 13:15:41.723000,https://github.com/yucornetto/MGMatting,263,Mask guided matting via progressive refinement network,"https://scholar.google.com/scholar?cluster=8590826732366464123&hl=en&as_sdt=0,5",26,2021
Calibrated RGB-D Salient Object Detection,121,cvpr,5,1,2023-06-03 13:15:41.915000,https://github.com/jiwei0921/DCF,28,Calibrated RGB-D salient object detection,"https://scholar.google.com/scholar?cluster=3242411088032394204&hl=en&as_sdt=0,33",2,2021
Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model,11,cvpr,2,0,2023-06-03 13:15:42.106000,https://github.com/julilien/PLDepth,11,Monocular depth estimation via listwise ranking using the plackett-luce model,"https://scholar.google.com/scholar?cluster=6351741257494355738&hl=en&as_sdt=0,50",4,2021
Deep Gradient Projection Networks for Pan-sharpening,69,cvpr,5,1,2023-06-03 13:15:42.297000,https://github.com/xsxjtu/GPPNN,17,Deep gradient projection networks for pan-sharpening,"https://scholar.google.com/scholar?cluster=11529217582036454415&hl=en&as_sdt=0,14",2,2021
4D Panoptic LiDAR Segmentation,47,cvpr,18,0,2023-06-03 13:15:42.488000,https://github.com/mehmetaygun/4d-pls,122,4d panoptic lidar segmentation,"https://scholar.google.com/scholar?cluster=17594560123506208573&hl=en&as_sdt=0,5",9,2021
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking,17,cvpr,4,1,2023-06-03 13:15:42.680000,https://github.com/researchmm/NEAS,19,One-shot neural ensemble architecture search by diversity-guided search space shrinking,"https://scholar.google.com/scholar?cluster=12076680641965090429&hl=en&as_sdt=0,33",2,2021
OSTeC: One-Shot Texture Completion,35,cvpr,28,10,2023-06-03 13:15:42.871000,https://github.com/barisgecer/OSTeC,161,Ostec: One-shot texture completion,"https://scholar.google.com/scholar?cluster=5464854048494867761&hl=en&as_sdt=0,33",11,2021
TDN: Temporal Difference Networks for Efficient Action Recognition,208,cvpr,54,5,2023-06-03 13:15:43.063000,https://github.com/MCG-NJU/TDN,343,Tdn: Temporal difference networks for efficient action recognition,"https://scholar.google.com/scholar?cluster=1977279486299235361&hl=en&as_sdt=0,23",10,2021
Disentangled Cycle Consistency for Highly-Realistic Virtual Try-On,49,cvpr,22,8,2023-06-03 13:15:43.256000,https://github.com/ChongjianGE/DCTON,97,Disentangled cycle consistency for highly-realistic virtual try-on,"https://scholar.google.com/scholar?cluster=113728232790763583&hl=en&as_sdt=0,31",10,2021
Robust Representation Learning With Feedback for Single Image Deraining,58,cvpr,4,1,2023-06-03 13:15:43.447000,https://github.com/LI-Hao-SJTU/DerainRLNet,21,Robust representation learning with feedback for single image deraining,"https://scholar.google.com/scholar?cluster=12649381605129869613&hl=en&as_sdt=0,5",3,2021
Natural Adversarial Examples,728,cvpr,46,5,2023-06-03 13:15:43.638000,https://github.com/hendrycks/natural-adv-examples,531,Natural adversarial examples,"https://scholar.google.com/scholar?cluster=1426370300375211168&hl=en&as_sdt=0,5",11,2021
LiBRe: A Practical Bayesian Approach to Adversarial Detection,26,cvpr,3,1,2023-06-03 13:15:43.830000,https://github.com/thudzj/ScalableBDL,28,Libre: A practical bayesian approach to adversarial detection,"https://scholar.google.com/scholar?cluster=1702209423642411203&hl=en&as_sdt=0,5",2,2021
AdaBins: Depth Estimation Using Adaptive Bins,377,cvpr,146,34,2023-06-03 13:15:44.035000,https://github.com/shariqfarooq123/AdaBins,602,Adabins: Depth estimation using adaptive bins,"https://scholar.google.com/scholar?cluster=11112701312974631954&hl=en&as_sdt=0,5",14,2021
Mirror3D: Depth Refinement for Mirror Surfaces,9,cvpr,3,0,2023-06-03 13:15:44.226000,https://github.com/3dlg-hcvc/mirror3d,32,Mirror3d: Depth refinement for mirror surfaces,"https://scholar.google.com/scholar?cluster=12280701474597360801&hl=en&as_sdt=0,10",6,2021
ArtCoder: An End-to-End Method for Generating Scanning-Robust Stylized QR Codes,3,cvpr,12,1,2023-06-03 13:15:44.418000,https://github.com/SwordHolderSH/ArtCoder,53,Artcoder: an end-to-end method for generating scanning-robust stylized QR codes,"https://scholar.google.com/scholar?cluster=18061832072642751316&hl=en&as_sdt=0,44",3,2021
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning,270,cvpr,40,21,2023-06-03 13:15:44.610000,https://github.com/zdaxie/PixPro,307,Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning,"https://scholar.google.com/scholar?cluster=13904438580236939165&hl=en&as_sdt=0,15",11,2021
CorrNet3D: Unsupervised End-to-End Learning of Dense Correspondence for 3D Point Clouds,36,cvpr,2,5,2023-06-03 13:15:44.800000,https://github.com/ZENGYIMING-EAMON/CorrNet3D,38,CorrNet3D: Unsupervised end-to-end learning of dense correspondence for 3D point clouds,"https://scholar.google.com/scholar?cluster=12120661589110257282&hl=en&as_sdt=0,5",2,2021
Exploring Simple Siamese Representation Learning,2191,cvpr,164,12,2023-06-03 13:15:44.992000,https://github.com/facebookresearch/simsiam,952,Exploring simple siamese representation learning,"https://scholar.google.com/scholar?cluster=14102911635221168501&hl=en&as_sdt=0,11",12,2021
Learning To Count Everything,39,cvpr,53,7,2023-06-03 13:15:45.183000,https://github.com/cvlab-stonybrook/LearningToCountEverything,238,Learning to count everything,"https://scholar.google.com/scholar?cluster=17975781940861778585&hl=en&as_sdt=0,5",11,2021
CAMERAS: Enhanced Resolution and Sanity Preserving Class Activation Mapping for Image Saliency,36,cvpr,12,1,2023-06-03 13:15:45.374000,https://github.com/VisMIL/CAMERAS,44,CAMERAS: Enhanced resolution and sanity preserving class activation mapping for image saliency,"https://scholar.google.com/scholar?cluster=13540735522001981759&hl=en&as_sdt=0,5",3,2021
Normal Integration via Inverse Plane Fitting With Minimum Point-to-Plane Distance,11,cvpr,4,0,2023-06-03 13:15:45.565000,https://github.com/hoshino042/NormalIntegration,28,Normal integration via inverse plane fitting with minimum point-to-plane distance,"https://scholar.google.com/scholar?cluster=7866083634746250024&hl=en&as_sdt=0,11",2,2021
SSN: Soft Shadow Network for Image Compositing,11,cvpr,1,3,2023-06-03 13:15:45.757000,https://github.com/ShengCN/SSN_SoftShadowNet,11,SSN: Soft shadow network for image compositing,"https://scholar.google.com/scholar?cluster=14557357004981423877&hl=en&as_sdt=0,41",3,2021
Coarse-Fine Networks for Temporal Activity Detection in Videos,26,cvpr,7,1,2023-06-03 13:15:45.949000,https://github.com/kkahatapitiya/Coarse-Fine-Networks,51,Coarse-fine networks for temporal activity detection in videos,"https://scholar.google.com/scholar?cluster=11401655372406151148&hl=en&as_sdt=0,14",2,2021
VinVL: Revisiting Visual Representations in Vision-Language Models,486,cvpr,21,28,2023-06-03 13:15:46.140000,https://github.com/pzzhang/VinVL,327,Vinvl: Revisiting visual representations in vision-language models,"https://scholar.google.com/scholar?cluster=1213049958817267584&hl=en&as_sdt=0,5",9,2021
MongeNet: Efficient Sampler for Geometric Deep Learning,1,cvpr,0,0,2023-06-03 13:15:46.331000,https://github.com/lebrat/MongeNet,10,MongeNet: Efficient Sampler for Geometric Deep Learning,"https://scholar.google.com/scholar?cluster=1235305287797759193&hl=en&as_sdt=0,21",5,2021
SurFree: A Fast Surrogate-Free Black-Box Attack,45,cvpr,7,4,2023-06-03 13:15:46.523000,https://github.com/t-maho/SurFree,35,Surfree: a fast surrogate-free black-box attack,"https://scholar.google.com/scholar?cluster=4516658040961875887&hl=en&as_sdt=0,19",2,2021
Visualizing Adapted Knowledge in Domain Transfer,38,cvpr,14,0,2023-06-03 13:15:46.714000,https://github.com/hou-yz/DA_visualization,85,Visualizing adapted knowledge in domain transfer,"https://scholar.google.com/scholar?cluster=8771147707141610474&hl=en&as_sdt=0,5",3,2021
CondenseNet V2: Sparse Feature Reactivation for Deep Networks,30,cvpr,19,0,2023-06-03 13:15:46.905000,https://github.com/jianghaojun/CondenseNetV2,76,Condensenet v2: Sparse feature reactivation for deep networks,"https://scholar.google.com/scholar?cluster=13410483781005276408&hl=en&as_sdt=0,11",2,2021
Robust and Accurate Object Detection via Adversarial Learning,47,cvpr,1452,146,2023-06-03 13:15:47.096000,https://github.com/google/automl,5885,Robust and accurate object detection via adversarial learning,"https://scholar.google.com/scholar?cluster=8271637616216749842&hl=en&as_sdt=0,36",158,2021
Beyond Image to Depth: Improving Depth Prediction Using Echoes,28,cvpr,12,1,2023-06-03 13:15:47.288000,https://github.com/krantiparida/beyond-image-to-depth,36,Beyond image to depth: Improving depth prediction using echoes,"https://scholar.google.com/scholar?cluster=17991952600453670438&hl=en&as_sdt=0,5",6,2021
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes With Biharmonic Coordinates,9,cvpr,5,1,2023-06-03 13:15:47.480000,https://github.com/Colin97/DeepMetaHandles,75,Deepmetahandles: Learning deformation meta-handles of 3d meshes with biharmonic coordinates,"https://scholar.google.com/scholar?cluster=13848121966732049005&hl=en&as_sdt=0,5",6,2021
Layout-Guided Novel View Synthesis From a Single Indoor Panorama,8,cvpr,1,3,2023-06-03 13:15:47.671000,https://github.com/bluestyle97/PNVS,30,Layout-guided novel view synthesis from a single indoor panorama,"https://scholar.google.com/scholar?cluster=14997634611461665441&hl=en&as_sdt=0,33",7,2021
Prototype-Supervised Adversarial Network for Targeted Attack of Deep Hashing,24,cvpr,15,0,2023-06-03 13:15:47.862000,https://github.com/xunguangwang/ProS-GAN,37,Prototype-supervised adversarial network for targeted attack of deep hashing,"https://scholar.google.com/scholar?cluster=11194317984062930130&hl=en&as_sdt=0,47",1,2021
Sequential Graph Convolutional Network for Active Learning,58,cvpr,17,5,2023-06-03 13:15:48.054000,https://github.com/razvancaramalau/Sequential-GCN-for-Active-Learning,47,Sequential graph convolutional network for active learning,"https://scholar.google.com/scholar?cluster=4042132635090650658&hl=en&as_sdt=0,33",2,2021
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection,123,cvpr,26,3,2023-06-03 13:15:48.247000,https://github.com/fjchange/MIST_VAD,99,Mist: Multiple instance self-training framework for video anomaly detection,"https://scholar.google.com/scholar?cluster=12776159988616565203&hl=en&as_sdt=0,41",5,2021
Progressive Semantic Segmentation,44,cvpr,8,4,2023-06-03 13:15:48.438000,https://github.com/VinAIResearch/MagNet,107,Progressive semantic segmentation,"https://scholar.google.com/scholar?cluster=8312905002752442421&hl=en&as_sdt=0,31",6,2021
Bottom-Up Human Pose Estimation via Disentangled Keypoint Regression,116,cvpr,76,32,2023-06-03 13:15:48.631000,https://github.com/HRNet/DEKR,384,Bottom-up human pose estimation via disentangled keypoint regression,"https://scholar.google.com/scholar?cluster=11348760748613165934&hl=en&as_sdt=0,50",13,2021
Learning the Best Pooling Strategy for Visual Semantic Embedding,107,cvpr,16,1,2023-06-03 13:15:48.824000,https://github.com/woodfrog/vse_infty,127,Learning the best pooling strategy for visual semantic embedding,"https://scholar.google.com/scholar?cluster=9446201351656979528&hl=en&as_sdt=0,47",4,2021
Deep Two-View Structure-From-Motion Revisited,24,cvpr,10,6,2023-06-03 13:15:49.040000,https://github.com/jytime/Deep-SfM-Revisited,153,Deep two-view structure-from-motion revisited,"https://scholar.google.com/scholar?cluster=677162948445926784&hl=en&as_sdt=0,47",9,2021
CoMoGAN: Continuous Model-Guided Image-to-Image Translation,26,cvpr,13,2,2023-06-03 13:15:49.231000,https://github.com/cv-rits/CoMoGAN,171,CoMoGAN: continuous model-guided image-to-image translation,"https://scholar.google.com/scholar?cluster=7891088022641417036&hl=en&as_sdt=0,5",8,2021
Regressive Domain Adaptation for Unsupervised Keypoint Detection,32,cvpr,0,0,2023-06-03 13:15:49.422000,https://github.com/thuml/Regressive-Domain-Adaptation-for-Unsupervised-Keypoint-Detection,3,Regressive domain adaptation for unsupervised keypoint detection,"https://scholar.google.com/scholar?cluster=6049330673110118719&hl=en&as_sdt=0,5",4,2021
LASR: Learning Articulated Shape Reconstruction From a Monocular Video,56,cvpr,21,7,2023-06-03 13:15:49.614000,https://github.com/google/lasr,161,Lasr: Learning articulated shape reconstruction from a monocular video,"https://scholar.google.com/scholar?cluster=11712030734106144818&hl=en&as_sdt=0,3",10,2021
Self-Supervised Video Hashing via Bidirectional Transformers,19,cvpr,6,0,2023-06-03 13:15:49.805000,https://github.com/Lily1994/BTH,28,Self-supervised video hashing via bidirectional transformers,"https://scholar.google.com/scholar?cluster=767073233350909973&hl=en&as_sdt=0,38",1,2021
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding,40,cvpr,19,7,2023-06-03 13:15:49.998000,https://github.com/Gorilla-Lab-SCUT/AffordanceNet,53,3d affordancenet: A benchmark for visual object affordance understanding,"https://scholar.google.com/scholar?cluster=18000757458595963631&hl=en&as_sdt=0,5",6,2021
Safe Local Motion Planning With Self-Supervised Freespace Forecasting,16,cvpr,15,4,2023-06-03 13:15:50.190000,https://github.com/peiyunh/ff,95,Safe local motion planning with self-supervised freespace forecasting,"https://scholar.google.com/scholar?cluster=13817545767696206441&hl=en&as_sdt=0,14",4,2021
Deep Implicit Templates for 3D Shape Representation,68,cvpr,18,4,2023-06-03 13:15:50.381000,https://github.com/ZhengZerong/DeepImplicitTemplates,148,Deep implicit templates for 3d shape representation,"https://scholar.google.com/scholar?cluster=17287580575195685138&hl=en&as_sdt=0,15",14,2021
Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration,48,cvpr,60,8,2023-06-03 13:15:50.573000,https://github.com/SeanChenxy/HandMesh,278,Camera-space hand mesh recovery via semantic aggregation and adaptive 2d-1d registration,"https://scholar.google.com/scholar?cluster=6668605664947680509&hl=en&as_sdt=0,39",10,2021
Semi-Supervised Semantic Segmentation With Cross Pseudo Supervision,320,cvpr,66,17,2023-06-03 13:15:50.775000,https://github.com/charlesCXK/TorchSemiSeg,425,Semi-supervised semantic segmentation with cross pseudo supervision,"https://scholar.google.com/scholar?cluster=10749248141312892990&hl=en&as_sdt=0,5",7,2021
Can Audio-Visual Integration Strengthen Robustness Under Multimodal Attacks?,19,cvpr,1,0,2023-06-03 13:15:50.967000,https://github.com/YapengTian/AV-Robustness-CVPR21,22,Can audio-visual integration strengthen robustness under multimodal attacks?,"https://scholar.google.com/scholar?cluster=5649193960055691180&hl=en&as_sdt=0,14",3,2021
Deep Lucas-Kanade Homography for Multimodal Image Alignment,21,cvpr,25,1,2023-06-03 13:15:51.159000,https://github.com/placeforyiming/CVPR21-Deep-Lucas-Kanade-Homography,95,Deep lucas-kanade homography for multimodal image alignment,"https://scholar.google.com/scholar?cluster=2059111033949296544&hl=en&as_sdt=0,45",2,2021
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain Decomposition,10,cvpr,17,2,2023-06-03 13:15:51.351000,https://github.com/hjwdzh/DeepLM,149,Deeplm: Large-scale nonlinear least squares on deep learning frameworks using stochastic domain decomposition,"https://scholar.google.com/scholar?cluster=12633562592566231920&hl=en&as_sdt=0,33",8,2021
DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation,28,cvpr,4,1,2023-06-03 13:15:51.542000,https://github.com/Seung-Hun-Lee/DRANet,18,Dranet: Disentangling representation and adaptation networks for unsupervised cross-domain adaptation,"https://scholar.google.com/scholar?cluster=6969833385245966621&hl=en&as_sdt=0,18",1,2021
Ranking Neural Checkpoints,17,cvpr,7286,1013,2023-06-03 13:15:51.734000,https://github.com/google-research/google-research,29546,Ranking neural checkpoints,"https://scholar.google.com/scholar?cluster=3088841370184707243&hl=en&as_sdt=0,23",726,2021
clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation,92,cvpr,22,4,2023-06-03 13:15:51.925000,https://github.com/jocpae/clDice,126,clDice-a novel topology-preserving loss function for tubular structure segmentation,"https://scholar.google.com/scholar?cluster=17149814151594930474&hl=en&as_sdt=0,5",3,2021
Background Splitting: Finding Rare Classes in a Sea of Background,6,cvpr,0,0,2023-06-03 13:15:52.116000,https://github.com/fpoms/background-splitting,3,Background splitting: Finding rare classes in a sea of background,"https://scholar.google.com/scholar?cluster=11809603228299738022&hl=en&as_sdt=0,34",3,2021
Open-Vocabulary Object Detection Using Captions,139,cvpr,22,6,2023-06-03 13:15:52.308000,https://github.com/alirezazareian/ovr-cnn,166,Open-vocabulary object detection using captions,"https://scholar.google.com/scholar?cluster=5639294152401428048&hl=en&as_sdt=0,5",5,2021
Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target Generation,24,cvpr,2,2,2023-06-03 13:15:52.499000,https://github.com/yhjo09/AdaTarget,28,Tackling the ill-posedness of super-resolution through adaptive target generation,"https://scholar.google.com/scholar?cluster=7049541202600827172&hl=en&as_sdt=0,5",1,2021
Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing,21,cvpr,1,0,2023-06-03 13:15:52.690000,https://github.com/MetaVQA/MetaVQA,8,Perception matters: Detecting perception failures of vqa models using metamorphic testing,"https://scholar.google.com/scholar?cluster=7526712713500067804&hl=en&as_sdt=0,5",2,2021
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions,65,cvpr,10,11,2023-06-03 13:15:52.882000,https://github.com/doc-doc/NExT-QA,56,Next-qa: Next phase of question-answering to explaining temporal actions,"https://scholar.google.com/scholar?cluster=11207932043863275607&hl=en&as_sdt=0,5",2,2021
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization,55,cvpr,5,4,2023-06-03 13:15:53.073000,https://github.com/Panxjia/SPA_CVPR2021,45,Unveiling the potential of structure preserving for weakly supervised object localization,"https://scholar.google.com/scholar?cluster=3260854309875570131&hl=en&as_sdt=0,33",3,2021
Generative Classifiers as a Basis for Trustworthy Image Classification,25,cvpr,1,0,2023-06-03 13:15:53.264000,https://github.com/VLL-HD/trustworthy_GCs,13,Generative classifiers as a basis for trustworthy image classification,"https://scholar.google.com/scholar?cluster=10855361604109918004&hl=en&as_sdt=0,47",2,2021
Dual-Stream Multiple Instance Learning Network for Whole Slide Image Classification With Self-Supervised Contrastive Learning,231,cvpr,71,44,2023-06-03 13:15:53.455000,https://github.com/binli123/dsmil-wsi,219,Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning,"https://scholar.google.com/scholar?cluster=15982402023145475497&hl=en&as_sdt=0,5",0,2021
Faster Meta Update Strategy for Noise-Robust Deep Learning,42,cvpr,0,2,2023-06-03 13:15:53.646000,https://github.com/youjiangxu/FaMUS,25,Faster meta update strategy for noise-robust deep learning,"https://scholar.google.com/scholar?cluster=7111457941814893962&hl=en&as_sdt=0,33",7,2021
Spatial Feature Calibration and Temporal Fusion for Effective One-Stage Video Instance Segmentation,46,cvpr,7,3,2023-06-03 13:15:53.844000,https://github.com/MinghanLi/STMask,34,Spatial feature calibration and temporal fusion for effective one-stage video instance segmentation,"https://scholar.google.com/scholar?cluster=14636005859389180477&hl=en&as_sdt=0,33",4,2021
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks,35,cvpr,8,2,2023-06-03 13:15:54.048000,https://github.com/AIprogrammer/AdvMix,28,When human pose estimation meets robustness: Adversarial algorithms and benchmarks,"https://scholar.google.com/scholar?cluster=13416935760826741532&hl=en&as_sdt=0,35",4,2021
ContactOpt: Optimizing Contact To Improve Grasps,56,cvpr,11,5,2023-06-03 13:15:54.240000,https://github.com/facebookresearch/ContactOpt,52,Contactopt: Optimizing contact to improve grasps,"https://scholar.google.com/scholar?cluster=13104249403147976503&hl=en&as_sdt=0,5",8,2021
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association,14,cvpr,4,1,2023-06-03 13:15:54.431000,https://github.com/KID-7391/seeking-the-shape-of-sound,15,Seeking the shape of sound: An adaptive framework for learning voice-face association,"https://scholar.google.com/scholar?cluster=7403329124013493851&hl=en&as_sdt=0,23",2,2021
Structure-Aware Face Clustering on a Large-Scale Graph With 107 Nodes,21,cvpr,15,3,2023-06-03 13:15:54.622000,https://github.com/sstzal/STAR-FC,92,Structure-aware face clustering on a large-scale graph with 107 nodes,"https://scholar.google.com/scholar?cluster=4463882150445151384&hl=en&as_sdt=0,5",4,2021
The Spatially-Correlative Loss for Various Image Translation Tasks,66,cvpr,10,5,2023-06-03 13:15:54.816000,https://github.com/lyndonzheng/F-LSeSim,93,The spatially-correlative loss for various image translation tasks,"https://scholar.google.com/scholar?cluster=6306284278828725649&hl=en&as_sdt=0,14",8,2021
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification,45,cvpr,4,2,2023-06-03 13:15:55.009000,https://github.com/onuriel/PermutedAdaIN,37,Permuted adain: Reducing the bias towards global statistics in image classification,"https://scholar.google.com/scholar?cluster=1412952333184497967&hl=en&as_sdt=0,5",1,2021
Depth From Camera Motion and Object Detection,15,cvpr,31,0,2023-06-03 13:15:55.200000,https://github.com/griffbr/ODMD,176,Depth from camera motion and object detection,"https://scholar.google.com/scholar?cluster=1322192234030050947&hl=en&as_sdt=0,33",5,2021
Task Programming: Learning Data Efficient Behavior Representations,33,cvpr,7,0,2023-06-03 13:15:55.391000,https://github.com/neuroethology/TREBA,66,Task programming: Learning data efficient behavior representations,"https://scholar.google.com/scholar?cluster=9615784878132303296&hl=en&as_sdt=0,11",3,2021
NeRD: Neural 3D Reflection Symmetry Detector,13,cvpr,20,0,2023-06-03 13:15:55.585000,https://github.com/zhou13/nerd,92,NeRD: Neural 3d reflection symmetry detector,"https://scholar.google.com/scholar?cluster=16779121922618028359&hl=en&as_sdt=0,5",7,2021
PPR10K: A Large-Scale Portrait Photo Retouching Dataset With Human-Region Mask and Group-Level Consistency,20,cvpr,18,6,2023-06-03 13:15:55.777000,https://github.com/csjliang/PPR10K,202,Ppr10k: A large-scale portrait photo retouching dataset with human-region mask and group-level consistency,"https://scholar.google.com/scholar?cluster=10349099180609975418&hl=en&as_sdt=0,5",7,2021
Self-Supervised Pillar Motion Learning for Autonomous Driving,40,cvpr,9,10,2023-06-03 13:15:55.969000,https://github.com/qcraftai/pillar-motion,107,Self-supervised pillar motion learning for autonomous driving,"https://scholar.google.com/scholar?cluster=17858842091607083280&hl=en&as_sdt=0,34",9,2021
CoLA: Weakly-Supervised Temporal Action Localization With Snippet Contrastive Learning,81,cvpr,7,3,2023-06-03 13:15:56.161000,https://github.com/zhang-can/CoLA,53,Cola: Weakly-supervised temporal action localization with snippet contrastive learning,"https://scholar.google.com/scholar?cluster=4349341322540262681&hl=en&as_sdt=0,29",3,2021
Flow-Based Kernel Prior With Application to Blind Super-Resolution,80,cvpr,21,5,2023-06-03 13:15:56.351000,https://github.com/JingyunLiang/FKP,136,Flow-based kernel prior with application to blind super-resolution,"https://scholar.google.com/scholar?cluster=7847212286517432309&hl=en&as_sdt=0,11",10,2021
Stable View Synthesis,227,cvpr,34,16,2023-06-03 13:15:56.543000,https://github.com/intel-isl/StableViewSynthesis,205,Non-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video,"https://scholar.google.com/scholar?cluster=1613764570957363647&hl=en&as_sdt=0,11",13,2021
Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation,45,cvpr,2,3,2023-06-03 13:15:56.735000,https://github.com/YapengTian/CCOL-CVPR21,19,Cyclic co-learning of sounding object visual grounding and sound separation,"https://scholar.google.com/scholar?cluster=9875784287659877960&hl=en&as_sdt=0,33",4,2021
End-to-End Object Detection With Fully Convolutional Network,148,cvpr,38,2,2023-06-03 13:15:56.926000,https://github.com/Megvii-BaseDetection/DeFCN,477,End-to-end object detection with fully convolutional network,"https://scholar.google.com/scholar?cluster=5174677530138095658&hl=en&as_sdt=0,5",22,2021
"Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark",58,cvpr,28,14,2023-06-03 13:15:57.118000,https://github.com/VisDrone/DroneCrowd,113,"Detection, tracking, and counting meets drones in crowds: A benchmark","https://scholar.google.com/scholar?cluster=10273494687004701769&hl=en&as_sdt=0,5",4,2021
Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes,36,cvpr,19,0,2023-06-03 13:15:57.311000,https://github.com/CompVis/brushstroke-parameterized-style-transfer,156,Rethinking style transfer: From pixels to parameterized brushstrokes,"https://scholar.google.com/scholar?cluster=12590451920139992694&hl=en&as_sdt=0,10",16,2021
Beyond Static Features for Temporally Consistent 3D Human Pose and Shape From a Video,101,cvpr,38,32,2023-06-03 13:15:57.502000,https://github.com/hongsukchoi/TCMR_RELEASE,227,Beyond static features for temporally consistent 3d human pose and shape from a video,"https://scholar.google.com/scholar?cluster=8912045762488288563&hl=en&as_sdt=0,5",8,2021
Bottom-Up Shift and Reasoning for Referring Image Segmentation,38,cvpr,0,4,2023-06-03 13:15:57.693000,https://github.com/incredibleXM/BUSNet,7,Bottom-up shift and reasoning for referring image segmentation,"https://scholar.google.com/scholar?cluster=14023100108932815940&hl=en&as_sdt=0,31",2,2021
M3DSSD: Monocular 3D Single Stage Object Detector,49,cvpr,7,10,2023-06-03 13:15:57.885000,https://github.com/mumianyuxin/M3DSSD,65,M3dssd: Monocular 3d single stage object detector,"https://scholar.google.com/scholar?cluster=12492206230359978451&hl=en&as_sdt=0,31",13,2021
Weakly Supervised Video Salient Object Detection,44,cvpr,4,3,2023-06-03 13:15:58.076000,https://github.com/wangbo-zhao/WSVSOD,35,Weakly supervised video salient object detection,"https://scholar.google.com/scholar?cluster=14690171157801340077&hl=en&as_sdt=0,5",4,2021
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion,30,cvpr,239,79,2023-06-03 13:15:58.268000,https://github.com/TRI-ML/packnet-sfm,1134,Sparse auxiliary networks for unified monocular depth prediction and completion,"https://scholar.google.com/scholar?cluster=5557051410324779465&hl=en&as_sdt=0,10",56,2021
MagFace: A Universal Representation for Face Recognition and Quality Assessment,261,cvpr,81,13,2023-06-03 13:15:58.459000,https://github.com/IrvingMeng/MagFace,545,Magface: A universal representation for face recognition and quality assessment,"https://scholar.google.com/scholar?cluster=9877457203953760593&hl=en&as_sdt=0,36",15,2021
Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising,82,cvpr,6,3,2023-06-03 13:15:58.651000,https://github.com/PangTongyao/Recorrupted-to-Recorrupted-Unsupervised-Deep-Learning-for-Image-Denoising,33,Recorrupted-to-recorrupted: unsupervised deep learning for image denoising,"https://scholar.google.com/scholar?cluster=6045827484143611791&hl=en&as_sdt=0,5",1,2021
Objects Are Different: Flexible Monocular 3D Object Detection,118,cvpr,33,43,2023-06-03 13:15:58.843000,https://github.com/zhangyp15/MonoFlex,183,Objects are different: Flexible monocular 3d object detection,"https://scholar.google.com/scholar?cluster=2733828785339010244&hl=en&as_sdt=0,34",12,2021
Pixel-Wise Anomaly Detection in Complex Driving Scenes,58,cvpr,36,7,2023-06-03 13:15:59.056000,https://github.com/giandbt/synboost,79,Pixel-wise anomaly detection in complex driving scenes,"https://scholar.google.com/scholar?cluster=8082507999808537249&hl=en&as_sdt=0,5",5,2021
Reconsidering Representation Alignment for Multi-View Clustering,44,cvpr,9,0,2023-06-03 13:15:59.247000,https://github.com/DanielTrosten/mvc,27,Reconsidering representation alignment for multi-view clustering,"https://scholar.google.com/scholar?cluster=3476025378540823396&hl=en&as_sdt=0,39",1,2021
Weakly Supervised Action Selection Learning in Video,32,cvpr,3,0,2023-06-03 13:15:59.440000,https://github.com/layer6ai-labs/ASL,18,Weakly supervised action selection learning in video,"https://scholar.google.com/scholar?cluster=15048001711067828269&hl=en&as_sdt=0,5",4,2021
Learning To Segment Actions From Visual and Language Instructions via Differentiable Weak Sequence Alignment,24,cvpr,2,1,2023-06-03 13:15:59.636000,https://github.com/Yuhan-Shen/VisualNarrationProceL-CVPR21,12,Learning to segment actions from visual and language instructions via differentiable weak sequence alignment,"https://scholar.google.com/scholar?cluster=8040398527278299224&hl=en&as_sdt=0,5",2,2021
ArtEmis: Affective Language for Visual Art,81,cvpr,29,5,2023-06-03 13:15:59.827000,https://github.com/optas/artemis,283,Artemis: Affective language for visual art,"https://scholar.google.com/scholar?cluster=7401331222172180327&hl=en&as_sdt=0,36",15,2021
Bi-GCN: Binary Graph Convolutional Network,25,cvpr,3,1,2023-06-03 13:16:00.020000,https://github.com/bywmm/Bi-GCN,19,Bi-gcn: Binary graph convolutional network,"https://scholar.google.com/scholar?cluster=6044194484441711712&hl=en&as_sdt=0,26",3,2021
Regularization Strategy for Point Cloud via Rigidly Mixed Sample,49,cvpr,2,0,2023-06-03 13:16:00.213000,https://github.com/dogyoonlee/RSMix-official,26,Regularization strategy for point cloud via rigidly mixed sample,"https://scholar.google.com/scholar?cluster=16427219818216326322&hl=en&as_sdt=0,5",3,2021
Semantic Image Matting,51,cvpr,28,11,2023-06-03 13:16:00.404000,https://github.com/nowsyn/SIM,203,Semantic image matting,"https://scholar.google.com/scholar?cluster=10423945365556659252&hl=en&as_sdt=0,33",33,2021
Learning Normal Dynamics in Videos With Meta Prototype Network,67,cvpr,40,11,2023-06-03 13:16:00.596000,https://github.com/ktr-hubrt/MPN,111,Learning normal dynamics in videos with meta prototype network,"https://scholar.google.com/scholar?cluster=6117923428097082519&hl=en&as_sdt=0,31",5,2021
Iterative Filter Adaptive Network for Single Image Defocus Deblurring,49,cvpr,34,1,2023-06-03 13:16:00.787000,https://github.com/codeslake/IFAN,189,Iterative filter adaptive network for single image defocus deblurring,"https://scholar.google.com/scholar?cluster=16009138990466242309&hl=en&as_sdt=0,36",8,2021
Domain Adaptation With Auxiliary Target Domain-Oriented Classifier,80,cvpr,10,1,2023-06-03 13:16:00.979000,https://github.com/tim-learn/atdoc,52,Domain adaptation with auxiliary target domain-oriented classifier,"https://scholar.google.com/scholar?cluster=3091667709867176809&hl=en&as_sdt=0,31",4,2021
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning,50,cvpr,13,2,2023-06-03 13:16:01.171000,https://github.com/coolbeam/UPFlow_pytorch,102,Upflow: Upsampling pyramid for unsupervised optical flow learning,"https://scholar.google.com/scholar?cluster=11318037521411053550&hl=en&as_sdt=0,25",3,2021
SRDAN: Scale-Aware and Range-Aware Domain Adaptation Network for Cross-Dataset 3D Object Detection,29,cvpr,1,1,2023-06-03 13:16:01.364000,https://github.com/zhangweichen2006/SRDAN_Open,18,SRDAN: Scale-aware and range-aware domain adaptation network for cross-dataset 3D object detection,"https://scholar.google.com/scholar?cluster=7839981633147231797&hl=en&as_sdt=0,33",1,2021
HLA-Face: Joint High-Low Adaptation for Low Light Face Detection,34,cvpr,21,5,2023-06-03 13:16:01.556000,https://github.com/daooshee/HLA-Face-Code,84,Hla-face: Joint high-low adaptation for low light face detection,"https://scholar.google.com/scholar?cluster=6652977768124314222&hl=en&as_sdt=0,5",3,2021
OTA: Optimal Transport Assignment for Object Detection,210,cvpr,22,12,2023-06-03 13:16:01.748000,https://github.com/Megvii-BaseDetection/OTA,223,Ota: Optimal transport assignment for object detection,"https://scholar.google.com/scholar?cluster=14858856697824684994&hl=en&as_sdt=0,5",6,2021
Learning Student Networks in the Wild,13,cvpr,131,3,2023-06-03 13:16:01.939000,https://github.com/huawei-noah/Data-Efficient-Model-Compression,593,Learning student networks in the wild,"https://scholar.google.com/scholar?cluster=3331284398779789233&hl=en&as_sdt=0,10",18,2021
DECOR-GAN: 3D Shape Detailization by Conditional Refinement,30,cvpr,14,0,2023-06-03 13:16:02.132000,https://github.com/czq142857/DECOR-GAN,78,Decor-gan: 3d shape detailization by conditional refinement,"https://scholar.google.com/scholar?cluster=16104682814401267505&hl=en&as_sdt=0,44",6,2021
Single Image Reflection Removal With Absorption Effect,24,cvpr,0,3,2023-06-03 13:16:02.324000,https://github.com/q-zh/absorption,16,Single image reflection removal with absorption effect,"https://scholar.google.com/scholar?cluster=17472651740886034037&hl=en&as_sdt=0,5",4,2021
Skeleton Merger: An Unsupervised Aligned Keypoint Detector,18,cvpr,12,0,2023-06-03 13:16:02.516000,https://github.com/eliphatfs/SkeletonMerger,52,Skeleton merger: an unsupervised aligned keypoint detector,"https://scholar.google.com/scholar?cluster=3287128831504521304&hl=en&as_sdt=0,40",2,2021
Distilling Knowledge via Knowledge Review,144,cvpr,32,12,2023-06-03 13:16:02.708000,https://github.com/Jia-Research-Lab/ReviewKD,207,Distilling knowledge via knowledge review,"https://scholar.google.com/scholar?cluster=13522722160846228296&hl=en&as_sdt=0,5",5,2021
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification,83,cvpr,2,1,2023-06-03 13:16:02.900000,https://github.com/zijian-hu/SimPLE,55,Simple: Similar pseudo label exploitation for semi-supervised classification,"https://scholar.google.com/scholar?cluster=7368075088888364718&hl=en&as_sdt=0,11",4,2021
Semi-Supervised Semantic Segmentation With Directional Context-Aware Consistency,95,cvpr,19,4,2023-06-03 13:16:03.092000,https://github.com/dvlab-research/Context-Aware-Consistency,145,Semi-supervised semantic segmentation with directional context-aware consistency,"https://scholar.google.com/scholar?cluster=9088511643861823190&hl=en&as_sdt=0,5",5,2021
Lips Don't Lie: A Generalisable and Robust Approach To Face Forgery Detection,146,cvpr,20,7,2023-06-03 13:16:03.284000,https://github.com/ahaliassos/lipforensics,83,Lips don't lie: A generalisable and robust approach to face forgery detection,"https://scholar.google.com/scholar?cluster=3005144873143034600&hl=en&as_sdt=0,44",1,2021
Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning,72,cvpr,13,17,2023-06-03 13:16:03.476000,https://github.com/zzr-idam/4KDehazing,53,Ultra-high-definition image dehazing via multi-guided bilateral learning,"https://scholar.google.com/scholar?cluster=18108425284541652551&hl=en&as_sdt=0,14",1,2021
Learning Multi-Scale Photo Exposure Correction,73,cvpr,50,3,2023-06-03 13:16:03.668000,https://github.com/mahmoudnafifi/Exposure_Correction,387,Learning multi-scale photo exposure correction,"https://scholar.google.com/scholar?cluster=119133651095954002&hl=en&as_sdt=0,23",30,2021
Context-Aware Layout to Image Generation With Enhanced Object Appearance,24,cvpr,9,4,2023-06-03 13:16:03.859000,https://github.com/wtliao/layout2img,40,Context-aware layout to image generation with enhanced object appearance,"https://scholar.google.com/scholar?cluster=8921550057506450307&hl=en&as_sdt=0,25",6,2021
Incremental Few-Shot Instance Segmentation,39,cvpr,11,10,2023-06-03 13:16:04.059000,https://github.com/danganea/iMTFA,64,Incremental few-shot instance segmentation,"https://scholar.google.com/scholar?cluster=580990789331538729&hl=en&as_sdt=0,5",1,2021
Learning Semantic Person Image Generation by Region-Adaptive Normalization,37,cvpr,8,2,2023-06-03 13:16:04.252000,https://github.com/cszy98/SPGNet,45,Learning semantic person image generation by region-adaptive normalization,"https://scholar.google.com/scholar?cluster=5300402096253707265&hl=en&as_sdt=0,43",2,2021
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images,7,cvpr,2,1,2023-06-03 13:16:04.443000,https://github.com/fkluger/cuboids_revisited,26,Cuboids revisited: Learning robust 3d shape fitting to single rgb images,"https://scholar.google.com/scholar?cluster=6326954861024214134&hl=en&as_sdt=0,5",3,2021
Learning To Filter: Siamese Relation Network for Robust Tracking,71,cvpr,5,8,2023-06-03 13:16:04.634000,https://github.com/hqucv/siamrn,33,Learning to filter: Siamese relation network for robust tracking,"https://scholar.google.com/scholar?cluster=10061535957424557181&hl=en&as_sdt=0,5",4,2021
Combining Semantic Guidance and Deep Reinforcement Learning for Generating Human Level Paintings,15,cvpr,3,0,2023-06-03 13:16:04.825000,https://github.com/1jsingh/semantic-guidance,21,Combining semantic guidance and deep reinforcement learning for generating human level paintings,"https://scholar.google.com/scholar?cluster=7762655623863077115&hl=en&as_sdt=0,5",2,2021
Exemplar-Based Open-Set Panoptic Segmentation Network,22,cvpr,8,3,2023-06-03 13:16:05.016000,https://github.com/jd730/EOPSN,50,Exemplar-based open-set panoptic segmentation network,"https://scholar.google.com/scholar?cluster=17800986122777045921&hl=en&as_sdt=0,33",2,2021
Plan2Scene: Converting Floorplans to 3D Scenes,12,cvpr,45,3,2023-06-03 13:16:05.207000,https://github.com/3dlg-hcvc/plan2scene,365,Plan2scene: Converting floorplans to 3d scenes,"https://scholar.google.com/scholar?cluster=5907462671091392877&hl=en&as_sdt=0,5",10,2021
DatasetGAN: Efficient Labeled Data Factory With Minimal Human Effort,165,cvpr,43,16,2023-06-03 13:16:05.399000,https://github.com/nv-tlabs/datasetGAN_release,317,Datasetgan: Efficient labeled data factory with minimal human effort,"https://scholar.google.com/scholar?cluster=16812738650431521204&hl=en&as_sdt=0,47",14,2021
Reciprocal Transformations for Unsupervised Video Object Segmentation,39,cvpr,3,7,2023-06-03 13:16:05.590000,https://github.com/OliverRensu/RTNet,23,Reciprocal transformations for unsupervised video object segmentation,"https://scholar.google.com/scholar?cluster=15190002387103388309&hl=en&as_sdt=0,5",1,2021
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning Over Traffic Events,33,cvpr,1,1,2023-06-03 13:16:05.782000,https://github.com/SUTDCV/SUTD-TrafficQA,32,Sutd-trafficqa: A question answering benchmark and an efficient network for video reasoning over traffic events,"https://scholar.google.com/scholar?cluster=13846762936798544230&hl=en&as_sdt=0,33",3,2021
Deep RGB-D Saliency Detection With Depth-Sensitive Attention and Automatic Multi-Modal Fusion,108,cvpr,8,6,2023-06-03 13:16:05.983000,https://github.com/sunpeng1996/DSA2F,50,Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion,"https://scholar.google.com/scholar?cluster=3989746836066136362&hl=en&as_sdt=0,5",3,2021
SuperMix: Supervising the Mixing Data Augmentation,54,cvpr,21,1,2023-06-03 13:16:06.176000,https://github.com/alldbi/SuperMix,86,Supermix: Supervising the mixing data augmentation,"https://scholar.google.com/scholar?cluster=14244427449152120178&hl=en&as_sdt=0,31",6,2021
OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations,29,cvpr,1,1,2023-06-03 13:16:06.367000,https://github.com/tanyang1231/OTCE_Transferability_CVPR21,15,Otce: A transferability metric for cross-domain cross-task representations,"https://scholar.google.com/scholar?cluster=15121493805699552808&hl=en&as_sdt=0,5",1,2021
Reformulating HOI Detection As Adaptive Set Prediction,72,cvpr,1,7,2023-06-03 13:16:06.558000,https://github.com/yoyomimi/AS-Net,48,Reformulating hoi detection as adaptive set prediction,"https://scholar.google.com/scholar?cluster=8880745463913547947&hl=en&as_sdt=0,5",3,2021
Strengthen Learning Tolerance for Weakly Supervised Object Localization,39,cvpr,3,4,2023-06-03 13:16:06.750000,https://github.com/gyguo/SLT-Net,7,Strengthen learning tolerance for weakly supervised object localization,"https://scholar.google.com/scholar?cluster=4452129180625629860&hl=en&as_sdt=0,5",2,2021
Connecting What To Say With Where To Look by Modeling Human Attention Traces,19,cvpr,7,5,2023-06-03 13:16:06.941000,https://github.com/facebookresearch/connect-caption-and-trace,75,Connecting what to say with where to look by modeling human attention traces,"https://scholar.google.com/scholar?cluster=1166819972433413158&hl=en&as_sdt=0,21",8,2021
Adaptive Weighted Discriminator for Training Generative Adversarial Networks,10,cvpr,5,3,2023-06-03 13:16:07.133000,https://github.com/vasily789/adaptive-weighted-gans,6,Adaptive weighted discriminator for training generative adversarial networks,"https://scholar.google.com/scholar?cluster=17772724676833431602&hl=en&as_sdt=0,5",1,2021
Mesh Saliency: An Independent Perceptual Measure or a Derivative of Image Saliency?,8,cvpr,2,0,2023-06-03 13:16:07.324000,https://github.com/rsong/MIMO-GAN,6,Mesh saliency: An independent perceptual measure or a derivative of image saliency?,"https://scholar.google.com/scholar?cluster=10937218748729316652&hl=en&as_sdt=0,5",1,2021
Transformation Driven Visual Reasoning,11,cvpr,6,0,2023-06-03 13:16:07.516000,https://github.com/hughplay/TVR,34,Transformation driven visual reasoning,"https://scholar.google.com/scholar?cluster=4106340116896467389&hl=en&as_sdt=0,5",5,2021
Multi-Perspective LSTM for Joint Visual Representation Learning,8,cvpr,3,1,2023-06-03 13:16:07.708000,https://github.com/arsm/MPLSTM,7,Multi-perspective LSTM for joint visual representation learning,"https://scholar.google.com/scholar?cluster=6230236178292556864&hl=en&as_sdt=0,14",1,2021
"Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges",90,cvpr,54,39,2023-06-03 13:16:07.900000,https://github.com/QingyongHu/SensatUrban,426,"Towards semantic segmentation of urban-scale 3D point clouds: A dataset, benchmarks and challenges","https://scholar.google.com/scholar?cluster=16464957660439610842&hl=en&as_sdt=0,5",17,2021
Exponential Moving Average Normalization for Self-Supervised and Semi-Supervised Learning,66,cvpr,13,2,2023-06-03 13:16:08.091000,https://github.com/amazon-research/exponential-moving-average-normalization,89,Exponential moving average normalization for self-supervised and semi-supervised learning,"https://scholar.google.com/scholar?cluster=5348747885279347992&hl=en&as_sdt=0,39",11,2021
Towards Open World Object Detection,223,cvpr,153,16,2023-06-03 13:16:08.282000,https://github.com/JosephKJ/OWOD,949,Towards open world object detection,"https://scholar.google.com/scholar?cluster=9715328489246217151&hl=en&as_sdt=0,11",24,2021
Probabilistic Embeddings for Cross-Modal Retrieval,106,cvpr,16,0,2023-06-03 13:16:08.474000,https://github.com/naver-ai/pcme,101,Probabilistic embeddings for cross-modal retrieval,"https://scholar.google.com/scholar?cluster=6129118202702830234&hl=en&as_sdt=0,10",4,2021
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object Segmentation,7,cvpr,5,2,2023-06-03 13:16:08.665000,https://github.com/scutpaul/DANet,27,Delving deep into many-to-many attention for few-shot video object segmentation,"https://scholar.google.com/scholar?cluster=13910171345976330359&hl=en&as_sdt=0,33",1,2021
PD-GAN: Probabilistic Diverse GAN for Image Inpainting,126,cvpr,11,6,2023-06-03 13:16:08.861000,https://github.com/KumapowerLIU/PD-GAN,96,Pd-gan: Probabilistic diverse gan for image inpainting,"https://scholar.google.com/scholar?cluster=11543458897685198295&hl=en&as_sdt=0,33",10,2021
Group Collaborative Learning for Co-Salient Object Detection,51,cvpr,14,0,2023-06-03 13:16:09.056000,https://github.com/fanq15/GCoNet,48,Group collaborative learning for co-salient object detection,"https://scholar.google.com/scholar?cluster=421463151410607475&hl=en&as_sdt=0,39",6,2021
Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification,57,cvpr,6,5,2023-06-03 13:16:09.247000,https://github.com/FutabaSakuraXD/Farewell-to-Mutual-Information-Variational-Distiilation-for-Cross-Modal-Person-Re-identification,38,Farewell to mutual information: Variational distillation for cross-modal person re-identification,"https://scholar.google.com/scholar?cluster=7519285784266258551&hl=en&as_sdt=0,24",1,2021
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening,116,cvpr,27,6,2023-06-03 13:16:09.439000,https://github.com/shachoi/RobustNet,180,Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening,"https://scholar.google.com/scholar?cluster=14695435356519848244&hl=en&as_sdt=0,15",4,2021
FrameExit: Conditional Early Exiting for Efficient Video Recognition,38,cvpr,7,4,2023-06-03 13:16:09.630000,https://github.com/Qualcomm-AI-research/FrameExit,32,Frameexit: Conditional early exiting for efficient video recognition,"https://scholar.google.com/scholar?cluster=14023967671092786083&hl=en&as_sdt=0,43",5,2021
Pre-Trained Image Processing Transformer,903,cvpr,58,16,2023-06-03 13:16:09.822000,https://github.com/huawei-noah/Pretrained-IPT,360,Pre-trained image processing transformer,"https://scholar.google.com/scholar?cluster=5512802662340022027&hl=en&as_sdt=0,33",10,2021
Quasi-Dense Similarity Learning for Multiple Object Tracking,192,cvpr,59,14,2023-06-03 13:16:10.013000,https://github.com/SysCV/qdtrack,353,Quasi-dense similarity learning for multiple object tracking,"https://scholar.google.com/scholar?cluster=340622669702709711&hl=en&as_sdt=0,33",20,2021
HDR Environment Map Estimation for Real-Time Augmented Reality,16,cvpr,10,0,2023-06-03 13:16:10.205000,https://github.com/apple/ml-envmapnet,46,HDR environment map estimation for real-time augmented reality,"https://scholar.google.com/scholar?cluster=5493655089609620234&hl=en&as_sdt=0,10",8,2021
Panoptic-PolarNet: Proposal-Free LiDAR Point Cloud Panoptic Segmentation,58,cvpr,26,19,2023-06-03 13:16:10.397000,https://github.com/edwardzhou130/Panoptic-PolarNet,147,Panoptic-polarnet: Proposal-free lidar point cloud panoptic segmentation,"https://scholar.google.com/scholar?cluster=15412248683790508859&hl=en&as_sdt=0,5",3,2021
Distilling Object Detectors via Decoupled Features,103,cvpr,10,12,2023-06-03 13:16:10.588000,https://github.com/ggjy/DeFeat.pytorch,105,Distilling object detectors via decoupled features,"https://scholar.google.com/scholar?cluster=9224300720018831889&hl=en&as_sdt=0,14",4,2021
Spherical Confidence Learning for Face Recognition,37,cvpr,7,2,2023-06-03 13:16:10.780000,https://github.com/MathsShen/SCF,71,Spherical confidence learning for face recognition,"https://scholar.google.com/scholar?cluster=2804038155970346173&hl=en&as_sdt=0,33",1,2021
Sparse R-CNN: End-to-End Object Detection With Learnable Proposals,571,cvpr,181,33,2023-06-03 13:16:10.980000,https://github.com/PeizeSun/SparseR-CNN,1243,Sparse r-cnn: End-to-end object detection with learnable proposals,"https://scholar.google.com/scholar?cluster=2104521862399993019&hl=en&as_sdt=0,5",18,2021
Roof-GAN: Learning To Generate Roof Geometry and Relations for Residential Houses,8,cvpr,3,2,2023-06-03 13:16:11.172000,https://github.com/yi-ming-qian/roofgan,31,Roof-gan: Learning to generate roof geometry and relations for residential houses,"https://scholar.google.com/scholar?cluster=13615776309818015997&hl=en&as_sdt=0,5",4,2021
VIP-DeepLab: Learning Visual Perception With Depth-Aware Video Panoptic Segmentation,93,cvpr,25,6,2023-06-03 13:16:11.364000,https://github.com/joe-siyuan-qiao/ViP-DeepLab,205,Vip-deeplab: Learning visual perception with depth-aware video panoptic segmentation,"https://scholar.google.com/scholar?cluster=9655790843364940778&hl=en&as_sdt=0,5",13,2021
How Privacy-Preserving Are Line Clouds? Recovering Scene Details From 3D Lines,8,cvpr,4,1,2023-06-03 13:16:11.555000,https://github.com/kunalchelani/Line2Point,37,How privacy-preserving are line clouds? recovering scene details from 3d lines,"https://scholar.google.com/scholar?cluster=6922577891184748806&hl=en&as_sdt=0,29",2,2021
Stay Positive: Non-Negative Image Synthesis for Augmented Reality,1,cvpr,4,0,2023-06-03 13:16:11.746000,https://github.com/katieluo88/staypositive,26,Stay Positive: Non-Negative Image Synthesis for Augmented Reality,"https://scholar.google.com/scholar?cluster=8999551926409003763&hl=en&as_sdt=0,47",3,2021
Multi-View 3D Reconstruction of a Texture-Less Smooth Surface of Unknown Generic Reflectance,10,cvpr,4,0,2023-06-03 13:16:11.938000,https://github.com/za-cheng/PM-PMVS,63,Multi-view 3d reconstruction of a texture-less smooth surface of unknown generic reflectance,"https://scholar.google.com/scholar?cluster=14122645277689137445&hl=en&as_sdt=0,5",11,2021
Regularizing Generative Adversarial Networks Under Limited Data,79,cvpr,17,3,2023-06-03 13:16:12.132000,https://github.com/google/lecam-gan,156,Regularizing generative adversarial networks under limited data,"https://scholar.google.com/scholar?cluster=2171644461537225779&hl=en&as_sdt=0,5",11,2021
PhD Learning: Learning With Pompeiu-Hausdorff Distances for Video-Based Vehicle Re-Identification,18,cvpr,6,2,2023-06-03 13:16:12.323000,https://github.com/emdata-ailab/PhD-Learning,13,Phd learning: Learning with pompeiu-hausdorff distances for video-based vehicle re-identification,"https://scholar.google.com/scholar?cluster=15651193690723423145&hl=en&as_sdt=0,33",1,2021
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators,108,cvpr,46273,1204,2023-06-03 13:16:12.515000,https://github.com/tensorflow/models,75885,Mobiledets: Searching for object detection architectures for mobile accelerators,"https://scholar.google.com/scholar?cluster=11292448814138773366&hl=en&as_sdt=0,5",2774,2021
Regularizing Neural Networks via Adversarial Model Perturbation,62,cvpr,8,1,2023-06-03 13:16:12.707000,https://github.com/hiyouga/AMP-Regularizer,31,Regularizing neural networks via adversarial model perturbation,"https://scholar.google.com/scholar?cluster=159593877140258646&hl=en&as_sdt=0,44",2,2021
Digital Gimbal: End-to-End Deep Image Stabilization With Learnable Exposure Times,3,cvpr,2,3,2023-06-03 13:16:12.902000,https://github.com/omer11a/digital-gimbal,12,Digital gimbal: End-to-end deep image stabilization with learnable exposure times,"https://scholar.google.com/scholar?cluster=260596067970143906&hl=en&as_sdt=0,5",2,2021
Open World Compositional Zero-Shot Learning,60,cvpr,21,6,2023-06-03 13:16:13.094000,https://github.com/ExplainableML/czsl,85,Open world compositional zero-shot learning,"https://scholar.google.com/scholar?cluster=17915577558306787897&hl=en&as_sdt=0,31",4,2021
Continual Learning via Bit-Level Information Preserving,19,cvpr,3,1,2023-06-03 13:16:13.286000,https://github.com/Yujun-Shi/BLIP,37,Continual learning via bit-level information preserving,"https://scholar.google.com/scholar?cluster=5481229777141324219&hl=en&as_sdt=0,31",2,2021
Contrastive Neural Architecture Search With Neural Architecture Comparators,29,cvpr,4,0,2023-06-03 13:16:13.477000,https://github.com/chenyaofo/CTNAS,36,Contrastive neural architecture search with neural architecture comparators,"https://scholar.google.com/scholar?cluster=6536937962792264866&hl=en&as_sdt=0,5",6,2021
Rethinking Text Segmentation: A Novel Dataset and a Text-Specific Refinement Approach,24,cvpr,24,3,2023-06-03 13:16:13.669000,https://github.com/SHI-Labs/Rethinking-Text-Segmentation,201,Rethinking text segmentation: A novel dataset and a text-specific refinement approach,"https://scholar.google.com/scholar?cluster=85559860054228346&hl=en&as_sdt=0,5",17,2021
SIPSA-Net: Shift-Invariant Pan Sharpening With Moving Object Alignment for Satellite Imagery,13,cvpr,1,2,2023-06-03 13:16:13.867000,https://github.com/brachiohyup/SIPSA,14,Sipsa-net: Shift-invariant pan sharpening with moving object alignment for satellite imagery,"https://scholar.google.com/scholar?cluster=10916348569860936671&hl=en&as_sdt=0,5",1,2021
HOTR: End-to-End Human-Object Interaction Detection With Transformers,140,cvpr,20,14,2023-06-03 13:16:14.062000,https://github.com/kakaobrain/HOTR,122,Hotr: End-to-end human-object interaction detection with transformers,"https://scholar.google.com/scholar?cluster=12457555581764369519&hl=en&as_sdt=0,22",6,2021
Unpaired Image-to-Image Translation via Latent Energy Transport,22,cvpr,9,1,2023-06-03 13:16:14.254000,https://github.com/YangNaruto/latent-energy-transport,43,Unpaired image-to-image translation via latent energy transport,"https://scholar.google.com/scholar?cluster=3604013830472222214&hl=en&as_sdt=0,5",3,2021
Generalizable Pedestrian Detection: The Elephant in the Room,78,cvpr,153,0,2023-06-03 13:16:14.446000,https://github.com/hasanirtiza/Pedestron,631,Generalizable pedestrian detection: The elephant in the room,"https://scholar.google.com/scholar?cluster=1841952340529293898&hl=en&as_sdt=0,11",18,2021
STMTrack: Template-Free Visual Tracking With Space-Time Memory Networks,128,cvpr,13,9,2023-06-03 13:16:14.637000,https://github.com/fzh0917/STMTrack,70,Stmtrack: Template-free visual tracking with space-time memory networks,"https://scholar.google.com/scholar?cluster=15248432581458880980&hl=en&as_sdt=0,43",2,2021
Triple-Cooperative Video Shadow Detection,23,cvpr,4,1,2023-06-03 13:16:14.829000,https://github.com/eraserNut/ViSha,25,Triple-cooperative video shadow detection,"https://scholar.google.com/scholar?cluster=14498873794346957980&hl=en&as_sdt=0,10",4,2021
3D-to-2D Distillation for Indoor Scene Parsing,30,cvpr,3,7,2023-06-03 13:16:15.021000,https://github.com/liuzhengzhe/3D-to-2D-Distillation-for-Indoor-Scene-Parsing,44,3d-to-2d distillation for indoor scene parsing,"https://scholar.google.com/scholar?cluster=1157879971757768482&hl=en&as_sdt=0,34",3,2021
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis,5,cvpr,0,0,2023-06-03 13:16:15.213000,https://github.com/AlvinZhuyx/camera_pose_representation,16,Learning neural representation of camera pose with matrix representation of pose shift via view synthesis,"https://scholar.google.com/scholar?cluster=11279118821440888049&hl=en&as_sdt=0,5",2,2021
Refining Pseudo Labels With Clustering Consensus Over Generations for Unsupervised Object Re-Identification,63,cvpr,4,0,2023-06-03 13:16:15.405000,https://github.com/2han9x1a0release/RLCC,9,Refining pseudo labels with clustering consensus over generations for unsupervised object re-identification,"https://scholar.google.com/scholar?cluster=9495158567965592980&hl=en&as_sdt=0,23",1,2021
The Lottery Tickets Hypothesis for Supervised and Self-Supervised Pre-Training in Computer Vision Models,88,cvpr,13,2,2023-06-03 13:16:15.597000,https://github.com/VITA-Group/CV_LTH_Pre-training,61,The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models,"https://scholar.google.com/scholar?cluster=9665171368816644761&hl=en&as_sdt=0,5",14,2021
PML: Progressive Margin Loss for Long-Tailed Age Classification,38,cvpr,2,2,2023-06-03 13:16:15.788000,https://github.com/SanyeungWang/PML,12,Pml: Progressive margin loss for long-tailed age classification,"https://scholar.google.com/scholar?cluster=18212278929738151115&hl=en&as_sdt=0,5",1,2021
Single Image Depth Prediction With Wavelet Decomposition,38,cvpr,36,0,2023-06-03 13:16:15.980000,https://github.com/nianticlabs/wavelet-monodepth,214,Single image depth prediction with wavelet decomposition,"https://scholar.google.com/scholar?cluster=7966803353137342558&hl=en&as_sdt=0,39",12,2021
EnD: Entangling and Disentangling Deep Representations for Bias Correction,64,cvpr,3,1,2023-06-03 13:16:16.172000,https://github.com/EIDOSlab/entangling-disentangling-bias,15,End: Entangling and disentangling deep representations for bias correction,"https://scholar.google.com/scholar?cluster=2823444988595280671&hl=en&as_sdt=0,44",4,2021
ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows,78,cvpr,22,6,2023-06-03 13:16:16.363000,https://github.com/pkuanjie/ArtFlow,133,Artflow: Unbiased image style transfer via reversible neural flows,"https://scholar.google.com/scholar?cluster=3369519100876703278&hl=en&as_sdt=0,1",6,2021
Implicit Feature Alignment: Learn To Convert Text Recognizer to Text Spotter,8,cvpr,11,6,2023-06-03 13:16:16.554000,https://github.com/Wang-Tianwei/Implicit-feature-alignment,60,Implicit feature alignment: learn to convert text recognizer to text spotter,"https://scholar.google.com/scholar?cluster=16153700515830086108&hl=en&as_sdt=0,5",4,2021
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network,62,cvpr,12,2,2023-06-03 13:16:16.746000,https://github.com/ruiliu-ai/DivCo,65,Divco: Diverse conditional image synthesis via contrastive generative adversarial network,"https://scholar.google.com/scholar?cluster=3013413064043278208&hl=en&as_sdt=0,33",3,2021
KOALAnet: Blind Super-Resolution Using Kernel-Oriented Adaptive Local Adjustment,38,cvpr,4,2,2023-06-03 13:16:16.938000,https://github.com/hjSim/KOALAnet,33,Koalanet: Blind super-resolution using kernel-oriented adaptive local adjustment,"https://scholar.google.com/scholar?cluster=7559850928591885532&hl=en&as_sdt=0,5",5,2021
Zillow Indoor Dataset: Annotated Floor Plans With 360deg Panoramas and 3D Room Layouts,30,cvpr,10,0,2023-06-03 13:16:17.130000,https://github.com/zillow/zind,132,Zillow indoor dataset: Annotated floor plans with 360deg panoramas and 3d room layouts,"https://scholar.google.com/scholar?cluster=480781525344538560&hl=en&as_sdt=0,5",16,2021
Positive Sample Propagation Along the Audio-Visual Event Line,45,cvpr,10,0,2023-06-03 13:16:17.321000,https://github.com/jasongief/PSP_CVPR_2021,35,Positive sample propagation along the audio-visual event line,"https://scholar.google.com/scholar?cluster=3043211004122309264&hl=en&as_sdt=0,39",3,2021
Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization,30,cvpr,3,4,2023-06-03 13:16:17.512000,https://github.com/deel-ai/deel-lip,70,Achieving robustness in classification using optimal transport with hinge regularization,"https://scholar.google.com/scholar?cluster=14482711934050746997&hl=en&as_sdt=0,5",7,2021
Distilling Causal Effect of Data in Class-Incremental Learning,116,cvpr,7,2,2023-06-03 13:16:17.704000,https://github.com/JoyHuYY1412/DDE_CIL,55,Distilling causal effect of data in class-incremental learning,"https://scholar.google.com/scholar?cluster=616466842345848267&hl=en&as_sdt=0,5",2,2021
Real-Time High-Resolution Background Matting,117,cvpr,920,39,2023-06-03 13:16:17.896000,https://github.com/PeterL1n/BackgroundMattingV2,6294,Real-time high-resolution background matting,"https://scholar.google.com/scholar?cluster=14815088791302815348&hl=en&as_sdt=0,48",147,2021
Self-Supervised Geometric Perception,22,cvpr,2,0,2023-06-03 13:16:18.088000,https://github.com/theNded/SGP,145,Self-supervised geometric perception,"https://scholar.google.com/scholar?cluster=12972960961540767864&hl=en&as_sdt=0,14",27,2021
PiCIE: Unsupervised Semantic Segmentation Using Invariance and Equivariance in Clustering,87,cvpr,33,2,2023-06-03 13:16:18.279000,https://github.com/janghyuncho/PiCIE,176,Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering,"https://scholar.google.com/scholar?cluster=14972346295060017825&hl=en&as_sdt=0,5",3,2021
Metadata Normalization,12,cvpr,3,2,2023-06-03 13:16:18.470000,https://github.com/mlu355/MetadataNorm,18,Metadata normalization,"https://scholar.google.com/scholar?cluster=2606098392012969749&hl=en&as_sdt=0,23",2,2021
UnrealPerson: An Adaptive Pipeline Towards Costless Person Re-Identification,39,cvpr,8,6,2023-06-03 13:16:18.662000,https://github.com/FlyHighest/UnrealPerson,70,Unrealperson: An adaptive pipeline towards costless person re-identification,"https://scholar.google.com/scholar?cluster=14136359213002758168&hl=en&as_sdt=0,44",4,2021
CoSMo: Content-Style Modulation for Image Retrieval With Text Feedback,40,cvpr,10,2,2023-06-03 13:16:18.855000,https://github.com/postBG/CosMo.pytorch,57,Cosmo: Content-style modulation for image retrieval with text feedback,"https://scholar.google.com/scholar?cluster=7877571534622037277&hl=en&as_sdt=0,5",3,2021
"A Multiplexed Network for End-to-End, Multilingual OCR",23,cvpr,6,0,2023-06-03 13:16:19.055000,https://github.com/facebookresearch/MultiplexedOCR,64,"A multiplexed network for end-to-end, multilingual OCR","https://scholar.google.com/scholar?cluster=18230155525657851457&hl=en&as_sdt=0,47",19,2021
Discriminative Appearance Modeling With Multi-Track Pooling for Real-Time Multi-Object Tracking,53,cvpr,3,0,2023-06-03 13:16:19.246000,https://github.com/chkim403/blstm-mtp,21,Discriminative appearance modeling with multi-track pooling for real-time multi-object tracking,"https://scholar.google.com/scholar?cluster=11021460782125895378&hl=en&as_sdt=0,33",3,2021
General Multi-Label Image Classification With Transformers,135,cvpr,35,6,2023-06-03 13:16:19.437000,https://github.com/QData/C-Tran,179,General multi-label image classification with transformers,"https://scholar.google.com/scholar?cluster=13893159385470963321&hl=en&as_sdt=0,5",8,2021
UAV-Human: A Large Benchmark for Human Behavior Understanding With Unmanned Aerial Vehicles,92,cvpr,9,8,2023-06-03 13:16:19.630000,https://github.com/SUTDCV/UAV-Human,143,Uav-human: A large benchmark for human behavior understanding with unmanned aerial vehicles,"https://scholar.google.com/scholar?cluster=131638545922960226&hl=en&as_sdt=0,5",6,2021
Context-Aware Biaffine Localizing Network for Temporal Sentence Grounding,86,cvpr,1,5,2023-06-03 13:16:19.821000,https://github.com/liudaizong/CBLN,20,Context-aware biaffine localizing network for temporal sentence grounding,"https://scholar.google.com/scholar?cluster=13944337781888254186&hl=en&as_sdt=0,25",7,2021
Auto-Exposure Fusion for Single-Image Shadow Removal,67,cvpr,28,12,2023-06-03 13:16:20.013000,https://github.com/tsingqguo/exposure-fusion-shadow-removal,156,Auto-exposure fusion for single-image shadow removal,"https://scholar.google.com/scholar?cluster=8800214440851984824&hl=en&as_sdt=0,3",7,2021
Populating 3D Scenes by Learning Human-Scene Interaction,59,cvpr,11,9,2023-06-03 13:16:20.204000,https://github.com/mohamedhassanmus/POSA,87,Populating 3D scenes by learning human-scene interaction,"https://scholar.google.com/scholar?cluster=15438549382159555424&hl=en&as_sdt=0,5",3,2021
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation,201,cvpr,58,10,2023-06-03 13:16:20.396000,https://github.com/weihaox/TediGAN,335,Tedigan: Text-guided diverse face image generation and manipulation,"https://scholar.google.com/scholar?cluster=2355571043587775935&hl=en&as_sdt=0,5",10,2021
Exploring Sparsity in Image Super-Resolution for Efficient Inference,114,cvpr,28,20,2023-06-03 13:16:20.588000,https://github.com/LongguangWang/SMSR,228,Exploring sparsity in image super-resolution for efficient inference,"https://scholar.google.com/scholar?cluster=2087939017149023760&hl=en&as_sdt=0,33",7,2021
Simpler Certified Radius Maximization by Propagating Covariances,3,cvpr,1,0,2023-06-03 13:16:20.779000,https://github.com/zhenxingjian/Propagating_Covariance,4,Simpler certified radius maximization by propagating covariances,"https://scholar.google.com/scholar?cluster=9172669133376835254&hl=en&as_sdt=0,33",2,2021
StylePeople: A Generative Model of Fullbody Human Avatars,38,cvpr,6,0,2023-06-03 13:16:20.973000,https://github.com/Dolorousrtur/style-people,23,Stylepeople: A generative model of fullbody human avatars,"https://scholar.google.com/scholar?cluster=10279676245017267770&hl=en&as_sdt=0,5",1,2021
Memory-Efficient Network for Large-Scale Video Compressive Sensing,39,cvpr,3,1,2023-06-03 13:16:21.165000,https://github.com/BoChenGroup/RevSCI-net,20,Memory-efficient network for large-scale video compressive sensing,"https://scholar.google.com/scholar?cluster=16316385234608503752&hl=en&as_sdt=0,31",1,2021
Neural Deformation Graphs for Globally-Consistent Non-Rigid Reconstruction,49,cvpr,18,1,2023-06-03 13:16:21.356000,https://github.com/AljazBozic/NeuralGraph,141,Neural deformation graphs for globally-consistent non-rigid reconstruction,"https://scholar.google.com/scholar?cluster=12870418421299018371&hl=en&as_sdt=0,23",6,2021
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search,30,cvpr,10,4,2023-06-03 13:16:21.548000,https://github.com/luminxu/ViPNAS,43,Vipnas: Efficient video pose estimation via neural architecture search,"https://scholar.google.com/scholar?cluster=463881621209957672&hl=en&as_sdt=0,7",5,2021
BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation,100,cvpr,9,6,2023-06-03 13:16:21.746000,https://github.com/jbeomlee93/BBAM,93,Bbam: Bounding box attribution map for weakly supervised semantic and instance segmentation,"https://scholar.google.com/scholar?cluster=13387821059089379890&hl=en&as_sdt=0,14",5,2021
Amalgamating Knowledge From Heterogeneous Graph Neural Networks,52,cvpr,1,2,2023-06-03 13:16:21.937000,https://github.com/ycjing/AmalgamateGNN.PyTorch,15,Amalgamating knowledge from heterogeneous graph neural networks,"https://scholar.google.com/scholar?cluster=8671883522813146854&hl=en&as_sdt=0,44",2,2021
Retinex-Inspired Unrolling With Cooperative Prior Architecture Search for Low-Light Image Enhancement,207,cvpr,24,6,2023-06-03 13:16:22.129000,https://github.com/KarelZhang/RUAS,76,Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement,"https://scholar.google.com/scholar?cluster=3809086358231988437&hl=en&as_sdt=0,5",3,2021
Predator: Registration of 3D Point Clouds With Low Overlap,195,cvpr,57,5,2023-06-03 13:16:22.321000,https://github.com/ShengyuH/OverlapPredator,363,Predator: Registration of 3d point clouds with low overlap,"https://scholar.google.com/scholar?cluster=15458777390719597114&hl=en&as_sdt=0,5",12,2021
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation,74,cvpr,23,2,2023-06-03 13:16:22.512000,https://github.com/W-zx-Y/DANNet,100,Dannet: A one-stage domain adaptation network for unsupervised nighttime semantic segmentation,"https://scholar.google.com/scholar?cluster=13005306794607282066&hl=en&as_sdt=0,5",2,2021
PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training,64,cvpr,7,1,2023-06-03 13:16:22.704000,https://github.com/lukemelas/pixmatch,33,Pixmatch: Unsupervised domain adaptation via pixelwise consistency training,"https://scholar.google.com/scholar?cluster=16396239953331018584&hl=en&as_sdt=0,5",4,2021
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion,103,cvpr,17,10,2023-06-03 13:16:22.896000,https://github.com/ShiQiu0419/BAAF-Net,95,Semantic segmentation for real point cloud scenes via bilateral augmentation and adaptive fusion,"https://scholar.google.com/scholar?cluster=2401797052665971808&hl=en&as_sdt=0,33",6,2021
DI-Fusion: Online Implicit 3D Reconstruction With Deep Priors,46,cvpr,11,3,2023-06-03 13:16:23.088000,https://github.com/huangjh-pub/di-fusion,107,Di-fusion: Online implicit 3d reconstruction with deep priors,"https://scholar.google.com/scholar?cluster=10163665950409560820&hl=en&as_sdt=0,5",3,2021
Learning Graph Embeddings for Compositional Zero-Shot Learning,80,cvpr,21,6,2023-06-03 13:16:23.281000,https://github.com/ExplainableML/czsl,85,Learning graph embeddings for compositional zero-shot learning,"https://scholar.google.com/scholar?cluster=16016610818419068365&hl=en&as_sdt=0,5",4,2021
Neural Side-by-Side: Predicting Human Preferences for No-Reference Super-Resolution Evaluation,11,cvpr,4,2,2023-06-03 13:16:23.473000,https://github.com/KhrulkovV/NeuralSBS,14,Neural side-by-side: Predicting human preferences for no-reference super-resolution evaluation,"https://scholar.google.com/scholar?cluster=12587015866754201082&hl=en&as_sdt=0,5",2,2021
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification,93,cvpr,23,3,2023-06-03 13:16:23.665000,https://github.com/HeliosZhao/M3L,52,Learning to generalize unseen domains via memory-based multi-source meta-learning for person re-identification,"https://scholar.google.com/scholar?cluster=14604118849107032618&hl=en&as_sdt=0,41",3,2021
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics,19,cvpr,0,0,2023-06-03 13:16:23.866000,https://github.com/git-disl/DP-Ensemble,4,Boosting ensemble accuracy by revisiting ensemble diversity metrics,"https://scholar.google.com/scholar?cluster=17960956130360902631&hl=en&as_sdt=0,33",4,2021
"img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation",87,cvpr,108,1,2023-06-03 13:16:24.062000,https://github.com/vitoralbiero/img2pose,537,"img2pose: Face alignment and detection via 6dof, face pose estimation","https://scholar.google.com/scholar?cluster=4532542063231978095&hl=en&as_sdt=0,21",23,2021
Binary TTC: A Temporal Geofence for Autonomous Navigation,18,cvpr,9,2,2023-06-03 13:16:24.255000,https://github.com/NVlabs/BiTTC,47,Binary ttc: A temporal geofence for autonomous navigation,"https://scholar.google.com/scholar?cluster=5422438333716948548&hl=en&as_sdt=0,5",9,2021
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization,31,cvpr,7,2,2023-06-03 13:16:24.446000,https://github.com/IRMVLab/PWCLONet,46,Pwclo-net: Deep lidar odometry in 3d point clouds using hierarchical embedding mask optimization,"https://scholar.google.com/scholar?cluster=7292224928962753730&hl=en&as_sdt=0,14",4,2021
Limitations of Post-Hoc Feature Alignment for Robustness,17,cvpr,1,0,2023-06-03 13:16:24.638000,https://github.com/collin-burns/feature-alignment,9,Limitations of post-hoc feature alignment for robustness,"https://scholar.google.com/scholar?cluster=10007615184186415315&hl=en&as_sdt=0,5",1,2021
Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias,20,cvpr,0,1,2023-06-03 13:16:24.829000,https://github.com/yzhao520/CPP,9,Camera pose matters: Improving depth prediction by mitigating pose distribution bias,"https://scholar.google.com/scholar?cluster=159639015212632476&hl=en&as_sdt=0,44",5,2021
Trajectory Prediction With Latent Belief Energy-Based Model,47,cvpr,4,1,2023-06-03 13:16:25.020000,https://github.com/bpucla/lbebm,14,Trajectory prediction with latent belief energy-based model,"https://scholar.google.com/scholar?cluster=16762426174098635430&hl=en&as_sdt=0,31",3,2021
Dynamic Slimmable Network,78,cvpr,19,5,2023-06-03 13:16:25.212000,https://github.com/changlin31/DS-Net,206,Dynamic slimmable network,"https://scholar.google.com/scholar?cluster=4748499622697401157&hl=en&as_sdt=0,5",9,2021
Coordinate Attention for Efficient Mobile Network Design,1251,cvpr,111,40,2023-06-03 13:16:25.403000,https://github.com/Andrew-Qibin/CoordAttention,812,Coordinate attention for efficient mobile network design,"https://scholar.google.com/scholar?cluster=3090051676751795724&hl=en&as_sdt=0,23",9,2021
UP-DETR: Unsupervised Pre-Training for Object Detection With Transformers,333,cvpr,68,5,2023-06-03 13:16:25.595000,https://github.com/dddzg/up-detr,445,Up-detr: Unsupervised pre-training for object detection with transformers,"https://scholar.google.com/scholar?cluster=10828507269261066901&hl=en&as_sdt=0,13",13,2021
Domain-Independent Dominance of Adaptive Methods,16,cvpr,3,1,2023-06-03 13:16:25.786000,https://github.com/lolemacs/avagrad,5,Domain-independent dominance of adaptive methods,"https://scholar.google.com/scholar?cluster=4724538541608089973&hl=en&as_sdt=0,5",4,2021
Intentonomy: A Dataset and Study Towards Human Intent Understanding,15,cvpr,3,2,2023-06-03 13:16:25.977000,https://github.com/KMnP/intentonomy,30,Intentonomy: a dataset and study towards human intent understanding,"https://scholar.google.com/scholar?cluster=5268870345003195142&hl=en&as_sdt=0,5",6,2021
Weakly Supervised Learning of Rigid 3D Scene Flow,52,cvpr,17,0,2023-06-03 13:16:26.170000,https://github.com/zgojcic/Rigid3DSceneFlow,125,Weakly supervised learning of rigid 3D scene flow,"https://scholar.google.com/scholar?cluster=12559706025847463188&hl=en&as_sdt=0,5",7,2021
SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements,62,cvpr,12,6,2023-06-03 13:16:26.362000,https://github.com/qianlim/SCALE,137,SCALE: Modeling clothed humans with a surface codec of articulated local elements,"https://scholar.google.com/scholar?cluster=719449730726585903&hl=en&as_sdt=0,36",8,2021
End-to-End Rotation Averaging With Multi-Source Propagation,12,cvpr,0,1,2023-06-03 13:16:26.554000,https://github.com/sfu-gruvi-3dv/msp_rot_avg,4,End-to-end rotation averaging with multi-source propagation,"https://scholar.google.com/scholar?cluster=13738694698799406974&hl=en&as_sdt=0,38",4,2021
Efficient Regional Memory Network for Video Object Segmentation,80,cvpr,14,5,2023-06-03 13:16:26.749000,https://github.com/hzxie/RMNet,80,Efficient regional memory network for video object segmentation,"https://scholar.google.com/scholar?cluster=988648905528030221&hl=en&as_sdt=0,11",6,2021
"Permute, Quantize, and Fine-Tune: Efficient Compression of Neural Networks",23,cvpr,14,2,2023-06-03 13:16:26.941000,https://github.com/uber-research/permute-quantize-finetune,138,"Permute, quantize, and fine-tune: Efficient compression of neural networks","https://scholar.google.com/scholar?cluster=9142582804391009886&hl=en&as_sdt=0,15",10,2021
Learning Placeholders for Open-Set Recognition,101,cvpr,5,0,2023-06-03 13:16:27.134000,https://github.com/zhoudw-zdw/CVPR21-Proser,62,Learning placeholders for open-set recognition,"https://scholar.google.com/scholar?cluster=3578115835054885638&hl=en&as_sdt=0,11",1,2021
Learning Triadic Belief Dynamics in Nonverbal Communication From Videos,12,cvpr,1,1,2023-06-03 13:16:27.325000,https://github.com/LifengFan/Triadic-Belief-Dynamics,24,Learning triadic belief dynamics in nonverbal communication from videos,"https://scholar.google.com/scholar?cluster=15365483338824697316&hl=en&as_sdt=0,10",1,2021
Rethinking and Improving the Robustness of Image Style Transfer,61,cvpr,8,3,2023-06-03 13:16:27.517000,https://github.com/peiwang062/swag,68,Rethinking and improving the robustness of image style transfer,"https://scholar.google.com/scholar?cluster=1388867253415843451&hl=en&as_sdt=0,11",2,2021
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation,150,cvpr,10,2,2023-06-03 13:16:27.709000,https://github.com/Reagan1311/ASGNet,100,Adaptive prototype learning and allocation for few-shot segmentation,"https://scholar.google.com/scholar?cluster=6066343110768130599&hl=en&as_sdt=0,5",4,2021
Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders,16,cvpr,5,0,2023-06-03 13:16:27.901000,https://github.com/junhocho/HGCAE,39,Unsupervised hyperbolic representation learning via message passing auto-encoders,"https://scholar.google.com/scholar?cluster=935372689415120335&hl=en&as_sdt=0,5",3,2021
Im2Vec: Synthesizing Vector Graphics Without Vector Supervision,49,cvpr,41,7,2023-06-03 13:16:28.093000,https://github.com/preddy5/Im2Vec,248,Im2vec: Synthesizing vector graphics without vector supervision,"https://scholar.google.com/scholar?cluster=11546943917570278518&hl=en&as_sdt=0,5",8,2021
HVPR: Hybrid Voxel-Point Representation for Single-Stage 3D Object Detection,62,cvpr,10,10,2023-06-03 13:16:28.284000,https://github.com/cvlab-yonsei/HVPR,41,Hvpr: Hybrid voxel-point representation for single-stage 3d object detection,"https://scholar.google.com/scholar?cluster=4407966554044764458&hl=en&as_sdt=0,44",4,2021
Exploring Data-Efficient 3D Scene Understanding With Contrastive Scene Contexts,141,cvpr,29,12,2023-06-03 13:16:28.476000,https://github.com/facebookresearch/ContrastiveSceneContexts,202,Exploring data-efficient 3d scene understanding with contrastive scene contexts,"https://scholar.google.com/scholar?cluster=13865762697551268322&hl=en&as_sdt=0,33",14,2021
SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud Based Place Recognition,56,cvpr,4,1,2023-06-03 13:16:28.669000,https://github.com/Yan-Xia/SOE-Net,27,SOE-Net: A self-attention and orientation encoding network for point cloud based place recognition,"https://scholar.google.com/scholar?cluster=5474105332958847763&hl=en&as_sdt=0,44",1,2021
Deep Video Matting via Spatio-Temporal Alignment and Aggregation,27,cvpr,6,6,2023-06-03 14:28:28.573000,https://github.com/nowsyn/DVM,81,Deep video matting via spatio-temporal alignment and aggregation,"https://scholar.google.com/scholar?cluster=9990319638495859460&hl=en&as_sdt=0,5",11,2021
ID-Unet: Iterative Soft and Hard Deformation for View Synthesis,1,cvpr,1,2,2023-06-03 14:28:28.766000,https://github.com/MingyuY/Iterative-view-synthesis,17,ID-Unet: Iterative Soft and Hard Deformation for View Synthesis,"https://scholar.google.com/scholar?cluster=337572587298032510&hl=en&as_sdt=0,5",3,2021
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation,134,cvpr,7,14,2023-06-03 14:28:28.960000,https://github.com/NUST-Machine-Intelligence-Laboratory/nsrom,29,Non-salient region object mining for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=9153345519997499260&hl=en&as_sdt=0,5",3,2021
Adaptive Class Suppression Loss for Long-Tail Object Detection,48,cvpr,17,8,2023-06-03 14:28:29.153000,https://github.com/CASIA-IVA-Lab/ACSL,70,Adaptive class suppression loss for long-tail object detection,"https://scholar.google.com/scholar?cluster=12268764715246462942&hl=en&as_sdt=0,5",5,2021
AutoDO: Robust AutoAugment for Biased Data With Label Noise via Scalable Probabilistic Implicit Differentiation,11,cvpr,2,1,2023-06-03 14:28:29.346000,https://github.com/gudovskiy/autodo,23,Autodo: Robust autoaugment for biased data with label noise via scalable probabilistic implicit differentiation,"https://scholar.google.com/scholar?cluster=7377035415006974852&hl=en&as_sdt=0,33",3,2021
Mask-ToF: Learning Microlens Masks for Flying Pixel Correction in Time-of-Flight Imaging,11,cvpr,4,0,2023-06-03 14:28:29.547000,https://github.com/princeton-computational-imaging/MaskToF,12,Mask-tof: Learning microlens masks for flying pixel correction in time-of-flight imaging,"https://scholar.google.com/scholar?cluster=11766099077329725847&hl=en&as_sdt=0,5",2,2021
"Simultaneously Localize, Segment and Rank the Camouflaged Objects",107,cvpr,22,4,2023-06-03 14:28:29.744000,https://github.com/JingZhang617/COD-Rank-Localize-and-Segment,60,"Simultaneously localize, segment and rank the camouflaged objects","https://scholar.google.com/scholar?cluster=6965015719759523118&hl=en&as_sdt=0,5",4,2021
Bridging the Visual Gap: Wide-Range Image Blending,12,cvpr,13,1,2023-06-03 14:28:29.938000,https://github.com/julia0607/Wide-Range-Image-Blending,70,Bridging the visual gap: Wide-range image blending,"https://scholar.google.com/scholar?cluster=11098300865501310926&hl=en&as_sdt=0,5",5,2021
DeepVideoMVS: Multi-View Stereo on Video With Recurrent Spatio-Temporal Fusion,51,cvpr,27,2,2023-06-03 14:28:30.131000,https://github.com/ardaduz/deep-video-mvs,189,Deepvideomvs: Multi-view stereo on video with recurrent spatio-temporal fusion,"https://scholar.google.com/scholar?cluster=8843388339490171974&hl=en&as_sdt=0,50",6,2021
Network Quantization With Element-Wise Gradient Scaling,56,cvpr,15,6,2023-06-03 14:28:30.331000,https://github.com/cvlab-yonsei/EWGS,69,Network quantization with element-wise gradient scaling,"https://scholar.google.com/scholar?cluster=10683464565989960816&hl=en&as_sdt=0,19",5,2021
NeuralRecon: Real-Time Coherent 3D Reconstruction From Monocular Video,118,cvpr,248,50,2023-06-03 14:28:30.524000,https://github.com/zju3dv/NeuralRecon,1618,NeuralRecon: Real-time coherent 3D reconstruction from monocular video,"https://scholar.google.com/scholar?cluster=5140458549603125226&hl=en&as_sdt=0,28",48,2021
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning,141,cvpr,14,4,2023-06-03 14:28:30.718000,https://github.com/google-research/crest,83,Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning,"https://scholar.google.com/scholar?cluster=9401501311900508186&hl=en&as_sdt=0,33",6,2021
Towards Accurate Text-Based Image Captioning With Content Diversity Exploration,41,cvpr,10,0,2023-06-03 14:28:30.911000,https://github.com/guanghuixu/AnchorCaptioner,30,Towards accurate text-based image captioning with content diversity exploration,"https://scholar.google.com/scholar?cluster=9321598868313110662&hl=en&as_sdt=0,5",3,2021
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder,24,cvpr,27,0,2023-06-03 14:28:31.105000,https://github.com/taldatech/soft-intro-vae-pytorch,174,Soft-IntroVAE: Analyzing and improving the introspective variational autoencoder,"https://scholar.google.com/scholar?cluster=5200997054602397790&hl=en&as_sdt=0,5",8,2021
Towards Diverse Paragraph Captioning for Untrimmed Videos,20,cvpr,3,4,2023-06-03 14:28:31.297000,https://github.com/syuqings/video-paragraph,64,Towards diverse paragraph captioning for untrimmed videos,"https://scholar.google.com/scholar?cluster=12726499520512547812&hl=en&as_sdt=0,47",4,2021
Relevance-CAM: Your Model Already Knows Where To Look,32,cvpr,12,3,2023-06-03 14:28:31.491000,https://github.com/mongeoroo/Relevance-CAM,30,Relevance-cam: Your model already knows where to look,"https://scholar.google.com/scholar?cluster=14789771741969512393&hl=en&as_sdt=0,14",3,2021
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation,118,cvpr,20,7,2023-06-03 14:28:31.685000,https://github.com/bowenc0221/boundary-iou-api,185,Boundary iou: Improving object-centric image segmentation evaluation,"https://scholar.google.com/scholar?cluster=10684515185169166483&hl=en&as_sdt=0,9",8,2021
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation,140,cvpr,99,86,2023-06-03 14:28:31.878000,https://github.com/Jeff-sjtu/HybrIK,751,Hybrik: A hybrid analytical-neural inverse kinematics solution for 3d human pose and shape estimation,"https://scholar.google.com/scholar?cluster=5227418509922795277&hl=en&as_sdt=0,5",19,2021
Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning,17,cvpr,0,0,2023-06-03 14:28:32.071000,https://github.com/flywithcloud/STHS,4,Hardness sampling for self-training based transductive zero-shot learning,"https://scholar.google.com/scholar?cluster=15258068797066662007&hl=en&as_sdt=0,5",2,2021
HoHoNet: 360 Indoor Holistic Understanding With Latent Horizontal Features,86,cvpr,22,9,2023-06-03 14:28:32.264000,https://github.com/sunset1995/HoHoNet,91,Hohonet: 360 indoor holistic understanding with latent horizontal features,"https://scholar.google.com/scholar?cluster=9387492226265607287&hl=en&as_sdt=0,33",6,2021
Learning To Recover 3D Scene Shape From a Single Image,96,cvpr,127,7,2023-06-03 14:28:32.458000,https://github.com/aim-uofa/AdelaiDepth,879,Learning to recover 3d scene shape from a single image,"https://scholar.google.com/scholar?cluster=2544157410497185829&hl=en&as_sdt=0,33",35,2021
Stochastic Image-to-Video Synthesis Using cINNs,22,cvpr,22,1,2023-06-03 14:28:32.652000,https://github.com/CompVis/image2video-synthesis-using-cINNs,150,Stochastic image-to-video synthesis using cinns,"https://scholar.google.com/scholar?cluster=9331470818129713954&hl=en&as_sdt=0,15",12,2021
Intrinsic Image Harmonization,38,cvpr,11,1,2023-06-03 14:28:32.845000,https://github.com/zhenglab/IntrinsicHarmony,46,Intrinsic image harmonization,"https://scholar.google.com/scholar?cluster=10659074732290532957&hl=en&as_sdt=0,30",5,2021
Learning To Fuse Asymmetric Feature Maps in Siamese Trackers,55,cvpr,2,8,2023-06-03 14:28:33.038000,https://github.com/wencheng256/SiamBAN-ACM,26,Learning to fuse asymmetric feature maps in siamese trackers,"https://scholar.google.com/scholar?cluster=8592317804502913899&hl=en&as_sdt=0,11",3,2021
NeX: Real-Time View Synthesis With Neural Basis Expansion,163,cvpr,71,33,2023-06-03 14:28:33.232000,https://github.com/nex-mpi/nex-code,568,Nex: Real-time view synthesis with neural basis expansion,"https://scholar.google.com/scholar?cluster=12655957296636411683&hl=en&as_sdt=0,5",24,2021
Jigsaw Clustering for Unsupervised Visual Representation Learning,41,cvpr,6,3,2023-06-03 14:28:33.425000,https://github.com/Jia-Research-Lab/JigsawClustering,76,Jigsaw clustering for unsupervised visual representation learning,"https://scholar.google.com/scholar?cluster=14392047762998246798&hl=en&as_sdt=0,22",4,2021
FS-Net: Fast Shape-Based Network for Category-Level 6D Object Pose Estimation With Decoupled Rotation Mechanism,78,cvpr,11,10,2023-06-03 14:28:33.619000,https://github.com/DC1991/FS-Net,82,Fs-net: Fast shape-based network for category-level 6d object pose estimation with decoupled rotation mechanism,"https://scholar.google.com/scholar?cluster=10283183897943492910&hl=en&as_sdt=0,22",8,2021
Ego-Exo: Transferring Visual Representations From Third-Person to First-Person Videos,29,cvpr,4,1,2023-06-03 14:28:33.812000,https://github.com/facebookresearch/Ego-Exo,24,Ego-exo: Transferring visual representations from third-person to first-person videos,"https://scholar.google.com/scholar?cluster=13422051591584617675&hl=en&as_sdt=0,10",6,2021
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink,60,cvpr,5,2,2023-06-03 14:28:34.005000,https://github.com/RjDuan/Advlight,26,Adversarial laser beam: Effective physical-world attack to dnns in a blink,"https://scholar.google.com/scholar?cluster=686104465748748989&hl=en&as_sdt=0,5",1,2021
Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection,58,cvpr,8,7,2023-06-03 14:28:34.198000,https://github.com/SDL-GuoZonghao/BeyondBoundingBox,35,Beyond bounding-box: Convex-hull feature adaptation for oriented and densely packed object detection,"https://scholar.google.com/scholar?cluster=11137887749868459671&hl=en&as_sdt=0,21",1,2021
Unsupervised Human Pose Estimation Through Transforming Shape Templates,27,cvpr,7,4,2023-06-03 14:28:34.392000,https://github.com/lschmidtke/shape_templates,27,Unsupervised human pose estimation through transforming shape templates,"https://scholar.google.com/scholar?cluster=1876170363487737133&hl=en&as_sdt=0,5",2,2021
Multiple Instance Captioning: Learning Representations From Histopathology Textbooks and Articles,17,cvpr,65,8,2023-06-03 14:28:34.585000,https://github.com/kdexd/virtex,544,Multiple instance captioning: Learning representations from histopathology textbooks and articles,"https://scholar.google.com/scholar?cluster=5825797168550832253&hl=en&as_sdt=0,10",14,2021
Parser-Free Virtual Try-On via Distilling Appearance Flows,73,cvpr,120,59,2023-06-03 14:28:34.778000,https://github.com/geyuying/PF-AFN,437,Parser-free virtual try-on via distilling appearance flows,"https://scholar.google.com/scholar?cluster=396392339053930240&hl=en&as_sdt=0,44",21,2021
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence,19,cvpr,0,2,2023-06-03 14:28:34.971000,https://github.com/leejaeyong7/PMNC,8,Patchmatch-based neighborhood consensus for semantic correspondence,"https://scholar.google.com/scholar?cluster=3815206428916496134&hl=en&as_sdt=0,14",4,2021
RGB-D Local Implicit Function for Depth Completion of Transparent Objects,36,cvpr,9,1,2023-06-03 14:28:35.165000,https://github.com/NVlabs/implicit_depth,48,RGB-D local implicit function for depth completion of transparent objects,"https://scholar.google.com/scholar?cluster=17103198080435903893&hl=en&as_sdt=0,41",5,2021
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation,151,cvpr,37,3,2023-06-03 14:28:35.358000,https://github.com/THU-DA-6D-Pose-Group/GDR-Net,190,Gdr-net: Geometry-guided direct regression network for monocular 6d object pose estimation,"https://scholar.google.com/scholar?cluster=14463958793485183598&hl=en&as_sdt=0,5",11,2021
Rethinking BiSeNet for Real-Time Semantic Segmentation,234,cvpr,129,67,2023-06-03 14:28:35.552000,https://github.com/MichaelFan01/STDC-Seg,595,Rethinking bisenet for real-time semantic segmentation,"https://scholar.google.com/scholar?cluster=315767785825564397&hl=en&as_sdt=0,5",16,2021
Representative Forgery Mining for Fake Face Detection,75,cvpr,10,2,2023-06-03 14:28:35.747000,https://github.com/crywang/RFM,68,Representative forgery mining for fake face detection,"https://scholar.google.com/scholar?cluster=2408328817690602874&hl=en&as_sdt=0,10",3,2021
Fingerspelling Detection in American Sign Language,17,cvpr,0,0,2023-06-03 14:28:35.941000,https://github.com/chevalierNoir/FS-Detection,13,Fingerspelling detection in american sign language,"https://scholar.google.com/scholar?cluster=16474260967419890042&hl=en&as_sdt=0,44",1,2021
Look Closer To Segment Better: Boundary Patch Refinement for Instance Segmentation,45,cvpr,19,6,2023-06-03 14:28:36.134000,https://github.com/tinyalpha/BPR,153,Look closer to segment better: Boundary patch refinement for instance segmentation,"https://scholar.google.com/scholar?cluster=5028520281881833573&hl=en&as_sdt=0,19",3,2021
Temporal Modulation Network for Controllable Space-Time Video Super-Resolution,48,cvpr,14,12,2023-06-03 14:28:36.328000,https://github.com/CS-GangXu/TMNet,102,Temporal modulation network for controllable space-time video super-resolution,"https://scholar.google.com/scholar?cluster=11881856033234337089&hl=en&as_sdt=0,30",4,2021
Time Lens: Event-Based Video Frame Interpolation,72,cvpr,84,13,2023-06-03 14:28:36.522000,https://github.com/uzh-rpg/rpg_timelens,565,Time lens: Event-based video frame interpolation,"https://scholar.google.com/scholar?cluster=13487669745126624319&hl=en&as_sdt=0,34",23,2021
Transformer Interpretability Beyond Attention Visualization,299,cvpr,186,3,2023-06-03 14:28:36.715000,https://github.com/hila-chefer/Transformer-Explainability,1339,Transformer interpretability beyond attention visualization,"https://scholar.google.com/scholar?cluster=8730617665338917129&hl=en&as_sdt=0,5",20,2021
Information-Theoretic Segmentation by Inpainting Error Maximization,11,cvpr,0,0,2023-06-03 14:28:36.908000,https://github.com/lolemacs/iem,4,Information-theoretic segmentation by inpainting error maximization,"https://scholar.google.com/scholar?cluster=9388185421636497003&hl=en&as_sdt=0,33",3,2021
Improving Calibration for Long-Tailed Recognition,127,cvpr,21,1,2023-06-03 14:28:37.101000,https://github.com/Jia-Research-Lab/MiSLAS,121,Improving calibration for long-tailed recognition,"https://scholar.google.com/scholar?cluster=1886209916702481819&hl=en&as_sdt=0,29",4,2021
DeFLOCNet: Deep Image Editing via Flexible Low-Level Controls,24,cvpr,3,3,2023-06-03 14:28:37.295000,https://github.com/KumapowerLIU/DeFLOCNet,44,Deflocnet: Deep image editing via flexible low-level controls,"https://scholar.google.com/scholar?cluster=12804292002191235287&hl=en&as_sdt=0,38",8,2021
Efficient Conditional GAN Transfer With Knowledge Propagation Across Classes,19,cvpr,4,1,2023-06-03 14:28:37.488000,https://github.com/mshahbazi72/cGANTransfer,43,Efficient conditional gan transfer with knowledge propagation across classes,"https://scholar.google.com/scholar?cluster=4584178947356205399&hl=en&as_sdt=0,5",7,2021
Can We Characterize Tasks Without Labels or Features?,7,cvpr,0,3,2023-06-03 14:28:37.682000,https://github.com/BramSW/task_characterization_cvpr_2021,11,Can We Characterize Tasks Without Labels or Features?,"https://scholar.google.com/scholar?cluster=9452455746785873680&hl=en&as_sdt=0,47",3,2021
RefineMask: Towards High-Quality Instance Segmentation With Fine-Grained Features,65,cvpr,31,0,2023-06-03 14:28:37.876000,https://github.com/zhanggang001/RefineMask,201,Refinemask: Towards high-quality instance segmentation with fine-grained features,"https://scholar.google.com/scholar?cluster=4381772034468775587&hl=en&as_sdt=0,5",5,2021
Fully Convolutional Scene Graph Generation,54,cvpr,2,7,2023-06-03 14:28:38.069000,https://github.com/liuhengyue/fcsgg,24,Fully convolutional scene graph generation,"https://scholar.google.com/scholar?cluster=15981776743724250181&hl=en&as_sdt=0,14",2,2021
Global2Local: Efficient Structure Search for Video Action Segmentation,47,cvpr,6,1,2023-06-03 14:28:38.262000,https://github.com/ShangHua-Gao/G2L-search,56,Global2local: Efficient structure search for video action segmentation,"https://scholar.google.com/scholar?cluster=12416380547225353104&hl=en&as_sdt=0,10",4,2021
Adversarial Robustness Under Long-Tailed Distribution,43,cvpr,9,1,2023-06-03 14:28:38.456000,https://github.com/wutong16/Adversarial_Long-Tail,92,Adversarial robustness under long-tailed distribution,"https://scholar.google.com/scholar?cluster=177693986831593820&hl=en&as_sdt=0,14",3,2021
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation,119,cvpr,10,5,2023-06-03 14:28:38.650000,https://github.com/najaemin92/fixbi,52,Fixbi: Bridging domain spaces for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=1372156062624791071&hl=en&as_sdt=0,1",2,2021
Adaptive Consistency Regularization for Semi-Supervised Transfer Learning,48,cvpr,15,0,2023-06-03 14:28:38.843000,https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning,96,Adaptive consistency regularization for semi-supervised transfer learning,"https://scholar.google.com/scholar?cluster=13702640495534978161&hl=en&as_sdt=0,8",4,2021
MoViNets: Mobile Video Networks for Efficient Video Recognition,124,cvpr,46272,1204,2023-06-03 14:28:39.036000,https://github.com/tensorflow/models,75885,Movinets: Mobile video networks for efficient video recognition,"https://scholar.google.com/scholar?cluster=8326744381641246666&hl=en&as_sdt=0,47",2774,2021
Augmentation Strategies for Learning With Noisy Labels,67,cvpr,13,0,2023-06-03 14:28:39.229000,https://github.com/KentoNishi/Augmentation-for-LNL,110,Augmentation strategies for learning with noisy labels,"https://scholar.google.com/scholar?cluster=2022581435379681044&hl=en&as_sdt=0,34",6,2021
Energy-Based Learning for Scene Graph Generation,88,cvpr,12,4,2023-06-03 14:28:39.425000,https://github.com/mods333/energy-based-scene-graph,81,Energy-based learning for scene graph generation,"https://scholar.google.com/scholar?cluster=7477049772793408087&hl=en&as_sdt=0,40",4,2021
Learning Camera Localization via Dense Scene Matching,19,cvpr,8,8,2023-06-03 14:28:39.618000,https://github.com/Tangshitao/Dense-Scene-Matching,71,Learning camera localization via dense scene matching,"https://scholar.google.com/scholar?cluster=11626173334165213359&hl=en&as_sdt=0,5",4,2021
UV-Net: Learning From Boundary Representations,23,cvpr,10,3,2023-06-03 14:28:39.811000,https://github.com/AutodeskAILab/UV-Net,39,Uv-net: Learning from boundary representations,"https://scholar.google.com/scholar?cluster=7470446315945873143&hl=en&as_sdt=0,33",6,2021
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation,125,cvpr,12,1,2023-06-03 14:28:40.007000,https://github.com/talreiss/PANDA,78,Panda: Adapting pretrained features for anomaly detection and segmentation,"https://scholar.google.com/scholar?cluster=16336360069322644038&hl=en&as_sdt=0,5",7,2021
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation,46,cvpr,4,3,2023-06-03 14:28:40.201000,https://github.com/yairkit/flowstep3d,23,Flowstep3d: Model unrolling for self-supervised scene flow estimation,"https://scholar.google.com/scholar?cluster=6101207113765783303&hl=en&as_sdt=0,5",1,2021
MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation,62,cvpr,5,0,2023-06-03 14:28:40.394000,https://github.com/sanjaykariyappa/maze,13,Maze: Data-free model stealing attack using zeroth-order gradient estimation,"https://scholar.google.com/scholar?cluster=11127018640325172921&hl=en&as_sdt=0,5",1,2021
ReDet: A Rotation-Equivariant Detector for Aerial Object Detection,221,cvpr,74,86,2023-06-03 14:28:40.589000,https://github.com/csuhan/ReDet,346,Redet: A rotation-equivariant detector for aerial object detection,"https://scholar.google.com/scholar?cluster=419805668893772133&hl=en&as_sdt=0,43",5,2021
Invertible Image Signal Processing,54,cvpr,35,5,2023-06-03 14:28:40.782000,https://github.com/yzxing87/Invertible-ISP,295,Invertible image signal processing,"https://scholar.google.com/scholar?cluster=10310908298402673581&hl=en&as_sdt=0,5",10,2021
Prototypical Cross-Domain Self-Supervised Learning for Few-Shot Unsupervised Domain Adaptation,84,cvpr,24,3,2023-06-03 14:28:40.976000,https://github.com/zhengzangw/PCS-FUDA,74,Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=9827357876251193092&hl=en&as_sdt=0,14",4,2021
Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation,8,cvpr,0,0,2023-06-03 14:28:41.174000,https://github.com/JBNU-VL/RelEx,1,Building reliable explanations of unreliable neural networks: locally smoothing perspective of model interpretation,"https://scholar.google.com/scholar?cluster=12474704611863090740&hl=en&as_sdt=0,5",1,2021
DAT: Training Deep Networks Robust To Label-Noise by Matching the Feature Distributions,12,cvpr,1,0,2023-06-03 14:28:41.367000,https://github.com/Tyqnn0323/DAT,11,Dat: Training deep networks robust to label-noise by matching the feature distributions,"https://scholar.google.com/scholar?cluster=11314817048313794234&hl=en&as_sdt=0,5",1,2021
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation,281,cvpr,32,17,2023-06-03 14:28:41.561000,https://github.com/betterze/StyleSpace,281,Stylespace analysis: Disentangled controls for stylegan image generation,"https://scholar.google.com/scholar?cluster=5513339767475462290&hl=en&as_sdt=0,31",6,2021
Keep Your Eyes on the Lane: Real-Time Attention-Guided Lane Detection,156,cvpr,154,11,2023-06-03 14:28:41.754000,https://github.com/lucastabelini/LaneATT,536,Keep your eyes on the lane: Real-time attention-guided lane detection,"https://scholar.google.com/scholar?cluster=13590135080871318855&hl=en&as_sdt=0,11",14,2021
FAPIS: A Few-Shot Anchor-Free Part-Based Instance Segmenter,16,cvpr,0,2,2023-06-03 14:28:41.948000,https://github.com/ducminhkhoi/FAPIS,9,Fapis: A few-shot anchor-free part-based instance segmenter,"https://scholar.google.com/scholar?cluster=8316840674657705612&hl=en&as_sdt=0,10",3,2021
Rethinking the Heatmap Regression for Bottom-Up Human Pose Estimation,78,cvpr,20,6,2023-06-03 14:28:42.141000,https://github.com/greatlog/SWAHR-HumanPose,115,Rethinking the heatmap regression for bottom-up human pose estimation,"https://scholar.google.com/scholar?cluster=15048897974803491120&hl=en&as_sdt=0,16",1,2021
Self-Supervised Multi-Frame Monocular Scene Flow,30,cvpr,17,2,2023-06-03 14:28:42.334000,https://github.com/visinf/multi-mono-sf,93,Self-supervised multi-frame monocular scene flow,"https://scholar.google.com/scholar?cluster=16870624673404241507&hl=en&as_sdt=0,5",4,2021
From Semantic Categories to Fixations: A Novel Weakly-Supervised Visual-Auditory Saliency Detection Approach,21,cvpr,6,3,2023-06-03 14:28:42.528000,https://github.com/guotaowang/STANet,14,From semantic categories to fixations: A novel weakly-supervised visual-auditory saliency detection approach,"https://scholar.google.com/scholar?cluster=17437126615806655697&hl=en&as_sdt=0,33",1,2021
Differentiable Diffusion for Dense Depth Estimation From Multi-View Images,2,cvpr,3,4,2023-06-03 14:28:42.721000,https://github.com/brownvc/diffdiffdepth,42,Differentiable Diffusion for Dense Depth Estimation from Multi-view Images,"https://scholar.google.com/scholar?cluster=14682164473312226217&hl=en&as_sdt=0,5",6,2021
Deep Compositional Metric Learning,33,cvpr,3,2,2023-06-03 14:28:42.914000,https://github.com/wzzheng/DCML,19,Deep compositional metric learning,"https://scholar.google.com/scholar?cluster=18408262231237431015&hl=en&as_sdt=0,31",1,2021
Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation,57,cvpr,8,0,2023-06-03 14:28:43.108000,https://github.com/lijichang/CVPR2021-SSDA,20,Cross-domain adaptive clustering for semi-supervised domain adaptation,"https://scholar.google.com/scholar?cluster=4324721633700837868&hl=en&as_sdt=0,36",1,2021
Combined Depth Space Based Architecture Search for Person Re-Identification,63,cvpr,4,2,2023-06-03 14:28:43.301000,https://github.com/solicucu/ReID,8,Combined depth space based architecture search for person re-identification,"https://scholar.google.com/scholar?cluster=13106955053088039794&hl=en&as_sdt=0,21",3,2021
Self-Supervised Augmentation Consistency for Adapting Semantic Segmentation,121,cvpr,35,3,2023-06-03 14:28:43.495000,https://github.com/visinf/da-sac,139,Self-supervised augmentation consistency for adapting semantic segmentation,"https://scholar.google.com/scholar?cluster=13973519484328469003&hl=en&as_sdt=0,34",5,2021
Multispectral Photometric Stereo for Spatially-Varying Spectral Reflectances: A Well Posed Problem?,5,cvpr,1,0,2023-06-03 14:28:43.688000,https://github.com/GH-HOME/MultispectralPS,7,Multispectral photometric stereo for spatially-varying spectral reflectances: A well posed problem?,"https://scholar.google.com/scholar?cluster=18314906697531227171&hl=en&as_sdt=0,5",2,2021
Diverse Branch Block: Building a Convolution as an Inception-Like Unit,111,cvpr,41,11,2023-06-03 14:28:43.881000,https://github.com/DingXiaoH/DiverseBranchBlock,278,Diverse branch block: Building a convolution as an inception-like unit,"https://scholar.google.com/scholar?cluster=7567904351838028390&hl=en&as_sdt=0,5",8,2021
Towards High Fidelity Face Relighting With Realistic Shadows,31,cvpr,23,1,2023-06-03 14:28:44.075000,https://github.com/andrewhou1/Shadow-Mask-Face-Relighting,128,Towards high fidelity face relighting with realistic shadows,"https://scholar.google.com/scholar?cluster=8434084467277614158&hl=en&as_sdt=0,14",7,2021
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling,13,cvpr,10,0,2023-06-03 14:28:44.269000,https://github.com/HuangZhiChao95/FewShotMotionTransfer,66,Few-shot human motion transfer by personalized geometry and texture modeling,"https://scholar.google.com/scholar?cluster=8295394362259851909&hl=en&as_sdt=0,33",4,2021
Post-Hoc Uncertainty Calibration for Domain Drift Scenarios,28,cvpr,1,0,2023-06-03 14:28:44.462000,https://github.com/tochris/calibration-domain-drift,5,Post-hoc uncertainty calibration for domain drift scenarios,"https://scholar.google.com/scholar?cluster=12414565283757295439&hl=en&as_sdt=0,34",3,2021
Multi-View Multi-Person 3D Pose Estimation With Plane Sweep Stereo,25,cvpr,7,13,2023-06-03 14:28:44.655000,https://github.com/jiahaoLjh/PlaneSweepPose,76,Multi-view multi-person 3d pose estimation with plane sweep stereo,"https://scholar.google.com/scholar?cluster=12677974094976459690&hl=en&as_sdt=0,45",10,2021
Progressive Semantic-Aware Style Transformation for Blind Face Restoration,65,cvpr,67,3,2023-06-03 14:28:44.849000,https://github.com/chaofengc/PSFRGAN,346,Progressive semantic-aware style transformation for blind face restoration,"https://scholar.google.com/scholar?cluster=15285979209219261599&hl=en&as_sdt=0,41",20,2021
Joint Generative and Contrastive Learning for Unsupervised Person Re-Identification,101,cvpr,10,12,2023-06-03 14:28:45.042000,https://github.com/chenhao2345/GCL,40,Joint generative and contrastive learning for unsupervised person re-identification,"https://scholar.google.com/scholar?cluster=719213803358993434&hl=en&as_sdt=0,3",6,2021
Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression,42,cvpr,0,3,2023-06-03 14:28:45.236000,https://github.com/alloldman/CKR,21,Room-and-object aware knowledge reasoning for remote embodied referring expression,"https://scholar.google.com/scholar?cluster=4131689644457640359&hl=en&as_sdt=0,11",0,2021
CDFI: Compression-Driven Network Design for Frame Interpolation,50,cvpr,17,0,2023-06-03 14:28:45.429000,https://github.com/tding1/CDFI,102,Cdfi: Compression-driven network design for frame interpolation,"https://scholar.google.com/scholar?cluster=4051392930031828023&hl=en&as_sdt=0,5",5,2021
Repetitive Activity Counting by Sight and Sound,22,cvpr,7,1,2023-06-03 14:28:45.623000,https://github.com/xiaobai1217/RepetitionCounting,21,Repetitive activity counting by sight and sound,"https://scholar.google.com/scholar?cluster=7710274650463090325&hl=en&as_sdt=0,5",2,2021
HyperSeg: Patch-Wise Hypernetwork for Real-Time Semantic Segmentation,96,cvpr,36,8,2023-06-03 14:28:45.817000,https://github.com/YuvalNirkin/hyperseg,190,Hyperseg: Patch-wise hypernetwork for real-time semantic segmentation,"https://scholar.google.com/scholar?cluster=285703110602718016&hl=en&as_sdt=0,15",9,2021
Partition-Guided GANs,13,cvpr,1,0,2023-06-03 14:28:46.014000,https://github.com/alisadeghian/PGMGAN,12,Partition-guided gans,"https://scholar.google.com/scholar?cluster=2694571426590294284&hl=en&as_sdt=0,5",4,2021
VarifocalNet: An IoU-Aware Dense Object Detector,316,cvpr,50,27,2023-06-03 14:28:46.217000,https://github.com/hyz-xmaster/VarifocalNet,320,Varifocalnet: An iou-aware dense object detector,"https://scholar.google.com/scholar?cluster=18267268624032061291&hl=en&as_sdt=0,47",9,2021
Distribution Alignment: A Unified Framework for Long-Tail Visual Recognition,144,cvpr,9,6,2023-06-03 14:28:46.411000,https://github.com/Megvii-BaseDetection/DisAlign,111,Distribution alignment: A unified framework for long-tail visual recognition,"https://scholar.google.com/scholar?cluster=14063730215715012129&hl=en&as_sdt=0,5",10,2021
GATSBI: Generative Agent-Centric Spatio-Temporal Object Interaction,5,cvpr,0,0,2023-06-03 14:28:46.604000,https://github.com/mch5048/gatsbi,11,Gatsbi: Generative agent-centric spatio-temporal object interaction,"https://scholar.google.com/scholar?cluster=8979640912156754679&hl=en&as_sdt=0,33",1,2021
pixelNeRF: Neural Radiance Fields From One or Few Images,598,cvpr,176,44,2023-06-03 14:28:46.798000,https://github.com/sxyu/pixel-nerf,1176,pixelnerf: Neural radiance fields from one or few images,"https://scholar.google.com/scholar?cluster=12682303033306081766&hl=en&as_sdt=0,47",21,2021
GIRAFFE: Representing Scenes As Compositional Generative Neural Feature Fields,496,cvpr,165,3,2023-06-03 14:28:46.991000,https://github.com/autonomousvision/giraffe,1190,Giraffe: Representing scenes as compositional generative neural feature fields,"https://scholar.google.com/scholar?cluster=15268522676622913459&hl=en&as_sdt=0,5",20,2021
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization,97,cvpr,40,19,2023-06-03 14:28:47.184000,https://github.com/Siyu-C/ACAR-Net,182,Actor-context-actor relation network for spatio-temporal action localization,"https://scholar.google.com/scholar?cluster=11514981785933912243&hl=en&as_sdt=0,14",13,2021
Navigating the GAN Parameter Space for Semantic Image Editing,53,cvpr,34,0,2023-06-03 14:28:47.378000,https://github.com/yandex-research/navigan,298,Navigating the gan parameter space for semantic image editing,"https://scholar.google.com/scholar?cluster=2075635132698586720&hl=en&as_sdt=0,5",9,2021
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition,73,cvpr,8,0,2023-06-03 14:28:47.571000,https://github.com/BIT-DA/MetaSAug,55,Metasaug: Meta semantic augmentation for long-tailed visual recognition,"https://scholar.google.com/scholar?cluster=5904217657972980159&hl=en&as_sdt=0,1",2,2021
Online Multiple Object Tracking With Cross-Task Synergy,38,cvpr,10,7,2023-06-03 14:28:47.765000,https://github.com/songguocode/TADAM,57,Online multiple object tracking with cross-task synergy,"https://scholar.google.com/scholar?cluster=359887446945404307&hl=en&as_sdt=0,44",3,2021
Audio-Visual Instance Discrimination with Cross-Modal Agreement,178,cvpr,19,9,2023-06-03 14:28:47.958000,https://github.com/facebookresearch/AVID-CMA,123,Audio-visual instance discrimination with cross-modal agreement,"https://scholar.google.com/scholar?cluster=885326186401082715&hl=en&as_sdt=0,44",10,2021
Disentangling Label Distribution for Long-Tailed Visual Recognition,120,cvpr,11,0,2023-06-03 14:28:48.151000,https://github.com/hyperconnect/LADE,84,Disentangling label distribution for long-tailed visual recognition,"https://scholar.google.com/scholar?cluster=7519970297883302935&hl=en&as_sdt=0,34",10,2021
DyGLIP: A Dynamic Graph Model With Link Prediction for Accurate Multi-Camera Multiple Object Tracking,22,cvpr,2,9,2023-06-03 14:28:48.345000,https://github.com/uark-cviu/DyGLIP,64,Dyglip: A dynamic graph model with link prediction for accurate multi-camera multiple object tracking,"https://scholar.google.com/scholar?cluster=351386239477940751&hl=en&as_sdt=0,5",13,2021
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding,184,cvpr,47,40,2023-06-03 14:28:48.548000,https://github.com/MegviiDetection/FSCE,255,Fsce: Few-shot object detection via contrastive proposal encoding,"https://scholar.google.com/scholar?cluster=3308996211432317749&hl=en&as_sdt=0,23",10,2021
Deep Dual Consecutive Network for Human Pose Estimation,66,cvpr,60,19,2023-06-03 14:28:48.753000,https://github.com/Pose-Group/DCPose,322,Deep dual consecutive network for human pose estimation,"https://scholar.google.com/scholar?cluster=4759985264284548369&hl=en&as_sdt=0,34",8,2021
How Well Do Self-Supervised Models Transfer?,174,cvpr,26,0,2023-06-03 14:28:48.946000,https://github.com/linusericsson/ssl-transfer,163,How well do self-supervised models transfer?,"https://scholar.google.com/scholar?cluster=7330299423743444329&hl=en&as_sdt=0,47",8,2021
GAN Prior Embedded Network for Blind Face Restoration in the Wild,114,cvpr,398,96,2023-06-03 14:28:49.139000,https://github.com/yangxy/GPEN,1966,Gan prior embedded network for blind face restoration in the wild,"https://scholar.google.com/scholar?cluster=13614110850816025937&hl=en&as_sdt=0,33",56,2021
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection,36,cvpr,5,10,2023-06-03 14:28:49.332000,https://github.com/VDIGPKU/OPANAS,43,Opanas: One-shot path aggregation network architecture search for object detection,"https://scholar.google.com/scholar?cluster=17614105059584946104&hl=en&as_sdt=0,5",4,2021
AutoInt: Automatic Integration for Fast Neural Volume Rendering,143,cvpr,19,0,2023-06-03 14:28:49.526000,https://github.com/computational-imaging/automatic-integration,165,Autoint: Automatic integration for fast neural volume rendering,"https://scholar.google.com/scholar?cluster=4848577432262489185&hl=en&as_sdt=0,5",13,2021
The Lottery Ticket Hypothesis for Object Recognition,43,cvpr,1,4,2023-06-03 14:28:49.719000,https://github.com/Sharath-girish/LTH-ObjectRecognition,18,The lottery ticket hypothesis for object recognition,"https://scholar.google.com/scholar?cluster=11434363513543078883&hl=en&as_sdt=0,10",4,2021
Pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis,446,cvpr,76,27,2023-06-03 14:28:49.919000,https://github.com/marcoamonteiro/pi-GAN,393,pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis,"https://scholar.google.com/scholar?cluster=5520148630577999505&hl=en&as_sdt=0,41",11,2021
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking,305,cvpr,41,16,2023-06-03 14:28:50.112000,https://github.com/594422814/TransformerTrack,245,Transformer meets tracker: Exploiting temporal context for robust visual tracking,"https://scholar.google.com/scholar?cluster=9233219793813194550&hl=en&as_sdt=0,5",5,2021
Slimmable Compressive Autoencoders for Practical Neural Image Compression,38,cvpr,2,3,2023-06-03 14:28:50.306000,https://github.com/FireFYF/SlimCAE,38,Slimmable compressive autoencoders for practical neural image compression,"https://scholar.google.com/scholar?cluster=5676193261669468177&hl=en&as_sdt=0,5",2,2021
Transformer Tracking,508,cvpr,97,46,2023-06-03 14:28:50.499000,https://github.com/chenxin-dlut/TransT,496,Transformer tracking,"https://scholar.google.com/scholar?cluster=10722665686456251008&hl=en&as_sdt=0,14",9,2021
Equivariant Point Network for 3D Point Cloud Analysis,56,cvpr,7,3,2023-06-03 14:28:50.693000,https://github.com/nintendops/EPN_PointCloud,88,Equivariant point network for 3d point cloud analysis,"https://scholar.google.com/scholar?cluster=4127793259562200898&hl=en&as_sdt=0,33",5,2021
Structured Scene Memory for Vision-Language Navigation,61,cvpr,7,4,2023-06-03 14:28:50.886000,https://github.com/HanqingWangAI/SSM-VLN,34,Structured scene memory for vision-language navigation,"https://scholar.google.com/scholar?cluster=15348911801382495252&hl=en&as_sdt=0,33",2,2021
Depth Completion With Twin Surface Extrapolation at Occlusion Boundaries,39,cvpr,7,5,2023-06-03 14:28:51.081000,https://github.com/imransai/TWISE,42,Depth completion with twin surface extrapolation at occlusion boundaries,"https://scholar.google.com/scholar?cluster=14151538568708825284&hl=en&as_sdt=0,1",2,2021
Self-Attention Based Text Knowledge Mining for Text Detection,20,cvpr,3,3,2023-06-03 14:28:51.273000,https://github.com/CVI-SZU/STKM,41,Self-attention based text knowledge mining for text detection,"https://scholar.google.com/scholar?cluster=10173785649147986287&hl=en&as_sdt=0,33",5,2021
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic,82,cvpr,41,3,2023-06-03 14:28:51.467000,https://github.com/Xiangtaokong/ClassSR,325,Classsr: A general framework to accelerate super-resolution networks by data characteristic,"https://scholar.google.com/scholar?cluster=15500329011658079939&hl=en&as_sdt=0,40",3,2021
Learning the Superpixel in a Non-Iterative and Lifelong Manner,18,cvpr,16,13,2023-06-03 14:28:51.661000,https://github.com/zh460045050/LNSNet,44,Learning the superpixel in a non-iterative and lifelong manner,"https://scholar.google.com/scholar?cluster=7645654626633181885&hl=en&as_sdt=0,39",2,2021
Efficient Initial Pose-Graph Generation for Global SfM,15,cvpr,8,2,2023-06-03 14:28:51.854000,https://github.com/danini/pose-graph-initialization,41,Efficient initial pose-graph generation for global sfm,"https://scholar.google.com/scholar?cluster=10390214568687825189&hl=en&as_sdt=0,5",12,2021
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy,19,cvpr,2,3,2023-06-03 14:28:52.049000,https://github.com/hqucv/dmtrack,21,Distractor-aware fast tracking via dynamic convolutions and mot philosophy,"https://scholar.google.com/scholar?cluster=13442299740744634912&hl=en&as_sdt=0,5",8,2021
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning,44,cvpr,11,2,2023-06-03 14:28:52.242000,https://github.com/yanbeic/CCL,77,Distilling audio-visual knowledge by compositional contrastive learning,"https://scholar.google.com/scholar?cluster=17588021199787406593&hl=en&as_sdt=0,33",5,2021
MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation,68,cvpr,10,0,2023-06-03 14:28:52.436000,https://github.com/tjiiv-cprg/MonoRUn,98,Monorun: Monocular 3d object detection by reconstruction and uncertainty propagation,"https://scholar.google.com/scholar?cluster=11633936977756915168&hl=en&as_sdt=0,5",8,2021
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing,55,cvpr,7,7,2023-06-03 14:28:52.630000,https://github.com/tfzhou/MG-HumanParsing,73,Differentiable multi-granularity human representation learning for instance-aware human semantic parsing,"https://scholar.google.com/scholar?cluster=10601974880031634539&hl=en&as_sdt=0,9",13,2021
Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans,315,cvpr,125,0,2023-06-03 14:28:52.824000,https://github.com/zju3dv/neuralbody,804,Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans,"https://scholar.google.com/scholar?cluster=9135636288565980674&hl=en&as_sdt=0,44",42,2021
Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset With Limited Computational Resources,12,cvpr,2,6,2023-06-03 14:28:53.019000,https://github.com/pengyuLPY/Virtual-Fully-Connected-Layer,21,Virtual fully-connected layer: Training a large-scale face recognition dataset with limited computational resources,"https://scholar.google.com/scholar?cluster=12479292701668402823&hl=en&as_sdt=0,47",2,2021
One Shot Face Swapping on Megapixels,79,cvpr,36,22,2023-06-03 14:28:53.213000,https://github.com/zyainfal/One-Shot-Face-Swapping-on-Megapixels,271,One shot face swapping on megapixels,"https://scholar.google.com/scholar?cluster=2088011683119816417&hl=en&as_sdt=0,21",16,2021
Learning Delaunay Surface Elements for Mesh Reconstruction,20,cvpr,12,1,2023-06-03 14:28:53.407000,https://github.com/mrakotosaon/dse-meshing,38,Learning delaunay surface elements for mesh reconstruction,"https://scholar.google.com/scholar?cluster=16915408577903371249&hl=en&as_sdt=0,5",6,2021
PAConv: Position Adaptive Convolution With Dynamic Kernel Assembling on Point Clouds,218,cvpr,37,4,2023-06-03 14:28:53.603000,https://github.com/CVMI-Lab/PAConv,245,Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds,"https://scholar.google.com/scholar?cluster=8457792520671281421&hl=en&as_sdt=0,48",7,2021
Predicting Human Scanpaths in Visual Question Answering,18,cvpr,3,0,2023-06-03 14:28:53.796000,https://github.com/chenxy99/Scanpaths,14,Predicting human scanpaths in visual question answering,"https://scholar.google.com/scholar?cluster=8570058457085367295&hl=en&as_sdt=0,5",2,2021
DetectoRS: Detecting Objects With Recursive Feature Pyramid and Switchable Atrous Convolution,495,cvpr,188,50,2023-06-03 14:28:53.990000,https://github.com/joe-siyuan-qiao/DetectoRS,1118,Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution,"https://scholar.google.com/scholar?cluster=3466946125865762121&hl=en&as_sdt=0,33",36,2021
Monte Carlo Scene Search for 3D Scene Understanding,11,cvpr,2,1,2023-06-03 14:28:54.184000,https://github.com/vevenom/MonteScene,17,Monte Carlo scene search for 3D scene understanding,"https://scholar.google.com/scholar?cluster=1260848938814070643&hl=en&as_sdt=0,3",5,2021
MIST: Multiple Instance Spatial Transformer,7,cvpr,1,0,2023-06-03 14:28:54.376000,https://github.com/ubc-vision/mist,25,MIST: Multiple instance spatial transformer,"https://scholar.google.com/scholar?cluster=1866821344209049802&hl=en&as_sdt=0,5",3,2021
Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-Localization in Large Scenes From Body-Mounted Sensors,52,cvpr,12,1,2023-06-03 14:28:54.570000,https://github.com/aymenmir1/hps,72,Human poseitioning system (hps): 3d human pose estimation and self-localization in large scenes from body-mounted sensors,"https://scholar.google.com/scholar?cluster=3530345321739728254&hl=en&as_sdt=0,10",6,2021
Unsupervised Pre-Training for Person Re-Identification,68,cvpr,22,8,2023-06-03 14:28:54.763000,https://github.com/DengpanFu/LUPerson,158,Unsupervised pre-training for person re-identification,"https://scholar.google.com/scholar?cluster=9308264680885243047&hl=en&as_sdt=0,14",7,2021
Rethinking Channel Dimensions for Efficient Model Design,48,cvpr,63,2,2023-06-03 14:28:54.957000,https://github.com/clovaai/rexnet,444,Rethinking channel dimensions for efficient model design,"https://scholar.google.com/scholar?cluster=14969696303509372057&hl=en&as_sdt=0,33",16,2021
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation,232,cvpr,167,51,2023-06-03 14:28:55.150000,https://github.com/xinge008/Cylinder3D,707,Cylindrical and asymmetrical 3d convolution networks for lidar segmentation,"https://scholar.google.com/scholar?cluster=428342647351348811&hl=en&as_sdt=0,5",15,2021
DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation,41,cvpr,6,6,2023-06-03 14:28:55.343000,https://github.com/aliyun/DCT-Mask,66,Dct-mask: Discrete cosine transform mask representation for instance segmentation,"https://scholar.google.com/scholar?cluster=16116062821910974025&hl=en&as_sdt=0,33",6,2021
Dynamic Domain Adaptation for Efficient Inference,8,cvpr,8,1,2023-06-03 14:28:55.537000,https://github.com/BIT-DA/DDA,28,Dynamic domain adaptation for efficient inference,"https://scholar.google.com/scholar?cluster=17885613617005564756&hl=en&as_sdt=0,23",1,2021
Learning To Recommend Frame for Interactive Video Object Segmentation in the Wild,8,cvpr,4,1,2023-06-03 14:28:55.736000,https://github.com/svip-lab/IVOS-W,44,Learning to recommend frame for interactive video object segmentation in the wild,"https://scholar.google.com/scholar?cluster=5011051962198707605&hl=en&as_sdt=0,5",6,2021
"Re-Labeling ImageNet: From Single to Multi-Labels, From Global to Localized Labels",95,cvpr,39,2,2023-06-03 14:28:55.929000,https://github.com/naver-ai/relabel_imagenet,377,"Re-labeling imagenet: from single to multi-labels, from global to localized labels","https://scholar.google.com/scholar?cluster=12310992157550430603&hl=en&as_sdt=0,36",11,2021
Interventional Video Grounding With Dual Contrastive Learning,80,cvpr,0,7,2023-06-03 14:28:56.124000,https://github.com/nanguoshun/IVG,7,Interventional video grounding with dual contrastive learning,"https://scholar.google.com/scholar?cluster=2403210337017808628&hl=en&as_sdt=0,5",5,2021
GMOT-40: A Benchmark for Generic Multiple Object Tracking,14,cvpr,2,0,2023-06-03 14:28:56.318000,https://github.com/Spritea/GMOT40,39,Gmot-40: A benchmark for generic multiple object tracking,"https://scholar.google.com/scholar?cluster=11014649565197412325&hl=en&as_sdt=0,11",3,2021
Cross Modal Focal Loss for RGBD Face Anti-Spoofing,56,cvpr,0,0,2023-06-03 14:28:56.512000,https://github.com/anjith2006/bob.paper.cross_modal_focal_loss_cvpr2021,10,Cross modal focal loss for rgbd face anti-spoofing,"https://scholar.google.com/scholar?cluster=12940960528013032461&hl=en&as_sdt=0,33",2,2021
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting,72,cvpr,9,4,2023-06-03 14:28:56.706000,https://github.com/chen-judge/RGBTCrowdCounting,45,Cross-modal collaborative representation learning and a large-scale rgbt benchmark for crowd counting,"https://scholar.google.com/scholar?cluster=6548341927243916638&hl=en&as_sdt=0,15",4,2021
Tangent Space Backpropagation for 3D Transformation Groups,22,cvpr,36,19,2023-06-03 14:28:56.899000,https://github.com/princeton-vl/lietorch,535,Tangent space backpropagation for 3d transformation groups,"https://scholar.google.com/scholar?cluster=10038910471790624583&hl=en&as_sdt=0,40",15,2021
Cross-Modal Contrastive Learning for Text-to-Image Generation,178,cvpr,14,19,2023-06-03 14:28:57.093000,https://github.com/google-research/xmcgan_image_generation,98,Cross-modal contrastive learning for text-to-image generation,"https://scholar.google.com/scholar?cluster=6680839752426556602&hl=en&as_sdt=0,14",4,2021
Few-Shot Classification With Feature Map Reconstruction Networks,124,cvpr,15,1,2023-06-03 14:28:57.286000,https://github.com/Tsingularity/FRN,82,Few-shot classification with feature map reconstruction networks,"https://scholar.google.com/scholar?cluster=16309286926460217904&hl=en&as_sdt=0,5",4,2021
Image Super-Resolution With Non-Local Sparse Attention,207,cvpr,16,2,2023-06-03 14:28:57.480000,https://github.com/HarukiYqM/Non-Local-Sparse-Attention,152,Image super-resolution with non-local sparse attention,"https://scholar.google.com/scholar?cluster=1709843943888104380&hl=en&as_sdt=0,5",3,2021
SMPLicit: Topology-Aware Generative Model for Clothed People,106,cvpr,27,13,2023-06-03 14:28:57.673000,https://github.com/enriccorona/SMPLicit,235,Smplicit: Topology-aware generative model for clothed people,"https://scholar.google.com/scholar?cluster=9252689260924385414&hl=en&as_sdt=0,5",10,2021
SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks,140,cvpr,29,7,2023-06-03 14:28:57.867000,https://github.com/shunsukesaito/SCANimate,253,SCANimate: Weakly supervised learning of skinned clothed avatar networks,"https://scholar.google.com/scholar?cluster=17897461219740040607&hl=en&as_sdt=0,5",10,2021
Categorical Depth Distribution Network for Monocular 3D Object Detection,231,cvpr,59,22,2023-06-03 14:28:58.060000,https://github.com/TRAILab/CaDDN,311,Categorical depth distribution network for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=24498449239586378&hl=en&as_sdt=0,39",5,2021
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization,17,cvpr,7286,1013,2023-06-03 14:28:58.254000,https://github.com/google-research/google-research,29546,Learning view-disentangled human pose representation by contrastive cross-view mutual information maximization,"https://scholar.google.com/scholar?cluster=2922115036876098901&hl=en&as_sdt=0,23",726,2021
Towards Long-Form Video Understanding,64,cvpr,7,5,2023-06-03 14:28:58.447000,https://github.com/chaoyuaw/lvu,71,Towards long-form video understanding,"https://scholar.google.com/scholar?cluster=10827722935938975238&hl=en&as_sdt=0,10",2,2021
On Semantic Similarity in Video Retrieval,42,cvpr,2,0,2023-06-03 14:28:58.641000,https://github.com/mwray/Semantic-Video-Retrieval,53,On semantic similarity in video retrieval,"https://scholar.google.com/scholar?cluster=10863589681444958355&hl=en&as_sdt=0,14",9,2021
Noise-Resistant Deep Metric Learning With Ranking-Based Instance Selection,27,cvpr,5,1,2023-06-03 14:28:58.834000,https://github.com/alibaba-edu/Ranking-based-Instance-Selection,29,Noise-resistant deep metric learning with ranking-based instance selection,"https://scholar.google.com/scholar?cluster=3956427373032683933&hl=en&as_sdt=0,3",3,2021
Representation Learning via Global Temporal Alignment and Cycle-Consistency,32,cvpr,4,5,2023-06-03 14:28:59.027000,https://github.com/hadjisma/VideoAlignment,42,Representation learning via global temporal alignment and cycle-consistency,"https://scholar.google.com/scholar?cluster=6263659697815819074&hl=en&as_sdt=0,14",7,2021
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search,16,cvpr,0,0,2023-06-03 14:28:59.221000,https://github.com/kcyu2014/nas-landmarkreg,9,Landmark regularization: Ranking guided super-net training in neural architecture search,"https://scholar.google.com/scholar?cluster=4693351170344874099&hl=en&as_sdt=0,5",2,2021
End-to-End Human Pose and Mesh Reconstruction with Transformers,339,cvpr,88,18,2023-06-03 14:28:59.414000,https://github.com/microsoft/MeshTransformer,521,End-to-end human pose and mesh reconstruction with transformers,"https://scholar.google.com/scholar?cluster=12956114412627599893&hl=en&as_sdt=0,14",17,2021
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework,50,cvpr,30,3,2023-06-03 14:28:59.608000,https://github.com/Hzzone/MTLFace,131,When age-invariant face recognition meets face age synthesis: A multi-task learning framework,"https://scholar.google.com/scholar?cluster=13757601612779033095&hl=en&as_sdt=0,46",9,2021
Cross-Iteration Batch Normalization,88,cvpr,22,4,2023-06-03 14:28:59.801000,https://github.com/Howal/Cross-iterationBatchNorm,126,Cross-iteration batch normalization,"https://scholar.google.com/scholar?cluster=3743158606346871914&hl=en&as_sdt=0,33",6,2021
De-Rendering the World's Revolutionary Artefacts,16,cvpr,11,3,2023-06-03 14:28:59.994000,https://github.com/elliottwu/sorderender,52,De-rendering the world's revolutionary artefacts,"https://scholar.google.com/scholar?cluster=637677617740599049&hl=en&as_sdt=0,24",6,2021
Robust Instance Segmentation Through Reasoning About Multi-Object Occlusion,20,cvpr,8,2,2023-06-03 14:29:00.188000,https://github.com/XD7479/Multi-Object-Occlusion,26,Robust instance segmentation through reasoning about multi-object occlusion,"https://scholar.google.com/scholar?cluster=13930657581866394608&hl=en&as_sdt=0,4",3,2021
Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow,22,cvpr,5,8,2023-06-03 14:29:00.382000,https://github.com/uof1745-cmd/PCN,37,Progressively complementary network for fisheye image rectification using appearance flow,"https://scholar.google.com/scholar?cluster=12233705436054244389&hl=en&as_sdt=0,5",3,2021
"BABEL: Bodies, Action and Behavior With English Labels",60,cvpr,6,0,2023-06-03 14:29:00.576000,https://github.com/abhinanda-punnakkal/BABEL,122,"BABEL: bodies, action and behavior with English labels","https://scholar.google.com/scholar?cluster=4012164611659135605&hl=en&as_sdt=0,41",4,2021
Back to the Feature: Learning Robust Camera Localization From Pixels To Pose,126,cvpr,87,20,2023-06-03 14:29:00.776000,https://github.com/cvg/pixloc,647,Back to the feature: Learning robust camera localization from pixels to pose,"https://scholar.google.com/scholar?cluster=13820242433868080224&hl=en&as_sdt=0,39",50,2021
From Rain Generation to Rain Removal,46,cvpr,16,5,2023-06-03 14:29:00.969000,https://github.com/hongwang01/VRGNet,50,From rain generation to rain removal,"https://scholar.google.com/scholar?cluster=11428573310197006048&hl=en&as_sdt=0,5",2,2021
Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals,55,cvpr,9,15,2023-06-03 14:29:01.163000,https://github.com/qiank10/MVDNet,74,Robust multimodal vehicle detection in foggy weather using complementary lidar and radar signals,"https://scholar.google.com/scholar?cluster=18183830905353060568&hl=en&as_sdt=0,7",3,2021
Learning Parallel Dense Correspondence From Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction,12,cvpr,1,0,2023-06-03 14:29:01.356000,https://github.com/tangjiapeng/LPDC-Net,28,Learning parallel dense correspondence from spatio-temporal descriptors for efficient and robust 4d reconstruction,"https://scholar.google.com/scholar?cluster=11099306128444077591&hl=en&as_sdt=0,33",5,2021
VDSM: Unsupervised Video Disentanglement With State-Space Modeling and Deep Mixtures of Experts,7,cvpr,1,0,2023-06-03 14:29:01.554000,https://github.com/matthewvowels1/DisentanglingSequences,20,Vdsm: Unsupervised video disentanglement with state-space modeling and deep mixtures of experts,"https://scholar.google.com/scholar?cluster=15689859843532812856&hl=en&as_sdt=0,23",1,2021
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving,236,cvpr,152,3,2023-06-03 14:29:01.759000,https://github.com/autonomousvision/transfuser,784,Multi-modal fusion transformer for end-to-end autonomous driving,"https://scholar.google.com/scholar?cluster=10455103882616133990&hl=en&as_sdt=0,5",24,2021
Deformed Implicit Field: Modeling 3D Shapes With Learned Dense Correspondence,79,cvpr,16,8,2023-06-03 14:29:01.952000,https://github.com/microsoft/DIF-Net,107,Deformed implicit field: Modeling 3d shapes with learned dense correspondence,"https://scholar.google.com/scholar?cluster=3920937918828019885&hl=en&as_sdt=0,3",9,2021
Deep Homography for Efficient Stereo Image Compression,24,cvpr,12,2,2023-06-03 14:29:02.146000,https://github.com/ywz978020607/HESIC,49,Deep homography for efficient stereo image compression,"https://scholar.google.com/scholar?cluster=11291893566376592800&hl=en&as_sdt=0,5",4,2021
VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization,64,cvpr,87,0,2023-06-03 14:29:02.340000,https://github.com/shadow2496/VITON-HD,371,Viton-hd: High-resolution virtual try-on via misalignment-aware normalization,"https://scholar.google.com/scholar?cluster=14147239110505271848&hl=en&as_sdt=0,5",29,2021
Training Networks in Null Space of Feature Covariance for Continual Learning,49,cvpr,2,1,2023-06-03 14:29:02.534000,https://github.com/ShipengWang/Adam-NSCL,34,Training networks in null space of feature covariance for continual learning,"https://scholar.google.com/scholar?cluster=3981676700337551569&hl=en&as_sdt=0,44",1,2021
Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks,29,cvpr,4,2,2023-06-03 14:29:02.729000,https://github.com/fwilliams/neural-splines,62,Neural splines: Fitting 3d surfaces with infinitely-wide neural networks,"https://scholar.google.com/scholar?cluster=1751086253494726077&hl=en&as_sdt=0,5",4,2021
LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search,83,cvpr,54,16,2023-06-03 14:29:02.922000,https://github.com/researchmm/LightTrack,322,Lighttrack: Finding lightweight neural networks for object tracking via one-shot architecture search,"https://scholar.google.com/scholar?cluster=6211078804998152856&hl=en&as_sdt=0,34",17,2021
Visual Room Rearrangement,67,cvpr,12,1,2023-06-03 14:29:03.116000,https://github.com/allenai/robothor-challenge,53,Visual room rearrangement,"https://scholar.google.com/scholar?cluster=711651683418692059&hl=en&as_sdt=0,34",44,2021
Few-Shot 3D Point Cloud Semantic Segmentation,51,cvpr,28,10,2023-06-03 14:29:03.310000,https://github.com/Na-Z/attMPTI,124,Few-shot 3d point cloud semantic segmentation,"https://scholar.google.com/scholar?cluster=11867037827768862449&hl=en&as_sdt=0,14",7,2021
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation,255,cvpr,47,17,2023-06-03 14:29:03.504000,https://github.com/microsoft/ProDA,252,Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=4978603340943430786&hl=en&as_sdt=0,10",6,2021
Adaptive Consistency Prior Based Deep Network for Image Denoising,74,cvpr,8,4,2023-06-03 14:29:03.698000,https://github.com/chaoren88/DeamNet,40,Adaptive consistency prior based deep network for image denoising,"https://scholar.google.com/scholar?cluster=9558877767879141008&hl=en&as_sdt=0,5",1,2021
Unsupervised Real-World Image Super Resolution via Domain-Distance Aware Training,73,cvpr,23,19,2023-06-03 14:29:03.891000,https://github.com/ShuhangGu/DASR,212,Unsupervised real-world image super resolution via domain-distance aware training,"https://scholar.google.com/scholar?cluster=438254105640002831&hl=en&as_sdt=0,22",9,2021
Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination,48,cvpr,8,0,2023-06-03 14:29:04.085000,https://github.com/frank-xwang/CLD-UnsupervisedLearning,92,Unsupervised feature learning by cross-level instance-group discrimination,"https://scholar.google.com/scholar?cluster=5575362988777240669&hl=en&as_sdt=0,5",8,2021
Equalization Loss v2: A New Gradient Balance Approach for Long-Tailed Object Detection,81,cvpr,22,14,2023-06-03 14:29:04.278000,https://github.com/tztztztztz/eqlv2,139,Equalization loss v2: A new gradient balance approach for long-tailed object detection,"https://scholar.google.com/scholar?cluster=5397807898365416838&hl=en&as_sdt=0,5",7,2021
Tracking Pedestrian Heads in Dense Crowd,46,cvpr,8,2,2023-06-03 14:29:04.471000,https://github.com/Sentient07/HeadHunter--T,35,Tracking pedestrian heads in dense crowd,"https://scholar.google.com/scholar?cluster=14185015812291628203&hl=en&as_sdt=0,5",3,2021
Primitive Representation Learning for Scene Text Recognition,50,cvpr,14,12,2023-06-03 14:29:04.666000,https://github.com/RuijieJ/pren,77,Primitive representation learning for scene text recognition,"https://scholar.google.com/scholar?cluster=2679302935830848317&hl=en&as_sdt=0,5",2,2021
SDD-FIQA: Unsupervised Face Image Quality Assessment With Similarity Distribution Distance,53,cvpr,207,59,2023-06-03 14:29:04.859000,https://github.com/Tencent/TFace,1044,SDD-FIQA: unsupervised face image quality assessment with similarity distribution distance,"https://scholar.google.com/scholar?cluster=7724786486839901478&hl=en&as_sdt=0,23",34,2021
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts,321,cvpr,12,1,2023-06-03 14:29:05.052000,https://github.com/google-research-datasets/conceptual-12m,252,Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,"https://scholar.google.com/scholar?cluster=3375736515991008673&hl=en&as_sdt=0,14",13,2021
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks,43,cvpr,2,2,2023-06-03 14:29:05.251000,https://github.com/XinshaoAmosWang/ProSelfLC-CVPR2021,57,Proselflc: Progressive self label correction for training robust deep neural networks,"https://scholar.google.com/scholar?cluster=14790390926220732400&hl=en&as_sdt=0,5",3,2021
Generalized Few-Shot Object Detection Without Forgetting,110,cvpr,3,2,2023-06-03 14:29:05.444000,https://github.com/Megvii-BaseDetection/GFSD,36,Generalized few-shot object detection without forgetting,"https://scholar.google.com/scholar?cluster=6196888269192586051&hl=en&as_sdt=0,10",5,2021
Learning To Segment Rigid Motions From Two Frames,28,cvpr,18,8,2023-06-03 14:29:05.643000,https://github.com/gengshan-y/rigidmask,163,Learning to segment rigid motions from two frames,"https://scholar.google.com/scholar?cluster=16220174865769682176&hl=en&as_sdt=0,39",11,2021
Truly Shift-Invariant Convolutional Neural Networks,40,cvpr,7,4,2023-06-03 14:29:05.838000,https://github.com/achaman2/truly_shift_invariant_cnns,48,Truly shift-invariant convolutional neural networks,"https://scholar.google.com/scholar?cluster=596645945924258419&hl=en&as_sdt=0,33",2,2021
Learning Continuous Image Representation With Local Implicit Image Function,275,cvpr,132,18,2023-06-03 14:29:06.031000,https://github.com/yinboc/liif,1084,Learning continuous image representation with local implicit image function,"https://scholar.google.com/scholar?cluster=17688179025247323276&hl=en&as_sdt=0,3",23,2021
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data,23,cvpr,13,4,2023-06-03 14:29:06.225000,https://github.com/jw9730/setvae,63,Setvae: Learning hierarchical composition for generative modeling of set-structured data,"https://scholar.google.com/scholar?cluster=10751598209000319269&hl=en&as_sdt=0,21",3,2021
Multi-Label Learning From Single Positive Labels,43,cvpr,15,0,2023-06-03 14:29:06.419000,https://github.com/elijahcole/single-positive-multi-label,78,Multi-label learning from single positive labels,"https://scholar.google.com/scholar?cluster=10629946850600064814&hl=en&as_sdt=0,5",7,2021
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching,116,cvpr,21,15,2023-06-03 14:29:06.612000,https://github.com/gallenszl/CFNet,119,Cfnet: Cascade and fused cost volume for robust stereo matching,"https://scholar.google.com/scholar?cluster=6636929522099977175&hl=en&as_sdt=0,25",7,2021
LiDAR-Based Panoptic Segmentation via Dynamic Shifting Network,52,cvpr,29,11,2023-06-03 14:29:06.805000,https://github.com/hongfz16/DS-Net,219,Lidar-based panoptic segmentation via dynamic shifting network,"https://scholar.google.com/scholar?cluster=8868905053187909969&hl=en&as_sdt=0,33",10,2021
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning,63,cvpr,10,5,2023-06-03 14:29:06.999000,https://github.com/zhukaii/SPPR,79,Self-promoted prototype refinement for few-shot class-incremental learning,"https://scholar.google.com/scholar?cluster=14305352558080676320&hl=en&as_sdt=0,14",5,2021
SiamMOT: Siamese Multi-Object Tracking,86,cvpr,61,31,2023-06-03 14:29:07.192000,https://github.com/amazon-research/siam-mot,455,Siammot: Siamese multi-object tracking,"https://scholar.google.com/scholar?cluster=10406043046175863207&hl=en&as_sdt=0,5",18,2021
Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning,85,cvpr,6,1,2023-06-03 14:29:07.386000,https://github.com/guopengf/FL-MRCM,40,Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning,"https://scholar.google.com/scholar?cluster=9660062839000972634&hl=en&as_sdt=0,10",2,2021
Separating Skills and Concepts for Novel Visual Question Answering,26,cvpr,1,6,2023-06-03 14:29:07.580000,https://github.com/SpencerWhitehead/novelvqa,20,Separating skills and concepts for novel visual question answering,"https://scholar.google.com/scholar?cluster=5432885246099890718&hl=en&as_sdt=0,26",5,2021
Self-Aligned Video Deraining With Transmission-Depth Consistency,14,cvpr,2,6,2023-06-03 14:29:07.774000,https://github.com/wending94/Self-Aligned-Video-Deraining-with-Transmission-Depth-Consistency,9,Self-aligned video deraining with transmission-depth consistency,"https://scholar.google.com/scholar?cluster=16493647877794878130&hl=en&as_sdt=0,21",1,2021
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction,55,cvpr,20,2,2023-06-03 14:29:07.967000,https://github.com/Andy97/DeepMLS,110,Deep implicit moving least-squares functions for 3D reconstruction,"https://scholar.google.com/scholar?cluster=8086388192176560197&hl=en&as_sdt=0,14",4,2021
SwiftNet: Real-Time Video Object Segmentation,76,cvpr,8,6,2023-06-03 14:29:08.161000,https://github.com/haochenheheda/SwiftNet,71,Swiftnet: Real-time video object segmentation,"https://scholar.google.com/scholar?cluster=15131125620840130629&hl=en&as_sdt=0,25",3,2021
Scene Text Retrieval via Joint Text Detection and Similarity Learning,20,cvpr,6,6,2023-06-03 14:29:08.355000,https://github.com/lanfeng4659/STR-TDSL,79,Scene text retrieval via joint text detection and similarity learning,"https://scholar.google.com/scholar?cluster=3224927623216328056&hl=en&as_sdt=0,39",14,2021
QPIC: Query-Based Pairwise Human-Object Interaction Detection With Image-Wide Contextual Information,90,cvpr,28,21,2023-06-03 14:29:08.553000,https://github.com/hitachi-rd-cv/qpic,114,Qpic: Query-based pairwise human-object interaction detection with image-wide contextual information,"https://scholar.google.com/scholar?cluster=2040091967364618062&hl=en&as_sdt=0,14",8,2021
Mutual Graph Learning for Camouflaged Object Detection,97,cvpr,7,3,2023-06-03 14:29:08.763000,https://github.com/fanyang587/MGL,37,Mutual graph learning for camouflaged object detection,"https://scholar.google.com/scholar?cluster=2736803402775181033&hl=en&as_sdt=0,5",2,2021
Contrastive Embedding for Generalized Zero-Shot Learning,113,cvpr,18,1,2023-06-03 14:29:08.956000,https://github.com/Hanzy1996/CE-GZSL,77,Contrastive embedding for generalized zero-shot learning,"https://scholar.google.com/scholar?cluster=16849290189422535119&hl=en&as_sdt=0,3",1,2021
Home Action Genome: Cooperative Compositional Action Understanding,33,cvpr,3,2,2023-06-03 14:29:09.150000,https://github.com/nishantrai18/homage,11,Home action genome: Cooperative compositional action understanding,"https://scholar.google.com/scholar?cluster=10699455751162519671&hl=en&as_sdt=0,5",3,2021
Involution: Inverting the Inherence of Convolution for Visual Recognition,182,cvpr,172,23,2023-06-03 14:29:09.343000,https://github.com/d-li14/involution,1282,Involution: Inverting the inherence of convolution for visual recognition,"https://scholar.google.com/scholar?cluster=393441868853886664&hl=en&as_sdt=0,34",16,2021
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification,86,cvpr,11,0,2023-06-03 14:29:09.536000,https://github.com/FlyingRoastDuck/MetaCam_DSCE,59,Joint noise-tolerant learning and meta camera shift adaptation for unsupervised person re-identification,"https://scholar.google.com/scholar?cluster=13113254359526621910&hl=en&as_sdt=0,36",3,2021
Less Is More: ClipBERT for Video-and-Language Learning via Sparse Sampling,345,cvpr,80,12,2023-06-03 14:29:09.730000,https://github.com/jayleicn/ClipBERT,636,Less is more: Clipbert for video-and-language learning via sparse sampling,"https://scholar.google.com/scholar?cluster=16820126260126878886&hl=en&as_sdt=0,47",8,2021
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies,19,cvpr,1,0,2023-06-03 14:29:09.923000,https://github.com/JimmyCai91/DLT,16,Deep lesion tracker: monitoring lesions in 4D longitudinal imaging studies,"https://scholar.google.com/scholar?cluster=18233692910243885982&hl=en&as_sdt=0,33",2,2021
Self-Supervised Learning of Depth Inference for Multi-View Stereo,28,cvpr,10,6,2023-06-03 14:29:10.118000,https://github.com/JiayuYANG/Self-supervised-CVP-MVSNet,77,Self-supervised learning of depth inference for multi-view stereo,"https://scholar.google.com/scholar?cluster=11457735974106134030&hl=en&as_sdt=0,5",9,2021
ST3D: Self-Training for Unsupervised Domain Adaptation on 3D Object Detection,98,cvpr,37,2,2023-06-03 14:29:10.311000,https://github.com/CVMI-Lab/ST3D,247,St3d: Self-training for unsupervised domain adaptation on 3d object detection,"https://scholar.google.com/scholar?cluster=12232176416755323092&hl=en&as_sdt=0,5",13,2021
BRepNet: A Topological Message Passing System for Solid Models,30,cvpr,39,4,2023-06-03 14:29:10.505000,https://github.com/AutodeskAILab/Fusion360GalleryDataset,294,Brepnet: A topological message passing system for solid models,"https://scholar.google.com/scholar?cluster=13088699303570660399&hl=en&as_sdt=0,5",33,2021
Learning To Warp for Style Transfer,21,cvpr,6,3,2023-06-03 14:29:10.698000,https://github.com/xch-liu/learning-warp-st,31,Learning to warp for style transfer,"https://scholar.google.com/scholar?cluster=1585034475210210502&hl=en&as_sdt=0,10",1,2021
Deeply Shape-Guided Cascade for Instance Segmentation,17,cvpr,1,3,2023-06-03 14:29:10.892000,https://github.com/hding2455/DSC,14,Deeply shape-guided cascade for instance segmentation,"https://scholar.google.com/scholar?cluster=4542003749813626262&hl=en&as_sdt=0,44",1,2021
Encoding in Style: A StyleGAN Encoder for Image-to-Image Translation,652,cvpr,537,2,2023-06-03 14:29:11.085000,https://github.com/eladrich/pixel2style2pixel,2934,Encoding in style: a stylegan encoder for image-to-image translation,"https://scholar.google.com/scholar?cluster=12264250297849199750&hl=en&as_sdt=0,47",64,2021
Enriching ImageNet With Human Similarity Judgments and Psychological Embeddings,32,cvpr,6,8,2023-06-03 14:29:11.280000,https://github.com/roads/psiz,28,Enriching imagenet with human similarity judgments and psychological embeddings,"https://scholar.google.com/scholar?cluster=18339232715543273339&hl=en&as_sdt=0,5",3,2021
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback,97,cvpr,37,13,2023-06-03 14:29:11.474000,https://github.com/XiaoxiaoGuo/fashion-iq,109,Fashion iq: A new dataset towards retrieving images by natural language feedback,"https://scholar.google.com/scholar?cluster=13059282150878702635&hl=en&as_sdt=0,5",3,2021
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection,158,cvpr,55,27,2023-06-03 14:29:11.667000,https://github.com/implus/GFocalV2,455,Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection,"https://scholar.google.com/scholar?cluster=12458152692195612427&hl=en&as_sdt=0,5",14,2021
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal,45,cvpr,6,13,2023-06-03 14:29:11.861000,https://github.com/wangxiang1230/SSTAP,60,Self-supervised learning for semi-supervised temporal action proposal,"https://scholar.google.com/scholar?cluster=13058870912069214775&hl=en&as_sdt=0,44",4,2021
Exploiting Spatial Dimensions of Latent in GAN for Real-Time Image Editing,96,cvpr,79,19,2023-06-03 14:29:12.055000,https://github.com/naver-ai/StyleMapGAN,442,Exploiting spatial dimensions of latent in gan for real-time image editing,"https://scholar.google.com/scholar?cluster=4637129633542110179&hl=en&as_sdt=0,34",10,2021
Learning Cross-Modal Retrieval With Noisy Labels,45,cvpr,9,2,2023-06-03 14:29:12.249000,https://github.com/penghu-cs/MRL,40,Learning cross-modal retrieval with noisy labels,"https://scholar.google.com/scholar?cluster=8371167861789755514&hl=en&as_sdt=0,36",3,2021
Model-Based 3D Hand Reconstruction via Self-Supervised Learning,57,cvpr,11,9,2023-06-03 14:29:12.443000,https://github.com/TerenceCYJ/S2HAND,79,Model-based 3d hand reconstruction via self-supervised learning,"https://scholar.google.com/scholar?cluster=8411707964425961039&hl=en&as_sdt=0,3",6,2021
"Your ""Flamingo"" is My ""Bird"": Fine-Grained, or Not",54,cvpr,9,0,2023-06-03 14:29:12.637000,https://github.com/PRIS-CV/Fine-Grained-or-Not,47,"Your"" Flamingo"" is my"" Bird"": fine-grained, or not","https://scholar.google.com/scholar?cluster=4159273575784882414&hl=en&as_sdt=0,20",3,2021
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth,136,cvpr,75,32,2023-06-03 14:29:12.831000,https://github.com/nianticlabs/manydepth,545,The temporal opportunist: Self-supervised multi-frame monocular depth,"https://scholar.google.com/scholar?cluster=10836634820207184106&hl=en&as_sdt=0,26",17,2021
Linear Semantics in Generative Adversarial Networks,15,cvpr,4,0,2023-06-03 14:29:13.024000,https://github.com/AtlantixJJ/LinearGAN,25,Linear semantics in generative adversarial networks,"https://scholar.google.com/scholar?cluster=13931738749710537337&hl=en&as_sdt=0,38",4,2021
Semi-Supervised Video Deraining With Dynamical Rain Generator,37,cvpr,19,7,2023-06-03 14:29:13.219000,https://github.com/zsyOAOA/S2VD,59,Semi-supervised video deraining with dynamical rain generator,"https://scholar.google.com/scholar?cluster=12212442081427603950&hl=en&as_sdt=0,21",6,2021
DG-Font: Deformable Generative Networks for Unsupervised Font Generation,43,cvpr,33,37,2023-06-03 14:29:13.412000,https://github.com/ecnuycxie/DG-Font,163,Dg-font: Deformable generative networks for unsupervised font generation,"https://scholar.google.com/scholar?cluster=5676121317228703912&hl=en&as_sdt=0,5",4,2021
Mesoscopic Photogrammetry With an Unstabilized Phone Camera,9,cvpr,5,0,2023-06-03 14:29:13.606000,https://github.com/kevinczhou/mesoscopic-photogrammetry,27,Mesoscopic photogrammetry with an unstabilized phone camera,"https://scholar.google.com/scholar?cluster=5544038158039929878&hl=en&as_sdt=0,5",3,2021
Robust Reflection Removal With Reflection-Free Flash-Only Cues,25,cvpr,27,2,2023-06-03 14:29:13.799000,https://github.com/ChenyangLEI/flash-reflection-removal,177,Robust reflection removal with reflection-free flash-only cues,"https://scholar.google.com/scholar?cluster=17618252109389744590&hl=en&as_sdt=0,5",4,2021
Real-Time Selfie Video Stabilization,7,cvpr,5,4,2023-06-03 14:29:13.993000,https://github.com/jiy173/selfievideostabilization,40,Real-time selfie video stabilization,"https://scholar.google.com/scholar?cluster=9199625127240779541&hl=en&as_sdt=0,14",3,2021
Diverse Semantic Image Synthesis via Probability Distribution Modeling,30,cvpr,7,1,2023-06-03 14:29:14.187000,https://github.com/tzt101/INADE,50,Diverse semantic image synthesis via probability distribution modeling,"https://scholar.google.com/scholar?cluster=3606979518541326034&hl=en&as_sdt=0,5",4,2021
Self-Generated Defocus Blur Detection via Dual Adversarial Discriminators,12,cvpr,9,3,2023-06-03 14:29:14.381000,https://github.com/shangcai1/SG,20,Self-generated defocus blur detection via dual adversarial discriminators,"https://scholar.google.com/scholar?cluster=8864367165223509629&hl=en&as_sdt=0,5",1,2021
Body2Hands: Learning To Infer 3D Hands From Conversational Gesture Body Dynamics,32,cvpr,14,4,2023-06-03 14:29:14.574000,https://github.com/facebookresearch/body2hands,92,Body2hands: Learning to infer 3d hands from conversational gesture body dynamics,"https://scholar.google.com/scholar?cluster=8082217403370037880&hl=en&as_sdt=0,11",10,2021
Semantic Scene Completion via Integrating Instances and Scene In-the-Loop,27,cvpr,0,1,2023-06-03 14:29:14.773000,https://github.com/yjcaimeow/SISNet,10,Semantic scene completion via integrating instances and scene in-the-loop,"https://scholar.google.com/scholar?cluster=10278535956132345017&hl=en&as_sdt=0,21",2,2021
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection,53,cvpr,20,1,2023-06-03 14:29:14.967000,https://github.com/abhi1kumar/groomed_nms,79,Groomed-nms: Grouped mathematically differentiable nms for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=17502801882249020372&hl=en&as_sdt=0,5",9,2021
Unsupervised Visual Representation Learning by Tracking Patches in Video,16,cvpr,5,2,2023-06-03 14:29:15.160000,https://github.com/microsoft/CtP,44,Unsupervised visual representation learning by tracking patches in video,"https://scholar.google.com/scholar?cluster=11577331929387498194&hl=en&as_sdt=0,3",5,2021
Deep Learning in Latent Space for Video Prediction and Compression,42,cvpr,3,1,2023-06-03 14:29:15.354000,https://github.com/BowenL0218/Video_Compression,16,Deep learning in latent space for video prediction and compression,"https://scholar.google.com/scholar?cluster=17100088282737430240&hl=en&as_sdt=0,34",1,2021
3D Human Action Representation Learning via Cross-View Consistency Pursuit,89,cvpr,18,7,2023-06-03 14:29:15.547000,https://github.com/LinguoLi/CrosSCLR,48,3d human action representation learning via cross-view consistency pursuit,"https://scholar.google.com/scholar?cluster=14585779787909883755&hl=en&as_sdt=0,5",5,2021
Unsupervised Degradation Representation Learning for Blind Super-Resolution,169,cvpr,47,67,2023-06-03 14:29:15.756000,https://github.com/LongguangWang/DASR,336,Unsupervised degradation representation learning for blind super-resolution,"https://scholar.google.com/scholar?cluster=12938651263779779258&hl=en&as_sdt=0,39",10,2021
Deep Analysis of CNN-Based Spatio-Temporal Representations for Action Recognition,69,cvpr,43,9,2023-06-03 14:29:15.949000,https://github.com/IBM/action-recognition-pytorch,207,Deep analysis of cnn-based spatio-temporal representations for action recognition,"https://scholar.google.com/scholar?cluster=13442779659830969305&hl=en&as_sdt=0,5",13,2021
Semi-Supervised Action Recognition With Temporal Contrastive Learning,56,cvpr,6,4,2023-06-03 14:29:16.143000,https://github.com/CVIR/TCL,50,Semi-supervised action recognition with temporal contrastive learning,"https://scholar.google.com/scholar?cluster=16627879401168255798&hl=en&as_sdt=0,21",4,2021
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View Transformation,55,cvpr,19,10,2023-06-03 14:29:16.336000,https://github.com/JonDoe-297/cross-view,129,Projecting your view attentively: Monocular road scene layout estimation via cross-view transformation,"https://scholar.google.com/scholar?cluster=9398792801654115525&hl=en&as_sdt=0,43",4,2021
Adaptive Aggregation Networks for Class-Incremental Learning,113,cvpr,67,27,2023-06-03 14:29:16.530000,https://github.com/yaoyao-liu/class-incremental-learning,420,Adaptive aggregation networks for class-incremental learning,"https://scholar.google.com/scholar?cluster=3356118117821233260&hl=en&as_sdt=0,11",13,2021
Part-Aware Panoptic Segmentation,31,cvpr,15,2,2023-06-03 14:29:16.724000,https://github.com/tue-mps/panoptic_parts,95,Part-aware panoptic segmentation,"https://scholar.google.com/scholar?cluster=6655933110036314416&hl=en&as_sdt=0,4",8,2021
SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation,90,cvpr,8,5,2023-06-03 14:29:16.917000,https://github.com/goodproj13/SG-Net,58,Sg-net: Spatial granularity network for one-stage video instance segmentation,"https://scholar.google.com/scholar?cluster=15843316366415866499&hl=en&as_sdt=0,23",7,2021
Hierarchical and Partially Observable Goal-Driven Policy Learning With Goals Relational Graph,12,cvpr,2,3,2023-06-03 14:29:17.111000,https://github.com/Xin-Ye-1/HRL-GRG,11,Hierarchical and partially observable goal-driven policy learning with goals relational graph,"https://scholar.google.com/scholar?cluster=6089087711305508639&hl=en&as_sdt=0,39",2,2021
Convolutional Hough Matching Networks,37,cvpr,10,1,2023-06-03 14:29:17.305000,https://github.com/juhongm999/chm,72,Convolutional hough matching networks,"https://scholar.google.com/scholar?cluster=3616198346725754055&hl=en&as_sdt=0,14",1,2021
Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos,85,cvpr,14,8,2023-06-03 14:29:17.499000,https://github.com/hehefan/P4Transformer,119,Point 4d transformer networks for spatio-temporal modeling in point cloud videos,"https://scholar.google.com/scholar?cluster=1837130648312459327&hl=en&as_sdt=0,5",3,2021
Deep Convolutional Dictionary Learning for Image Denoising,51,cvpr,18,4,2023-06-03 14:29:17.692000,https://github.com/natezhenghy/DCDicL_denoising,101,Deep convolutional dictionary learning for image denoising,"https://scholar.google.com/scholar?cluster=1839135958435369844&hl=en&as_sdt=0,44",4,2021
Dynamic Class Queue for Large Scale Face Recognition in the Wild,11,cvpr,11,4,2023-06-03 14:29:17.886000,https://github.com/bilylee/DCQ,55,Dynamic class queue for large scale face recognition in the wild,"https://scholar.google.com/scholar?cluster=17570798662013091752&hl=en&as_sdt=0,5",4,2021
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation,67,cvpr,7,11,2023-06-03 14:29:18.080000,https://github.com/liuzhengzhe/One-Thing-One-Click,46,One thing one click: A self-training approach for weakly supervised 3d semantic segmentation,"https://scholar.google.com/scholar?cluster=11937309607531868823&hl=en&as_sdt=0,5",6,2021
Clusformer: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition,23,cvpr,2,0,2023-06-03 14:29:18.278000,https://github.com/uark-cviu/Intraformer,12,Clusformer: A transformer based clustering approach to unsupervised large-scale face and visual landmark recognition,"https://scholar.google.com/scholar?cluster=15842403157814716357&hl=en&as_sdt=0,14",2,2021
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations From Self-Trained Negative Adversaries,104,cvpr,18,0,2023-06-03 14:29:18.472000,https://github.com/maple-research-lab/AdCo,162,Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries,"https://scholar.google.com/scholar?cluster=17767222965679946506&hl=en&as_sdt=0,5",7,2021
Learned Initializations for Optimizing Coordinate-Based Neural Representations,158,cvpr,22,4,2023-06-03 14:29:18.666000,https://github.com/tancik/learnit,143,Learned initializations for optimizing coordinate-based neural representations,"https://scholar.google.com/scholar?cluster=6727393976512171217&hl=en&as_sdt=0,5",8,2021
FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds,18,cvpr,2,4,2023-06-03 14:29:18.860000,https://github.com/InterDigitalInc/FESTA,18,Festa: Flow estimation via spatial-temporal attention for scene point clouds,"https://scholar.google.com/scholar?cluster=10745735900488186828&hl=en&as_sdt=0,45",1,2021
Uncertainty-Aware Joint Salient Object and Camouflaged Object Detection,94,cvpr,9,4,2023-06-03 14:29:19.053000,https://github.com/JingZhang617/Joint_COD_SOD,43,Uncertainty-aware joint salient object and camouflaged object detection,"https://scholar.google.com/scholar?cluster=7630240259379039565&hl=en&as_sdt=0,48",3,2021
You Only Look One-Level Feature,337,cvpr,118,27,2023-06-03 14:29:19.248000,https://github.com/megvii-model/YOLOF,799,You only look one-level feature,"https://scholar.google.com/scholar?cluster=8367071398417962813&hl=en&as_sdt=0,21",20,2021
EvDistill: Asynchronous Events To End-Task Learning via Bidirectional Reconstruction-Guided Cross-Modal Knowledge Distillation,34,cvpr,3,4,2023-06-03 14:29:19.443000,https://github.com/addisonwang2013/evdistill,22,Evdistill: Asynchronous events to end-task learning via bidirectional reconstruction-guided cross-modal knowledge distillation,"https://scholar.google.com/scholar?cluster=9062827688836585541&hl=en&as_sdt=0,33",2,2021
PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation,87,cvpr,54,4,2023-06-03 14:29:19.640000,https://github.com/jfzhang95/PoseAug,342,Poseaug: A differentiable pose augmentation framework for 3d human pose estimation,"https://scholar.google.com/scholar?cluster=18834802810197113&hl=en&as_sdt=0,41",19,2021
Transferable Semantic Augmentation for Domain Adaptation,65,cvpr,11,6,2023-06-03 14:29:19.833000,https://github.com/BIT-DA/TSA,68,Transferable semantic augmentation for domain adaptation,"https://scholar.google.com/scholar?cluster=6287196979078308522&hl=en&as_sdt=0,36",2,2021
Seesaw Loss for Long-Tailed Instance Segmentation,144,cvpr,8691,801,2023-06-03 14:29:20.028000,https://github.com/open-mmlab/mmdetection,24422,Seesaw loss for long-tailed instance segmentation,"https://scholar.google.com/scholar?cluster=10365484825680988313&hl=en&as_sdt=0,38",372,2021
No Frame Left Behind: Full Video Action Recognition,24,cvpr,3,0,2023-06-03 14:29:20.221000,https://github.com/L-KID/Full-Video-Action-Recognition,15,No frame left behind: Full video action recognition,"https://scholar.google.com/scholar?cluster=1704422255390141383&hl=en&as_sdt=0,33",1,2021
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer,51,cvpr,1166,249,2023-06-03 14:29:20.416000,https://github.com/PaddlePaddle/PaddleGAN,6980,Drafting and revision: Laplacian pyramid network for fast high-quality artistic style transfer,"https://scholar.google.com/scholar?cluster=3871486821033857987&hl=en&as_sdt=0,5",102,2021
Adversarial Generation of Continuous Images,107,cvpr,23,6,2023-06-03 14:29:20.610000,https://github.com/universome/inr-gan,226,Adversarial generation of continuous images,"https://scholar.google.com/scholar?cluster=13576341953482945188&hl=en&as_sdt=0,5",10,2021
Watching You: Global-Guided Reciprocal Learning for Video-Based Person Re-Identification,51,cvpr,6,3,2023-06-03 14:29:20.804000,https://github.com/flysnowtiger/GRL,18,Watching you: Global-guided reciprocal learning for video-based person re-identification,"https://scholar.google.com/scholar?cluster=928417901228249912&hl=en&as_sdt=0,41",1,2021
Polygonal Building Extraction by Frame Field Learning,32,cvpr,55,24,2023-06-03 14:29:20.997000,https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning,227,Polygonal building extraction by frame field learning,"https://scholar.google.com/scholar?cluster=5467064830111076941&hl=en&as_sdt=0,51",13,2021
Image Generators With Conditionally-Independent Pixel Synthesis,114,cvpr,36,13,2023-06-03 14:29:21.191000,https://github.com/saic-mdal/CIPS,203,Image generators with conditionally-independent pixel synthesis,"https://scholar.google.com/scholar?cluster=2624387036194918896&hl=en&as_sdt=0,29",9,2021
NeuralFusion: Online Depth Fusion in Latent Space,30,cvpr,2,0,2023-06-03 14:29:21.385000,https://github.com/weders/NeuralFusion,59,Neuralfusion: Online depth fusion in latent space,"https://scholar.google.com/scholar?cluster=14651258106620525182&hl=en&as_sdt=0,14",5,2021
PU-GCN: Point Cloud Upsampling Using Graph Convolutional Networks,104,cvpr,16,10,2023-06-03 14:29:21.580000,https://github.com/guochengqian/PU-GCN,132,Pu-gcn: Point cloud upsampling using graph convolutional networks,"https://scholar.google.com/scholar?cluster=16007941439711255758&hl=en&as_sdt=0,5",9,2021
How2Sign: A Large-Scale Multimodal Dataset for Continuous American Sign Language,74,cvpr,1,8,2023-06-03 14:29:21.773000,https://github.com/how2sign/how2sign.github.io,7,How2sign: a large-scale multimodal dataset for continuous american sign language,"https://scholar.google.com/scholar?cluster=6693792152789343772&hl=en&as_sdt=0,3",4,2021
A Closer Look at Fourier Spectrum Discrepancies for CNN-Generated Images Detection,36,cvpr,5,0,2023-06-03 14:29:21.967000,https://github.com/sutd-visual-computing-group/Fourier-Discrepancies-CNN-Detection,24,A closer look at fourier spectrum discrepancies for cnn-generated images detection,"https://scholar.google.com/scholar?cluster=16628031137386261195&hl=en&as_sdt=0,10",5,2021
MaX-DeepLab: End-to-End Panoptic Segmentation With Mask Transformers,319,cvpr,155,30,2023-06-03 14:29:22.165000,https://github.com/google-research/deeplab2,904,Max-deeplab: End-to-end panoptic segmentation with mask transformers,"https://scholar.google.com/scholar?cluster=5792560058636996973&hl=en&as_sdt=0,5",23,2021
Dynamic Head: Unifying Object Detection Heads With Attentions,215,cvpr,61,30,2023-06-03 14:29:22.359000,https://github.com/microsoft/DynamicHead,574,Dynamic head: Unifying object detection heads with attentions,"https://scholar.google.com/scholar?cluster=1319826694668830613&hl=en&as_sdt=0,5",38,2021
Counterfactual VQA: A Cause-Effect Look at Language Bias,226,cvpr,13,3,2023-06-03 14:29:22.551000,https://github.com/yuleiniu/cfvqa,103,Counterfactual vqa: A cause-effect look at language bias,"https://scholar.google.com/scholar?cluster=16921277254023051511&hl=en&as_sdt=0,4",1,2021
Learning a Proposal Classifier for Multiple Object Tracking,78,cvpr,8,11,2023-06-03 14:29:22.759000,https://github.com/daip13/LPC_MOT,72,Learning a proposal classifier for multiple object tracking,"https://scholar.google.com/scholar?cluster=17248423667886589275&hl=en&as_sdt=0,13",2,2021
Denoise and Contrast for Category Agnostic Shape Completion,19,cvpr,3,0,2023-06-03 14:29:22.953000,https://github.com/antoalli/Deco,32,Denoise and contrast for category agnostic shape completion,"https://scholar.google.com/scholar?cluster=4770719809788299633&hl=en&as_sdt=0,14",3,2021
Indoor Panorama Planar 3D Reconstruction via Divide and Conquer,6,cvpr,8,4,2023-06-03 14:29:23.146000,https://github.com/sunset1995/panoplane360,38,Indoor panorama planar 3d reconstruction via divide and conquer,"https://scholar.google.com/scholar?cluster=3193687115703792849&hl=en&as_sdt=0,25",4,2021
Multi-Attentional Deepfake Detection,270,cvpr,51,20,2023-06-03 14:29:23.340000,https://github.com/yoctta/multiple-attention,184,Multi-attentional deepfake detection,"https://scholar.google.com/scholar?cluster=17591479096007453523&hl=en&as_sdt=0,11",4,2021
RAFT-3D: Scene Flow Using Rigid-Motion Embeddings,66,cvpr,22,10,2023-06-03 14:29:23.534000,https://github.com/princeton-vl/RAFT-3D,195,Raft-3d: Scene flow using rigid-motion embeddings,"https://scholar.google.com/scholar?cluster=4984780804505362232&hl=en&as_sdt=0,23",8,2021
Consistent Instance False Positive Improves Fairness in Face Recognition,38,cvpr,207,59,2023-06-03 14:29:23.727000,https://github.com/Tencent/TFace,1044,Consistent instance false positive improves fairness in face recognition,"https://scholar.google.com/scholar?cluster=13248267076396056645&hl=en&as_sdt=0,5",34,2021
Neighbor2Neighbor: Self-Supervised Denoising From Single Noisy Images,131,cvpr,32,9,2023-06-03 14:29:23.921000,https://github.com/TaoHuang2018/Neighbor2Neighbor,192,Neighbor2neighbor: Self-supervised denoising from single noisy images,"https://scholar.google.com/scholar?cluster=3848352971463411925&hl=en&as_sdt=0,18",8,2021
Exploring intermediate representation for monocular vehicle pose estimation,22,cvpr,19,7,2023-06-03 14:29:24.114000,https://github.com/Nicholasli1995/EgoNet,151,Exploring intermediate representation for monocular vehicle pose estimation,"https://scholar.google.com/scholar?cluster=14896714302792961653&hl=en&as_sdt=0,47",5,2021
Capturing Omni-Range Context for Omnidirectional Segmentation,51,cvpr,4,0,2023-06-03 14:29:24.308000,https://github.com/elnino9ykl/WildPASS,24,Capturing omni-range context for omnidirectional segmentation,"https://scholar.google.com/scholar?cluster=16295518216625722547&hl=en&as_sdt=0,33",1,2021
FaceSec: A Fine-Grained Robustness Evaluation Framework for Face Recognition Systems,12,cvpr,1,0,2023-06-03 14:29:24.501000,https://github.com/KnowledgeDiscovery/FaceSec,2,Facesec: A fine-grained robustness evaluation framework for face recognition systems,"https://scholar.google.com/scholar?cluster=2525111220508051406&hl=en&as_sdt=0,44",0,2021
Practical Single-Image Super-Resolution Using Look-Up Table,29,cvpr,19,7,2023-06-03 14:29:24.695000,https://github.com/yhjo09/SR-LUT,130,Practical single-image super-resolution using look-up table,"https://scholar.google.com/scholar?cluster=15412161541559387188&hl=en&as_sdt=0,31",5,2021
Face Forensics in the Wild,36,cvpr,1,1,2023-06-03 14:29:24.888000,https://github.com/tfzhou/FFIW,40,Face forensics in the wild,"https://scholar.google.com/scholar?cluster=18250568401405564851&hl=en&as_sdt=0,11",4,2021
SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation,21,cvpr,9,2,2023-06-03 14:29:25.082000,https://github.com/sanghyun-son/srwarp,94,SRWarp: Generalized image super-resolution under arbitrary transformation,"https://scholar.google.com/scholar?cluster=2617927953153752522&hl=en&as_sdt=0,33",7,2021
Point Cloud Upsampling via Disentangled Refinement,72,cvpr,7,10,2023-06-03 14:29:25.277000,https://github.com/liruihui/Dis-PU,57,Point cloud upsampling via disentangled refinement,"https://scholar.google.com/scholar?cluster=621432179935144707&hl=en&as_sdt=0,33",3,2021
Riggable 3D Face Reconstruction via In-Network Optimization,28,cvpr,11,10,2023-06-03 14:29:25.470000,https://github.com/zqbai-jeremy/INORig,134,Riggable 3d face reconstruction via in-network optimization,"https://scholar.google.com/scholar?cluster=1590327969363368005&hl=en&as_sdt=0,3",11,2021
Anti-Aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation,33,cvpr,6,3,2023-06-03 14:29:25.664000,https://github.com/Bibkiller/ASR,13,Anti-aliasing semantic reconstruction for few-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=10066436287730165739&hl=en&as_sdt=0,44",1,2021
Masksembles for Uncertainty Estimation,44,cvpr,10,9,2023-06-03 14:29:25.857000,https://github.com/nikitadurasov/masksembles,87,Masksembles for uncertainty estimation,"https://scholar.google.com/scholar?cluster=8793330525655669666&hl=en&as_sdt=0,5",3,2021
S2R-DepthNet: Learning a Generalizable Depth-Specific Structural Representation,34,cvpr,35,11,2023-06-03 14:29:26.051000,https://github.com/microsoft/S2R-DepthNet,161,S2r-depthnet: Learning a generalizable depth-specific structural representation,"https://scholar.google.com/scholar?cluster=13231358957211408773&hl=en&as_sdt=0,10",10,2021
ACTION-Net: Multipath Excitation for Action Recognition,96,cvpr,45,7,2023-06-03 14:29:26.245000,https://github.com/V-Sense/ACTION-Net,182,Action-net: Multipath excitation for action recognition,"https://scholar.google.com/scholar?cluster=7419477302114680323&hl=en&as_sdt=0,5",11,2021
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation,125,cvpr,65,47,2023-06-03 14:29:26.438000,https://github.com/ethnhe/FFB6D,220,Ffb6d: A full flow bidirectional fusion network for 6d pose estimation,"https://scholar.google.com/scholar?cluster=18291440380943792483&hl=en&as_sdt=0,33",7,2021
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures,13,cvpr,0,0,2023-06-03 14:29:26.631000,https://github.com/ofsoundof/Heterogeneity_Hypothesis,9,The heterogeneity hypothesis: Finding layer-wise differentiated network architectures,"https://scholar.google.com/scholar?cluster=4465443810300018794&hl=en&as_sdt=0,5",3,2021
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation,141,cvpr,34,19,2023-06-03 14:29:26.825000,https://github.com/microsoft/CoCosNet-v2,321,Cocosnet v2: Full-resolution correspondence learning for image translation,"https://scholar.google.com/scholar?cluster=11302776795234140037&hl=en&as_sdt=0,5",19,2021
Three Ways To Improve Semantic Segmentation With Self-Supervised Depth Estimation,54,cvpr,28,4,2023-06-03 14:29:27.019000,https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth,224,Three ways to improve semantic segmentation with self-supervised depth estimation,"https://scholar.google.com/scholar?cluster=15846260331884670403&hl=en&as_sdt=0,5",9,2021
A Fourier-Based Framework for Domain Generalization,165,cvpr,20,5,2023-06-03 14:29:27.213000,https://github.com/MediaBrain-SJTU/FACT,120,A fourier-based framework for domain generalization,"https://scholar.google.com/scholar?cluster=3133270315407407724&hl=en&as_sdt=0,3",1,2021
Self-Supervised Motion Learning From Static Images,21,cvpr,28,4,2023-06-03 14:29:27.407000,https://github.com/alibaba-mmai-research/TAdaConv,163,Self-supervised motion learning from static images,"https://scholar.google.com/scholar?cluster=15224416147268474185&hl=en&as_sdt=0,5",8,2021
iMiGUE: An Identity-Free Video Dataset for Micro-Gesture Understanding and Emotion Analysis,33,cvpr,4,1,2023-06-03 14:29:27.599000,https://github.com/linuxsino/iMiGUE,33,iMiGUE: An identity-free video dataset for micro-gesture understanding and emotion analysis,"https://scholar.google.com/scholar?cluster=16225884599772598439&hl=en&as_sdt=0,4",3,2021
Probabilistic Modeling of Semantic Ambiguity for Scene Graph Generation,44,cvpr,7,0,2023-06-03 14:29:27.794000,https://github.com/IIGROUP/PUM,17,Probabilistic modeling of semantic ambiguity for scene graph generation,"https://scholar.google.com/scholar?cluster=13595340087031567164&hl=en&as_sdt=0,33",3,2021
Robust Reference-Based Super-Resolution via C2-Matching,31,cvpr,31,10,2023-06-03 14:29:27.991000,https://github.com/yumingj/C2-Matching,168,Robust reference-based super-resolution via c2-matching,"https://scholar.google.com/scholar?cluster=14240637213632886344&hl=en&as_sdt=0,33",6,2021
NBNet: Noise Basis Learning for Image Denoising With Subspace Projection,104,cvpr,21,13,2023-06-03 14:29:28.186000,https://github.com/megvii-research/NBNet,121,Nbnet: Noise basis learning for image denoising with subspace projection,"https://scholar.google.com/scholar?cluster=11692369828723140658&hl=en&as_sdt=0,14",6,2021
Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food,27,cvpr,18,6,2023-06-03 14:29:28.380000,https://github.com/google-research-datasets/Nutrition5k,82,Nutrition5k: Towards automatic nutritional understanding of generic food,"https://scholar.google.com/scholar?cluster=515079697257173152&hl=en&as_sdt=0,5",10,2021
Temporal-Relational CrossTransformers for Few-Shot Action Recognition,81,cvpr,18,7,2023-06-03 14:29:28.576000,https://github.com/tobyperrett/trx,88,Temporal-relational crosstransformers for few-shot action recognition,"https://scholar.google.com/scholar?cluster=7442330698160577215&hl=en&as_sdt=0,15",6,2021
Scale-Aware Automatic Augmentation for Object Detection,26,cvpr,21,2,2023-06-03 14:29:28.769000,https://github.com/Jia-Research-Lab/SA-AutoAug,191,Scale-aware automatic augmentation for object detection,"https://scholar.google.com/scholar?cluster=9825632568720049899&hl=en&as_sdt=0,18",9,2021
Prioritized Architecture Sampling With Monto-Carlo Tree Search,30,cvpr,7,2,2023-06-03 14:29:28.963000,https://github.com/xiusu/NAS-Bench-Macro,37,Prioritized architecture sampling with monto-carlo tree search,"https://scholar.google.com/scholar?cluster=16897259555170724856&hl=en&as_sdt=0,15",2,2021
Towards Robust Classification Model by Counterfactual and Invariant Data Generation,13,cvpr,1,1,2023-06-03 14:29:29.156000,https://github.com/zzzace2000/robust_cls_model,13,Towards robust classification model by counterfactual and invariant data generation,"https://scholar.google.com/scholar?cluster=2156886024917599782&hl=en&as_sdt=0,31",4,2021
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing,36,cvpr,6,2,2023-06-03 14:29:29.352000,https://github.com/xyvirtualgroup/MetaSCI-CVPR2021,15,Metasci: Scalable and adaptive reconstruction for video compressive sensing,"https://scholar.google.com/scholar?cluster=18366339974379385458&hl=en&as_sdt=0,5",3,2021
"Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition",129,cvpr,64,64,2023-06-03 14:29:29.546000,https://github.com/FangShancheng/ABINet,366,"Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition","https://scholar.google.com/scholar?cluster=15361793719091164859&hl=en&as_sdt=0,39",15,2021
Benchmarking Representation Learning for Natural World Image Collections,78,cvpr,8,2,2023-06-03 14:29:29.740000,https://github.com/visipedia/newt,30,Benchmarking representation learning for natural world image collections,"https://scholar.google.com/scholar?cluster=4259630619061410335&hl=en&as_sdt=0,21",6,2021
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space,180,cvpr,30,8,2023-06-03 14:29:29.934000,https://github.com/liuquande/FedDG-ELCFS,192,Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,"https://scholar.google.com/scholar?cluster=17451959948999352784&hl=en&as_sdt=0,33",8,2021
Lifting 2D StyleGAN for 3D-Aware Face Generation,49,cvpr,13,6,2023-06-03 14:29:30.127000,https://github.com/seasonSH/LiftedGAN,71,Lifting 2d stylegan for 3d-aware face generation,"https://scholar.google.com/scholar?cluster=8041055593232332694&hl=en&as_sdt=0,39",5,2021
Neural Architecture Search With Random Labels,36,cvpr,8,1,2023-06-03 14:29:30.322000,https://github.com/megvii-model/RLNAS,19,Neural architecture search with random labels,"https://scholar.google.com/scholar?cluster=118988540291447923&hl=en&as_sdt=0,44",3,2021
PGT: A Progressive Method for Training Models on Long Videos,7,cvpr,2,0,2023-06-03 14:29:30.516000,https://github.com/BoPang1996/PGT,27,Pgt: A progressive method for training models on long videos,"https://scholar.google.com/scholar?cluster=7192011502397574518&hl=en&as_sdt=0,3",5,2021
Modeling Multi-Label Action Dependencies for Temporal Action Localization,36,cvpr,6,5,2023-06-03 14:29:30.710000,https://github.com/ptirupat/MLAD,38,Modeling multi-label action dependencies for temporal action localization,"https://scholar.google.com/scholar?cluster=8930430680494415006&hl=en&as_sdt=0,34",3,2021
UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering,35,cvpr,18,0,2023-06-03 14:29:30.904000,https://github.com/mbanani/unsupervisedRR,124,Unsupervisedr&r: Unsupervised point cloud registration via differentiable rendering,"https://scholar.google.com/scholar?cluster=6647988051204069461&hl=en&as_sdt=0,33",9,2021
BASAR:Black-Box Attack on Skeletal Action Recognition,18,cvpr,4,0,2023-06-03 14:29:31.097000,https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition,13,BASAR: black-box attack on skeletal action recognition,"https://scholar.google.com/scholar?cluster=11009120545836176905&hl=en&as_sdt=0,5",3,2021
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation,91,cvpr,12,5,2023-06-03 14:29:31.291000,https://github.com/leofansq/SCF-Net,60,SCF-Net: Learning spatial contextual features for large-scale point cloud segmentation,"https://scholar.google.com/scholar?cluster=17206305368169054421&hl=en&as_sdt=0,7",3,2021
AGORA: Avatars in Geography Optimized for Regression Analysis,102,cvpr,6,5,2023-06-03 14:29:31.485000,https://github.com/pixelite1201/agora_evaluation,84,AGORA: Avatars in geography optimized for regression analysis,"https://scholar.google.com/scholar?cluster=4438936443116737153&hl=en&as_sdt=0,26",4,2021
SPSG: Self-Supervised Photometric Scene Generation From RGB-D Scans,24,cvpr,8,7,2023-06-03 14:29:31.679000,https://github.com/angeladai/spsg,89,Spsg: Self-supervised photometric scene generation from rgb-d scans,"https://scholar.google.com/scholar?cluster=11927983500029925680&hl=en&as_sdt=0,5",2,2021
Style-Based Point Generator With Adversarial Rendering for Point Cloud Completion,55,cvpr,19,11,2023-06-03 14:29:31.873000,https://github.com/microsoft/SpareNet,119,Style-based point generator with adversarial rendering for point cloud completion,"https://scholar.google.com/scholar?cluster=7429583694910140003&hl=en&as_sdt=0,5",9,2021
Dense Relation Distillation With Context-Aware Aggregation for Few-Shot Object Detection,101,cvpr,32,10,2023-06-03 14:29:32.066000,https://github.com/hzhupku/DCNet,128,Dense relation distillation with context-aware aggregation for few-shot object detection,"https://scholar.google.com/scholar?cluster=4992189010011367767&hl=en&as_sdt=0,3",6,2021
Fast and Accurate Model Scaling,55,cvpr,240,24,2023-06-03 14:29:32.260000,https://github.com/facebookresearch/pycls,2054,Fast and accurate model scaling,"https://scholar.google.com/scholar?cluster=9002801528721217017&hl=en&as_sdt=0,5",60,2021
Shape and Material Capture at Home,22,cvpr,9,2,2023-06-03 14:29:32.454000,https://github.com/dlichy/ShapeAndMaterial,96,Shape and material capture at home,"https://scholar.google.com/scholar?cluster=13374088501981115936&hl=en&as_sdt=0,33",11,2021
Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE,91,cvpr,18,11,2023-06-03 14:29:32.648000,https://github.com/USTC-JialunPeng/Diverse-Structure-Inpainting,159,Generating diverse structure for image inpainting with hierarchical VQ-VAE,"https://scholar.google.com/scholar?cluster=14110506647886220408&hl=en&as_sdt=0,41",7,2021
Progressive Domain Expansion Network for Single Domain Generalization,57,cvpr,4,4,2023-06-03 14:29:32.841000,https://github.com/lileicv/PDEN,28,Progressive domain expansion network for single domain generalization,"https://scholar.google.com/scholar?cluster=1011572642759353909&hl=en&as_sdt=0,5",3,2021
T-vMF Similarity for Regularizing Intra-Class Feature Distribution,11,cvpr,2,1,2023-06-03 14:29:33.035000,https://github.com/tk1980/tvMF,17,T-vMF similarity for regularizing intra-class feature distribution,"https://scholar.google.com/scholar?cluster=4762125977095337484&hl=en&as_sdt=0,5",2,2021
Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation,57,cvpr,24,7,2023-06-03 14:29:33.230000,https://github.com/MingiJi/FRSKD,81,Refine myself by teaching myself: Feature refinement via self-knowledge distillation,"https://scholar.google.com/scholar?cluster=6538235984072024546&hl=en&as_sdt=0,5",2,2021
Self-Supervised Visibility Learning for Novel View Synthesis,12,cvpr,1,1,2023-06-03 14:29:33.423000,https://github.com/shiyujiao/SVNVS,22,Self-supervised visibility learning for novel view synthesis,"https://scholar.google.com/scholar?cluster=153982858179223103&hl=en&as_sdt=0,36",7,2021
End-to-End Human Object Interaction Detection With HOI Transformer,120,cvpr,18,10,2023-06-03 14:29:33.616000,https://github.com/bbepoch/HoiTransformer,133,End-to-end human object interaction detection with hoi transformer,"https://scholar.google.com/scholar?cluster=5255251082771415776&hl=en&as_sdt=0,33",8,2021
ZeroScatter: Domain Transfer for Long Distance Imaging and Vision Through Scattering Media,7,cvpr,2,0,2023-06-03 14:29:33.810000,https://github.com/princeton-computational-imaging/ZeroScatter,0,Zeroscatter: Domain transfer for long distance imaging and vision through scattering media,"https://scholar.google.com/scholar?cluster=3485742634528152783&hl=en&as_sdt=0,8",2,2021
DeFlow: Learning Complex Image Degradations From Unpaired Data With Conditional Flows,27,cvpr,8,5,2023-06-03 14:29:34.003000,https://github.com/volflow/DeFlow,88,Deflow: Learning complex image degradations from unpaired data with conditional flows,"https://scholar.google.com/scholar?cluster=3776519392557986005&hl=en&as_sdt=0,5",8,2021
Multi-Shot Temporal Event Localization: A Benchmark,51,cvpr,4,2,2023-06-03 14:29:34.197000,https://github.com/xlliu7/muses,53,Multi-shot temporal event localization: a benchmark,"https://scholar.google.com/scholar?cluster=14661904446510964410&hl=en&as_sdt=0,10",9,2021
Spatially-Adaptive Pixelwise Networks for Fast Image Translation,52,cvpr,13,9,2023-06-03 14:29:34.390000,https://github.com/tamarott/ASAPNet,105,Spatially-adaptive pixelwise networks for fast image translation,"https://scholar.google.com/scholar?cluster=3202338836992468439&hl=en&as_sdt=0,3",3,2021
Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks,32,cvpr,19,11,2023-06-03 14:29:34.584000,https://github.com/3dpose/3D-Multi-Person-Pose,139,Monocular 3D multi-person pose estimation by integrating top-down and bottom-up networks,"https://scholar.google.com/scholar?cluster=14886592634309994640&hl=en&as_sdt=0,5",9,2021
Anycost GANs for Interactive Image Synthesis and Editing,50,cvpr,94,5,2023-06-03 14:29:34.778000,https://github.com/mit-han-lab/anycost-gan,741,Anycost gans for interactive image synthesis and editing,"https://scholar.google.com/scholar?cluster=15620697692677396536&hl=en&as_sdt=0,5",24,2021
Depth-Conditioned Dynamic Message Propagation for Monocular 3D Object Detection,69,cvpr,1,1,2023-06-03 14:29:34.972000,https://github.com/fudan-zvg/DDMP,22,Depth-conditioned dynamic message propagation for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=6723784930267964298&hl=en&as_sdt=0,5",11,2021
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation,54,cvpr,16,1,2023-06-03 14:29:35.166000,https://github.com/lxtGH/PFSegNets,115,Pointflow: Flowing semantics through points for aerial image segmentation,"https://scholar.google.com/scholar?cluster=9470013847827825734&hl=en&as_sdt=0,33",4,2021
"Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs",22,cvpr,1,4,2023-06-03 14:29:35.359000,https://github.com/a514514772/hijackgan,46,"Hijack-gan: Unintended-use of pretrained, black-box gans","https://scholar.google.com/scholar?cluster=16144626559950746585&hl=en&as_sdt=0,11",7,2021
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-Bit Neural Networks via Guided Distribution Calibration,12,cvpr,11,0,2023-06-03 14:29:35.553000,https://github.com/szq0214/S2-BNN,55,S2-bnn: Bridging the gap between self-supervised real and 1-bit neural networks via guided distribution calibration,"https://scholar.google.com/scholar?cluster=15859220157567518026&hl=en&as_sdt=0,5",2,2021
Deep Stable Learning for Out-of-Distribution Generalization,114,cvpr,33,11,2023-06-03 14:29:35.750000,https://github.com/xxgege/StableNet,156,Deep stable learning for out-of-distribution generalization,"https://scholar.google.com/scholar?cluster=4482914226187027731&hl=en&as_sdt=0,5",4,2021
LiDAR R-CNN: An Efficient and Universal 3D Object Detector,124,cvpr,54,0,2023-06-03 14:29:35.944000,https://github.com/tusimple/LiDAR_RCNN,301,Lidar r-cnn: An efficient and universal 3d object detector,"https://scholar.google.com/scholar?cluster=4694333607807583383&hl=en&as_sdt=0,5",19,2021
Region-Aware Adaptive Instance Normalization for Image Harmonization,65,cvpr,23,7,2023-06-03 14:29:36.137000,https://github.com/junleen/RainNet,150,Region-aware adaptive instance normalization for image harmonization,"https://scholar.google.com/scholar?cluster=64086957601024951&hl=en&as_sdt=0,3",4,2021
Content-Aware GAN Compression,24,cvpr,7,5,2023-06-03 14:29:36.331000,https://github.com/lychenyoko/content-aware-gan-compression,59,Content-aware gan compression,"https://scholar.google.com/scholar?cluster=12142259723395782933&hl=en&as_sdt=0,14",8,2021
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking,28,cvpr,2,1,2023-06-03 14:29:36.524000,https://github.com/VISION-SJTU/IoUattack,37,Iou attack: Towards temporally coherent black-box adversarial attack for visual object tracking,"https://scholar.google.com/scholar?cluster=9565166137480646657&hl=en&as_sdt=0,33",2,2021
Temporal Action Segmentation From Timestamp Supervision,42,cvpr,4,5,2023-06-03 14:29:36.718000,https://github.com/ZheLi2020/TimestampActionSeg,29,Temporal action segmentation from timestamp supervision,"https://scholar.google.com/scholar?cluster=17472327853496912277&hl=en&as_sdt=0,5",2,2021
Deep Graph Matching Under Quadratic Constraint,21,cvpr,5,0,2023-06-03 14:29:36.912000,https://github.com/Zerg-Overmind/QC-DGM,56,Deep graph matching under quadratic constraint,"https://scholar.google.com/scholar?cluster=11789189759154030584&hl=en&as_sdt=0,34",2,2021
SMD-Nets: Stereo Mixture Density Networks,31,cvpr,26,7,2023-06-03 14:29:37.106000,https://github.com/fabiotosi92/SMD-Nets,128,Smd-nets: Stereo mixture density networks,"https://scholar.google.com/scholar?cluster=17071509949921577102&hl=en&as_sdt=0,1",15,2021
FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise,24,cvpr,11,1,2023-06-03 14:29:37.299000,https://github.com/csm9493/FBI-Denoiser,40,Fbi-denoiser: Fast blind image denoiser for poisson-gaussian noise,"https://scholar.google.com/scholar?cluster=6701577429489508076&hl=en&as_sdt=0,5",4,2021
Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning,18,cvpr,0,0,2023-06-03 14:29:37.493000,https://github.com/insomnia94/ISREG,13,Iterative shrinking for referring expression grounding using deep reinforcement learning,"https://scholar.google.com/scholar?cluster=8719908244946589094&hl=en&as_sdt=0,22",1,2021
Line Segment Detection Using Transformers Without Edges,57,cvpr,32,19,2023-06-03 14:29:37.686000,https://github.com/mlpc-ucsd/LETR,171,Line segment detection using transformers without edges,"https://scholar.google.com/scholar?cluster=15147732543453778667&hl=en&as_sdt=0,10",6,2021
Simulating Unknown Target Models for Query-Efficient Black-Box Attacks,37,cvpr,13,8,2023-06-03 14:29:37.880000,https://github.com/machanic/SimulatorAttack,52,Simulating unknown target models for query-efficient black-box attacks,"https://scholar.google.com/scholar?cluster=16262882922319372233&hl=en&as_sdt=0,29",2,2021
Diffusion Probabilistic Models for 3D Point Cloud Generation,200,cvpr,64,7,2023-06-03 14:29:38.074000,https://github.com/luost26/diffusion-point-cloud,426,Diffusion probabilistic models for 3d point cloud generation,"https://scholar.google.com/scholar?cluster=6434863982682241420&hl=en&as_sdt=0,5",7,2021
Towards Real-World Blind Face Restoration With Generative Facial Prior,188,cvpr,4601,223,2023-06-03 14:29:38.268000,https://github.com/TencentARC/GFPGAN,29335,Towards real-world blind face restoration with generative facial prior,"https://scholar.google.com/scholar?cluster=872284588023168927&hl=en&as_sdt=0,36",433,2021
Learning Feature Aggregation for Deep 3D Morphable Models,15,cvpr,3,2,2023-06-03 14:29:38.462000,https://github.com/zxchen110/Deep3DMM,39,Learning feature aggregation for deep 3d morphable models,"https://scholar.google.com/scholar?cluster=16387988667029350078&hl=en&as_sdt=0,5",6,2021
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise,34,cvpr,185,10,2023-06-03 14:29:38.656000,https://github.com/PAIR-code/saliency,880,Guided integrated gradients: An adaptive path method for removing noise,"https://scholar.google.com/scholar?cluster=4588840794025051198&hl=en&as_sdt=0,5",25,2021
Self-Guided and Cross-Guided Learning for Few-Shot Segmentation,83,cvpr,7,1,2023-06-03 14:29:38.849000,https://github.com/zbf1991/SCL,34,Self-guided and cross-guided learning for few-shot segmentation,"https://scholar.google.com/scholar?cluster=14569435996116603561&hl=en&as_sdt=0,32",2,2021
Data-Free Model Extraction,72,cvpr,12,1,2023-06-03 14:29:39.043000,https://github.com/cake-lab/datafree-model-extraction,47,Data-free model extraction,"https://scholar.google.com/scholar?cluster=5802396338589095656&hl=en&as_sdt=0,5",2,2021
Group-aware Label Transfer for Domain Adaptive Person Re-identification,121,cvpr,20,5,2023-06-03 14:29:39.237000,https://github.com/zkcys001/UDAStrongBaseline,134,Group-aware label transfer for domain adaptive person re-identification,"https://scholar.google.com/scholar?cluster=12841225369142791209&hl=en&as_sdt=0,31",6,2021
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging,99,cvpr,181,3,2023-06-03 14:29:39.431000,https://github.com/compphoto/BoostingMonocularDepth,1271,Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging,"https://scholar.google.com/scholar?cluster=5466386634265922533&hl=en&as_sdt=0,11",26,2021
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification,76,cvpr,3,8,2023-06-03 14:29:39.624000,https://github.com/DoubtedSteam/MPANet,47,Discover cross-modality nuances for visible-infrared person re-identification,"https://scholar.google.com/scholar?cluster=4915606615889437853&hl=en&as_sdt=0,21",2,2021
Track To Detect and Segment: An Online Multi-Object Tracker,181,cvpr,111,15,2023-06-03 14:29:39.818000,https://github.com/JialianW/TraDeS,534,Track to detect and segment: An online multi-object tracker,"https://scholar.google.com/scholar?cluster=17485550280397785036&hl=en&as_sdt=0,5",7,2021
Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking,63,cvpr,20,7,2023-06-03 14:29:40.012000,https://github.com/jiaweihe1996/GMTracker,100,Learnable graph matching: Incorporating graph partitioning with deep feature learning for multiple object tracking,"https://scholar.google.com/scholar?cluster=1702587870012806584&hl=en&as_sdt=0,5",5,2021
PISE: Person Image Synthesis and Editing With Decoupled GAN,65,cvpr,30,6,2023-06-03 14:29:40.208000,https://github.com/Zhangjinso/PISE,113,Pise: Person image synthesis and editing with decoupled gan,"https://scholar.google.com/scholar?cluster=13465017855213911775&hl=en&as_sdt=0,5",4,2021
Neighborhood Contrastive Learning for Novel Class Discovery,63,cvpr,11,1,2023-06-03 14:29:40.402000,https://github.com/zhunzhong07/NCL,64,Neighborhood contrastive learning for novel class discovery,"https://scholar.google.com/scholar?cluster=2585700352575875279&hl=en&as_sdt=0,14",4,2021
Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching,33,cvpr,8,5,2023-06-03 14:29:40.596000,https://github.com/dut-media-lab/Image-Stitching,55,Leveraging line-point consistence to preserve structures for wide parallax image stitching,"https://scholar.google.com/scholar?cluster=16787291560595966284&hl=en&as_sdt=0,5",1,2021
Effective Sparsification of Neural Networks With Global Sparsity Constraint,34,cvpr,3,0,2023-06-03 14:29:40.790000,https://github.com/x-zho14/ProbMask-official,21,Effective sparsification of neural networks with global sparsity constraint,"https://scholar.google.com/scholar?cluster=4938285737416489900&hl=en&as_sdt=0,39",2,2021
3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection,68,cvpr,16,1,2023-06-03 14:29:40.984000,https://github.com/thu17cyz/3DIoUMatch,139,3dioumatch: Leveraging iou prediction for semi-supervised 3d object detection,"https://scholar.google.com/scholar?cluster=14495081406750503778&hl=en&as_sdt=0,33",8,2021
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation,72,cvpr,12,6,2023-06-03 14:29:41.178000,https://github.com/lijin118/CGDM,48,Cross-domain gradient discrepancy minimization for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=11532677679716875800&hl=en&as_sdt=0,5",4,2021
Neural Camera Simulators,12,cvpr,3,0,2023-06-03 14:29:41.372000,https://github.com/ken-ouyang/neural_image_simulator,47,Neural camera simulators,"https://scholar.google.com/scholar?cluster=15236530149266601836&hl=en&as_sdt=0,5",5,2021
Time Adaptive Recurrent Neural Network,9,cvpr,1,0,2023-06-03 14:29:41.565000,https://github.com/anilkagak2/TARNN,6,Time adaptive recurrent neural network,"https://scholar.google.com/scholar?cluster=4889697386978033964&hl=en&as_sdt=0,10",3,2021
Training Generative Adversarial Networks in One Stage,8,cvpr,4,2,2023-06-03 14:29:41.769000,https://github.com/zju-vipa/OSGAN,33,Training generative adversarial networks in one stage,"https://scholar.google.com/scholar?cluster=16137972989735452920&hl=en&as_sdt=0,33",4,2021
Neighborhood Normalization for Robust Geometric Feature Learning,2,cvpr,1,0,2023-06-03 14:29:41.963000,https://github.com/lppllppl920/NeighborhoodNormalization-Pytorch,11,Neighborhood normalization for robust geometric feature learning,"https://scholar.google.com/scholar?cluster=10239322737251894205&hl=en&as_sdt=0,5",2,2021
Dictionary-Guided Scene Text Recognition,32,cvpr,38,10,2023-06-03 14:29:42.156000,https://github.com/VinAIResearch/dict-guided,115,Dictionary-guided scene text recognition,"https://scholar.google.com/scholar?cluster=8967702556645030278&hl=en&as_sdt=0,33",5,2021
Glance and Gaze: Inferring Action-Aware Points for One-Stage Human-Object Interaction Detection,51,cvpr,2,5,2023-06-03 14:29:42.350000,https://github.com/SherlockHolmes221/GGNet,25,Glance and gaze: Inferring action-aware points for one-stage human-object interaction detection,"https://scholar.google.com/scholar?cluster=2160691661775397806&hl=en&as_sdt=0,5",3,2021
Activate or Not: Learning Customized Activation,78,cvpr,33,11,2023-06-03 14:29:42.543000,https://github.com/nmaac/acon,196,Activate or not: Learning customized activation,"https://scholar.google.com/scholar?cluster=3965043952264591548&hl=en&as_sdt=0,39",6,2021
Improving Unsupervised Image Clustering With Robust Learning,65,cvpr,15,3,2023-06-03 14:29:42.736000,https://github.com/deu30303/RUC,135,Improving unsupervised image clustering with robust learning,"https://scholar.google.com/scholar?cluster=6576670889862952105&hl=en&as_sdt=0,44",4,2021
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud,209,cvpr,135,11,2023-06-03 14:29:42.930000,https://github.com/Vegeta2020/SE-SSD,786,SE-SSD: Self-ensembling single-stage object detector from point cloud,"https://scholar.google.com/scholar?cluster=10191050231224943760&hl=en&as_sdt=0,33",23,2021
Intra-Inter Camera Similarity for Unsupervised Person Re-Identification,108,cvpr,11,3,2023-06-03 14:29:43.125000,https://github.com/SY-Xuan/IICS,49,Intra-inter camera similarity for unsupervised person re-identification,"https://scholar.google.com/scholar?cluster=11842546335779954449&hl=en&as_sdt=0,44",2,2021
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning,61,cvpr,16,1,2023-06-03 14:29:43.319000,https://github.com/valeoai/obow,93,Obow: Online bag-of-visual-words generation for self-supervised learning,"https://scholar.google.com/scholar?cluster=2623281973933580357&hl=en&as_sdt=0,48",6,2021
Learning To Relate Depth and Semantics for Unsupervised Domain Adaptation,30,cvpr,3,2,2023-06-03 14:29:43.514000,https://github.com/susaha/ctrl-uda,34,Learning to relate depth and semantics for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=4558299063475731681&hl=en&as_sdt=0,39",3,2021
Efficient Feature Transformations for Discriminative and Generative Continual Learning,35,cvpr,9,1,2023-06-03 14:29:43.707000,https://github.com/vkverma01/EFT,15,Efficient feature transformations for discriminative and generative continual learning,"https://scholar.google.com/scholar?cluster=7328688430510216163&hl=en&as_sdt=0,33",2,2021
Learning a Self-Expressive Network for Subspace Clustering,30,cvpr,16,3,2023-06-03 14:29:43.902000,https://github.com/zhangsz1998/self-expressive-network,29,Learning a self-expressive network for subspace clustering,"https://scholar.google.com/scholar?cluster=4731734417551609114&hl=en&as_sdt=0,5",3,2021
Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling,7,cvpr,3,5,2023-06-03 14:29:44.096000,https://github.com/ding3820/MIMO-VRN,39,Video rescaling networks with joint optimization strategies for downscaling and upscaling,"https://scholar.google.com/scholar?cluster=4864358806213212308&hl=en&as_sdt=0,5",3,2021
Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild With Pose Annotations,101,cvpr,257,25,2023-06-03 14:29:44.290000,https://github.com/google-research-datasets/Objectron,2113,Objectron: A large scale dataset of object-centric videos in the wild with pose annotations,"https://scholar.google.com/scholar?cluster=9975718100726506827&hl=en&as_sdt=0,1",65,2021
Nearest Neighbor Matching for Deep Clustering,46,cvpr,7,7,2023-06-03 14:29:44.483000,https://github.com/ZhiyuanDang/NNM,47,Nearest neighbor matching for deep clustering,"https://scholar.google.com/scholar?cluster=18404115884091038105&hl=en&as_sdt=0,5",1,2021
Meta Batch-Instance Normalization for Generalizable Person Re-Identification,77,cvpr,14,6,2023-06-03 14:29:44.677000,https://github.com/bismex/MetaBIN,61,Meta batch-instance normalization for generalizable person re-identification,"https://scholar.google.com/scholar?cluster=10789721847854617735&hl=en&as_sdt=0,31",5,2021
Asymmetric Metric Learning for Knowledge Transfer,28,cvpr,3,1,2023-06-03 14:29:44.875000,https://github.com/budnikm/aml,21,Asymmetric metric learning for knowledge transfer,"https://scholar.google.com/scholar?cluster=9333606311348889691&hl=en&as_sdt=0,19",2,2021
MOOD: Multi-Level Out-of-Distribution Detection,70,cvpr,7,1,2023-06-03 14:29:45.069000,https://github.com/deeplearning-wisc/MOOD,33,Mood: Multi-level out-of-distribution detection,"https://scholar.google.com/scholar?cluster=7895474226980823918&hl=en&as_sdt=0,5",2,2021
Neural Surface Maps,61,cvpr,7,0,2023-06-03 14:29:45.263000,https://github.com/luca-morreale/neural_surface_maps,52,Using dose-surface maps to predict radiation-induced rectal bleeding: a neural network approach,"https://scholar.google.com/scholar?cluster=6539814741598517079&hl=en&as_sdt=0,33",4,2021
Fair Attribute Classification Through Latent Space De-Biasing,82,cvpr,13,3,2023-06-03 14:29:45.457000,https://github.com/princetonvisualai/gan-debiasing,64,Fair attribute classification through latent space de-biasing,"https://scholar.google.com/scholar?cluster=13757258113259373561&hl=en&as_sdt=0,5",3,2021
Neural Geometric Level of Detail: Real-Time Rendering With Implicit 3D Shapes,204,cvpr,83,14,2023-06-03 14:29:45.651000,https://github.com/nv-tlabs/nglod,737,Neural geometric level of detail: Real-time rendering with implicit 3D shapes,"https://scholar.google.com/scholar?cluster=10286239463531488500&hl=en&as_sdt=0,5",43,2021
Where and What? Examining Interpretable Disentangled Representations,16,cvpr,6,1,2023-06-03 14:29:45.845000,https://github.com/zhuxinqimac/PS-SC,42,Where and what? examining interpretable disentangled representations,"https://scholar.google.com/scholar?cluster=13765523375006484062&hl=en&as_sdt=0,5",4,2021
On the Difficulty of Membership Inference Attacks,38,cvpr,4,0,2023-06-03 14:29:46.039000,https://github.com/shrezaei/MI-Attack,26,On the difficulty of membership inference attacks,"https://scholar.google.com/scholar?cluster=469372699667799648&hl=en&as_sdt=0,21",1,2021
On Feature Normalization and Data Augmentation,98,cvpr,19,1,2023-06-03 14:29:46.232000,https://github.com/Boyiliee/MoEx,140,On feature normalization and data augmentation,"https://scholar.google.com/scholar?cluster=1162199557648697785&hl=en&as_sdt=0,3",9,2021
Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes,26,cvpr,9,2,2023-06-03 14:29:46.427000,https://github.com/zzh-tech/RSCD,85,Towards rolling shutter correction and deblurring in dynamic scenes,"https://scholar.google.com/scholar?cluster=17987973685524534879&hl=en&as_sdt=0,5",1,2021
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning,160,cvpr,1143,348,2023-06-03 14:29:46.620000,https://github.com/facebookresearch/SlowFast,5685,A large-scale study on unsupervised spatiotemporal representation learning,"https://scholar.google.com/scholar?cluster=13005810490237461760&hl=en&as_sdt=0,26",97,2021
Motion Representations for Articulated Animation,89,cvpr,317,47,2023-06-03 14:29:46.814000,https://github.com/snap-research/articulated-animation,987,Motion representations for articulated animation,"https://scholar.google.com/scholar?cluster=4275053062048652612&hl=en&as_sdt=0,10",37,2021
Bipartite Graph Network With Adaptive Message Passing for Unbiased Scene Graph Generation,103,cvpr,0,0,2023-06-03 14:29:47.008000,https://github.com/Scarecrow0/BGNN-SGG,18,Bipartite graph network with adaptive message passing for unbiased scene graph generation,"https://scholar.google.com/scholar?cluster=4359404067695101369&hl=en&as_sdt=0,50",4,2021
Center-Based 3D Object Detection and Tracking,719,cvpr,411,90,2023-06-03 14:29:47.202000,https://github.com/tianweiy/CenterPoint,1564,Center-based 3d object detection and tracking,"https://scholar.google.com/scholar?cluster=14703570951981014637&hl=en&as_sdt=0,5",35,2021
Delving Into Localization Errors for Monocular 3D Object Detection,108,cvpr,26,11,2023-06-03 14:29:47.395000,https://github.com/xinzhuma/monodle,136,Delving into localization errors for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=2447420766194254891&hl=en&as_sdt=0,5",6,2021
Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps,16,cvpr,3,1,2023-06-03 14:29:47.589000,https://github.com/yuk6heo/GIS-RAmap,42,Guided interactive video object segmentation using reliability-based attention maps,"https://scholar.google.com/scholar?cluster=12749278709618358583&hl=en&as_sdt=0,47",4,2021
Dynamic Metric Learning: Towards a Scalable Metric Space To Accommodate Multiple Semantic Scales,7,cvpr,6,5,2023-06-03 14:29:47.791000,https://github.com/SupetZYK/DynamicMetricLearning,40,Dynamic metric learning: Towards a scalable metric space to accommodate multiple semantic scales,"https://scholar.google.com/scholar?cluster=913124457320121939&hl=en&as_sdt=0,14",3,2021
RepVGG: Making VGG-Style ConvNets Great Again,650,cvpr,421,38,2023-06-03 14:29:47.985000,https://github.com/DingXiaoH/RepVGG,3017,Repvgg: Making vgg-style convnets great again,"https://scholar.google.com/scholar?cluster=13451922491452459802&hl=en&as_sdt=0,5",37,2021
MotionRNN: A Flexible Model for Video Prediction With Spacetime-Varying Motions,63,cvpr,6,0,2023-06-03 14:29:48.179000,https://github.com/thuml/MotionRNN,31,MotionRNN: A flexible model for video prediction with spacetime-varying motions,"https://scholar.google.com/scholar?cluster=4417864762791101141&hl=en&as_sdt=0,43",5,2021
Decoupled Dynamic Filter Networks,52,cvpr,32,19,2023-06-03 14:29:48.374000,https://github.com/theFoxofSky/ddf,188,Decoupled dynamic filter networks,"https://scholar.google.com/scholar?cluster=17168807110468665999&hl=en&as_sdt=0,11",8,2021
Rainbow Memory: Continual Learning With a Memory of Diverse Samples,126,cvpr,24,2,2023-06-03 14:29:48.568000,https://github.com/clovaai/rainbow-memory,99,Rainbow memory: Continual learning with a memory of diverse samples,"https://scholar.google.com/scholar?cluster=10725666326497252898&hl=en&as_sdt=0,3",8,2021
Learning Discriminative Prototypes With Dynamic Time Warping,17,cvpr,2,2,2023-06-03 14:29:48.762000,https://github.com/BorealisAI/TSC-Disc-Proto,28,Learning discriminative prototypes with dynamic time warping,"https://scholar.google.com/scholar?cluster=991425475026271688&hl=en&as_sdt=0,11",5,2021
Scaled-YOLOv4: Scaling Cross Stage Partial Network,927,cvpr,572,308,2023-06-03 14:29:48.956000,https://github.com/WongKinYiu/ScaledYOLOv4,2001,Scaled-yolov4: Scaling cross stage partial network,"https://scholar.google.com/scholar?cluster=12563424232401784595&hl=en&as_sdt=0,31",43,2021
CompositeTasking: Understanding Images by Spatial Composition of Tasks,4,cvpr,3,0,2023-06-03 14:29:49.150000,https://github.com/nikola3794/composite-tasking,15,Compositetasking: Understanding images by spatial composition of tasks,"https://scholar.google.com/scholar?cluster=7767913695392902057&hl=en&as_sdt=0,33",2,2021
MOS: Towards Scaling Out-of-Distribution Detection for Large Semantic Space,74,cvpr,8,1,2023-06-03 14:29:49.344000,https://github.com/deeplearning-wisc/large_scale_ood,67,Mos: Towards scaling out-of-distribution detection for large semantic space,"https://scholar.google.com/scholar?cluster=13006779397232953748&hl=en&as_sdt=0,31",4,2021
Knowledge Evolution in Neural Networks,12,cvpr,15,1,2023-06-03 14:29:49.538000,https://github.com/ahmdtaha/knowledge_evolution,80,Knowledge evolution in neural networks,"https://scholar.google.com/scholar?cluster=3087440363792839160&hl=en&as_sdt=0,5",6,2021
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network,4,cvpr,1,4,2023-06-03 14:29:49.732000,https://github.com/aupendu/sr-uncertainty,9,Fast bayesian uncertainty estimation and reduction of batch normalized single image super-resolution network,"https://scholar.google.com/scholar?cluster=17164325024638181776&hl=en&as_sdt=0,5",5,2021
CLCC: Contrastive Learning for Color Constancy,24,cvpr,10,4,2023-06-03 14:29:49.926000,https://github.com/howardyclo/clcc-cvpr21,62,Clcc: Contrastive learning for color constancy,"https://scholar.google.com/scholar?cluster=13787766363168154908&hl=en&as_sdt=0,33",2,2021
i3DMM: Deep Implicit 3D Morphable Model of Human Heads,62,cvpr,8,4,2023-06-03 14:29:50.120000,https://github.com/tarun738/i3DMM,73,i3dmm: Deep implicit 3d morphable model of human heads,"https://scholar.google.com/scholar?cluster=3694474654602862218&hl=en&as_sdt=0,31",7,2021
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World,70,cvpr,10,15,2023-06-03 14:29:50.313000,https://github.com/nlsde-safety-team/DualAttentionAttack,38,Dual attention suppression attack: Generate adversarial camouflage in physical world,"https://scholar.google.com/scholar?cluster=16023884810065017598&hl=en&as_sdt=0,43",1,2021
Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes,32,cvpr,5,2,2023-06-03 14:29:50.507000,https://github.com/BERYLSHEEP/AdvStyle,37,Discovering interpretable latent space directions of gans beyond binary attributes,"https://scholar.google.com/scholar?cluster=7798115278399994494&hl=en&as_sdt=0,5",3,2021
Siamese Natural Language Tracker: Tracking by Natural Language Descriptions With Siamese Trackers,14,cvpr,1,5,2023-06-03 14:29:50.701000,https://github.com/fredfung007/snlt,12,Siamese natural language tracker: Tracking by natural language descriptions with siamese trackers,"https://scholar.google.com/scholar?cluster=8024494078691569582&hl=en&as_sdt=0,23",2,2021
3D Object Detection With Pointformer,200,cvpr,12,10,2023-06-03 14:29:50.896000,https://github.com/Vladimir2506/Pointformer,132,3d object detection with pointformer,"https://scholar.google.com/scholar?cluster=586972148582341814&hl=en&as_sdt=0,5",6,2021
Video Prediction Recalling Long-Term Motion Context via Memory Alignment Learning,54,cvpr,22,5,2023-06-03 14:29:51.090000,https://github.com/sangmin-git/LMC-Memory,75,Video prediction recalling long-term motion context via memory alignment learning,"https://scholar.google.com/scholar?cluster=9038631636731141167&hl=en&as_sdt=0,5",5,2021
Weakly-Supervised Physically Unconstrained Gaze Estimation,22,cvpr,0,4,2023-06-03 14:29:51.284000,https://github.com/NVlabs/weakly-supervised-gaze,26,Weakly-supervised physically unconstrained gaze estimation,"https://scholar.google.com/scholar?cluster=14324809649739090860&hl=en&as_sdt=0,11",11,2021
Detecting Human-Object Interaction via Fabricated Compositional Learning,33,cvpr,10,17,2023-06-03 14:29:51.478000,https://github.com/zhihou7/HOI-CL,73,Detecting human-object interaction via fabricated compositional learning,"https://scholar.google.com/scholar?cluster=5434504416185304789&hl=en&as_sdt=0,3",2,2021
VirTex: Learning Visual Representations From Textual Annotations,276,cvpr,65,8,2023-06-03 14:29:51.672000,https://github.com/kdexd/virtex,544,Virtex: Learning visual representations from textual annotations,"https://scholar.google.com/scholar?cluster=11811062397412531509&hl=en&as_sdt=0,5",14,2021
Learning by Planning: Language-Guided Global Image Editing,17,cvpr,1,0,2023-06-03 14:29:51.865000,https://github.com/jshi31/T2ONet,17,Learning by planning: Language-guided global image editing,"https://scholar.google.com/scholar?cluster=14328793630988060308&hl=en&as_sdt=0,5",4,2021
Visual Semantic Role Labeling for Video Understanding,27,cvpr,7,6,2023-06-03 14:29:52.059000,https://github.com/TheShadow29/VidSitu,47,Visual semantic role labeling for video understanding,"https://scholar.google.com/scholar?cluster=15170013693598531079&hl=en&as_sdt=0,26",3,2021
Quantifying Explainers of Graph Neural Networks in Computational Pathology,51,cvpr,7,4,2023-06-03 14:29:52.253000,https://github.com/histocartography/patho-quant-explainer,32,Quantifying explainers of graph neural networks in computational pathology,"https://scholar.google.com/scholar?cluster=14536985190039177341&hl=en&as_sdt=0,5",5,2021
Bidirectional Projection Network for Cross Dimension Scene Understanding,68,cvpr,16,11,2023-06-03 14:29:52.448000,https://github.com/wbhu/BPNet,153,Bidirectional projection network for cross dimension scene understanding,"https://scholar.google.com/scholar?cluster=909010016987049964&hl=en&as_sdt=0,34",10,2021
SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation,97,cvpr,2,10,2023-06-03 14:29:52.642000,https://github.com/dukebw/SSTVOS,85,Sstvos: Sparse spatiotemporal transformers for video object segmentation,"https://scholar.google.com/scholar?cluster=17407640623691452231&hl=en&as_sdt=0,36",15,2021
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation,29,cvpr,53,0,2023-06-03 14:29:52.836000,https://github.com/ssarfraz/FINCH-CLustering,277,Temporally-weighted hierarchical clustering for unsupervised action segmentation,"https://scholar.google.com/scholar?cluster=1085728939717454514&hl=en&as_sdt=0,5",22,2021
Event-Based Synthetic Aperture Imaging With a Hybrid Network,16,cvpr,4,4,2023-06-03 14:29:53.029000,https://github.com/dvs-whu/E-SAI,21,Event-based synthetic aperture imaging with a hybrid network,"https://scholar.google.com/scholar?cluster=5205630784265455546&hl=en&as_sdt=0,47",1,2021
Inferring CAD Modeling Sequences Using Zone Graphs,27,cvpr,3,0,2023-06-03 14:29:53.223000,https://github.com/brownvc/zone-graphs,11,Inferring cad modeling sequences using zone graphs,"https://scholar.google.com/scholar?cluster=17691275069099402496&hl=en&as_sdt=0,5",4,2021
PCLs: Geometry-Aware Neural Reconstruction of 3D Pose With Perspective Crop Layers,13,cvpr,2,2,2023-06-03 14:29:53.418000,https://github.com/yu-frank/PerspectiveCropLayers,28,PCLs: Geometry-aware neural reconstruction of 3D pose with perspective crop layers,"https://scholar.google.com/scholar?cluster=16880214436381760635&hl=en&as_sdt=0,48",3,2021
Learning Statistical Texture for Semantic Segmentation,76,cvpr,13,8,2023-06-03 14:29:53.612000,https://github.com/lanyunzhu99/Learning-Statistical-Texture-for-Semantic-Segmentation,83,Learning statistical texture for semantic segmentation,"https://scholar.google.com/scholar?cluster=4204628405750422958&hl=en&as_sdt=0,50",1,2021
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution,60,cvpr,21,21,2023-06-03 14:29:53.806000,https://github.com/dvlab-research/MASA-SR,133,Masa-sr: Matching acceleration and spatial adaptation for reference-based image super-resolution,"https://scholar.google.com/scholar?cluster=6920155242295106042&hl=en&as_sdt=0,22",4,2021
Lifelong Person Re-Identification via Adaptive Knowledge Accumulation,35,cvpr,12,5,2023-06-03 14:29:54,https://github.com/TPCD/LifelongReID,75,Lifelong person re-identification via adaptive knowledge accumulation,"https://scholar.google.com/scholar?cluster=9217481327609976325&hl=en&as_sdt=0,33",4,2021
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework,20,cvpr,0,2,2023-06-03 14:29:54.195000,https://github.com/yellowtownhz/IE-KD,9,Revisiting knowledge distillation: An inheritance and exploration framework,"https://scholar.google.com/scholar?cluster=11634573678140367870&hl=en&as_sdt=0,33",4,2021
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning,89,cvpr,12,1,2023-06-03 14:29:54.389000,https://github.com/nayeemrizve/invariance-equivariance,33,Exploring complementary strengths of invariant and equivariant representations for few-shot learning,"https://scholar.google.com/scholar?cluster=8981528282077205030&hl=en&as_sdt=0,10",3,2021
Fine-Grained Angular Contrastive Learning With Coarse Labels,38,cvpr,5,4,2023-06-03 14:29:54.584000,https://github.com/guybuk/ANCOR,41,Fine-grained angular contrastive learning with coarse labels,"https://scholar.google.com/scholar?cluster=12938298068029448198&hl=en&as_sdt=0,5",4,2021
Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation With Manipulable Semantics,16,cvpr,0,2,2023-06-03 14:29:54.787000,https://github.com/chiamuyu/PI-Net,0,Perceptual indistinguishability-net (pi-net): Facial image obfuscation with manipulable semantics,"https://scholar.google.com/scholar?cluster=12885698987020065072&hl=en&as_sdt=0,3",2,2021
DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images,25,cvpr,3,3,2023-06-03 14:29:54.982000,https://github.com/DeepTag/cardiac_tagging_motion_estimation,36,Deeptag: An unsupervised deep learning method for motion tracking on cardiac tagging magnetic resonance images,"https://scholar.google.com/scholar?cluster=18201651370453784927&hl=en&as_sdt=0,39",1,2021
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning?,24,cvpr,2,0,2023-06-03 14:29:55.176000,https://github.com/akshaymehra24/poisoning_certified_defenses,11,How robust are randomized smoothing based defenses to data poisoning?,"https://scholar.google.com/scholar?cluster=17001375147769153298&hl=en&as_sdt=0,19",2,2021
Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation Priors,16,cvpr,1,0,2023-06-03 14:29:55.371000,https://github.com/ucker/SCI-TV-FFDNet,16,Effective snapshot compressive-spectral imaging via deep denoising and total variation priors,"https://scholar.google.com/scholar?cluster=17987622509604935453&hl=en&as_sdt=0,5",1,2021
Explore Image Deblurring via Encoded Blur Kernel Space,34,cvpr,32,8,2023-06-03 14:29:55.564000,https://github.com/VinAIResearch/blur-kernel-space-exploring,122,Explore image deblurring via encoded blur kernel space,"https://scholar.google.com/scholar?cluster=7082219498382175790&hl=en&as_sdt=0,5",6,2021
Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction,25,cvpr,5,1,2023-06-03 14:29:55.759000,https://github.com/syguan96/BOA,52,Bilevel online adaptation for out-of-domain human mesh reconstruction,"https://scholar.google.com/scholar?cluster=14857518758485826444&hl=en&as_sdt=0,39",3,2021
Lipstick Ain't Enough: Beyond Color Matching for In-the-Wild Makeup Transfer,29,cvpr,47,4,2023-06-03 14:29:55.958000,https://github.com/VinAIResearch/CPM,282,Lipstick ain't enough: beyond color matching for in-the-wild makeup transfer,"https://scholar.google.com/scholar?cluster=7205626386020322671&hl=en&as_sdt=0,1",8,2021
Dynamic Transfer for Multi-Source Domain Adaptation,40,cvpr,10,1,2023-06-03 14:29:56.152000,https://github.com/liyunsheng13/DRT,56,Dynamic transfer for multi-source domain adaptation,"https://scholar.google.com/scholar?cluster=12382977104623000243&hl=en&as_sdt=0,33",4,2021
Rethinking Graph Neural Architecture Search From Message-Passing,32,cvpr,8,2,2023-06-03 14:29:56.347000,https://github.com/phython96/GNAS-MP,48,Rethinking graph neural architecture search from message-passing,"https://scholar.google.com/scholar?cluster=378250720919825769&hl=en&as_sdt=0,35",2,2021
DER: Dynamically Expandable Representation for Class Incremental Learning,193,cvpr,18,7,2023-06-03 14:29:56.540000,https://github.com/Rhyssiyan/DER-ClassIL.pytorch,115,Der: Dynamically expandable representation for class incremental learning,"https://scholar.google.com/scholar?cluster=15603649429564073640&hl=en&as_sdt=0,5",8,2021
Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks,43,cvpr,6,1,2023-06-03 14:29:56.735000,https://github.com/xxlong0/ESTDepth,65,Multi-view depth estimation using epipolar spatio-temporal networks,"https://scholar.google.com/scholar?cluster=10710437807161824313&hl=en&as_sdt=0,5",10,2021
Multi-Stage Progressive Image Restoration,686,cvpr,179,25,2023-06-03 14:29:56.928000,https://github.com/swz30/MPRNet,935,Multi-stage progressive image restoration,"https://scholar.google.com/scholar?cluster=14988305211504802629&hl=en&as_sdt=0,33",16,2021
PointNetLK Revisited,36,cvpr,3,7,2023-06-03 14:29:57.122000,https://github.com/Lilac-Lee/PointNetLK_Revisited,34,Pointnetlk revisited,"https://scholar.google.com/scholar?cluster=13463747400628018914&hl=en&as_sdt=0,47",4,2021
Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation,36,cvpr,18,2,2023-06-03 14:29:57.316000,https://github.com/Evgeneus/Graph-Domain-Adaptaion,84,Curriculum graph co-teaching for multi-target domain adaptation,"https://scholar.google.com/scholar?cluster=11292159721380637620&hl=en&as_sdt=0,5",3,2021
Seeing Out of the Box: End-to-End Pre-Training for Vision-Language Representation Learning,158,cvpr,18,9,2023-06-03 14:29:57.511000,https://github.com/researchmm/soho,201,Seeing out of the box: End-to-end pre-training for vision-language representation learning,"https://scholar.google.com/scholar?cluster=9824266083837816532&hl=en&as_sdt=0,33",10,2021
TearingNet: Point Cloud Autoencoder To Learn Topology-Friendly Representations,26,cvpr,5,1,2023-06-03 14:29:57.709000,https://github.com/InterDigitalInc/TearingNet,23,Tearingnet: Point cloud autoencoder to learn topology-friendly representations,"https://scholar.google.com/scholar?cluster=5359166351945292780&hl=en&as_sdt=0,15",0,2021
LAFEAT: Piercing Through Adversarial Defenses With Latent Features,31,cvpr,3,1,2023-06-03 14:29:57.904000,https://github.com/lafeat/lafeat,16,Lafeat: Piercing through adversarial defenses with latent features,"https://scholar.google.com/scholar?cluster=5272292491144016057&hl=en&as_sdt=0,36",3,2021
Convolutional Dynamic Alignment Networks for Interpretable Classifications,25,cvpr,4,0,2023-06-03 14:29:58.098000,https://github.com/moboehle/CoDA-Nets,26,Convolutional dynamic alignment networks for interpretable classifications,"https://scholar.google.com/scholar?cluster=11243314331412028333&hl=en&as_sdt=0,3",5,2021
RSG: A Simple but Effective Module for Learning Imbalanced Datasets,43,cvpr,23,3,2023-06-03 14:29:58.292000,https://github.com/Jianf-Wang/RSG,119,Rsg: A simple but effective module for learning imbalanced datasets,"https://scholar.google.com/scholar?cluster=5310416236723527828&hl=en&as_sdt=0,5",8,2021
Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On,42,cvpr,16,0,2023-06-03 14:29:58.486000,https://github.com/isantesteban/vto-garment-collisions,82,Self-supervised collision handling via generative 3d garment models for virtual try-on,"https://scholar.google.com/scholar?cluster=6415265380053021701&hl=en&as_sdt=0,46",6,2021
Goal-Oriented Gaze Estimation for Zero-Shot Learning,66,cvpr,9,11,2023-06-03 14:29:58.681000,https://github.com/osierboy/GEM-ZSL,53,Goal-oriented gaze estimation for zero-shot learning,"https://scholar.google.com/scholar?cluster=17663151732667468023&hl=en&as_sdt=0,5",9,2021
Model-Contrastive Federated Learning,293,cvpr,42,0,2023-06-03 14:29:58.875000,https://github.com/QinbinLi/MOON,171,Model-contrastive federated learning,"https://scholar.google.com/scholar?cluster=13936275944182725477&hl=en&as_sdt=0,31",1,2021
CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild,65,cvpr,5,10,2023-06-03 14:29:59.069000,https://github.com/bastianwandt/CanonPose,31,Canonpose: Self-supervised monocular 3d human pose estimation in the wild,"https://scholar.google.com/scholar?cluster=14702812907982478084&hl=en&as_sdt=0,5",2,2021
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation,42,cvpr,2,3,2023-06-03 14:29:59.264000,https://github.com/jhuang81/weak-sup-visual-grounding,12,Improving weakly supervised visual grounding by contrastive knowledge distillation,"https://scholar.google.com/scholar?cluster=7795877753807004303&hl=en&as_sdt=0,5",1,2021
The Blessings of Unlabeled Background in Untrimmed Videos,22,cvpr,0,0,2023-06-03 14:29:59.458000,https://github.com/liuyuancv/WTAL_blessing,10,The blessings of unlabeled background in untrimmed videos,"https://scholar.google.com/scholar?cluster=10544715704849785953&hl=en&as_sdt=0,23",5,2021
Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment,30,cvpr,2,2,2023-06-03 14:29:59.651000,https://github.com/days1011/HLAGCN,27,Hierarchical layout-aware graph convolutional network for unified aesthetics assessment,"https://scholar.google.com/scholar?cluster=17323273798242072327&hl=en&as_sdt=0,6",5,2021
Instance Localization for Self-Supervised Detection Pretraining,94,cvpr,12,13,2023-06-03 14:29:59.846000,https://github.com/limbo0000/InstanceLoc,137,Instance localization for self-supervised detection pretraining,"https://scholar.google.com/scholar?cluster=11482573304091107646&hl=en&as_sdt=0,44",9,2021
TAP: Text-Aware Pre-Training for Text-VQA and Text-Caption,82,cvpr,10,8,2023-06-03 14:30:00.039000,https://github.com/microsoft/TAP,66,Tap: Text-aware pre-training for text-vqa and text-caption,"https://scholar.google.com/scholar?cluster=3036316404812091265&hl=en&as_sdt=0,19",4,2021
Fast End-to-End Learning on Protein Surfaces,70,cvpr,40,21,2023-06-03 14:30:00.233000,https://github.com/FreyrS/dMaSIF,144,Fast end-to-end learning on protein surfaces,"https://scholar.google.com/scholar?cluster=4710178074647509921&hl=en&as_sdt=0,5",7,2021
Playable Video Generation,28,cvpr,25,2,2023-06-03 14:30:00.429000,https://github.com/willi-menapace/PlayableVideoGeneration,138,Playable video generation,"https://scholar.google.com/scholar?cluster=351628226572607542&hl=en&as_sdt=0,6",9,2021
Multimodal Motion Prediction With Stacked Transformers,139,cvpr,45,6,2023-06-03 14:30:00.623000,https://github.com/decisionforce/mmTransformer,272,Multimodal motion prediction with stacked transformers,"https://scholar.google.com/scholar?cluster=13959105563081015589&hl=en&as_sdt=0,5",23,2021
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression,28,cvpr,2,0,2023-06-03 14:30:00.817000,https://github.com/Li-Wanhua/POEs,27,Learning probabilistic ordinal embeddings for uncertainty-aware regression,"https://scholar.google.com/scholar?cluster=17605894500743092318&hl=en&as_sdt=0,44",2,2021
DexYCB: A Benchmark for Capturing Hand Grasping of Objects,93,cvpr,19,14,2023-06-03 14:30:01.011000,https://github.com/NVlabs/dex-ycb-toolkit,121,DexYCB: A benchmark for capturing hand grasping of objects,"https://scholar.google.com/scholar?cluster=3318238416703754159&hl=en&as_sdt=0,5",12,2021
StyleMix: Separating Content and Style for Enhanced Data Augmentation,43,cvpr,2,0,2023-06-03 14:30:01.209000,https://github.com/alsdml/StyleMix,26,Stylemix: Separating content and style for enhanced data augmentation,"https://scholar.google.com/scholar?cluster=208585168421421993&hl=en&as_sdt=0,5",1,2021
Generative Interventions for Causal Learning,28,cvpr,3,2,2023-06-03 14:30:01.403000,https://github.com/cvlab-columbia/GenInt,16,Generative interventions for causal learning,"https://scholar.google.com/scholar?cluster=11092479042334328503&hl=en&as_sdt=0,10",6,2021
Pushing It Out of the Way: Interactive Visual Navigation,11,cvpr,4,2,2023-06-03 14:30:01.606000,https://github.com/KuoHaoZeng/Interactive_Visual_Navigation,26,Pushing it out of the way: Interactive visual navigation,"https://scholar.google.com/scholar?cluster=4756576468045174679&hl=en&as_sdt=0,5",2,2021
Kaleido-BERT: Vision-Language Pre-Training on Fashion Domain,82,cvpr,18,1,2023-06-03 14:30:01.813000,https://github.com/mczhuge/Kaleido-BERT,258,Kaleido-bert: Vision-language pre-training on fashion domain,"https://scholar.google.com/scholar?cluster=2308625659154777541&hl=en&as_sdt=0,11",2,2021
VS-Net: Voting With Segmentation for Visual Localization,18,cvpr,16,4,2023-06-03 14:30:02.007000,https://github.com/zju3dv/VS-Net,80,VS-Net: Voting with segmentation for visual localization,"https://scholar.google.com/scholar?cluster=8854412944119215291&hl=en&as_sdt=0,5",13,2021
LoFTR: Detector-Free Local Feature Matching With Transformers,361,cvpr,266,51,2023-06-03 14:30:02.202000,https://github.com/zju3dv/LoFTR,1626,LoFTR: Detector-free local feature matching with transformers,"https://scholar.google.com/scholar?cluster=3868256673952367025&hl=en&as_sdt=0,28",44,2021
Understanding the Robustness of Skeleton-Based Action Recognition Under Adversarial Attack,25,cvpr,2,0,2023-06-03 14:30:02.396000,https://github.com/realcrane/SMART,8,Understanding the robustness of skeleton-based action recognition under adversarial attack,"https://scholar.google.com/scholar?cluster=12734671026203588219&hl=en&as_sdt=0,10",3,2021
What if We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels,43,cvpr,27,0,2023-06-03 14:30:02.590000,https://github.com/ku21fan/STR-Fewer-Labels,155,What if we only use real datasets for scene text recognition? toward scene text recognition with fewer labels,"https://scholar.google.com/scholar?cluster=13302443587131676313&hl=en&as_sdt=0,5",6,2021
HR-NAS: Searching Efficient High-Resolution Neural Architectures With Lightweight Transformers,36,cvpr,16,7,2023-06-03 14:30:02.801000,https://github.com/dingmyu/HR-NAS,132,Hr-nas: Searching efficient high-resolution neural architectures with lightweight transformers,"https://scholar.google.com/scholar?cluster=17653983911048379132&hl=en&as_sdt=0,41",5,2021
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules,17,cvpr,1,1,2023-06-03 14:30:02.995000,https://github.com/aurooj/WeakGroundedVQA_Capsules,17,Found a reason for me? weakly-supervised grounded visual question answering using capsules,"https://scholar.google.com/scholar?cluster=17446502598026051954&hl=en&as_sdt=0,5",3,2021
MetaCorrection: Domain-Aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation,65,cvpr,13,5,2023-06-03 14:30:03.203000,https://github.com/cyang-cityu/MetaCorrection,42,Metacorrection: Domain-aware meta loss correction for unsupervised domain adaptation in semantic segmentation,"https://scholar.google.com/scholar?cluster=7034870002645181090&hl=en&as_sdt=0,7",3,2021
Recurrent Multi-View Alignment Network for Unsupervised Surface Registration,23,cvpr,56,4,2023-06-03 14:30:03.399000,https://github.com/WanquanF/RMA-Net,206,Recurrent multi-view alignment network for unsupervised surface registration,"https://scholar.google.com/scholar?cluster=9021684109606820328&hl=en&as_sdt=0,5",27,2021
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents,47,cvpr,1,1,2023-06-03 14:30:03.594000,https://github.com/bdokim/LaPred,8,Lapred: Lane-aware prediction of multi-modal future trajectories of dynamic agents,"https://scholar.google.com/scholar?cluster=11065684318584494696&hl=en&as_sdt=0,3",1,2021
Prototype Completion With Primitive Knowledge for Few-Shot Learning,63,cvpr,4,9,2023-06-03 14:30:03.799000,https://github.com/zhangbq-research/Prototype_Completion_for_FSL,45,Prototype completion with primitive knowledge for few-shot learning,"https://scholar.google.com/scholar?cluster=12917805060629414812&hl=en&as_sdt=0,5",2,2021
Binary Graph Neural Networks,32,cvpr,8,1,2023-06-03 14:30:03.994000,https://github.com/mbahri/binary_gnn,32,Binary graph neural networks,"https://scholar.google.com/scholar?cluster=6199225357039878391&hl=en&as_sdt=0,10",4,2021
LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity,20,cvpr,3,3,2023-06-03 14:30:04.187000,https://github.com/davidhalladay/LayoutTransformer,43,Layouttransformer: Scene layout generation with conceptual and spatial diversity,"https://scholar.google.com/scholar?cluster=11988789825986082424&hl=en&as_sdt=0,5",5,2021
Practical Wide-Angle Portraits Correction With Deep Structured Models,9,cvpr,2,4,2023-06-03 14:30:04.382000,https://github.com/TanJing94/Deep_Portraits_Correction,28,Practical wide-angle portraits correction with deep structured models,"https://scholar.google.com/scholar?cluster=8572601471367978174&hl=en&as_sdt=0,10",7,2021
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration,127,cvpr,28,19,2023-06-03 14:30:04.576000,https://github.com/QingyongHu/SpinNet,211,Spinnet: Learning a general surface descriptor for 3d point cloud registration,"https://scholar.google.com/scholar?cluster=11824391955586440793&hl=en&as_sdt=0,23",13,2021
SOLD2: Self-Supervised Occlusion-Aware Line Description and Detection,32,cvpr,70,10,2023-06-03 14:30:04.770000,https://github.com/cvg/SOLD2,480,SOLD2: Self-supervised occlusion-aware line description and detection,"https://scholar.google.com/scholar?cluster=9149041813653182481&hl=en&as_sdt=0,22",17,2021
Flow-Guided One-Shot Talking Face Generation With a High-Resolution Audio-Visual Dataset,85,cvpr,35,11,2023-06-03 14:30:04.964000,https://github.com/MRzzm/HDTF,190,Flow-guided one-shot talking face generation with a high-resolution audio-visual dataset,"https://scholar.google.com/scholar?cluster=3087372429218443775&hl=en&as_sdt=0,10",14,2021
Cycle4Completion: Unpaired Point Cloud Completion Using Cycle Transformation With Missing Region Coding,55,cvpr,1,4,2023-06-03 14:30:05.158000,https://github.com/diviswen/Cycle4Completion,24,Cycle4completion: Unpaired point cloud completion using cycle transformation with missing region coding,"https://scholar.google.com/scholar?cluster=16379264061617867074&hl=en&as_sdt=0,10",2,2021
VIGOR: Cross-View Image Geo-Localization Beyond One-to-One Retrieval,51,cvpr,6,0,2023-06-03 14:30:05.352000,https://github.com/Jeff-Zilence/VIGOR,44,Vigor: Cross-view image geo-localization beyond one-to-one retrieval,"https://scholar.google.com/scholar?cluster=14572148874110352574&hl=en&as_sdt=0,33",3,2021
CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation,19,cvpr,5,2,2023-06-03 14:30:05.546000,https://github.com/MCG-NJU/CGA-Net,21,Cga-net: Category guided aggregation for point cloud semantic segmentation,"https://scholar.google.com/scholar?cluster=12499546067760980965&hl=en&as_sdt=0,50",4,2021
Neural Prototype Trees for Interpretable Fine-Grained Image Recognition,92,cvpr,15,1,2023-06-03 14:30:05.740000,https://github.com/M-Nauta/ProtoTree,74,Neural prototype trees for interpretable fine-grained image recognition,"https://scholar.google.com/scholar?cluster=14898137726824988889&hl=en&as_sdt=0,48",1,2021
Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization,7,cvpr,1,0,2023-06-03 14:30:05.934000,https://github.com/AISIGSJTU/SEBR,4,Robust bayesian neural networks by spectral expectation bound regularization,"https://scholar.google.com/scholar?cluster=18356692445207348282&hl=en&as_sdt=0,5",5,2021
3D CNNs With Adaptive Temporal Feature Resolutions,17,cvpr,3,2,2023-06-03 14:30:06.130000,https://github.com/SimilarityGuidedSampling/Similarity-Guided-Sampling,24,3D CNNs with adaptive temporal feature resolutions,"https://scholar.google.com/scholar?cluster=17042700411859549213&hl=en&as_sdt=0,3",6,2021
Multiple Instance Active Learning for Object Detection,59,cvpr,44,0,2023-06-03 14:30:06.324000,https://github.com/yuantn/MI-AOD,287,Multiple instance active learning for object detection,"https://scholar.google.com/scholar?cluster=6683819680492257566&hl=en&as_sdt=0,5",2,2021
Birds of a Feather: Capturing Avian Shape Models From Images,9,cvpr,2,0,2023-06-03 14:30:06.518000,https://github.com/yufu-wang/aves,12,Birds of a feather: Capturing avian shape models from images,"https://scholar.google.com/scholar?cluster=4271237650976131227&hl=en&as_sdt=0,5",3,2021
Neural Parts: Learning Expressive 3D Shape Abstractions With Invertible Neural Networks,60,cvpr,20,2,2023-06-03 14:30:06.712000,https://github.com/paschalidoud/neural_parts,166,Neural parts: Learning expressive 3d shape abstractions with invertible neural networks,"https://scholar.google.com/scholar?cluster=7980732082971902518&hl=en&as_sdt=0,50",10,2021
Back-Tracing Representative Points for Voting-Based 3D Object Detection in Point Clouds,56,cvpr,15,4,2023-06-03 14:30:06.906000,https://github.com/cheng052/BRNet,89,Back-tracing representative points for voting-based 3d object detection in point clouds,"https://scholar.google.com/scholar?cluster=5172119343963315393&hl=en&as_sdt=0,5",3,2021
Rotation Equivariant Siamese Networks for Tracking,27,cvpr,1,4,2023-06-03 14:30:07.101000,https://github.com/dkgupta90/re-siamnet,25,Rotation equivariant siamese networks for tracking,"https://scholar.google.com/scholar?cluster=2650574729569060906&hl=en&as_sdt=0,10",8,2021
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds,43,cvpr,7,4,2023-06-03 14:30:07.295000,https://github.com/weiyithu/PV-RAFT,47,Pv-raft: Point-voxel correlation fields for scene flow estimation of point clouds,"https://scholar.google.com/scholar?cluster=997486946043239338&hl=en&as_sdt=0,5",3,2021
High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network,52,cvpr,45,1,2023-06-03 14:30:07.489000,https://github.com/csjliang/LPTN,386,High-resolution photorealistic image translation in real-time: A laplacian pyramid translation network,"https://scholar.google.com/scholar?cluster=14166501793789279224&hl=en&as_sdt=0,5",12,2021
Improving the Efficiency and Robustness of Deepfakes Detection Through Precise Geometric Features,59,cvpr,12,22,2023-06-03 14:30:07.684000,https://github.com/frederickszk/LRNet,72,Improving the efficiency and robustness of deepfakes detection through precise geometric features,"https://scholar.google.com/scholar?cluster=18274114420858694149&hl=en&as_sdt=0,5",2,2021
End-to-End Video Instance Segmentation With Transformers,487,cvpr,95,25,2023-06-03 14:30:07.878000,https://github.com/Epiphqny/VisTR,704,End-to-end video instance segmentation with transformers,"https://scholar.google.com/scholar?cluster=10424310778620231784&hl=en&as_sdt=0,48",12,2021
PatchmatchNet: Learned Multi-View Patchmatch Stereo,133,cvpr,63,8,2023-06-03 14:30:08.072000,https://github.com/FangjinhuaWang/PatchmatchNet,424,Patchmatchnet: Learned multi-view patchmatch stereo,"https://scholar.google.com/scholar?cluster=18322732022474881641&hl=en&as_sdt=0,16",10,2021
Sketch2Model: View-Aware 3D Modeling From Single Free-Hand Sketches,31,cvpr,8,5,2023-06-03 14:30:08.266000,https://github.com/bennyguo/sketch2model,36,Sketch2model: View-aware 3d modeling from single free-hand sketches,"https://scholar.google.com/scholar?cluster=4931108810150583856&hl=en&as_sdt=0,47",4,2021
A Second-Order Approach to Learning With Instance-Dependent Label Noise,71,cvpr,9,2,2023-06-03 14:30:08.461000,https://github.com/UCSC-REAL/CAL,45,A second-order approach to learning with instance-dependent label noise,"https://scholar.google.com/scholar?cluster=9161510088364032397&hl=en&as_sdt=0,31",3,2021
Taming Transformers for High-Resolution Image Synthesis,851,cvpr,931,129,2023-06-03 14:30:08.654000,https://github.com/CompVis/taming-transformers,4299,Taming transformers for high-resolution image synthesis,"https://scholar.google.com/scholar?cluster=12650581806598225058&hl=en&as_sdt=0,5",75,2021
Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation,154,cvpr,157,44,2023-06-03 14:30:08.849000,https://github.com/Hangz-nju-cuhk/Talking-Face_PC-AVS,771,Pose-controllable talking face generation by implicitly modularized audio-visual representation,"https://scholar.google.com/scholar?cluster=16709572247327062137&hl=en&as_sdt=0,36",21,2021
Learning the Predictability of the Future,44,cvpr,24,0,2023-06-03 14:30:09.043000,https://github.com/cvlab-columbia/hyperfuture,149,Learning the predictability of the future,"https://scholar.google.com/scholar?cluster=16527013686624401149&hl=en&as_sdt=0,33",12,2021
"Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion",90,cvpr,60,1,2023-06-03 14:30:09.248000,https://github.com/hkchengrex/MiVOS,398,"Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion","https://scholar.google.com/scholar?cluster=14171505212158565986&hl=en&as_sdt=0,5",14,2021
Robust Point Cloud Registration Framework Based on Deep Graph Matching,99,cvpr,13,8,2023-06-03 14:30:09.442000,https://github.com/fukexue/RGM,93,Robust point cloud registration framework based on deep graph matching,"https://scholar.google.com/scholar?cluster=14668089237724280371&hl=en&as_sdt=0,5",6,2021
COMPLETER: Incomplete Multi-View Clustering via Contrastive Prediction,150,cvpr,29,5,2023-06-03 14:30:09.636000,https://github.com/XLearning-SCU/2021-CVPR-Completer,85,Completer: Incomplete multi-view clustering via contrastive prediction,"https://scholar.google.com/scholar?cluster=12580655597958281447&hl=en&as_sdt=0,11",3,2021
Learning Better Visual Dialog Agents With Pretrained Visual-Linguistic Representation,12,cvpr,4,2,2023-06-03 14:30:09.830000,https://github.com/amazon-research/read-up,8,Learning better visual dialog agents with pretrained visual-linguistic representation,"https://scholar.google.com/scholar?cluster=17634548932371863893&hl=en&as_sdt=0,44",2,2021
Image-to-Image Translation via Hierarchical Style Disentanglement,59,cvpr,50,15,2023-06-03 14:30:10.024000,https://github.com/imlixinyang/HiSD,365,Image-to-image translation via hierarchical style disentanglement,"https://scholar.google.com/scholar?cluster=14902320438514616115&hl=en&as_sdt=0,5",7,2021
Restoring Extremely Dark Images in Real Time,27,cvpr,22,7,2023-06-03 14:30:10.218000,https://github.com/MohitLamba94/Restoring-Extremely-Dark-Images-In-Real-Time,129,Restoring extremely dark images in real time,"https://scholar.google.com/scholar?cluster=17820256892745594301&hl=en&as_sdt=0,3",10,2021
What Can Style Transfer and Paintings Do for Model Robustness?,9,cvpr,0,0,2023-06-03 14:30:10.413000,https://github.com/hubertsgithub/style_painting_robustness,4,What can style transfer and paintings do for model robustness?,"https://scholar.google.com/scholar?cluster=11744425304200168858&hl=en&as_sdt=0,22",2,2021
DeepSurfels: Learning Online Appearance Fusion,10,cvpr,7,1,2023-06-03 14:30:10.607000,https://github.com/onlinereconstruction/deep_surfels,59,DeepSurfels: Learning online appearance fusion,"https://scholar.google.com/scholar?cluster=712300087980113706&hl=en&as_sdt=0,32",5,2021
Unbiased Mean Teacher for Cross-Domain Object Detection,133,cvpr,8,4,2023-06-03 14:30:10.802000,https://github.com/kinredon/umt,72,Unbiased mean teacher for cross-domain object detection,"https://scholar.google.com/scholar?cluster=16143837810291267401&hl=en&as_sdt=0,35",6,2021
Beyond Max-Margin: Class Margin Equilibrium for Few-Shot Object Detection,94,cvpr,12,30,2023-06-03 14:30:10.996000,https://github.com/Bohao-Lee/CME,58,Beyond max-margin: Class margin equilibrium for few-shot object detection,"https://scholar.google.com/scholar?cluster=2030833382199618587&hl=en&as_sdt=0,33",1,2021
Learning Dynamic Network Using a Reuse Gate Function in Semi-Supervised Video Object Segmentation,21,cvpr,3,6,2023-06-03 14:30:11.191000,https://github.com/HYOJINPARK/Reuse_VOS,23,Learning dynamic network using a reuse gate function in semi-supervised video object segmentation,"https://scholar.google.com/scholar?cluster=13940373744389019037&hl=en&as_sdt=0,10",4,2021
FSDR: Frequency Space Domain Randomization for Domain Generalization,100,cvpr,4,4,2023-06-03 14:30:11.384000,https://github.com/jxhuang0508/FSDR,29,Fsdr: Frequency space domain randomization for domain generalization,"https://scholar.google.com/scholar?cluster=13379053152992456686&hl=en&as_sdt=0,33",2,2021
PLOP: Learning Without Forgetting for Continual Semantic Segmentation,107,cvpr,22,6,2023-06-03 14:30:11.579000,https://github.com/arthurdouillard/CVPR2021_PLOP,124,Plop: Learning without forgetting for continual semantic segmentation,"https://scholar.google.com/scholar?cluster=7315634125927972961&hl=en&as_sdt=0,5",4,2021
Teachers Do More Than Teach: Compressing Image-to-Image Models,30,cvpr,18,4,2023-06-03 14:30:11.773000,https://github.com/snap-research/CAT,164,Teachers do more than teach: Compressing image-to-image models,"https://scholar.google.com/scholar?cluster=8225246153105742254&hl=en&as_sdt=0,5",8,2021
L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute Editing,36,cvpr,7,6,2023-06-03 14:30:11.967000,https://github.com/rucmlcv/L2M-GAN,53,L2m-gan: Learning to manipulate latent space semantics for facial attribute editing,"https://scholar.google.com/scholar?cluster=12602747068550124262&hl=en&as_sdt=0,10",3,2021
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments,13,cvpr,10,0,2023-06-03 14:30:12.161000,https://github.com/siyandong/NeuralRouting,66,Robust neural routing through space partitions for camera relocalization in dynamic indoor environments,"https://scholar.google.com/scholar?cluster=9962218736530737887&hl=en&as_sdt=0,5",9,2021
Understanding Failures of Deep Networks via Robust Feature Extraction,42,cvpr,3,0,2023-06-03 14:30:12.355000,https://github.com/singlasahil14/barlow,34,Understanding failures of deep networks via robust feature extraction,"https://scholar.google.com/scholar?cluster=10378262686408966783&hl=en&as_sdt=0,33",1,2021
Learning Temporal Consistency for Low Light Video Enhancement From Single Images,50,cvpr,20,6,2023-06-03 14:30:12.550000,https://github.com/zkawfanx/StableLLVE,109,Learning temporal consistency for low light video enhancement from single images,"https://scholar.google.com/scholar?cluster=3430684384737804668&hl=en&as_sdt=0,5",8,2021
Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation,41,cvpr,2,2,2023-06-03 14:30:12.743000,https://github.com/astuti/ILA-DA,22,Instance level affinity-based transfer for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=6192092144156470763&hl=en&as_sdt=0,5",5,2021
Anchor-Free Person Search,67,cvpr,32,7,2023-06-03 14:30:12.937000,https://github.com/daodaofr/AlignPS,160,Anchor-free person search,"https://scholar.google.com/scholar?cluster=370980907530218862&hl=en&as_sdt=0,36",4,2021
Omni-Supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning,27,cvpr,6,4,2023-06-03 14:30:13.132000,https://github.com/azuki-miho/RFCR,22,Omni-supervised point cloud segmentation via gradual receptive field component reasoning,"https://scholar.google.com/scholar?cluster=9475016043088711593&hl=en&as_sdt=0,3",2,2021
Shallow Feature Matters for Weakly Supervised Object Localization,50,cvpr,8,2,2023-06-03 14:30:13.325000,https://github.com/weijun88/SPOL,21,Shallow feature matters for weakly supervised object localization,"https://scholar.google.com/scholar?cluster=3339581433972365106&hl=en&as_sdt=0,26",1,2021
RSTNet: Captioning With Adaptive Attention on Visual and Non-Visual Words,117,cvpr,29,2,2023-06-03 14:30:13.520000,https://github.com/zhangxuying1004/RSTNet,99,Rstnet: Captioning with adaptive attention on visual and non-visual words,"https://scholar.google.com/scholar?cluster=5068866602340287584&hl=en&as_sdt=0,14",3,2021
PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors,115,cvpr,16,8,2023-06-03 14:30:13.713000,https://github.com/zychen-ustc/PSD-Principled-Synthetic-to-Real-Dehazing-Guided-by-Physical-Priors,88,PSD: Principled synthetic-to-real dehazing guided by physical priors,"https://scholar.google.com/scholar?cluster=3301317357252121587&hl=en&as_sdt=0,33",2,2021
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling,59,cvpr,20,1,2023-06-03 14:30:13.907000,https://github.com/facebookresearch/AttentiveNAS,96,Attentivenas: Improving neural architecture search via attentive sampling,"https://scholar.google.com/scholar?cluster=8315277370906805362&hl=en&as_sdt=0,5",9,2021
Multiresolution Knowledge Distillation for Anomaly Detection,173,cvpr,20,0,2023-06-03 14:30:14.101000,https://github.com/Niousha12/Knowledge_Distillation_AD,50,Multiresolution knowledge distillation for anomaly detection,"https://scholar.google.com/scholar?cluster=12042429450923024445&hl=en&as_sdt=0,34",0,2021
Towards Evaluating and Training Verifiably Robust Neural Networks,16,cvpr,0,2,2023-06-03 14:30:14.295000,https://github.com/ZhaoyangLyu/VerifiablyRobustNN,15,Towards evaluating and training verifiably robust neural networks,"https://scholar.google.com/scholar?cluster=7927009820415851005&hl=en&as_sdt=0,5",2,2021
All Labels Are Not Created Equal: Enhancing Semi-Supervision via Label Grouping and Co-Training,40,cvpr,3,0,2023-06-03 14:30:14.490000,https://github.com/islam-nassar/semco,43,All labels are not created equal: Enhancing semi-supervision via label grouping and co-training,"https://scholar.google.com/scholar?cluster=12399027302616476471&hl=en&as_sdt=0,33",3,2021
Relation-aware Instance Refinement for Weakly Supervised Visual Grounding,32,cvpr,5,2,2023-06-03 14:30:14.684000,https://github.com/youngfly11/ReIR-WeaklyGrounding.pytorch,22,Relation-aware instance refinement for weakly supervised visual grounding,"https://scholar.google.com/scholar?cluster=12243869184345891895&hl=en&as_sdt=0,5",2,2021
PMP-Net: Point Cloud Completion by Learning Multi-Step Point Moving Paths,99,cvpr,12,9,2023-06-03 14:30:14.878000,https://github.com/diviswen/PMP-Net,37,Pmp-net: Point cloud completion by learning multi-step point moving paths,"https://scholar.google.com/scholar?cluster=3446048452262850232&hl=en&as_sdt=0,44",2,2021
Spatially-Invariant Style-Codes Controlled Makeup Transfer,28,cvpr,36,13,2023-06-03 14:30:15.072000,https://github.com/makeuptransfer/SCGAN,128,Spatially-invariant style-codes controlled makeup transfer,"https://scholar.google.com/scholar?cluster=5979466171705280461&hl=en&as_sdt=0,5",5,2021
ReAgent: Point Cloud Registration Using Imitation and Reinforcement Learning,18,cvpr,13,1,2023-06-03 14:30:15.267000,https://github.com/dornik/reagent,40,Reagent: Point cloud registration using imitation and reinforcement learning,"https://scholar.google.com/scholar?cluster=12039411482069665809&hl=en&as_sdt=0,33",2,2021
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?,122,cvpr,27,5,2023-06-03 14:30:15.462000,https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation,146,Few-shot segmentation without meta-learning: A good transductive inference is all you need?,"https://scholar.google.com/scholar?cluster=16904542927834091495&hl=en&as_sdt=0,5",8,2021
Neural Scene Graphs for Dynamic Scenes,121,cvpr,28,6,2023-06-03 14:30:15.655000,https://github.com/princeton-computational-imaging/neural-scene-graphs,184,Neural scene graphs for dynamic scenes,"https://scholar.google.com/scholar?cluster=6249527695742813290&hl=en&as_sdt=0,5",7,2021
Lite-HRNet: A Lightweight High-Resolution Network,144,cvpr,115,58,2023-06-03 14:30:15.849000,https://github.com/HRNet/Lite-HRNet,724,Lite-hrnet: A lightweight high-resolution network,"https://scholar.google.com/scholar?cluster=10774532196436088435&hl=en&as_sdt=0,5",21,2021
Revamping Cross-Modal Recipe Retrieval With Hierarchical Transformers and Self-Supervised Learning,38,cvpr,19,0,2023-06-03 14:30:16.043000,https://github.com/amzn/image-to-recipe-transformers,75,Revamping cross-modal recipe retrieval with hierarchical transformers and self-supervised learning,"https://scholar.google.com/scholar?cluster=3856631632033542711&hl=en&as_sdt=0,5",6,2021
Semantic Palette: Guiding Scene Generation With Class Proportions,2,cvpr,3,0,2023-06-03 14:30:16.238000,https://github.com/valeoai/SemanticPalette,24,Semantic palette: Guiding scene generation with class proportions,"https://scholar.google.com/scholar?cluster=17263626928391641174&hl=en&as_sdt=0,3",3,2021
Rethinking Semantic Segmentation From a Sequence-to-Sequence Perspective With Transformers,1614,cvpr,145,10,2023-06-03 14:30:16.432000,https://github.com/fudan-zvg/SETR,939,Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,"https://scholar.google.com/scholar?cluster=2013933719074368496&hl=en&as_sdt=0,5",35,2021
Picasso: A CUDA-Based Library for Deep Learning Over 3D Meshes,9,cvpr,18,6,2023-06-03 14:30:16.626000,https://github.com/hlei-ziyan/Picasso,103,Picasso: A cuda-based library for deep learning over 3d meshes,"https://scholar.google.com/scholar?cluster=16489285615655819626&hl=en&as_sdt=0,5",13,2021
Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality,24,cvpr,9,3,2023-06-03 14:30:16.820000,https://github.com/affect2mm/emotion-timeseries,11,Affect2mm: Affective analysis of multimedia content using emotion causality,"https://scholar.google.com/scholar?cluster=17792171219469660003&hl=en&as_sdt=0,19",1,2021
Deep Occlusion-Aware Instance Segmentation With Overlapping BiLayers,101,cvpr,67,9,2023-06-03 14:30:17.014000,https://github.com/lkeab/BCNet,459,Deep occlusion-aware instance segmentation with overlapping bilayers,"https://scholar.google.com/scholar?cluster=15732260026637551039&hl=en&as_sdt=0,5",9,2021
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective,70,cvpr,6,0,2023-06-03 14:30:17.208000,https://github.com/jeremy313/Soteria,39,Soteria: Provable defense against privacy leakage in federated learning from representation perspective,"https://scholar.google.com/scholar?cluster=1639045703363354875&hl=en&as_sdt=0,39",2,2021
Learning Optical Flow From Still Images,23,cvpr,11,1,2023-06-03 14:30:17.402000,https://github.com/mattpoggi/depthstillation,141,Learning optical flow from still images,"https://scholar.google.com/scholar?cluster=16680029073395272488&hl=en&as_sdt=0,5",3,2021
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments From a Single Moving Camera,39,cvpr,84,2,2023-06-03 14:30:17.596000,https://github.com/Brummi/MonoRec,531,MonoRec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera,"https://scholar.google.com/scholar?cluster=12286893036914513750&hl=en&as_sdt=0,21",27,2021
Linguistic Structures As Weak Supervision for Visual Scene Graph Generation,27,cvpr,4,0,2023-06-03 14:30:17.807000,https://github.com/yekeren/WSSGG,35,Linguistic structures as weak supervision for visual scene graph generation,"https://scholar.google.com/scholar?cluster=15867273824482740665&hl=en&as_sdt=0,43",2,2021
From Shadow Generation To Shadow Removal,40,cvpr,8,6,2023-06-03 14:30:18.002000,https://github.com/hhqweasd/G2R-ShadowNet,41,From shadow generation to shadow removal,"https://scholar.google.com/scholar?cluster=8295209265303745577&hl=en&as_sdt=0,39",3,2021
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation,120,cvpr,16,10,2023-06-03 14:30:18.196000,https://github.com/jbeomlee93/AdvCAM,114,Anti-adversarially manipulated attributions for weakly and semi-supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=11730723595404757151&hl=en&as_sdt=0,31",2,2021
SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization,8,cvpr,0,2,2023-06-03 15:10:22.685000,https://github.com/canjie-luo/real-300k,29,SimAN: exploring self-supervised representation learning of scene text via similarity-aware normalization,"https://scholar.google.com/scholar?cluster=8731827534213223271&hl=en&as_sdt=0,44",3,2022
Estimating Example Difficulty Using Variance of Gradients,41,cvpr,6,0,2023-06-03 15:10:22.879000,https://github.com/chirag126/VOG,55,Estimating example difficulty using variance of gradients,"https://scholar.google.com/scholar?cluster=11875282308322336079&hl=en&as_sdt=0,26",3,2022
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast,35,cvpr,12,3,2023-06-03 15:10:23.137000,https://github.com/usr922/wseg,114,Weakly supervised semantic segmentation by pixel-to-prototype contrast,"https://scholar.google.com/scholar?cluster=9426593234701255657&hl=en&as_sdt=0,10",2,2022
Learning Adaptive Warping for Real-World Rolling Shutter Correction,5,cvpr,2,0,2023-06-03 15:10:23.356000,https://github.com/ljzycmd/bsrsc,25,Learning adaptive warping for real-world rolling shutter correction,"https://scholar.google.com/scholar?cluster=16345027106223087803&hl=en&as_sdt=0,36",3,2022
Learning To Affiliate: Mutual Centralized Learning for Few-Shot Classification,16,cvpr,2,0,2023-06-03 15:10:23.590000,https://github.com/LouieYang/MCL,38,Learning to affiliate: Mutual centralized learning for few-shot classification,"https://scholar.google.com/scholar?cluster=14447790618194656650&hl=en&as_sdt=0,3",3,2022
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions,9,cvpr,5,1,2023-06-03 15:10:23.836000,https://github.com/nvlabs/bongard-hoi,49,Bongard-hoi: Benchmarking few-shot visual reasoning for human-object interactions,"https://scholar.google.com/scholar?cluster=9397175359738479859&hl=en&as_sdt=0,33",7,2022
Recurrent Dynamic Embedding for Video Object Segmentation,14,cvpr,6,3,2023-06-03 15:10:24.072000,https://github.com/limingxing00/rde-vos-cvpr2022,31,Recurrent dynamic embedding for video object segmentation,"https://scholar.google.com/scholar?cluster=748792613032702791&hl=en&as_sdt=0,33",3,2022
Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism,10,cvpr,0,3,2023-06-03 15:10:24.312000,https://github.com/dkxocl/rosseta,4,Continual object detection via prototypical task correlation guided gating mechanism,"https://scholar.google.com/scholar?cluster=12695099468525271301&hl=en&as_sdt=0,5",1,2022
Deep Hierarchical Semantic Segmentation,35,cvpr,22,6,2023-06-03 15:10:24.522000,https://github.com/0liliulei/hieraseg,229,Deep hierarchical semantic segmentation,"https://scholar.google.com/scholar?cluster=15631366303943962005&hl=en&as_sdt=0,21",9,2022
DATA: Domain-Aware and Task-Aware Self-Supervised Learning,3,cvpr,3,3,2023-06-03 15:10:24.767000,https://github.com/gaia-vision/gaia-ssl,18,DATA: Domain-Aware and Task-Aware Self-supervised Learning,"https://scholar.google.com/scholar?cluster=10551788358785783150&hl=en&as_sdt=0,34",2,2022
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection From Point Clouds,41,cvpr,16,12,2023-06-03 15:10:25.015000,https://github.com/skyhehe123/voxset,165,Voxel set transformer: A set-to-set approach to 3d object detection from point clouds,"https://scholar.google.com/scholar?cluster=509520809209241334&hl=en&as_sdt=0,5",3,2022
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM,134,cvpr,149,1,2023-06-03 15:10:25.289000,https://github.com/cvg/nice-slam,1021,Nice-slam: Neural implicit scalable encoding for slam,"https://scholar.google.com/scholar?cluster=2376705359809252522&hl=en&as_sdt=0,14",34,2022
Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion,29,cvpr,12,2,2023-06-03 15:10:25.484000,https://github.com/gutianpei/mid,98,Stochastic trajectory prediction via motion indeterminacy diffusion,"https://scholar.google.com/scholar?cluster=8008640819060084074&hl=en&as_sdt=0,33",9,2022
Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning,25,cvpr,0,3,2023-06-03 15:10:25.690000,https://github.com/xduxyli/scen-master,16,Siamese contrastive embedding network for compositional zero-shot learning,"https://scholar.google.com/scholar?cluster=381868240874280468&hl=en&as_sdt=0,14",2,2022
Cross-Modal Map Learning for Vision and Language Navigation,13,cvpr,2,4,2023-06-03 15:10:25.893000,https://github.com/ggeorgak11/cm2,22,Cross-modal map learning for vision and language navigation,"https://scholar.google.com/scholar?cluster=10974280323957175956&hl=en&as_sdt=0,5",3,2022
Do Learned Representations Respect Causal Relationships?,2,cvpr,2,1,2023-06-03 15:10:26.132000,https://github.com/human-analysis/causal-relations-between-representations,16,Do learned representations respect causal relationships?,"https://scholar.google.com/scholar?cluster=5558982647399599200&hl=en&as_sdt=0,47",2,2022
ZebraPose: Coarse To Fine Surface Encoding for 6DoF Object Pose Estimation,27,cvpr,10,6,2023-06-03 15:10:26.379000,https://github.com/suyz526/zebrapose,64,Zebrapose: Coarse to fine surface encoding for 6dof object pose estimation,"https://scholar.google.com/scholar?cluster=12848982769215820402&hl=en&as_sdt=0,33",9,2022
Incremental Transformer Structure Enhanced Image Inpainting With Masking Positional Encoding,41,cvpr,30,1,2023-06-03 15:10:26.611000,https://github.com/dqiaole/zits_inpainting,235,Incremental transformer structure enhanced image inpainting with masking positional encoding,"https://scholar.google.com/scholar?cluster=12152435237473254275&hl=en&as_sdt=0,43",13,2022
Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation,50,cvpr,11,7,2023-06-03 15:10:26.831000,https://github.com/xulianuwa/mctformer,96,Multi-class token transformer for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=9207086502844896554&hl=en&as_sdt=0,5",5,2022
CRIS: CLIP-Driven Referring Image Segmentation,68,cvpr,31,5,2023-06-03 15:10:27.054000,https://github.com/DerrickWang005/CRIS.pytorch,167,Cris: Clip-driven referring image segmentation,"https://scholar.google.com/scholar?cluster=12396058489435840533&hl=en&as_sdt=0,41",2,2022
CVF-SID: Cyclic Multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise From Image,19,cvpr,7,10,2023-06-03 15:10:27.325000,https://github.com/reyhanehne/cvf-sid_pytorch,48,CVF-SID: Cyclic multi-variate function for self-supervised image denoising by disentangling noise from image,"https://scholar.google.com/scholar?cluster=12432404920328258294&hl=en&as_sdt=0,5",1,2022
FaceFormer: Speech-Driven 3D Facial Animation With Transformers,36,cvpr,103,53,2023-06-03 15:10:27.548000,https://github.com/EvelynFan/FaceFormer,504,Faceformer: Speech-driven 3d facial animation with transformers,"https://scholar.google.com/scholar?cluster=4890874188830606098&hl=en&as_sdt=0,39",10,2022
Blind2Unblind: Self-Supervised Image Denoising With Visible Blind Spots,28,cvpr,11,2,2023-06-03 15:10:27.807000,https://github.com/demonsjin/blind2unblind,81,Blind2unblind: Self-supervised image denoising with visible blind spots,"https://scholar.google.com/scholar?cluster=11607429091423964704&hl=en&as_sdt=0,5",2,2022
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization,49,cvpr,10,1,2023-06-03 15:10:28.059000,https://github.com/ybzh/efdm,126,Exact feature distribution matching for arbitrary style transfer and domain generalization,"https://scholar.google.com/scholar?cluster=11528215253653754225&hl=en&as_sdt=0,5",4,2022
Exploring Patch-Wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks,23,cvpr,3,2,2023-06-03 15:10:28.263000,https://github.com/jcy132/Hneg_SRC,46,Exploring patch-wise semantic relation for contrastive learning in image-to-image translation tasks,"https://scholar.google.com/scholar?cluster=6005655039000553018&hl=en&as_sdt=0,11",5,2022
CLRNet: Cross Layer Refinement Network for Lane Detection,27,cvpr,75,78,2023-06-03 15:10:28.482000,https://github.com/Turoad/clrnet,318,Clrnet: Cross layer refinement network for lane detection,"https://scholar.google.com/scholar?cluster=11442601300141569813&hl=en&as_sdt=0,25",11,2022
Balanced and Hierarchical Relation Learning for One-Shot Object Detection,3,cvpr,7,5,2023-06-03 15:10:28.716000,https://github.com/hero-y/bhrl,35,Balanced and hierarchical relation learning for one-shot object detection,"https://scholar.google.com/scholar?cluster=12345820201585228534&hl=en&as_sdt=0,5",1,2022
Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts,40,cvpr,6,3,2023-06-03 15:10:28.937000,https://github.com/Phoenix1153/ViT_OOD_generalization,80,Delving deep into the generalization of vision transformers under distribution shifts,"https://scholar.google.com/scholar?cluster=3383711996123690231&hl=en&as_sdt=0,10",3,2022
LGT-Net: Indoor Panoramic Room Layout Estimation With Geometry-Aware Transformer Network,10,cvpr,5,0,2023-06-03 15:10:29.170000,https://github.com/zhigangjiang/LGT-Net,50,Lgt-net: Indoor panoramic room layout estimation with geometry-aware transformer network,"https://scholar.google.com/scholar?cluster=11535993822419894663&hl=en&as_sdt=0,36",2,2022
Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning,15,cvpr,12,11,2023-06-03 15:10:29.379000,https://github.com/jiahao-uts/slpt-master,79,Sparse local patch transformer for robust face alignment and landmarks inherent relation learning,"https://scholar.google.com/scholar?cluster=12559311512689037820&hl=en&as_sdt=0,34",3,2022
Motion-Aware Contrastive Video Representation Learning via Foreground-Background Merging,21,cvpr,7,6,2023-06-03 15:10:29.595000,https://github.com/mark12ding/fame,38,Motion-aware contrastive video representation learning via foreground-background merging,"https://scholar.google.com/scholar?cluster=6148259841571528152&hl=en&as_sdt=0,3",3,2022
SIOD: Single Instance Annotated per Category per Image for Object Detection,6,cvpr,0,2,2023-06-03 15:10:29.831000,https://github.com/solicucu/siod,19,SIOD: single instance annotated per category per image for object detection,"https://scholar.google.com/scholar?cluster=16538856504701001407&hl=en&as_sdt=0,5",3,2022
Uformer: A General U-Shaped Transformer for Image Restoration,416,cvpr,94,33,2023-06-03 15:10:30.031000,https://github.com/ZhendongWang6/Uformer,587,Uformer: A general u-shaped transformer for image restoration,"https://scholar.google.com/scholar?cluster=14031000766044293652&hl=en&as_sdt=0,5",13,2022
Accelerating DETR Convergence via Semantic-Aligned Matching,34,cvpr,45,10,2023-06-03 15:10:30.278000,https://github.com/zhanggongjie/sam-detr,261,Accelerating DETR convergence via semantic-aligned matching,"https://scholar.google.com/scholar?cluster=6658129475220097194&hl=en&as_sdt=0,19",10,2022
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos,18,cvpr,7,12,2023-06-03 15:10:30.483000,https://github.com/ttlmh/bridge-prompt,74,Bridge-prompt: Towards ordinal action understanding in instructional videos,"https://scholar.google.com/scholar?cluster=4812875950233018558&hl=en&as_sdt=0,33",3,2022
Vision Transformer With Deformable Attention,113,cvpr,49,11,2023-06-03 15:10:30.711000,https://github.com/leaplabthu/dat,497,Vision transformer with deformable attention,"https://scholar.google.com/scholar?cluster=11231231375435598889&hl=en&as_sdt=0,5",10,2022
High-Resolution Face Swapping via Latent Semantics Disentanglement,22,cvpr,5,8,2023-06-03 15:10:30.948000,https://github.com/cnnlstm/fslsd_hires,78,High-resolution face swapping via latent semantics disentanglement,"https://scholar.google.com/scholar?cluster=13488189955819125408&hl=en&as_sdt=0,5",8,2022
SimMatch: Semi-Supervised Learning With Similarity Matching,51,cvpr,8,0,2023-06-03 15:10:31.154000,https://github.com/kylezheng1997/simmatch,71,Simmatch: Semi-supervised learning with similarity matching,"https://scholar.google.com/scholar?cluster=9332236623932380397&hl=en&as_sdt=0,23",2,2022
"Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing",9,cvpr,7,0,2023-06-03 15:10:31.373000,https://github.com/open-air-sun/cerberus,65,"Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing","https://scholar.google.com/scholar?cluster=13536647247810424831&hl=en&as_sdt=0,5",3,2022
RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic Scenes,11,cvpr,0,0,2023-06-03 15:10:31.575000,https://github.com/twhui/rm-depth,33,Rm-depth: Unsupervised learning of recurrent monocular depth in dynamic scenes,"https://scholar.google.com/scholar?cluster=4268400877815672740&hl=en&as_sdt=0,5",14,2022
Deep Vanishing Point Detection: Geometric Priors Make Dataset Variations Vanish,9,cvpr,5,1,2023-06-03 15:10:31.779000,https://github.com/yanconglin/vanishingpoint_houghtransform_gaussiansphere,48,Deep vanishing point detection: Geometric priors make dataset variations vanish,"https://scholar.google.com/scholar?cluster=17537664596776285133&hl=en&as_sdt=0,10",2,2022
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks,14,cvpr,10,2,2023-06-03 15:10:32.013000,https://github.com/wanyugroup/cvpr2022-orphicx,25,Orphicx: A causality-inspired latent variable model for interpreting graph neural networks,"https://scholar.google.com/scholar?cluster=7768511965986962434&hl=en&as_sdt=0,5",1,2022
Cloning Outfits From Real-World Images to 3D Characters for Generalizable Person Re-Identification,2,cvpr,7,2,2023-06-03 15:10:32.231000,https://github.com/yanan-wang-cs/clonedperson,59,Cloning outfits from real-world images to 3d characters for generalizable person re-identification,"https://scholar.google.com/scholar?cluster=18433748154228429403&hl=en&as_sdt=0,14",4,2022
ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo,2,cvpr,3,2,2023-06-03 15:10:32.481000,https://github.com/younglbw/crhd-3k,28,ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo,"https://scholar.google.com/scholar?cluster=17398337412524506783&hl=en&as_sdt=0,10",6,2022
Expanding Low-Density Latent Regions for Open-Set Object Detection,8,cvpr,9,18,2023-06-03 15:10:32.687000,https://github.com/csuhan/opendet2,75,Expanding low-density latent regions for open-set object detection,"https://scholar.google.com/scholar?cluster=10754307422790465317&hl=en&as_sdt=0,5",1,2022
Quantifying Societal Bias Amplification in Image Captioning,14,cvpr,1,0,2023-06-03 15:10:32.893000,https://github.com/rebnej/lick-caption-bias,6,Quantifying societal bias amplification in image captioning,"https://scholar.google.com/scholar?cluster=8737005229784815941&hl=en&as_sdt=0,5",1,2022
Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data,2,cvpr,11,2,2023-06-03 15:10:33.107000,https://github.com/storymy/take-off-eyeglasses,81,Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data,"https://scholar.google.com/scholar?cluster=12184173752936978394&hl=en&as_sdt=0,18",6,2022
Exploring Dual-Task Correlation for Pose Guided Person Image Generation,12,cvpr,10,1,2023-06-03 15:10:33.340000,https://github.com/pangzecheung/dual-task-pose-transformer-network,75,Exploring dual-task correlation for pose guided person image generation,"https://scholar.google.com/scholar?cluster=1347522366895866728&hl=en&as_sdt=0,5",1,2022
StyleSwin: Transformer-Based GAN for High-Resolution Image Generation,57,cvpr,36,6,2023-06-03 15:10:33.556000,https://github.com/microsoft/StyleSwin,395,Styleswin: Transformer-based gan for high-resolution image generation,"https://scholar.google.com/scholar?cluster=14580693445961229120&hl=en&as_sdt=0,44",12,2022
Abandoning the Bayer-Filter To See in the Dark,11,cvpr,11,2,2023-06-03 15:10:33.771000,https://github.com/tcl-ailab/erase_bayer-filter_to_see_in_the_dark,79,Abandoning the Bayer-filter to see in the dark,"https://scholar.google.com/scholar?cluster=11347672181310935976&hl=en&as_sdt=0,5",5,2022
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity,16,cvpr,10,3,2023-06-03 15:10:34.003000,https://github.com/facebookresearch/Generic-Grouping,109,Open-world instance segmentation: Exploiting pseudo ground truth from learned pairwise affinity,"https://scholar.google.com/scholar?cluster=255635617827796771&hl=en&as_sdt=0,44",6,2022
Neural Rays for Occlusion-Aware Image-Based Rendering,63,cvpr,28,9,2023-06-03 15:10:34.220000,https://github.com/liuyuan-pal/NeuRay,342,Neural rays for occlusion-aware image-based rendering,"https://scholar.google.com/scholar?cluster=4279364007436951215&hl=en&as_sdt=0,33",34,2022
Reinforced Structured State-Evolution for Vision-Language Navigation,10,cvpr,0,2,2023-06-03 15:10:34.486000,https://github.com/chenjinyubuaa/sevol,9,Reinforced structured state-evolution for vision-language navigation,"https://scholar.google.com/scholar?cluster=2018245891569790517&hl=en&as_sdt=0,5",1,2022
Modeling 3D Layout for Group Re-Identification,5,cvpr,1,0,2023-06-03 15:10:34.684000,https://github.com/linlyac/city1m-dataset,11,Modeling 3D Layout For Group Re-Identification,"https://scholar.google.com/scholar?cluster=315402371651311719&hl=en&as_sdt=0,10",1,2022
Forward Compatible Training for Large-Scale Embedding Retrieval Systems,7,cvpr,9,0,2023-06-03 15:10:34.891000,https://github.com/apple/ml-fct,44,Forward compatible training for large-scale embedding retrieval systems,"https://scholar.google.com/scholar?cluster=14484277701269720936&hl=en&as_sdt=0,23",6,2022
"Toward Fast, Flexible, and Robust Low-Light Image Enhancement",93,cvpr,47,1,2023-06-03 15:10:35.518000,https://github.com/vis-opt-group/sci,281,"Toward fast, flexible, and robust low-light image enhancement","https://scholar.google.com/scholar?cluster=867483774840364548&hl=en&as_sdt=0,33",5,2022
CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning,14,cvpr,10,0,2023-06-03 15:10:35.751000,https://github.com/yue-fan/cossl,44,Cossl: Co-learning of representation and classifier for imbalanced semi-supervised learning,"https://scholar.google.com/scholar?cluster=10704215977252277173&hl=en&as_sdt=0,5",3,2022
Everything at Once - Multi-Modal Fusion Transformer for Video Retrieval,51,cvpr,12,4,2023-06-03 15:10:35.998000,https://github.com/ninatu/everything_at_once,63,Everything at once-multi-modal fusion transformer for video retrieval,"https://scholar.google.com/scholar?cluster=1129241930799490053&hl=en&as_sdt=0,33",2,2022
Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph,30,cvpr,3,1,2023-06-03 15:10:36.219000,https://github.com/wangsiwei2010/cvpr22-imvc-cbg,23,Highly-efficient incomplete large-scale multi-view clustering with consensus bipartite graph,"https://scholar.google.com/scholar?cluster=7592965778131723796&hl=en&as_sdt=0,5",1,2022
Discovering Objects That Can Move,12,cvpr,1,3,2023-06-03 15:10:36.420000,https://github.com/zpbao/discovery_obj_move,38,Discovering objects that can move,"https://scholar.google.com/scholar?cluster=9817615571190342933&hl=en&as_sdt=0,11",2,2022
Neural Template: Topology-Aware Reconstruction and Disentangled Generation of 3D Meshes,7,cvpr,6,2,2023-06-03 15:10:36.615000,https://github.com/edward1997104/Neural-Template,35,Neural template: Topology-aware reconstruction and disentangled generation of 3d meshes,"https://scholar.google.com/scholar?cluster=1818240477523491291&hl=en&as_sdt=0,44",12,2022
Weakly Supervised High-Fidelity Clothing Model Generation,1,cvpr,11,4,2023-06-03 15:10:36.809000,https://github.com/RuiLiFeng/Deep-Generative-Projection,64,Weakly Supervised High-Fidelity Clothing Model Generation,"https://scholar.google.com/scholar?cluster=20866160009981622&hl=en&as_sdt=0,1",8,2022
Towards Principled Disentanglement for Domain Generalization,37,cvpr,6,0,2023-06-03 15:10:37.004000,https://github.com/hlzhang109/ddg,43,Towards principled disentanglement for domain generalization,"https://scholar.google.com/scholar?cluster=13483038458715901543&hl=en&as_sdt=0,5",4,2022
Self-Supervised Learning of Object Parts for Semantic Segmentation,25,cvpr,9,1,2023-06-03 15:10:37.205000,https://github.com/mkuuwaujinga/leopart,79,Self-supervised learning of object parts for semantic segmentation,"https://scholar.google.com/scholar?cluster=10023428544428348799&hl=en&as_sdt=0,33",2,2022
AdaFace: Quality Adaptive Margin for Face Recognition,58,cvpr,65,42,2023-06-03 15:10:37.416000,https://github.com/mk-minchul/adaface,390,Adaface: Quality adaptive margin for face recognition,"https://scholar.google.com/scholar?cluster=15806761742105148856&hl=en&as_sdt=0,33",6,2022
Knowledge Mining With Scene Text for Fine-Grained Recognition,3,cvpr,0,1,2023-06-03 15:10:37.611000,https://github.com/lanfeng4659/knowledgeminingwithscenetext,34,Knowledge mining with scene text for fine-grained recognition,"https://scholar.google.com/scholar?cluster=4597110415855400448&hl=en&as_sdt=0,47",2,2022
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution,36,cvpr,6,7,2023-06-03 15:10:37.804000,https://github.com/zhaozixiang1228/gdsr-dctnet,59,Discrete cosine transform network for guided depth map super-resolution,"https://scholar.google.com/scholar?cluster=9625270855075879979&hl=en&as_sdt=0,32",1,2022
Swin Transformer V2: Scaling Up Capacity and Resolution,438,cvpr,1790,150,2023-06-03 15:10:37.999000,https://github.com/microsoft/Swin-Transformer,10934,Swin transformer v2: Scaling up capacity and resolution,"https://scholar.google.com/scholar?cluster=15329990143169836178&hl=en&as_sdt=0,10",125,2022
Learning Soft Estimator of Keypoint Scale and Orientation With Probabilistic Covariant Loss,1,cvpr,0,0,2023-06-03 15:10:38.193000,https://github.com/elvintanhust/s3esti,1,Learning Soft Estimator of Keypoint Scale and Orientation with Probabilistic Covariant Loss,"https://scholar.google.com/scholar?cluster=4755977681804564967&hl=en&as_sdt=0,6",2,2022
TransGeo: Transformer Is All You Need for Cross-View Image Geo-Localization,25,cvpr,11,2,2023-06-03 15:10:38.413000,https://github.com/jeff-zilence/transgeo2022,56,Transgeo: Transformer is all you need for cross-view image geo-localization,"https://scholar.google.com/scholar?cluster=7235703548829998428&hl=en&as_sdt=0,11",4,2022
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects,8,cvpr,89,2,2023-06-03 15:10:38.610000,https://github.com/dlr-rm/3dobjecttracking,449,Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects,"https://scholar.google.com/scholar?cluster=1442948309573312269&hl=en&as_sdt=0,5",16,2022
Multi-Instance Point Cloud Registration by Efficient Correspondence Clustering,4,cvpr,2,2,2023-06-03 15:10:38.803000,https://github.com/sjtu-visys/multi-instant-reg,26,Multi-instance point cloud registration by efficient correspondence clustering,"https://scholar.google.com/scholar?cluster=9998594732732329471&hl=en&as_sdt=0,21",5,2022
OSSO: Obtaining Skeletal Shape From Outside,8,cvpr,29,1,2023-06-03 15:10:38.997000,https://github.com/MarilynKeller/OSSO,171,OSSO: Obtaining Skeletal Shape from Outside,"https://scholar.google.com/scholar?cluster=12271885870131613985&hl=en&as_sdt=0,33",11,2022
SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention,4,cvpr,0,0,2023-06-03 15:10:39.191000,https://github.com/mwoedlinger/sasic,10,Sasic: Stereo image compression with latent shifts and stereo attention,"https://scholar.google.com/scholar?cluster=12235598835426789378&hl=en&as_sdt=0,47",1,2022
Contrastive Boundary Learning for Point Cloud Segmentation,39,cvpr,10,11,2023-06-03 15:10:39.439000,https://github.com/liyaotang/contrastboundary,101,Contrastive boundary learning for point cloud segmentation,"https://scholar.google.com/scholar?cluster=9432037003873850722&hl=en&as_sdt=0,1",3,2022
CVNet: Contour Vibration Network for Building Extraction,1,cvpr,1,2,2023-06-03 15:10:39.633000,https://github.com/xzq-njust/cvnet,16,CVNet: Contour Vibration Network for Building Extraction,"https://scholar.google.com/scholar?cluster=1053642954273826873&hl=en&as_sdt=0,33",1,2022
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution,23,cvpr,17,6,2023-06-03 15:10:39.828000,https://github.com/csjliang/ldl,187,Details or artifacts: A locally discriminative learning approach to realistic image super-resolution,"https://scholar.google.com/scholar?cluster=17562703024059944790&hl=en&as_sdt=0,5",7,2022
RAGO: Recurrent Graph Optimizer for Multiple Rotation Averaging,2,cvpr,0,0,2023-06-03 15:10:40.023000,https://github.com/sfu-gruvi-3dv/rago,14,RAGO: Recurrent graph optimizer for multiple rotation averaging,"https://scholar.google.com/scholar?cluster=2898145836232418250&hl=en&as_sdt=0,5",7,2022
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint,7,cvpr,1,1,2023-06-03 15:10:40.219000,https://github.com/cr-gjx/scc,15,Alleviating semantics distortion in unsupervised low-level image-to-image translation via structure consistency constraint,"https://scholar.google.com/scholar?cluster=6940101841878746410&hl=en&as_sdt=0,43",2,2022
Hyperbolic Image Segmentation,18,cvpr,6,3,2023-06-03 15:10:40.416000,https://github.com/minaghadimiatigh/hyperbolicimagesegmentation,50,Hyperbolic image segmentation,"https://scholar.google.com/scholar?cluster=6754485731620585801&hl=en&as_sdt=0,33",9,2022
Projective Manifold Gradient Layer for Deep Rotation Regression,18,cvpr,2,0,2023-06-03 15:10:40.611000,https://github.com/jychen18/rpmg,58,Projective manifold gradient layer for deep rotation regression,"https://scholar.google.com/scholar?cluster=3379264059637576430&hl=en&as_sdt=0,33",4,2022
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search,9,cvpr,0,2,2023-06-03 15:10:40.805000,https://github.com/centaurus982034/arch-graph,3,Arch-graph: Acyclic architecture relation predictor for task-transferable neural architecture search,"https://scholar.google.com/scholar?cluster=57321944077030732&hl=en&as_sdt=0,33",2,2022
On Aliased Resizing and Surprising Subtleties in GAN Evaluation,71,cvpr,51,20,2023-06-03 15:10:40.999000,https://github.com/GaParmar/clean-fid,679,On aliased resizing and surprising subtleties in gan evaluation,"https://scholar.google.com/scholar?cluster=11437769662814607169&hl=en&as_sdt=0,43",7,2022
TransRank: Self-Supervised Video Representation Learning via Ranking-Based Transformation Recognition,5,cvpr,0,1,2023-06-03 15:10:41.194000,https://github.com/kennymckormick/transrank,19,Transrank: Self-supervised video representation learning via ranking-based transformation recognition,"https://scholar.google.com/scholar?cluster=12065866732236053878&hl=en&as_sdt=0,36",5,2022
Invariant Grounding for Video Question Answering,29,cvpr,3,2,2023-06-03 15:10:41.405000,https://github.com/yl3800/igv,22,Invariant grounding for video question answering,"https://scholar.google.com/scholar?cluster=12491881695567377041&hl=en&as_sdt=0,5",2,2022
DiSparse: Disentangled Sparsification for Multitask Model Compression,5,cvpr,4,0,2023-06-03 15:10:41.600000,https://github.com/shi-labs/disparse-multitask-model-compression,11,DiSparse: Disentangled Sparsification for Multitask Model Compression,"https://scholar.google.com/scholar?cluster=6072417934305435185&hl=en&as_sdt=0,5",2,2022
A Style-Aware Discriminator for Controllable Image Translation,11,cvpr,9,0,2023-06-03 15:10:41.794000,https://github.com/kunheek/style-aware-discriminator,104,A style-aware discriminator for controllable image translation,"https://scholar.google.com/scholar?cluster=845533795214129869&hl=en&as_sdt=0,5",4,2022
Lepard: Learning Partial Point Cloud Matching in Rigid and Deformable Scenes,32,cvpr,16,11,2023-06-03 15:10:41.989000,https://github.com/rabbityl/lepard,127,Lepard: Learning partial point cloud matching in rigid and deformable scenes,"https://scholar.google.com/scholar?cluster=457295611521329031&hl=en&as_sdt=0,33",7,2022
Moving Window Regression: A Novel Approach to Ordinal Regression,12,cvpr,8,12,2023-06-03 15:10:42.183000,https://github.com/nhshin-mcl/mwr,53,Moving window regression: a novel approach to ordinal regression,"https://scholar.google.com/scholar?cluster=449198826622568241&hl=en&as_sdt=0,33",1,2022
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference,44,cvpr,15,5,2023-06-03 15:10:42.377000,https://github.com/hushell/pmf_cvpr22,102,Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference,"https://scholar.google.com/scholar?cluster=8309949889630795104&hl=en&as_sdt=0,5",3,2022
ICON: Implicit Clothed Humans Obtained From Normals,84,cvpr,199,29,2023-06-03 15:10:42.571000,https://github.com/yuliangxiu/icon,1367,ICON: implicit clothed humans obtained from normals,"https://scholar.google.com/scholar?cluster=8550715599070401133&hl=en&as_sdt=0,5",43,2022
ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification,26,cvpr,10,4,2023-06-03 15:10:42.764000,https://github.com/FBLADL/ACPL,42,ACPL: Anti-curriculum pseudo-labelling for semi-supervised medical image classification,"https://scholar.google.com/scholar?cluster=5770820468040495027&hl=en&as_sdt=0,33",2,2022
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection,18,cvpr,7,0,2023-06-03 15:10:42.959000,https://github.com/siyueyu/dcfm,24,Democracy does matter: Comprehensive feature mining for co-salient object detection,"https://scholar.google.com/scholar?cluster=13942408903712812761&hl=en&as_sdt=0,31",2,2022
VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention,31,cvpr,11,10,2023-06-03 15:10:43.158000,https://github.com/gorilla-lab-scut/vista,117,Vista: Boosting 3d object detection via dual cross-view spatial attention,"https://scholar.google.com/scholar?cluster=3435830337411000222&hl=en&as_sdt=0,14",2,2022
Shape From Polarization for Complex Scenes in the Wild,14,cvpr,5,1,2023-06-03 15:10:43.353000,https://github.com/chenyanglei/sfp-wild,43,Shape from polarization for complex scenes in the wild,"https://scholar.google.com/scholar?cluster=16191419047666639012&hl=en&as_sdt=0,34",6,2022
Learning to Deblur Using Light Field Generated and Real Defocus Images,6,cvpr,3,0,2023-06-03 15:10:43.557000,https://github.com/lingyanruan/DRBNet,65,Learning to deblur using light field generated and real defocus images,"https://scholar.google.com/scholar?cluster=15735702944594019309&hl=en&as_sdt=0,34",3,2022
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes,5,cvpr,8,1,2023-06-03 15:10:43.750000,https://github.com/vilab-ucsd/photoscene,71,PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes,"https://scholar.google.com/scholar?cluster=9334128890479120246&hl=en&as_sdt=0,44",8,2022
Self-Supervised Predictive Learning: A Negative-Free Method for Sound Source Localization in Visual Scenes,14,cvpr,1,3,2023-06-03 15:10:43.945000,https://github.com/zjsong/sspl,25,Self-supervised predictive learning: A negative-free method for sound source localization in visual scenes,"https://scholar.google.com/scholar?cluster=7448875633647844847&hl=en&as_sdt=0,5",1,2022
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment,19,cvpr,2,0,2023-06-03 15:10:44.140000,https://github.com/stevenshaw1999/rssa,39,Few shot generative model adaption via relaxed spatial structural alignment,"https://scholar.google.com/scholar?cluster=1584682891522159994&hl=en&as_sdt=0,10",2,2022
Versatile Multi-Modal Pre-Training for Human-Centric Perception,7,cvpr,7,2,2023-06-03 15:10:44.415000,https://github.com/hongfz16/hcmoco,109,Versatile multi-modal pre-training for human-centric perception,"https://scholar.google.com/scholar?cluster=10243356763111938200&hl=en&as_sdt=0,5",9,2022
Comparing Correspondences: Video Prediction With Correspondence-Wise Losses,7,cvpr,0,1,2023-06-03 15:10:44.686000,https://github.com/dangeng/CorrWiseLosses,22,Comparing correspondences: Video prediction with correspondence-wise losses,"https://scholar.google.com/scholar?cluster=2052252333693513180&hl=en&as_sdt=0,5",1,2022
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection,14,cvpr,17,9,2023-06-03 15:10:44.880000,https://github.com/icvteam/pgnet,130,Pyramid grafting network for one-stage high resolution saliency detection,"https://scholar.google.com/scholar?cluster=10086340238621691361&hl=en&as_sdt=0,33",14,2022
SNUG: Self-Supervised Neural Dynamic Garments,19,cvpr,12,6,2023-06-03 15:10:45.073000,https://github.com/isantesteban/snug,146,Snug: Self-supervised neural dynamic garments,"https://scholar.google.com/scholar?cluster=17441211965878867334&hl=en&as_sdt=0,5",8,2022
Enhancing Adversarial Training With Second-Order Statistics of Weights,17,cvpr,3,0,2023-06-03 15:10:45.268000,https://github.com/alexkael/s2o,18,Enhancing adversarial training with second-order statistics of weights,"https://scholar.google.com/scholar?cluster=3568098907834682547&hl=en&as_sdt=0,16",1,2022
Multi-View Consistent Generative Adversarial Networks for 3D-Aware Image Synthesis,22,cvpr,9,2,2023-06-03 15:10:45.462000,https://github.com/Xuanmeng-Zhang/MVCGAN,83,Multi-view consistent generative adversarial networks for 3d-aware image synthesis,"https://scholar.google.com/scholar?cluster=1900688015204602379&hl=en&as_sdt=0,23",7,2022
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation,17,cvpr,16,0,2023-06-03 15:10:45.656000,https://github.com/bit-da/ripu,119,Towards fewer annotations: Active learning via region impurity and prediction uncertainty for domain adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=907514128348245282&hl=en&as_sdt=0,5",7,2022
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo,19,cvpr,2,1,2023-06-03 15:10:45.850000,https://github.com/ChaoningZhang/Dual-temperature,26,Dual temperature helps contrastive learning without many negative samples: Towards understanding and simplifying moco,"https://scholar.google.com/scholar?cluster=11904217105191322390&hl=en&as_sdt=0,44",3,2022
Memory-Augmented Non-Local Attention for Video Super-Resolution,11,cvpr,2,2,2023-06-03 15:10:46.044000,https://github.com/jiy173/MANA,34,Memory-augmented non-local attention for video super-resolution,"https://scholar.google.com/scholar?cluster=15635881739608714616&hl=en&as_sdt=0,5",2,2022
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding,82,cvpr,26,9,2023-06-03 15:10:46.238000,https://github.com/mohamedafham/crosspoint,180,Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding,"https://scholar.google.com/scholar?cluster=9260010001336706874&hl=en&as_sdt=0,5",7,2022
Classification-Then-Grounding: Reformulating Video Scene Graphs As Temporal Bipartite Graphs,11,cvpr,4,1,2023-06-03 15:10:46.432000,https://github.com/Dawn-LX/VidSGG-BIG,33,Classification-then-grounding: Reformulating video scene graphs as temporal bipartite graphs,"https://scholar.google.com/scholar?cluster=10991905322136021916&hl=en&as_sdt=0,33",3,2022
Transformer-Empowered Multi-Scale Contextual Matching and Aggregation for Multi-Contrast MRI Super-Resolution,11,cvpr,8,2,2023-06-03 15:10:46.626000,https://github.com/xaimi-lab/mcmrsr,28,Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution,"https://scholar.google.com/scholar?cluster=3541046959677804256&hl=en&as_sdt=0,33",1,2022
Robust Optimization As Data Augmentation for Large-Scale Graphs,15,cvpr,18,6,2023-06-03 15:10:46.820000,https://github.com/devnkong/FLAG,129,Robust optimization as data augmentation for large-scale graphs,"https://scholar.google.com/scholar?cluster=4102533396606051676&hl=en&as_sdt=0,31",3,2022
Safe Self-Refinement for Transformer-Based Domain Adaptation,22,cvpr,7,0,2023-06-03 15:10:47.014000,https://github.com/tsun/ssrt,28,Safe self-refinement for transformer-based domain adaptation,"https://scholar.google.com/scholar?cluster=3114236035785050488&hl=en&as_sdt=0,5",1,2022
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection,43,cvpr,15,6,2023-06-03 15:10:47.208000,https://github.com/ristea/sspcab,126,Self-supervised predictive convolutional attentive block for anomaly detection,"https://scholar.google.com/scholar?cluster=12000700045591707888&hl=en&as_sdt=0,5",4,2022
StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions,15,cvpr,5,0,2023-06-03 15:10:47.406000,https://github.com/lukasHoel/stylemesh,117,Stylemesh: Style transfer for indoor 3d scene reconstructions,"https://scholar.google.com/scholar?cluster=8459063223930073677&hl=en&as_sdt=0,21",7,2022
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks With Implicit Gradients,6,cvpr,1,1,2023-06-03 15:10:47.600000,https://github.com/KaidongLi/pytorch-LatticePointClassifier,6,Robust structured declarative classifiers for 3d point clouds: Defending adversarial attacks with implicit gradients,"https://scholar.google.com/scholar?cluster=5834510496620017164&hl=en&as_sdt=0,33",2,2022
Bridging Video-Text Retrieval With Multiple Choice Questions,45,cvpr,15,9,2023-06-03 15:10:47.793000,https://github.com/tencentarc/mcq,114,Bridging video-text retrieval with multiple choice questions,"https://scholar.google.com/scholar?cluster=12347663869868417847&hl=en&as_sdt=0,5",4,2022
Improving the Transferability of Targeted Adversarial Examples Through Object-Based Diverse Input,16,cvpr,1,0,2023-06-03 15:10:47.987000,https://github.com/dreamflake/odi,13,Improving the transferability of targeted adversarial examples through object-based diverse input,"https://scholar.google.com/scholar?cluster=10812581714488271969&hl=en&as_sdt=0,5",1,2022
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis,53,cvpr,58,8,2023-06-03 15:10:48.181000,https://github.com/tobran/DF-GAN,246,Df-gan: A simple and effective baseline for text-to-image synthesis,"https://scholar.google.com/scholar?cluster=10536626672413829585&hl=en&as_sdt=0,5",7,2022
Class-Incremental Learning With Strong Pre-Trained Models,19,cvpr,1,0,2023-06-03 15:10:48.375000,https://github.com/amazon-research/sp-cil,5,Class-incremental learning with strong pre-trained models,"https://scholar.google.com/scholar?cluster=4887808237223642830&hl=en&as_sdt=0,5",9,2022
Splicing ViT Features for Semantic Appearance Transfer,34,cvpr,25,4,2023-06-03 15:10:48.569000,https://github.com/omerbt/Splice,294,Splicing vit features for semantic appearance transfer,"https://scholar.google.com/scholar?cluster=8197809886716310130&hl=en&as_sdt=0,33",8,2022
CoNeRF: Controllable Neural Radiance Fields,33,cvpr,12,2,2023-06-03 15:10:48.763000,https://github.com/kacperkan/conerf,67,Conerf: Controllable neural radiance fields,"https://scholar.google.com/scholar?cluster=531094580070165718&hl=en&as_sdt=0,5",5,2022
Depth-Aware Generative Adversarial Network for Talking Head Video Generation,34,cvpr,104,27,2023-06-03 15:10:48.957000,https://github.com/harlanhong/cvpr2022-dagan,718,Depth-aware generative adversarial network for talking head video generation,"https://scholar.google.com/scholar?cluster=16409913294426978444&hl=en&as_sdt=0,5",25,2022
ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer,14,cvpr,8,2,2023-06-03 15:10:49.150000,https://github.com/rhgao/objectfolder,121,Objectfolder 2.0: A multisensory object dataset for sim2real transfer,"https://scholar.google.com/scholar?cluster=18321501639603224905&hl=en&as_sdt=0,29",5,2022
Neural Texture Extraction and Distribution for Controllable Person Image Synthesis,16,cvpr,20,10,2023-06-03 15:10:49.344000,https://github.com/renyurui/neural-texture-extraction-distribution,136,Neural texture extraction and distribution for controllable person image synthesis,"https://scholar.google.com/scholar?cluster=1232018497986225753&hl=en&as_sdt=0,5",7,2022
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes,7,cvpr,8,1,2023-06-03 15:10:49.538000,https://github.com/dbash/zerowaste,28,Zerowaste dataset: Towards deformable object segmentation in cluttered scenes,"https://scholar.google.com/scholar?cluster=13526608056498843239&hl=en&as_sdt=0,5",2,2022
Implicit Feature Decoupling With Depthwise Quantization,0,cvpr,0,0,2023-06-03 15:10:49.732000,https://github.com/fostiropoulos/depthwise-quantization,7,Implicit Feature Decoupling with Depthwise Quantization,"https://scholar.google.com/scholar?cluster=457355122912411711&hl=en&as_sdt=0,26",1,2022
POCO: Point Convolution for Surface Reconstruction,30,cvpr,18,8,2023-06-03 15:10:49.926000,https://github.com/valeoai/poco,131,Poco: Point convolution for surface reconstruction,"https://scholar.google.com/scholar?cluster=7702492245699818369&hl=en&as_sdt=0,18",8,2022
Graph-Context Attention Networks for Size-Varied Deep Graph Matching,3,cvpr,4,1,2023-06-03 15:10:50.119000,https://github.com/zhehengjiang/gcan,8,Graph-context Attention Networks for Size-varied Deep Graph Matching,"https://scholar.google.com/scholar?cluster=12111473869885871155&hl=en&as_sdt=0,5",2,2022
Few-Shot Font Generation by Learning Fine-Grained Local Styles,8,cvpr,4,17,2023-06-03 15:10:50.313000,https://github.com/tlc121/FsFont,36,Few-Shot Font Generation by Learning Fine-Grained Local Styles,"https://scholar.google.com/scholar?cluster=8704102275018994274&hl=en&as_sdt=0,33",1,2022
The DEVIL Is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting,3,cvpr,4,4,2023-06-03 15:10:50.507000,https://github.com/MichiganCOG/devil,21,The devil is in the details: A diagnostic evaluation benchmark for video inpainting,"https://scholar.google.com/scholar?cluster=13208463904457021068&hl=en&as_sdt=0,11",4,2022
Category Contrast for Unsupervised Domain Adaptation in Visual Tasks,43,cvpr,3,5,2023-06-03 15:10:50.701000,https://github.com/jxhuang0508/CaCo,20,Category contrast for unsupervised domain adaptation in visual tasks,"https://scholar.google.com/scholar?cluster=651008442347880918&hl=en&as_sdt=0,5",1,2022
Shifting More Attention to Visual Backbone: Query-Modulated Refinement Networks for End-to-End Visual Grounding,11,cvpr,3,10,2023-06-03 15:10:50.895000,https://github.com/lukeforeveryoung/qrnet,27,Shifting more attention to visual backbone: Query-modulated refinement networks for end-to-end visual grounding,"https://scholar.google.com/scholar?cluster=5221723935694903097&hl=en&as_sdt=0,2",1,2022
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering,20,cvpr,3,1,2023-06-03 15:10:51.089000,https://github.com/vipulgupta1011/swapmix,14,Swapmix: Diagnosing and regularizing the over-reliance on visual context in visual question answering,"https://scholar.google.com/scholar?cluster=7815124774187094531&hl=en&as_sdt=0,33",1,2022
FENeRF: Face Editing in Neural Radiance Fields,57,cvpr,18,4,2023-06-03 15:10:51.283000,https://github.com/MrTornado24/FENeRF,202,Fenerf: Face editing in neural radiance fields,"https://scholar.google.com/scholar?cluster=8441399333131745733&hl=en&as_sdt=0,33",15,2022
Remember Intentions: Retrospective-Memory-Based Trajectory Prediction,20,cvpr,11,3,2023-06-03 15:10:51.477000,https://github.com/mediabrain-sjtu/memonet,92,Remember intentions: retrospective-memory-based trajectory prediction,"https://scholar.google.com/scholar?cluster=8617365985309626194&hl=en&as_sdt=0,5",4,2022
Contextualized Spatio-Temporal Contrastive Learning With Self-Supervision,8,cvpr,46271,1204,2023-06-03 15:10:51.678000,https://github.com/tensorflow/models,75886,Contextualized spatio-temporal contrastive learning with self-supervision,"https://scholar.google.com/scholar?cluster=13207353650961441981&hl=en&as_sdt=0,33",2774,2022
FLOAT: Factorized Learning of Object Attributes for Improved Multi-Object Multi-Part Scene Parsing,4,cvpr,0,0,2023-06-03 15:10:51.872000,https://github.com/floatseg/floatseg.github.io,0,Float: Factorized learning of object attributes for improved multi-object multi-part scene parsing,"https://scholar.google.com/scholar?cluster=14572531130161976236&hl=en&as_sdt=0,22",0,2022
SPAct: Self-Supervised Privacy Preservation for Action Recognition,14,cvpr,0,4,2023-06-03 15:10:52.065000,https://github.com/daveishan/spact,16,Spact: Self-supervised privacy preservation for action recognition,"https://scholar.google.com/scholar?cluster=5134352976127146556&hl=en&as_sdt=0,33",1,2022
FocusCut: Diving Into a Focus View in Interactive Segmentation,15,cvpr,4,3,2023-06-03 15:10:52.259000,https://github.com/frazerlin/focuscut,18,Focuscut: Diving into a focus view in interactive segmentation,"https://scholar.google.com/scholar?cluster=15635194282665947002&hl=en&as_sdt=0,5",1,2022
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning,34,cvpr,18,2,2023-06-03 15:10:52.454000,https://github.com/Liangqiong/ViT-FL-main,67,Rethinking architecture design for tackling data heterogeneity in federated learning,"https://scholar.google.com/scholar?cluster=11871442867624728681&hl=en&as_sdt=0,33",4,2022
De-Rendering 3D Objects in the Wild,11,cvpr,9,3,2023-06-03 15:10:52.648000,https://github.com/brummi/derender3d,122,De-rendering 3D Objects in the Wild,"https://scholar.google.com/scholar?cluster=1935620080686118847&hl=en&as_sdt=0,3",7,2022
Global Sensing and Measurements Reuse for Image Compressed Sensing,3,cvpr,1,1,2023-06-03 15:10:52.841000,https://github.com/fze0012/mr-ccsnet,3,Global Sensing and Measurements Reuse for Image Compressed Sensing,"https://scholar.google.com/scholar?cluster=6542901026232183837&hl=en&as_sdt=0,47",1,2022
DETReg: Unsupervised Pretraining With Region Priors for Object Detection,50,cvpr,37,27,2023-06-03 15:10:53.036000,https://github.com/amirbar/detreg,309,Detreg: Unsupervised pretraining with region priors for object detection,"https://scholar.google.com/scholar?cluster=10551610679278163665&hl=en&as_sdt=0,22",17,2022
Learning ABCs: Approximate Bijective Correspondence for Isolating Factors of Variation With Weak Supervision,0,cvpr,7286,1013,2023-06-03 15:10:53.230000,https://github.com/google-research/google-research,29545,Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision,"https://scholar.google.com/scholar?cluster=13054823983554523598&hl=en&as_sdt=0,47",726,2022
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack,19,cvpr,2,0,2023-06-03 15:10:53.424000,https://github.com/liuye6666/adaptive_auto_attack,40,Practical evaluation of adversarial robustness via adaptive auto attack,"https://scholar.google.com/scholar?cluster=11985714371020199162&hl=en&as_sdt=0,21",3,2022
Online Continual Learning on a Contaminated Data Stream With Blurry Task Boundaries,19,cvpr,5,2,2023-06-03 15:10:53.618000,https://github.com/clovaai/puridiver,38,Online continual learning on a contaminated data stream with blurry task boundaries,"https://scholar.google.com/scholar?cluster=4641150768738034616&hl=en&as_sdt=0,5",4,2022
Learning To Imagine: Diversify Memory for Incremental Learning Using Unlabeled Data,8,cvpr,2,1,2023-06-03 15:10:53.812000,https://github.com/TOM-tym/Learn-to-Imagine,11,Learning to imagine: Diversify memory for incremental learning using unlabeled data,"https://scholar.google.com/scholar?cluster=5026291664933280388&hl=en&as_sdt=0,33",1,2022
Cross-View Transformers for Real-Time Map-View Semantic Segmentation,71,cvpr,58,32,2023-06-03 15:10:54.006000,https://github.com/bradyz/cross_view_transformers,424,Cross-view transformers for real-time map-view semantic segmentation,"https://scholar.google.com/scholar?cluster=1491587142118840508&hl=en&as_sdt=0,3",15,2022
Make It Move: Controllable Image-to-Video Generation With Text Descriptions,12,cvpr,7,2,2023-06-03 15:10:54.203000,https://github.com/youncy-hu/mage,28,Make it move: Controllable image-to-video generation with text descriptions,"https://scholar.google.com/scholar?cluster=3516336906711406652&hl=en&as_sdt=0,1",1,2022
VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks,81,cvpr,16,5,2023-06-03 15:10:54.405000,https://github.com/ylsung/vl_adapter,173,Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks,"https://scholar.google.com/scholar?cluster=6414220699607528663&hl=en&as_sdt=0,5",7,2022
Neural Points: Point Cloud Representation With Neural Fields for Arbitrary Upsampling,10,cvpr,33,13,2023-06-03 15:10:54.598000,https://github.com/wanquanf/neuralpoints,220,Neural points: point cloud representation with neural fields for arbitrary upsampling,"https://scholar.google.com/scholar?cluster=7916530615220861431&hl=en&as_sdt=0,33",14,2022
Fire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask Prediction,11,cvpr,2,2,2023-06-03 15:10:54.792000,https://github.com/selkerdawy/FTWT,6,Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction,"https://scholar.google.com/scholar?cluster=916820794753097681&hl=en&as_sdt=0,43",1,2022
FIFO: Learning Fog-Invariant Features for Foggy Scene Segmentation,11,cvpr,18,2,2023-06-03 15:10:54.986000,https://github.com/sohyun-l/fifo,77,Fifo: Learning fog-invariant features for foggy scene segmentation,"https://scholar.google.com/scholar?cluster=10025377918833329993&hl=en&as_sdt=0,5",3,2022
Accelerating Video Object Segmentation With Compressed Video,7,cvpr,4,2,2023-06-03 15:10:55.181000,https://github.com/kai422/covos,35,Accelerating video object segmentation with compressed video,"https://scholar.google.com/scholar?cluster=6742993136450637773&hl=en&as_sdt=0,33",3,2022
Bi-Directional Object-Context Prioritization Learning for Saliency Ranking,6,cvpr,0,2,2023-06-03 15:10:55.375000,https://github.com/grassbro/ocor,19,Bi-directional object-context prioritization learning for saliency ranking,"https://scholar.google.com/scholar?cluster=3067181998464015249&hl=en&as_sdt=0,37",6,2022
Unsupervised Visual Representation Learning by Online Constrained K-Means,9,cvpr,4,0,2023-06-03 15:10:55.570000,https://github.com/idstcv/coke,12,Unsupervised visual representation learning by online constrained k-means,"https://scholar.google.com/scholar?cluster=5554234163511357212&hl=en&as_sdt=0,5",0,2022
FastDOG: Fast Discrete Optimization on GPU,3,cvpr,5,0,2023-06-03 15:10:55.763000,https://github.com/lpmp/bdd,30,FastDOG: Fast discrete optimization on GPU,"https://scholar.google.com/scholar?cluster=15262271901525322084&hl=en&as_sdt=0,5",2,2022
What Do Navigation Agents Learn About Their Environment?,8,cvpr,3,0,2023-06-03 15:10:55.957000,https://github.com/allenai/isee,17,What do navigation agents learn about their environment?,"https://scholar.google.com/scholar?cluster=5532012740599620331&hl=en&as_sdt=0,44",5,2022
Self-Supervised Equivariant Learning for Oriented Keypoint Detection,6,cvpr,4,1,2023-06-03 15:10:56.151000,https://github.com/bluedream1121/REKD,49,Self-supervised equivariant learning for oriented keypoint detection,"https://scholar.google.com/scholar?cluster=18376839789551926333&hl=en&as_sdt=0,5",2,2022
Improving Adversarial Transferability via Neuron Attribution-Based Attacks,28,cvpr,2,0,2023-06-03 15:10:56.345000,https://github.com/jpzhang1810/naa,27,Improving adversarial transferability via neuron attribution-based attacks,"https://scholar.google.com/scholar?cluster=3102917020132255936&hl=en&as_sdt=0,16",2,2022
Focal and Global Knowledge Distillation for Detectors,74,cvpr,36,10,2023-06-03 15:10:56.539000,https://github.com/yzd-v/FGD,291,Focal and global knowledge distillation for detectors,"https://scholar.google.com/scholar?cluster=14151291256828319904&hl=en&as_sdt=0,34",2,2022
Instance-Wise Occlusion and Depth Orders in Natural Scenes,6,cvpr,1,1,2023-06-03 15:10:56.734000,https://github.com/POSTECH-CVLab/InstaOrder,26,Instance-wise occlusion and depth orders in natural scenes,"https://scholar.google.com/scholar?cluster=17301949382953129928&hl=en&as_sdt=0,11",2,2022
Learning To Prompt for Continual Learning,112,cvpr,33,4,2023-06-03 15:10:56.928000,https://github.com/google-research/l2p,278,Learning to prompt for continual learning,"https://scholar.google.com/scholar?cluster=11127330701624169778&hl=en&as_sdt=0,22",7,2022
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-Shot Learning,18,cvpr,7,6,2023-06-03 15:10:57.122000,https://github.com/stomachcold/hctransformers,42,Attribute surrogates learning and spectral tokens pooling in transformers for few-shot learning,"https://scholar.google.com/scholar?cluster=17677971297733661657&hl=en&as_sdt=0,11",5,2022
Contour-Hugging Heatmaps for Landmark Detection,3,cvpr,2,0,2023-06-03 15:10:57.316000,https://github.com/jfm15/contourhuggingheatmaps,7,Contour-Hugging Heatmaps for Landmark Detection,"https://scholar.google.com/scholar?cluster=15311814784447519217&hl=en&as_sdt=0,10",1,2022
Generalized Category Discovery,51,cvpr,13,8,2023-06-03 15:10:57.510000,https://github.com/sgvaze/generalized-category-discovery,138,Generalized category discovery,"https://scholar.google.com/scholar?cluster=5154769386036347309&hl=en&as_sdt=0,33",14,2022
FreeSOLO: Learning To Segment Objects Without Annotations,30,cvpr,31,11,2023-06-03 15:10:57.706000,https://github.com/nvlabs/freesolo,284,Freesolo: Learning to segment objects without annotations,"https://scholar.google.com/scholar?cluster=3726545131723476858&hl=en&as_sdt=0,48",5,2022
GANSeg: Learning To Segment by Unsupervised Hierarchical Image Generation,7,cvpr,1,1,2023-06-03 15:10:57.900000,https://github.com/xingzhehe/ganseg,14,Ganseg: Learning to segment by unsupervised hierarchical image generation,"https://scholar.google.com/scholar?cluster=8381112320248526584&hl=en&as_sdt=0,44",1,2022
Enhancing Adversarial Robustness for Deep Metric Learning,5,cvpr,1,0,2023-06-03 15:10:58.094000,https://github.com/cdluminate/robdml,18,Enhancing adversarial robustness for deep metric learning,"https://scholar.google.com/scholar?cluster=6859977703388701066&hl=en&as_sdt=0,5",3,2022
Dense Learning Based Semi-Supervised Object Detection,18,cvpr,8,12,2023-06-03 15:10:58.288000,https://github.com/chenbinghui1/dsl,83,Dense learning based semi-supervised object detection,"https://scholar.google.com/scholar?cluster=3247052077202185212&hl=en&as_sdt=0,5",4,2022
TransforMatcher: Match-to-Match Attention for Semantic Correspondence,9,cvpr,2,1,2023-06-03 15:10:58.483000,https://github.com/wookiekim/transformatcher,31,TransforMatcher: Match-to-Match Attention for Semantic Correspondence,"https://scholar.google.com/scholar?cluster=15104802067612466032&hl=en&as_sdt=0,5",4,2022
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation,52,cvpr,17,5,2023-06-03 15:10:58.677000,https://github.com/facebookresearch/HRViT,159,Multi-scale high-resolution vision transformer for semantic segmentation,"https://scholar.google.com/scholar?cluster=7367405585260744735&hl=en&as_sdt=0,5",10,2022
Optimal Correction Cost for Object Detection Evaluation,3,cvpr,0,1,2023-06-03 15:10:58.871000,https://github.com/mayu-ot/oc-cost,26,Optimal correction cost for object detection evaluation,"https://scholar.google.com/scholar?cluster=2642922896219719634&hl=en&as_sdt=0,5",2,2022
Robust Outlier Detection by De-Biasing VAE Likelihoods,4,cvpr,7286,1013,2023-06-03 15:10:59.065000,https://github.com/google-research/google-research,29545,Robust outlier detection by de-biasing VAE likelihoods,"https://scholar.google.com/scholar?cluster=11831436094055949436&hl=en&as_sdt=0,5",726,2022
Artistic Style Discovery With Independent Components,3,cvpr,1,0,2023-06-03 15:10:59.259000,https://github.com/shelsin/artins,11,Artistic style discovery with independent components,"https://scholar.google.com/scholar?cluster=11868555798863356936&hl=en&as_sdt=0,5",1,2022
Convolution of Convolution: Let Kernels Spatially Collaborate,0,cvpr,2,0,2023-06-03 15:10:59.452000,https://github.com/genera1z/convolutionofconvolution,8,Convolution of Convolution: Let Kernels Spatially Collaborate,"https://scholar.google.com/scholar?cluster=1747810192135914154&hl=en&as_sdt=0,5",1,2022
Point2Seq: Detecting 3D Objects As Sequences,7,cvpr,5,1,2023-06-03 15:10:59.647000,https://github.com/ocnflag/point2seq,61,Point2seq: detecting 3D objects as sequences,"https://scholar.google.com/scholar?cluster=6567908324401744618&hl=en&as_sdt=0,10",7,2022
Look for the Change: Learning Object States and State-Modifying Actions From Untrimmed Web Videos,5,cvpr,4,0,2023-06-03 15:10:59.841000,https://github.com/soCzech/LookForTheChange,28,Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos,"https://scholar.google.com/scholar?cluster=1537815863841327629&hl=en&as_sdt=0,5",4,2022
HyperStyle: StyleGAN Inversion With HyperNetworks for Real Image Editing,96,cvpr,108,1,2023-06-03 15:11:00.036000,https://github.com/yuval-alaluf/hyperstyle,925,Hyperstyle: Stylegan inversion with hypernetworks for real image editing,"https://scholar.google.com/scholar?cluster=13013295706162025578&hl=en&as_sdt=0,23",28,2022
Video-Text Representation Learning via Differentiable Weak Temporal Alignment,3,cvpr,1,0,2023-06-03 15:11:00.231000,https://github.com/mlvlab/vt-twins,14,Video-text representation learning via differentiable weak temporal alignment,"https://scholar.google.com/scholar?cluster=5591595499413413837&hl=en&as_sdt=0,5",0,2022
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition,11,cvpr,4,3,2023-06-03 15:11:00.426000,https://github.com/shiyuanh/tane,15,Task-adaptive negative envision for few-shot open-set recognition,"https://scholar.google.com/scholar?cluster=4099282274649852201&hl=en&as_sdt=0,5",6,2022
Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery,9,cvpr,1,0,2023-06-03 15:11:00.626000,https://github.com/muliyangm/comex,9,Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery,"https://scholar.google.com/scholar?cluster=17405901485879363588&hl=en&as_sdt=0,36",1,2022
MixFormer: Mixing Features Across Windows and Dimensions,30,cvpr,1087,190,2023-06-03 15:11:00.821000,https://github.com/PaddlePaddle/PaddleClas,4860,Mixformer: Mixing features across windows and dimensions,"https://scholar.google.com/scholar?cluster=16965662268880636582&hl=en&as_sdt=0,5",73,2022
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network,26,cvpr,10,1,2023-06-03 15:11:01.015000,https://github.com/wooseoklee4/ap-bsn,57,Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network,"https://scholar.google.com/scholar?cluster=11985940273976088551&hl=en&as_sdt=0,5",2,2022
Interpretable Part-Whole Hierarchies and Conceptual-Semantic Relationships in Neural Networks,6,cvpr,9,3,2023-06-03 15:11:01.229000,https://github.com/mmlab-cv/Agglomerator,24,Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks,"https://scholar.google.com/scholar?cluster=17205796139419550527&hl=en&as_sdt=0,10",3,2022
Not All Points Are Equal: Learning Highly Efficient Point-Based Detectors for 3D LiDAR Point Clouds,84,cvpr,50,18,2023-06-03 15:11:01.428000,https://github.com/yifanzhang713/ia-ssd,315,Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds,"https://scholar.google.com/scholar?cluster=16655541821133235810&hl=en&as_sdt=0,5",4,2022
DASO: Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning,17,cvpr,7,3,2023-06-03 15:11:01.622000,https://github.com/ytaek-oh/daso,55,DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning,"https://scholar.google.com/scholar?cluster=17144682580574038881&hl=en&as_sdt=0,5",2,2022
MonoDTR: Monocular 3D Object Detection With Depth-Aware Transformer,42,cvpr,13,6,2023-06-03 15:11:01.816000,https://github.com/kuanchihhuang/monodtr,103,Monodtr: Monocular 3d object detection with depth-aware transformer,"https://scholar.google.com/scholar?cluster=1805235227134475013&hl=en&as_sdt=0,33",8,2022
CycleMix: A Holistic Strategy for Medical Image Segmentation From Scribble Supervision,12,cvpr,13,5,2023-06-03 15:11:02.010000,https://github.com/bwgzk/cyclemix,56,Cyclemix: A holistic strategy for medical image segmentation from scribble supervision,"https://scholar.google.com/scholar?cluster=16924143345812990466&hl=en&as_sdt=0,5",3,2022
CLIP-Forge: Towards Zero-Shot Text-To-Shape Generation,82,cvpr,31,7,2023-06-03 15:11:02.218000,https://github.com/autodeskailab/clip-forge,314,Clip-forge: Towards zero-shot text-to-shape generation,"https://scholar.google.com/scholar?cluster=15450117600687919110&hl=en&as_sdt=0,5",17,2022
Learning Graph Regularisation for Guided Super-Resolution,10,cvpr,7,0,2023-06-03 15:11:02.415000,https://github.com/prs-eth/graph-super-resolution,35,Learning graph regularisation for guided super-resolution,"https://scholar.google.com/scholar?cluster=7635150475076541879&hl=en&as_sdt=0,15",2,2022
Voxel Field Fusion for 3D Object Detection,22,cvpr,9,3,2023-06-03 15:11:02.609000,https://github.com/dvlab-research/vff,87,Voxel field fusion for 3d object detection,"https://scholar.google.com/scholar?cluster=15196041616068317393&hl=en&as_sdt=0,33",3,2022
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo,28,cvpr,15,0,2023-06-03 15:11:02.805000,https://github.com/fangjinhuawang/itermvs,139,IterMVS: iterative probability estimation for efficient multi-view stereo,"https://scholar.google.com/scholar?cluster=10217528380961894363&hl=en&as_sdt=0,47",9,2022
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance With Expanded Views,9,cvpr,0,0,2023-06-03 15:11:02.999000,https://github.com/zhangjb416/Hierarchical-Augmentation,7,Rethinking the augmentation module in contrastive learning: Learning hierarchical augmentation invariance with expanded views,"https://scholar.google.com/scholar?cluster=4005108257678361504&hl=en&as_sdt=0,5",2,2022
FedCorr: Multi-Stage Federated Learning for Label Noise Correction,15,cvpr,5,0,2023-06-03 15:11:03.194000,https://github.com/xu-jingyi/fedcorr,22,Fedcorr: Multi-stage federated learning for label noise correction,"https://scholar.google.com/scholar?cluster=5669373680326837123&hl=en&as_sdt=0,33",1,2022
Depth-Guided Sparse Structure-From-Motion for Movies and TV Shows,3,cvpr,5,0,2023-06-03 15:11:03.387000,https://github.com/amazon-research/small-baseline-camera-tracking,59,Depth-Guided Sparse Structure-from-Motion for Movies and TV Shows,"https://scholar.google.com/scholar?cluster=1901915419593604514&hl=en&as_sdt=0,5",7,2022
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing,10,cvpr,0,2,2023-06-03 15:11:03.581000,https://github.com/luost26/equivariant-orientedmp,22,Equivariant point cloud analysis via learning orientations for message passing,"https://scholar.google.com/scholar?cluster=4920530705850028640&hl=en&as_sdt=0,5",6,2022
Source-Free Object Detection by Learning To Overlook Domain Style,18,cvpr,4,6,2023-06-03 15:11:03.775000,https://github.com/Flashkong/Source-Free-Object-Detection-by-Learning-to-Overlook-Domain-Style,28,Source-free object detection by learning to overlook domain style,"https://scholar.google.com/scholar?cluster=11890223003866230078&hl=en&as_sdt=0,20",2,2022
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization,9,cvpr,1,1,2023-06-03 15:11:03.969000,https://github.com/dongwei156/n2n,21,Node representation learning in graph via node-to-neighbourhood mutual information maximization,"https://scholar.google.com/scholar?cluster=11746290513076855187&hl=en&as_sdt=0,5",2,2022
One Step at a Time: Long-Horizon Vision-and-Language Navigation With Milestones,8,cvpr,1,1,2023-06-03 15:11:04.163000,https://github.com/chanhee-luke/m-track,8,One step at a time: Long-horizon vision-and-language navigation with milestones,"https://scholar.google.com/scholar?cluster=17829230908017130659&hl=en&as_sdt=0,5",2,2022
Point Cloud Pre-Training With Natural 3D Structures,9,cvpr,1,2,2023-06-03 15:11:04.357000,https://github.com/ryosuke-yamada/3dfractaldb,5,Point Cloud Pre-training with Natural 3D Structures,"https://scholar.google.com/scholar?cluster=4232774493484775617&hl=en&as_sdt=0,5",3,2022
SelfRecon: Self Reconstruction Your Digital Avatar From Monocular Video,49,cvpr,43,18,2023-06-03 15:11:04.551000,https://github.com/jby1993/selfreconcode,352,Selfrecon: Self reconstruction your digital avatar from monocular video,"https://scholar.google.com/scholar?cluster=8410658971247413341&hl=en&as_sdt=0,5",22,2022
Scene Consistency Representation Learning for Video Scene Segmentation,3,cvpr,9,2,2023-06-03 15:11:04.745000,https://github.com/TencentYoutuResearch/SceneSegmentation-SCRL,53,Scene Consistency Representation Learning for Video Scene Segmentation,"https://scholar.google.com/scholar?cluster=2564075373785184208&hl=en&as_sdt=0,11",7,2022
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis,19,cvpr,3,0,2023-06-03 15:11:04.941000,https://github.com/zhihengli-UR/StyleT2I,34,Stylet2i: Toward compositional and high-fidelity text-to-image synthesis,"https://scholar.google.com/scholar?cluster=6119835574966247948&hl=en&as_sdt=0,48",8,2022
FineDiving: A Fine-Grained Dataset for Procedure-Aware Action Quality Assessment,14,cvpr,7,6,2023-06-03 15:11:05.135000,https://github.com/xujinglin/finediving,75,Finediving: A fine-grained dataset for procedure-aware action quality assessment,"https://scholar.google.com/scholar?cluster=5125588138588766817&hl=en&as_sdt=0,5",3,2022
Self-Supervised Models Are Continual Learners,45,cvpr,15,3,2023-06-03 15:11:05.329000,https://github.com/donkeyshot21/cassle,84,Self-supervised models are continual learners,"https://scholar.google.com/scholar?cluster=8726502467009709179&hl=en&as_sdt=0,3",5,2022
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart,9,cvpr,6,1,2023-06-03 15:11:05.523000,https://github.com/P2333/Rectified-Rejection,28,Two coupled rejection metrics can tell adversarial examples apart,"https://scholar.google.com/scholar?cluster=9583967984455182755&hl=en&as_sdt=0,10",1,2022
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction,4,cvpr,10,4,2023-06-03 15:11:05.717000,https://github.com/woodfrog/heat,46,HEAT: Holistic Edge Attention Transformer for Structured Reconstruction,"https://scholar.google.com/scholar?cluster=18409780889355359989&hl=en&as_sdt=0,14",7,2022
Exploiting Explainable Metrics for Augmented SGD,2,cvpr,14,0,2023-06-03 15:11:05.911000,https://github.com/mahdihosseini/rmsgd,43,Exploiting Explainable Metrics for Augmented SGD,"https://scholar.google.com/scholar?cluster=9604666184151717265&hl=en&as_sdt=0,47",4,2022
REX: Reasoning-Aware and Grounded Explanation,6,cvpr,0,1,2023-06-03 15:11:06.105000,https://github.com/szzexpoi/rex,15,Rex: Reasoning-aware and grounded explanation,"https://scholar.google.com/scholar?cluster=8689640944520847019&hl=en&as_sdt=0,5",1,2022
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution,17,cvpr,18,4,2023-06-03 15:11:06.300000,https://github.com/picsart-ai-research/videoinr-continuous-space-time-super-resolution,215,Videoinr: Learning video implicit neural representation for continuous space-time super-resolution,"https://scholar.google.com/scholar?cluster=3606765168614463405&hl=en&as_sdt=0,5",5,2022
Improving Neural Implicit Surfaces Geometry With Patch Warping,41,cvpr,12,4,2023-06-03 15:11:06.494000,https://github.com/fdarmon/neuralwarp,188,Improving neural implicit surfaces geometry with patch warping,"https://scholar.google.com/scholar?cluster=3623427518324463702&hl=en&as_sdt=0,32",8,2022
EvUnroll: Neuromorphic Events Based Rolling Shutter Image Correction,7,cvpr,1,1,2023-06-03 15:11:06.689000,https://github.com/zxyemo/evunroll,24,EvUnroll: Neuromorphic events based rolling shutter image correction,"https://scholar.google.com/scholar?cluster=4997635481202221489&hl=en&as_sdt=0,5",1,2022
Gait Recognition in the Wild With Dense 3D Representations and a Benchmark,35,cvpr,12,2,2023-06-03 15:11:06.883000,https://github.com/Gait3D/Gait3D-Benchmark,93,Gait recognition in the wild with dense 3d representations and a benchmark,"https://scholar.google.com/scholar?cluster=12338378403312062338&hl=en&as_sdt=0,5",5,2022
"AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation",50,cvpr,18,7,2023-06-03 15:11:07.078000,https://github.com/yccyenchicheng/AutoSDF,182,"Autosdf: Shape priors for 3d completion, reconstruction and generation","https://scholar.google.com/scholar?cluster=11137881856007355965&hl=en&as_sdt=0,5",9,2022
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior,3,cvpr,4,2,2023-06-03 15:11:07.272000,https://github.com/ozgurkara99/ISNAS-DIP,27,Isnas-dip: Image-specific neural architecture search for deep image prior,"https://scholar.google.com/scholar?cluster=13979102315997014403&hl=en&as_sdt=0,5",3,2022
A Unified Query-Based Paradigm for Point Cloud Understanding,11,cvpr,6,5,2023-06-03 15:11:07.467000,https://github.com/dvlab-research/deepvision3d,97,A unified query-based paradigm for point cloud understanding,"https://scholar.google.com/scholar?cluster=15160520958505330608&hl=en&as_sdt=0,11",5,2022
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values,9,cvpr,0,1,2023-06-03 15:11:07.661000,https://github.com/AhmedImtiazPrio/magnet-polarity,9,Polarity sampling: Quality and diversity control of pre-trained generative networks via singular values,"https://scholar.google.com/scholar?cluster=13683632591453970770&hl=en&as_sdt=0,37",1,2022
Learning From All Vehicles,37,cvpr,52,17,2023-06-03 15:11:07.854000,https://github.com/dotchen/LAV,336,Learning from all vehicles,"https://scholar.google.com/scholar?cluster=16218506036464706349&hl=en&as_sdt=0,15",10,2022
End-to-End Referring Video Object Segmentation With Multimodal Transformers,28,cvpr,67,15,2023-06-03 15:11:08.049000,https://github.com/mttr2021/MTTR,623,End-to-end referring video object segmentation with multimodal transformers,"https://scholar.google.com/scholar?cluster=948851395326952237&hl=en&as_sdt=0,15",7,2022
Style-Structure Disentangled Features and Normalizing Flows for Diverse Icon Colorization,5,cvpr,1,0,2023-06-03 15:11:08.243000,https://github.com/djosix/IconFlow,18,Style-Structure Disentangled Features and Normalizing Flows for Diverse Icon Colorization,"https://scholar.google.com/scholar?cluster=1432476942087797797&hl=en&as_sdt=0,22",2,2022
Towards Driving-Oriented Metric for Lane Detection Models,5,cvpr,3,1,2023-06-03 15:11:08.437000,https://github.com/asguard-uci/ld-metric,22,Towards driving-oriented metric for lane detection models,"https://scholar.google.com/scholar?cluster=123008160880562675&hl=en&as_sdt=0,47",3,2022
REGTR: End-to-End Point Cloud Correspondences With Transformers,44,cvpr,16,11,2023-06-03 15:11:08.631000,https://github.com/yewzijian/regtr,134,Regtr: End-to-end point cloud correspondences with transformers,"https://scholar.google.com/scholar?cluster=1787546847961088663&hl=en&as_sdt=0,38",7,2022
MAT: Mask-Aware Transformer for Large Hole Image Inpainting,61,cvpr,52,39,2023-06-03 15:11:08.825000,https://github.com/fenglinglwb/mat,427,Mat: Mask-aware transformer for large hole image inpainting,"https://scholar.google.com/scholar?cluster=13226075639445229258&hl=en&as_sdt=0,16",7,2022
XYDeblur: Divide and Conquer for Single Image Deblurring,6,cvpr,0,1,2023-06-03 15:11:09.019000,https://github.com/Seowon-Ji/XYDeblur,12,XYDeblur: divide and conquer for single image deblurring,"https://scholar.google.com/scholar?cluster=1150348206342417431&hl=en&as_sdt=0,50",1,2022
Neural 3D Scene Reconstruction With the Manhattan-World Assumption,39,cvpr,32,4,2023-06-03 15:11:09.213000,https://github.com/zju3dv/manhattan_sdf,413,Neural 3d scene reconstruction with the manhattan-world assumption,"https://scholar.google.com/scholar?cluster=16772687487738165&hl=en&as_sdt=0,14",22,2022
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes,11,cvpr,1,5,2023-06-03 15:11:09.416000,https://github.com/4dvlab/stcrowd,39,Stcrowd: A multimodal dataset for pedestrian perception in crowded scenes,"https://scholar.google.com/scholar?cluster=2069889439247779884&hl=en&as_sdt=0,5",3,2022
Style-Based Global Appearance Flow for Virtual Try-On,16,cvpr,30,23,2023-06-03 15:11:09.610000,https://github.com/senhe/flow-style-vton,191,Style-based global appearance flow for virtual try-on,"https://scholar.google.com/scholar?cluster=4991537933937419909&hl=en&as_sdt=0,33",7,2022
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment,4,cvpr,1,2,2023-06-03 15:11:09.804000,https://github.com/zengyiming-eamon/idea-net,15,Idea-net: Dynamic 3d point cloud interpolation via deep embedding alignment,"https://scholar.google.com/scholar?cluster=9916120322274645032&hl=en&as_sdt=0,33",4,2022
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens,41,cvpr,7,3,2023-06-03 15:11:09.999000,https://github.com/hustvl/MSG-Transformer,73,Msg-transformer: Exchanging local spatial information by manipulating messenger tokens,"https://scholar.google.com/scholar?cluster=12949437642882722873&hl=en&as_sdt=0,10",4,2022
Semi-Supervised Video Semantic Segmentation With Inter-Frame Feature Reconstruction,2,cvpr,2,3,2023-06-03 15:11:10.193000,https://github.com/jfzhuang/ifr,24,Semi-supervised video semantic segmentation with inter-frame feature reconstruction,"https://scholar.google.com/scholar?cluster=9308252690102339389&hl=en&as_sdt=0,33",2,2022
UniCon: Combating Label Noise Through Uniform Selection and Contrastive Learning,23,cvpr,10,1,2023-06-03 15:11:10.388000,https://github.com/nazmul-karim170/unicon-noisy-label,42,Unicon: Combating label noise through uniform selection and contrastive learning,"https://scholar.google.com/scholar?cluster=13840763414391094365&hl=en&as_sdt=0,11",2,2022
Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection,11,cvpr,0,0,2023-06-03 15:11:10.582000,https://github.com/SDret/Pytorch-implementation-for-Rethinking-Reconstruction-Autoencoder-Based-Out-of-Distribution-Detection,4,Rethinking reconstruction autoencoder-based out-of-distribution detection,"https://scholar.google.com/scholar?cluster=15354982576440685482&hl=en&as_sdt=0,21",1,2022
Ray3D: Ray-Based 3D Human Pose Estimation for Monocular Absolute 3D Localization,17,cvpr,11,0,2023-06-03 15:11:10.776000,https://github.com/YxZhxn/Ray3D,95,Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization,"https://scholar.google.com/scholar?cluster=10359759403461468779&hl=en&as_sdt=0,33",4,2022
Amodal Segmentation Through Out-of-Task and Out-of-Distribution Generalization With a Bayesian Model,8,cvpr,5,1,2023-06-03 15:11:10.970000,https://github.com/yihongsun/bayesian-amodal,13,Amodal segmentation through out-of-task and out-of-distribution generalization with a bayesian model,"https://scholar.google.com/scholar?cluster=12805769980060504688&hl=en&as_sdt=0,16",1,2022
E-CIR: Event-Enhanced Continuous Intensity Recovery,8,cvpr,2,6,2023-06-03 15:11:11.165000,https://github.com/chensong1995/e-cir,37,E-cir: Event-enhanced continuous intensity recovery,"https://scholar.google.com/scholar?cluster=8773035017781760200&hl=en&as_sdt=0,21",3,2022
ASM-Loc: Action-Aware Segment Modeling for Weakly-Supervised Temporal Action Localization,29,cvpr,3,1,2023-06-03 15:11:11.359000,https://github.com/boheumd/asm-loc,29,ASM-Loc: action-aware segment modeling for weakly-supervised temporal action localization,"https://scholar.google.com/scholar?cluster=16395330686982601785&hl=en&as_sdt=0,33",4,2022
PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition,4,cvpr,4,3,2023-06-03 15:11:11.553000,https://github.com/Morpheus3000/PIE-Net,20,PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition,"https://scholar.google.com/scholar?cluster=6666076693919758502&hl=en&as_sdt=0,33",2,2022
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes,7,cvpr,8,1,2023-06-03 15:11:11.747000,https://github.com/qq456cvb/CanonicalVoting,43,Canonical voting: Towards robust oriented bounding box detection in 3d scenes,"https://scholar.google.com/scholar?cluster=6939279992146812577&hl=en&as_sdt=0,8",6,2022
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond,16,cvpr,4,4,2023-06-03 15:11:11.942000,https://github.com/yuyi-sd/robust_rain_removal,27,Towards robust rain removal against adversarial attacks: A comprehensive benchmark analysis and beyond,"https://scholar.google.com/scholar?cluster=18112822762459252278&hl=en&as_sdt=0,33",2,2022
QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation,15,cvpr,10,7,2023-06-03 15:11:12.139000,https://github.com/sapphire497/query-selected-attention,58,QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation,"https://scholar.google.com/scholar?cluster=14517662462895397737&hl=en&as_sdt=0,19",4,2022
Object-Aware Video-Language Pre-Training for Retrieval,38,cvpr,2,2,2023-06-03 15:11:12.334000,https://github.com/FingerRec/OA-Transformer,56,Object-aware video-language pre-training for retrieval,"https://scholar.google.com/scholar?cluster=3612334662404575783&hl=en&as_sdt=0,23",5,2022
AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception,3,cvpr,2,3,2023-06-03 15:11:12.528000,https://github.com/hustvl/azinorm,52,Azinorm: Exploiting the radial symmetry of point cloud for azimuth-normalized 3d perception,"https://scholar.google.com/scholar?cluster=17891238883434683826&hl=en&as_sdt=0,24",9,2022
End-to-End Multi-Person Pose Estimation With Transformers,22,cvpr,10,21,2023-06-03 15:11:12.723000,https://github.com/hikvision-research/opera,105,End-to-end multi-person pose estimation with transformers,"https://scholar.google.com/scholar?cluster=9055383074551993802&hl=en&as_sdt=0,5",5,2022
Surface Reconstruction From Point Clouds by Learning Predictive Context Priors,14,cvpr,26,6,2023-06-03 15:11:12.916000,https://github.com/mabaorui/predictablecontextprior,147,Surface reconstruction from point clouds by learning predictive context priors,"https://scholar.google.com/scholar?cluster=8429996318459430707&hl=en&as_sdt=0,11",12,2022
Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection,1,cvpr,2,0,2023-06-03 15:11:13.112000,https://github.com/xudangliatiger/ape-loss,17,Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection,"https://scholar.google.com/scholar?cluster=8362901440504014468&hl=en&as_sdt=0,37",2,2022
Parametric Scattering Networks,8,cvpr,3,1,2023-06-03 15:11:13.307000,https://github.com/bentherien/ParametricScatteringNetworks,19,Parametric scattering networks,"https://scholar.google.com/scholar?cluster=8228401933277258066&hl=en&as_sdt=0,44",3,2022
3DeformRS: Certifying Spatial Deformations on Point Clouds,4,cvpr,0,0,2023-06-03 15:11:13.501000,https://github.com/gaperezsa/3deformrs,7,3deformrs: Certifying spatial deformations on point clouds,"https://scholar.google.com/scholar?cluster=11736963952581582642&hl=en&as_sdt=0,33",2,2022
Cannot See the Forest for the Trees: Aggregating Multiple Viewpoints To Better Classify Objects in Videos,1,cvpr,0,0,2023-06-03 15:11:13.695000,https://github.com/sukjunhwang/set_classifier,10,Cannot see the forest for the trees: Aggregating multiple viewpoints to better classify objects in videos,"https://scholar.google.com/scholar?cluster=6793993206629715589&hl=en&as_sdt=0,33",1,2022
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection,60,cvpr,37,15,2023-06-03 15:11:13.892000,https://github.com/ChenhongyiYang/QueryDet-PyTorch,331,Querydet: Cascaded sparse query for accelerating high-resolution small object detection,"https://scholar.google.com/scholar?cluster=10062072141692223467&hl=en&as_sdt=0,5",5,2022
BatchFormer: Learning To Explore Sample Relationships for Robust Representation Learning,23,cvpr,17,11,2023-06-03 15:11:14.086000,https://github.com/zhihou7/batchformer,210,Batchformer: Learning to explore sample relationships for robust representation learning,"https://scholar.google.com/scholar?cluster=5463271069505253831&hl=en&as_sdt=0,39",6,2022
BEHAVE: Dataset and Method for Tracking Human Object Interactions,37,cvpr,5,1,2023-06-03 15:11:14.280000,https://github.com/xiexh20/behave-dataset,91,Behave: Dataset and method for tracking human object interactions,"https://scholar.google.com/scholar?cluster=4342080892167060274&hl=en&as_sdt=0,44",4,2022
Learning Multi-View Aggregation in the Wild for Large-Scale 3D Semantic Segmentation,12,cvpr,21,0,2023-06-03 15:11:14.474000,https://github.com/drprojects/deepviewagg,190,Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation,"https://scholar.google.com/scholar?cluster=9792934371946072434&hl=en&as_sdt=0,5",10,2022
Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation,15,cvpr,7,8,2023-06-03 15:11:14.668000,https://github.com/megvii-research/treeenergyloss,89,Tree energy loss: Towards sparsely annotated semantic segmentation,"https://scholar.google.com/scholar?cluster=2731242151515492345&hl=en&as_sdt=0,22",3,2022
Align and Prompt: Video-and-Language Pre-Training With Entity Prompts,59,cvpr,17,3,2023-06-03 15:11:14.862000,https://github.com/salesforce/alpro,154,Align and prompt: Video-and-language pre-training with entity prompts,"https://scholar.google.com/scholar?cluster=6820441926009627924&hl=en&as_sdt=0,33",7,2022
Physically Disentangled Intra- and Inter-Domain Adaptation for Varicolored Haze Removal,8,cvpr,1,2,2023-06-03 15:11:15.056000,https://github.com/huayuuu/pdi2a-cvpr2022,9,Physically disentangled intra-and inter-domain adaptation for varicolored haze removal,"https://scholar.google.com/scholar?cluster=10179998555559623646&hl=en&as_sdt=0,33",3,2022
Grounding Answers for Visual Questions Asked by Visually Impaired People,12,cvpr,0,0,2023-06-03 15:11:15.250000,https://github.com/ccychongyanchen/vizwizvqagroundingcrowdsourcing,2,Grounding answers for visual questions asked by visually impaired people,"https://scholar.google.com/scholar?cluster=4669087414208305470&hl=en&as_sdt=0,5",1,2022
Representation Compensation Networks for Continual Semantic Segmentation,24,cvpr,10,3,2023-06-03 15:11:15.444000,https://github.com/zhangchbin/rcil,83,Representation compensation networks for continual semantic segmentation,"https://scholar.google.com/scholar?cluster=11771024087882660153&hl=en&as_sdt=0,15",6,2022
HyperInverter: Improving StyleGAN Inversion via Hypernetwork,48,cvpr,11,4,2023-06-03 15:11:15.638000,https://github.com/VinAIResearch/HyperInverter,96,Hyperinverter: Improving stylegan inversion via hypernetwork,"https://scholar.google.com/scholar?cluster=14781112251673612176&hl=en&as_sdt=0,5",4,2022
Cross Modal Retrieval With Querybank Normalisation,27,cvpr,2,0,2023-06-03 15:11:15.832000,https://github.com/ioanacroi/qb-norm,45,Cross modal retrieval with querybank normalisation,"https://scholar.google.com/scholar?cluster=15468839386400527047&hl=en&as_sdt=0,18",3,2022
Learning To Solve Hard Minimal Problems,15,cvpr,15,5,2023-06-03 15:11:16.026000,https://github.com/petrhruby97/learning_minimal,137,Learning to solve hard minimal problems,"https://scholar.google.com/scholar?cluster=11913159081706253526&hl=en&as_sdt=0,5",6,2022
Dataset Distillation by Matching Training Trajectories,71,cvpr,38,12,2023-06-03 15:11:16.221000,https://github.com/georgecazenavette/mtt-distillation,294,Dataset distillation by matching training trajectories,"https://scholar.google.com/scholar?cluster=13091100064813132171&hl=en&as_sdt=0,43",8,2022
VisualGPT: Data-Efficient Adaptation of Pretrained Language Models for Image Captioning,49,cvpr,41,4,2023-06-03 15:11:16.422000,https://github.com/Vision-CAIR/VisualGPT,253,Visualgpt: Data-efficient adaptation of pretrained language models for image captioning,"https://scholar.google.com/scholar?cluster=15606514929492571666&hl=en&as_sdt=0,5",12,2022
Hire-MLP: Vision MLP via Hierarchical Rearrangement,50,cvpr,3,2,2023-06-03 15:11:16.616000,https://github.com/ggjy/Hire-Wave-MLP.pytorch,31,Hire-mlp: Vision mlp via hierarchical rearrangement,"https://scholar.google.com/scholar?cluster=17492654469175814635&hl=en&as_sdt=0,26",1,2022
Forward Compatible Few-Shot Class-Incremental Learning,50,cvpr,22,0,2023-06-03 15:11:16.811000,https://github.com/zhoudw-zdw/cvpr22-fact,76,Forward compatible few-shot class-incremental learning,"https://scholar.google.com/scholar?cluster=12196932409258641037&hl=en&as_sdt=0,23",1,2022
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation,14,cvpr,10,2,2023-06-03 15:11:17.006000,https://github.com/rpng/suo_slam,113,Symmetry and uncertainty-aware object SLAM for 6DoF object pose estimation,"https://scholar.google.com/scholar?cluster=15700441079704915036&hl=en&as_sdt=0,5",11,2022
Towards Implicit Text-Guided 3D Shape Generation,32,cvpr,7,4,2023-06-03 15:11:17.200000,https://github.com/liuzhengzhe/towards-implicit-text-guided-shape-generation,65,Towards implicit text-guided 3d shape generation,"https://scholar.google.com/scholar?cluster=8575693710278496357&hl=en&as_sdt=0,34",6,2022
MAD: A Scalable Dataset for Language Grounding in Videos From Movie Audio Descriptions,20,cvpr,2,0,2023-06-03 15:11:17.395000,https://github.com/Soldelli/MAD,93,Mad: A scalable dataset for language grounding in videos from movie audio descriptions,"https://scholar.google.com/scholar?cluster=8414164954934852684&hl=en&as_sdt=0,47",8,2022
Learning To Recognize Procedural Activities With Distant Supervision,24,cvpr,3,3,2023-06-03 15:11:17.589000,https://github.com/facebookresearch/video-distant-supervision,33,Learning to recognize procedural activities with distant supervision,"https://scholar.google.com/scholar?cluster=15863923512848354874&hl=en&as_sdt=0,5",5,2022
Weakly Supervised Rotation-Invariant Aerial Object Detection Network,11,cvpr,0,12,2023-06-03 15:11:17.783000,https://github.com/xiaoxfeng/rinet,23,Weakly supervised rotation-invariant aerial object detection network,"https://scholar.google.com/scholar?cluster=113609856048887940&hl=en&as_sdt=0,21",3,2022
CellTypeGraph: A New Geometric Computer Vision Benchmark,0,cvpr,0,0,2023-06-03 15:11:17.977000,https://github.com/hci-unihd/plant-celltype,0,CellTypeGraph: A New Geometric Computer Vision Benchmark,"https://scholar.google.com/scholar?cluster=8014091386846368210&hl=en&as_sdt=0,10",1,2022
Modeling Motion With Multi-Modal Features for Text-Based Video Segmentation,3,cvpr,1,0,2023-06-03 15:11:18.171000,https://github.com/wangbo-zhao/2022cvpr-mmmmtbvs,13,Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation,"https://scholar.google.com/scholar?cluster=8613868801694252953&hl=en&as_sdt=0,5",4,2022
Clustering Plotted Data by Image Segmentation,0,cvpr,2,0,2023-06-03 15:11:18.365000,https://github.com/tareknaous/visual-clustering,23,Clustering Plotted Data by Image Segmentation,"https://scholar.google.com/scholar?cluster=10570301673869618107&hl=en&as_sdt=0,22",2,2022
Does Text Attract Attention on E-Commerce Images: A Novel Saliency Prediction Dataset and Method,4,cvpr,0,1,2023-06-03 15:11:18.559000,https://github.com/leafy-lee/E-commercial-dataset,6,Does text attract attention on e-commerce images: A novel saliency prediction dataset and method,"https://scholar.google.com/scholar?cluster=1686138418539189344&hl=en&as_sdt=0,24",2,2022
Finding Badly Drawn Bunnies,2,cvpr,0,0,2023-06-03 15:11:18.763000,https://github.com/yanglan0225/SketchX-Quantifying-Sketch-Quality,0,Finding Badly Drawn Bunnies,"https://scholar.google.com/scholar?cluster=1940515327150977628&hl=en&as_sdt=0,9",1,2022
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding,10,cvpr,6,3,2023-06-03 15:11:18.961000,https://github.com/SUTDCV/Animal-Kingdom,61,Animal kingdom: A large and diverse dataset for animal behavior understanding,"https://scholar.google.com/scholar?cluster=10308328840642657146&hl=en&as_sdt=0,45",5,2022
Topologically-Aware Deformation Fields for Single-View 3D Reconstruction,9,cvpr,6,0,2023-06-03 15:11:19.169000,https://github.com/ShivamDuggal4/TARS3D,73,Topologically-aware deformation fields for single-view 3D reconstruction,"https://scholar.google.com/scholar?cluster=14199759879009906149&hl=en&as_sdt=0,5",5,2022
Transforming Model Prediction for Tracking,58,cvpr,577,55,2023-06-03 15:11:19.372000,https://github.com/visionml/pytracking,2783,Transforming model prediction for tracking,"https://scholar.google.com/scholar?cluster=10187246864084975926&hl=en&as_sdt=0,5",90,2022
Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling,24,cvpr,5,6,2023-06-03 15:11:19.567000,https://github.com/hbdat/cvpr22_cross_modal_pseudo_labeling,35,Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling,"https://scholar.google.com/scholar?cluster=14556078157971376175&hl=en&as_sdt=0,33",3,2022
ScaleNet: A Shallow Architecture for Scale Estimation,7,cvpr,0,1,2023-06-03 15:11:19.761000,https://github.com/axelbarroso/scalenet,41,ScaleNet: A Shallow Architecture for Scale Estimation,"https://scholar.google.com/scholar?cluster=8424038844973851199&hl=en&as_sdt=0,5",4,2022
EPro-PnP: Generalized End-to-End Probabilistic Perspective-N-Points for Monocular Object Pose Estimation,33,cvpr,101,42,2023-06-03 15:11:19.955000,https://github.com/tjiiv-cprg/epro-pnp,918,Epro-pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation,"https://scholar.google.com/scholar?cluster=11247035745945214455&hl=en&as_sdt=0,5",14,2022
Bounded Adversarial Attack on Deep Content Features,2,cvpr,0,0,2023-06-03 15:11:20.149000,https://github.com/qiulingxu/d2b,7,Bounded Adversarial Attack on Deep Content Features,"https://scholar.google.com/scholar?cluster=9624388745955287603&hl=en&as_sdt=0,5",2,2022
Generating Diverse and Natural 3D Human Motions From Text,50,cvpr,23,7,2023-06-03 15:11:20.344000,https://github.com/EricGuo5513/text-to-motion,240,Generating diverse and natural 3d human motions from text,"https://scholar.google.com/scholar?cluster=16675508405550857870&hl=en&as_sdt=0,14",8,2022
Fine-Grained Temporal Contrastive Learning for Weakly-Supervised Temporal Action Localization,26,cvpr,6,0,2023-06-03 15:11:20.538000,https://github.com/mengyuanchen21/cvpr2022-ftcl,34,Fine-grained temporal contrastive learning for weakly-supervised temporal action localization,"https://scholar.google.com/scholar?cluster=10330161142886546867&hl=en&as_sdt=0,5",1,2022
Semi-Supervised Semantic Segmentation With Error Localization Network,22,cvpr,2,0,2023-06-03 15:11:20.732000,https://github.com/kinux98/SSL_ELN,31,Semi-supervised semantic segmentation with error localization network,"https://scholar.google.com/scholar?cluster=9646327588389678444&hl=en&as_sdt=0,5",0,2022
Self-Supervised Keypoint Discovery in Behavioral Videos,11,cvpr,5,1,2023-06-03 15:11:20.926000,https://github.com/neuroethology/bkind,28,Self-supervised keypoint discovery in behavioral videos,"https://scholar.google.com/scholar?cluster=4049771218934132755&hl=en&as_sdt=0,5",1,2022
Anomaly Detection via Reverse Distillation From One-Class Embedding,65,cvpr,14,10,2023-06-03 15:11:21.121000,https://github.com/hq-deng/RD4AD,105,Anomaly detection via reverse distillation from one-class embedding,"https://scholar.google.com/scholar?cluster=1619880648717969759&hl=en&as_sdt=0,5",1,2022
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning,17,cvpr,5,4,2023-06-03 15:11:21.315000,https://github.com/yujun-shi/cwd,29,Mimicking the oracle: an initial phase decorrelation approach for class incremental learning,"https://scholar.google.com/scholar?cluster=9500075217247674212&hl=en&as_sdt=0,39",2,2022
Fine-Grained Object Classification via Self-Supervised Pose Alignment,8,cvpr,8,2,2023-06-03 15:11:21.513000,https://github.com/yangxh11/p2p-net,11,Fine-grained object classification via self-supervised pose alignment,"https://scholar.google.com/scholar?cluster=17823640387451848288&hl=en&as_sdt=0,22",2,2022
Robust Image Forgery Detection Over Online Social Network Shared Images,13,cvpr,15,3,2023-06-03 15:11:21.708000,https://github.com/highwaywu/imageforensicsosn,59,Robust image forgery detection over online social network shared images,"https://scholar.google.com/scholar?cluster=461741873970320663&hl=en&as_sdt=0,5",2,2022
Connecting the Complementary-View Videos: Joint Camera Identification and Subject Association,3,cvpr,2,0,2023-06-03 15:11:21.902000,https://github.com/ruizehan/dmha,4,Connecting the complementary-view videos: joint camera identification and subject association,"https://scholar.google.com/scholar?cluster=12084710914271153563&hl=en&as_sdt=0,5",2,2022
Text2Mesh: Text-Driven Neural Stylization for Meshes,107,cvpr,106,19,2023-06-03 15:11:22.096000,https://github.com/threedle/text2mesh,807,Text2mesh: Text-driven neural stylization for meshes,"https://scholar.google.com/scholar?cluster=15584170954771362381&hl=en&as_sdt=0,33",22,2022
RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition,1,cvpr,3,2,2023-06-03 15:11:22.290000,https://github.com/Vision-CAIR/RelTransformer,25,RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition,"https://scholar.google.com/scholar?cluster=646768930427188436&hl=en&as_sdt=0,5",4,2022
End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps,4,cvpr,6,0,2023-06-03 15:11:22.484000,https://github.com/kguo-cs/tdor,44,End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps,"https://scholar.google.com/scholar?cluster=15193013313739918653&hl=en&as_sdt=0,5",1,2022
FWD: Real-Time Novel View Synthesis With Forward Warping and Depth,11,cvpr,4,0,2023-06-03 15:11:22.678000,https://github.com/caoang327/fwd_code,48,FWD: Real-time Novel View Synthesis with Forward Warping and Depth,"https://scholar.google.com/scholar?cluster=7308540082301250363&hl=en&as_sdt=0,5",4,2022
Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation,21,cvpr,7,2,2023-06-03 15:11:22.872000,https://github.com/leonhlj/rskp,37,Weakly supervised temporal action localization via representative snippet knowledge propagation,"https://scholar.google.com/scholar?cluster=726122753819898575&hl=en&as_sdt=0,5",0,2022
E2EC: An End-to-End Contour-Based Method for High-Quality High-Speed Instance Segmentation,14,cvpr,39,2,2023-06-03 15:11:23.067000,https://github.com/zhang-tao-whu/e2ec,170,E2ec: An end-to-end contour-based method for high-quality high-speed instance segmentation,"https://scholar.google.com/scholar?cluster=11299120623399261857&hl=en&as_sdt=0,33",11,2022
Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection,22,cvpr,3,4,2023-06-03 15:11:23.261000,https://github.com/ahaliassos/RealForensics,30,Leveraging real talking faces via self-supervision for robust forgery detection,"https://scholar.google.com/scholar?cluster=1775977373440976785&hl=en&as_sdt=0,33",3,2022
HDNet: High-Resolution Dual-Domain Learning for Spectral Compressive Imaging,29,cvpr,70,0,2023-06-03 15:11:23.455000,https://github.com/caiyuanhao1998/MST,383,Hdnet: High-resolution dual-domain learning for spectral compressive imaging,"https://scholar.google.com/scholar?cluster=12878162031636014383&hl=en&as_sdt=0,21",6,2022
Self-Supervised Image-Specific Prototype Exploration for Weakly Supervised Semantic Segmentation,26,cvpr,9,3,2023-06-03 15:11:23.649000,https://github.com/chenqi1126/sipe,61,Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=1564943123188101109&hl=en&as_sdt=0,33",2,2022
Embracing Single Stride 3D Object Detector With Sparse Transformer,86,cvpr,57,11,2023-06-03 15:11:23.843000,https://github.com/tusimple/sst,495,Embracing single stride 3d object detector with sparse transformer,"https://scholar.google.com/scholar?cluster=5426394606047885053&hl=en&as_sdt=0,33",16,2022
Clothes-Changing Person Re-Identification With RGB Modality Only,32,cvpr,13,0,2023-06-03 15:11:24.037000,https://github.com/guxinqian/simple-ccreid,88,Clothes-changing person re-identification with RGB modality only,"https://scholar.google.com/scholar?cluster=12056992038637931655&hl=en&as_sdt=0,5",6,2022
Relieving Long-Tailed Instance Segmentation via Pairwise Class Balance,6,cvpr,5,2,2023-06-03 15:11:24.231000,https://github.com/megvii-research/pcb,32,Relieving long-tailed instance segmentation via pairwise class balance,"https://scholar.google.com/scholar?cluster=2220206009039188533&hl=en&as_sdt=0,33",6,2022
Weakly Supervised Object Localization As Domain Adaption,9,cvpr,9,8,2023-06-03 15:11:24.432000,https://github.com/zh460045050/da-wsol_cvpr2022,45,Weakly supervised object localization as domain adaption,"https://scholar.google.com/scholar?cluster=2600801820537187981&hl=en&as_sdt=0,1",2,2022
Chitransformer: Towards Reliable Stereo From Cues,1,cvpr,4,5,2023-06-03 15:11:24.627000,https://github.com/isl-cv/chitransformer,18,ChiTransformer: Towards Reliable Stereo from Cues,"https://scholar.google.com/scholar?cluster=10615531645814342576&hl=en&as_sdt=0,11",3,2022
3D-Aware Image Synthesis via Learning Structural and Textural Representations,62,cvpr,13,5,2023-06-03 15:11:24.822000,https://github.com/genforce/volumegan,119,3d-aware image synthesis via learning structural and textural representations,"https://scholar.google.com/scholar?cluster=12063912311085349984&hl=en&as_sdt=0,5",16,2022
Part-Based Pseudo Label Refinement for Unsupervised Person Re-Identification,38,cvpr,9,3,2023-06-03 15:11:25.016000,https://github.com/yoonkicho/pplr,49,Part-based pseudo label refinement for unsupervised person re-identification,"https://scholar.google.com/scholar?cluster=17618561039665370644&hl=en&as_sdt=0,33",4,2022
OW-DETR: Open-World Detection Transformer,51,cvpr,29,28,2023-06-03 15:11:25.211000,https://github.com/akshitac8/ow-detr,167,Ow-detr: Open-world detection transformer,"https://scholar.google.com/scholar?cluster=5601871542106060008&hl=en&as_sdt=0,43",7,2022
MatteFormer: Transformer-Based Image Matting via Prior-Tokens,19,cvpr,18,13,2023-06-03 15:11:25.406000,https://github.com/webtoon/matteformer,163,Matteformer: Transformer-based image matting via prior-tokens,"https://scholar.google.com/scholar?cluster=16921043057179614554&hl=en&as_sdt=0,33",13,2022
Correlation Verification for Image Retrieval,19,cvpr,8,7,2023-06-03 15:11:25.600000,https://github.com/sungonce/cvnet,120,Correlation verification for image retrieval,"https://scholar.google.com/scholar?cluster=2980584558572412452&hl=en&as_sdt=0,33",11,2022
Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer,27,cvpr,229,13,2023-06-03 15:11:25.794000,https://github.com/williamyang1991/DualStyleGAN,1431,Pastiche master: exemplar-based high-resolution portrait style transfer,"https://scholar.google.com/scholar?cluster=11846236417895763951&hl=en&as_sdt=0,5",29,2022
Exploring Structure-Aware Transformer Over Interaction Proposals for Human-Object Interaction Detection,14,cvpr,7,2,2023-06-03 15:11:25.988000,https://github.com/zyong812/stip,35,Exploring structure-aware transformer over interaction proposals for human-object interaction detection,"https://scholar.google.com/scholar?cluster=1963693257595361023&hl=en&as_sdt=0,44",3,2022
Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching,8,cvpr,5,2,2023-06-03 15:11:26.183000,https://github.com/nicolasdonati/duo-fm,20,Deep orientation-aware functional maps: Tackling symmetry issues in shape matching,"https://scholar.google.com/scholar?cluster=12388684276488108070&hl=en&as_sdt=0,10",2,2022
MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation,80,cvpr,63,4,2023-06-03 15:11:26.377000,https://github.com/Vegetebird/MHFormer,392,Mhformer: Multi-hypothesis transformer for 3d human pose estimation,"https://scholar.google.com/scholar?cluster=18177167198432349205&hl=en&as_sdt=0,43",10,2022
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training,1,cvpr,0,2,2023-06-03 15:11:26.572000,https://github.com/yihong-97/stict,9,Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training,"https://scholar.google.com/scholar?cluster=13259195031281612001&hl=en&as_sdt=0,5",1,2022
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions With Superior OOD Generalization,36,cvpr,2,0,2023-06-03 15:11:26.767000,https://github.com/dteney/collages-dataset,13,Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization,"https://scholar.google.com/scholar?cluster=1721231897643169077&hl=en&as_sdt=0,23",2,2022
Robust and Accurate Superquadric Recovery: A Probabilistic Approach,11,cvpr,7,0,2023-06-03 15:11:26.961000,https://github.com/bmlklwx/ems-superquadric_fitting,30,Robust and accurate superquadric recovery: a probabilistic approach,"https://scholar.google.com/scholar?cluster=2984483543173194652&hl=en&as_sdt=0,44",4,2022
Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene Graphs With Language Structures via Dependency Relationships,2,cvpr,3,1,2023-06-03 15:11:27.156000,https://github.com/bigai-research/vlgae,20,Unsupervised vision-language parsing: Seamlessly bridging visual scene graphs with language structures via dependency relationships,"https://scholar.google.com/scholar?cluster=12646197136058540206&hl=en&as_sdt=0,5",5,2022
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities,14,cvpr,0,0,2023-06-03 15:11:27.349000,https://github.com/assembly101/assembly101.github.io,0,Assembly101: A large-scale multi-view video dataset for understanding procedural activities,"https://scholar.google.com/scholar?cluster=16985062727042180828&hl=en&as_sdt=0,5",0,2022
Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation,24,cvpr,12,13,2023-06-03 15:11:27.542000,https://github.com/prstrive/unimvsnet,179,Rethinking depth estimation for multi-view stereo: A unified representation,"https://scholar.google.com/scholar?cluster=17372943946694395522&hl=en&as_sdt=0,44",13,2022
Query and Attention Augmentation for Knowledge-Based Explainable Reasoning,6,cvpr,0,1,2023-06-03 15:11:27.736000,https://github.com/superjohnzhang/qaa,2,Query and attention augmentation for knowledge-based explainable reasoning,"https://scholar.google.com/scholar?cluster=4154689667310680190&hl=en&as_sdt=0,44",0,2022
DPICT: Deep Progressive Image Compression Using Trit-Planes,7,cvpr,4,3,2023-06-03 15:11:27.930000,https://github.com/jaehanlee-mcl/DPICT,19,DPICT: Deep progressive image compression using trit-planes,"https://scholar.google.com/scholar?cluster=2664539776570963601&hl=en&as_sdt=0,11",2,2022
Sparse Instance Activation for Real-Time Instance Segmentation,30,cvpr,66,47,2023-06-03 15:11:28.124000,https://github.com/hustvl/sparseinst,492,Sparse instance activation for real-time instance segmentation,"https://scholar.google.com/scholar?cluster=2803945007747990107&hl=en&as_sdt=0,34",15,2022
Long-Tailed Recognition via Weight Balancing,40,cvpr,8,10,2023-06-03 15:11:28.318000,https://github.com/shadealsha/ltr-weight-balancing,89,Long-tailed recognition via weight balancing,"https://scholar.google.com/scholar?cluster=356035519616132526&hl=en&as_sdt=0,50",3,2022
Interactron: Embodied Adaptive Object Detection,7,cvpr,6,3,2023-06-03 15:11:28.511000,https://github.com/allenai/interactron,37,Interactron: Embodied adaptive object detection,"https://scholar.google.com/scholar?cluster=2226951975941177652&hl=en&as_sdt=0,14",4,2022
Text to Image Generation With Semantic-Spatial Aware GAN,38,cvpr,25,10,2023-06-03 15:11:28.705000,https://github.com/wtliao/text2image,145,Text to image generation with semantic-spatial aware gan,"https://scholar.google.com/scholar?cluster=14150640205241370220&hl=en&as_sdt=0,34",4,2022
Can You Spot the Chameleon? Adversarially Camouflaging Images From Co-Salient Object Detection,7,cvpr,4,0,2023-06-03 15:11:28.899000,https://github.com/tsingqguo/jadena,13,Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection,"https://scholar.google.com/scholar?cluster=14325818615867904227&hl=en&as_sdt=0,5",2,2022
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition,54,cvpr,8,8,2023-06-03 15:11:29.093000,https://github.com/facebookresearch/memvit,111,Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition,"https://scholar.google.com/scholar?cluster=12972864106896201781&hl=en&as_sdt=0,5",3,2022
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures,43,cvpr,7,1,2023-06-03 15:11:29.287000,https://github.com/andyzoujm/pixmix,87,Pixmix: Dreamlike pictures comprehensively improve safety measures,"https://scholar.google.com/scholar?cluster=4245256336048193979&hl=en&as_sdt=0,37",2,2022
Learning From Temporal Gradient for Semi-Supervised Action Recognition,29,cvpr,0,0,2023-06-03 15:11:29.481000,https://github.com/lambert-x/video-semisup,22,Learning from temporal gradient for semi-supervised action recognition,"https://scholar.google.com/scholar?cluster=7437324692925065540&hl=en&as_sdt=0,36",2,2022
Multidimensional Belief Quantification for Label-Efficient Meta-Learning,2,cvpr,0,0,2023-06-03 15:11:29.674000,https://github.com/pandeydeep9/units-ml-cvpr-22,0,Multidimensional Belief Quantification for Label-Efficient Meta-Learning,"https://scholar.google.com/scholar?cluster=9438698509298365616&hl=en&as_sdt=0,5",1,2022
Generalizable Cross-Modality Medical Image Segmentation via Style Augmentation and Dual Normalization,7,cvpr,12,8,2023-06-03 15:11:29.868000,https://github.com/zzzqzhou/dual-normalization,64,Generalizable cross-modality medical image segmentation via style augmentation and dual normalization,"https://scholar.google.com/scholar?cluster=17086350682355681270&hl=en&as_sdt=0,5",6,2022
SoftCollage: A Differentiable Probabilistic Tree Generator for Image Collage,0,cvpr,0,1,2023-06-03 15:11:30.062000,https://github.com/chineseyjh/softcollage,2,SoftCollage: A Differentiable Probabilistic Tree Generator for Image Collage,"https://scholar.google.com/scholar?cluster=4406629323630144736&hl=en&as_sdt=0,5",3,2022
Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigation,15,cvpr,4,1,2023-06-03 15:11:30.256000,https://github.com/yiconghong/discrete-continuous-vln,49,Bridging the gap between learning in discrete and continuous environments for vision-and-language navigation,"https://scholar.google.com/scholar?cluster=7397070476015817841&hl=en&as_sdt=0,5",2,2022
Interactive Segmentation and Visualization for Tiny Objects in Multi-Megapixel Images,1,cvpr,1,2,2023-06-03 15:11:30.458000,https://github.com/cy-xu/cosmic-conn,21,Interactive Segmentation and Visualization for Tiny Objects in Multi-megapixel Images,"https://scholar.google.com/scholar?cluster=3316866732036438866&hl=en&as_sdt=0,5",2,2022
A Unified Framework for Implicit Sinkhorn Differentiation,8,cvpr,2,1,2023-06-03 15:11:30.651000,https://github.com/marvin-eisenberger/implicit-sinkhorn,55,A unified framework for implicit sinkhorn differentiation,"https://scholar.google.com/scholar?cluster=1150414225562497871&hl=en&as_sdt=0,11",6,2022
Learning Deep Implicit Functions for 3D Shapes With Dynamic Code Clouds,16,cvpr,1,1,2023-06-03 15:11:30.845000,https://github.com/lity20/dccdif,44,Learning deep implicit functions for 3D shapes with dynamic code clouds,"https://scholar.google.com/scholar?cluster=2465694364536697059&hl=en&as_sdt=0,47",4,2022
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning,79,cvpr,64,11,2023-06-03 15:11:31.038000,https://github.com/mahmoodlab/hipt,320,Scaling vision transformers to gigapixel images via hierarchical self-supervised learning,"https://scholar.google.com/scholar?cluster=5125878088803584631&hl=en&as_sdt=0,47",6,2022
Revisiting Temporal Alignment for Video Restoration,5,cvpr,1,3,2023-06-03 15:11:31.233000,https://github.com/redrock303/revisiting-temporal-alignment-for-video-restoration,63,Revisiting temporal alignment for video restoration,"https://scholar.google.com/scholar?cluster=5475915994061531926&hl=en&as_sdt=0,5",9,2022
Neural Reflectance for Shape Recovery With Shadow Handling,15,cvpr,2,0,2023-06-03 15:11:31.439000,https://github.com/junxuan-li/neural-reflectance-ps,21,Neural reflectance for shape recovery with shadow handling,"https://scholar.google.com/scholar?cluster=16791493094484768855&hl=en&as_sdt=0,41",9,2022
OVE6D: Object Viewpoint Encoding for Depth-Based 6D Object Pose Estimation,9,cvpr,1,0,2023-06-03 15:11:31.633000,https://github.com/dingdingcai/ove6d-pose,40,OVE6D: Object viewpoint encoding for depth-based 6D object pose estimation,"https://scholar.google.com/scholar?cluster=3482949006972018064&hl=en&as_sdt=0,33",2,2022
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-Robust Makeup Transfer,25,cvpr,10,3,2023-06-03 15:11:31.827000,https://github.com/cgcl-codes/amt-gan,50,Protecting facial privacy: generating adversarial identity masks via style-robust makeup transfer,"https://scholar.google.com/scholar?cluster=3562262560419959176&hl=en&as_sdt=0,5",2,2022
Surface Representation for Point Clouds,45,cvpr,23,10,2023-06-03 15:11:32.020000,https://github.com/hancyran/RepSurf,291,Surface representation for point clouds,"https://scholar.google.com/scholar?cluster=16356578138709083224&hl=en&as_sdt=0,10",7,2022
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection,7,cvpr,2,3,2023-06-03 15:11:32.214000,https://github.com/mcg-nju/ddm,37,Progressive attention on multi-level dense difference maps for generic event boundary detection,"https://scholar.google.com/scholar?cluster=16948962825604969208&hl=en&as_sdt=0,19",2,2022
Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation,24,cvpr,3,3,2023-06-03 15:11:32.408000,https://github.com/pose-group/fami-pose,31,Temporal feature alignment and mutual information maximization for video-based human pose estimation,"https://scholar.google.com/scholar?cluster=16375178533019515072&hl=en&as_sdt=0,5",3,2022
DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides,6,cvpr,41,0,2023-06-03 15:11:32.602000,https://github.com/nadeemlab/deepliif,93,DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides,"https://scholar.google.com/scholar?cluster=10654659031128175256&hl=en&as_sdt=0,5",6,2022
Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing,20,cvpr,7,9,2023-06-03 15:11:32.795000,https://github.com/adobe-research/sam_inversion,162,Spatially-adaptive multilayer selection for gan inversion and editing,"https://scholar.google.com/scholar?cluster=7575642883414053316&hl=en&as_sdt=0,7",9,2022
Revisiting Weakly Supervised Pre-Training of Visual Perception Models,13,cvpr,8,0,2023-06-03 15:11:32.989000,https://github.com/facebookresearch/SWAG,151,Revisiting weakly supervised pre-training of visual perception models,"https://scholar.google.com/scholar?cluster=2449052747833361751&hl=en&as_sdt=0,23",7,2022
Joint Global and Local Hierarchical Priors for Learned Image Compression,18,cvpr,0,1,2023-06-03 15:11:33.184000,https://github.com/naver-ai/informer,15,Joint global and local hierarchical priors for learned image compression,"https://scholar.google.com/scholar?cluster=7421054896403806364&hl=en&as_sdt=0,21",3,2022
Optical Flow Estimation for Spiking Camera,16,cvpr,3,1,2023-06-03 15:11:33.378000,https://github.com/acnext/optical-flow-for-spiking-camera,27,Optical flow estimation for spiking camera,"https://scholar.google.com/scholar?cluster=12653261529232305450&hl=en&as_sdt=0,44",2,2022
Image Segmentation Using Text and Image Prompts,41,cvpr,62,1,2023-06-03 15:11:33.571000,https://github.com/timojl/clipseg,687,Image segmentation using text and image prompts,"https://scholar.google.com/scholar?cluster=3090742342199483621&hl=en&as_sdt=0,5",11,2022
Towards Robust Adaptive Object Detection Under Noisy Annotations,10,cvpr,3,0,2023-06-03 15:11:33.765000,https://github.com/cityu-aim-group/nlte,25,Towards robust adaptive object detection under noisy annotations,"https://scholar.google.com/scholar?cluster=11536411322683282826&hl=en&as_sdt=0,5",3,2022
InstaFormer: Instance-Aware Image-to-Image Translation With Transformer,12,cvpr,5,5,2023-06-03 15:11:33.959000,https://github.com/KU-CVLAB/InstaFormer,36,InstaFormer: Instance-aware image-to-image translation with transformer,"https://scholar.google.com/scholar?cluster=549260384572579726&hl=en&as_sdt=0,33",4,2022
Open-Vocabulary One-Stage Detection With Hierarchical Visual-Language Knowledge Distillation,20,cvpr,0,7,2023-06-03 15:11:34.152000,https://github.com/mengqidyangge/hierkd,29,Open-vocabulary one-stage detection with hierarchical visual-language knowledge distillation,"https://scholar.google.com/scholar?cluster=12711419494084177503&hl=en&as_sdt=0,7",3,2022
Global Tracking Transformers,36,cvpr,54,30,2023-06-03 15:11:34.346000,https://github.com/xingyizhou/GTR,332,Global tracking transformers,"https://scholar.google.com/scholar?cluster=7616713930375867128&hl=en&as_sdt=0,33",11,2022
Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation,18,cvpr,2,1,2023-06-03 15:11:34.540000,https://github.com/hanqingwangai/ccc-vln,25,Counterfactual cycle-consistent learning for instruction following and generation in vision-language navigation,"https://scholar.google.com/scholar?cluster=15743594750672630990&hl=en&as_sdt=0,5",3,2022
Online Convolutional Re-Parameterization,11,cvpr,14,7,2023-06-03 15:11:34.733000,https://github.com/jugghm/orepa_cvpr2022,134,Online convolutional re-parameterization,"https://scholar.google.com/scholar?cluster=7390880606712026339&hl=en&as_sdt=0,33",3,2022
GMFlow: Learning Optical Flow via Global Matching,78,cvpr,27,1,2023-06-03 15:11:34.927000,https://github.com/haofeixu/gmflow,369,Gmflow: Learning optical flow via global matching,"https://scholar.google.com/scholar?cluster=15062223482476318452&hl=en&as_sdt=0,5",17,2022
Evaluation-Oriented Knowledge Distillation for Deep Face Recognition,10,cvpr,207,59,2023-06-03 15:11:35.121000,https://github.com/Tencent/TFace,1044,Evaluation-oriented knowledge distillation for deep face recognition,"https://scholar.google.com/scholar?cluster=8381131670097997378&hl=en&as_sdt=0,33",34,2022
Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation,24,cvpr,7,6,2023-06-03 15:11:35.314000,https://github.com/alvinliu0/HA2G,86,Learning hierarchical cross-modal association for co-speech gesture generation,"https://scholar.google.com/scholar?cluster=13701242356515161539&hl=en&as_sdt=0,5",5,2022
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization,19,cvpr,3,3,2023-06-03 15:11:35.508000,https://github.com/jmiemirza/dua,36,The norm must go on: dynamic unsupervised domain adaptation by normalization,"https://scholar.google.com/scholar?cluster=5716711288485693280&hl=en&as_sdt=0,14",1,2022
Improving Subgraph Recognition With Variational Graph Information Bottleneck,14,cvpr,1,1,2023-06-03 15:11:35.702000,https://github.com/samyu0304/vgib,9,Improving subgraph recognition with variational graph information bottleneck,"https://scholar.google.com/scholar?cluster=11871225659355307615&hl=en&as_sdt=0,26",1,2022
Learning To Prompt for Open-Vocabulary Object Detection With Vision-Language Model,74,cvpr,19,10,2023-06-03 15:11:35.895000,https://github.com/dyabel/detpro,130,Learning to prompt for open-vocabulary object detection with vision-language model,"https://scholar.google.com/scholar?cluster=601454080714466837&hl=en&as_sdt=0,23",4,2022
TransRAC: Encoding Multi-Scale Temporal Correlation With Transformers for Repetitive Action Counting,11,cvpr,19,2,2023-06-03 15:11:36.089000,https://github.com/sviprepetitioncounting/transrac,85,TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting,"https://scholar.google.com/scholar?cluster=15728575545685962694&hl=en&as_sdt=0,5",2,2022
Decoupled Multi-Task Learning With Cyclical Self-Regulation for Face Parsing,3,cvpr,4364,899,2023-06-03 15:11:36.282000,https://github.com/deepinsight/insightface,15516,Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing,"https://scholar.google.com/scholar?cluster=13609332630211171705&hl=en&as_sdt=0,5",476,2022
Recurrent Glimpse-Based Decoder for Detection With Transformer,10,cvpr,3,2,2023-06-03 15:11:36.476000,https://github.com/zhechen/deformable-detr-rego,39,Recurrent glimpse-based decoder for detection with transformer,"https://scholar.google.com/scholar?cluster=5408647100029634036&hl=en&as_sdt=0,44",6,2022
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-Time Image Enhancement,14,cvpr,16,7,2023-06-03 15:11:36.670000,https://github.com/imcharlesy/adaint,133,AdaInt: learning adaptive intervals for 3D lookup tables on real-time image enhancement,"https://scholar.google.com/scholar?cluster=17080526266081796756&hl=en&as_sdt=0,5",4,2022
SimMIM: A Simple Framework for Masked Image Modeling,405,cvpr,72,27,2023-06-03 15:11:36.864000,https://github.com/microsoft/simmim,735,Simmim: A simple framework for masked image modeling,"https://scholar.google.com/scholar?cluster=17018195497378444438&hl=en&as_sdt=0,33",22,2022
RegionCLIP: Region-Based Language-Image Pretraining,116,cvpr,35,7,2023-06-03 15:11:37.058000,https://github.com/microsoft/regionclip,437,Regionclip: Region-based language-image pretraining,"https://scholar.google.com/scholar?cluster=10020660180667111529&hl=en&as_sdt=0,5",11,2022
Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation,7,cvpr,3,1,2023-06-03 15:11:37.251000,https://github.com/dnjs3594/Eigencontours,21,Eigencontours: Novel contour descriptors based on low-rank approximation,"https://scholar.google.com/scholar?cluster=8251832155492813353&hl=en&as_sdt=0,5",1,2022
ContrastMask: Contrastive Learning To Segment Every Thing,10,cvpr,2,0,2023-06-03 15:11:37.453000,https://github.com/huiserwang/ContrastMask,25,Contrastmask: Contrastive learning to segment every thing,"https://scholar.google.com/scholar?cluster=7085743912913543637&hl=en&as_sdt=0,5",2,2022
Video Frame Interpolation Transformer,47,cvpr,9,2,2023-06-03 15:11:37.647000,https://github.com/zhshi0816/video-frame-interpolation-transformer,80,Video frame interpolation transformer,"https://scholar.google.com/scholar?cluster=12304212309446586855&hl=en&as_sdt=0,5",4,2022
Efficient Deep Embedded Subspace Clustering,15,cvpr,3,3,2023-06-03 15:11:37.841000,https://github.com/jinyucai95/edesc-pytorch,16,Efficient deep embedded subspace clustering,"https://scholar.google.com/scholar?cluster=10194611374694627243&hl=en&as_sdt=0,14",1,2022
Omni-DETR: Omni-Supervised Object Detection With Transformers,22,cvpr,6,9,2023-06-03 15:11:38.034000,https://github.com/amazon-research/omni-detr,51,Omni-DETR: Omni-supervised object detection with transformers,"https://scholar.google.com/scholar?cluster=11715587431329321132&hl=en&as_sdt=0,33",8,2022
An Empirical Study of End-to-End Temporal Action Detection,23,cvpr,8,7,2023-06-03 15:11:38.228000,https://github.com/xlliu7/E2E-TAD,73,An empirical study of end-to-end temporal action detection,"https://scholar.google.com/scholar?cluster=5494069457110925541&hl=en&as_sdt=0,47",9,2022
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping,12,cvpr,4,2,2023-06-03 15:11:38.442000,https://github.com/jazzcharles/cream,7,Cream: Weakly supervised object localization via class re-activation mapping,"https://scholar.google.com/scholar?cluster=3281762841192972651&hl=en&as_sdt=0,5",3,2022
Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification,27,cvpr,4,0,2023-06-03 15:11:38.636000,https://github.com/xlearning-scu/2022-cvpr-dart,24,Learning with twin noisy labels for visible-infrared person re-identification,"https://scholar.google.com/scholar?cluster=13680969413637491164&hl=en&as_sdt=0,33",1,2022
3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces,4,cvpr,7,0,2023-06-03 15:11:38.830000,https://github.com/simofoti/3dvae-swapdisentangled,42,3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces,"https://scholar.google.com/scholar?cluster=12890348998824103228&hl=en&as_sdt=0,5",2,2022
RestoreFormer: High-Quality Blind Face Restoration From Undegraded Key-Value Pairs,17,cvpr,22,8,2023-06-03 15:11:39.024000,https://github.com/wzhouxiff/restoreformer,166,Restoreformer: High-quality blind face restoration from undegraded key-value pairs,"https://scholar.google.com/scholar?cluster=473848415754205723&hl=en&as_sdt=0,33",9,2022
Text Spotting Transformers,20,cvpr,18,6,2023-06-03 15:11:39.218000,https://github.com/mlpc-ucsd/testr,148,Text spotting transformers,"https://scholar.google.com/scholar?cluster=16098958964518654862&hl=en&as_sdt=0,38",9,2022
Mask-Guided Spectral-Wise Transformer for Efficient Hyperspectral Image Reconstruction,68,cvpr,70,0,2023-06-03 15:11:39.411000,https://github.com/caiyuanhao1998/MST,383,Mask-guided spectral-wise transformer for efficient hyperspectral image reconstruction,"https://scholar.google.com/scholar?cluster=18064431115673337162&hl=en&as_sdt=0,33",6,2022
Subspace Adversarial Training,15,cvpr,2,3,2023-06-03 15:11:39.604000,https://github.com/nblt/sub-at,19,Subspace adversarial training,"https://scholar.google.com/scholar?cluster=9851312696966203784&hl=en&as_sdt=0,1",2,2022
Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production,11,cvpr,4,1,2023-06-03 15:11:39.798000,https://github.com/bensaunders27/meinedgs-translation-protocols,1,Signing at scale: Learning to co-articulate signs for large-scale photo-realistic sign language production,"https://scholar.google.com/scholar?cluster=1092614695854902852&hl=en&as_sdt=0,48",2,2022
HINT: Hierarchical Neuron Concept Explainer,5,cvpr,0,0,2023-06-03 15:11:39.992000,https://github.com/antonotnawang/hint,17,HINT: Hierarchical Neuron Concept Explainer,"https://scholar.google.com/scholar?cluster=2904754647179872906&hl=en&as_sdt=0,33",2,2022
Background Activation Suppression for Weakly Supervised Object Localization,14,cvpr,5,1,2023-06-03 15:11:40.185000,https://github.com/wpy1999/bas,37,Background activation suppression for weakly supervised object localization,"https://scholar.google.com/scholar?cluster=2550068495177253363&hl=en&as_sdt=0,5",3,2022
Fine-Grained Predicates Learning for Scene Graph Generation,13,cvpr,2,5,2023-06-03 15:11:40.379000,https://github.com/xinyulyu/fgpl,15,Fine-grained predicates learning for scene graph generation,"https://scholar.google.com/scholar?cluster=17296242378351545458&hl=en&as_sdt=0,10",1,2022
Explore Spatio-Temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline,4,cvpr,2,0,2023-06-03 15:11:40.573000,https://github.com/calayzhou/iod-video,20,Explore spatio-temporal aggregation for insubstantial object detection: benchmark dataset and baseline,"https://scholar.google.com/scholar?cluster=3759906862959702128&hl=en&as_sdt=0,21",2,2022
Target-Aware Dual Adversarial Learning and a Multi-Scenario Multi-Modality Benchmark To Fuse Infrared and Visible for Object Detection,71,cvpr,8,0,2023-06-03 15:11:40.767000,https://github.com/dlut-dimt/tardal,69,Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection,"https://scholar.google.com/scholar?cluster=7585968095673204997&hl=en&as_sdt=0,22",1,2022
Motion-From-Blur: 3D Shape and Motion Estimation of Motion-Blurred Objects in Videos,3,cvpr,4,0,2023-06-03 15:11:40.960000,https://github.com/rozumden/motionfromblur,14,Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos,"https://scholar.google.com/scholar?cluster=8288633015175756727&hl=en&as_sdt=0,33",1,2022
EMOCA: Emotion Driven Monocular Face Capture and Animation,24,cvpr,52,34,2023-06-03 15:11:41.155000,https://github.com/radekd91/emoca,448,EMOCA: Emotion driven monocular face capture and animation,"https://scholar.google.com/scholar?cluster=14870788722517102341&hl=en&as_sdt=0,44",16,2022
Graph-Based Spatial Transformer With Memory Replay for Multi-Future Pedestrian Trajectory Prediction,16,cvpr,5,0,2023-06-03 15:11:41.348000,https://github.com/jacobieee/st-mr,26,Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction,"https://scholar.google.com/scholar?cluster=2776895015094473235&hl=en&as_sdt=0,5",2,2022
Backdoor Attacks on Self-Supervised Learning,36,cvpr,5,1,2023-06-03 15:11:41.542000,https://github.com/UMBCvision/SSL-Backdoor,46,Backdoor attacks on self-supervised learning,"https://scholar.google.com/scholar?cluster=15861275843610371613&hl=en&as_sdt=0,36",2,2022
TableFormer: Table Structure Understanding With Transformers,18,cvpr,10,2,2023-06-03 15:11:41.736000,https://github.com/ibm/synthtabnet,72,Tableformer: Table structure understanding with transformers,"https://scholar.google.com/scholar?cluster=4379564279718802932&hl=en&as_sdt=0,14",8,2022
Deep Rectangling for Image Stitching: A Learning Baseline,8,cvpr,32,16,2023-06-03 15:11:41.929000,https://github.com/nie-lang/deeprectangling,158,Deep Rectangling for Image Stitching: A Learning Baseline,"https://scholar.google.com/scholar?cluster=1158357885550377928&hl=en&as_sdt=0,33",4,2022
PCL: Proxy-Based Contrastive Learning for Domain Generalization,23,cvpr,6,7,2023-06-03 15:11:42.123000,https://github.com/yaoxufeng/PCL-Proxy-based-Contrastive-Learning-for-Domain-Generalization,47,PCL: Proxy-based Contrastive Learning for Domain Generalization,"https://scholar.google.com/scholar?cluster=4912215381230769577&hl=en&as_sdt=0,34",1,2022
HumanNeRF: Free-Viewpoint Rendering of Moving People From Monocular Video,112,cvpr,74,31,2023-06-03 15:11:42.317000,https://github.com/chungyiweng/humannerf,629,Humannerf: Free-viewpoint rendering of moving people from monocular video,"https://scholar.google.com/scholar?cluster=4802417386337124922&hl=en&as_sdt=0,33",17,2022
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion,20,cvpr,13,5,2023-06-03 15:11:42.511000,https://github.com/yuyanli0831/omnifusion,62,Omnifusion: 360 monocular depth estimation via geometry-aware fusion,"https://scholar.google.com/scholar?cluster=10062127993927788502&hl=en&as_sdt=0,33",4,2022
Unsupervised Learning of Accurate Siamese Tracking,23,cvpr,0,3,2023-06-03 15:11:42.705000,https://github.com/florinshum/ulast,16,Unsupervised learning of accurate Siamese tracking,"https://scholar.google.com/scholar?cluster=4089155253019708361&hl=en&as_sdt=0,5",1,2022
Grounded Language-Image Pre-Training,200,cvpr,121,46,2023-06-03 15:11:42.899000,https://github.com/microsoft/GLIP,1290,Grounded language-image pre-training,"https://scholar.google.com/scholar?cluster=6004268348151288098&hl=en&as_sdt=0,5",44,2022
Semi-Weakly-Supervised Learning of Complex Actions From Instructional Task Videos,4,cvpr,0,0,2023-06-03 15:11:43.092000,https://github.com/yuhan-shen/swsl,2,Semi-weakly-supervised learning of complex actions from instructional task videos,"https://scholar.google.com/scholar?cluster=5990181969650959339&hl=en&as_sdt=0,5",1,2022
Unified Transformer Tracker for Object Tracking,24,cvpr,1,5,2023-06-03 15:11:43.286000,https://github.com/flowerfan/trackron,45,Unified transformer tracker for object tracking,"https://scholar.google.com/scholar?cluster=2294005191343201895&hl=en&as_sdt=0,33",9,2022
Non-Parametric Depth Distribution Modelling Based Depth Inference for Multi-View Stereo,7,cvpr,3,1,2023-06-03 15:11:43.480000,https://github.com/nvlabs/np-cvp-mvsnet,22,Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo,"https://scholar.google.com/scholar?cluster=17721662070981337764&hl=en&as_sdt=0,33",5,2022
Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition,6,cvpr,1,1,2023-06-03 15:11:43.674000,https://github.com/82magnolia/ev_tta,8,Ev-tta: Test-time adaptation for event-based object recognition,"https://scholar.google.com/scholar?cluster=14941098914432467048&hl=en&as_sdt=0,31",1,2022
Advancing High-Resolution Video-Language Representation With Large-Scale Video Transcriptions,32,cvpr,12,0,2023-06-03 15:11:43.867000,https://github.com/microsoft/xpretrain,284,Advancing high-resolution video-language representation with large-scale video transcriptions,"https://scholar.google.com/scholar?cluster=3591972109861256721&hl=en&as_sdt=0,4",12,2022
Equalized Focal Loss for Dense Long-Tailed Object Detection,39,cvpr,62,24,2023-06-03 15:11:44.061000,https://github.com/modeltc/eod,397,Equalized focal loss for dense long-tailed object detection,"https://scholar.google.com/scholar?cluster=3753161556540928995&hl=en&as_sdt=0,10",19,2022
Neural Face Identification in a 2D Wireframe Projection of a Manifold Object,3,cvpr,1,1,2023-06-03 15:11:44.254000,https://github.com/manycore-research/faceformer,39,Neural face identification in a 2d wireframe projection of a manifold object,"https://scholar.google.com/scholar?cluster=5718507503258968760&hl=en&as_sdt=0,25",2,2022
DeepDPM: Deep Clustering With an Unknown Number of Clusters,20,cvpr,68,10,2023-06-03 15:11:44.447000,https://github.com/bgu-cs-vil/deepdpm,693,DeepDPM: Deep Clustering With an Unknown Number of Clusters,"https://scholar.google.com/scholar?cluster=7335598410750852147&hl=en&as_sdt=0,44",9,2022
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation,22,cvpr,9,4,2023-06-03 15:11:44.642000,https://github.com/liuzechun/nonuniform-to-uniform-quantization,78,Nonuniform-to-uniform quantization: Towards accurate quantization via generalized straight-through estimation,"https://scholar.google.com/scholar?cluster=777827710441638860&hl=en&as_sdt=0,10",2,2022
Cross-Architecture Self-Supervised Video Representation Learning,8,cvpr,2,1,2023-06-03 15:11:44.835000,https://github.com/guoshengcv/cacl,21,Cross-architecture self-supervised video representation learning,"https://scholar.google.com/scholar?cluster=5088671574638326251&hl=en&as_sdt=0,5",3,2022
Unsupervised Domain Adaptation for Nighttime Aerial Tracking,20,cvpr,16,0,2023-06-03 15:11:45.029000,https://github.com/vision4robotics/udat,98,Unsupervised domain adaptation for nighttime aerial tracking,"https://scholar.google.com/scholar?cluster=18364001837684502950&hl=en&as_sdt=0,5",3,2022
High-Resolution Image Harmonization via Collaborative Dual Transformations,21,cvpr,5,1,2023-06-03 15:11:45.223000,https://github.com/bcmi/CDTNet-High-Resolution-Image-Harmonization,78,High-resolution image harmonization via collaborative dual transformations,"https://scholar.google.com/scholar?cluster=12649598176028163151&hl=en&as_sdt=0,5",12,2022
SimVP: Simpler Yet Better Video Prediction,24,cvpr,25,7,2023-06-03 15:11:45.417000,https://github.com/gaozhangyang/simvp-simpler-yet-better-video-prediction,108,Simvp: Simpler yet better video prediction,"https://scholar.google.com/scholar?cluster=319517229086919990&hl=en&as_sdt=0,39",2,2022
Balanced Multimodal Learning via On-the-Fly Gradient Modulation,25,cvpr,12,24,2023-06-03 15:11:45.610000,https://github.com/gewu-lab/ogm-ge_cvpr2022,144,Balanced multimodal learning via on-the-fly gradient modulation,"https://scholar.google.com/scholar?cluster=5090317183372596343&hl=en&as_sdt=0,44",2,2022
Transferable Sparse Adversarial Attack,5,cvpr,0,0,2023-06-03 15:11:45.804000,https://github.com/NiCE-X/TSAA,1,Transferable sparse adversarial attack,"https://scholar.google.com/scholar?cluster=13275656247152317725&hl=en&as_sdt=0,33",0,2022
Object Localization Under Single Coarse Point Supervision,7,cvpr,73,26,2023-06-03 15:11:45.999000,https://github.com/ucas-vg/pointtinybenchmark,573,Object localization under single coarse point supervision,"https://scholar.google.com/scholar?cluster=11197749454980040204&hl=en&as_sdt=0,5",21,2022
Causality Inspired Representation Learning for Domain Generalization,30,cvpr,11,8,2023-06-03 15:11:46.195000,https://github.com/bit-da/cirl,94,Causality inspired representation learning for domain generalization,"https://scholar.google.com/scholar?cluster=14938904289268146133&hl=en&as_sdt=0,44",0,2022
StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation,137,cvpr,48,13,2023-06-03 15:11:46.390000,https://github.com/royorel/StyleSDF,490,Stylesdf: High-resolution 3d-consistent image and geometry generation,"https://scholar.google.com/scholar?cluster=6766726929083092838&hl=en&as_sdt=0,47",28,2022
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection,5,cvpr,2,2,2023-06-03 15:11:46.587000,https://github.com/ritmininglab/bn-svp,7,Bayesian nonparametric submodular video partition for robust anomaly detection,"https://scholar.google.com/scholar?cluster=3607857779398730024&hl=en&as_sdt=0,21",1,2022
"Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency",14,cvpr,5,0,2023-06-03 15:11:46.782000,https://github.com/yanangu/dvc,17,"Not just selection, but exploration: Online class-incremental continual learning via dual view consistency","https://scholar.google.com/scholar?cluster=8555368968556268636&hl=en&as_sdt=0,5",1,2022
GLAMR: Global Occlusion-Aware Human Mesh Recovery With Dynamic Cameras,37,cvpr,25,17,2023-06-03 15:11:46.976000,https://github.com/nvlabs/glamr,289,GLAMR: Global occlusion-aware human mesh recovery with dynamic cameras,"https://scholar.google.com/scholar?cluster=9390433960838526357&hl=en&as_sdt=0,33",27,2022
Topology Preserving Local Road Network Estimation From Single Onboard Camera Image,14,cvpr,5,2,2023-06-03 15:11:47.171000,https://github.com/ybarancan/topologicallanegraph,47,Topology preserving local road network estimation from single onboard camera image,"https://scholar.google.com/scholar?cluster=11662846550390952880&hl=en&as_sdt=0,5",7,2022
FocalClick: Towards Practical Interactive Image Segmentation,29,cvpr,32,4,2023-06-03 15:11:47.365000,https://github.com/XavierCHEN34/ClickSEG,131,FocalClick: towards practical interactive image segmentation,"https://scholar.google.com/scholar?cluster=3062520042557091780&hl=en&as_sdt=0,5",2,2022
Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes,9,cvpr,16,0,2023-06-03 15:11:47.558000,https://github.com/dongkwonjin/eigenlanes,115,Eigenlanes: Data-driven lane descriptors for structurally diverse lanes,"https://scholar.google.com/scholar?cluster=8563383129778081242&hl=en&as_sdt=0,5",5,2022
ISDNet: Integrating Shallow and Deep Networks for Efficient Ultra-High Resolution Segmentation,8,cvpr,2,4,2023-06-03 15:11:47.752000,https://github.com/cedricgsh/isdnet,34,Isdnet: Integrating shallow and deep networks for efficient ultra-high resolution segmentation,"https://scholar.google.com/scholar?cluster=17316168847977566364&hl=en&as_sdt=0,5",3,2022
B-Cos Networks: Alignment Is All We Need for Interpretability,13,cvpr,8,3,2023-06-03 15:11:47.946000,https://github.com/moboehle/b-cos,84,B-Cos networks: alignment is all we need for interpretability,"https://scholar.google.com/scholar?cluster=7013378357046496018&hl=en&as_sdt=0,23",3,2022
Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation,5,cvpr,3,0,2023-06-03 15:11:48.141000,https://github.com/akmtn/OmniDreamer,22,Diverse plausible 360-degree image outpainting for efficient 3dcg background creation,"https://scholar.google.com/scholar?cluster=14309141439600165743&hl=en&as_sdt=0,31",1,2022
Burst Image Restoration and Enhancement,32,cvpr,16,4,2023-06-03 15:11:48.334000,https://github.com/akshaydudhane16/bipnet,96,Burst image restoration and enhancement,"https://scholar.google.com/scholar?cluster=2235176340185033730&hl=en&as_sdt=0,33",6,2022
Multi-Level Feature Learning for Contrastive Multi-View Clustering,35,cvpr,15,7,2023-06-03 15:11:48.528000,https://github.com/SubmissionsIn/MFLVC,41,Multi-level feature learning for contrastive multi-view clustering,"https://scholar.google.com/scholar?cluster=11544394085965907218&hl=en&as_sdt=0,10",2,2022
A Variational Bayesian Method for Similarity Learning in Non-Rigid Image Registration,0,cvpr,2,1,2023-06-03 15:11:48.721000,https://github.com/dgrzech/learnsim,15,A variational Bayesian method for similarity learning in non-rigid image registration,"https://scholar.google.com/scholar?cluster=12266065616950652821&hl=en&as_sdt=0,47",1,2022
What Makes Transfer Learning Work for Medical Images: Feature Reuse & Other Factors,15,cvpr,2,0,2023-06-03 15:11:48.915000,https://github.com/chrismats/feature-reuse,12,What makes transfer learning work for medical images: feature reuse & other factors,"https://scholar.google.com/scholar?cluster=10091390179375265575&hl=en&as_sdt=0,5",3,2022
CAFE: Learning To Condense Dataset by Aligning Features,42,cvpr,4,1,2023-06-03 15:11:49.109000,https://github.com/kaiwang960112/cafe,50,Cafe: Learning to condense dataset by aligning features,"https://scholar.google.com/scholar?cluster=189180043954036993&hl=en&as_sdt=0,5",8,2022
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free,11,cvpr,2,0,2023-06-03 15:11:49.303000,https://github.com/vita-group/backdoor-lth,22,Quarantine: Sparsity can uncover the trojan attack trigger for free,"https://scholar.google.com/scholar?cluster=8805004245573032259&hl=en&as_sdt=0,10",8,2022
Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis,8,cvpr,5,0,2023-06-03 15:11:49.496000,https://github.com/facebookresearch/facestar,92,Audio-visual speech codecs: Rethinking audio-visual speech enhancement by re-synthesis,"https://scholar.google.com/scholar?cluster=11556289608283151891&hl=en&as_sdt=0,41",8,2022
Generalized Few-Shot Semantic Segmentation,27,cvpr,11,4,2023-06-03 15:11:49.690000,https://github.com/dvlab-research/gfs-seg,46,Generalized few-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=1972190624221467279&hl=en&as_sdt=0,33",1,2022
Towards Real-World Navigation With Deep Differentiable Planners,1,cvpr,3,0,2023-06-03 15:11:49.884000,https://github.com/shuishida/calvin,12,Towards real-world navigation with deep differentiable planners,"https://scholar.google.com/scholar?cluster=4300137872951403656&hl=en&as_sdt=0,10",2,2022
Why Discard if You Can Recycle?: A Recycling Max Pooling Module for 3D Point Cloud Analysis,9,cvpr,2,0,2023-06-03 15:11:50.078000,https://github.com/jiajingchen113322/Recycle_Maxpooling_Module,11,Why discard if you can recycle?: A recycling max pooling module for 3d point cloud analysis,"https://scholar.google.com/scholar?cluster=2177156517217224296&hl=en&as_sdt=0,5",3,2022
Localized Adversarial Domain Generalization,5,cvpr,2,1,2023-06-03 15:11:50.272000,https://github.com/zwvews/ladg,7,Localized Adversarial Domain Generalization,"https://scholar.google.com/scholar?cluster=5644223777517706008&hl=en&as_sdt=0,43",1,2022
"Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation",23,cvpr,11,1,2023-06-03 15:11:50.467000,https://github.com/lxtgh/video-k-net,132,"Video k-net: A simple, strong, and unified baseline for video segmentation","https://scholar.google.com/scholar?cluster=17741523309785170156&hl=en&as_sdt=0,5",6,2022
Learning From Pixel-Level Noisy Label: A New Perspective for Light Field Saliency Detection,5,cvpr,4,3,2023-06-03 15:11:50.661000,https://github.com/olobbcode/noiself,12,Learning from pixel-level noisy label: A new perspective for light field saliency detection,"https://scholar.google.com/scholar?cluster=3274959735332722801&hl=en&as_sdt=0,5",1,2022
X-Trans2Cap: Cross-Modal Knowledge Transfer Using Transformer for 3D Dense Captioning,19,cvpr,3,6,2023-06-03 15:11:50.854000,https://github.com/curryyuan/x-trans2cap,31,X-trans2cap: Cross-modal knowledge transfer using transformer for 3d dense captioning,"https://scholar.google.com/scholar?cluster=16937276053226702586&hl=en&as_sdt=0,1",3,2022
Multi-View Depth Estimation by Fusing Single-View Depth Probability With Multi-View Geometry,13,cvpr,13,7,2023-06-03 15:11:51.048000,https://github.com/baegwangbin/magnet,162,Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry,"https://scholar.google.com/scholar?cluster=1886059432415129607&hl=en&as_sdt=0,10",10,2022
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data,27,cvpr,18,0,2023-06-03 15:11:51.241000,https://github.com/valeoai/slidr,116,Image-to-lidar self-supervised distillation for autonomous driving data,"https://scholar.google.com/scholar?cluster=7983433851687883883&hl=en&as_sdt=0,5",9,2022
AlignQ: Alignment Quantization With ADMM-Based Correlation Preservation,2,cvpr,0,2,2023-06-03 15:11:51.436000,https://github.com/tinganchen/alignq,7,AlignQ: Alignment Quantization with ADMM-based Correlation Preservation,"https://scholar.google.com/scholar?cluster=6551086000020541347&hl=en&as_sdt=0,10",2,2022
SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization,8,cvpr,0,3,2023-06-03 15:11:51.629000,https://github.com/ychang686/scs-co,15,Scs-co: Self-consistent style contrastive learning for image harmonization,"https://scholar.google.com/scholar?cluster=829043008214387637&hl=en&as_sdt=0,33",8,2022
CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields,122,cvpr,14,13,2023-06-03 15:11:51.823000,https://github.com/cassiePython/CLIPNeRF,215,Clip-nerf: Text-and-image driven manipulation of neural radiance fields,"https://scholar.google.com/scholar?cluster=4864817613756311341&hl=en&as_sdt=0,5",15,2022
Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention,5,cvpr,3,1,2023-06-03 15:11:52.016000,https://github.com/yuyang0901/lavise,14,Explaining deep convolutional neural networks via latent visual-semantic filter attention,"https://scholar.google.com/scholar?cluster=2638927577424870038&hl=en&as_sdt=0,44",2,2022
Self-Distillation From the Last Mini-Batch for Consistency Regularization,13,cvpr,4,2,2023-06-03 15:11:52.211000,https://github.com/meta-knowledge-lab/dlb,31,Self-distillation from the last mini-batch for consistency regularization,"https://scholar.google.com/scholar?cluster=4161549003342654024&hl=en&as_sdt=0,5",4,2022
Homography Loss for Monocular 3D Object Detection,13,cvpr,1,3,2023-06-03 15:11:52.405000,https://github.com/gujiaqivadin/HomographyLoss,48,Homography loss for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=6731379779152512615&hl=en&as_sdt=0,5",9,2022
Interactive Multi-Class Tiny-Object Detection,7,cvpr,8,6,2023-06-03 15:11:52.599000,https://github.com/chungyi347/interactive-multi-class-tiny-object-detection,117,Interactive Multi-Class Tiny-Object Detection,"https://scholar.google.com/scholar?cluster=2359034833759547214&hl=en&as_sdt=0,33",3,2022
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation,5,cvpr,2,1,2023-06-03 15:11:52.793000,https://github.com/robinwang1/art-point,20,Art-point: Improving rotation robustness of point cloud classifiers via adversarial rotation,"https://scholar.google.com/scholar?cluster=8517568599940731232&hl=en&as_sdt=0,31",1,2022
Stable Long-Term Recurrent Video Super-Resolution,2,cvpr,1,0,2023-06-03 15:11:52.987000,https://github.com/bjmch/MRVSR,13,Stable long-term recurrent video super-resolution,"https://scholar.google.com/scholar?cluster=8822280917744930483&hl=en&as_sdt=0,5",1,2022
Targeted Supervised Contrastive Learning for Long-Tailed Recognition,60,cvpr,8,4,2023-06-03 15:11:53.180000,https://github.com/lth14/targeted-supcon,71,Targeted supervised contrastive learning for long-tailed recognition,"https://scholar.google.com/scholar?cluster=13733873656995544681&hl=en&as_sdt=0,33",1,2022
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes,16,cvpr,5,0,2023-06-03 15:11:53.374000,https://github.com/brown-ivl/ConDor,39,Condor: Self-supervised canonicalization of 3d pose for partial shapes,"https://scholar.google.com/scholar?cluster=17821245970665188554&hl=en&as_sdt=0,49",1,2022
Balanced Contrastive Learning for Long-Tailed Visual Recognition,36,cvpr,8,6,2023-06-03 15:11:53.568000,https://github.com/flamiezhu/bcl,60,Balanced contrastive learning for long-tailed visual recognition,"https://scholar.google.com/scholar?cluster=1251493437962452403&hl=en&as_sdt=0,23",2,2022
Bandits for Structure Perturbation-Based Black-Box Attacks To Graph Neural Networks With Theoretical Guarantees,2,cvpr,0,1,2023-06-03 15:11:53.762000,https://github.com/metaoblivion/bandit_gnn_attack,2,Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees,"https://scholar.google.com/scholar?cluster=9420293099745156582&hl=en&as_sdt=0,48",1,2022
Exploring Endogenous Shift for Cross-Domain Detection: A Large-Scale Benchmark and Perturbation Suppression Network,4,cvpr,5,1,2023-06-03 15:11:53.956000,https://github.com/DIG-Beihang/PSN,21,Exploring Endogenous Shift for Cross-domain Detection: A Large-scale Benchmark and Perturbation Suppression Network,"https://scholar.google.com/scholar?cluster=8345745909201314754&hl=en&as_sdt=0,5",0,2022
A Hybrid Quantum-Classical Algorithm for Robust Fitting,11,cvpr,1,0,2023-06-03 15:11:54.149000,https://github.com/dadung/hqc-robust-fitting,4,A hybrid quantum-classical algorithm for robust fitting,"https://scholar.google.com/scholar?cluster=9615275407828505733&hl=en&as_sdt=0,26",1,2022
Slimmable Domain Adaptation,6,cvpr,1,0,2023-06-03 15:11:54.343000,https://github.com/hik-lab/slimda,4,Slimmable domain adaptation,"https://scholar.google.com/scholar?cluster=8676942822111500758&hl=en&as_sdt=0,5",1,2022
Decoupling Makes Weakly Supervised Local Feature Better,17,cvpr,3,0,2023-06-03 15:11:54.537000,https://github.com/The-Learning-And-Vision-Atelier-LAVA/PoSFeat,88,Decoupling makes weakly supervised local feature better,"https://scholar.google.com/scholar?cluster=17838776311448192851&hl=en&as_sdt=0,5",5,2022
VisCUIT: Visual Auditor for Bias in CNN Image Classifier,4,cvpr,0,0,2023-06-03 15:11:54.738000,https://github.com/poloclub/viscuit,6,VisCUIT: Visual auditor for bias in CNN image classifier,"https://scholar.google.com/scholar?cluster=2224841334807001363&hl=en&as_sdt=0,33",6,2022
Human Instance Matting via Mutual Guidance and Multi-Instance Refinement,4,cvpr,3,0,2023-06-03 15:11:54.932000,https://github.com/nowsyn/instmatt,62,Human instance matting via mutual guidance and multi-instance refinement,"https://scholar.google.com/scholar?cluster=7734851440095865996&hl=en&as_sdt=0,43",17,2022
DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow,12,cvpr,3,1,2023-06-03 15:11:55.125000,https://github.com/zihuazheng/dip,33,Dip: Deep inverse patchmatch for high-resolution optical flow,"https://scholar.google.com/scholar?cluster=14772753701936328240&hl=en&as_sdt=0,31",13,2022
A Unified Model for Line Projections in Catadioptric Cameras With Rotationally Symmetric Mirrors,1,cvpr,0,0,2023-06-03 15:11:55.320000,https://github.com/pmiraldo/line-projection-catadioptric,0,A Unified Model for Line Projections in Catadioptric Cameras with Rotationally Symmetric Mirrors,"https://scholar.google.com/scholar?cluster=17611314101887791279&hl=en&as_sdt=0,26",1,2022
DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition,19,cvpr,2,2,2023-06-03 15:11:55.513000,https://github.com/uark-cviu/direcformer,18,Direcformer: A directed attention in transformer approach to robust action recognition,"https://scholar.google.com/scholar?cluster=15291191071526338462&hl=en&as_sdt=0,41",1,2022
SwinTextSpotter: Scene Text Spotting via Better Synergy Between Text Detection and Text Recognition,25,cvpr,35,12,2023-06-03 15:11:55.708000,https://github.com/mxin262/swintextspotter,221,Swintextspotter: Scene text spotting via better synergy between text detection and text recognition,"https://scholar.google.com/scholar?cluster=6989674085539462525&hl=en&as_sdt=0,5",7,2022
Few-Shot Object Detection With Fully Cross-Transformer,44,cvpr,8,14,2023-06-03 15:11:55.902000,https://github.com/guangxinghan/fct,45,Few-shot object detection with fully cross-transformer,"https://scholar.google.com/scholar?cluster=2983619289198144662&hl=en&as_sdt=0,48",4,2022
Video Frame Interpolation With Transformer,32,cvpr,13,12,2023-06-03 15:11:56.097000,https://github.com/dvlab-research/vfiformer,90,Video frame interpolation with transformer,"https://scholar.google.com/scholar?cluster=17295051963064398777&hl=en&as_sdt=0,34",5,2022
TCTrack: Temporal Contexts for Aerial Tracking,40,cvpr,30,5,2023-06-03 15:11:56.298000,https://github.com/vision4robotics/tctrack,118,TCTrack: Temporal contexts for aerial tracking,"https://scholar.google.com/scholar?cluster=1435906032426961702&hl=en&as_sdt=0,14",2,2022
iPLAN: Interactive and Procedural Layout Planning,4,cvpr,3,1,2023-06-03 15:11:56.492000,https://github.com/realcrane/iPLAN-Interactive-and-Procedural-Layout-Planning,16,iPLAN: interactive and procedural layout planning,"https://scholar.google.com/scholar?cluster=2268894692471172520&hl=en&as_sdt=0,10",5,2022
GAN-Supervised Dense Visual Alignment,33,cvpr,118,6,2023-06-03 15:11:56.691000,https://github.com/wpeebles/gangealing,982,Gan-supervised dense visual alignment,"https://scholar.google.com/scholar?cluster=16638477680398641872&hl=en&as_sdt=0,5",16,2022
GIFS: Neural Implicit Function for General Shape Representation,15,cvpr,4,0,2023-06-03 15:11:56.886000,https://github.com/jianglongye/gifs,69,GIFS: Neural implicit function for general shape representation,"https://scholar.google.com/scholar?cluster=13905620613363965452&hl=en&as_sdt=0,33",6,2022
PhysFormer: Facial Video-Based Physiological Measurement With Temporal Difference Transformer,30,cvpr,7,3,2023-06-03 15:11:57.081000,https://github.com/zitongyu/physformer,65,PhysFormer: facial video-based physiological measurement with temporal difference transformer,"https://scholar.google.com/scholar?cluster=8053588590478703627&hl=en&as_sdt=0,44",6,2022
Balanced MSE for Imbalanced Visual Regression,23,cvpr,27,0,2023-06-03 15:11:57.292000,https://github.com/jiawei-ren/BalancedMSE,297,Balanced mse for imbalanced visual regression,"https://scholar.google.com/scholar?cluster=3746607576114564694&hl=en&as_sdt=0,5",8,2022
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction,3,cvpr,5,1,2023-06-03 15:11:57.487000,https://github.com/leolyliu/HOI4D-Instructions,25,HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction,"https://scholar.google.com/scholar?cluster=932811307134293169&hl=en&as_sdt=0,39",1,2022
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning,22,cvpr,6,4,2023-06-03 15:11:57.681000,https://github.com/mmendiet/fedalign,37,Local learning matters: Rethinking data heterogeneity in federated learning,"https://scholar.google.com/scholar?cluster=17308444446907301376&hl=en&as_sdt=0,33",2,2022
Collaborative Transformers for Grounded Situation Recognition,5,cvpr,6,0,2023-06-03 15:11:57.876000,https://github.com/jhcho99/coformer,33,Collaborative transformers for grounded situation recognition,"https://scholar.google.com/scholar?cluster=4025222753784961174&hl=en&as_sdt=0,33",4,2022
Blind Image Super-Resolution With Elaborate Degradation Modeling on Noise and Kernel,8,cvpr,6,2,2023-06-03 15:11:58.071000,https://github.com/zsyOAOA/BSRDM,77,Blind image super-resolution with elaborate degradation modeling on noise and kernel,"https://scholar.google.com/scholar?cluster=8130499388651638958&hl=en&as_sdt=0,19",2,2022
UnweaveNet: Unweaving Activity Stories,4,cvpr,0,0,2023-06-03 15:11:58.270000,https://github.com/willprice/activity-stories,3,UnweaveNet: Unweaving Activity Stories,"https://scholar.google.com/scholar?cluster=11666756894901674634&hl=en&as_sdt=0,33",2,2022
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces From 3D MRI Scans With Geometric Deep Neural Networks,6,cvpr,8,2,2023-06-03 15:11:58.472000,https://github.com/ai-med/vox2cortex,28,Vox2cortex: fast explicit reconstruction of cortical surfaces from 3D MRI scans with geometric deep neural networks,"https://scholar.google.com/scholar?cluster=16656997846799811932&hl=en&as_sdt=0,33",2,2022
Progressive End-to-End Object Detection in Crowded Scenes,19,cvpr,6,11,2023-06-03 15:11:58.666000,https://github.com/megvii-model/iter-e2edet,73,Progressive end-to-end object detection in crowded scenes,"https://scholar.google.com/scholar?cluster=17039945862035123188&hl=en&as_sdt=0,33",3,2022
Look Closer To Supervise Better: One-Shot Font Generation via Component-Based Discriminator,10,cvpr,5,6,2023-06-03 15:11:58.861000,https://github.com/kyxscut/CG-GAN,65,Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator,"https://scholar.google.com/scholar?cluster=1654991629295666254&hl=en&as_sdt=0,33",4,2022
ScanQA: 3D Question Answering for Spatial Scene Understanding,16,cvpr,1,3,2023-06-03 15:11:59.055000,https://github.com/atr-dbi/scanqa,45,ScanQA: 3D question answering for spatial scene understanding,"https://scholar.google.com/scholar?cluster=13073090147692852508&hl=en&as_sdt=0,5",6,2022
PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images,5,cvpr,0,0,2023-06-03 15:11:59.248000,https://github.com/LZleejean/FutureHouse,11,Phyir: Physics-based inverse rendering for panoramic indoor images,"https://scholar.google.com/scholar?cluster=9072496348607100712&hl=en&as_sdt=0,10",2,2022
Class-Incremental Learning by Knowledge Distillation With Adaptive Feature Consolidation,42,cvpr,2,2,2023-06-03 15:11:59.444000,https://github.com/kminsoo/afc,40,Class-incremental learning by knowledge distillation with adaptive feature consolidation,"https://scholar.google.com/scholar?cluster=5517490982946328291&hl=en&as_sdt=0,5",1,2022
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis,2,cvpr,0,0,2023-06-03 15:11:59.639000,https://github.com/autoswap/autoswap_cvpr_2022,0,Automatic synthesis of diverse weak supervision sources for behavior analysis,"https://scholar.google.com/scholar?cluster=8850774509311392995&hl=en&as_sdt=0,5",1,2022
Beyond Fixation: Dynamic Window Visual Transformer,10,cvpr,3,2,2023-06-03 15:11:59.833000,https://github.com/pzhren/dw-vit,22,Beyond fixation: Dynamic window visual transformer,"https://scholar.google.com/scholar?cluster=3432971989735915692&hl=en&as_sdt=0,5",1,2022
No-Reference Point Cloud Quality Assessment via Domain Adaptation,13,cvpr,2,0,2023-06-03 15:12:00.027000,https://github.com/qi-yangsjtu/it-pcqa,6,No-reference point cloud quality assessment via domain adaptation,"https://scholar.google.com/scholar?cluster=11704421630157221695&hl=en&as_sdt=0,22",3,2022
DyRep: Bootstrapping Training With Dynamic Re-Parameterization,11,cvpr,4,1,2023-06-03 15:12:00.221000,https://github.com/hunto/dyrep,36,Dyrep: Bootstrapping training with dynamic re-parameterization,"https://scholar.google.com/scholar?cluster=9004725926464672087&hl=en&as_sdt=0,23",2,2022
Faithful Extreme Rescaling via Generative Prior Reciprocated Invertible Representations,4,cvpr,1,5,2023-06-03 15:12:00.415000,https://github.com/cszzx/grain,11,Faithful extreme rescaling via generative prior reciprocated invertible representations,"https://scholar.google.com/scholar?cluster=14399422148181390780&hl=en&as_sdt=0,5",3,2022
Bailando: 3D Dance Generation by Actor-Critic GPT With Choreographic Memory,32,cvpr,51,23,2023-06-03 15:12:00.609000,https://github.com/lisiyao21/bailando,284,Bailando: 3d dance generation by actor-critic gpt with choreographic memory,"https://scholar.google.com/scholar?cluster=6112913323950957761&hl=en&as_sdt=0,31",13,2022
Comprehending and Ordering Semantics for Image Captioning,27,cvpr,118,9,2023-06-03 15:12:00.802000,https://github.com/yehli/xmodaler,961,Comprehending and ordering semantics for image captioning,"https://scholar.google.com/scholar?cluster=18418607301133785994&hl=en&as_sdt=0,44",35,2022
CDGNet: Class Distribution Guided Network for Human Parsing,10,cvpr,1,5,2023-06-03 15:12:00.996000,https://github.com/tjpulkl/cdgnet,22,Cdgnet: Class distribution guided network for human parsing,"https://scholar.google.com/scholar?cluster=1642651212496196669&hl=en&as_sdt=0,5",1,2022
"Proto2Proto: Can You Recognize the Car, the Way I Do?",6,cvpr,1,3,2023-06-03 15:12:01.189000,https://github.com/archmaester/proto2proto,17,"Proto2Proto: Can you recognize the car, the way I do?","https://scholar.google.com/scholar?cluster=14652242116280681807&hl=en&as_sdt=0,5",4,2022
A Large-Scale Comprehensive Dataset and Copy-Overlap Aware Evaluation Protocol for Segment-Level Video Copy Detection,3,cvpr,13,6,2023-06-03 15:12:01.384000,https://github.com/alipay/vcsl,86,A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection,"https://scholar.google.com/scholar?cluster=3798718632373715926&hl=en&as_sdt=0,33",2,2022
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution,3,cvpr,3,2,2023-06-03 15:12:01.577000,https://github.com/lonelyhope/dada,17,Dual adversarial adaptation for cross-device real-world image super-resolution,"https://scholar.google.com/scholar?cluster=12585024585484187646&hl=en&as_sdt=0,48",3,2022
TVConv: Efficient Translation Variant Convolution for Layout-Aware Visual Processing,7,cvpr,5,3,2023-06-03 15:12:01.771000,https://github.com/jierunchen/tvconv,34,Tvconv: Efficient translation variant convolution for layout-aware visual processing,"https://scholar.google.com/scholar?cluster=17158794433146143278&hl=en&as_sdt=0,47",2,2022
GaTector: A Unified Framework for Gaze Object Prediction,8,cvpr,12,5,2023-06-03 15:12:01.965000,https://github.com/CodeMonsterPHD/GaTector-A-Unified-Framework-for-Gaze-Object-Prediction,62,Gatector: A unified framework for gaze object prediction,"https://scholar.google.com/scholar?cluster=14449412475373341664&hl=en&as_sdt=0,33",3,2022
FS6D: Few-Shot 6D Pose Estimation of Novel Objects,20,cvpr,0,8,2023-06-03 15:12:02.159000,https://github.com/ethnhe/FS6D-PyTorch,57,FS6D: Few-shot 6D pose estimation of novel objects,"https://scholar.google.com/scholar?cluster=3399054124824947985&hl=en&as_sdt=0,5",17,2022
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-Based Visual Question Answering,14,cvpr,15,13,2023-06-03 15:12:02.352000,https://github.com/andersonstra/mukea,68,Mukea: Multimodal knowledge extraction and accumulation for knowledge-based visual question answering,"https://scholar.google.com/scholar?cluster=3791605051552354810&hl=en&as_sdt=0,31",5,2022
Reflection and Rotation Symmetry Detection via Equivariant Learning,3,cvpr,2,0,2023-06-03 15:12:02.546000,https://github.com/ahyunSeo/EquiSym,13,Reflection and Rotation Symmetry Detection via Equivariant Learning,"https://scholar.google.com/scholar?cluster=14920322158551539092&hl=en&as_sdt=0,5",2,2022
Simple but Effective: CLIP Embeddings for Embodied AI,72,cvpr,10,0,2023-06-03 15:12:02.740000,https://github.com/allenai/embodied-clip,66,Simple but effective: Clip embeddings for embodied ai,"https://scholar.google.com/scholar?cluster=7221336460417039958&hl=en&as_sdt=0,5",6,2022
HeadNeRF: A Real-Time NeRF-Based Parametric Head Model,74,cvpr,41,13,2023-06-03 15:12:02.934000,https://github.com/crishy1995/headnerf,344,Headnerf: A real-time nerf-based parametric head model,"https://scholar.google.com/scholar?cluster=4600116112972239361&hl=en&as_sdt=0,10",20,2022
CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild,10,cvpr,8,0,2023-06-03 15:12:03.128000,https://github.com/qq456cvb/cppf,38,Cppf: Towards robust category-level 9d pose estimation in the wild,"https://scholar.google.com/scholar?cluster=16756938709076228236&hl=en&as_sdt=0,25",5,2022
NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition,6,cvpr,9,2,2023-06-03 15:12:03.328000,https://github.com/tencentyouturesearch/visualrecognition-nommer,23,Nommer: Nominate synergistic context in vision transformer for visual recognition,"https://scholar.google.com/scholar?cluster=1755354855617861897&hl=en&as_sdt=0,5",4,2022
Interactive Disentanglement: Learning Concepts by Interacting With Their Prototype Representations,9,cvpr,1,1,2023-06-03 15:12:03.522000,https://github.com/ml-research/xiconceptlearning,11,Interactive disentanglement: Learning concepts by interacting with their prototype representations,"https://scholar.google.com/scholar?cluster=10270028716607829754&hl=en&as_sdt=0,5",2,2022
Direct Voxel Grid Optimization: Super-Fast Convergence for Radiance Fields Reconstruction,229,cvpr,98,44,2023-06-03 15:12:03.716000,https://github.com/sunset1995/directvoxgo,858,Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction,"https://scholar.google.com/scholar?cluster=579849009103222086&hl=en&as_sdt=0,5",22,2022
Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information,11,cvpr,11,2,2023-06-03 15:12:03.911000,https://github.com/ylingfeng/dynamicmlp,72,Dynamic mlp for fine-grained image classification by leveraging geographical and temporal information,"https://scholar.google.com/scholar?cluster=16547765185441112176&hl=en&as_sdt=0,5",2,2022
Learning Affinity From Attention: End-to-End Weakly-Supervised Semantic Segmentation With Transformers,46,cvpr,24,12,2023-06-03 15:12:04.105000,https://github.com/rulixiang/afa,165,Learning affinity from attention: end-to-end weakly-supervised semantic segmentation with transformers,"https://scholar.google.com/scholar?cluster=6515573409385377039&hl=en&as_sdt=0,5",7,2022
Continual Test-Time Domain Adaptation,77,cvpr,18,3,2023-06-03 15:12:04.299000,https://github.com/qinenergy/cotta,126,Continual test-time domain adaptation,"https://scholar.google.com/scholar?cluster=4325500781447230872&hl=en&as_sdt=0,47",4,2022
URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement,55,cvpr,8,11,2023-06-03 15:12:04.494000,https://github.com/andersonyong/uretinex-net,92,Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement,"https://scholar.google.com/scholar?cluster=17361373328251331183&hl=en&as_sdt=0,5",1,2022
Omnivore: A Single Model for Many Visual Modalities,70,cvpr,34,4,2023-06-03 15:12:04.688000,https://github.com/facebookresearch/omnivore,493,Omnivore: A single model for many visual modalities,"https://scholar.google.com/scholar?cluster=7775892631592466813&hl=en&as_sdt=0,39",19,2022
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection,52,cvpr,46,16,2023-06-03 15:12:04.888000,https://github.com/air-thu/dair-v2x,249,Dair-v2x: A large-scale dataset for vehicle-infrastructure cooperative 3d object detection,"https://scholar.google.com/scholar?cluster=8270292026134965075&hl=en&as_sdt=0,22",16,2022
FvOR: Robust Joint Shape and Pose Optimization for Few-View Object Reconstruction,4,cvpr,0,6,2023-06-03 15:12:05.082000,https://github.com/zhenpeiyang/fvor,47,Fvor: Robust joint shape and pose optimization for few-view object reconstruction,"https://scholar.google.com/scholar?cluster=7729610362589957780&hl=en&as_sdt=0,5",5,2022
Fair Contrastive Learning for Facial Attribute Classification,15,cvpr,1,4,2023-06-03 15:12:05.276000,https://github.com/sungho-coolg/fscl,29,Fair contrastive learning for facial attribute classification,"https://scholar.google.com/scholar?cluster=14869759203355701449&hl=en&as_sdt=0,43",3,2022
Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer,5,cvpr,6,4,2023-06-03 15:12:05.473000,https://github.com/megvii-research/portraits_correction,24,Semi-supervised wide-angle portraits correction by multi-scale transformer,"https://scholar.google.com/scholar?cluster=9231394585762666093&hl=en&as_sdt=0,5",9,2022
IDR: Self-Supervised Image Denoising via Iterative Data Refinement,20,cvpr,9,4,2023-06-03 15:12:05.666000,https://github.com/zhangyi-3/idr,92,Idr: Self-supervised image denoising via iterative data refinement,"https://scholar.google.com/scholar?cluster=12367270937320830051&hl=en&as_sdt=0,33",2,2022
Is Mapping Necessary for Realistic PointGoal Navigation?,12,cvpr,1,1,2023-06-03 15:12:05.864000,https://github.com/rpartsey/pointgoal-navigation,26,Is Mapping Necessary for Realistic PointGoal Navigation?,"https://scholar.google.com/scholar?cluster=10798037831721761839&hl=en&as_sdt=0,18",5,2022
CSWin Transformer: A General Vision Transformer Backbone With Cross-Shaped Windows,370,cvpr,70,29,2023-06-03 15:12:06.058000,https://github.com/microsoft/CSWin-Transformer,444,Cswin transformer: A general vision transformer backbone with cross-shaped windows,"https://scholar.google.com/scholar?cluster=4431453089685809340&hl=en&as_sdt=0,31",14,2022
Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification,15,cvpr,4,3,2023-06-03 15:12:06.252000,https://github.com/yohnguan/nagcn,13,Node-aligned graph convolutional network for whole-slide image representation and classification,"https://scholar.google.com/scholar?cluster=1360362531401615377&hl=en&as_sdt=0,44",2,2022
Generating Representative Samples for Few-Shot Classification,12,cvpr,4,4,2023-06-03 15:12:06.447000,https://github.com/cvlab-stonybrook/fsl-rsvae,22,Generating representative samples for few-shot classification,"https://scholar.google.com/scholar?cluster=7637142350361596542&hl=en&as_sdt=0,23",5,2022
Reduce Information Loss in Transformers for Pluralistic Image Inpainting,18,cvpr,10,12,2023-06-03 15:12:06.641000,https://github.com/liuqk3/put,121,Reduce information loss in transformers for pluralistic image inpainting,"https://scholar.google.com/scholar?cluster=11378273171717273533&hl=en&as_sdt=0,33",6,2022
FaceVerse: A Fine-Grained and Detail-Controllable 3D Face Morphable Model From a Hybrid Dataset,21,cvpr,34,19,2023-06-03 15:12:06.835000,https://github.com/lizhenwangt/faceverse,282,Faceverse: a fine-grained and detail-controllable 3d face morphable model from a hybrid dataset,"https://scholar.google.com/scholar?cluster=5396647356780098251&hl=en&as_sdt=0,33",20,2022
Learning To Detect Mobile Objects From LiDAR Scans Without Labels,6,cvpr,11,0,2023-06-03 15:12:07.030000,https://github.com/yurongyou/modest,67,Learning to detect mobile objects from LiDAR scans without labels,"https://scholar.google.com/scholar?cluster=16603727928145793930&hl=en&as_sdt=0,5",4,2022
Occlusion-Robust Face Alignment Using a Viewpoint-Invariant Hierarchical Network Architecture,9,cvpr,0,0,2023-06-03 15:12:07.232000,https://github.com/zhuccly/glomface-face-alignment,4,Occlusion-robust face alignment using a viewpoint-invariant hierarchical network architecture,"https://scholar.google.com/scholar?cluster=4101505147024090904&hl=en&as_sdt=0,33",1,2022
Look Back and Forth: Video Super-Resolution With Explicit Temporal Difference Modeling,13,cvpr,2,5,2023-06-03 15:12:07.426000,https://github.com/junpan19/ETDM,21,Look back and forth: video super-resolution with explicit temporal difference modeling,"https://scholar.google.com/scholar?cluster=5067571171321230160&hl=en&as_sdt=0,22",1,2022
BasicVSR++: Improving Video Super-Resolution With Enhanced Propagation and Alignment,157,cvpr,831,47,2023-06-03 15:12:07.620000,https://github.com/open-mmlab/mmediting,4831,BasicVSR++: Improving video super-resolution with enhanced propagation and alignment,"https://scholar.google.com/scholar?cluster=5012395728633702594&hl=en&as_sdt=0,5",93,2022
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration,13,cvpr,5,0,2023-06-03 15:12:07.814000,https://github.com/mdca-loss/mdca-calibration,23,A stitch in time saves nine: A train-time regularizing loss for improved neural network calibration,"https://scholar.google.com/scholar?cluster=7650029386959535850&hl=en&as_sdt=0,43",1,2022
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection,17,cvpr,4,4,2023-06-03 15:12:08.009000,https://github.com/lianqing11/monojsg,26,Monojsg: Joint semantic and geometric cost volume for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=883360811947562124&hl=en&as_sdt=0,36",8,2022
Efficient Classification of Very Large Images With Tiny Objects,13,cvpr,8,2,2023-06-03 15:12:08.202000,https://github.com/timqqt/pytorch-zoom-in-network,14,Efficient classification of very large images with tiny objects,"https://scholar.google.com/scholar?cluster=3901059019264442877&hl=en&as_sdt=0,38",2,2022
EnvEdit: Environment Editing for Vision-and-Language Navigation,23,cvpr,1,3,2023-06-03 15:12:08.396000,https://github.com/jialuli-luka/envedit,25,Envedit: Environment editing for vision-and-language navigation,"https://scholar.google.com/scholar?cluster=18427906199760934116&hl=en&as_sdt=0,33",1,2022
SWEM: Towards Real-Time Video Object Segmentation With Sequential Weighted Expectation-Maximization,13,cvpr,5,1,2023-06-03 15:12:08.591000,https://github.com/lmm077/SWEM,22,SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization,"https://scholar.google.com/scholar?cluster=2748681599667255123&hl=en&as_sdt=0,5",1,2022
CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation,18,cvpr,16,0,2023-06-03 15:12:08.785000,https://github.com/mcg-nju/camliflow,169,Camliflow: bidirectional camera-lidar fusion for joint optical flow and scene flow estimation,"https://scholar.google.com/scholar?cluster=5956757280702342234&hl=en&as_sdt=0,29",6,2022
Generating Diverse 3D Reconstructions From a Single Occluded Face Image,1,cvpr,1,1,2023-06-03 15:12:08.979000,https://github.com/human-analysis/diverse3dface,13,Generating Diverse 3D Reconstructions from a Single Occluded Face Image,"https://scholar.google.com/scholar?cluster=9546018249568825792&hl=en&as_sdt=0,5",5,2022
BNV-Fusion: Dense 3D Reconstruction Using Bi-Level Neural Volume Fusion,7,cvpr,16,2,2023-06-03 15:12:09.173000,https://github.com/likojack/bnv_fusion,119,Bnv-fusion: dense 3D reconstruction using bi-level neural volume fusion,"https://scholar.google.com/scholar?cluster=14606313558077227641&hl=en&as_sdt=0,44",6,2022
RBGNet: Ray-Based Grouping for 3D Object Detection,24,cvpr,2,0,2023-06-03 15:12:09.367000,https://github.com/haiyang-w/rbgnet,30,Rbgnet: Ray-based grouping for 3d object detection,"https://scholar.google.com/scholar?cluster=8309048489827668417&hl=en&as_sdt=0,5",2,2022
Context-Aware Video Reconstruction for Rolling Shutter Cameras,5,cvpr,4,1,2023-06-03 15:12:09.561000,https://github.com/gitcvfb/cvr,28,Context-Aware Video Reconstruction for Rolling Shutter Cameras,"https://scholar.google.com/scholar?cluster=8427580102771448979&hl=en&as_sdt=0,14",1,2022
Stand-Alone Inter-Frame Attention in Video Models,22,cvpr,0,6,2023-06-03 15:12:09.755000,https://github.com/fuchenustc/sifa,26,Stand-alone inter-frame attention in video models,"https://scholar.google.com/scholar?cluster=18361346800836500084&hl=en&as_sdt=0,31",2,2022
WildNet: Learning Domain Generalized Semantic Segmentation From the Wild,21,cvpr,5,2,2023-06-03 15:12:09.949000,https://github.com/suhyeonlee/wildnet,43,WildNet: Learning domain generalized semantic segmentation from the wild,"https://scholar.google.com/scholar?cluster=6566806038357537625&hl=en&as_sdt=0,39",5,2022
Masked Feature Prediction for Self-Supervised Visual Pre-Training,261,cvpr,1143,348,2023-06-03 15:12:10.144000,https://github.com/facebookresearch/SlowFast,5685,Masked feature prediction for self-supervised visual pre-training,"https://scholar.google.com/scholar?cluster=12617218192144474322&hl=en&as_sdt=0,5",97,2022
Robust Contrastive Learning Against Noisy Views,29,cvpr,7,3,2023-06-03 15:12:10.337000,https://github.com/chingyaoc/rince,60,Robust contrastive learning against noisy views,"https://scholar.google.com/scholar?cluster=10458213393879693296&hl=en&as_sdt=0,5",3,2022
Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening,11,cvpr,1,0,2023-06-03 15:12:10.531000,https://github.com/yggame/mdcun,7,Memory-augmented deep conditional unfolding network for pan-sharpening,"https://scholar.google.com/scholar?cluster=797504698814434691&hl=en&as_sdt=0,5",1,2022
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage,19,cvpr,10,0,2023-06-03 15:12:10.725000,https://github.com/zhuohangli/ggl,33,Auditing privacy defenses in federated learning via generative gradient leakage,"https://scholar.google.com/scholar?cluster=8227785622283493262&hl=en&as_sdt=0,10",4,2022
Certified Patch Robustness via Smoothed Vision Transformers,31,cvpr,4,0,2023-06-03 15:12:10.920000,https://github.com/madrylab/smoothed-vit,37,Certified patch robustness via smoothed vision transformers,"https://scholar.google.com/scholar?cluster=8940759754720060702&hl=en&as_sdt=0,35",8,2022
RSTT: Real-Time Spatial Temporal Transformer for Space-Time Video Super-Resolution,21,cvpr,17,6,2023-06-03 15:12:11.114000,https://github.com/llmpass/RSTT,98,Rstt: Real-time spatial temporal transformer for space-time video super-resolution,"https://scholar.google.com/scholar?cluster=832312865512185750&hl=en&as_sdt=0,5",3,2022
Large-Scale Pre-Training for Person Re-Identification With Noisy Labels,9,cvpr,6,4,2023-06-03 15:12:11.308000,https://github.com/dengpanfu/luperson-nl,51,Large-scale pre-training for person re-identification with noisy labels,"https://scholar.google.com/scholar?cluster=16484480159530632998&hl=en&as_sdt=0,5",4,2022
Task Discrepancy Maximization for Fine-Grained Few-Shot Classification,6,cvpr,5,0,2023-06-03 15:12:11.502000,https://github.com/leesb7426/cvpr2022-task-discrepancy-maximization-for-fine-grained-few-shot-classification,22,Task discrepancy maximization for fine-grained few-shot classification,"https://scholar.google.com/scholar?cluster=17653340683364988907&hl=en&as_sdt=0,33",2,2022
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy,13,cvpr,2,2,2023-06-03 15:12:11.697000,https://github.com/vita-group/diverse-vit,18,The principle of diversity: Training stronger vision transformers calls for reducing all levels of redundancy,"https://scholar.google.com/scholar?cluster=3780615921970982956&hl=en&as_sdt=0,14",7,2022
Feature Erasing and Diffusion Network for Occluded Person Re-Identification,25,cvpr,5,3,2023-06-03 15:12:11.891000,https://github.com/ZacharyWang-007/FED-Occluded-ReID,28,Feature erasing and diffusion network for occluded person re-identification,"https://scholar.google.com/scholar?cluster=2126509024889842797&hl=en&as_sdt=0,5",2,2022
FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction,37,cvpr,12,3,2023-06-03 15:12:12.096000,https://github.com/gaoliang13/feddc,55,Feddc: Federated learning with non-iid data via local drift decoupling and correction,"https://scholar.google.com/scholar?cluster=7952817299548197443&hl=en&as_sdt=0,33",2,2022
Active Learning by Feature Mixing,17,cvpr,10,5,2023-06-03 15:12:12.290000,https://github.com/aminparvaneh/alpha_mix_active_learning,65,Active learning by feature mixing,"https://scholar.google.com/scholar?cluster=6445697373568669972&hl=en&as_sdt=0,11",1,2022
Partial Class Activation Attention for Semantic Segmentation,10,cvpr,2,3,2023-06-03 15:12:12.485000,https://github.com/lsa1997/pcaa,29,Partial Class Activation Attention for Semantic Segmentation,"https://scholar.google.com/scholar?cluster=13925216483993369069&hl=en&as_sdt=0,33",1,2022
Semantic Segmentation by Early Region Proxy,9,cvpr,3,6,2023-06-03 15:12:12.680000,https://github.com/yif-zhang/regionproxy,60,Semantic segmentation by early region proxy,"https://scholar.google.com/scholar?cluster=8801540507147368813&hl=en&as_sdt=0,47",1,2022
Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation,33,cvpr,103,18,2023-06-03 15:12:12.875000,https://github.com/cardwing/codes-for-pvkd,424,Point-to-voxel knowledge distillation for lidar semantic segmentation,"https://scholar.google.com/scholar?cluster=16004793191083746672&hl=en&as_sdt=0,5",39,2022
Class-Aware Contrastive Semi-Supervised Learning,32,cvpr,12,4,2023-06-03 15:12:13.069000,https://github.com/tencentyouturesearch/classification-semicls,72,Class-aware contrastive semi-supervised learning,"https://scholar.google.com/scholar?cluster=4852221294569160326&hl=en&as_sdt=0,33",4,2022
Debiased Learning From Naturally Imbalanced Pseudo-Labels,19,cvpr,4,1,2023-06-03 15:12:13.271000,https://github.com/frank-xwang/debiased-pseudo-labeling,75,Debiased learning from naturally imbalanced pseudo-labels,"https://scholar.google.com/scholar?cluster=14652156265549585566&hl=en&as_sdt=0,33",3,2022
RNNPose: Recurrent 6-DoF Object Pose Refinement With Robust Correspondence Field Estimation and Pose Optimization,23,cvpr,12,4,2023-06-03 15:12:13.490000,https://github.com/decayale/rnnpose,93,RNNPose: Recurrent 6-DoF object pose refinement with robust correspondence field estimation and pose optimization,"https://scholar.google.com/scholar?cluster=10937328809991428101&hl=en&as_sdt=0,10",7,2022
Conditional Prompt Learning for Vision-Language Models,219,cvpr,109,39,2023-06-03 15:12:13.683000,https://github.com/kaiyangzhou/coop,943,Conditional prompt learning for vision-language models,"https://scholar.google.com/scholar?cluster=737352693355482724&hl=en&as_sdt=0,14",16,2022
Towards Efficient Data Free Black-Box Adversarial Attack,17,cvpr,2,4,2023-06-03 15:12:13.877000,https://github.com/jhjiezhang/Data-Free-Transfer-Attack,10,Towards efficient data free black-box adversarial attack,"https://scholar.google.com/scholar?cluster=15067901193060387901&hl=en&as_sdt=0,5",1,2022
Affine Medical Image Registration With Coarse-To-Fine Vision Transformer,13,cvpr,3,0,2023-06-03 15:12:14.071000,https://github.com/cwmok/C2FViT,86,Affine medical image registration with coarse-to-fine vision transformer,"https://scholar.google.com/scholar?cluster=10120158526622203013&hl=en&as_sdt=0,33",2,2022
LD-ConGR: A Large RGB-D Video Dataset for Long-Distance Continuous Gesture Recognition,2,cvpr,0,0,2023-06-03 15:12:14.277000,https://github.com/diananini/ld-congr-cvpr2022,13,LD-ConGR: A large RGB-D video dataset for long-distance continuous gesture recognition,"https://scholar.google.com/scholar?cluster=4555687839329941537&hl=en&as_sdt=0,44",2,2022
Depth-Supervised NeRF: Fewer Views and Faster Training for Free,234,cvpr,107,58,2023-06-03 15:12:14.476000,https://github.com/dunbar12138/DSNeRF,608,Depth-supervised nerf: Fewer views and faster training for free,"https://scholar.google.com/scholar?cluster=13165818652354577840&hl=en&as_sdt=0,5",17,2022
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning,23,cvpr,1,0,2023-06-03 15:12:14.670000,https://github.com/allenhaozhu/ease,6,EASE: Unsupervised discriminant subspace learning for transductive few-shot learning,"https://scholar.google.com/scholar?cluster=10528704964344248072&hl=en&as_sdt=0,33",2,2022
A Differentiable Two-Stage Alignment Scheme for Burst Image Reconstruction With Large Shift,2,cvpr,2,0,2023-06-03 15:12:14.864000,https://github.com/guoshi28/2stagealign,32,A differentiable two-stage alignment scheme for burst image reconstruction with large shift,"https://scholar.google.com/scholar?cluster=9965757813907820626&hl=en&as_sdt=0,41",5,2022
Thin-Plate Spline Motion Model for Image Animation,22,cvpr,398,54,2023-06-03 15:12:15.058000,https://github.com/yoyo-nb/thin-plate-spline-motion-model,2347,Thin-plate spline motion model for image animation,"https://scholar.google.com/scholar?cluster=17087395403195931350&hl=en&as_sdt=0,10",59,2022
UCC: Uncertainty Guided Cross-Head Co-Training for Semi-Supervised Semantic Segmentation,10,cvpr,15,6,2023-06-03 15:12:15.252000,https://github.com/voldemortX/DST-CBC,125,Ucc: Uncertainty guided cross-head co-training for semi-supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=8607789258562041257&hl=en&as_sdt=0,5",5,2022
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping,25,cvpr,5,7,2023-06-03 15:12:15.447000,https://github.com/otaheri/GOAL,81,Goal: Generating 4d whole-body motion for hand-object grasping,"https://scholar.google.com/scholar?cluster=8144375827903585464&hl=en&as_sdt=0,10",12,2022
On the Road to Online Adaptation for Semantic Image Segmentation,8,cvpr,3,0,2023-06-03 15:12:15.640000,https://github.com/naver/oasis,18,On the road to online adaptation for semantic image segmentation,"https://scholar.google.com/scholar?cluster=9925554197632369771&hl=en&as_sdt=0,5",5,2022
Adversarial Texture for Fooling Person Detectors in the Physical World,26,cvpr,2,5,2023-06-03 15:12:15.834000,https://github.com/WhoTHU/Adversarial_Texture,35,Adversarial texture for fooling person detectors in the physical world,"https://scholar.google.com/scholar?cluster=17634783504972477237&hl=en&as_sdt=0,33",1,2022
Masked Autoencoders Are Scalable Vision Learners,2008,cvpr,925,95,2023-06-03 15:12:16.028000,https://github.com/facebookresearch/mae,5516,Masked autoencoders are scalable vision learners,"https://scholar.google.com/scholar?cluster=16837829726140559426&hl=en&as_sdt=0,5",58,2022
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training,3,cvpr,1,1,2023-06-03 15:12:16.222000,https://github.com/theot1/fw-at-adapt,5,Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training,"https://scholar.google.com/scholar?cluster=10641470324585550829&hl=en&as_sdt=0,33",1,2022
TO-FLOW: Efficient Continuous Normalizing Flows With Temporal Optimization Adjoint With Moving Speed,1,cvpr,1,0,2023-06-03 15:12:16.417000,https://github.com/studying910/TO-FLOW,2,TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed,"https://scholar.google.com/scholar?cluster=12750283594068318928&hl=en&as_sdt=0,5",1,2022
Point-BERT: Pre-Training 3D Point Cloud Transformers With Masked Point Modeling,160,cvpr,43,17,2023-06-03 15:12:16.612000,https://github.com/lulutang0608/Point-BERT,375,Point-bert: Pre-training 3d point cloud transformers with masked point modeling,"https://scholar.google.com/scholar?cluster=17327663970405370182&hl=en&as_sdt=0,39",8,2022
RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising,2,cvpr,0,2,2023-06-03 15:12:16.806000,https://github.com/schellmi42/RADU,3,RADU: Ray-aligned depth update convolutions for ToF data denoising,"https://scholar.google.com/scholar?cluster=174828791614676296&hl=en&as_sdt=0,10",1,2022
Arbitrary-Scale Image Synthesis,8,cvpr,1,3,2023-06-03 15:12:17,https://github.com/vglsd/scaleparty,39,Arbitrary-scale image synthesis,"https://scholar.google.com/scholar?cluster=11591722873956369365&hl=en&as_sdt=0,47",10,2022
Crowd Counting in the Frequency Domain,16,cvpr,1,1,2023-06-03 15:12:17.195000,https://github.com/wbshu/Crowd_Counting_in_the_Frequency_Domain,10,Crowd counting in the frequency domain,"https://scholar.google.com/scholar?cluster=9496116420869260350&hl=en&as_sdt=0,47",1,2022
Rethinking Visual Geo-Localization for Large-Scale Applications,21,cvpr,46,0,2023-06-03 15:12:17.388000,https://github.com/gmberton/cosplace,158,Rethinking visual geo-localization for large-scale applications,"https://scholar.google.com/scholar?cluster=13291227342716907771&hl=en&as_sdt=0,47",6,2022
Retrieval-Based Spatially Adaptive Normalization for Semantic Image Synthesis,4,cvpr,1,2,2023-06-03 15:12:17.582000,https://github.com/shi-yupeng/resail-for-sis,18,Retrieval-based Spatially Adaptive Normalization for Semantic Image Synthesis,"https://scholar.google.com/scholar?cluster=18237388723556776672&hl=en&as_sdt=0,5",3,2022
ViM: Out-of-Distribution With Virtual-Logit Matching,46,cvpr,9,1,2023-06-03 15:12:17.776000,https://github.com/haoqiwang/vim,54,Vim: Out-of-distribution with virtual-logit matching,"https://scholar.google.com/scholar?cluster=14640587043811012846&hl=en&as_sdt=0,32",4,2022
Learning sRGB-to-Raw-RGB De-Rendering With Content-Aware Metadata,6,cvpr,0,3,2023-06-03 15:12:17.970000,https://github.com/samsunglabs/content-aware-metadata,15,Learning sRGB-to-Raw-RGB De-rendering with Content-Aware Metadata,"https://scholar.google.com/scholar?cluster=6215997292002228315&hl=en&as_sdt=0,32",4,2022
NPBG++: Accelerating Neural Point-Based Graphics,19,cvpr,3,0,2023-06-03 15:12:18.164000,https://github.com/rakhimovv/npbgpp,72,NPBG++: Accelerating neural point-based graphics,"https://scholar.google.com/scholar?cluster=4431996804075666654&hl=en&as_sdt=0,10",7,2022
Towards an End-to-End Framework for Flow-Guided Video Inpainting,28,cvpr,74,36,2023-06-03 15:12:18.358000,https://github.com/MCG-NKU/E2FGVI,733,Towards an end-to-end framework for flow-guided video inpainting,"https://scholar.google.com/scholar?cluster=6491078858607146383&hl=en&as_sdt=0,5",14,2022
A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres,4,cvpr,4,2,2023-06-03 15:12:18.552000,https://github.com/jw09191/MNET,31,A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres,"https://scholar.google.com/scholar?cluster=4033819530386431935&hl=en&as_sdt=0,5",3,2022
Dual-Key Multimodal Backdoors for Visual Question Answering,6,cvpr,2,1,2023-06-03 15:12:18.746000,https://github.com/SRI-CSL/TrinityMultimodalTrojAI,21,Dual-key multimodal backdoors for visual question answering,"https://scholar.google.com/scholar?cluster=2510015101914994133&hl=en&as_sdt=0,5",19,2022
On Learning Contrastive Representations for Learning With Noisy Labels,13,cvpr,1,4,2023-06-03 15:12:18.941000,https://github.com/liyi01827/noisy-contrastive,20,On learning contrastive representations for learning with noisy labels,"https://scholar.google.com/scholar?cluster=17517537665735016772&hl=en&as_sdt=0,5",1,2022
Adaptive Early-Learning Correction for Segmentation From Noisy Annotations,29,cvpr,8,4,2023-06-03 15:12:19.136000,https://github.com/kangningthu/adele,62,Adaptive early-learning correction for segmentation from noisy annotations,"https://scholar.google.com/scholar?cluster=530625488460245511&hl=en&as_sdt=0,20",3,2022
Unsupervised Domain Generalization by Learning a Bridge Across Domains,8,cvpr,6,2,2023-06-03 15:12:19.330000,https://github.com/leokarlin/brad,16,Unsupervised domain generalization by learning a bridge across domains,"https://scholar.google.com/scholar?cluster=3954970380405955444&hl=en&as_sdt=0,5",4,2022
Multi-Scale Memory-Based Video Deblurring,1,cvpr,1,6,2023-06-03 15:12:19.524000,https://github.com/jibo27/memdeblur,25,Multi-Scale Memory-Based Video Deblurring,"https://scholar.google.com/scholar?cluster=10514050726384410318&hl=en&as_sdt=0,33",4,2022
Learning Trajectory-Aware Transformer for Video Super-Resolution,25,cvpr,11,6,2023-06-03 15:12:19.718000,https://github.com/researchmm/TTVSR,155,Learning trajectory-aware transformer for video super-resolution,"https://scholar.google.com/scholar?cluster=14254697616696818680&hl=en&as_sdt=0,36",6,2022
BACON: Band-Limited Coordinate Networks for Multiscale Scene Representation,49,cvpr,13,1,2023-06-03 15:12:19.912000,https://github.com/computational-imaging/bacon,150,Bacon: Band-limited coordinate networks for multiscale scene representation,"https://scholar.google.com/scholar?cluster=9022690859414463287&hl=en&as_sdt=0,33",9,2022
A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching,3,cvpr,0,0,2023-06-03 15:12:20.106000,https://github.com/paul0noah/sm-comb,7,A scalable combinatorial solver for elastic geometrically consistent 3d shape matching,"https://scholar.google.com/scholar?cluster=458979282895060638&hl=en&as_sdt=0,38",1,2022
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification,20,cvpr,31,0,2023-06-03 15:12:20.300000,https://github.com/shengcailiao/QAConv,180,Graph sampling based deep metric learning for generalizable person re-identification,"https://scholar.google.com/scholar?cluster=14531702412735248957&hl=en&as_sdt=0,11",5,2022
Large Loss Matters in Weakly Supervised Multi-Label Classification,15,cvpr,3,3,2023-06-03 15:12:20.494000,https://github.com/snucml/largelossmatters,30,Large loss matters in weakly supervised multi-label classification,"https://scholar.google.com/scholar?cluster=2355235211209439942&hl=en&as_sdt=0,11",4,2022
Geometric Structure Preserving Warp for Natural Image Stitching,1,cvpr,9,4,2023-06-03 15:12:20.688000,https://github.com/flowerduo/ges-gsp-stitching,34,Geometric Structure Preserving Warp for Natural Image Stitching,"https://scholar.google.com/scholar?cluster=17391311720268949298&hl=en&as_sdt=0,5",0,2022
Undoing the Damage of Label Shift for Cross-Domain Semantic Segmentation,13,cvpr,4,0,2023-06-03 15:12:20.882000,https://github.com/manmanjun/undoing_uda,16,Undoing the damage of label shift for cross-domain semantic segmentation,"https://scholar.google.com/scholar?cluster=18242190270392725268&hl=en&as_sdt=0,5",1,2022
Attention Concatenation Volume for Accurate and Efficient Stereo Matching,39,cvpr,32,15,2023-06-03 15:12:21.076000,https://github.com/gangweix/acvnet,273,Attention concatenation volume for accurate and efficient stereo matching,"https://scholar.google.com/scholar?cluster=9546790128088034646&hl=en&as_sdt=0,32",10,2022
Reference-Based Video Super-Resolution Using Multi-Camera Video Triplets,11,cvpr,19,2,2023-06-03 15:12:21.270000,https://github.com/codeslake/RefVSR,170,Reference-based video super-resolution using multi-camera video triplets,"https://scholar.google.com/scholar?cluster=8492238296973860495&hl=en&as_sdt=0,5",4,2022
Focal Length and Object Pose Estimation via Render and Compare,5,cvpr,9,0,2023-06-03 15:12:21.475000,https://github.com/ponimatkin/focalpose,61,Focal length and object pose estimation via render and compare,"https://scholar.google.com/scholar?cluster=4375857119956184026&hl=en&as_sdt=0,33",6,2022
GPV-Pose: Category-Level Object Pose Estimation via Geometry-Guided Point-Wise Voting,19,cvpr,12,8,2023-06-03 15:12:21.670000,https://github.com/lolrudy/gpv_pose,50,Gpv-pose: Category-level object pose estimation via geometry-guided point-wise voting,"https://scholar.google.com/scholar?cluster=15383980007181086969&hl=en&as_sdt=0,11",4,2022
Dynamic 3D Gaze From Afar: Deep Gaze Estimation From Temporal Eye-Head-Body Coordination,5,cvpr,5,3,2023-06-03 15:12:21.864000,https://github.com/kyotovision-public/dynamic-3d-gaze-from-afar,36,Dynamic 3d gaze from afar: Deep gaze estimation from temporal eye-head-body coordination,"https://scholar.google.com/scholar?cluster=14505854089634926626&hl=en&as_sdt=0,22",3,2022
Trustworthy Long-Tailed Classification,24,cvpr,10,5,2023-06-03 15:12:22.058000,https://github.com/lblaoke/tlc,41,Trustworthy long-tailed classification,"https://scholar.google.com/scholar?cluster=13156619284166385316&hl=en&as_sdt=0,51",1,2022
GraftNet: Towards Domain Generalized Stereo Matching With a Broad-Spectrum and Task-Oriented Feature,13,cvpr,5,7,2023-06-03 15:12:22.252000,https://github.com/spadeliu/graft-psmnet,22,Graftnet: Towards domain generalized stereo matching with a broad-spectrum and task-oriented feature,"https://scholar.google.com/scholar?cluster=18400465047273061750&hl=en&as_sdt=0,33",2,2022
GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection,24,cvpr,10,1,2023-06-03 15:12:22.446000,https://github.com/yueliao/gen-vlkt,59,Gen-vlkt: Simplify association and enhance interaction understanding for hoi detection,"https://scholar.google.com/scholar?cluster=12388064586661056861&hl=en&as_sdt=0,5",5,2022
Towards Total Recall in Industrial Anomaly Detection,165,cvpr,79,43,2023-06-03 15:12:22.641000,https://github.com/amazon-research/patchcore-inspection,397,Towards total recall in industrial anomaly detection,"https://scholar.google.com/scholar?cluster=5206095115312316594&hl=en&as_sdt=0,5",13,2022
Global Matching With Overlapping Attention for Optical Flow Estimation,21,cvpr,7,4,2023-06-03 15:12:22.840000,https://github.com/xiaofeng94/gmflownet,73,Global matching with overlapping attention for optical flow estimation,"https://scholar.google.com/scholar?cluster=17198361630852312350&hl=en&as_sdt=0,39",5,2022
HairMapper: Removing Hair From Portraits Using GANs,1,cvpr,29,0,2023-06-03 15:12:23.034000,https://github.com/oneThousand1000/HairMapper,199,HairMapper: Removing Hair from Portraits Using GANs,"https://scholar.google.com/scholar?cluster=15262097110531580905&hl=en&as_sdt=0,5",9,2022
Rethinking Efficient Lane Detection via Curve Modeling,27,cvpr,121,27,2023-06-03 15:12:23.228000,https://github.com/voldemortX/pytorch-auto-drive,665,Rethinking efficient lane detection via curve modeling,"https://scholar.google.com/scholar?cluster=949946091219062353&hl=en&as_sdt=0,5",10,2022
Image Dehazing Transformer With Transmission-Aware 3D Position Embedding,41,cvpr,9,13,2023-06-03 15:12:23.421000,https://github.com/Li-Chongyi/Dehamer,39,Image dehazing transformer with transmission-aware 3D position embedding,"https://scholar.google.com/scholar?cluster=3873993523856645959&hl=en&as_sdt=0,5",2,2022
Active Teacher for Semi-Supervised Object Detection,11,cvpr,6,0,2023-06-03 15:12:23.615000,https://github.com/hunterj-lin/activeteacher,45,Active teacher for semi-supervised object detection,"https://scholar.google.com/scholar?cluster=16254437278639517990&hl=en&as_sdt=0,5",2,2022
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes,26,cvpr,5,1,2023-06-03 15:12:23.809000,https://github.com/jdonnelly36/Deformable-ProtoPNet,26,Deformable protopnet: An interpretable image classifier using deformable prototypes,"https://scholar.google.com/scholar?cluster=10120980699796367660&hl=en&as_sdt=0,14",2,2022
Audio-Adaptive Activity Recognition Across Video Domains,13,cvpr,0,0,2023-06-03 15:12:24.003000,https://github.com/xiaobai1217/DomainAdaptation,15,Audio-adaptive activity recognition across video domains,"https://scholar.google.com/scholar?cluster=4675606367456358670&hl=en&as_sdt=0,33",3,2022
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification,51,cvpr,12,10,2023-06-03 15:12:24.197000,https://github.com/hrzhang1123/dtfd-mil,67,Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification,"https://scholar.google.com/scholar?cluster=14285175044896723615&hl=en&as_sdt=0,1",1,2022
Perturbed and Strict Mean Teachers for Semi-Supervised Semantic Segmentation,47,cvpr,14,0,2023-06-03 15:12:24.391000,https://github.com/yyliu01/ps-mt,149,Perturbed and strict mean teachers for semi-supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=8198064819499128394&hl=en&as_sdt=0,44",4,2022
Deep Generalized Unfolding Networks for Image Restoration,29,cvpr,18,4,2023-06-03 15:12:24.585000,https://github.com/mc-e/deep-generalized-unfolding-networks-for-image-restoration,86,Deep generalized unfolding networks for image restoration,"https://scholar.google.com/scholar?cluster=7494856167361955847&hl=en&as_sdt=0,5",6,2022
IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation,35,cvpr,14,23,2023-06-03 15:12:24.779000,https://github.com/ltkong218/ifrnet,177,Ifrnet: Intermediate feature refine network for efficient frame interpolation,"https://scholar.google.com/scholar?cluster=17285578741262758001&hl=en&as_sdt=0,33",9,2022
H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection,13,cvpr,4,0,2023-06-03 15:12:24.979000,https://github.com/xuyunqiu/h2fa_r-cnn,21,H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection,"https://scholar.google.com/scholar?cluster=14474288568007525729&hl=en&as_sdt=0,44",2,2022
Toward Practical Monocular Indoor Depth Estimation,7,cvpr,12,12,2023-06-03 15:12:25.173000,https://github.com/facebookresearch/DistDepth,153,Toward practical monocular indoor depth estimation,"https://scholar.google.com/scholar?cluster=14631084137186211122&hl=en&as_sdt=0,1",8,2022
Focal Sparse Convolutional Networks for 3D Object Detection,63,cvpr,30,2,2023-06-03 15:12:25.366000,https://github.com/dvlab-research/focalsconv,317,Focal sparse convolutional networks for 3d object detection,"https://scholar.google.com/scholar?cluster=17217899982806844888&hl=en&as_sdt=0,5",3,2022
A ConvNet for the 2020s,1373,cvpr,626,55,2023-06-03 15:12:25.560000,https://github.com/facebookresearch/ConvNeXt,4980,A convnet for the 2020s,"https://scholar.google.com/scholar?cluster=14443907969977981621&hl=en&as_sdt=0,5",31,2022
Nested Collaborative Learning for Long-Tailed Visual Recognition,22,cvpr,24,0,2023-06-03 15:12:25.754000,https://github.com/bazinga699/ncl,69,Nested collaborative learning for long-tailed visual recognition,"https://scholar.google.com/scholar?cluster=17817271762400185230&hl=en&as_sdt=0,10",1,2022
Restormer: Efficient Transformer for High-Resolution Image Restoration,430,cvpr,172,23,2023-06-03 15:12:25.948000,https://github.com/swz30/restormer,1093,Restormer: Efficient transformer for high-resolution image restoration,"https://scholar.google.com/scholar?cluster=16431204865977056518&hl=en&as_sdt=0,33",15,2022
Proactive Image Manipulation Detection,13,cvpr,5,1,2023-06-03 15:12:26.142000,https://github.com/vishal3477/proactive_imd,19,Proactive image manipulation detection,"https://scholar.google.com/scholar?cluster=6137308031728166809&hl=en&as_sdt=0,44",2,2022
Boosting Crowd Counting via Multifaceted Attention,39,cvpr,16,9,2023-06-03 15:12:26.336000,https://github.com/loralinh/boosting-crowd-counting-via-multifaceted-attention,65,Boosting crowd counting via multifaceted attention,"https://scholar.google.com/scholar?cluster=11874033844588786645&hl=en&as_sdt=0,31",4,2022
HODOR: High-Level Object Descriptors for Object Re-Segmentation in Video Learned From Static Images,4,cvpr,2,0,2023-06-03 15:12:26.530000,https://github.com/Ali2500/HODOR,15,HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images,"https://scholar.google.com/scholar?cluster=14744779499159128664&hl=en&as_sdt=0,5",1,2022
Learning Distinctive Margin Toward Active Domain Adaptation,9,cvpr,2,1,2023-06-03 15:12:26.724000,https://github.com/tencentyouturesearch/activelearning-sdm,15,Learning distinctive margin toward active domain adaptation,"https://scholar.google.com/scholar?cluster=9634979691431361691&hl=en&as_sdt=0,33",2,2022
StyTr2: Image Style Transfer With Transformers,54,cvpr,51,12,2023-06-03 15:12:26.918000,https://github.com/diyiiyiii/StyTR-2,225,Stytr2: Image style transfer with transformers,"https://scholar.google.com/scholar?cluster=11574050246452156935&hl=en&as_sdt=0,5",5,2022
BigDL 2.0: Seamless Scaling of AI Pipelines From Laptops to Distributed Cluster,2,cvpr,1084,744,2023-06-03 15:12:27.112000,https://github.com/intel-analytics/BigDL,4222,BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster,"https://scholar.google.com/scholar?cluster=9864215102515109742&hl=en&as_sdt=0,31",237,2022
Neural Inertial Localization,8,cvpr,9,3,2023-06-03 15:12:27.306000,https://github.com/Sachini/niloc,59,Neural inertial localization,"https://scholar.google.com/scholar?cluster=11014129419899900337&hl=en&as_sdt=0,48",11,2022
Contrastive Test-Time Adaptation,52,cvpr,6,2,2023-06-03 15:12:27.501000,https://github.com/DianCh/AdaContrast,64,Contrastive test-time adaptation,"https://scholar.google.com/scholar?cluster=12033489108280591471&hl=en&as_sdt=0,5",0,2022
BEVT: BERT Pretraining of Video Transformers,89,cvpr,15,2,2023-06-03 15:12:27.695000,https://github.com/xyzforever/bevt,140,Bevt: Bert pretraining of video transformers,"https://scholar.google.com/scholar?cluster=9527303198700083047&hl=en&as_sdt=0,14",6,2022
Attentive Fine-Grained Structured Sparsity for Image Restoration,9,cvpr,3,1,2023-06-03 15:12:27.889000,https://github.com/junghunoh/sls_cvpr2022,28,Attentive fine-grained structured sparsity for image restoration,"https://scholar.google.com/scholar?cluster=10207666943032449932&hl=en&as_sdt=0,43",2,2022
Automatic Relation-Aware Graph Network Proliferation,1,cvpr,2,0,2023-06-03 15:12:28.083000,https://github.com/phython96/ARGNP,21,Automatic Relation-aware Graph Network Proliferation,"https://scholar.google.com/scholar?cluster=2091863511758166713&hl=en&as_sdt=0,5",2,2022
Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation,39,cvpr,4,9,2023-06-03 15:12:28.282000,https://github.com/maeve07/rca,61,Regional semantic contrast and aggregation for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=5084328956172259959&hl=en&as_sdt=0,5",3,2022
VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning,15,cvpr,0,4,2023-06-03 15:12:28.491000,https://github.com/wenjiaxu/vgse,34,Vgse: Visually-grounded semantic embeddings for zero-shot learning,"https://scholar.google.com/scholar?cluster=7139969826440179766&hl=en&as_sdt=0,10",5,2022
Contextual Instance Decoupling for Robust Multi-Person Pose Estimation,7,cvpr,9,4,2023-06-03 15:12:28.685000,https://github.com/kennethwdk/cid,38,Contextual Instance Decoupling for Robust Multi-Person Pose Estimation,"https://scholar.google.com/scholar?cluster=9573347418062099087&hl=en&as_sdt=0,5",1,2022
NightLab: A Dual-Level Architecture With Hardness Detection for Segmentation at Night,9,cvpr,1,6,2023-06-03 15:12:28.880000,https://github.com/xdeng7/nightlab,24,NightLab: A dual-level architecture with hardness detection for segmentation at night,"https://scholar.google.com/scholar?cluster=8582137563664570244&hl=en&as_sdt=0,47",7,2022
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation,38,cvpr,17,0,2023-06-03 15:12:29.074000,https://github.com/zhaozhengchen/recam,111,Class re-activation maps for weakly-supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=8897020011930575520&hl=en&as_sdt=0,5",2,2022
Catching Both Gray and Black Swans: Open-Set Supervised Anomaly Detection,19,cvpr,10,6,2023-06-03 15:12:29.269000,https://github.com/choubo/dra,55,Catching both gray and black swans: Open-set supervised anomaly detection,"https://scholar.google.com/scholar?cluster=3212757597363903396&hl=en&as_sdt=0,36",5,2022
A Simple Data Mixing Prior for Improving Self-Supervised Learning,11,cvpr,0,1,2023-06-03 15:12:29.463000,https://github.com/oliverrensu/sdmp,18,A simple data mixing prior for improving self-supervised learning,"https://scholar.google.com/scholar?cluster=17623687333052042987&hl=en&as_sdt=0,50",1,2022
InfoGCN: Representation Learning for Human Skeleton-Based Action Recognition,27,cvpr,6,5,2023-06-03 15:12:29.657000,https://github.com/stnoah1/infogcn,68,Infogcn: Representation learning for human skeleton-based action recognition,"https://scholar.google.com/scholar?cluster=7150747450236425563&hl=en&as_sdt=0,51",2,2022
TransWeather: Transformer-Based Restoration of Images Degraded by Adverse Weather Conditions,50,cvpr,30,3,2023-06-03 15:12:29.851000,https://github.com/jeya-maria-jose/TransWeather,97,Transweather: Transformer-based restoration of images degraded by adverse weather conditions,"https://scholar.google.com/scholar?cluster=563900006853828372&hl=en&as_sdt=0,44",6,2022
Multimodal Colored Point Cloud to Image Alignment,0,cvpr,0,0,2023-06-03 15:12:30.045000,https://github.com/RotsteinNoam/Multimodal-Colored-Point-Cloud-to-Image-Alignment,3,Multimodal Colored Point Cloud to Image Alignment,"https://scholar.google.com/scholar?cluster=1382373453724028501&hl=en&as_sdt=0,5",2,2022
P3Depth: Monocular Depth Estimation With a Piecewise Planarity Prior,28,cvpr,14,7,2023-06-03 15:12:30.240000,https://github.com/syscv/p3depth,105,P3depth: Monocular depth estimation with a piecewise planarity prior,"https://scholar.google.com/scholar?cluster=13429119031106756950&hl=en&as_sdt=0,48",15,2022
MotionAug: Augmentation With Physical Correction for Human Motion Prediction,2,cvpr,2,1,2023-06-03 15:12:30.433000,https://github.com/meaten/motionaug,51,MotionAug: Augmentation with Physical Correction for Human Motion Prediction,"https://scholar.google.com/scholar?cluster=14753224110233710322&hl=en&as_sdt=0,5",3,2022
Crafting Better Contrastive Views for Siamese Representation Learning,49,cvpr,26,4,2023-06-03 15:12:30.628000,https://github.com/xyupeng/contrastivecrop,262,Crafting better contrastive views for siamese representation learning,"https://scholar.google.com/scholar?cluster=17284983713036766691&hl=en&as_sdt=0,5",8,2022
Unsupervised Deraining: Where Contrastive Learning Meets Self-Similarity,6,cvpr,2,2,2023-06-03 15:12:30.822000,https://github.com/yunguo224/NLCL,15,Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity,"https://scholar.google.com/scholar?cluster=4923405697489110935&hl=en&as_sdt=0,39",1,2022
Diffusion Autoencoders: Toward a Meaningful and Decodable Representation,88,cvpr,85,25,2023-06-03 15:12:31.016000,https://github.com/phizaz/diffae,556,Diffusion autoencoders: Toward a meaningful and decodable representation,"https://scholar.google.com/scholar?cluster=1181873611642798324&hl=en&as_sdt=0,33",9,2022
Continual Learning for Visual Search With Backward Consistent Feature Embedding,5,cvpr,1,0,2023-06-03 15:12:31.209000,https://github.com/ivclab/cvs,12,Continual learning for visual search with backward consistent feature embedding,"https://scholar.google.com/scholar?cluster=12075123014734661371&hl=en&as_sdt=0,5",4,2022
BANMo: Building Animatable 3D Neural Models From Many Casual Videos,58,cvpr,51,18,2023-06-03 15:12:31.403000,https://github.com/facebookresearch/banmo,454,Banmo: Building animatable 3d neural models from many casual videos,"https://scholar.google.com/scholar?cluster=18221797548394232954&hl=en&as_sdt=0,36",14,2022
Simple Multi-Dataset Detection,40,cvpr,51,16,2023-06-03 15:12:31.597000,https://github.com/xingyizhou/UniDet,439,Simple multi-dataset detection,"https://scholar.google.com/scholar?cluster=17207953557216789132&hl=en&as_sdt=0,19",14,2022
Knowledge Distillation With the Reused Teacher Classifier,35,cvpr,11,1,2023-06-03 15:12:31.790000,https://github.com/DefangChen/SimKD,56,Knowledge distillation with the reused teacher classifier,"https://scholar.google.com/scholar?cluster=5464853461403136078&hl=en&as_sdt=0,14",3,2022
Language As Queries for Referring Video Object Segmentation,32,cvpr,25,20,2023-06-03 15:12:31.984000,https://github.com/wjn922/referformer,270,Language as queries for referring video object segmentation,"https://scholar.google.com/scholar?cluster=15824157200018556836&hl=en&as_sdt=0,5",7,2022
Efficient Two-Stage Detection of Human-Object Interactions With a Novel Unary-Pairwise Transformer,22,cvpr,26,1,2023-06-03 15:12:32.179000,https://github.com/fredzzhang/upt,118,Efficient two-stage detection of human-object interactions with a novel unary-pairwise transformer,"https://scholar.google.com/scholar?cluster=632464747819696562&hl=en&as_sdt=0,39",6,2022
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving,19,cvpr,3,0,2023-06-03 15:12:32.373000,https://github.com/HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection,37,Investigating the impact of multi-lidar placement on object detection for autonomous driving,"https://scholar.google.com/scholar?cluster=16325083479721146482&hl=en&as_sdt=0,36",4,2022
Audio-Visual Generalised Zero-Shot Learning With Cross-Modal Attention and Language,13,cvpr,1,0,2023-06-03 15:12:32.567000,https://github.com/explainableml/avca-gzsl,26,Audio-visual generalised zero-shot learning with cross-modal attention and language,"https://scholar.google.com/scholar?cluster=4699171220201152909&hl=en&as_sdt=0,39",5,2022
Dense Depth Priors for Neural Radiance Fields From Sparse Input Views,91,cvpr,39,17,2023-06-03 15:12:32.761000,https://github.com/barbararoessle/dense_depth_priors_nerf,297,Dense depth priors for neural radiance fields from sparse input views,"https://scholar.google.com/scholar?cluster=7927762412074311485&hl=en&as_sdt=0,5",9,2022
Unified Contrastive Learning in Image-Text-Label Space,66,cvpr,23,4,2023-06-03 15:12:32.955000,https://github.com/microsoft/unicl,288,Unified contrastive learning in image-text-label space,"https://scholar.google.com/scholar?cluster=6817248240551752310&hl=en&as_sdt=0,21",20,2022
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation,11,cvpr,4,9,2023-06-03 15:12:33.149000,https://github.com/xnowbzhao/sapcu,27,Self-supervised arbitrary-scale point clouds upsampling via implicit neural representation,"https://scholar.google.com/scholar?cluster=1053863024980929514&hl=en&as_sdt=0,47",0,2022
AdaMixer: A Fast-Converging Query-Based Object Detector,33,cvpr,21,3,2023-06-03 15:12:33.343000,https://github.com/mcg-nju/adamixer,212,Adamixer: A fast-converging query-based object detector,"https://scholar.google.com/scholar?cluster=10270081680505767341&hl=en&as_sdt=0,5",6,2022
Unifying Motion Deblurring and Frame Interpolation With Events,20,cvpr,6,9,2023-06-03 15:12:33.537000,https://github.com/xiangz-0/evdi,53,Unifying motion deblurring and frame interpolation with events,"https://scholar.google.com/scholar?cluster=17050992619743623191&hl=en&as_sdt=0,5",2,2022
Deep Unlearning via Randomized Conditionally Independent Hessians,13,cvpr,3,0,2023-06-03 15:12:33.731000,https://github.com/vsingh-group/LCODEC-deep-unlearning,8,Deep unlearning via randomized conditionally independent hessians,"https://scholar.google.com/scholar?cluster=10430632878924637911&hl=en&as_sdt=0,21",2,2022
Shape-Invariant 3D Adversarial Point Clouds,13,cvpr,5,2,2023-06-03 15:12:33.925000,https://github.com/shikiw/si-adv,22,Shape-invariant 3D Adversarial Point Clouds,"https://scholar.google.com/scholar?cluster=12297100581137050112&hl=en&as_sdt=0,33",4,2022
Fast Point Transformer,50,cvpr,36,4,2023-06-03 15:12:34.119000,https://github.com/POSTECH-CVLab/FastPointTransformer,222,Fast point transformer,"https://scholar.google.com/scholar?cluster=13465362774675393232&hl=en&as_sdt=0,18",5,2022
VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation,10,cvpr,4,5,2023-06-03 15:12:34.314000,https://github.com/suhohan95/visolo,24,Visolo: Grid-based space-time aggregation for efficient online video instance segmentation,"https://scholar.google.com/scholar?cluster=5346503725753054267&hl=en&as_sdt=0,33",1,2022
Patch-Level Representation Learning for Self-Supervised Vision Transformers,16,cvpr,6,5,2023-06-03 15:12:34.507000,https://github.com/alinlab/selfpatch,50,Patch-level representation learning for self-supervised vision transformers,"https://scholar.google.com/scholar?cluster=15793792687497316784&hl=en&as_sdt=0,5",2,2022
LAS-AT: Adversarial Training With Learnable Attack Strategy,31,cvpr,9,3,2023-06-03 15:12:34.701000,https://github.com/jiaxiaojunqaq/las-at,81,LAS-AT: adversarial training with learnable attack strategy,"https://scholar.google.com/scholar?cluster=3476669686081471952&hl=en&as_sdt=0,33",2,2022
Incremental Learning in Semantic Segmentation From Image Labels,23,cvpr,7,2,2023-06-03 15:12:34.895000,https://github.com/fcdl94/wilson,44,Incremental learning in semantic segmentation from image labels,"https://scholar.google.com/scholar?cluster=13395790776688983530&hl=en&as_sdt=0,14",3,2022
Sylph: A Hypernetwork Framework for Incremental Few-Shot Object Detection,16,cvpr,7,5,2023-06-03 15:12:35.089000,https://github.com/facebookresearch/sylph-few-shot-detection,42,Sylph: A hypernetwork framework for incremental few-shot object detection,"https://scholar.google.com/scholar?cluster=13105389242995861997&hl=en&as_sdt=0,5",7,2022
Playable Environments: Video Manipulation in Space and Time,3,cvpr,8,2,2023-06-03 15:12:35.284000,https://github.com/willi-menapace/PlayableEnvironments,68,Playable environments: Video manipulation in space and time,"https://scholar.google.com/scholar?cluster=1770031922644467021&hl=en&as_sdt=0,10",4,2022
Stereo Magnification With Multi-Layer Images,4,cvpr,7,3,2023-06-03 15:12:35.484000,https://github.com/SamsungLabs/MLI,112,Stereo magnification with multi-layer images,"https://scholar.google.com/scholar?cluster=737377969220391544&hl=en&as_sdt=0,33",9,2022
Unsupervised Pre-Training for Temporal Action Localization Tasks,15,cvpr,0,3,2023-06-03 15:12:35.678000,https://github.com/zhang-can/up-tal,29,Unsupervised pre-training for temporal action localization tasks,"https://scholar.google.com/scholar?cluster=14964804060777605755&hl=en&as_sdt=0,34",8,2022
Mining Multi-View Information: A Strong Self-Supervised Framework for Depth-Based 3D Hand Pose and Mesh Estimation,4,cvpr,0,1,2023-06-03 15:12:35.872000,https://github.com/renfeitemp/mmi,1,Mining multi-view information: a strong self-supervised framework for depth-based 3D hand pose and mesh estimation,"https://scholar.google.com/scholar?cluster=3031787085939220183&hl=en&as_sdt=0,33",3,2022
Stereo Depth From Events Cameras: Concentrate and Focus on the Future,4,cvpr,4,3,2023-06-03 15:12:36.079000,https://github.com/yonseivnl/se-cff,24,Stereo Depth from Events Cameras: Concentrate and Focus on the Future,"https://scholar.google.com/scholar?cluster=2178875549537086265&hl=en&as_sdt=0,33",3,2022
Hierarchical Modular Network for Video Captioning,17,cvpr,6,5,2023-06-03 15:12:36.299000,https://github.com/marcusnerva/hmn,43,Hierarchical modular network for video captioning,"https://scholar.google.com/scholar?cluster=17569403582558149847&hl=en&as_sdt=0,5",2,2022
Alignment-Uniformity Aware Representation Learning for Zero-Shot Video Classification,6,cvpr,2,4,2023-06-03 15:12:36.493000,https://github.com/ShipuLoveMili/CVPR2022-AURL,12,Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification,"https://scholar.google.com/scholar?cluster=9472317875374648532&hl=en&as_sdt=0,5",1,2022
Maintaining Reasoning Consistency in Compositional Visual Question Answering,4,cvpr,1,0,2023-06-03 15:12:36.687000,https://github.com/jingchenchen/reasoningconsistency-vqa,8,Maintaining Reasoning Consistency in Compositional Visual Question Answering,"https://scholar.google.com/scholar?cluster=6788429748643513720&hl=en&as_sdt=0,5",1,2022
"Knowledge Distillation As Efficient Pre-Training: Faster Convergence, Higher Data-Efficiency, and Better Transferability",9,cvpr,7,1,2023-06-03 15:12:36.882000,https://github.com/cvmi-lab/kdep,61,"Knowledge distillation as efficient pre-training: Faster convergence, higher data-efficiency, and better transferability","https://scholar.google.com/scholar?cluster=9159082480783598932&hl=en&as_sdt=0,5",2,2022
Structure-Aware Motion Transfer With Deformable Anchor Model,13,cvpr,3,0,2023-06-03 15:12:37.076000,https://github.com/jialetao/dam,62,Structure-aware motion transfer with deformable anchor model,"https://scholar.google.com/scholar?cluster=3964743732361453407&hl=en&as_sdt=0,47",3,2022
Integrative Few-Shot Learning for Classification and Segmentation,21,cvpr,13,1,2023-06-03 15:12:37.271000,https://github.com/dahyun-kang/ifsl,98,Integrative few-shot learning for classification and segmentation,"https://scholar.google.com/scholar?cluster=6985902245534667569&hl=en&as_sdt=0,5",6,2022
Automated Progressive Learning for Efficient Training of Vision Transformers,9,cvpr,2,1,2023-06-03 15:12:37.465000,https://github.com/changlin31/autoprog,23,Automated progressive learning for efficient training of vision transformers,"https://scholar.google.com/scholar?cluster=1217682386744714657&hl=en&as_sdt=0,34",5,2022
Pix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation,37,cvpr,30,6,2023-06-03 15:12:37.659000,https://github.com/hexagonprime/pix2nerf,235,Pix2nerf: Unsupervised conditional p-gan for single image to neural radiance fields translation,"https://scholar.google.com/scholar?cluster=7833643077156866497&hl=en&as_sdt=0,18",29,2022
Learning Fair Classifiers With Partially Annotated Group Labels,21,cvpr,1,1,2023-06-03 15:12:37.853000,https://github.com/naver-ai/cgl_fairness,14,Learning fair classifiers with partially annotated group labels,"https://scholar.google.com/scholar?cluster=3333887381827250053&hl=en&as_sdt=0,5",3,2022
GroupViT: Semantic Segmentation Emerges From Text Supervision,111,cvpr,47,31,2023-06-03 15:12:38.047000,https://github.com/NVlabs/GroupViT,592,Groupvit: Semantic segmentation emerges from text supervision,"https://scholar.google.com/scholar?cluster=11386191630559851929&hl=en&as_sdt=0,5",11,2022
Constrained Few-Shot Class-Incremental Learning,33,cvpr,12,4,2023-06-03 15:12:38.242000,https://github.com/ibm/constrained-fscil,31,Constrained few-shot class-incremental learning,"https://scholar.google.com/scholar?cluster=10728009599579655554&hl=en&as_sdt=0,10",2,2022
Occlusion-Aware Cost Constructor for Light Field Depth Estimation,20,cvpr,10,0,2023-06-03 15:12:38.438000,https://github.com/yingqianwang/oacc-net,44,Occlusion-aware cost constructor for light field depth estimation,"https://scholar.google.com/scholar?cluster=16782358215197438633&hl=en&as_sdt=0,33",3,2022
Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholds,15,cvpr,1,1,2023-06-03 15:12:38.639000,https://github.com/gaviotas/amn,30,Threshold matters in WSSS: manipulating the activation for the robust and accurate segmentation model against thresholds,"https://scholar.google.com/scholar?cluster=11996776905163955629&hl=en&as_sdt=0,36",2,2022
DeeCap: Dynamic Early Exiting for Efficient Image Captioning,12,cvpr,4,3,2023-06-03 15:12:38.833000,https://github.com/feizc/deecap,13,Deecap: dynamic early exiting for efficient image captioning,"https://scholar.google.com/scholar?cluster=13789650658206614224&hl=en&as_sdt=0,36",1,2022
Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation,24,cvpr,5,9,2023-06-03 15:12:39.027000,https://github.com/dongxingning/sha-gcl-for-sgg,25,Stacked hybrid-attention and group collaborative learning for unbiased scene graph generation,"https://scholar.google.com/scholar?cluster=1697948611889236463&hl=en&as_sdt=0,34",2,2022
ROCA: Robust CAD Model Retrieval and Alignment From a Single Image,14,cvpr,18,2,2023-06-03 15:12:39.221000,https://github.com/cangumeli/ROCA,142,ROCA: robust CAD model retrieval and alignment from a single image,"https://scholar.google.com/scholar?cluster=5577489245033976027&hl=en&as_sdt=0,32",10,2022
RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning,15,cvpr,10,3,2023-06-03 15:12:39.415000,https://github.com/xmed-lab/rscfed,36,RSCFed: random sampling consensus federated semi-supervised learning,"https://scholar.google.com/scholar?cluster=2751746282003399252&hl=en&as_sdt=0,5",1,2022
Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow,6,cvpr,1,0,2023-06-03 15:12:39.609000,https://github.com/siwensun/neural_diffeomorphic_flow--ndf,24,Topology-preserving shape reconstruction and registration via neural diffeomorphic flow,"https://scholar.google.com/scholar?cluster=3712174266299153732&hl=en&as_sdt=0,5",4,2022
Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks,0,cvpr,0,1,2023-06-03 15:12:39.804000,https://github.com/asudahkzj/wnet,19,Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks,"https://scholar.google.com/scholar?cluster=6130397205515703605&hl=en&as_sdt=0,5",1,2022
TransMVSNet: Global Context-Aware Multi-View Stereo Network With Transformers,35,cvpr,22,15,2023-06-03 15:12:39.998000,https://github.com/megviirobot/transmvsnet,206,Transmvsnet: Global context-aware multi-view stereo network with transformers,"https://scholar.google.com/scholar?cluster=16695593325302942442&hl=en&as_sdt=0,5",18,2022
ARCS: Accurate Rotation and Correspondence Search,7,cvpr,2,0,2023-06-03 15:12:40.192000,https://github.com/liangzu/arcs,11,ARCS: Accurate Rotation and Correspondence Search,"https://scholar.google.com/scholar?cluster=15062673513826669415&hl=en&as_sdt=0,11",2,2022
PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents,23,cvpr,126,41,2023-06-03 15:12:40.386000,https://github.com/microsoft/table-transformer,838,PubTables-1M: Towards comprehensive table extraction from unstructured documents,"https://scholar.google.com/scholar?cluster=12915409103598553551&hl=en&as_sdt=0,10",26,2022
iFS-RCNN: An Incremental Few-Shot Instance Segmenter,11,cvpr,1,3,2023-06-03 15:12:40.588000,https://github.com/ducminhkhoi/iFS-RCNN,8,ifs-rcnn: An incremental few-shot instance segmenter,"https://scholar.google.com/scholar?cluster=1987868693644391825&hl=en&as_sdt=0,14",2,2022
Learning To Anticipate Future With Dynamic Context Removal,3,cvpr,1,1,2023-06-03 15:12:40.783000,https://github.com/allenxuuu/dcr,11,Learning to anticipate future with dynamic context removal,"https://scholar.google.com/scholar?cluster=6895510277602702886&hl=en&as_sdt=0,14",2,2022
Meta-Attention for ViT-Backed Continual Learning,11,cvpr,6,3,2023-06-03 15:12:40.977000,https://github.com/zju-vipa/meat-til,30,Meta-attention for ViT-backed Continual Learning,"https://scholar.google.com/scholar?cluster=6877499540163265050&hl=en&as_sdt=0,5",9,2022
Perception Prioritized Training of Diffusion Models,32,cvpr,8,5,2023-06-03 15:12:41.171000,https://github.com/jychoi118/p2-weighting,108,Perception prioritized training of diffusion models,"https://scholar.google.com/scholar?cluster=7208192797566706742&hl=en&as_sdt=0,11",5,2022
The Majority Can Help the Minority: Context-Rich Minority Oversampling for Long-Tailed Classification,38,cvpr,4,1,2023-06-03 15:12:41.365000,https://github.com/naver-ai/cmo,37,The majority can help the minority: Context-rich minority oversampling for long-tailed classification,"https://scholar.google.com/scholar?cluster=10301410724598778072&hl=en&as_sdt=0,33",5,2022
IntentVizor: Towards Generic Query Guided Interactive Video Summarization,4,cvpr,2,3,2023-06-03 15:12:41.559000,https://github.com/jnzs1836/intent-vizor,12,Intentvizor: Towards generic query guided interactive video summarization,"https://scholar.google.com/scholar?cluster=2207381737708119534&hl=en&as_sdt=0,5",2,2022
HairCLIP: Design Your Hair by Text and Reference Image,40,cvpr,57,17,2023-06-03 15:12:41.753000,https://github.com/wty-ustc/hairclip,383,Hairclip: Design your hair by text and reference image,"https://scholar.google.com/scholar?cluster=12674767256504102765&hl=en&as_sdt=0,5",21,2022
BppAttack: Stealthy and Efficient Trojan Attacks Against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning,17,cvpr,2,1,2023-06-03 15:12:41.948000,https://github.com/ru-system-software-and-security/bppattack,7,Bppattack: Stealthy and efficient trojan attacks against deep neural networks via image quantization and contrastive adversarial learning,"https://scholar.google.com/scholar?cluster=4389089579155481243&hl=en&as_sdt=0,33",1,2022
OakInk: A Large-Scale Knowledge Repository for Understanding Hand-Object Interaction,6,cvpr,2,1,2023-06-03 15:12:42.142000,https://github.com/lixiny/oakink,54,OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction,"https://scholar.google.com/scholar?cluster=3071838002660193479&hl=en&as_sdt=0,5",8,2022
Bootstrapping ViTs: Towards Liberating Vision Transformers From Pre-Training,2,cvpr,1,1,2023-06-03 15:12:42.338000,https://github.com/zhfeing/bootstrapping-vits-pytorch,18,Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training,"https://scholar.google.com/scholar?cluster=13025716192629045249&hl=en&as_sdt=0,14",2,2022
Ensembling Off-the-Shelf Models for GAN Training,34,cvpr,23,5,2023-06-03 15:12:42.533000,https://github.com/nupurkmr9/vision-aided-gan,356,Ensembling off-the-shelf models for gan training,"https://scholar.google.com/scholar?cluster=16368483157712189937&hl=en&as_sdt=0,10",13,2022
SwinBERT: End-to-End Transformers With Sparse Attention for Video Captioning,55,cvpr,25,31,2023-06-03 15:12:42.727000,https://github.com/microsoft/swinbert,196,Swinbert: End-to-end transformers with sparse attention for video captioning,"https://scholar.google.com/scholar?cluster=4460723879849250498&hl=en&as_sdt=0,21",6,2022
Multiview Transformers for Video Recognition,92,cvpr,313,163,2023-06-03 15:12:42.921000,https://github.com/google-research/scenic,2214,Multiview transformers for video recognition,"https://scholar.google.com/scholar?cluster=17352308175817904899&hl=en&as_sdt=0,5",37,2022
Segment and Complete: Defending Object Detectors Against Adversarial Patch Attacks With Robust Patch Detection,9,cvpr,0,0,2023-06-03 15:12:43.115000,https://github.com/joellliu/segmentandcomplete,9,Segment and complete: Defending object detectors against adversarial patch attacks with robust patch detection,"https://scholar.google.com/scholar?cluster=12183766093634556320&hl=en&as_sdt=0,5",3,2022
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation,9,cvpr,3,1,2023-06-03 15:12:43.309000,https://github.com/batmanlab/mspc,60,Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation,"https://scholar.google.com/scholar?cluster=11637513455568079018&hl=en&as_sdt=0,5",3,2022
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information,4,cvpr,4,0,2023-06-03 15:12:43.503000,https://github.com/YorkUCVIL/Static-Dynamic-Interpretability,12,A deeper dive into what deep spatiotemporal networks encode: Quantifying static vs. dynamic information,"https://scholar.google.com/scholar?cluster=7943918121272471561&hl=en&as_sdt=0,5",0,2022
Bringing Old Films Back to Life,11,cvpr,36,19,2023-06-03 15:12:43.697000,https://github.com/raywzy/bringing-old-films-back-to-life,414,Bringing old films back to life,"https://scholar.google.com/scholar?cluster=6118739834400301680&hl=en&as_sdt=0,5",50,2022
Total Variation Optimization Layers for Computer Vision,0,cvpr,2,0,2023-06-03 15:12:43.891000,https://github.com/raymondyeh07/tv_layers_for_cv,38,Total Variation Optimization Layers for Computer Vision,"https://scholar.google.com/scholar?cluster=12050761721992626528&hl=en&as_sdt=0,5",3,2022
Style Transformer for Image Inversion and Editing,16,cvpr,13,6,2023-06-03 15:12:44.085000,https://github.com/sapphire497/style-transformer,163,Style transformer for image inversion and editing,"https://scholar.google.com/scholar?cluster=8290213132016159700&hl=en&as_sdt=0,5",10,2022
E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition,16,cvpr,1,0,2023-06-03 15:12:44.279000,https://github.com/egocentricvision/n-epic-kitchens,12,E2 (go) motion: Motion augmented event stream for egocentric action recognition,"https://scholar.google.com/scholar?cluster=1462656507073042180&hl=en&as_sdt=0,5",2,2022
Defensive Patches for Robust Recognition in the Physical World,11,cvpr,2,1,2023-06-03 15:12:44.473000,https://github.com/nlsde-safety-team/defensivepatch,9,Defensive patches for robust recognition in the physical world,"https://scholar.google.com/scholar?cluster=9698214259713422763&hl=en&as_sdt=0,10",1,2022
An Empirical Study of Training End-to-End Vision-and-Language Transformers,142,cvpr,30,9,2023-06-03 15:12:44.667000,https://github.com/zdou0830/meter,295,An empirical study of training end-to-end vision-and-language transformers,"https://scholar.google.com/scholar?cluster=9718581961231347788&hl=en&as_sdt=0,47",6,2022
Sequential Voting With Relational Box Fields for Active Object Detection,3,cvpr,0,0,2023-06-03 15:12:44.862000,https://github.com/fuqichen1998/SequentialVotingDet,8,Sequential voting with relational box fields for active object detection,"https://scholar.google.com/scholar?cluster=13889336509562179097&hl=en&as_sdt=0,5",2,2022
Bridging Global Context Interactions for High-Fidelity Image Completion,16,cvpr,11,12,2023-06-03 15:12:45.055000,https://github.com/lyndonzheng/TFill,125,Bridging global context interactions for high-fidelity image completion,"https://scholar.google.com/scholar?cluster=13010832561767605118&hl=en&as_sdt=0,31",12,2022
Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification,6,cvpr,3,1,2023-06-03 15:12:45.250000,https://github.com/tencentailabhealthcare/mmdynamics,27,Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification,"https://scholar.google.com/scholar?cluster=2314283102131265897&hl=en&as_sdt=0,5",2,2022
Learning Transferable Human-Object Interaction Detector With Natural Language Supervision,12,cvpr,4,5,2023-06-03 15:12:45.444000,https://github.com/scwangdyd/promting_hoi,16,Learning transferable human-object interaction detector with natural language supervision,"https://scholar.google.com/scholar?cluster=17111948876585943469&hl=en&as_sdt=0,5",2,2022
Unsupervised Homography Estimation With Coplanarity-Aware GAN,9,cvpr,3,8,2023-06-03 15:12:45.638000,https://github.com/megvii-research/homogan,26,Unsupervised homography estimation with coplanarity-aware gan,"https://scholar.google.com/scholar?cluster=3382226077000244726&hl=en&as_sdt=0,33",7,2022
Fourier Document Restoration for Robust Document Dewarping and Recognition,12,cvpr,0,0,2023-06-03 15:12:45.832000,https://github.com/wanghun/wanghun,3,Fourier document restoration for robust document dewarping and recognition,"https://scholar.google.com/scholar?cluster=2048456094872715675&hl=en&as_sdt=0,26",3,2022
Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds,9,cvpr,6,2,2023-06-03 15:12:46.027000,https://github.com/leolyj/dca-srsfe,32,Deformation and correspondence aware unsupervised synthetic-to-real scene flow estimation for point clouds,"https://scholar.google.com/scholar?cluster=1545562343831911281&hl=en&as_sdt=0,33",4,2022
Dist-PU: Positive-Unlabeled Learning From a Label Distribution Perspective,2,cvpr,1,0,2023-06-03 15:12:46.221000,https://github.com/ray-rui/dist-pu-positive-unlabeled-learning-from-a-label-distribution-perspective,7,Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective,"https://scholar.google.com/scholar?cluster=16272078311893231994&hl=en&as_sdt=0,33",1,2022
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection,8,cvpr,1,0,2023-06-03 15:12:46.415000,https://github.com/mlvlab/cpchoi,7,Consistency learning via decoding path augmentation for transformers in human object interaction detection,"https://scholar.google.com/scholar?cluster=13117051392972890341&hl=en&as_sdt=0,47",7,2022
Learn From Others and Be Yourself in Heterogeneous Federated Learning,24,cvpr,2,3,2023-06-03 15:12:46.609000,https://github.com/wenkehuang/fccl,21,Learn from others and be yourself in heterogeneous federated learning,"https://scholar.google.com/scholar?cluster=7393762203425732147&hl=en&as_sdt=0,31",5,2022
Learning With Neighbor Consistency for Noisy Labels,14,cvpr,313,163,2023-06-03 15:12:46.803000,https://github.com/google-research/scenic,2214,Learning with neighbor consistency for noisy labels,"https://scholar.google.com/scholar?cluster=6760478343448133888&hl=en&as_sdt=0,24",37,2022
Semantic-Aware Auto-Encoders for Self-Supervised Representation Learning,3,cvpr,0,2,2023-06-03 15:12:46.997000,https://github.com/wanggrun/semantic-aware-ae,4,Semantic-aware auto-encoders for self-supervised representation learning,"https://scholar.google.com/scholar?cluster=4002747980342038386&hl=en&as_sdt=0,33",2,2022
Rethinking Minimal Sufficient Representation in Contrastive Learning,20,cvpr,4,0,2023-06-03 15:12:47.193000,https://github.com/haoqing-wang/infocl,47,Rethinking minimal sufficient representation in contrastive learning,"https://scholar.google.com/scholar?cluster=13924377043758377223&hl=en&as_sdt=0,34",1,2022
Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light,0,cvpr,1,3,2023-06-03 15:12:47.387000,https://github.com/yuhuaxu/monostereofusion,35,Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light,"https://scholar.google.com/scholar?cluster=16208499283420000901&hl=en&as_sdt=0,5",9,2022
Consistent Explanations by Contrastive Learning,7,cvpr,1,0,2023-06-03 15:12:47.581000,https://github.com/UCDvision/CGC,16,Consistent Explanations by Contrastive Learning,"https://scholar.google.com/scholar?cluster=17257429371166714029&hl=en&as_sdt=0,47",2,2022
Not All Tokens Are Equal: Human-Centric Visual Analysis via Token Clustering Transformer,30,cvpr,18,2,2023-06-03 15:12:47.775000,https://github.com/zengwang430521/tcformer,153,Not all tokens are equal: Human-centric visual analysis via token clustering transformer,"https://scholar.google.com/scholar?cluster=4314076215516968526&hl=en&as_sdt=0,11",7,2022
Effective Conditioned and Composed Image Retrieval Combining CLIP-Based Features,16,cvpr,8,1,2023-06-03 15:12:47.969000,https://github.com/ABaldrati/CLIP4Cir,47,Effective conditioned and composed image retrieval combining CLIP-based features,"https://scholar.google.com/scholar?cluster=1998074726378483082&hl=en&as_sdt=0,5",2,2022
Practical Stereo Matching via Cascaded Recurrent Network With Adaptive Correlation,52,cvpr,43,29,2023-06-03 15:12:48.163000,https://github.com/megvii-research/crestereo,355,Practical stereo matching via cascaded recurrent network with adaptive correlation,"https://scholar.google.com/scholar?cluster=15829098413988895779&hl=en&as_sdt=0,5",13,2022
Salient-to-Broad Transition for Video Person Re-Identification,14,cvpr,1,2,2023-06-03 15:12:48.357000,https://github.com/baist/sinet,14,Salient-to-broad transition for video person re-identification,"https://scholar.google.com/scholar?cluster=8320015800738134478&hl=en&as_sdt=0,39",2,2022
RePaint: Inpainting Using Denoising Diffusion Probabilistic Models,211,cvpr,109,30,2023-06-03 15:12:48.551000,https://github.com/andreas128/RePaint,1388,Repaint: Inpainting using denoising diffusion probabilistic models,"https://scholar.google.com/scholar?cluster=15137047640877691296&hl=en&as_sdt=0,5",41,2022
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions,27,cvpr,12,2,2023-06-03 15:12:48.744000,https://github.com/christsa/dgrasp,39,D-grasp: Physically plausible dynamic grasp synthesis for hand-object interactions,"https://scholar.google.com/scholar?cluster=6734315347717258916&hl=en&as_sdt=0,33",2,2022
RepMLPNet: Hierarchical Vision MLP With Re-Parameterized Locality,22,cvpr,42,4,2023-06-03 15:12:48.938000,https://github.com/DingXiaoH/RepMLP,273,Repmlpnet: Hierarchical vision mlp with re-parameterized locality,"https://scholar.google.com/scholar?cluster=5100164982725834850&hl=en&as_sdt=0,5",11,2022
Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction,11,cvpr,6,5,2023-06-03 15:12:49.132000,https://github.com/705062791/pgbig,29,Progressively generating better initial guesses towards next stages for high-quality human motion prediction,"https://scholar.google.com/scholar?cluster=8589033064850986626&hl=en&as_sdt=0,23",1,2022
"Show, Deconfound and Tell: Image Captioning With Causal Inference",14,cvpr,1,5,2023-06-03 15:12:49.326000,https://github.com/cumtgg/ciic,7,"Show, deconfound and tell: Image captioning with causal inference","https://scholar.google.com/scholar?cluster=11785674868146524668&hl=en&as_sdt=0,23",1,2022
DR.VIC: Decomposition and Reasoning for Video Individual Counting,6,cvpr,9,8,2023-06-03 15:12:49.520000,https://github.com/taohan10200/drnet,44,Dr. vic: Decomposition and reasoning for video individual counting,"https://scholar.google.com/scholar?cluster=6120306712778926266&hl=en&as_sdt=0,33",5,2022
Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding,18,cvpr,5,0,2023-06-03 15:12:49.715000,https://github.com/leaplabthu/pseudo-q,116,Pseudo-q: Generating pseudo language queries for visual grounding,"https://scholar.google.com/scholar?cluster=1079438410491832434&hl=en&as_sdt=0,14",2,2022
ImFace: A Nonlinear 3D Morphable Face Model With Implicit Neural Representations,14,cvpr,7,1,2023-06-03 15:12:49.909000,https://github.com/mingwuzheng/imface,125,Imface: A nonlinear 3d morphable face model with implicit neural representations,"https://scholar.google.com/scholar?cluster=11337079410561771178&hl=en&as_sdt=0,44",23,2022
MobRecon: Mobile-Friendly Hand Mesh Reconstruction From Monocular Image,26,cvpr,60,8,2023-06-03 15:12:50.104000,https://github.com/SeanChenxy/HandMesh,278,MobRecon: Mobile-friendly hand mesh reconstruction from monocular image,"https://scholar.google.com/scholar?cluster=2734254068249927802&hl=en&as_sdt=0,5",10,2022
The Neurally-Guided Shape Parser: Grammar-Based Labeling of 3D Shape Regions With Approximate Inference,6,cvpr,0,0,2023-06-03 15:12:50.299000,https://github.com/rkjones4/ngsp,4,The neurally-guided shape parser: Grammar-based labeling of 3d shape regions with approximate inference,"https://scholar.google.com/scholar?cluster=1666091857334650840&hl=en&as_sdt=0,5",3,2022
AlignMixup: Improving Representations by Interpolating Aligned Features,17,cvpr,8,4,2023-06-03 15:12:50.502000,https://github.com/shashankvkt/alignmixup_cvpr22,55,Alignmixup: Improving representations by interpolating aligned features,"https://scholar.google.com/scholar?cluster=15402867223438087088&hl=en&as_sdt=0,26",1,2022
Novel Class Discovery in Semantic Segmentation,10,cvpr,3,2,2023-06-03 15:12:50.696000,https://github.com/HeliosZhao/NCDSS,57,Novel class discovery in semantic segmentation,"https://scholar.google.com/scholar?cluster=13123220317420170415&hl=en&as_sdt=0,44",4,2022
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging,8,cvpr,8,0,2023-06-03 15:12:50.891000,https://github.com/jianzhangcs/herosnet,21,Herosnet: Hyperspectral explicable reconstruction and optimal sampling deep network for snapshot compressive imaging,"https://scholar.google.com/scholar?cluster=9806896834866016887&hl=en&as_sdt=0,5",2,2022
MAXIM: Multi-Axis MLP for Image Processing,124,cvpr,81,12,2023-06-03 15:12:51.085000,https://github.com/google-research/maxim,716,Maxim: Multi-axis mlp for image processing,"https://scholar.google.com/scholar?cluster=18275282813589182456&hl=en&as_sdt=0,5",12,2022
Disentangling Visual Embeddings for Attributes and Objects,16,cvpr,2,2,2023-06-03 15:12:51.279000,https://github.com/nirat1606/oadis,24,Disentangling visual embeddings for attributes and objects,"https://scholar.google.com/scholar?cluster=215939798860441698&hl=en&as_sdt=0,5",4,2022
Detecting Deepfakes With Self-Blended Images,37,cvpr,22,18,2023-06-03 15:12:51.475000,https://github.com/mapooon/selfblendedimages,121,Detecting deepfakes with self-blended images,"https://scholar.google.com/scholar?cluster=7314584816604343182&hl=en&as_sdt=0,5",7,2022
Towards Better Understanding Attribution Methods,9,cvpr,4,0,2023-06-03 15:12:51.669000,https://github.com/sukrutrao/attribution-evaluation,11,Towards better understanding attribution methods,"https://scholar.google.com/scholar?cluster=13578269228145112248&hl=en&as_sdt=0,44",2,2022
MiniViT: Compressing Vision Transformers With Weight Multiplexing,47,cvpr,166,22,2023-06-03 15:12:51.864000,https://github.com/microsoft/cream,1064,Minivit: Compressing vision transformers with weight multiplexing,"https://scholar.google.com/scholar?cluster=14067747907803223122&hl=en&as_sdt=0,5",25,2022
On the Integration of Self-Attention and Convolution,87,cvpr,36,16,2023-06-03 15:12:52.059000,https://github.com/leaplabthu/acmix,281,On the integration of self-attention and convolution,"https://scholar.google.com/scholar?cluster=17860676705797643505&hl=en&as_sdt=0,18",4,2022
PSTR: End-to-End One-Step Person Search With Transformers,10,cvpr,8,7,2023-06-03 15:12:52.254000,https://github.com/jialecao001/pstr,31,PSTR: End-to-end one-step person search with transformers,"https://scholar.google.com/scholar?cluster=17186904653130910323&hl=en&as_sdt=0,31",5,2022
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph Propagation,5,cvpr,0,0,2023-06-03 15:12:52.449000,https://github.com/dante0shy/W4DTS,4,Weakly supervised segmentation on outdoor 4d point clouds with temporal matching and spatial graph propagation,"https://scholar.google.com/scholar?cluster=8736466185006926519&hl=en&as_sdt=0,11",1,2022
Consistency Driven Sequential Transformers Attention Model for Partially Observable Scenes,7,cvpr,1,1,2023-06-03 15:12:52.643000,https://github.com/samrudhdhirangrej/STAM-Sequential-Transformers-Attention-Model,5,Consistency driven sequential transformers attention model for partially observable scenes,"https://scholar.google.com/scholar?cluster=12665179691187026215&hl=en&as_sdt=0,5",2,2022
NFormer: Robust Person Re-Identification With Neighbor Transformer,29,cvpr,9,7,2023-06-03 15:12:52.836000,https://github.com/haochenheheda/nformer,56,Nformer: Robust person re-identification with neighbor transformer,"https://scholar.google.com/scholar?cluster=17136560426512678248&hl=en&as_sdt=0,14",1,2022
NeurMiPs: Neural Mixture of Planar Experts for View Synthesis,7,cvpr,10,4,2023-06-03 15:12:53.030000,https://github.com/zhihao-lin/neurmips,105,Neurmips: Neural mixture of planar experts for view synthesis,"https://scholar.google.com/scholar?cluster=1632486663608785078&hl=en&as_sdt=0,5",7,2022
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation,123,cvpr,91,14,2023-06-03 15:12:53.224000,https://github.com/gwang-kim/diffusionclip,592,Diffusionclip: Text-guided diffusion models for robust image manipulation,"https://scholar.google.com/scholar?cluster=9357336449213296541&hl=en&as_sdt=0,5",7,2022
Unseen Classes at a Later Time? No Problem,0,cvpr,4,0,2023-06-03 15:12:53.419000,https://github.com/sumitramalagi/unseen-classes-at-a-later-time,12,Unseen Classes at a Later Time? No Problem,"https://scholar.google.com/scholar?cluster=9796461758714223638&hl=en&as_sdt=0,36",1,2022
"GlideNet: Global, Local and Intrinsic Based Dense Embedding NETwork for Multi-Category Attributes Prediction",5,cvpr,2,0,2023-06-03 15:12:53.613000,https://github.com/kareem-metwaly/glidenet,24,"Glidenet: Global, local and intrinsic based dense embedding network for multi-category attributes prediction","https://scholar.google.com/scholar?cluster=13841981352415211893&hl=en&as_sdt=0,22",1,2022
Learning the Degradation Distribution for Blind Image Super-Resolution,12,cvpr,10,17,2023-06-03 15:12:53.807000,https://github.com/greatlog/UnpairedSR,135,Learning the degradation distribution for blind image super-resolution,"https://scholar.google.com/scholar?cluster=11157268463116975803&hl=en&as_sdt=0,34",5,2022
Self-Supervised Spatial Reasoning on Multi-View Line Drawings,0,cvpr,1,0,2023-06-03 15:12:54.001000,https://github.com/ai4ce/Contrastive-SPARE3D,21,Self-supervised Spatial Reasoning on Multi-View Line Drawings,"https://scholar.google.com/scholar?cluster=12979398971543214260&hl=en&as_sdt=0,19",3,2022
Hybrid Relation Guided Set Matching for Few-Shot Action Recognition,18,cvpr,4,12,2023-06-03 15:12:54.195000,https://github.com/alibaba-mmai-research/HyRSM,21,Hybrid relation guided set matching for few-shot action recognition,"https://scholar.google.com/scholar?cluster=3952513944084278223&hl=en&as_sdt=0,33",2,2022
Delving Into the Estimation Shift of Batch Normalization in a Network,8,cvpr,5,5,2023-06-03 15:12:54.389000,https://github.com/huangleibuaa/xbnblock,25,Delving into the estimation shift of batch normalization in a network,"https://scholar.google.com/scholar?cluster=4738166450324685205&hl=en&as_sdt=0,47",2,2022
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks,8,cvpr,7,0,2023-06-03 15:12:54.583000,https://github.com/fawazsammani/nlxgpt,31,NLX-GPT: A model for natural language explanations in vision and vision-language tasks,"https://scholar.google.com/scholar?cluster=8662989333140452131&hl=en&as_sdt=0,34",1,2022
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic Images,17,cvpr,6,3,2023-06-03 15:12:54.778000,https://github.com/zzw-szu/cdcl,23,Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images,"https://scholar.google.com/scholar?cluster=1318173233917005478&hl=en&as_sdt=0,5",1,2022
Joint Forecasting of Panoptic Segmentations With Difference Attention,1,cvpr,2,1,2023-06-03 15:12:54.971000,https://github.com/cgraber/psf-diffattn,4,Joint forecasting of panoptic segmentations with difference attention,"https://scholar.google.com/scholar?cluster=11350100219269203678&hl=en&as_sdt=0,21",1,2022
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation,4,cvpr,1,1,2023-06-03 15:12:55.165000,https://github.com/yztang4/warpinggan,16,Warpinggan: Warping multiple uniform priors for adversarial 3d point cloud generation,"https://scholar.google.com/scholar?cluster=1870479647707327308&hl=en&as_sdt=0,5",1,2022
Frame-Wise Action Representations for Long Videos via Sequence Contrastive Learning,10,cvpr,6,3,2023-06-03 15:12:55.359000,https://github.com/minghchen/carl_code,60,Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning,"https://scholar.google.com/scholar?cluster=15909063164280067703&hl=en&as_sdt=0,5",3,2022
"Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild",4,cvpr,2,0,2023-06-03 15:12:55.553000,https://github.com/cvlab-stonybrook/HandLer,20,"Forward propagation, backward regression, and pose association for hand tracking in the wild","https://scholar.google.com/scholar?cluster=13815293027556568808&hl=en&as_sdt=0,5",6,2022
Generalized Binary Search Network for Highly-Efficient Multi-View Stereo,12,cvpr,11,7,2023-06-03 15:12:55.747000,https://github.com/mizhenxing/gbi-net,94,Generalized binary search network for highly-efficient multi-view stereo,"https://scholar.google.com/scholar?cluster=2067795094219418786&hl=en&as_sdt=0,5",9,2022
Face2Exp: Combating Data Biases for Facial Expression Recognition,20,cvpr,3,0,2023-06-03 15:12:55.941000,https://github.com/danzeng1990/face2exp,10,Face2exp: Combating data biases for facial expression recognition,"https://scholar.google.com/scholar?cluster=6305348363652601881&hl=en&as_sdt=0,26",0,2022
Towards Robust and Reproducible Active Learning Using Neural Networks,28,cvpr,4,0,2023-06-03 15:12:56.135000,https://github.com/prateekmunjal/torchal,48,Towards robust and reproducible active learning using neural networks,"https://scholar.google.com/scholar?cluster=9506328256170962398&hl=en&as_sdt=0,33",2,2022
ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework,6,cvpr,2,6,2023-06-03 15:12:56.329000,https://github.com/ganwanshui/es6d,27,ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework,"https://scholar.google.com/scholar?cluster=11437985035935432033&hl=en&as_sdt=0,33",3,2022
Mega-NERF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs,49,cvpr,44,19,2023-06-03 15:12:56.523000,https://github.com/cmusatyalab/mega-nerf,346,Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs,"https://scholar.google.com/scholar?cluster=9397631381611401&hl=en&as_sdt=0,5",15,2022
Temporally Efficient Vision Transformer for Video Instance Segmentation,20,cvpr,18,9,2023-06-03 15:12:56.717000,https://github.com/hustvl/tevit,214,Temporally efficient vision transformer for video instance segmentation,"https://scholar.google.com/scholar?cluster=12121457393934602812&hl=en&as_sdt=0,36",7,2022
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization,23,cvpr,8,0,2023-06-03 15:12:56.912000,https://github.com/ynysjtu/ood_bench,42,Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization,"https://scholar.google.com/scholar?cluster=12360806136384688467&hl=en&as_sdt=0,5",3,2022
Forecasting From LiDAR via Future Object Detection,10,cvpr,9,0,2023-06-03 15:12:57.106000,https://github.com/neeharperi/futuredet,104,Forecasting from lidar via future object detection,"https://scholar.google.com/scholar?cluster=9283857749595095965&hl=en&as_sdt=0,33",3,2022
The Devil Is in the Margin: Margin-Based Label Smoothing for Network Calibration,14,cvpr,6,1,2023-06-03 15:12:57.301000,https://github.com/by-liu/mbls,41,The devil is in the margin: Margin-based label smoothing for network calibration,"https://scholar.google.com/scholar?cluster=3433503507084425850&hl=en&as_sdt=0,5",1,2022
OnePose: One-Shot Object Pose Estimation Without CAD Models,36,cvpr,67,28,2023-06-03 15:12:57.500000,https://github.com/zju3dv/OnePose,791,Onepose: One-shot object pose estimation without cad models,"https://scholar.google.com/scholar?cluster=3971107129233795900&hl=en&as_sdt=0,33",56,2022
Cloth-Changing Person Re-Identification From a Single Image With Gait Prediction and Regularization,41,cvpr,5,2,2023-06-03 15:12:57.694000,https://github.com/jinx-USTC/GI-ReID,31,Cloth-changing person re-identification from a single image with gait prediction and regularization,"https://scholar.google.com/scholar?cluster=13715470973363687442&hl=en&as_sdt=0,5",5,2022
CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow,36,cvpr,5,1,2023-06-03 15:12:57.888000,https://github.com/askerlee/craft,58,Craft: Cross-attentional flow transformer for robust optical flow,"https://scholar.google.com/scholar?cluster=15464678858494458398&hl=en&as_sdt=0,33",3,2022
Federated Class-Incremental Learning,30,cvpr,17,2,2023-06-03 15:12:58.083000,https://github.com/conditionwang/fcil,66,Federated class-incremental learning,"https://scholar.google.com/scholar?cluster=1565556417311154296&hl=en&as_sdt=0,5",4,2022
Self-Supervised Deep Image Restoration via Adaptive Stochastic Gradient Langevin Dynamics,7,cvpr,0,0,2023-06-03 15:12:58.277000,https://github.com/wang-weixi/restricted_sampling,10,Self-supervised deep image restoration via adaptive stochastic gradient langevin dynamics,"https://scholar.google.com/scholar?cluster=7828036028065181633&hl=en&as_sdt=0,5",2,2022
"Extracting Triangular 3D Models, Materials, and Lighting From Images",83,cvpr,193,52,2023-06-03 15:12:58.471000,https://github.com/NVlabs/nvdiffrec,1703,"Extracting triangular 3d models, materials, and lighting from images","https://scholar.google.com/scholar?cluster=6158309756121688689&hl=en&as_sdt=0,31",51,2022
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent From the Decision Boundary Perspective,31,cvpr,12,0,2023-06-03 15:12:58.667000,https://github.com/somepago/dbviz,66,Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective,"https://scholar.google.com/scholar?cluster=8786312838221119947&hl=en&as_sdt=0,34",3,2022
Cross-Modal Background Suppression for Audio-Visual Event Localization,13,cvpr,5,0,2023-06-03 15:12:58.862000,https://github.com/marmot-xy/cmbs,22,Cross-modal background suppression for audio-visual event localization,"https://scholar.google.com/scholar?cluster=6736750991894490913&hl=en&as_sdt=0,10",1,2022
Parameter-Free Online Test-Time Adaptation,25,cvpr,4,3,2023-06-03 15:12:59.057000,https://github.com/fiveai/lame,53,Parameter-free online test-time adaptation,"https://scholar.google.com/scholar?cluster=2855726690492926827&hl=en&as_sdt=0,5",5,2022
Multi-View Transformer for 3D Visual Grounding,13,cvpr,1,4,2023-06-03 15:12:59.254000,https://github.com/sega-hsj/mvt-3dvg,44,Multi-view transformer for 3d visual grounding,"https://scholar.google.com/scholar?cluster=17924715872503683586&hl=en&as_sdt=0,10",4,2022
Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels,12,cvpr,0,0,2023-06-03 15:12:59.448000,https://github.com/yikai-wang/spr-lnl,14,Scalable penalized regression for noise detection in learning with noisy labels,"https://scholar.google.com/scholar?cluster=458554296339547749&hl=en&as_sdt=0,10",1,2022
SIGMA: Semantic-Complete Graph Matching for Domain Adaptive Object Detection,30,cvpr,11,2,2023-06-03 15:12:59.643000,https://github.com/cityu-aim-group/sigma,106,Sigma: Semantic-complete graph matching for domain adaptive object detection,"https://scholar.google.com/scholar?cluster=17887538672732382103&hl=en&as_sdt=0,5",6,2022
Continual Predictive Learning From Videos,4,cvpr,2,2,2023-06-03 15:12:59.838000,https://github.com/jc043/cpl,10,Continual Predictive Learning from Videos,"https://scholar.google.com/scholar?cluster=17445780936647782462&hl=en&as_sdt=0,5",1,2022
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning,10,cvpr,2,0,2023-06-03 15:13:00.033000,https://github.com/yitewang/metantk-nas,11,Global convergence of maml and theory-inspired neural architecture search for few-shot learning,"https://scholar.google.com/scholar?cluster=14137481107584919193&hl=en&as_sdt=0,47",3,2022
Knowledge Distillation: A Good Teacher Is Patient and Consistent,103,cvpr,58,6,2023-06-03 15:13:00.228000,https://github.com/google-research/big_vision,878,Knowledge distillation: A good teacher is patient and consistent,"https://scholar.google.com/scholar?cluster=12041530417862145268&hl=en&as_sdt=0,5",23,2022
"No Pain, Big Gain: Classify Dynamic Point Cloud Sequences With Static Models by Fitting Feature-Level Space-Time Surfaces",4,cvpr,0,0,2023-06-03 15:13:00.422000,https://github.com/jx-zhong-for-academic-purpose/kinet,18,"No pain, big gain: classify dynamic point cloud sequences with static models by fitting feature-level space-time surfaces","https://scholar.google.com/scholar?cluster=14397724581913029354&hl=en&as_sdt=0,5",8,2022
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation,35,cvpr,3,1,2023-06-03 15:13:00.625000,https://github.com/qymeng94/dsr,30,Training high-performance low-latency spiking neural networks by differentiation on spike representation,"https://scholar.google.com/scholar?cluster=3967951227165330050&hl=en&as_sdt=0,5",1,2022
HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction,25,cvpr,79,6,2023-06-03 15:13:00.819000,https://github.com/ZikangZhou/HiVT,426,Hivt: Hierarchical vector transformer for multi-agent motion prediction,"https://scholar.google.com/scholar?cluster=3886543382399789682&hl=en&as_sdt=0,33",33,2022
Scribble-Supervised LiDAR Semantic Segmentation,24,cvpr,17,0,2023-06-03 15:13:01.013000,https://github.com/ouenal/scribblekitti,118,Scribble-supervised lidar semantic segmentation,"https://scholar.google.com/scholar?cluster=13978048439602258574&hl=en&as_sdt=0,10",11,2022
Structured Sparse R-CNN for Direct Scene Graph Generation,17,cvpr,1,0,2023-06-03 15:13:01.208000,https://github.com/mcg-nju/structured-sparse-rcnn,48,Structured sparse r-cnn for direct scene graph generation,"https://scholar.google.com/scholar?cluster=3862789668091273745&hl=en&as_sdt=0,5",2,2022
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space,22,cvpr,10,2,2023-06-03 15:13:01.403000,https://github.com/arnav0400/vit-slim,70,Vision transformer slimming: Multi-dimension searching in continuous optimization space,"https://scholar.google.com/scholar?cluster=11243993643991610263&hl=en&as_sdt=0,5",5,2022
Implicit Sample Extension for Unsupervised Person Re-Identification,22,cvpr,1087,190,2023-06-03 15:13:01.598000,https://github.com/PaddlePaddle/PaddleClas,4860,Implicit sample extension for unsupervised person re-identification,"https://scholar.google.com/scholar?cluster=11097071489515751336&hl=en&as_sdt=0,11",73,2022
"Label, Verify, Correct: A Simple Few Shot Object Detection Method",31,cvpr,12,13,2023-06-03 15:13:01.793000,https://github.com/prannaykaul/lvc,67,"Label, verify, correct: A simple few shot object detection method","https://scholar.google.com/scholar?cluster=15981199466446115851&hl=en&as_sdt=0,7",3,2022
Energy-Based Latent Aligner for Incremental Learning,19,cvpr,7,0,2023-06-03 15:13:01.988000,https://github.com/josephkj/eli,39,Energy-based latent aligner for incremental learning,"https://scholar.google.com/scholar?cluster=7831440552872014227&hl=en&as_sdt=0,14",3,2022
Autoregressive Image Generation Using Residual Quantization,44,cvpr,65,8,2023-06-03 15:13:02.182000,https://github.com/kakaobrain/rq-vae-transformer,566,Autoregressive image generation using residual quantization,"https://scholar.google.com/scholar?cluster=5953877530421044413&hl=en&as_sdt=0,10",15,2022
Learning To Estimate Robust 3D Human Mesh From In-the-Wild Crowded Scenes,18,cvpr,11,13,2023-06-03 15:13:02.380000,https://github.com/hongsukchoi/3dcrowdnet_release,124,Learning to estimate robust 3D human mesh from in-the-wild crowded scenes,"https://scholar.google.com/scholar?cluster=2094586402537632937&hl=en&as_sdt=0,5",9,2022
HL-Net: Heterophily Learning Network for Scene Graph Generation,13,cvpr,1,2,2023-06-03 15:13:02.575000,https://github.com/siml3/hl-net,8,HL-Net: Heterophily learning network for scene graph generation,"https://scholar.google.com/scholar?cluster=6746926126987228342&hl=en&as_sdt=0,11",1,2022
TeachAugment: Data Augmentation Optimization Using Teacher Knowledge,16,cvpr,13,0,2023-06-03 15:13:02.769000,https://github.com/DensoITLab/TeachAugment,57,Teachaugment: Data augmentation optimization using teacher knowledge,"https://scholar.google.com/scholar?cluster=8610314008643441319&hl=en&as_sdt=0,36",4,2022
Towards Semi-Supervised Deep Facial Expression Recognition With an Adaptive Confidence Margin,22,cvpr,5,3,2023-06-03 15:13:02.964000,https://github.com/hangyu94/ada-cm,46,Towards semi-supervised deep facial expression recognition with an adaptive confidence margin,"https://scholar.google.com/scholar?cluster=2363323039551556812&hl=en&as_sdt=0,7",1,2022
Group R-CNN for Weakly Semi-Supervised Object Detection With Points,11,cvpr,11,5,2023-06-03 15:13:03.158000,https://github.com/jshilong/grouprcnn,132,Group R-CNN for weakly semi-supervised object detection with points,"https://scholar.google.com/scholar?cluster=5091840667952394824&hl=en&as_sdt=0,5",5,2022
Weakly Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal Learning,17,cvpr,3,2,2023-06-03 15:13:03.352000,https://github.com/minghangz/cpl,32,Weakly Supervised Temporal Sentence Grounding with Gaussian-based Contrastive Proposal Learning,"https://scholar.google.com/scholar?cluster=16465723860400402568&hl=en&as_sdt=0,5",2,2022
A Conservative Approach for Unbiased Learning on Unknown Biases,6,cvpr,1,1,2023-06-03 15:13:03.547000,https://github.com/aandyjeon/ubnet,23,A conservative approach for unbiased learning on unknown biases,"https://scholar.google.com/scholar?cluster=5903607608052787146&hl=en&as_sdt=0,21",1,2022
Masked-Attention Mask Transformer for Universal Image Segmentation,364,cvpr,274,118,2023-06-03 15:13:03.741000,https://github.com/facebookresearch/Mask2Former,1524,Masked-attention mask transformer for universal image segmentation,"https://scholar.google.com/scholar?cluster=10375739191012965737&hl=en&as_sdt=0,6",27,2022
Weakly-Supervised Action Transition Learning for Stochastic Human Motion Prediction,11,cvpr,3,3,2023-06-03 15:13:03.936000,https://github.com/wei-mao-2019/wat,34,Weakly-supervised action transition learning for stochastic human motion prediction,"https://scholar.google.com/scholar?cluster=3476343182791072133&hl=en&as_sdt=0,5",1,2022
Task-Specific Inconsistency Alignment for Domain Adaptive Object Detection,14,cvpr,3,5,2023-06-03 15:13:04.130000,https://github.com/mcg-nju/tia,31,Task-specific inconsistency alignment for domain adaptive object detection,"https://scholar.google.com/scholar?cluster=7513023529480256736&hl=en&as_sdt=0,24",4,2022
Large-Scale Video Panoptic Segmentation in the Wild: A Benchmark,18,cvpr,6,2,2023-06-03 15:13:04.325000,https://github.com/vipseg-dataset/vipseg-dataset,105,Large-scale video panoptic segmentation in the wild: A benchmark,"https://scholar.google.com/scholar?cluster=567776318564767438&hl=en&as_sdt=0,5",2,2022
Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation,7,cvpr,2,0,2023-06-03 15:13:04.519000,https://github.com/dzh19990407/lbdt,19,Language-bridged spatial-temporal interaction for referring video object segmentation,"https://scholar.google.com/scholar?cluster=6573116128933368170&hl=en&as_sdt=0,5",1,2022
GrainSpace: A Large-Scale Dataset for Fine-Grained and Domain-Adaptive Recognition of Cereal Grains,1,cvpr,1,1,2023-06-03 15:13:04.713000,https://github.com/hellodfan/grainspace,6,GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains,"https://scholar.google.com/scholar?cluster=11520823297240729054&hl=en&as_sdt=0,5",2,2022
BokehMe: When Neural Rendering Meets Classical Rendering,10,cvpr,4,1,2023-06-03 15:13:04.916000,https://github.com/juewenpeng/bokehme,140,BokehMe: When neural rendering meets classical rendering,"https://scholar.google.com/scholar?cluster=11129971862430927906&hl=en&as_sdt=0,11",3,2022
Whose Hands Are These? Hand Detection and Hand-Body Association in the Wild,9,cvpr,5,1,2023-06-03 15:13:05.111000,https://github.com/cvlab-stonybrook/BodyHands,53,Whose hands are these? hand detection and hand-body association in the wild,"https://scholar.google.com/scholar?cluster=2993714044954142948&hl=en&as_sdt=0,14",6,2022
Learning Modal-Invariant and Temporal-Memory for Video-Based Visible-Infrared Person Re-Identification,5,cvpr,4,2,2023-06-03 15:13:05.322000,https://github.com/vcm-project233/mitml,24,Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification,"https://scholar.google.com/scholar?cluster=6715456311955986100&hl=en&as_sdt=0,5",2,2022
What Matters for Meta-Learning Vision Regression Tasks?,11,cvpr,5,0,2023-06-03 15:13:05.520000,https://github.com/boschresearch/what-matters-for-meta-learning,17,What Matters For Meta-Learning Vision Regression Tasks?,"https://scholar.google.com/scholar?cluster=13546300489255251342&hl=en&as_sdt=0,44",3,2022
Cross Domain Object Detection by Target-Perceived Dual Branch Distillation,14,cvpr,2,8,2023-06-03 15:13:05.714000,https://github.com/feobi1999/tdd,28,Cross domain object detection by target-perceived dual branch distillation,"https://scholar.google.com/scholar?cluster=2857882908165147607&hl=en&as_sdt=0,33",1,2022
Video Swin Transformer,579,cvpr,182,61,2023-06-03 15:13:05.909000,https://github.com/SwinTransformer/Video-Swin-Transformer,1131,Video swin transformer,"https://scholar.google.com/scholar?cluster=5833041667751260373&hl=en&as_sdt=0,33",9,2022
Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation,19,cvpr,5,10,2023-06-03 15:13:06.103000,https://github.com/hi-ft/erd,40,Overcoming catastrophic forgetting in incremental object detection via elastic response distillation,"https://scholar.google.com/scholar?cluster=1433907519873386658&hl=en&as_sdt=0,33",1,2022
Frequency-Driven Imperceptible Adversarial Attack on Semantic Similarity,12,cvpr,9,3,2023-06-03 15:13:06.301000,https://github.com/LinQinLiang/SSAH-adversarial-attack,32,Frequency-driven imperceptible adversarial attack on semantic similarity,"https://scholar.google.com/scholar?cluster=17065701980780256748&hl=en&as_sdt=0,33",0,2022
BoxeR: Box-Attention for 2D and 3D Transformers,12,cvpr,18,6,2023-06-03 15:13:06.506000,https://github.com/kienduynguyen/boxer,121,Boxer: Box-attention for 2d and 3d transformers,"https://scholar.google.com/scholar?cluster=1805226328438105949&hl=en&as_sdt=0,11",4,2022
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction With Relational Reasoning,19,cvpr,16,3,2023-06-03 15:13:06.700000,https://github.com/mediabrain-sjtu/groupnet,69,Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning,"https://scholar.google.com/scholar?cluster=4430607748119163601&hl=en&as_sdt=0,5",1,2022
ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds,9,cvpr,0,0,2023-06-03 15:13:06.895000,https://github.com/georg-bn/zz-net,3,Zz-net: A universal rotation equivariant architecture for 2d point clouds,"https://scholar.google.com/scholar?cluster=14073603853135717915&hl=en&as_sdt=0,5",2,2022
SVIP: Sequence VerIfication for Procedures in Videos,6,cvpr,2,3,2023-06-03 15:13:07.089000,https://github.com/svip-lab/SVIP-Sequence-VerIfication-for-Procedures-in-Videos,17,SVIP: Sequence VerIfication for Procedures in Videos,"https://scholar.google.com/scholar?cluster=1027862013166223118&hl=en&as_sdt=0,5",2,2022
Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation,13,cvpr,3,4,2023-06-03 15:13:07.283000,https://github.com/dayan-guan/usrn,24,Unbiased subclass regularization for semi-supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=9582218535208088942&hl=en&as_sdt=0,5",1,2022
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation,15,cvpr,14,4,2023-06-03 15:13:07.477000,https://github.com/princeton-vl/coupled-iterative-refinement,73,Coupled iterative refinement for 6D multi-object pose estimation,"https://scholar.google.com/scholar?cluster=11571991360126090293&hl=en&as_sdt=0,14",8,2022
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction,5,cvpr,3,3,2023-06-03 15:13:07.671000,https://github.com/koulakis/h-nne,36,Hierarchical nearest neighbor graph embedding for efficient dimensionality reduction,"https://scholar.google.com/scholar?cluster=11819915220514598721&hl=en&as_sdt=0,23",3,2022
Decoupling Zero-Shot Semantic Segmentation,45,cvpr,3,4,2023-06-03 15:13:07.865000,https://github.com/dingjiansw101/zegformer,136,Decoupling zero-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=14815843761254993307&hl=en&as_sdt=0,5",9,2022
"Neural Emotion Director: Speech-Preserving Semantic Control of Facial Expressions in ""In-the-Wild"" Videos",4,cvpr,18,5,2023-06-03 15:13:08.060000,https://github.com/foivospar/NED,111,"Neural Emotion Director: Speech-Preserving Semantic Control of Facial Expressions in"" In-the-Wild"" Videos","https://scholar.google.com/scholar?cluster=18047484222356560316&hl=en&as_sdt=0,5",6,2022
PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models,8,cvpr,1,0,2023-06-03 15:13:08.254000,https://github.com/chiutaiyin/pca-knowledge-distillation,12,PCA-based knowledge distillation towards lightweight and content-style balanced photorealistic style transfer models,"https://scholar.google.com/scholar?cluster=383904189920752008&hl=en&as_sdt=0,5",2,2022
Transformer Tracking With Cyclic Shifting Window Attention,19,cvpr,6,8,2023-06-03 15:13:08.448000,https://github.com/skyesong38/cswintt,49,Transformer tracking with cyclic shifting window attention,"https://scholar.google.com/scholar?cluster=12734796414080676153&hl=en&as_sdt=0,33",3,2022
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability,17,cvpr,3,2,2023-06-03 15:13:08.642000,https://github.com/jhl-hust/svre,18,Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability,"https://scholar.google.com/scholar?cluster=6215208880030340350&hl=en&as_sdt=0,5",1,2022
Unknown-Aware Object Detection: Learning What You Don't Know From Videos in the Wild,26,cvpr,11,0,2023-06-03 15:13:08.837000,https://github.com/deeplearning-wisc/stud,95,Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild,"https://scholar.google.com/scholar?cluster=18001485986532838919&hl=en&as_sdt=0,5",4,2022
Panoptic SegFormer: Delving Deeper Into Panoptic Segmentation With Transformers,38,cvpr,28,8,2023-06-03 15:13:09.031000,https://github.com/zhiqi-li/Panoptic-SegFormer,174,Panoptic segformer: Delving deeper into panoptic segmentation with transformers,"https://scholar.google.com/scholar?cluster=11135517644142739642&hl=en&as_sdt=0,44",4,2022
Towards Understanding Adversarial Robustness of Optical Flow Networks,7,cvpr,3,0,2023-06-03 15:13:09.226000,https://github.com/lmb-freiburg/understanding_flow_robustness,8,Towards understanding adversarial robustness of optical flow networks,"https://scholar.google.com/scholar?cluster=12225060266995149980&hl=en&as_sdt=0,4",4,2022
Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning,9,cvpr,3,2,2023-06-03 15:13:09.422000,https://github.com/ljin0429/hist,13,Hypergraph-induced semantic tuplet loss for deep metric learning,"https://scholar.google.com/scholar?cluster=15475244425319619070&hl=en&as_sdt=0,5",1,2022
Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning,2,cvpr,1,0,2023-06-03 15:13:09.616000,https://github.com/naver-ai/burn,10,Unsupervised representation learning for binary networks by joint classifier learning,"https://scholar.google.com/scholar?cluster=11918637577590771091&hl=en&as_sdt=0,10",1,2022
Computing Wasserstein-p Distance Between Images With Linear Cost,4,cvpr,1,1,2023-06-03 15:13:09.810000,https://github.com/ucascnic/cudaot,47,Computing Wasserstein-p Distance Between Images with Linear Cost,"https://scholar.google.com/scholar?cluster=15704609829457034992&hl=en&as_sdt=0,34",1,2022
Investigating Tradeoffs in Real-World Video Super-Resolution,28,cvpr,103,34,2023-06-03 15:13:10.005000,https://github.com/ckkelvinchan/realbasicvsr,666,Investigating tradeoffs in real-world video super-resolution,"https://scholar.google.com/scholar?cluster=12914353721061047169&hl=en&as_sdt=0,33",13,2022
Differentiable Stereopsis: Meshes From Multiple Views Using Differentiable Rendering,11,cvpr,6,2,2023-06-03 15:13:10.200000,https://github.com/shubham-goel/ds,63,Differentiable Stereopsis: Meshes from multiple views using differentiable rendering,"https://scholar.google.com/scholar?cluster=12089062565847805968&hl=en&as_sdt=0,5",13,2022
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation,43,cvpr,38,20,2023-06-03 15:13:10.394000,https://github.com/hustvl/TopFormer,314,TopFormer: Token pyramid transformer for mobile semantic segmentation,"https://scholar.google.com/scholar?cluster=4250030993072671612&hl=en&as_sdt=0,39",8,2022
The Devil Is in the Labels: Noisy Label Correction for Robust Scene Graph Generation,25,cvpr,1,0,2023-06-03 15:13:10.589000,https://github.com/muktilin/nice,18,The devil is in the labels: Noisy label correction for robust scene graph generation,"https://scholar.google.com/scholar?cluster=13903966642474117404&hl=en&as_sdt=0,18",1,2022
High Quality Segmentation for Ultra High-Resolution Images,8,cvpr,42,18,2023-06-03 15:13:10.783000,https://github.com/dvlab-research/Entity,443,High quality segmentation for ultra high-resolution images,"https://scholar.google.com/scholar?cluster=1606621854462362861&hl=en&as_sdt=0,21",20,2022
Global Tracking via Ensemble of Local Trackers,7,cvpr,0,2,2023-06-03 15:13:10.977000,https://github.com/zikunzhou/gtelt,13,Global tracking via ensemble of local trackers,"https://scholar.google.com/scholar?cluster=7160061588239539027&hl=en&as_sdt=0,5",4,2022
3DAC: Learning Attribute Compression for Point Clouds,10,cvpr,2,0,2023-06-03 15:13:11.171000,https://github.com/fatPeter/ThreeDAC,10,3dac: Learning attribute compression for point clouds,"https://scholar.google.com/scholar?cluster=13998291749300738323&hl=en&as_sdt=0,31",2,2022
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation,47,cvpr,9,1,2023-06-03 15:13:11.366000,https://github.com/yz93/lavt-ris,124,Lavt: Language-aware vision transformer for referring image segmentation,"https://scholar.google.com/scholar?cluster=10996452673462236592&hl=en&as_sdt=0,14",3,2022
Aesthetic Text Logo Synthesis via Content-Aware Layout Inferring,5,cvpr,33,9,2023-06-03 15:13:11.561000,https://github.com/yizhiwang96/textlogolayout,222,Aesthetic text logo synthesis via content-aware layout inferring,"https://scholar.google.com/scholar?cluster=9299651509951295587&hl=en&as_sdt=0,5",7,2022
Video Demoireing With Relation-Based Temporal Consistency,3,cvpr,10,3,2023-06-03 15:13:11.755000,https://github.com/CVMI-Lab/VideoDemoireing,57,Video Demoireing With Relation-Based Temporal Consistency,"https://scholar.google.com/scholar?cluster=14408025049447784264&hl=en&as_sdt=0,10",2,2022
Oriented RepPoints for Aerial Object Detection,62,cvpr,43,28,2023-06-03 15:13:11.949000,https://github.com/LiWentomng/OrientedRepPoints,229,Oriented reppoints for aerial object detection,"https://scholar.google.com/scholar?cluster=1528825781662204302&hl=en&as_sdt=0,5",5,2022
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning,29,cvpr,3,9,2023-06-03 15:13:12.144000,https://github.com/shiming-chen/msdn,44,Msdn: Mutually semantic distillation network for zero-shot learning,"https://scholar.google.com/scholar?cluster=15021999470702813013&hl=en&as_sdt=0,39",4,2022
MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video,41,cvpr,8,22,2023-06-03 15:13:12.338000,https://github.com/JinluZhang1126/MixSTE,126,Mixste: Seq2seq mixed spatio-temporal encoder for 3d human pose estimation in video,"https://scholar.google.com/scholar?cluster=10669263595390628750&hl=en&as_sdt=0,22",20,2022
OccAM's Laser: Occlusion-Based Attribution Maps for 3D Object Detectors on LiDAR Data,4,cvpr,5,1,2023-06-03 15:13:12.532000,https://github.com/dschinagl/occam,31,OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data,"https://scholar.google.com/scholar?cluster=15765380783034039691&hl=en&as_sdt=0,15",3,2022
MS2DG-Net: Progressive Correspondence Learning via Multiple Sparse Semantics Dynamic Graph,11,cvpr,0,2,2023-06-03 15:13:12.739000,https://github.com/changcaiyang/ms2dg-net,3,MS2DG-Net: Progressive correspondence learning via multiple sparse semantics dynamic graph,"https://scholar.google.com/scholar?cluster=946237664573591912&hl=en&as_sdt=0,40",1,2022
DeepCurrents: Learning Implicit Representations of Shapes With Boundaries,3,cvpr,2,0,2023-06-03 15:13:12.933000,https://github.com/dmsm/DeepCurrents,39,DeepCurrents: Learning Implicit Representations of Shapes with Boundaries,"https://scholar.google.com/scholar?cluster=5989790284022450801&hl=en&as_sdt=0,44",4,2022
Sparse Fuse Dense: Towards High Quality 3D Detection With Depth Completion,48,cvpr,31,29,2023-06-03 15:13:13.127000,https://github.com/LittlePey/SFD,221,Sparse fuse dense: Towards high quality 3d detection with depth completion,"https://scholar.google.com/scholar?cluster=4337309210814836516&hl=en&as_sdt=0,5",4,2022
High-Fidelity GAN Inversion for Image Attribute Editing,109,cvpr,44,10,2023-06-03 15:13:13.321000,https://github.com/Tengfei-Wang/HFGI,405,High-fidelity gan inversion for image attribute editing,"https://scholar.google.com/scholar?cluster=9509221732364572204&hl=en&as_sdt=0,36",32,2022
Meta Distribution Alignment for Generalizable Person Re-Identification,7,cvpr,2,1,2023-06-03 15:13:13.516000,https://github.com/haoni0812/mda,12,Meta distribution alignment for generalizable person re-identification,"https://scholar.google.com/scholar?cluster=11619394144328926429&hl=en&as_sdt=0,33",5,2022
MaskGIT: Masked Generative Image Transformer,95,cvpr,33,9,2023-06-03 15:13:13.710000,https://github.com/google-research/maskgit,267,Maskgit: Masked generative image transformer,"https://scholar.google.com/scholar?cluster=6249368678331828868&hl=en&as_sdt=0,5",15,2022
Instance-Aware Dynamic Neural Network Quantization,3,cvpr,131,3,2023-06-03 15:13:13.904000,https://github.com/huawei-noah/Efficient-Computing,593,Instance-aware dynamic neural network quantization,"https://scholar.google.com/scholar?cluster=3797816427904047286&hl=en&as_sdt=0,33",18,2022
All-in-One Image Restoration for Unknown Corruption,40,cvpr,12,4,2023-06-03 15:13:14.099000,https://github.com/xlearning-scu/2022-cvpr-airnet,83,All-in-one image restoration for unknown corruption,"https://scholar.google.com/scholar?cluster=10807347079650485654&hl=en&as_sdt=0,5",2,2022
Selective-Supervised Contrastive Learning With Noisy Labels,39,cvpr,13,5,2023-06-03 15:13:14.293000,https://github.com/shikunli/sel-cl,74,Selective-supervised contrastive learning with noisy labels,"https://scholar.google.com/scholar?cluster=7191762310287055252&hl=en&as_sdt=0,5",2,2022
Optimizing Video Prediction via Video Frame Interpolation,9,cvpr,6,0,2023-06-03 15:13:14.492000,https://github.com/YueWuHKUST/CVPR2022-Optimizing-Video-Prediction-via-Video-Frame-Interpolation,34,Optimizing video prediction via video frame interpolation,"https://scholar.google.com/scholar?cluster=12432696605609230243&hl=en&as_sdt=0,5",2,2022
Plenoxels: Radiance Fields Without Neural Networks,176,cvpr,335,74,2023-06-03 15:13:14.686000,https://github.com/sxyu/svox2,2517,Plenoxels: Radiance fields without neural networks,"https://scholar.google.com/scholar?cluster=6850809471978145917&hl=en&as_sdt=0,5",52,2022
SimT: Handling Open-Set Noise for Domain Adaptive Semantic Segmentation,8,cvpr,6,1,2023-06-03 15:13:14.880000,https://github.com/cityu-aim-group/simt,20,Simt: handling open-set noise for domain adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=9175191664302585452&hl=en&as_sdt=0,1",2,2022
Exploring Effective Data for Surrogate Training Towards Black-Box Attack,9,cvpr,1,1,2023-06-03 15:13:15.074000,https://github.com/xuxiangsun/st-data,10,Exploring effective data for surrogate training towards black-box attack,"https://scholar.google.com/scholar?cluster=17446865475002756909&hl=en&as_sdt=0,19",2,2022
Continual Stereo Matching of Continuous Driving Scenes With Growing Architecture,4,cvpr,0,0,2023-06-03 15:13:15.269000,https://github.com/chzhang18/rag,7,Continual Stereo Matching of Continuous Driving Scenes With Growing Architecture,"https://scholar.google.com/scholar?cluster=13199977113410529761&hl=en&as_sdt=0,44",1,2022
PLAD: Learning To Infer Shape Programs With Pseudo-Labels and Approximate Distributions,3,cvpr,0,0,2023-06-03 15:13:15.463000,https://github.com/rkjones4/plad,4,PLAD: Learning to infer shape programs with pseudo-labels and approximate distributions,"https://scholar.google.com/scholar?cluster=2527616327110809598&hl=en&as_sdt=0,5",2,2022
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation,23,cvpr,12,5,2023-06-03 15:13:15.659000,https://github.com/cvi-szu/ccam,137,C2AM: contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation,"https://scholar.google.com/scholar?cluster=4112010978532867161&hl=en&as_sdt=0,5",5,2022
PTTR: Relational 3D Point Cloud Object Tracking With Transformer,34,cvpr,8,11,2023-06-03 15:13:15.853000,https://github.com/jasonkks/pttr,98,Pttr: Relational 3d point cloud object tracking with transformer,"https://scholar.google.com/scholar?cluster=6998636134614144247&hl=en&as_sdt=0,33",8,2022
A Self-Supervised Descriptor for Image Copy Detection,10,cvpr,10,1,2023-06-03 15:13:16.047000,https://github.com/facebookresearch/sscd-copy-detection,121,A self-supervised descriptor for image copy detection,"https://scholar.google.com/scholar?cluster=7715635298413748060&hl=en&as_sdt=0,31",6,2022
Industrial Style Transfer With Large-Scale Geometric Warping and Content Preservation,2,cvpr,6,4,2023-06-03 15:13:16.241000,https://github.com/jcyang98/inst,39,Industrial style transfer with large-scale geometric warping and content preservation,"https://scholar.google.com/scholar?cluster=802240836888971183&hl=en&as_sdt=0,10",3,2022
Modeling Image Composition for Complex Scene Generation,18,cvpr,0,5,2023-06-03 15:13:16.436000,https://github.com/johndreamer/twfa,7,Modeling image composition for complex scene generation,"https://scholar.google.com/scholar?cluster=16853523309090667212&hl=en&as_sdt=0,5",1,2022
Negative-Aware Attention Framework for Image-Text Matching,28,cvpr,8,6,2023-06-03 15:13:16.630000,https://github.com/crossmodalgroup/naaf,74,Negative-aware attention framework for image-text matching,"https://scholar.google.com/scholar?cluster=17580877734718681453&hl=en&as_sdt=0,34",1,2022
On the Importance of Asymmetry for Siamese Representation Learning,22,cvpr,5,0,2023-06-03 15:13:16.824000,https://github.com/facebookresearch/asym-siam,90,On the importance of asymmetry for siamese representation learning,"https://scholar.google.com/scholar?cluster=11575843394769845742&hl=en&as_sdt=0,23",10,2022
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better Than Dot-Product Self-Attention,1,cvpr,0,0,2023-06-03 15:13:17.019000,https://github.com/wiedersehne/paramixer,16,Paramixer: Parameterizing mixing links in sparse factors works better than dot-product self-attention,"https://scholar.google.com/scholar?cluster=4581359001664044296&hl=en&as_sdt=0,3",2,2022
An Image Patch Is a Wave: Phase-Aware Vision MLP,44,cvpr,648,59,2023-06-03 15:13:17.213000,https://github.com/huawei-noah/CV-backbones,3301,An image patch is a wave: Phase-aware vision mlp,"https://scholar.google.com/scholar?cluster=7355726372164978103&hl=en&as_sdt=0,44",47,2022
Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving,18,cvpr,11,3,2023-06-03 15:13:17.408000,https://github.com/revisitq/Pseudo-Stereo-3D,65,Pseudo-stereo for monocular 3d object detection in autonomous driving,"https://scholar.google.com/scholar?cluster=1891144391570964122&hl=en&as_sdt=0,23",8,2022
Shunted Self-Attention via Multi-Scale Token Aggregation,54,cvpr,20,20,2023-06-03 15:13:17.601000,https://github.com/oliverrensu/shunted-transformer,184,Shunted self-attention via multi-scale token aggregation,"https://scholar.google.com/scholar?cluster=8471894567270514506&hl=en&as_sdt=0,5",5,2022
DenseCLIP: Language-Guided Dense Prediction With Context-Aware Prompting,144,cvpr,26,4,2023-06-03 15:13:17.796000,https://github.com/raoyongming/denseclip,392,Denseclip: Language-guided dense prediction with context-aware prompting,"https://scholar.google.com/scholar?cluster=12719486318898360519&hl=en&as_sdt=0,5",3,2022
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions,17,cvpr,10,3,2023-06-03 15:13:17.990000,https://github.com/nv-nguyen/template-pose,107,Templates for 3D object pose estimation revisited: generalization to new objects and robustness to occlusions,"https://scholar.google.com/scholar?cluster=474175578152573031&hl=en&as_sdt=0,5",4,2022
Shadows Can Be Dangerous: Stealthy and Effective Physical-World Adversarial Attack by Natural Phenomenon,37,cvpr,3,4,2023-06-03 15:13:18.184000,https://github.com/hncszyq/ShadowAttack,39,Shadows can be dangerous: Stealthy and effective physical-world adversarial attack by natural phenomenon,"https://scholar.google.com/scholar?cluster=363481114525663897&hl=en&as_sdt=0,34",1,2022
Sketching Without Worrying: Noise-Tolerant Sketch-Based Image Retrieval,23,cvpr,0,1,2023-06-03 15:13:18.379000,https://github.com/ayankumarbhunia/stroke_subset_selector-for-fgsbir,9,Sketching without worrying: Noise-tolerant sketch-based image retrieval,"https://scholar.google.com/scholar?cluster=1549676145286684211&hl=en&as_sdt=0,31",1,2022
Non-Isotropy Regularization for Proxy-Based Deep Metric Learning,16,cvpr,3,1,2023-06-03 15:13:18.574000,https://github.com/explainableml/nonisotropicproxydml,12,Non-isotropy regularization for proxy-based deep metric learning,"https://scholar.google.com/scholar?cluster=18444841473840429929&hl=en&as_sdt=0,33",4,2022
PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors,7,cvpr,3,4,2023-06-03 15:13:18.768000,https://github.com/naver/pump,31,PUMP: Pyramidal and uniqueness matching priors for unsupervised learning of local descriptors,"https://scholar.google.com/scholar?cluster=8551941411438314539&hl=en&as_sdt=0,47",4,2022
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-Based Motion Recognition,5,cvpr,3,0,2023-06-03 15:13:18.962000,https://github.com/damo-cv/motionrgbd,11,Decoupling and recoupling spatiotemporal representation for RGB-D-based motion recognition,"https://scholar.google.com/scholar?cluster=6391796257286266845&hl=en&as_sdt=0,39",0,2022
Deep Equilibrium Optical Flow Estimation,20,cvpr,17,0,2023-06-03 15:13:19.158000,https://github.com/locuslab/deq-flow,155,Deep equilibrium optical flow estimation,"https://scholar.google.com/scholar?cluster=4481040450726038983&hl=en&as_sdt=0,33",8,2022
Few-Shot Learning With Noisy Labels,11,cvpr,1,0,2023-06-03 15:13:19.352000,https://github.com/facebookresearch/noisy_few_shot,30,Few-shot learning with noisy labels,"https://scholar.google.com/scholar?cluster=17809989091527759685&hl=en&as_sdt=0,33",5,2022
Interactive Image Synthesis With Panoptic Layout Generation,5,cvpr,1,4,2023-06-03 15:13:19.551000,https://github.com/wb-finalking/PLGAN,16,Interactive Image Synthesis with Panoptic Layout Generation,"https://scholar.google.com/scholar?cluster=2666875388917869100&hl=en&as_sdt=0,24",3,2022
Attribute Group Editing for Reliable Few-Shot Image Generation,7,cvpr,8,8,2023-06-03 15:13:19.745000,https://github.com/unibester/age,48,Attribute Group Editing for Reliable Few-shot Image Generation,"https://scholar.google.com/scholar?cluster=12878484213141116778&hl=en&as_sdt=0,5",3,2022
PlanarRecon: Real-Time 3D Plane Detection and Reconstruction From Posed Monocular Videos,6,cvpr,8,4,2023-06-03 15:13:19.940000,https://github.com/neu-vi/PlanarRecon,232,PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos,"https://scholar.google.com/scholar?cluster=11352550170511398324&hl=en&as_sdt=0,5",11,2022
Local Texture Estimator for Implicit Representation Function,37,cvpr,28,0,2023-06-03 15:13:20.134000,https://github.com/jaewon-lee-b/lte,120,Local texture estimator for implicit representation function,"https://scholar.google.com/scholar?cluster=15673761794145810552&hl=en&as_sdt=0,3",4,2022
Motron: Multimodal Probabilistic Human Motion Forecasting,7,cvpr,0,0,2023-06-03 15:13:20.329000,https://github.com/TUM-AAS/motron-cvpr22,12,Motron: Multimodal probabilistic human motion forecasting,"https://scholar.google.com/scholar?cluster=5600714986446944153&hl=en&as_sdt=0,39",1,2022
"Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond",4,cvpr,0,1,2023-06-03 15:13:20.523000,https://github.com/thinklab-sjtu/robustmatch,12,"Appearance and structure aware robust deep visual graph matching: Attack, defense and beyond","https://scholar.google.com/scholar?cluster=10410063998921365943&hl=en&as_sdt=0,44",2,2022
Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression,16,cvpr,10,3,2023-06-03 15:13:20.717000,https://github.com/xiaosu-zhu/McQuic,82,Unified multivariate gaussian mixture for efficient neural image compression,"https://scholar.google.com/scholar?cluster=12208152737463536401&hl=en&as_sdt=0,44",1,2022
Feature Statistics Mixing Regularization for Generative Adversarial Networks,9,cvpr,3,2,2023-06-03 15:13:20.912000,https://github.com/naver-ai/fsmr,48,Feature statistics mixing regularization for generative adversarial networks,"https://scholar.google.com/scholar?cluster=3178196947805198698&hl=en&as_sdt=0,5",3,2022
Many-to-Many Splatting for Efficient Video Frame Interpolation,24,cvpr,6,0,2023-06-03 15:13:21.106000,https://github.com/feinanshan/m2m_vfi,74,Many-to-many splatting for efficient video frame interpolation,"https://scholar.google.com/scholar?cluster=9061197896951531138&hl=en&as_sdt=0,5",5,2022
RCP: Recurrent Closest Point for Point Cloud,10,cvpr,0,1,2023-06-03 15:13:21.301000,https://github.com/gxd1994/rcp,9,RCP: recurrent closest point for point cloud,"https://scholar.google.com/scholar?cluster=6174376460507552089&hl=en&as_sdt=0,23",3,2022
Geometric and Textural Augmentation for Domain Gap Reduction,6,cvpr,0,2,2023-06-03 15:13:21.500000,https://github.com/xch-liu/geom-tex-dg,7,Geometric and Textural Augmentation for Domain Gap Reduction,"https://scholar.google.com/scholar?cluster=9960592049133533503&hl=en&as_sdt=0,5",1,2022
Surpassing the Human Accuracy: Detecting Gallbladder Cancer From USG Images With Curriculum Learning,7,cvpr,2,0,2023-06-03 15:13:21.694000,https://github.com/sbasu276/GBCNet,12,Surpassing the human accuracy: detecting gallbladder cancer from USG images with curriculum learning,"https://scholar.google.com/scholar?cluster=11123929537083945888&hl=en&as_sdt=0,33",2,2022
A Dual Weighting Label Assignment Scheme for Object Detection,31,cvpr,16,12,2023-06-03 15:13:21.888000,https://github.com/strongwolf/dw,131,A dual weighting label assignment scheme for object detection,"https://scholar.google.com/scholar?cluster=2484140027818923745&hl=en&as_sdt=0,5",5,2022
A Keypoint-Based Global Association Network for Lane Detection,16,cvpr,25,20,2023-06-03 15:13:22.083000,https://github.com/wolfwjs/ganet,174,A keypoint-based global association network for lane detection,"https://scholar.google.com/scholar?cluster=5359779095767820120&hl=en&as_sdt=0,5",8,2022
Learning Multiple Adverse Weather Removal via Two-Stage Knowledge Learning and Multi-Contrastive Regularization: Toward a Unified Model,19,cvpr,11,8,2023-06-03 15:13:22.278000,https://github.com/fingerk28/two-stage-knowledge-for-multiple-adverse-weather-removal,98,Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model,"https://scholar.google.com/scholar?cluster=6389162819004784674&hl=en&as_sdt=0,5",3,2022
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning,19,cvpr,17,2,2023-06-03 15:13:22.474000,https://github.com/htdt/hyp_metric,132,Hyperbolic vision transformers: Combining improvements in metric learning,"https://scholar.google.com/scholar?cluster=11949602207830740830&hl=en&as_sdt=0,15",7,2022
L-Verse: Bidirectional Generation Between Image and Text,10,cvpr,4,1,2023-06-03 15:13:22.668000,https://github.com/tgisaturday/L-Verse,105,L-verse: Bidirectional generation between image and text,"https://scholar.google.com/scholar?cluster=1289245814456922276&hl=en&as_sdt=0,34",11,2022
Attributable Visual Similarity Learning,5,cvpr,5,0,2023-06-03 15:13:22.863000,https://github.com/zbr17/avsl,29,Attributable visual similarity learning,"https://scholar.google.com/scholar?cluster=1504278305019128751&hl=en&as_sdt=0,15",2,2022
Self-Supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection,37,cvpr,15,9,2023-06-03 15:13:23.057000,https://github.com/liangchen527/sladd,94,Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection,"https://scholar.google.com/scholar?cluster=12729341217248980339&hl=en&as_sdt=0,5",6,2022
DyTox: Transformers for Continual Learning With DYnamic TOken eXpansion,82,cvpr,18,4,2023-06-03 15:13:23.262000,https://github.com/arthurdouillard/dytox,111,Dytox: Transformers for continual learning with dynamic token expansion,"https://scholar.google.com/scholar?cluster=14790667600152766666&hl=en&as_sdt=0,5",3,2022
PanopticDepth: A Unified Framework for Depth-Aware Panoptic Segmentation,3,cvpr,4,5,2023-06-03 15:13:23.457000,https://github.com/naiyugao/panopticdepth,94,Panopticdepth: A unified framework for depth-aware panoptic segmentation,"https://scholar.google.com/scholar?cluster=6001282028310546811&hl=en&as_sdt=0,5",4,2022
Cross-Image Relational Knowledge Distillation for Semantic Segmentation,36,cvpr,16,2,2023-06-03 15:13:23.651000,https://github.com/winycg/cirkd,112,Cross-image relational knowledge distillation for semantic segmentation,"https://scholar.google.com/scholar?cluster=7831509579630976373&hl=en&as_sdt=0,5",4,2022
3D Shape Reconstruction From 2D Images With Disentangled Attribute Flow,16,cvpr,4,0,2023-06-03 15:13:23.846000,https://github.com/junshengzhou/3dattriflow,39,3D shape reconstruction from 2D images with disentangled attribute flow,"https://scholar.google.com/scholar?cluster=1789674502219608049&hl=en&as_sdt=0,5",6,2022
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning,6,cvpr,1,0,2023-06-03 15:13:24.040000,https://github.com/mgwillia/unsupervised-analysis,8,Beyond supervised vs. unsupervised: Representative benchmarking and analysis of image representation learning,"https://scholar.google.com/scholar?cluster=16835846862649846537&hl=en&as_sdt=0,33",2,2022
Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification,3,cvpr,2,2,2023-06-03 15:13:24.242000,https://github.com/ftd-wuchao/ccsfg,8,Camera-conditioned stable feature generation for isolated camera supervised person re-identification,"https://scholar.google.com/scholar?cluster=16513446344988595603&hl=en&as_sdt=0,5",2,2022
OpenTAL: Towards Open Set Temporal Action Localization,11,cvpr,1,1,2023-06-03 15:13:24.437000,https://github.com/Cogito2012/OpenTAL,42,Opental: Towards open set temporal action localization,"https://scholar.google.com/scholar?cluster=15497847232638129233&hl=en&as_sdt=0,5",4,2022
Discrete Time Convolution for Fast Event-Based Stereo,7,cvpr,0,2,2023-06-03 15:13:24.632000,https://github.com/Huawei-BIC/Discrete_Time_Convolution_for_Fast_Event_Based_Stereo,5,Discrete time convolution for fast event-based stereo,"https://scholar.google.com/scholar?cluster=10132978278685091738&hl=en&as_sdt=0,33",1,2022
Weakly Supervised Semantic Segmentation Using Out-of-Distribution Data,23,cvpr,7,5,2023-06-03 15:13:24.826000,https://github.com/naver-ai/w-ood,79,Weakly supervised semantic segmentation using out-of-distribution data,"https://scholar.google.com/scholar?cluster=10517312123859447200&hl=en&as_sdt=0,14",7,2022
Improving Visual Grounding With Visual-Linguistic Verification and Iterative Reasoning,19,cvpr,3,4,2023-06-03 15:13:25.020000,https://github.com/yangli18/vltvg,72,Improving visual grounding with visual-linguistic verification and iterative reasoning,"https://scholar.google.com/scholar?cluster=11859415308292435573&hl=en&as_sdt=0,5",2,2022
Point-Level Region Contrast for Object Detection Pre-Training,21,cvpr,2,0,2023-06-03 15:13:25.214000,https://github.com/facebookresearch/PLRC,31,Point-level region contrast for object detection pre-training,"https://scholar.google.com/scholar?cluster=8481021928568633762&hl=en&as_sdt=0,5",4,2022
ST++: Make Self-Training Work Better for Semi-Supervised Semantic Segmentation,77,cvpr,28,2,2023-06-03 15:13:25.409000,https://github.com/LiheYoung/ST-PlusPlus,184,St++: Make self-training work better for semi-supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=9712954716609052596&hl=en&as_sdt=0,47",5,2022
VisualHow: Multimodal Problem Solving,0,cvpr,1,0,2023-06-03 15:13:25.602000,https://github.com/formidify/visualhow,4,VisualHow: Multimodal Problem Solving,"https://scholar.google.com/scholar?cluster=18023386925378797214&hl=en&as_sdt=0,5",2,2022
Interacting Attention Graph for Single Image Two-Hand Reconstruction,32,cvpr,25,14,2023-06-03 15:13:25.797000,https://github.com/dw1010/intaghand,217,Interacting attention graph for single image two-hand reconstruction,"https://scholar.google.com/scholar?cluster=279346595374704806&hl=en&as_sdt=0,5",15,2022
OSSGAN: Open-Set Semi-Supervised Image Generation,0,cvpr,4,1,2023-06-03 15:13:25.991000,https://github.com/raven38/ossgan,20,OSSGAN: Open-Set Semi-Supervised Image Generation,"https://scholar.google.com/scholar?cluster=16258493984926978217&hl=en&as_sdt=0,5",1,2022
Failure Modes of Domain Generalization Algorithms,7,cvpr,1,0,2023-06-03 15:13:26.185000,https://github.com/tigrangalstyan/wilds,0,Failure modes of domain generalization algorithms,"https://scholar.google.com/scholar?cluster=3035832448773782127&hl=en&as_sdt=0,5",0,2022
Exploring and Evaluating Image Restoration Potential in Dynamic Scenes,4,cvpr,0,0,2023-06-03 15:13:26.380000,https://github.com/justones/irp,7,Exploring and evaluating image restoration potential in dynamic scenes,"https://scholar.google.com/scholar?cluster=3069214283886555277&hl=en&as_sdt=0,5",2,2022
Lite Vision Transformer With Enhanced Self-Attention,34,cvpr,9,5,2023-06-03 15:13:26.574000,https://github.com/chenglin-yang/lvt,110,Lite vision transformer with enhanced self-attention,"https://scholar.google.com/scholar?cluster=1596143178819191608&hl=en&as_sdt=0,47",3,2022
TrackFormer: Multi-Object Tracking With Transformers,352,cvpr,83,46,2023-06-03 15:13:26.769000,https://github.com/timmeinhardt/trackformer,384,Trackformer: Multi-object tracking with transformers,"https://scholar.google.com/scholar?cluster=11460907858590036405&hl=en&as_sdt=0,43",13,2022
Learning To Learn and Remember Super Long Multi-Domain Task Sequence,7,cvpr,0,0,2023-06-03 15:13:26.963000,https://github.com/joey-wang123/sdml,10,Learning to learn and remember super long multi-domain task sequence,"https://scholar.google.com/scholar?cluster=11670835409632826627&hl=en&as_sdt=0,44",1,2022
Bending Reality: Distortion-Aware Transformers for Adapting to Panoramic Semantic Segmentation,37,cvpr,13,1,2023-06-03 15:13:27.158000,https://github.com/jamycheung/trans4pass,49,Bending reality: Distortion-aware transformers for adapting to panoramic semantic segmentation,"https://scholar.google.com/scholar?cluster=15501859390969545209&hl=en&as_sdt=0,5",7,2022
ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation,19,cvpr,5,1,2023-06-03 15:13:27.361000,https://github.com/danielism97/st-mfnet,36,St-mfnet: A spatio-temporal multi-flow network for frame interpolation,"https://scholar.google.com/scholar?cluster=3349947361370227342&hl=en&as_sdt=0,5",5,2022
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-Learning,0,cvpr,0,0,2023-06-03 15:13:27.555000,https://github.com/transmuteai/metadock,4,Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning,"https://scholar.google.com/scholar?cluster=2138648671125260639&hl=en&as_sdt=0,5",0,2022
Decoupled Knowledge Distillation,117,cvpr,95,5,2023-06-03 15:13:27.749000,https://github.com/megvii-research/mdistiller,556,Decoupled knowledge distillation,"https://scholar.google.com/scholar?cluster=6183306406495914013&hl=en&as_sdt=0,7",6,2022
A Sampling-Based Approach for Efficient Clustering in Large Datasets,1,cvpr,1,0,2023-06-03 15:13:27.944000,https://github.com/ooub/peregrine,1,A sampling-based approach for efficient clustering in large datasets,"https://scholar.google.com/scholar?cluster=16296683083413569384&hl=en&as_sdt=0,33",1,2022
Reconstructing Surfaces for Sparse Point Clouds With On-Surface Priors,18,cvpr,25,2,2023-06-03 15:13:28.138000,https://github.com/mabaorui/onsurfaceprior,171,Reconstructing surfaces for sparse point clouds with on-surface priors,"https://scholar.google.com/scholar?cluster=4032557461331246649&hl=en&as_sdt=0,14",11,2022
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches,1,cvpr,3,1,2023-06-03 15:13:28.333000,https://github.com/sammica/simsac,17,Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches,"https://scholar.google.com/scholar?cluster=12745082851017557261&hl=en&as_sdt=0,10",5,2022
Noisy Boundaries: Lemon or Lemonade for Semi-Supervised Instance Segmentation?,9,cvpr,5,10,2023-06-03 15:13:28.528000,https://github.com/zhenyuw16/noisyboundaries,33,Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?,"https://scholar.google.com/scholar?cluster=15724815411476861632&hl=en&as_sdt=0,5",2,2022
Robust Equivariant Imaging: A Fully Unsupervised Framework for Learning To Image From Noisy and Partial Measurements,16,cvpr,6,0,2023-06-03 15:13:28.724000,https://github.com/edongdongchen/rei,67,Robust equivariant imaging: a fully unsupervised framework for learning to image from noisy and partial measurements,"https://scholar.google.com/scholar?cluster=12586813991314668221&hl=en&as_sdt=0,5",2,2022
End-to-End Semi-Supervised Learning for Video Action Detection,11,cvpr,4,0,2023-06-03 15:13:28.934000,https://github.com/AKASH2907/End-to-End-Semi-Supervised-Learning-for-Video-Action-Detection,28,End-to-end semi-supervised learning for video action detection,"https://scholar.google.com/scholar?cluster=4985348020758011045&hl=en&as_sdt=0,5",1,2022
Towards Layer-Wise Image Vectorization,8,cvpr,36,1,2023-06-03 15:13:29.129000,https://github.com/picsart-ai-research/live-layerwise-image-vectorization,287,Towards layer-wise image vectorization,"https://scholar.google.com/scholar?cluster=3203694956328431306&hl=en&as_sdt=0,10",12,2022
Deep Color Consistent Network for Low-Light Image Enhancement,25,cvpr,0,0,2023-06-03 15:13:29.324000,https://github.com/Ian0926/DCC-Net,14,Deep color consistent network for low-light image enhancement,"https://scholar.google.com/scholar?cluster=12246009956123058651&hl=en&as_sdt=0,14",1,2022
Scenic: A JAX Library for Computer Vision Research and Beyond,36,cvpr,313,163,2023-06-03 15:13:29.521000,https://github.com/google-research/scenic,2214,Scenic: A JAX library for computer vision research and beyond,"https://scholar.google.com/scholar?cluster=9687250935601109702&hl=en&as_sdt=0,5",37,2022
Real-Time Object Detection for Streaming Perception,16,cvpr,36,12,2023-06-03 15:13:29.716000,https://github.com/yancie-yjr/StreamYOLO,257,Real-time object detection for streaming perception,"https://scholar.google.com/scholar?cluster=10139464426827633103&hl=en&as_sdt=0,50",14,2022
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters,11,cvpr,2,2,2023-06-03 15:13:29.911000,https://github.com/paulgavrikov/cnn-filter-db,24,Cnn filter db: An empirical investigation of trained convolutional filters,"https://scholar.google.com/scholar?cluster=15125945935923890822&hl=en&as_sdt=0,11",2,2022
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets,2,cvpr,0,0,2023-06-03 15:13:30.105000,https://github.com/vsingh-group/datasetpooling,0,Equivariance allows handling multiple nuisance variables when analyzing pooled neuroimaging datasets,"https://scholar.google.com/scholar?cluster=10828542987860364997&hl=en&as_sdt=0,5",0,2022
CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings,5,cvpr,11,5,2023-06-03 15:13:30.300000,https://github.com/vita-group/cadtransformer,30,Cadtransformer: Panoptic symbol spotting transformer for cad drawings,"https://scholar.google.com/scholar?cluster=2657820499558458699&hl=en&as_sdt=0,5",14,2022
"ScePT: Scene-Consistent, Policy-Based Trajectory Predictions for Planning",18,cvpr,15,2,2023-06-03 15:13:30.495000,https://github.com/nvr-avg/scept,55,"Scept: Scene-consistent, policy-based trajectory predictions for planning","https://scholar.google.com/scholar?cluster=16859171496249935243&hl=en&as_sdt=0,5",7,2022
Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning,20,cvpr,17,4,2023-06-03 15:13:30.689000,https://github.com/snap-research/mmvid,152,Show me what and tell me how: Video synthesis via multimodal conditioning,"https://scholar.google.com/scholar?cluster=754350409916386261&hl=en&as_sdt=0,39",17,2022
Bi-Level Alignment for Cross-Domain Crowd Counting,7,cvpr,1,2,2023-06-03 15:13:30.884000,https://github.com/yankeegsj/bla,6,Bi-level alignment for cross-domain crowd counting,"https://scholar.google.com/scholar?cluster=885536784778493397&hl=en&as_sdt=0,5",2,2022
IntraQ: Learning Synthetic Images With Intra-Class Heterogeneity for Zero-Shot Network Quantization,25,cvpr,2,0,2023-06-03 15:13:31.077000,https://github.com/zysxmu/intraq,24,Intraq: Learning synthetic images with intra-class heterogeneity for zero-shot network quantization,"https://scholar.google.com/scholar?cluster=7599323507608007973&hl=en&as_sdt=0,5",1,2022
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors,1,cvpr,3,7,2023-06-03 15:13:31.272000,https://github.com/shenyi0220/cp-cluster,45,Confidence Propagation Cluster: Unleash Full Potential of Object Detectors,"https://scholar.google.com/scholar?cluster=5708961894169582507&hl=en&as_sdt=0,11",2,2022
Efficient Multi-View Stereo by Iterative Dynamic Cost Volume,7,cvpr,1,1,2023-06-03 15:13:31.466000,https://github.com/bdwsq1996/effi-mvs,36,Efficient multi-view stereo by iterative dynamic cost volume,"https://scholar.google.com/scholar?cluster=9228328506139728371&hl=en&as_sdt=0,5",6,2022
I M Avatar: Implicit Morphable Head Avatars From Videos,62,cvpr,53,2,2023-06-03 15:13:31.660000,https://github.com/zhengyuf/imavatar,534,Im avatar: Implicit morphable head avatars from videos,"https://scholar.google.com/scholar?cluster=16091393201921804100&hl=en&as_sdt=0,33",30,2022
ISNet: Shape Matters for Infrared Small Target Detection,23,cvpr,4,9,2023-06-03 15:13:31.855000,https://github.com/ruizhang97/isnet,73,ISNET: Shape matters for infrared small target detection,"https://scholar.google.com/scholar?cluster=13949452124724889742&hl=en&as_sdt=0,5",5,2022
Learning To Generate Line Drawings That Convey Geometry and Semantics,13,cvpr,37,14,2023-06-03 15:13:32.049000,https://github.com/carolineec/informative-drawings,259,Learning to generate line drawings that convey geometry and semantics,"https://scholar.google.com/scholar?cluster=13892686847311426908&hl=en&as_sdt=0,16",9,2022
Spatial Commonsense Graph for Object Localisation in Partial Scenes,6,cvpr,1,0,2023-06-03 15:13:32.243000,https://github.com/fgiuliari/spatialcommonsensegraph-dataset,6,Spatial commonsense graph for object localisation in partial scenes,"https://scholar.google.com/scholar?cluster=13436254565299915573&hl=en&as_sdt=0,10",4,2022
"Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way",25,cvpr,4,6,2023-06-03 15:13:32.438000,https://github.com/dlut-dimt/segmar,11,"Segment, magnify and reiterate: Detecting camouflaged objects the hard way","https://scholar.google.com/scholar?cluster=14180211102585612183&hl=en&as_sdt=0,5",2,2022
TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing,20,cvpr,13,9,2023-06-03 15:13:32.632000,https://github.com/billyxyb/transeditor,143,TransEditor: transformer-based dual-space GAN for highly controllable facial editing,"https://scholar.google.com/scholar?cluster=5195996324889296393&hl=en&as_sdt=0,5",12,2022
Multi-Modal Dynamic Graph Transformer for Visual Grounding,3,cvpr,0,3,2023-06-03 15:13:32.826000,https://github.com/iqua/m-dgt,12,Multi-Modal Dynamic Graph Transformer for Visual Grounding,"https://scholar.google.com/scholar?cluster=137668874215711392&hl=en&as_sdt=0,11",1,2022
Multi-Label Classification With Partial Annotations Using Class-Aware Selective Loss,9,cvpr,18,9,2023-06-03 15:13:33.021000,https://github.com/alibaba-miil/partiallabelingcsl,109,Multi-label classification with partial annotations using class-aware selective loss,"https://scholar.google.com/scholar?cluster=5212568544334689745&hl=en&as_sdt=0,5",2,2022
PointCLIP: Point Cloud Understanding by CLIP,98,cvpr,21,9,2023-06-03 15:13:33.217000,https://github.com/zrrskywalker/pointclip,238,Pointclip: Point cloud understanding by clip,"https://scholar.google.com/scholar?cluster=17352749194221255073&hl=en&as_sdt=0,33",10,2022
Weakly-Supervised Metric Learning With Cross-Module Communications for the Classification of Anterior Chamber Angle Images,0,cvpr,2,0,2023-06-03 15:13:33.411000,https://github.com/jingqi-h/gcnet,3,Weakly-supervised Metric Learning with Cross-Module Communications for the Classification of Anterior Chamber Angle Images,"https://scholar.google.com/scholar?cluster=12963996817116900790&hl=en&as_sdt=0,14",1,2022
Geometric Transformer for Fast and Robust Point Cloud Registration,76,cvpr,33,11,2023-06-03 15:13:33.606000,https://github.com/qinzheng93/geotransformer,301,Geometric transformer for fast and robust point cloud registration,"https://scholar.google.com/scholar?cluster=2426792360309580188&hl=en&as_sdt=0,32",8,2022
Rethinking Semantic Segmentation: A Prototype View,78,cvpr,28,12,2023-06-03 15:13:33.800000,https://github.com/tfzhou/protoseg,273,Rethinking semantic segmentation: A prototype view,"https://scholar.google.com/scholar?cluster=608072213696684716&hl=en&as_sdt=0,25",17,2022
Reusing the Task-Specific Classifier as a Discriminator: Discriminator-Free Adversarial Domain Adaptation,24,cvpr,2,0,2023-06-03 15:13:33.994000,https://github.com/xiaoachen98/daln,42,Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation,"https://scholar.google.com/scholar?cluster=3673441406066018173&hl=en&as_sdt=0,5",3,2022
Demystifying the Neural Tangent Kernel From a Practical Perspective: Can It Be Trusted for Neural Architecture Search Without Training?,1,cvpr,0,1,2023-06-03 15:13:34.189000,https://github.com/nutellamok/demystifyingntk,8,Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?,"https://scholar.google.com/scholar?cluster=13901165729995822789&hl=en&as_sdt=0,15",2,2022
Use All the Labels: A Hierarchical Multi-Label Contrastive Learning Framework,15,cvpr,12,6,2023-06-03 15:13:34.384000,https://github.com/salesforce/hierarchicalcontrastivelearning,110,Use all the labels: A hierarchical multi-label contrastive learning framework,"https://scholar.google.com/scholar?cluster=6099615041197158188&hl=en&as_sdt=0,31",7,2022
Bijective Mapping Network for Shadow Removal,12,cvpr,3,4,2023-06-03 15:13:34.579000,https://github.com/KevinJ-Huang/BMNet,20,Bijective mapping network for shadow removal,"https://scholar.google.com/scholar?cluster=9438636493673559588&hl=en&as_sdt=0,10",5,2022
SGTR: End-to-End Scene Graph Generation With Transformer,31,cvpr,5,7,2023-06-03 15:13:34.773000,https://github.com/scarecrow0/sgtr,47,Sgtr: End-to-end scene graph generation with transformer,"https://scholar.google.com/scholar?cluster=9105314878135623559&hl=en&as_sdt=0,14",5,2022
UMT: Unified Multi-Modal Transformers for Joint Video Moment Retrieval and Highlight Detection,22,cvpr,10,3,2023-06-03 15:13:34.968000,https://github.com/tencentarc/umt,111,Umt: Unified multi-modal transformers for joint video moment retrieval and highlight detection,"https://scholar.google.com/scholar?cluster=15051557957683222248&hl=en&as_sdt=0,50",4,2022
Causal Transportability for Visual Recognition,10,cvpr,5,5,2023-06-03 15:13:35.162000,https://github.com/cvlab-columbia/ct4recognition,17,Causal transportability for visual recognition,"https://scholar.google.com/scholar?cluster=3436681948530085748&hl=en&as_sdt=0,5",3,2022
Deep Depth From Focus With Differential Focus Volume,6,cvpr,3,0,2023-06-03 15:13:35.357000,https://github.com/fuy34/dfv,14,Deep Depth from Focus with Differential Focus Volume,"https://scholar.google.com/scholar?cluster=5829644517820004180&hl=en&as_sdt=0,5",1,2022
Set-Supervised Action Learning in Procedural Task Videos via Pairwise Order Consistency,4,cvpr,0,1,2023-06-03 15:13:35.552000,https://github.com/ZijiaLewisLu/CVPR22-POC,6,Set-supervised action learning in procedural task videos via pairwise order consistency,"https://scholar.google.com/scholar?cluster=4927415608110556903&hl=en&as_sdt=0,5",2,2022
Image Disentanglement Autoencoder for Steganography Without Embedding,8,cvpr,3,0,2023-06-03 15:13:35.746000,https://github.com/lemok00/ideas,23,Image disentanglement autoencoder for steganography without embedding,"https://scholar.google.com/scholar?cluster=2354197434468641333&hl=en&as_sdt=0,34",1,2022
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection,98,cvpr,435,118,2023-06-03 15:13:35.940000,https://github.com/tensorflow/lingvo,2727,Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection,"https://scholar.google.com/scholar?cluster=14573809017701835851&hl=en&as_sdt=0,33",122,2022
PolyWorld: Polygonal Building Extraction With Graph Neural Networks in Satellite Images,18,cvpr,20,5,2023-06-03 15:13:36.135000,https://github.com/zorzi-s/polyworldpretrainednetwork,115,Polyworld: Polygonal building extraction with graph neural networks in satellite images,"https://scholar.google.com/scholar?cluster=234175643715724732&hl=en&as_sdt=0,5",7,2022
DeepFace-EMD: Re-Ranking Using Patch-Wise Earth Mover's Distance Improves Out-of-Distribution Face Identification,10,cvpr,7,0,2023-06-03 15:13:36.329000,https://github.com/anguyen8/deepface-emd,42,DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification,"https://scholar.google.com/scholar?cluster=13946665513578338312&hl=en&as_sdt=0,11",3,2022
General Facial Representation Learning in a Visual-Linguistic Manner,34,cvpr,16,11,2023-06-03 15:13:36.529000,https://github.com/FacePerceiver/FaRL,215,General facial representation learning in a visual-linguistic manner,"https://scholar.google.com/scholar?cluster=6522643289800107970&hl=en&as_sdt=0,14",5,2022
HSC4D: Human-Centered 4D Scene Capture in Large-Scale Indoor-Outdoor Space Using Wearable IMUs and LiDAR,6,cvpr,3,0,2023-06-03 15:13:36.730000,https://github.com/climbingdaily/hsc4d,58,HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR,"https://scholar.google.com/scholar?cluster=837427202429812220&hl=en&as_sdt=0,34",2,2022
Face Relighting With Geometrically Consistent Shadows,13,cvpr,15,1,2023-06-03 15:13:36.924000,https://github.com/andrewhou1/geomconsistentfr,78,Face relighting with geometrically consistent shadows,"https://scholar.google.com/scholar?cluster=10705686767249373971&hl=en&as_sdt=0,50",3,2022
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation,21,cvpr,28,16,2023-06-03 15:13:37.119000,https://github.com/mit-han-lab/litepose,255,Lite pose: Efficient architecture design for 2d human pose estimation,"https://scholar.google.com/scholar?cluster=1827737403068851453&hl=en&as_sdt=0,44",23,2022
Interactiveness Field in Human-Object Interactions,13,cvpr,2,2,2023-06-03 15:13:37.313000,https://github.com/Foruck/Interactiveness-Field,8,Interactiveness field in human-object interactions,"https://scholar.google.com/scholar?cluster=13043850091789705922&hl=en&as_sdt=0,5",1,2022
Open-Set Text Recognition via Character-Context Decoupling,5,cvpr,4,0,2023-06-03 15:13:37.525000,https://github.com/lancercat/vsdf,20,Open-Set Text Recognition via Character-Context Decoupling,"https://scholar.google.com/scholar?cluster=16404682401144579187&hl=en&as_sdt=0,22",4,2022
It's All in the Teacher: Zero-Shot Quantization Brought Closer to the Teacher,8,cvpr,2,1,2023-06-03 15:13:37.719000,https://github.com/iamkanghyunchoi/ait,25,It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher,"https://scholar.google.com/scholar?cluster=13927349012197070959&hl=en&as_sdt=0,10",3,2022
Boosting Black-Box Attack With Partially Transferred Conditional Adversarial Distribution,15,cvpr,1,3,2023-06-03 15:13:37.914000,https://github.com/Kira0096/CGATTACK,20,Boosting black-box attack with partially transferred conditional adversarial distribution,"https://scholar.google.com/scholar?cluster=5846433864811147875&hl=en&as_sdt=0,10",1,2022
Image Animation With Perturbed Masks,0,cvpr,1,4,2023-06-03 15:13:38.108000,https://github.com/itsyoavshalev/Image-Animation-with-Perturbed-Masks,9,Image Animation with Perturbed Masks,"https://scholar.google.com/scholar?cluster=11353454438481556470&hl=en&as_sdt=0,10",1,2022
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-Resolution,14,cvpr,16,15,2023-06-03 15:13:38.303000,https://github.com/mjq11302010044/tatt,126,A text attention network for spatial deformation robust scene text image super-resolution,"https://scholar.google.com/scholar?cluster=3353700071146365451&hl=en&as_sdt=0,47",2,2022
Multi-Person Extreme Motion Prediction,12,cvpr,1,0,2023-06-03 15:13:38.498000,https://github.com/GUO-W/MultiMotion,42,Multi-person extreme motion prediction,"https://scholar.google.com/scholar?cluster=7534298147315314695&hl=en&as_sdt=0,23",2,2022
The Devil Is in the Details: Window-Based Attention for Image Compression,20,cvpr,14,6,2023-06-03 15:13:38.692000,https://github.com/googolxx/stf,104,The devil is in the details: Window-based attention for image compression,"https://scholar.google.com/scholar?cluster=10003124888209359259&hl=en&as_sdt=0,39",3,2022
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing,35,cvpr,18,15,2023-06-03 15:13:38.887000,https://github.com/wangzhuo2019/ssan,72,Domain generalization via shuffled style assembly for face anti-spoofing,"https://scholar.google.com/scholar?cluster=8882924232040294084&hl=en&as_sdt=0,44",4,2022
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network,4,cvpr,2,0,2023-06-03 15:13:39.081000,https://github.com/ByungKwanLee/Masking-Adversarial-Damage,26,Masking adversarial damage: Finding adversarial saliency for robust and sparse network,"https://scholar.google.com/scholar?cluster=4791658078003923972&hl=en&as_sdt=0,33",2,2022
OcclusionFusion: Occlusion-Aware Motion Estimation for Real-Time Dynamic 3D Reconstruction,9,cvpr,32,11,2023-06-03 15:13:39.278000,https://github.com/wenbin-lin/OcclusionFusion,212,Occlusionfusion: Occlusion-aware motion estimation for real-time dynamic 3d reconstruction,"https://scholar.google.com/scholar?cluster=419253631126996395&hl=en&as_sdt=0,14",25,2022
LARGE: Latent-Based Regression Through GAN Semantics,13,cvpr,7,1,2023-06-03 15:13:39.472000,https://github.com/YotamNitzan/LARGE,85,Large: Latent-based regression through gan semantics,"https://scholar.google.com/scholar?cluster=18373168955695458400&hl=en&as_sdt=0,36",5,2022
MonoScene: Monocular 3D Semantic Scene Completion,22,cvpr,51,0,2023-06-03 15:13:39.666000,https://github.com/cv-rits/MonoScene,459,Monoscene: Monocular 3d semantic scene completion,"https://scholar.google.com/scholar?cluster=8463919163265806404&hl=en&as_sdt=0,5",13,2022
Robust Fine-Tuning of Zero-Shot Models,166,cvpr,35,2,2023-06-03 15:13:39.861000,https://github.com/mlfoundations/wise-ft,354,Robust fine-tuning of zero-shot models,"https://scholar.google.com/scholar?cluster=12206160479867816283&hl=en&as_sdt=0,19",6,2022
Multi-Granularity Alignment Domain Adaptation for Object Detection,15,cvpr,1,1,2023-06-03 15:13:40.056000,https://github.com/tiankongzhang/mgada,14,Multi-granularity alignment domain adaptation for object detection,"https://scholar.google.com/scholar?cluster=5313352548254916730&hl=en&as_sdt=0,5",1,2022
Robust Federated Learning With Noisy and Heterogeneous Clients,17,cvpr,6,0,2023-06-03 15:13:40.250000,https://github.com/fangxiuwen/robust_fl,24,Robust federated learning with noisy and heterogeneous clients,"https://scholar.google.com/scholar?cluster=14357965830847102739&hl=en&as_sdt=0,48",2,2022
Human-Aware Object Placement for Visual Environment Reconstruction,24,cvpr,3,1,2023-06-03 15:13:40.447000,https://github.com/yhw-yhw/mover,81,Human-aware object placement for visual environment reconstruction,"https://scholar.google.com/scholar?cluster=17322252721160434094&hl=en&as_sdt=0,33",1,2022
Enabling Equivariance for Arbitrary Lie Groups,8,cvpr,1,0,2023-06-03 15:13:40.641000,https://github.com/lemacdonald/equivariant-convolutions,9,Enabling equivariance for arbitrary Lie groups,"https://scholar.google.com/scholar?cluster=15697784797839012160&hl=en&as_sdt=0,5",1,2022
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval,32,cvpr,7,3,2023-06-03 15:13:40.835000,https://github.com/layer6ai-labs/xpool,70,X-pool: Cross-modal language-video attention for text-video retrieval,"https://scholar.google.com/scholar?cluster=710257116862551104&hl=en&as_sdt=0,34",7,2022
Unbiased Teacher v2: Semi-Supervised Object Detection for Anchor-Free and Anchor-Based Detectors,28,cvpr,13,8,2023-06-03 15:13:41.030000,https://github.com/facebookresearch/unbiased-teacher-v2,80,Unbiased teacher v2: Semi-supervised object detection for anchor-free and anchor-based detectors,"https://scholar.google.com/scholar?cluster=15129909426072124916&hl=en&as_sdt=0,36",6,2022
Spatio-Temporal Relation Modeling for Few-Shot Action Recognition,26,cvpr,12,1,2023-06-03 15:13:41.224000,https://github.com/Anirudh257/strm,74,Spatio-temporal relation modeling for few-shot action recognition,"https://scholar.google.com/scholar?cluster=11681537223843253385&hl=en&as_sdt=0,5",3,2022
Gated2Gated: Self-Supervised Depth Estimation From Gated Images,2,cvpr,9,1,2023-06-03 15:13:41.418000,https://github.com/princeton-computational-imaging/gated2gated,28,Gated2Gated: Self-Supervised Depth Estimation from Gated Images,"https://scholar.google.com/scholar?cluster=2674295635751130518&hl=en&as_sdt=0,41",4,2022
Vector Quantized Diffusion Model for Text-to-Image Synthesis,180,cvpr,40,12,2023-06-03 15:13:41.613000,https://github.com/cientgu/vq-diffusion,328,Vector quantized diffusion model for text-to-image synthesis,"https://scholar.google.com/scholar?cluster=15334379520573776557&hl=en&as_sdt=0,33",7,2022
Mask Transfiner for High-Quality Instance Segmentation,31,cvpr,53,13,2023-06-03 15:13:41.807000,https://github.com/SysCV/transfiner,476,Mask transfiner for high-quality instance segmentation,"https://scholar.google.com/scholar?cluster=10396301663099841488&hl=en&as_sdt=0,34",11,2022
Ranking-Based Siamese Visual Tracking,13,cvpr,0,7,2023-06-03 15:13:42.001000,https://github.com/sansanfree/rbo,19,Ranking-based Siamese visual tracking,"https://scholar.google.com/scholar?cluster=8457311238070858364&hl=en&as_sdt=0,44",1,2022
CMT: Convolutional Neural Networks Meet Vision Transformers,245,cvpr,13,4,2023-06-03 15:13:42.196000,https://github.com/ggjy/cmt.pytorch,54,Cmt: Convolutional neural networks meet vision transformers,"https://scholar.google.com/scholar?cluster=18216985783540724512&hl=en&as_sdt=0,14",2,2022
SEEG: Semantic Energized Co-Speech Gesture Generation,19,cvpr,0,0,2023-06-03 15:13:42.390000,https://github.com/akira-l/seeg,25,SEEG: Semantic energized Co-speech gesture generation,"https://scholar.google.com/scholar?cluster=1159573125166509520&hl=en&as_sdt=0,33",3,2022
Optimizing Elimination Templates by Greedy Parameter Search,1,cvpr,0,0,2023-06-03 15:13:42.585000,https://github.com/martyushev/eliminationtemplates,4,Optimizing Elimination Templates by Greedy Parameter Search,"https://scholar.google.com/scholar?cluster=6836698749638815808&hl=en&as_sdt=0,5",5,2022
Unsupervised Image-to-Image Translation With Generative Prior,14,cvpr,14,2,2023-06-03 15:13:42.779000,https://github.com/williamyang1991/gp-unit,143,Unsupervised image-to-image translation with generative prior,"https://scholar.google.com/scholar?cluster=6364955953732635639&hl=en&as_sdt=0,31",5,2022
Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects,22,cvpr,8,0,2023-06-03 15:13:42.973000,https://github.com/nvlabs/watch-it-move,48,Watch it move: Unsupervised discovery of 3D joints for re-posing of articulated objects,"https://scholar.google.com/scholar?cluster=6834826585007350455&hl=en&as_sdt=0,5",14,2022
TransMix: Attend To Mix for Vision Transformers,35,cvpr,12,6,2023-06-03 15:13:43.168000,https://github.com/beckschen/transmix,136,Transmix: Attend to mix for vision transformers,"https://scholar.google.com/scholar?cluster=17300345725078470560&hl=en&as_sdt=0,5",11,2022
Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein Localization,2,cvpr,0,0,2023-06-03 15:13:43.362000,https://github.com/zinibrc/deepsloc,1,Multi-marginal Contrastive Learning for Multi-label Subcellular Protein Localization,"https://scholar.google.com/scholar?cluster=1035538080186509553&hl=en&as_sdt=0,5",1,2022
Hyperspherical Consistency Regularization,10,cvpr,1,0,2023-06-03 15:13:43.556000,https://github.com/chengtan9907/Hyperspherical-Consistency-Regularization,26,Hyperspherical consistency regularization,"https://scholar.google.com/scholar?cluster=10011600121781425421&hl=en&as_sdt=0,47",2,2022
HOP: History-and-Order Aware Pre-Training for Vision-and-Language Navigation,20,cvpr,0,4,2023-06-03 15:13:43.750000,https://github.com/yanyuanqiao/hop-vln,15,Hop: history-and-order aware pre-training for vision-and-language navigation,"https://scholar.google.com/scholar?cluster=8769821496264866268&hl=en&as_sdt=0,5",1,2022
"Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model",23,cvpr,3,0,2023-06-03 15:13:43.945000,https://github.com/zipengxuc/ppe,34,"Predict, prevent, and evaluate: Disentangled text-driven image manipulation empowered by pre-trained vision-language model","https://scholar.google.com/scholar?cluster=12976238228304314997&hl=en&as_sdt=0,33",3,2022
Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences,12,cvpr,61,2,2023-06-03 15:13:44.139000,https://github.com/PruneTruong/DenseMatching,475,Probabilistic warp consistency for weakly-supervised semantic correspondences,"https://scholar.google.com/scholar?cluster=5100855601029952411&hl=en&as_sdt=0,33",18,2022
Exploring the Equivalence of Siamese Self-Supervised Learning via a Unified Gradient Framework,26,cvpr,2,0,2023-06-03 15:13:44.333000,https://github.com/fundamentalvision/unigrad,27,Exploring the equivalence of siamese self-supervised learning via a unified gradient framework,"https://scholar.google.com/scholar?cluster=7990281981134691027&hl=en&as_sdt=0,23",1,2022
RU-Net: Regularized Unrolling Network for Scene Graph Generation,13,cvpr,2,1,2023-06-03 15:13:44.531000,https://github.com/siml3/ru-net,7,RU-Net: regularized unrolling network for scene graph generation,"https://scholar.google.com/scholar?cluster=3765664053223640870&hl=en&as_sdt=0,1",1,2022
Inertia-Guided Flow Completion and Style Fusion for Video Inpainting,9,cvpr,1,1,2023-06-03 15:13:44.726000,https://github.com/hitachinsk/isvi,31,Inertia-guided flow completion and style fusion for video inpainting,"https://scholar.google.com/scholar?cluster=7789864855703926573&hl=en&as_sdt=0,33",6,2022
Integrating Language Guidance Into Vision-Based Deep Metric Learning,13,cvpr,3,1,2023-06-03 15:13:44.920000,https://github.com/explainableml/languageguidance_for_dml,35,Integrating language guidance into vision-based deep metric learning,"https://scholar.google.com/scholar?cluster=14061212376736812793&hl=en&as_sdt=0,5",6,2022
Sound-Guided Semantic Image Manipulation,24,cvpr,12,5,2023-06-03 15:13:45.115000,https://github.com/kuai-lab/sound-guided-semantic-image-manipulation,71,Sound-guided semantic image manipulation,"https://scholar.google.com/scholar?cluster=13467636942293565039&hl=en&as_sdt=0,5",3,2022
Long-Tailed Visual Recognition via Gaussian Clouded Logit Adjustment,15,cvpr,3,0,2023-06-03 15:13:45.309000,https://github.com/keke921/gclloss,23,Long-tailed visual recognition via gaussian clouded logit adjustment,"https://scholar.google.com/scholar?cluster=14012492702147575648&hl=en&as_sdt=0,33",2,2022
Revisiting Domain Generalized Stereo Matching Networks From a Feature Consistency Perspective,10,cvpr,2,9,2023-06-03 15:13:45.504000,https://github.com/jiaw-z/fcstereo,43,Revisiting domain generalized stereo matching networks from a feature consistency perspective,"https://scholar.google.com/scholar?cluster=5902003836142023006&hl=en&as_sdt=0,5",1,2022
PartGlot: Learning Shape Part Segmentation From Language Reference Games,10,cvpr,3,2,2023-06-03 15:13:45.698000,https://github.com/63days/PartGlot,23,Partglot: Learning shape part segmentation from language reference games,"https://scholar.google.com/scholar?cluster=11569634995002123592&hl=en&as_sdt=0,33",3,2022
Point Density-Aware Voxels for LiDAR 3D Object Detection,48,cvpr,22,2,2023-06-03 15:13:45.892000,https://github.com/trailab/pdv,136,Point density-aware voxels for lidar 3d object detection,"https://scholar.google.com/scholar?cluster=8490405556180924949&hl=en&as_sdt=0,5",9,2022
Compositional Temporal Grounding With Structured Variational Cross-Graph Correspondence Learning,27,cvpr,1,6,2023-06-03 15:13:46.091000,https://github.com/yyjmjc/compositional-temporal-grounding,23,Compositional temporal grounding with structured variational cross-graph correspondence learning,"https://scholar.google.com/scholar?cluster=15824470747358710785&hl=en&as_sdt=0,33",1,2022
A Simple Episodic Linear Probe Improves Visual Recognition in the Wild,3,cvpr,5,0,2023-06-03 15:13:46.286000,https://github.com/akira-l/ELP,26,A simple episodic linear probe improves visual recognition in the wild,"https://scholar.google.com/scholar?cluster=11707850107783590325&hl=en&as_sdt=0,5",2,2022
Self-Supervised Video Transformer,35,cvpr,15,3,2023-06-03 15:13:46.481000,https://github.com/kahnchana/svt,83,Self-supervised video transformer,"https://scholar.google.com/scholar?cluster=11308628305789992240&hl=en&as_sdt=0,15",6,2022
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization,41,cvpr,37,5,2023-06-03 15:13:46.675000,https://github.com/lukemelas/deep-spectral-segmentation,203,Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization,"https://scholar.google.com/scholar?cluster=4695735279498312558&hl=en&as_sdt=0,23",8,2022
Complex Backdoor Detection by Symmetric Feature Differencing,10,cvpr,1,0,2023-06-03 15:13:46.869000,https://github.com/purduepaml/exray,8,Complex backdoor detection by symmetric feature differencing,"https://scholar.google.com/scholar?cluster=16516270233565857918&hl=en&as_sdt=0,5",1,2022
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition,21,cvpr,14,2,2023-06-03 15:13:47.063000,https://github.com/leaplabthu/adafocusv2,82,Adafocus v2: End-to-end training of spatial dynamic networks for video recognition,"https://scholar.google.com/scholar?cluster=12844185460204493668&hl=en&as_sdt=0,34",2,2022
Condensing CNNs With Partial Differential Equations,2,cvpr,3,1,2023-06-03 15:13:47.258000,https://github.com/anilkagak2/pde_globallayer,8,Condensing CNNs with Partial Differential Equations,"https://scholar.google.com/scholar?cluster=17289837267069094376&hl=en&as_sdt=0,5",2,2022
Unsupervised Hierarchical Semantic Segmentation With Multiview Cosegmentation and Clustering Transformers,14,cvpr,6,2,2023-06-03 15:13:47.452000,https://github.com/twke18/hsg,52,Unsupervised hierarchical semantic segmentation with multiview cosegmentation and clustering transformers,"https://scholar.google.com/scholar?cluster=8430936739774837199&hl=en&as_sdt=0,9",3,2022
Localization Distillation for Dense Object Detection,43,cvpr,50,31,2023-06-03 15:13:47.647000,https://github.com/HikariTJU/LD,310,Localization distillation for dense object detection,"https://scholar.google.com/scholar?cluster=10745444013023171384&hl=en&as_sdt=0,14",5,2022
Proper Reuse of Image Classification Features Improves Object Detection,9,cvpr,46271,1204,2023-06-03 15:13:47.840000,https://github.com/tensorflow/models,75885,Proper reuse of image classification features improves object detection,"https://scholar.google.com/scholar?cluster=5807746291493976653&hl=en&as_sdt=0,5",2774,2022
Kubric: A Scalable Dataset Generator,37,cvpr,176,55,2023-06-03 15:13:48.035000,https://github.com/google-research/kubric,1871,Kubric: A scalable dataset generator,"https://scholar.google.com/scholar?cluster=12844156757618831898&hl=en&as_sdt=0,5",36,2022
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds,31,cvpr,31,5,2023-06-03 15:13:48.229000,https://github.com/ghostish/open3dsot,191,Beyond 3d siamese tracking: A motion-centric paradigm for 3d single object tracking in point clouds,"https://scholar.google.com/scholar?cluster=111779171957440994&hl=en&as_sdt=0,5",11,2022
Optimal LED Spectral Multiplexing for NIR2RGB Translation,1,cvpr,1,1,2023-06-03 15:13:48.423000,https://github.com/cccyz/nir2rgb,4,Optimal LED Spectral Multiplexing for NIR2RGB Translation,"https://scholar.google.com/scholar?cluster=3544810694144583772&hl=en&as_sdt=0,34",1,2022
Non-Probability Sampling Network for Stochastic Human Trajectory Prediction,13,cvpr,10,0,2023-06-03 15:13:48.617000,https://github.com/inhwanbae/npsn,40,Non-probability sampling network for stochastic human trajectory prediction,"https://scholar.google.com/scholar?cluster=1248023743092759161&hl=en&as_sdt=0,19",4,2022
Neural Global Shutter: Learn To Restore Video From a Rolling Shutter Camera With Global Reset Feature,5,cvpr,0,0,2023-06-03 15:13:48.812000,https://github.com/lightchaserx/neural-global-shutter,11,Neural Global Shutter: Learn to Restore Video from a Rolling Shutter Camera with Global Reset Feature,"https://scholar.google.com/scholar?cluster=4222560518760472243&hl=en&as_sdt=0,5",5,2022
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection With Transformers,141,cvpr,62,61,2023-06-03 15:13:49.006000,https://github.com/xuyangbai/transfusion,463,Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,"https://scholar.google.com/scholar?cluster=12911220675230413174&hl=en&as_sdt=0,5",15,2022
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning,6,cvpr,3,1,2023-06-03 15:13:49.201000,https://github.com/zlijingtao/ResSFL,13,Ressfl: A resistance transfer framework for defending model inversion attack in split federated learning,"https://scholar.google.com/scholar?cluster=3765012831797072152&hl=en&as_sdt=0,5",1,2022
"DiRA: Discriminative, Restorative, and Adversarial Learning for Self-Supervised Medical Image Analysis",17,cvpr,8,4,2023-06-03 15:13:49.395000,https://github.com/jlianglab/dira,69,"DiRA: discriminative, restorative, and adversarial learning for self-supervised medical image analysis","https://scholar.google.com/scholar?cluster=10173276721588368719&hl=en&as_sdt=0,5",2,2022
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation,8,cvpr,4,5,2023-06-03 15:13:49.589000,https://github.com/mgholamikn/AdaptPose,16,AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation,"https://scholar.google.com/scholar?cluster=1711425847719753871&hl=en&as_sdt=0,31",3,2022
Per-Clip Video Object Segmentation,16,cvpr,3,2,2023-06-03 15:13:49.783000,https://github.com/pkyong95/PCVOS,51,Per-clip video object segmentation,"https://scholar.google.com/scholar?cluster=16681373339133330040&hl=en&as_sdt=0,33",5,2022
RAMA: A Rapid Multicut Algorithm on GPU,4,cvpr,12,1,2023-06-03 15:13:49.977000,https://github.com/pawelswoboda/rama,66,RAMA: A Rapid Multicut Algorithm on GPU,"https://scholar.google.com/scholar?cluster=1533239593347078884&hl=en&as_sdt=0,5",3,2022
Coarse-To-Fine Feature Mining for Video Semantic Segmentation,11,cvpr,5,3,2023-06-03 15:13:50.172000,https://github.com/guoleisun/vss-cffm,55,Coarse-to-fine feature mining for video semantic segmentation,"https://scholar.google.com/scholar?cluster=11463952495365012753&hl=en&as_sdt=0,33",4,2022
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation,31,cvpr,3,1,2023-06-03 15:13:50.366000,https://github.com/lslrh/cpsl,56,Class-balanced pixel-level self-labeling for domain adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=177219505636414694&hl=en&as_sdt=0,5",1,2022
"PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking",7,cvpr,11,3,2023-06-03 15:13:50.561000,https://github.com/andoer/posetrack21,49,"PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking","https://scholar.google.com/scholar?cluster=12602282575188654829&hl=en&as_sdt=0,5",3,2022
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching,3,cvpr,1,0,2023-06-03 15:13:50.755000,https://github.com/igitugraz/outputcodematching,4,Improving robustness against stealthy weight bit-flip attacks by output code matching,"https://scholar.google.com/scholar?cluster=6649120443721733048&hl=en&as_sdt=0,5",3,2022
Zoom in and Out: A Mixed-Scale Triplet Network for Camouflaged Object Detection,36,cvpr,14,3,2023-06-03 15:13:50.949000,https://github.com/lartpang/zoomnet,92,Zoom in and out: A mixed-scale triplet network for camouflaged object detection,"https://scholar.google.com/scholar?cluster=4424301545299413794&hl=en&as_sdt=0,47",6,2022
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation,121,cvpr,83,3,2023-06-03 15:13:51.143000,https://github.com/lhoyer/DAFormer,353,Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=4388759310460601633&hl=en&as_sdt=0,5",6,2022
MISF: Multi-Level Interactive Siamese Filtering for High-Fidelity Image Inpainting,15,cvpr,9,5,2023-06-03 15:13:51.338000,https://github.com/tsingqguo/misf,63,MISF: Multi-level interactive Siamese filtering for high-fidelity image inpainting,"https://scholar.google.com/scholar?cluster=16790016584547465514&hl=en&as_sdt=0,5",3,2022
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification,43,cvpr,19,2,2023-06-03 15:13:51.536000,https://github.com/Fei-Long121/DeepBDC,127,Joint distribution matters: Deep brownian distance covariance for few-shot classification,"https://scholar.google.com/scholar?cluster=16833673702731370777&hl=en&as_sdt=0,5",4,2022
LASER: LAtent SpacE Rendering for 2D Visual Localization,7,cvpr,0,0,2023-06-03 15:13:51.730000,https://github.com/zillow/laser,12,Laser: latent space rendering for 2d visual localization,"https://scholar.google.com/scholar?cluster=482933175726781739&hl=en&as_sdt=0,10",5,2022
Rethinking Spatial Invariance of Convolutional Networks for Object Counting,20,cvpr,9,2,2023-06-03 15:13:51.925000,https://github.com/zhiqic/rethinking-counting,39,Rethinking spatial invariance of convolutional networks for object counting,"https://scholar.google.com/scholar?cluster=2341300770995896985&hl=en&as_sdt=0,10",1,2022
An Efficient Training Approach for Very Large Scale Face Recognition,9,cvpr,9,5,2023-06-03 15:13:52.119000,https://github.com/tiandunx/FFC,82,An efficient training approach for very large scale face recognition,"https://scholar.google.com/scholar?cluster=13947234014694089490&hl=en&as_sdt=0,14",2,2022
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection,11,cvpr,3,4,2023-06-03 15:13:52.313000,https://github.com/jongmokkim/mix-unmix,30,Mum: Mix image tiles and unmix feature tiles for semi-supervised object detection,"https://scholar.google.com/scholar?cluster=7439091361445822478&hl=en&as_sdt=0,38",2,2022
The Devil Is in the Pose: Ambiguity-Free 3D Rotation-Invariant Learning via Pose-Aware Convolution,5,cvpr,2,1,2023-06-03 15:13:52.508000,https://github.com/GostInShell/PaRI-Conv,6,The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant Learning via Pose-aware Convolution,"https://scholar.google.com/scholar?cluster=8497699892320448024&hl=en&as_sdt=0,5",1,2022
Group Contextualization for Video Recognition,13,cvpr,2,2,2023-06-03 15:13:52.702000,https://github.com/haoyanbin918/group-contextualization,14,Group Contextualization for Video Recognition,"https://scholar.google.com/scholar?cluster=3668665525875112251&hl=en&as_sdt=0,21",1,2022
Few-Shot Keypoint Detection With Uncertainty Learning for Unseen Species,13,cvpr,0,1,2023-06-03 15:13:52.895000,https://github.com/alanlusun/few-shot-keypoint-detection,20,Few-shot keypoint detection with uncertainty learning for unseen species,"https://scholar.google.com/scholar?cluster=11935879951267374227&hl=en&as_sdt=0,15",5,2022
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation,13,cvpr,6,8,2023-06-03 15:13:53.090000,https://github.com/amingwu/single-dgod,30,Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation,"https://scholar.google.com/scholar?cluster=2264172047322664872&hl=en&as_sdt=0,33",3,2022
Neural RGB-D Surface Reconstruction,95,cvpr,55,14,2023-06-03 15:13:53.284000,https://github.com/dazinovic/neural-rgbd-surface-reconstruction,472,Neural rgb-d surface reconstruction,"https://scholar.google.com/scholar?cluster=9532725352404399195&hl=en&as_sdt=0,33",19,2022
3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection,3,cvpr,2,5,2023-06-03 15:13:53.479000,https://github.com/fjhzhixi/3d-sps,44,3d-sps: Single-stage 3d visual grounding via referred point progressive selection,"https://scholar.google.com/scholar?cluster=12602737653645741463&hl=en&as_sdt=0,43",2,2022
Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation,8,cvpr,2,1,2023-06-03 15:13:53.673000,https://github.com/jianf-wang/gbdl,46,Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation,"https://scholar.google.com/scholar?cluster=9467645249954304537&hl=en&as_sdt=0,5",1,2022
Cross-Domain Adaptive Teacher for Object Detection,36,cvpr,22,30,2023-06-03 15:13:53.867000,https://github.com/facebookresearch/adaptive_teacher,125,Cross-domain adaptive teacher for object detection,"https://scholar.google.com/scholar?cluster=2590113947712489483&hl=en&as_sdt=0,11",7,2022
Learning Multiple Dense Prediction Tasks From Partially Annotated Data,6,cvpr,0,2,2023-06-03 15:13:54.062000,https://github.com/vico-uoe/mtpsl,31,Learning multiple dense prediction tasks from partially annotated data,"https://scholar.google.com/scholar?cluster=5744915286654827216&hl=en&as_sdt=0,5",2,2022
Learning Non-Target Knowledge for Few-Shot Semantic Segmentation,18,cvpr,3,3,2023-06-03 15:13:54.256000,https://github.com/LIUYUANWEI98/NERTNet,26,Learning non-target knowledge for few-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=12689311458399310289&hl=en&as_sdt=0,14",2,2022
Accurate 3D Body Shape Regression Using Metric and Semantic Attributes,13,cvpr,30,15,2023-06-03 15:13:54.451000,https://github.com/muelea/shapy,221,Accurate 3D body shape regression using metric and semantic attributes,"https://scholar.google.com/scholar?cluster=4391312798610099614&hl=en&as_sdt=0,43",13,2022
Leveraging Adversarial Examples To Quantify Membership Information Leakage,6,cvpr,1,0,2023-06-03 15:13:54.645000,https://github.com/ganeshdg95/leveraging-adversarial-examples-to-quantify-membership-information-leakage,12,Leveraging adversarial examples to quantify membership information leakage,"https://scholar.google.com/scholar?cluster=13547022737227796799&hl=en&as_sdt=0,5",2,2022
Real-Time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders,4,cvpr,3,0,2023-06-03 15:13:54.840000,https://github.com/makamoa/hyplex,15,Real-time hyperspectral imaging in hardware via trained metasurface encoders,"https://scholar.google.com/scholar?cluster=11331603902909093284&hl=en&as_sdt=0,5",2,2022
Point-NeRF: Point-Based Neural Radiance Fields,93,cvpr,116,53,2023-06-03 15:13:55.034000,https://github.com/Xharlie/pointnerf,839,Point-nerf: Point-based neural radiance fields,"https://scholar.google.com/scholar?cluster=35440399082752988&hl=en&as_sdt=0,5",18,2022
Clean Implicit 3D Structure From Noisy 2D STEM Images,1,cvpr,1,0,2023-06-03 15:13:55.229000,https://github.com/hannahkniesel/implicit-electron-tomography,12,Clean implicit 3d structure from noisy 2d stem images,"https://scholar.google.com/scholar?cluster=8403324001091988092&hl=en&as_sdt=0,5",2,2022
Self-Augmented Unpaired Image Dehazing via Density and Depth Decomposition,34,cvpr,13,1,2023-06-03 15:13:55.423000,https://github.com/yan9-y/d4,59,Self-augmented unpaired image dehazing via density and depth decomposition,"https://scholar.google.com/scholar?cluster=14864964873917168900&hl=en&as_sdt=0,18",2,2022
PokeBNN: A Binary Pursuit of Lightweight Accuracy,15,cvpr,18,18,2023-06-03 15:13:55.617000,https://github.com/google/aqt,58,Pokebnn: A binary pursuit of lightweight accuracy,"https://scholar.google.com/scholar?cluster=5123791167800798572&hl=en&as_sdt=0,33",5,2022
UKPGAN: A General Self-Supervised Keypoint Detector,5,cvpr,3,0,2023-06-03 15:13:55.812000,https://github.com/qq456cvb/UKPGAN,48,Ukpgan: A general self-supervised keypoint detector,"https://scholar.google.com/scholar?cluster=7562868552807272633&hl=en&as_sdt=0,8",7,2022
Sparse Object-Level Supervision for Instance Segmentation With Pixel Embeddings,5,cvpr,6,2,2023-06-03 15:13:56.006000,https://github.com/kreshuklab/spoco,26,Sparse object-level supervision for instance segmentation with pixel embeddings,"https://scholar.google.com/scholar?cluster=6981889473218887560&hl=en&as_sdt=0,44",9,2022
How Do You Do It? Fine-Grained Action Understanding With Pseudo-Adverbs,7,cvpr,1,0,2023-06-03 15:13:56.201000,https://github.com/hazeld/pseudoadverbs,6,How do you do it? fine-grained action understanding with pseudo-adverbs,"https://scholar.google.com/scholar?cluster=3761537401906399664&hl=en&as_sdt=0,5",2,2022
Cascade Transformers for End-to-End Person Search,14,cvpr,13,7,2023-06-03 15:13:56.395000,https://github.com/kitware/coat,42,Cascade transformers for end-to-end person search,"https://scholar.google.com/scholar?cluster=15632434916836947360&hl=en&as_sdt=0,11",4,2022
Raw High-Definition Radar for Multi-Task Learning,16,cvpr,37,24,2023-06-03 15:13:56.589000,https://github.com/valeoai/radial,84,Raw high-definition radar for multi-task learning,"https://scholar.google.com/scholar?cluster=9265903791684045961&hl=en&as_sdt=0,10",8,2022
The Implicit Values of a Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement,6,cvpr,10,0,2023-06-03 15:13:56.784000,https://github.com/princeton-computational-imaging/hndr,59,The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement,"https://scholar.google.com/scholar?cluster=4961641186149767946&hl=en&as_sdt=0,44",8,2022
"PoseTriplet: Co-Evolving 3D Human Pose Estimation, Imitation, and Hallucination Under Self-Supervision",14,cvpr,23,4,2023-06-03 15:13:56.979000,https://github.com/garfield-kh/posetriplet,269,"PoseTriplet: co-evolving 3D human pose estimation, imitation, and hallucination under self-supervision","https://scholar.google.com/scholar?cluster=9893207106663949138&hl=en&as_sdt=0,14",18,2022
InsetGAN for Full-Body Image Generation,21,cvpr,17,3,2023-06-03 15:13:57.173000,https://github.com/afruehstueck/insetGAN,130,Insetgan for full-body image generation,"https://scholar.google.com/scholar?cluster=1898916154747224188&hl=en&as_sdt=0,5",8,2022
Coarse-To-Fine Q-Attention: Efficient Learning for Visual Robotic Manipulation via Discretisation,30,cvpr,23,6,2023-06-03 15:13:57.367000,https://github.com/stepjam/ARM,105,Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation,"https://scholar.google.com/scholar?cluster=14447254555897721646&hl=en&as_sdt=0,1",5,2022
ONCE-3DLanes: Building Monocular 3D Lane Detection,9,cvpr,11,11,2023-06-03 15:13:57.563000,https://github.com/once-3dlanes/once_3dlanes_benchmark,76,Once-3dlanes: Building monocular 3d lane detection,"https://scholar.google.com/scholar?cluster=6284174835150322196&hl=en&as_sdt=0,5",4,2022
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening,16,cvpr,11,9,2023-06-03 15:13:57.756000,https://github.com/wgcban/HyperTransformer,83,HyperTransformer: A textural and spectral feature fusion transformer for pansharpening,"https://scholar.google.com/scholar?cluster=14786042990833201883&hl=en&as_sdt=0,31",1,2022
Visual Abductive Reasoning,17,cvpr,10,3,2023-06-03 15:13:57.951000,https://github.com/leonnnop/var,104,Visual abductive reasoning,"https://scholar.google.com/scholar?cluster=12179295455727948525&hl=en&as_sdt=0,33",5,2022
MPViT: Multi-Path Vision Transformer for Dense Prediction,79,cvpr,33,11,2023-06-03 15:13:58.145000,https://github.com/youngwanLEE/MPViT,303,Mpvit: Multi-path vision transformer for dense prediction,"https://scholar.google.com/scholar?cluster=15814018180296500902&hl=en&as_sdt=0,5",9,2022
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models,9,cvpr,0,0,2023-06-03 15:13:58.365000,https://github.com/seekingdream/nicgslowdown,3,NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models,"https://scholar.google.com/scholar?cluster=18315829706085781932&hl=en&as_sdt=0,5",1,2022
Modulated Contrast for Versatile Image Synthesis,50,cvpr,5,4,2023-06-03 15:13:58.562000,https://github.com/fnzhan/monce,26,Modulated contrast for versatile image synthesis,"https://scholar.google.com/scholar?cluster=12701507388309605801&hl=en&as_sdt=0,44",3,2022
MonoGround: Detecting Monocular 3D Objects From the Ground,7,cvpr,5,3,2023-06-03 15:13:58.757000,https://github.com/cfzd/monoground,21,Monoground: Detecting monocular 3d objects from the ground,"https://scholar.google.com/scholar?cluster=2819960665559066982&hl=en&as_sdt=0,41",3,2022
Neural 3D Video Synthesis From Multi-View Video,59,cvpr,4,6,2023-06-03 15:13:58.951000,https://github.com/facebookresearch/neural_3d_video,187,Neural 3d video synthesis from multi-view video,"https://scholar.google.com/scholar?cluster=5276989799202751831&hl=en&as_sdt=0,44",9,2022
Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning,13,cvpr,5,1,2023-06-03 15:13:59.146000,https://github.com/Yfyangd/CVPR2022,2,Semiconductor defect detection by hybrid classical-quantum deep learning,"https://scholar.google.com/scholar?cluster=5593241258278623802&hl=en&as_sdt=0,5",2,2022
Learning What Not To Segment: A New Perspective on Few-Shot Segmentation,46,cvpr,32,9,2023-06-03 15:13:59.341000,https://github.com/chunbolang/BAM,187,Learning what not to segment: A new perspective on few-shot segmentation,"https://scholar.google.com/scholar?cluster=1275326702368315775&hl=en&as_sdt=0,5",3,2022
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing,24,cvpr,35,21,2023-06-03 15:13:59.542000,https://github.com/seasonSH/SemanticStyleGAN,208,Semanticstylegan: Learning compositional generative priors for controllable image synthesis and editing,"https://scholar.google.com/scholar?cluster=12283378212518776126&hl=en&as_sdt=0,5",8,2022
"StyleGAN-V: A Continuous Video Generator With the Price, Image Quality and Perks of StyleGAN2",61,cvpr,31,17,2023-06-03 15:13:59.737000,https://github.com/universome/stylegan-v,252,"Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2","https://scholar.google.com/scholar?cluster=7760725159980384673&hl=en&as_sdt=0,5",19,2022
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors,2,cvpr,1,0,2023-06-03 15:13:59.930000,https://github.com/poloclub/detector-detective,2,DetectorDetective: Investigating the effects of adversarial examples on object detectors,"https://scholar.google.com/scholar?cluster=3923002038356489512&hl=en&as_sdt=0,5",21,2022
Pin the Memory: Learning To Generalize Semantic Segmentation,15,cvpr,0,2,2023-06-03 15:14:00.125000,https://github.com/genie-kim/pinthememory,26,Pin the memory: Learning to generalize semantic segmentation,"https://scholar.google.com/scholar?cluster=16086647685465767136&hl=en&as_sdt=0,47",3,2022
Learning Where To Learn in Cross-View Self-Supervised Learning,11,cvpr,2,1,2023-06-03 15:14:00.319000,https://github.com/LayneH/LEWEL,24,Learning where to learn in cross-view self-supervised learning,"https://scholar.google.com/scholar?cluster=6776914221737060740&hl=en&as_sdt=0,14",2,2022
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching,7,cvpr,3,2,2023-06-03 15:14:00.514000,https://github.com/shiyaya/emscore,18,EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching,"https://scholar.google.com/scholar?cluster=9986430592002649643&hl=en&as_sdt=0,11",2,2022
Iterative Deep Homography Estimation,11,cvpr,6,13,2023-06-03 15:14:00.708000,https://github.com/imdumpl78/ihn,41,Iterative deep homography estimation,"https://scholar.google.com/scholar?cluster=6218921977782203274&hl=en&as_sdt=0,34",4,2022
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation,1,cvpr,2,1,2023-06-03 15:14:00.903000,https://github.com/wangzy22/SemAffiNet,41,SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation,"https://scholar.google.com/scholar?cluster=1397059046755751146&hl=en&as_sdt=0,5",2,2022
SNR-Aware Low-Light Image Enhancement,28,cvpr,11,14,2023-06-03 15:14:01.097000,https://github.com/dvlab-research/SNR-Aware-Low-Light-Enhance,67,SNR-aware low-light image enhancement,"https://scholar.google.com/scholar?cluster=2246956610590692796&hl=en&as_sdt=0,14",2,2022
Colar: Effective and Efficient Online Action Detection by Consulting Exemplars,12,cvpr,51,2,2023-06-03 15:14:01.292000,https://github.com/vividle/online-action-detection,259,Colar: Effective and efficient online action detection by consulting exemplars,"https://scholar.google.com/scholar?cluster=2120205662490355948&hl=en&as_sdt=0,44",30,2022
Structure-Aware Flow Generation for Human Body Reshaping,0,cvpr,8,3,2023-06-03 15:14:01.486000,https://github.com/jianqiangren/flowbasedbodyreshaping,91,Structure-Aware Flow Generation for Human Body Reshaping,"https://scholar.google.com/scholar?cluster=3734161270165986121&hl=en&as_sdt=0,5",12,2022
Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search,9,cvpr,3,6,2023-06-03 15:14:01.681000,https://github.com/euphoria16/shapley-nas,18,Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search,"https://scholar.google.com/scholar?cluster=10417871289325994119&hl=en&as_sdt=0,10",2,2022
3D Common Corruptions and Data Augmentation,15,cvpr,5,1,2023-06-03 15:14:01.875000,https://github.com/EPFL-VILAB/3DCommonCorruptions,61,3d common corruptions and data augmentation,"https://scholar.google.com/scholar?cluster=2157253668952811882&hl=en&as_sdt=0,33",5,2022
Vision-Language Pre-Training With Triple Contrastive Learning,88,cvpr,32,3,2023-06-03 15:14:02.070000,https://github.com/uta-smile/TCL,211,Vision-language pre-training with triple contrastive learning,"https://scholar.google.com/scholar?cluster=5621601248354598907&hl=en&as_sdt=0,10",6,2022
Injecting Semantic Concepts Into End-to-End Image Captioning,36,cvpr,0,8,2023-06-03 15:14:02.264000,https://github.com/jacobswan1/ViTCAP,34,Injecting semantic concepts into end-to-end image captioning,"https://scholar.google.com/scholar?cluster=9045895437530863415&hl=en&as_sdt=0,33",1,2022
SoftGroup for 3D Instance Segmentation on Point Clouds,65,cvpr,67,67,2023-06-03 15:14:02.459000,https://github.com/thangvubk/softgroup,271,Softgroup for 3d instance segmentation on point clouds,"https://scholar.google.com/scholar?cluster=10361837058618716429&hl=en&as_sdt=0,5",12,2022
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation,35,cvpr,17,2,2023-06-03 15:14:02.654000,https://github.com/pengtaojiang/l2g,46,L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=6552627678865502997&hl=en&as_sdt=0,33",3,2022
Deep Visual Geo-Localization Benchmark,16,cvpr,21,1,2023-06-03 15:14:02.848000,https://github.com/gmberton/deep-visual-geo-localization-benchmark,84,Deep visual geo-localization benchmark,"https://scholar.google.com/scholar?cluster=3325367119668373345&hl=en&as_sdt=0,14",2,2022
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks,21,cvpr,5,0,2023-06-03 15:14:03.042000,https://github.com/unispac/subnet-replacement-attack,12,Towards practical deployment-stage backdoor attack on deep neural networks,"https://scholar.google.com/scholar?cluster=17139328398709168157&hl=en&as_sdt=0,5",2,2022
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers,14,cvpr,3,0,2023-06-03 15:14:03.237000,https://github.com/intellabs/vl-interpret,62,Vl-interpret: An interactive visualization tool for interpreting vision-language transformers,"https://scholar.google.com/scholar?cluster=16119852457910791592&hl=en&as_sdt=0,33",9,2022
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement,10,cvpr,8,8,2023-06-03 15:14:03.431000,https://github.com/clovaai/BESTIE,41,Beyond semantic to instance segmentation: Weakly-supervised instance segmentation via semantic knowledge transfer and self-refinement,"https://scholar.google.com/scholar?cluster=6276145091064167820&hl=en&as_sdt=0,5",3,2022
Scaling Vision Transformers,449,cvpr,58,6,2023-06-03 15:14:03.626000,https://github.com/google-research/big_vision,878,Scaling vision transformers,"https://scholar.google.com/scholar?cluster=13501013621324561884&hl=en&as_sdt=0,14",23,2022
LiDAR Snowfall Simulation for Robust 3D Object Detection,27,cvpr,16,1,2023-06-03 15:14:03.820000,https://github.com/syscv/lidar_snow_sim,118,Lidar snowfall simulation for robust 3d object detection,"https://scholar.google.com/scholar?cluster=10885150940729856964&hl=en&as_sdt=0,5",11,2022
EDTER: Edge Detection With Transformer,31,cvpr,19,37,2023-06-03 15:14:04.017000,https://github.com/mengyangpu/edter,174,Edter: Edge detection with transformer,"https://scholar.google.com/scholar?cluster=13881045100514617650&hl=en&as_sdt=0,43",8,2022
Learned Queries for Efficient Local Attention,11,cvpr,6,5,2023-06-03 15:14:04.212000,https://github.com/moabarar/qna,104,Learned queries for efficient local attention,"https://scholar.google.com/scholar?cluster=5490257504184562365&hl=en&as_sdt=0,5",11,2022
MVS2D: Efficient Multi-View Stereo via Attention-Driven 2D Convolutions,20,cvpr,10,7,2023-06-03 15:14:04.407000,https://github.com/zhenpeiyang/MVS2D,102,Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions,"https://scholar.google.com/scholar?cluster=12068810377441312210&hl=en&as_sdt=0,33",12,2022
Stereoscopic Universal Perturbations Across Different Architectures and Datasets,6,cvpr,0,0,2023-06-03 15:14:04.601000,https://github.com/alexklwong/stereoscopic-universal-perturbations,15,Stereoscopic universal perturbations across different architectures and datasets,"https://scholar.google.com/scholar?cluster=2870130211892605355&hl=en&as_sdt=0,5",2,2022
Blended Diffusion for Text-Driven Editing of Natural Images,158,cvpr,33,0,2023-06-03 15:14:04.795000,https://github.com/omriav/blended-diffusion,419,Blended diffusion for text-driven editing of natural images,"https://scholar.google.com/scholar?cluster=5428518134765501665&hl=en&as_sdt=0,7",12,2022
Semantic-Aware Domain Generalized Segmentation,27,cvpr,9,0,2023-06-03 15:14:04.989000,https://github.com/leolyj/san-saw,123,Semantic-aware domain generalized segmentation,"https://scholar.google.com/scholar?cluster=5618227418226932379&hl=en&as_sdt=0,14",8,2022
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation,4,cvpr,2,0,2023-06-03 15:14:05.184000,https://github.com/Meowuu7/AutoGPart,23,Autogpart: Intermediate supervision search for generalizable 3d part segmentation,"https://scholar.google.com/scholar?cluster=10032151171864733868&hl=en&as_sdt=0,26",1,2022
Egocentric Scene Understanding via Multimodal Spatial Rectifier,1,cvpr,1,2,2023-06-03 15:14:05.378000,https://github.com/tien-d/EgoDepthNormal,6,Egocentric Scene Understanding via Multimodal Spatial Rectifier,"https://scholar.google.com/scholar?cluster=15125909252527214793&hl=en&as_sdt=0,41",1,2022
A Structured Dictionary Perspective on Implicit Neural Representations,21,cvpr,3,0,2023-06-03 15:14:05.573000,https://github.com/gortizji/inr_dictionaries,43,A structured dictionary perspective on implicit neural representations,"https://scholar.google.com/scholar?cluster=16723554554183827178&hl=en&as_sdt=0,5",11,2022
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels,85,cvpr,48,6,2023-06-03 15:14:05.768000,https://github.com/Haochen-Wang409/U2PL,333,Semi-supervised semantic segmentation using unreliable pseudo-labels,"https://scholar.google.com/scholar?cluster=8472948658429734246&hl=en&as_sdt=0,5",5,2022
AirObject: A Temporally Evolving Graph Embedding for Object Identification,1,cvpr,6,0,2023-06-03 15:14:05.962000,https://github.com/nik-v9/airobject,25,AirObject: A Temporally Evolving Graph Embedding for Object Identification,"https://scholar.google.com/scholar?cluster=13398015743537790569&hl=en&as_sdt=0,10",2,2022
Learning To Answer Questions in Dynamic Audio-Visual Scenarios,19,cvpr,5,3,2023-06-03 15:14:06.157000,https://github.com/GeWu-Lab/MUSIC-AVQA,47,Learning to answer questions in dynamic audio-visual scenarios,"https://scholar.google.com/scholar?cluster=12720175875337885368&hl=en&as_sdt=0,34",2,2022
Protecting Celebrities From DeepFake With Identity Consistency Transformer,16,cvpr,11,7,2023-06-03 15:14:06.352000,https://github.com/lightdxy/ict_deepfake,66,Protecting celebrities from deepfake with identity consistency transformer,"https://scholar.google.com/scholar?cluster=9563175433071478179&hl=en&as_sdt=0,44",2,2022
Synthetic Aperture Imaging With Events and Frames,4,cvpr,4,1,2023-06-03 15:14:06.547000,https://github.com/smjsc/ef-sai,6,Synthetic aperture imaging with events and frames,"https://scholar.google.com/scholar?cluster=829659197793998594&hl=en&as_sdt=0,36",2,2022
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning,11,cvpr,0,2,2023-06-03 15:14:06.740000,https://github.com/explainableml/kg-sp,8,Kg-sp: Knowledge guided simple primitives for open world compositional zero-shot learning,"https://scholar.google.com/scholar?cluster=3759909791813909467&hl=en&as_sdt=0,14",5,2022
CLIP-Event: Connecting Text and Images With Event Structures,42,cvpr,5,5,2023-06-03 15:14:06.935000,https://github.com/limanling/clip-event,81,Clip-event: Connecting text and images with event structures,"https://scholar.google.com/scholar?cluster=1378863951284050870&hl=en&as_sdt=0,31",10,2022
Stratified Transformer for 3D Point Cloud Segmentation,80,cvpr,37,32,2023-06-03 15:14:07.129000,https://github.com/dvlab-research/stratified-transformer,259,Stratified transformer for 3d point cloud segmentation,"https://scholar.google.com/scholar?cluster=1467220452108327164&hl=en&as_sdt=0,31",5,2022
Aug-NeRF: Training Stronger Neural Radiance Fields With Triple-Level Physically-Grounded Augmentations,17,cvpr,7,4,2023-06-03 15:14:07.324000,https://github.com/vita-group/aug-nerf,115,Aug-nerf: Training stronger neural radiance fields with triple-level physically-grounded augmentations,"https://scholar.google.com/scholar?cluster=8839625228911014075&hl=en&as_sdt=0,33",16,2022
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models,3,cvpr,3,0,2023-06-03 15:14:07.519000,https://github.com/amazon-research/stochastic-backpropagation,14,Stochastic backpropagation: a memory efficient strategy for training video models,"https://scholar.google.com/scholar?cluster=14270713915190496906&hl=en&as_sdt=0,33",7,2022
Semantic-Shape Adaptive Feature Modulation for Semantic Image Synthesis,8,cvpr,4,3,2023-06-03 15:14:07.715000,https://github.com/cszy98/safm,26,Semantic-shape adaptive feature modulation for semantic image synthesis,"https://scholar.google.com/scholar?cluster=17318944890457615747&hl=en&as_sdt=0,44",1,2022
FIBA: Frequency-Injection Based Backdoor Attack in Medical Image Analysis,17,cvpr,2,2,2023-06-03 15:14:07.910000,https://github.com/hazardfy/fiba,17,Fiba: Frequency-injection based backdoor attack in medical image analysis,"https://scholar.google.com/scholar?cluster=6976191591785103697&hl=en&as_sdt=0,33",2,2022
Commonality in Natural Images Rescues GANs: Pretraining GANs With Generic and Privacy-Free Synthetic Data,1,cvpr,0,0,2023-06-03 15:14:08.104000,https://github.com/friedronaldo/primitives-ps,33,Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data,"https://scholar.google.com/scholar?cluster=8826334023816517029&hl=en&as_sdt=0,33",1,2022
Day-to-Night Image Synthesis for Training Nighttime Neural ISPs,4,cvpr,3,1,2023-06-03 15:14:08.299000,https://github.com/samsunglabs/day-to-night,66,Day-to-Night Image Synthesis for Training Nighttime Neural ISPs,"https://scholar.google.com/scholar?cluster=10299773427115687036&hl=en&as_sdt=0,34",8,2022
Deep Constrained Least Squares for Blind Image Super-Resolution,21,cvpr,18,19,2023-06-03 15:14:08.493000,https://github.com/megvii-research/dcls-sr,170,Deep constrained least squares for blind image super-resolution,"https://scholar.google.com/scholar?cluster=11348834494517803103&hl=en&as_sdt=0,11",10,2022
Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning,17,cvpr,8,9,2023-06-03 15:14:08.688000,https://github.com/GT-RIPL/Xmodal-Ctx,51,Beyond a pre-trained object detector: Cross-modal textual and visual context for image captioning,"https://scholar.google.com/scholar?cluster=10614457451063447772&hl=en&as_sdt=0,5",2,2022
From Representation to Reasoning: Towards Both Evidence and Commonsense Reasoning for Video Question-Answering,10,cvpr,2,1,2023-06-03 15:14:08.883000,https://github.com/bcmi/causal-vidqa,34,From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering,"https://scholar.google.com/scholar?cluster=17266341443850491372&hl=en&as_sdt=0,10",8,2022
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion,44,cvpr,27,11,2023-06-03 15:14:09.077000,https://github.com/DanceTrack/DanceTrack,297,Dancetrack: Multi-object tracking in uniform appearance and diverse motion,"https://scholar.google.com/scholar?cluster=9529319158101525799&hl=en&as_sdt=0,18",5,2022
TubeDETR: Spatio-Temporal Video Grounding With Transformers,30,cvpr,8,4,2023-06-03 15:14:09.272000,https://github.com/antoyang/TubeDETR,124,Tubedetr: Spatio-temporal video grounding with transformers,"https://scholar.google.com/scholar?cluster=10434862692373421904&hl=en&as_sdt=0,47",3,2022
SLIC: Self-Supervised Learning With Iterative Clustering for Human Action Videos,6,cvpr,1,4,2023-06-03 15:14:09.466000,https://github.com/rvl-lab-utoronto/video_similarity_search,15,Slic: Self-supervised learning with iterative clustering for human action videos,"https://scholar.google.com/scholar?cluster=17806290374737598520&hl=en&as_sdt=0,47",2,2022
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection,25,cvpr,5,0,2023-06-03 15:14:09.660000,https://github.com/lilygeorgescu/ubnormal,47,Ubnormal: New benchmark for supervised open-set video anomaly detection,"https://scholar.google.com/scholar?cluster=8511572493070462818&hl=en&as_sdt=0,5",5,2022
Beyond Cross-View Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image,14,cvpr,8,1,2023-06-03 15:14:09.854000,https://github.com/shiyujiao/highlyaccurate,50,Beyond cross-view image retrieval: Highly accurate vehicle localization using satellite image,"https://scholar.google.com/scholar?cluster=3818502605593451777&hl=en&as_sdt=0,23",2,2022
On GANs and GMMs,136,neurips,19,1,2023-06-15 17:54:35.804000,https://github.com/eitanrich/gans-n-gmms,61,On gans and gmms,"https://scholar.google.com/scholar?cluster=809414118731916677&hl=en&as_sdt=0,44",3,2018
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks,198,neurips,15,3,2023-06-15 17:54:36.014000,https://github.com/hyeonseob-nam/Batch-Instance-Normalization,75,Batch-instance normalization for adaptively style-invariant neural networks,"https://scholar.google.com/scholar?cluster=10695085476541761892&hl=en&as_sdt=0,39",3,2018
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies,69,neurips,7,2,2023-06-15 17:54:36.209000,https://github.com/srsohn/subtask-graph-execution,12,Hierarchical reinforcement learning for zero-shot generalization with subtask dependencies,"https://scholar.google.com/scholar?cluster=15468349230439204109&hl=en&as_sdt=0,47",2,2018
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net,0,neurips,0,0,2023-06-15 17:54:36.402000,https://github.com/tmichoel/bayonet,1,Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net,"https://scholar.google.com/scholar?cluster=14797747024232630376&hl=en&as_sdt=0,39",1,2018
Streamlining Variational Inference for Constraint Satisfaction Problems,7,neurips,2,0,2023-06-15 17:54:36.595000,https://github.com/ermongroup/streamline-vi-csp,7,Streamlining variational inference for constraint satisfaction problems,"https://scholar.google.com/scholar?cluster=9129978297441572165&hl=en&as_sdt=0,18",6,2018
Critical initialisation for deep signal propagation in noisy rectifier neural networks,17,neurips,0,0,2023-06-15 17:54:36.788000,https://github.com/ElanVB/noisy_signal_prop,5,Critical initialisation for deep signal propagation in noisy rectifier neural networks,"https://scholar.google.com/scholar?cluster=1536287201347762714&hl=en&as_sdt=0,48",6,2018
COLA: Decentralized Linear Learning,117,neurips,5,0,2023-06-15 17:54:36.982000,https://github.com/epfml/cola,18,Cola: Decentralized linear learning,"https://scholar.google.com/scholar?cluster=15790148886977326889&hl=en&as_sdt=0,4",6,2018
A General Method for Amortizing Variational Filtering,29,neurips,8,0,2023-06-15 17:54:37.176000,https://github.com/joelouismarino/amortized-variational-filtering,44,A general method for amortizing variational filtering,"https://scholar.google.com/scholar?cluster=11262711494393358792&hl=en&as_sdt=0,14",7,2018
One-Shot Unsupervised Cross Domain Translation,116,neurips,17,2,2023-06-15 17:54:37.372000,https://github.com/sagiebenaim/OneShotTranslation,140,One-shot unsupervised cross domain translation,"https://scholar.google.com/scholar?cluster=16456724842379503316&hl=en&as_sdt=0,5",6,2018
Probabilistic Neural Programmed Networks for Scene Generation,13,neurips,3,0,2023-06-15 17:54:37.565000,https://github.com/Lucas2012/ProbabilisticNeuralProgrammedNetwork,40,Probabilistic neural programmed networks for scene generation,"https://scholar.google.com/scholar?cluster=7658453227892507452&hl=en&as_sdt=0,23",5,2018
On gradient regularizers for MMD GANs,94,neurips,7,0,2023-06-15 17:54:37.758000,https://github.com/MichaelArbel/Scaled-MMD-GAN,33,On gradient regularizers for MMD GANs,"https://scholar.google.com/scholar?cluster=12044208657387141906&hl=en&as_sdt=0,43",6,2018
Learning Plannable Representations with Causal InfoGAN,165,neurips,17,4,2023-06-15 17:54:37.952000,https://github.com/thanard/causal-infogan,83,Learning plannable representations with causal infogan,"https://scholar.google.com/scholar?cluster=11334480747970611889&hl=en&as_sdt=0,10",15,2018
The streaming rollout of deep networks - towards fully model-parallel execution,12,neurips,1,0,2023-06-15 17:54:38.146000,https://github.com/boschresearch/statestream,16,The streaming rollout of deep networks-towards fully model-parallel execution,"https://scholar.google.com/scholar?cluster=4918339413298728627&hl=en&as_sdt=0,10",5,2018
Generalisation in humans and deep neural networks,507,neurips,21,0,2023-06-15 17:54:38.339000,https://github.com/rgeirhos/generalisation-humans-DNNs,94,Generalisation in humans and deep neural networks,"https://scholar.google.com/scholar?cluster=16577111803298526010&hl=en&as_sdt=0,11",8,2018
Enhancing the Accuracy and Fairness of Human Decision Making,37,neurips,1,0,2023-06-15 17:54:38.533000,https://github.com/Networks-Learning/FairHumanDecisions,4,Enhancing the accuracy and fairness of human decision making,"https://scholar.google.com/scholar?cluster=9266559070813035929&hl=en&as_sdt=0,36",4,2018
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models,33,neurips,3,0,2023-06-15 17:54:38.726000,https://github.com/neitzal/adaptive-skip-intervals,25,Adaptive skip intervals: Temporal abstraction for recurrent dynamical models,"https://scholar.google.com/scholar?cluster=7596677518342590575&hl=en&as_sdt=0,5",4,2018
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks,22,neurips,1,0,2023-06-15 17:54:38.920000,https://github.com/zhangquan-ut/Lomax-delegate-racing-for-survival-analysis-with-competing-risks,1,Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks,"https://scholar.google.com/scholar?cluster=8420900743499045396&hl=en&as_sdt=0,44",1,2018
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries,143,neurips,97,11,2023-06-15 17:54:39.114000,https://github.com/amirgholami/pyhessian,538,Hessian-based analysis of large batch training and robustness to adversaries,"https://scholar.google.com/scholar?cluster=4488699145655690539&hl=en&as_sdt=0,44",13,2018
Bayesian Structure Learning by Recursive Bootstrap,15,neurips,9,0,2023-06-15 17:54:39.307000,https://github.com/IntelLabs/causality-lab,53,Bayesian structure learning by recursive bootstrap,"https://scholar.google.com/scholar?cluster=8741496663210631585&hl=en&as_sdt=0,5",10,2018
Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN,141,neurips,13,1,2023-06-15 17:54:39.501000,https://github.com/ssppp/GreedyHash,49,Greedy hash: Towards fast optimization for accurate hash coding in cnn,"https://scholar.google.com/scholar?cluster=5080578763427257320&hl=en&as_sdt=0,7",2,2018
CatBoost: unbiased boosting with categorical features,1994,neurips,1127,500,2023-06-15 17:54:39.694000,https://github.com/catboost/catboost,7187,CatBoost: unbiased boosting with categorical features,"https://scholar.google.com/scholar?cluster=15125594264257209192&hl=en&as_sdt=0,3",193,2018
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders,204,neurips,54,4,2023-06-15 17:54:39.888000,https://github.com/Microsoft/constrained-graph-variational-autoencoder,202,Constrained generation of semantically valid graphs via regularizing variational autoencoders,"https://scholar.google.com/scholar?cluster=8461416587658034730&hl=en&as_sdt=0,39",11,2018
Wasserstein Distributionally Robust Kalman Filtering,74,neurips,2,0,2023-06-15 17:54:40.081000,https://github.com/sorooshafiee/WKF,12,Wasserstein distributionally robust Kalman filtering,"https://scholar.google.com/scholar?cluster=3916790984259735894&hl=en&as_sdt=0,44",4,2018
Recurrently Controlled Recurrent Networks,22,neurips,5,0,2023-06-15 17:54:40.274000,https://github.com/vanzytay/NIPS2018_RCRN,23,Recurrently controlled recurrent networks,"https://scholar.google.com/scholar?cluster=119621077163762339&hl=en&as_sdt=0,22",3,2018
"Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data",26,neurips,3,0,2023-06-15 17:54:40.483000,https://github.com/OxCSML-BayesNP/HawkesNetOC,8,"Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data","https://scholar.google.com/scholar?cluster=3157293658449719005&hl=en&as_sdt=0,5",5,2018
Heterogeneous Multi-output Gaussian Process Prediction,75,neurips,15,1,2023-06-15 17:54:40.676000,https://github.com/pmorenoz/HetMOGP,46,Heterogeneous multi-output Gaussian process prediction,"https://scholar.google.com/scholar?cluster=16326528698943863964&hl=en&as_sdt=0,11",6,2018
SNIPER: Efficient Multi-Scale Training,526,neurips,449,116,2023-06-15 17:54:40.870000,https://github.com/MahyarNajibi/SNIPER,2674,Sniper: Efficient multi-scale training,"https://scholar.google.com/scholar?cluster=15792283057349312488&hl=en&as_sdt=0,33",84,2018
Delta-encoder: an effective sample synthesis method for few-shot object recognition,329,neurips,13,0,2023-06-15 17:54:41.064000,https://github.com/EliSchwartz/DeltaEncoder,50,Delta-encoder: an effective sample synthesis method for few-shot object recognition,"https://scholar.google.com/scholar?cluster=13986746272492724236&hl=en&as_sdt=0,3",13,2018
Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks,16,neurips,1,0,2023-06-15 17:54:41.259000,https://github.com/ZilinGao/GM-SOP,19,Global gated mixture of second-order pooling for improving deep convolutional neural networks,"https://scholar.google.com/scholar?cluster=12539085796049951238&hl=en&as_sdt=0,44",2,2018
Neural Code Comprehension: A Learnable Representation of Code Semantics,213,neurips,51,11,2023-06-15 17:54:41.473000,https://github.com/spcl/ncc,184,Neural code comprehension: A learnable representation of code semantics,"https://scholar.google.com/scholar?cluster=9627019893956716634&hl=en&as_sdt=0,5",12,2018
Structure-Aware Convolutional Neural Networks,46,neurips,4,2,2023-06-15 17:54:41.666000,https://github.com/vector-1127/SACNNs,25,Structure-aware convolutional neural networks,"https://scholar.google.com/scholar?cluster=15143914212740363018&hl=en&as_sdt=0,10",4,2018
Learning filter widths of spectral decompositions with wavelets,28,neurips,11,4,2023-06-15 17:54:41.860000,https://github.com/haidark/WaveletDeconv,32,Learning filter widths of spectral decompositions with wavelets,"https://scholar.google.com/scholar?cluster=1195090452223114657&hl=en&as_sdt=0,5",3,2018
BRUNO: A Deep Recurrent Model for Exchangeable Data,27,neurips,7,0,2023-06-15 17:54:42.053000,https://github.com/IraKorshunova/bruno,34,Bruno: A deep recurrent model for exchangeable data,"https://scholar.google.com/scholar?cluster=9358687651511071079&hl=en&as_sdt=0,5",6,2018
Gaussian Process Prior Variational Autoencoders,95,neurips,10,5,2023-06-15 17:54:42.247000,https://github.com/fpcasale/GPPVAE,67,Gaussian process prior variational autoencoders,"https://scholar.google.com/scholar?cluster=7294538008539835502&hl=en&as_sdt=0,3",8,2018
Variational Inference with Tail-adaptive f-Divergence,48,neurips,3,0,2023-06-15 17:54:42.442000,https://github.com/dilinwang820/adaptive-f-divergence,20,Variational inference with tail-adaptive f-divergence,"https://scholar.google.com/scholar?cluster=1588246766149700607&hl=en&as_sdt=0,5",3,2018
Generalizing to Unseen Domains via Adversarial Data Augmentation,588,neurips,20,1,2023-06-15 17:54:42.635000,https://github.com/ricvolpi/generalize-unseen-domains,113,Generalizing to unseen domains via adversarial data augmentation,"https://scholar.google.com/scholar?cluster=3314749587084034699&hl=en&as_sdt=0,33",4,2018
Isolating Sources of Disentanglement in Variational Autoencoders,1085,neurips,70,1,2023-06-15 17:54:42.830000,https://github.com/rtqichen/beta-tcvae,311,Isolating sources of disentanglement in variational autoencoders,"https://scholar.google.com/scholar?cluster=11372263911361899725&hl=en&as_sdt=0,5",12,2018
Learning to Share and Hide Intentions using Information Regularization,58,neurips,6,0,2023-06-15 17:54:43.023000,https://github.com/djstrouse/InfoMARL,19,Learning to share and hide intentions using information regularization,"https://scholar.google.com/scholar?cluster=17666377994780351102&hl=en&as_sdt=0,18",5,2018
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks,804,neurips,19,5,2023-06-15 17:54:43.216000,https://github.com/ashafahi/inceptionv3-transferLearn-poison,50,Poison frogs! targeted clean-label poisoning attacks on neural networks,"https://scholar.google.com/scholar?cluster=2909175979109217787&hl=en&as_sdt=0,5",4,2018
Non-metric Similarity Graphs for Maximum Inner Product Search,57,neurips,10,1,2023-06-15 17:54:43.410000,https://github.com/stanis-morozov/ip-nsw,38,Non-metric similarity graphs for maximum inner product search,"https://scholar.google.com/scholar?cluster=7566476240574710197&hl=en&as_sdt=0,49",5,2018
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction,125,neurips,22,6,2023-06-15 17:54:43.603000,https://github.com/shikorab/SceneGraph,68,Mapping images to scene graphs with permutation-invariant structured prediction,"https://scholar.google.com/scholar?cluster=10299834729999374704&hl=en&as_sdt=0,39",9,2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration,767,neurips,501,318,2023-06-15 17:54:43.797000,https://github.com/cornellius-gp/gpytorch,3139,Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration,"https://scholar.google.com/scholar?cluster=15805506961047915622&hl=en&as_sdt=0,25",55,2018
Attention in Convolutional LSTM for Gesture Recognition,105,neurips,51,19,2023-06-15 17:54:43.991000,https://github.com/GuangmingZhu/AttentionConvLSTM,205,Attention in convolutional LSTM for gesture recognition,"https://scholar.google.com/scholar?cluster=13184940893185979866&hl=en&as_sdt=0,47",4,2018
Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger,29,neurips,5,2,2023-06-15 17:54:44.186000,https://github.com/facebookresearch/starcraft_defogger,30,Forward modeling for partial observation strategy games-a starcraft defogger,"https://scholar.google.com/scholar?cluster=5562179615762953081&hl=en&as_sdt=0,5",13,2018
PacGAN: The power of two samples in generative adversarial networks,312,neurips,9,1,2023-06-15 17:54:44.379000,https://github.com/fjxmlzn/PacGAN,80,Pacgan: The power of two samples in generative adversarial networks,"https://scholar.google.com/scholar?cluster=14705983068913748289&hl=en&as_sdt=0,5",4,2018
Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages,30,neurips,2,0,2023-06-15 17:54:44.573000,https://github.com/forest-snow/mtanchor_demo,9,Multilingual anchoring: Interactive topic modeling and alignment across languages,"https://scholar.google.com/scholar?cluster=12128120271299187435&hl=en&as_sdt=0,5",1,2018
Sanity Checks for Saliency Maps,1530,neurips,13,11,2023-06-15 17:54:44.767000,https://github.com/adebayoj/sanity_checks_saliency,99,Sanity checks for saliency maps,"https://scholar.google.com/scholar?cluster=8767887416569707674&hl=en&as_sdt=0,41",10,2018
Deep Dynamical Modeling and Control of Unsteady Fluid Flows,126,neurips,19,0,2023-06-15 17:54:44.961000,https://github.com/sisl/deep_flow_control,38,Deep dynamical modeling and control of unsteady fluid flows,"https://scholar.google.com/scholar?cluster=8193012965395960760&hl=en&as_sdt=0,33",6,2018
Lifelong Inverse Reinforcement Learning,13,neurips,3,0,2023-06-15 17:54:45.154000,https://github.com/lifelong-ml/elirl,7,Lifelong inverse reinforcement learning,"https://scholar.google.com/scholar?cluster=8930935480048739276&hl=en&as_sdt=0,10",5,2018
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks,83,neurips,4,0,2023-06-15 17:54:45.350000,https://github.com/popgenmethods/defiNETti,18,A likelihood-free inference framework for population genetic data using exchangeable neural networks,"https://scholar.google.com/scholar?cluster=5564729426311739157&hl=en&as_sdt=0,5",4,2018
Inferring Networks From Random Walk-Based Node Similarities,7,neurips,13,0,2023-06-15 17:54:45.543000,https://github.com/cnmusco/graph-similarity-learning,30,Inferring networks from random walk-based node similarities,"https://scholar.google.com/scholar?cluster=4035487172765819261&hl=en&as_sdt=0,34",9,2018
Distributed $k$-Clustering for Data with Heavy Noise,22,neurips,1,0,2023-06-15 17:54:45.737000,https://github.com/xyguo/clusterz,9,Distributed -Clustering for Data with Heavy Noise,"https://scholar.google.com/scholar?cluster=4052545958640287143&hl=en&as_sdt=0,5",1,2018
Deepcode: Feedback Codes via Deep Learning,91,neurips,12,1,2023-06-15 17:54:45.930000,https://github.com/hyejikim1/Deepcode,15,Deepcode: Feedback codes via deep learning,"https://scholar.google.com/scholar?cluster=17328761776643473390&hl=en&as_sdt=0,31",4,2018
Hamiltonian Variational Auto-Encoder,82,neurips,2,0,2023-06-15 17:54:46.123000,https://github.com/anthonycaterini/hvae-nips,14,Hamiltonian variational auto-encoder,"https://scholar.google.com/scholar?cluster=13199503496722173919&hl=en&as_sdt=0,6",2,2018
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations,23,neurips,0,1,2023-06-15 17:54:46.317000,https://github.com/wangtongada/MRS,3,Multi-value rule sets for interpretable classification with feature-efficient representations,"https://scholar.google.com/scholar?cluster=13805737803480413432&hl=en&as_sdt=0,23",3,2018
ATOMO: Communication-efficient Learning via Atomic Sparsification,302,neurips,5,2,2023-06-15 17:54:46.510000,https://github.com/hwang595/ATOMO,23,Atomo: Communication-efficient learning via atomic sparsification,"https://scholar.google.com/scholar?cluster=8287483998499358971&hl=en&as_sdt=0,26",2,2018
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models,21,neurips,5,0,2023-06-15 17:54:46.703000,https://github.com/amber0309/ANM-MM,16,Causal inference and mechanism clustering of a mixture of additive noise models,"https://scholar.google.com/scholar?cluster=17153751836211673378&hl=en&as_sdt=0,5",2,2018
Scaling provable adversarial defenses,417,neurips,83,8,2023-06-15 17:54:46.897000,https://github.com/locuslab/convex_adversarial,357,Scaling provable adversarial defenses,"https://scholar.google.com/scholar?cluster=17860970585851528849&hl=en&as_sdt=0,33",16,2018
DropMax: Adaptive Variational Softmax,14,neurips,2,0,2023-06-15 17:54:47.091000,https://github.com/haebeom-lee/dropmax,18,DropMax: Adaptive variational softmax,"https://scholar.google.com/scholar?cluster=6113755016125254061&hl=en&as_sdt=0,5",1,2018
Automatic Program Synthesis of Long Programs with a Learned Garbage Collector,69,neurips,14,0,2023-06-15 17:54:47.284000,https://github.com/amitz25/PCCoder,44,Automatic program synthesis of long programs with a learned garbage collector,"https://scholar.google.com/scholar?cluster=8202429186928135403&hl=en&as_sdt=0,11",3,2018
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions,168,neurips,4,3,2023-06-15 17:54:47.478000,https://github.com/caus-am/dom_adapt,17,Domain adaptation by using causal inference to predict invariant conditional distributions,"https://scholar.google.com/scholar?cluster=3967372382720766256&hl=en&as_sdt=0,5",7,2018
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes,43,neurips,5,0,2023-06-15 17:54:47.672000,https://github.com/RonanFR/UCRL,25,Near optimal exploration-exploitation in non-communicating Markov decision processes,"https://scholar.google.com/scholar?cluster=6645061695976054329&hl=en&as_sdt=0,39",5,2018
Mesh-TensorFlow: Deep Learning for Supercomputers,301,neurips,248,98,2023-06-15 17:54:47.865000,https://github.com/tensorflow/mesh,1427,Mesh-tensorflow: Deep learning for supercomputers,"https://scholar.google.com/scholar?cluster=1887735754811341119&hl=en&as_sdt=0,18",48,2018
Semi-crowdsourced Clustering with Deep Generative Models,19,neurips,4,1,2023-06-15 17:54:48.058000,https://github.com/xinmei9322/semicrowd,10,Semi-crowdsourced clustering with deep generative models,"https://scholar.google.com/scholar?cluster=5214247288739307462&hl=en&as_sdt=0,6",3,2018
Scalable Laplacian K-modes,10,neurips,1,0,2023-06-15 17:54:48.252000,https://github.com/imtiazziko/SLK,10,Scalable laplacian K-modes,"https://scholar.google.com/scholar?cluster=673736975875501078&hl=en&as_sdt=0,5",1,2018
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models,1017,neurips,92,11,2023-06-15 17:54:48.453000,https://github.com/kchua/handful-of-trials,397,Deep reinforcement learning in a handful of trials using probabilistic dynamics models,"https://scholar.google.com/scholar?cluster=6248399848380977147&hl=en&as_sdt=0,44",15,2018
Inexact trust-region algorithms on Riemannian manifolds,20,neurips,5,0,2023-06-15 17:54:48.647000,https://github.com/hiroyuki-kasai/Subsampled-RTR,6,Inexact trust-region algorithms on Riemannian manifolds,"https://scholar.google.com/scholar?cluster=197435474681214281&hl=en&as_sdt=0,6",2,2018
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks,187,neurips,18,0,2023-06-15 17:54:48.841000,https://github.com/jinyyy666/mm-bp-snn,33,Hybrid macro/micro level backpropagation for training deep spiking neural networks,"https://scholar.google.com/scholar?cluster=6794497534863732123&hl=en&as_sdt=0,7",4,2018
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance,103,neurips,1,0,2023-06-15 17:54:49.034000,https://github.com/GiulsLu/OT-gradients,10,Differential properties of sinkhorn approximation for learning with wasserstein distance,"https://scholar.google.com/scholar?cluster=436330101781594143&hl=en&as_sdt=0,5",2,2018
Processing of missing data by neural networks,122,neurips,11,1,2023-06-15 17:54:49.228000,https://github.com/lstruski/Processing-of-missing-data-by-neural-networks,38,Processing of missing data by neural networks,"https://scholar.google.com/scholar?cluster=8626650856385111699&hl=en&as_sdt=0,5",0,2018
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo,91,neurips,10,1,2023-06-15 17:54:49.421000,https://github.com/cambridge-mlg/sghmc_dgp,26,Inference in deep Gaussian processes using stochastic gradient Hamiltonian Monte Carlo,"https://scholar.google.com/scholar?cluster=3764755113585298283&hl=en&as_sdt=0,15",7,2018
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior,35,neurips,1,0,2023-06-15 17:54:49.615000,https://github.com/beomjoonkim/MetaLearnBO,8,Regret bounds for meta bayesian optimization with an unknown gaussian process prior,"https://scholar.google.com/scholar?cluster=17688880368262090655&hl=en&as_sdt=0,33",4,2018
Large Margin Deep Networks for Classification,249,neurips,7319,1025,2023-06-15 17:54:49.809000,https://github.com/google-research/google-research,29774,Large margin deep networks for classification,"https://scholar.google.com/scholar?cluster=4375455714147672635&hl=en&as_sdt=0,5",727,2018
Multi-Task Learning as Multi-Objective Optimization,797,neurips,154,17,2023-06-15 17:54:50.003000,https://github.com/IntelVCL/MultiObjectiveOptimization,768,Multi-task learning as multi-objective optimization,"https://scholar.google.com/scholar?cluster=7092916310292802870&hl=en&as_sdt=0,5",19,2018
Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch,91,neurips,9,0,2023-06-15 17:54:50.197000,https://github.com/OsmanMalik/tucker-tensorsketch,22,Low-rank tucker decomposition of large tensors using tensorsketch,"https://scholar.google.com/scholar?cluster=14930463506395433719&hl=en&as_sdt=0,47",3,2018
But How Does It Work in Theory? Linear SVM with Random Features,55,neurips,1,1,2023-06-15 17:54:50.391000,https://github.com/syitong/randfourier,4,But how does it work in theory? Linear SVM with random features,"https://scholar.google.com/scholar?cluster=2923305469042609420&hl=en&as_sdt=0,5",4,2018
A Probabilistic U-Net for Segmentation of Ambiguous Images,437,neurips,96,15,2023-06-15 17:54:50.584000,https://github.com/SimonKohl/probabilistic_unet,518,A probabilistic u-net for segmentation of ambiguous images,"https://scholar.google.com/scholar?cluster=17567416838130660215&hl=en&as_sdt=0,11",20,2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks,246,neurips,7,1,2023-06-15 17:54:50.778000,https://github.com/ytsmiling/lmt,34,Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks,"https://scholar.google.com/scholar?cluster=17946280354894784321&hl=en&as_sdt=0,44",2,2018
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data,378,neurips,43,4,2023-06-15 17:54:50.972000,https://github.com/mariogeiger/se3cnn,181,3d steerable cnns: Learning rotationally equivariant features in volumetric data,"https://scholar.google.com/scholar?cluster=10898598436815000986&hl=en&as_sdt=0,5",10,2018
Reducing Network Agnostophobia,233,neurips,11,2,2023-06-15 17:54:51.165000,https://github.com/Vastlab/Reducing-Network-Agnostophobia,63,Reducing network agnostophobia,"https://scholar.google.com/scholar?cluster=13549236386686072567&hl=en&as_sdt=0,36",11,2018
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions,34,neurips,7,1,2023-06-15 17:54:51.359000,https://github.com/mhsung/deep-functional-dictionaries,38,Deep functional dictionaries: Learning consistent semantic structures on 3d models from functions,"https://scholar.google.com/scholar?cluster=9622270934005244916&hl=en&as_sdt=0,23",3,2018
Learning to Decompose and Disentangle Representations for Video Prediction,284,neurips,24,3,2023-06-15 17:54:51.553000,https://github.com/jthsieh/DDPAE-video-prediction,133,Learning to decompose and disentangle representations for video prediction,"https://scholar.google.com/scholar?cluster=3026670262984428356&hl=en&as_sdt=0,23",7,2018
Moonshine: Distilling with Cheap Convolutions,115,neurips,5,1,2023-06-15 17:54:51.751000,https://github.com/BayesWatch/pytorch-moonshine,33,Moonshine: Distilling with cheap convolutions,"https://scholar.google.com/scholar?cluster=1198937430039662694&hl=en&as_sdt=0,37",4,2018
Learning Conditioned Graph Structures for Interpretable Visual Question Answering,231,neurips,35,6,2023-06-15 17:54:51.945000,https://github.com/aimbrain/vqa-project,146,Learning conditioned graph structures for interpretable visual question answering,"https://scholar.google.com/scholar?cluster=16899155560172978534&hl=en&as_sdt=0,21",9,2018
Temporal Regularization for Markov Decision Process,23,neurips,1,0,2023-06-15 17:54:52.138000,https://github.com/pierthodo/temporal_regularization,6,Temporal regularization for markov decision process,"https://scholar.google.com/scholar?cluster=12308924458627658967&hl=en&as_sdt=0,6",4,2018
Meta-Reinforcement Learning of Structured Exploration Strategies,322,neurips,7,2,2023-06-15 17:54:52.332000,https://github.com/russellmendonca/maesn_suite,39,Meta-reinforcement learning of structured exploration strategies,"https://scholar.google.com/scholar?cluster=8837867565687609361&hl=en&as_sdt=0,44",4,2018
Unsupervised Attention-guided Image-to-Image Translation,320,neurips,49,21,2023-06-15 17:54:52.525000,https://github.com/AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation,322,Unsupervised attention-guided image-to-image translation,"https://scholar.google.com/scholar?cluster=912464851779595905&hl=en&as_sdt=0,48",11,2018
Approximate Knowledge Compilation by Online Collapsed Importance Sampling,21,neurips,0,0,2023-06-15 17:54:52.719000,https://github.com/UCLA-StarAI/Collapsed-Compilation,5,Approximate knowledge compilation by online collapsed importance sampling,"https://scholar.google.com/scholar?cluster=5801857808795259088&hl=en&as_sdt=0,10",4,2018
Reversible Recurrent Neural Networks,48,neurips,7,1,2023-06-15 17:54:52.912000,https://github.com/matthewjmackay/reversible-rnn,35,Reversible recurrent neural networks,"https://scholar.google.com/scholar?cluster=2936325833713118727&hl=en&as_sdt=0,44",3,2018
Regularization Learning Networks: Deep Learning for Tabular Datasets,79,neurips,6,2,2023-06-15 17:54:53.106000,https://github.com/irashavitt/regularization_learning_networks,31,Regularization learning networks: deep learning for tabular datasets,"https://scholar.google.com/scholar?cluster=12900371387873290272&hl=en&as_sdt=0,47",3,2018
On Learning Intrinsic Rewards for Policy Gradient Methods,160,neurips,10,2,2023-06-15 17:54:53.299000,https://github.com/Hwhitetooth/lirpg,54,On learning intrinsic rewards for policy gradient methods,"https://scholar.google.com/scholar?cluster=8658005357410230302&hl=en&as_sdt=0,45",4,2018
Single-Agent Policy Tree Search With Guarantees,27,neurips,15,1,2023-06-15 17:54:53.496000,https://github.com/deepmind/boxoban-levels,54,Single-agent policy tree search with guarantees,"https://scholar.google.com/scholar?cluster=17454634556201960088&hl=en&as_sdt=0,26",9,2018
Bias and Generalization in Deep Generative Models: An Empirical Study,99,neurips,8,0,2023-06-15 17:54:53.690000,https://github.com/ermongroup/BiasAndGeneralization,25,Bias and generalization in deep generative models: An empirical study,"https://scholar.google.com/scholar?cluster=17301681294706446940&hl=en&as_sdt=0,11",5,2018
Link Prediction Based on Graph Neural Networks,1395,neurips,129,24,2023-06-15 17:54:53.883000,https://github.com/muhanzhang/SEAL,493,Link prediction based on graph neural networks,"https://scholar.google.com/scholar?cluster=11968553220977234326&hl=en&as_sdt=0,5",12,2018
A flexible model for training action localization with varying levels of supervision,41,neurips,6,1,2023-06-15 17:54:54.074000,https://github.com/jalayrac/weakactionloc,17,A flexible model for training action localization with varying levels of supervision,"https://scholar.google.com/scholar?cluster=12745987706790622376&hl=en&as_sdt=0,5",4,2018
Generative Probabilistic Novelty Detection with Adversarial Autoencoders,295,neurips,31,7,2023-06-15 17:54:54.264000,https://github.com/podgorskiy/GPND,125,Generative probabilistic novelty detection with adversarial autoencoders,"https://scholar.google.com/scholar?cluster=13335383760622553502&hl=en&as_sdt=0,3",11,2018
Informative Features for Model Comparison,24,neurips,3,0,2023-06-15 17:54:54.460000,https://github.com/wittawatj/kernel-mod,17,Informative features for model comparison,"https://scholar.google.com/scholar?cluster=962836959160034441&hl=en&as_sdt=0,10",6,2018
Discrimination-aware Channel Pruning for Deep Neural Networks,615,neurips,27,9,2023-06-15 17:54:54.650000,https://github.com/SCUT-AILab/DCP,179,Discrimination-aware channel pruning for deep neural networks,"https://scholar.google.com/scholar?cluster=4423411645597495&hl=en&as_sdt=0,10",9,2018
On Fast Leverage Score Sampling and Optimal Learning,78,neurips,2,1,2023-06-15 17:54:54.841000,https://github.com/LCSL/bless,12,On fast leverage score sampling and optimal learning,"https://scholar.google.com/scholar?cluster=6173645811972804817&hl=en&as_sdt=0,44",9,2018
Robustness of conditional GANs to noisy labels,174,neurips,9,2,2023-06-15 17:54:55.031000,https://github.com/POLane16/Robust-Conditional-GAN,39,Robustness of conditional gans to noisy labels,"https://scholar.google.com/scholar?cluster=4597323022745403664&hl=en&as_sdt=0,10",3,2018
Legendre Decomposition for Tensors,14,neurips,4,0,2023-06-15 17:54:55.222000,https://github.com/mahito-sugiyama/Legendre-decomposition,12,Legendre decomposition for tensors,"https://scholar.google.com/scholar?cluster=12973396671492815941&hl=en&as_sdt=0,10",2,2018
SING: Symbol-to-Instrument Neural Generator,60,neurips,25,1,2023-06-15 17:54:55.412000,https://github.com/facebookresearch/SING,155,Sing: Symbol-to-instrument neural generator,"https://scholar.google.com/scholar?cluster=9576037029701279224&hl=en&as_sdt=0,33",10,2018
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks,99,neurips,7,2,2023-06-15 17:54:55.602000,https://github.com/sjblim/rmsn_nips_2018,30,Forecasting treatment responses over time using recurrent marginal structural networks,"https://scholar.google.com/scholar?cluster=9312966518414628527&hl=en&as_sdt=0,33",1,2018
Quadratic Decomposable Submodular Function Minimization,13,neurips,1,0,2023-06-15 17:54:55.793000,https://github.com/lipan00123/QDSDM,0,Quadratic decomposable submodular function minimization,"https://scholar.google.com/scholar?cluster=9668278212333240026&hl=en&as_sdt=0,33",1,2018
Deep Anomaly Detection Using Geometric Transformations,520,neurips,35,2,2023-06-15 17:54:55.983000,https://github.com/izikgo/AnomalyDetectionTransformations,154,Deep anomaly detection using geometric transformations,"https://scholar.google.com/scholar?cluster=15277146675093535725&hl=en&as_sdt=0,3",7,2018
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation,88,neurips,1,0,2023-06-15 17:54:56.174000,https://github.com/MeckyWu/subspace-match,15,Towards understanding learning representations: To what extent do different neural networks learn the same representation,"https://scholar.google.com/scholar?cluster=401428033641216502&hl=en&as_sdt=0,13",2,2018
An intriguing failing of convolutional neural networks and the CoordConv solution,730,neurips,37,7,2023-06-15 17:54:56.365000,https://github.com/uber-research/coordconv,202,An intriguing failing of convolutional neural networks and the coordconv solution,"https://scholar.google.com/scholar?cluster=1725137104710452960&hl=en&as_sdt=0,18",6,2018
A Smoother Way to Train Structured Prediction Models,18,neurips,4,0,2023-06-15 17:54:56.555000,https://github.com/krishnap25/casimir,2,A smoother way to train structured prediction models,"https://scholar.google.com/scholar?cluster=9176087356126828757&hl=en&as_sdt=0,10",2,2018
3D-Aware Scene Manipulation via Inverse Graphics,176,neurips,41,0,2023-06-15 17:54:56.746000,https://github.com/ysymyth/3D-SDN,262,3d-aware scene manipulation via inverse graphics,"https://scholar.google.com/scholar?cluster=1601238761105816866&hl=en&as_sdt=0,44",16,2018
Complex Gated Recurrent Neural Networks,51,neurips,11,0,2023-06-15 17:54:56.936000,https://github.com/v0lta/Complex-gated-recurrent-neural-networks,42,Complex gated recurrent neural networks,"https://scholar.google.com/scholar?cluster=10862653902258650151&hl=en&as_sdt=0,11",3,2018
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation,191,neurips,425,18,2023-06-15 17:54:57.127000,https://github.com/SullyChen/Autopilot-TensorFlow,1235,Scalable end-to-end autonomous vehicle testing via rare-event simulation,"https://scholar.google.com/scholar?cluster=5564001038044175212&hl=en&as_sdt=0,43",75,2018
Learning Loop Invariants for Program Verification,115,neurips,23,1,2023-06-15 17:54:57.318000,https://github.com/PL-ML/code2inv,74,Learning loop invariants for program verification,"https://scholar.google.com/scholar?cluster=6954633128371638771&hl=en&as_sdt=0,5",9,2018
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective,158,neurips,1,1,2023-06-15 17:54:57.508000,https://github.com/leiwu1990/sgd.stability,10,How sgd selects the global minima in over-parameterized learning: A dynamical stability perspective,"https://scholar.google.com/scholar?cluster=1980119340021099329&hl=en&as_sdt=0,33",2,2018
Neural Guided Constraint Logic Programming for Program Synthesis,35,neurips,9,0,2023-06-15 17:54:57.698000,https://github.com/xuexue/neuralkanren,85,Neural guided constraint logic programming for program synthesis,"https://scholar.google.com/scholar?cluster=5770275785272500195&hl=en&as_sdt=0,36",9,2018
Neural Ordinary Differential Equations,3210,neurips,848,61,2023-06-15 17:54:57.888000,https://github.com/rtqichen/torchdiffeq,4672,Neural ordinary differential equations,"https://scholar.google.com/scholar?cluster=13748354740225969894&hl=en&as_sdt=0,33",123,2018
Coupled Variational Bayes via Optimization Embedding,29,neurips,3,2,2023-06-15 17:54:58.079000,https://github.com/Hanjun-Dai/cvb,10,Coupled variational bayes via optimization embedding,"https://scholar.google.com/scholar?cluster=9010555957492755231&hl=en&as_sdt=0,5",5,2018
Policy Optimization via Importance Sampling,87,neurips,3,1,2023-06-15 17:54:58.271000,https://github.com/T3p/pois,12,Policy optimization via importance sampling,"https://scholar.google.com/scholar?cluster=16130728419946747088&hl=en&as_sdt=0,5",6,2018
Task-Driven Convolutional Recurrent Models of the Visual System,144,neurips,15,0,2023-06-15 17:54:58.479000,https://github.com/neuroailab/tnn,92,Task-driven convolutional recurrent models of the visual system,"https://scholar.google.com/scholar?cluster=11039722383223148947&hl=en&as_sdt=0,34",12,2018
Paraphrasing Complex Network: Network Compression via Factor Transfer,374,neurips,7,0,2023-06-15 17:54:58.670000,https://github.com/Jangho-Kim/Factor-Transfer-pytorch,14,Paraphrasing complex network: Network compression via factor transfer,"https://scholar.google.com/scholar?cluster=2520473274058783123&hl=en&as_sdt=0,5",1,2018
A Simple Cache Model for Image Recognition,20,neurips,0,0,2023-06-15 17:54:58.861000,https://github.com/eminorhan/simple-cache,2,A simple cache model for image recognition,"https://scholar.google.com/scholar?cluster=3091315690960335000&hl=en&as_sdt=0,29",3,2018
Learning Attractor Dynamics for Generative Memory,20,neurips,16,0,2023-06-15 17:54:59.051000,https://github.com/deepmind/dynamic-kanerva-machines,40,Learning attractor dynamics for generative memory,"https://scholar.google.com/scholar?cluster=9940290258944118765&hl=en&as_sdt=0,22",11,2018
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior,96,neurips,2,0,2023-06-15 17:54:59.241000,https://github.com/rddy/isql,27,Where do you think you're going?: Inferring beliefs about dynamics from behavior,"https://scholar.google.com/scholar?cluster=11438620297016616954&hl=en&as_sdt=0,22",6,2018
Image Inpainting via Generative Multi-column Convolutional Neural Networks,291,neurips,91,25,2023-06-15 17:54:59.431000,https://github.com/shepnerd/inpainting_gmcnn,400,Image inpainting via generative multi-column convolutional neural networks,"https://scholar.google.com/scholar?cluster=14919715529082387957&hl=en&as_sdt=0,40",20,2018
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices,29,neurips,3,0,2023-06-15 17:54:59.622000,https://github.com/zhenxingjian/SPD-SRU,14,A statistical recurrent model on the manifold of symmetric positive definite matrices,"https://scholar.google.com/scholar?cluster=5544428600595730510&hl=en&as_sdt=0,44",2,2018
Object-Oriented Dynamics Predictor,31,neurips,3,0,2023-06-15 17:54:59.813000,https://github.com/mig-zh/OODP,13,Object-oriented dynamics predictor,"https://scholar.google.com/scholar?cluster=1811390955386289421&hl=en&as_sdt=0,11",3,2018
To Trust Or Not To Trust A Classifier,387,neurips,46,2,2023-06-15 17:55:00.003000,https://github.com/google/TrustScore,167,To trust or not to trust a classifier,"https://scholar.google.com/scholar?cluster=9292152849001694574&hl=en&as_sdt=0,10",14,2018
Deep Reinforcement Learning of Marked Temporal Point Processes,97,neurips,18,1,2023-06-15 17:55:00.194000,https://github.com/Networks-Learning/tpprl,71,Deep reinforcement learning of marked temporal point processes,"https://scholar.google.com/scholar?cluster=10991436220054749409&hl=en&as_sdt=0,30",7,2018
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization,146,neurips,10,0,2023-06-15 17:55:00.384000,https://github.com/bargavj/distributedMachineLearning,25,Distributed learning without distress: Privacy-preserving empirical risk minimization,"https://scholar.google.com/scholar?cluster=10577380829443665980&hl=en&as_sdt=0,38",0,2018
Hybrid Knowledge Routed Modules for Large-scale Object Detection,74,neurips,19,15,2023-06-15 17:55:00.574000,https://github.com/chanyn/HKRM,98,Hybrid knowledge routed modules for large-scale object detection,"https://scholar.google.com/scholar?cluster=18227077982790889117&hl=en&as_sdt=0,5",9,2018
BRITS: Bidirectional Recurrent Imputation for Time Series,394,neurips,67,12,2023-06-15 17:55:00.764000,https://github.com/caow13/BRITS,173,Brits: Bidirectional recurrent imputation for time series,"https://scholar.google.com/scholar?cluster=17928129084181066672&hl=en&as_sdt=0,47",6,2018
"Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects",222,neurips,28,1,2023-06-15 17:55:00.955000,https://github.com/akosiorek/sqair,96,"Sequential attend, infer, repeat: Generative modelling of moving objects","https://scholar.google.com/scholar?cluster=7430884807828197721&hl=en&as_sdt=0,11",11,2018
Boosting Black Box Variational Inference,26,neurips,5,0,2023-06-15 17:55:01.145000,https://github.com/ratschlab/boosting-bbvi,7,Boosting black box variational inference,"https://scholar.google.com/scholar?cluster=493456481295082921&hl=en&as_sdt=0,43",4,2018
Transfer of Deep Reactive Policies for MDP Planning,23,neurips,1,0,2023-06-15 17:55:01.337000,https://github.com/dair-iitd/torpido,7,Transfer of deep reactive policies for mdp planning,"https://scholar.google.com/scholar?cluster=4580400729732661142&hl=en&as_sdt=0,10",4,2018
GILBO: One Metric to Measure Them All,16,neurips,322,16,2023-06-15 17:55:01.527000,https://github.com/google/compare_gan,1814,GILBO: One metric to measure them all,"https://scholar.google.com/scholar?cluster=14349686696431672115&hl=en&as_sdt=0,11",52,2018
"FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction",103,neurips,94,0,2023-06-15 17:55:01.718000,https://github.com/kevin-ssy/FishNet,545,"Fishnet: A versatile backbone for image, region, and pixel level prediction","https://scholar.google.com/scholar?cluster=8077266557125333363&hl=en&as_sdt=0,33",23,2018
Automatic differentiation in ML: Where we are and where we should be going,75,neurips,43,31,2023-06-15 17:55:01.909000,https://github.com/mila-udem/myia,454,Automatic differentiation in ML: Where we are and where we should be going,"https://scholar.google.com/scholar?cluster=11874990560582038809&hl=en&as_sdt=0,3",31,2018
Evolved Policy Gradients,228,neurips,56,7,2023-06-15 17:55:02.100000,https://github.com/openai/EPG,240,Evolved policy gradients,"https://scholar.google.com/scholar?cluster=17605986776756195620&hl=en&as_sdt=0,36",14,2018
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features,33,neurips,0,0,2023-06-15 17:55:02.291000,https://github.com/r3831/SAKPCA,0,Streaming Kernel PCA with Random Features,"https://scholar.google.com/scholar?cluster=17070435901311007360&hl=en&as_sdt=0,32",3,2018
Faster Neural Networks Straight from JPEG,175,neurips,40,12,2023-06-15 17:55:02.491000,https://github.com/uber-research/jpeg2dct,231,Faster neural networks straight from jpeg,"https://scholar.google.com/scholar?cluster=9617446820670115100&hl=en&as_sdt=0,5",11,2018
Visual Reinforcement Learning with Imagined Goals,456,neurips,520,39,2023-06-15 17:55:02.685000,https://github.com/vitchyr/rlkit,2161,Visual reinforcement learning with imagined goals,"https://scholar.google.com/scholar?cluster=5007292417648560707&hl=en&as_sdt=0,8",61,2018
Deep Generative Models for Distribution-Preserving Lossy Compression,103,neurips,8,2,2023-06-15 17:55:02.879000,https://github.com/mitscha/dplc,34,Deep generative models for distribution-preserving lossy compression,"https://scholar.google.com/scholar?cluster=10590142637711882209&hl=en&as_sdt=0,14",3,2018
"With Friends Like These, Who Needs Adversaries?",74,neurips,0,0,2023-06-15 17:55:03.072000,https://github.com/torrvision/whoneedsadversaries,12,"With friends like these, who needs adversaries?","https://scholar.google.com/scholar?cluster=5740676327222968631&hl=en&as_sdt=0,10",9,2018
"Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation",72,neurips,19,10,2023-06-15 17:55:03.266000,https://github.com/thusiyuan/cooperative_scene_parsing,91,"Cooperative holistic scene understanding: Unifying 3d object, layout, and camera pose estimation","https://scholar.google.com/scholar?cluster=5227625249975009897&hl=en&as_sdt=0,5",5,2018
Empirical Risk Minimization Under Fairness Constraints,383,neurips,6,0,2023-06-15 17:55:03.460000,https://github.com/jmikko/fair_ERM,36,Empirical risk minimization under fairness constraints,"https://scholar.google.com/scholar?cluster=5746250113194301793&hl=en&as_sdt=0,5",3,2018
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation,284,neurips,27,4,2023-06-15 17:55:03.654000,https://github.com/Alexander-H-Liu/UFDN,131,A unified feature disentangler for multi-domain image translation and manipulation,"https://scholar.google.com/scholar?cluster=6007789913986445498&hl=en&as_sdt=0,48",6,2018
The committee machine: Computational to statistical gaps in learning a two-layers neural network,79,neurips,0,0,2023-06-15 17:55:03.848000,https://github.com/benjaminaubin/TheCommitteeMachine,0,The committee machine: Computational to statistical gaps in learning a two-layers neural network,"https://scholar.google.com/scholar?cluster=4903323524016093175&hl=en&as_sdt=0,34",2,2018
Evolution-Guided Policy Gradient in Reinforcement Learning,183,neurips,52,2,2023-06-15 17:55:04.042000,https://github.com/ShawK91/erl_paper_nips18,172,Evolution-guided policy gradient in reinforcement learning,"https://scholar.google.com/scholar?cluster=7920725821302044195&hl=en&as_sdt=0,10",5,2018
Causal Inference with Noisy and Missing Covariates via Matrix Factorization,61,neurips,0,0,2023-06-15 17:55:04.236000,https://github.com/udellgroup/causal_mf_code,6,Causal inference with noisy and missing covariates via matrix factorization,"https://scholar.google.com/scholar?cluster=14104978633422349618&hl=en&as_sdt=0,7",3,2018
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation,28,neurips,0,0,2023-06-15 17:55:04.430000,https://github.com/jingnantes/hybrid-mst,8,Hybrid-MST: A hybrid active sampling strategy for pairwise preference aggregation,"https://scholar.google.com/scholar?cluster=13558401002999071074&hl=en&as_sdt=0,10",2,2018
A no-regret generalization of hierarchical softmax to extreme multi-label classification,81,neurips,16,6,2023-06-15 17:55:04.624000,https://github.com/mwydmuch/extremeText,147,A no-regret generalization of hierarchical softmax to extreme multi-label classification,"https://scholar.google.com/scholar?cluster=14171307998042582918&hl=en&as_sdt=0,3",13,2018
Rectangular Bounding Process,21,neurips,3,0,2023-06-15 17:55:04.818000,https://github.com/xuhuifan/RBP,3,Rectangular bounding process,"https://scholar.google.com/scholar?cluster=10618275895500216203&hl=en&as_sdt=0,34",1,2018
Constructing Unrestricted Adversarial Examples with Generative Models,240,neurips,15,6,2023-06-15 17:55:05.011000,https://github.com/ermongroup/generative_adversary,60,Constructing unrestricted adversarial examples with generative models,"https://scholar.google.com/scholar?cluster=14086270849571978699&hl=en&as_sdt=0,39",6,2018
Boosted Sparse and Low-Rank Tensor Regression,32,neurips,4,3,2023-06-15 17:55:05.208000,https://github.com/LifangHe/SURF,9,Boosted sparse and low-rank tensor regression,"https://scholar.google.com/scholar?cluster=13402681948996325867&hl=en&as_sdt=0,10",3,2018
Deep Neural Networks with Box Convolutions,15,neurips,36,3,2023-06-15 17:55:05.403000,https://github.com/shrubb/box-convolutions,513,Deep neural networks with box convolutions,"https://scholar.google.com/scholar?cluster=15004510562166029998&hl=en&as_sdt=0,43",17,2018
Learning Compressed Transforms with Low Displacement Rank,42,neurips,17,6,2023-06-15 17:55:05.594000,https://github.com/HazyResearch/structured-nets,57,Learning compressed transforms with low displacement rank,"https://scholar.google.com/scholar?cluster=8419515952370992696&hl=en&as_sdt=0,50",17,2018
Deep Defense: Training DNNs with Improved Adversarial Robustness,115,neurips,6,0,2023-06-15 17:55:05.784000,https://github.com/ZiangYan/deepdefense.pytorch,37,Deep defense: Training dnns with improved adversarial robustness,"https://scholar.google.com/scholar?cluster=6643757979178770669&hl=en&as_sdt=0,1",4,2018
Large-Scale Stochastic Sampling from the Probability Simplex,5,neurips,0,1,2023-06-15 17:55:05.975000,https://github.com/jbaker92/scir,2,Large-scale stochastic sampling from the probability simplex,"https://scholar.google.com/scholar?cluster=9892795582424041794&hl=en&as_sdt=0,43",2,2018
Adaptive Methods for Nonconvex Optimization,321,neurips,15,3,2023-06-15 17:55:06.166000,https://github.com/stefan-it/nmt-en-vi,51,Adaptive methods for nonconvex optimization,"https://scholar.google.com/scholar?cluster=13576720529696525340&hl=en&as_sdt=0,33",6,2018
Compact Generalized Non-local Network,166,neurips,41,0,2023-06-15 17:55:06.357000,https://github.com/KaiyuYue/cgnl-network.pytorch,259,Compact generalized non-local network,"https://scholar.google.com/scholar?cluster=12004705320658184806&hl=en&as_sdt=0,5",7,2018
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning,124,neurips,4,2,2023-06-15 17:55:06.548000,https://github.com/ylytju/sga,20,Stacked semantics-guided attention model for fine-grained zero-shot learning,"https://scholar.google.com/scholar?cluster=17870706793172229300&hl=en&as_sdt=0,10",1,2018
Banach Wasserstein GAN,214,neurips,10,1,2023-06-15 17:55:06.739000,https://github.com/adler-j/bwgan,31,Banach wasserstein gan,"https://scholar.google.com/scholar?cluster=10419609167162928003&hl=en&as_sdt=0,5",6,2018
Visual Object Networks: Image Generation with Disentangled 3D Representations,203,neurips,91,12,2023-06-15 17:55:06.930000,https://github.com/junyanz/VON,530,Visual object networks: Image generation with disentangled 3D representations,"https://scholar.google.com/scholar?cluster=3404291286977602499&hl=en&as_sdt=0,5",32,2018
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare,214,neurips,27,1,2023-06-15 17:55:07.120000,https://github.com/mp2893/mime,98,Mime: Multilevel medical embedding of electronic health records for predictive healthcare,"https://scholar.google.com/scholar?cluster=9778014794664384350&hl=en&as_sdt=0,47",7,2018
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams,74,neurips,0,1,2023-06-15 17:55:07.311000,https://github.com/lttam/PersistenceFisher,5,Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams,"https://scholar.google.com/scholar?cluster=1409702383947125765&hl=en&as_sdt=0,5",2,2018
Bilinear Attention Networks,720,neurips,102,2,2023-06-15 17:55:07.501000,https://github.com/jnhwkim/ban-vqa,515,Bilinear attention networks,"https://scholar.google.com/scholar?cluster=10383181412923835294&hl=en&as_sdt=0,5",17,2018
Constructing Fast Network through Deconstruction of Convolution,67,neurips,5,0,2023-06-15 17:55:07.692000,https://github.com/jyh2986/Active-Shift,31,Constructing fast network through deconstruction of convolution,"https://scholar.google.com/scholar?cluster=15893085353567655931&hl=en&as_sdt=0,14",3,2018
See and Think: Disentangling Semantic Scene Completion,65,neurips,10,9,2023-06-15 17:55:07.882000,https://github.com/ShiceLiu/SATNet,47,See and think: Disentangling semantic scene completion,"https://scholar.google.com/scholar?cluster=3218225429355211096&hl=en&as_sdt=0,10",5,2018
"Unsupervised Depth Estimation, 3D Face Rotation and Replacement",31,neurips,31,6,2023-06-15 17:55:08.073000,https://github.com/joelmoniz/DepthNets,124,"Unsupervised depth estimation, 3d face rotation and replacement","https://scholar.google.com/scholar?cluster=2371681385764042999&hl=en&as_sdt=0,44",8,2018
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language,14,neurips,7,2,2023-06-15 17:55:08.263000,https://github.com/google-research/autoconj,36,Autoconj: recognizing and exploiting conjugacy without a domain-specific language,"https://scholar.google.com/scholar?cluster=10948786372244458956&hl=en&as_sdt=0,34",11,2018
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance,78,neurips,26,2,2023-06-15 17:55:08.454000,https://github.com/ermongroup/ssdkl,65,Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance,"https://scholar.google.com/scholar?cluster=6491716866958005670&hl=en&as_sdt=0,35",8,2018
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video,44,neurips,8,0,2023-06-15 17:55:08.645000,https://github.com/sinzlab/Sinz2018_NIPS,3,Stimulus domain transfer in recurrent models for large scale cortical population prediction on video,"https://scholar.google.com/scholar?cluster=3426947555786993703&hl=en&as_sdt=0,5",3,2018
Norm matters: efficient and accurate normalization schemes in deep networks,166,neurips,3,0,2023-06-15 17:55:08.835000,https://github.com/eladhoffer/norm_matters,22,Norm matters: efficient and accurate normalization schemes in deep networks,"https://scholar.google.com/scholar?cluster=12023191299459902610&hl=en&as_sdt=0,29",4,2018
Dialog-based Interactive Image Retrieval,145,neurips,19,4,2023-06-15 17:55:09.026000,https://github.com/XiaoxiaoGuo/fashion-retrieval,66,Dialog-based interactive image retrieval,"https://scholar.google.com/scholar?cluster=4258300372823907612&hl=en&as_sdt=0,36",4,2018
Co-teaching: Robust training of deep neural networks with extremely noisy labels,1450,neurips,98,9,2023-06-15 17:55:09.217000,https://github.com/bhanML/Co-teaching,447,Co-teaching: Robust training of deep neural networks with extremely noisy labels,"https://scholar.google.com/scholar?cluster=1619874673011079691&hl=en&as_sdt=0,5",11,2018
Learning to Reason with Third Order Tensor Products,65,neurips,4,0,2023-06-15 17:55:09.407000,https://github.com/ischlag/TPR-RNN,39,Learning to reason with third order tensor products,"https://scholar.google.com/scholar?cluster=1859815740065749231&hl=en&as_sdt=0,43",4,2018
Deep Structured Prediction with Nonlinear Output Transformations,22,neurips,0,1,2023-06-15 17:55:09.597000,https://github.com/cgraber/NLStruct,11,Deep structured prediction with nonlinear output transformations,"https://scholar.google.com/scholar?cluster=14558697357825196777&hl=en&as_sdt=0,31",6,2018
Visualizing the Loss Landscape of Neural Nets,1487,neurips,345,23,2023-06-15 17:55:09.787000,https://github.com/tomgoldstein/loss-landscape,2379,Visualizing the loss landscape of neural nets,"https://scholar.google.com/scholar?cluster=11650483902238288010&hl=en&as_sdt=0,5",33,2018
Representation Learning for Treatment Effect Estimation from Observational Data,223,neurips,8,3,2023-06-15 17:55:09.979000,https://github.com/Osier-Yi/SITE,48,Representation learning for treatment effect estimation from observational data,"https://scholar.google.com/scholar?cluster=8473125110526248121&hl=en&as_sdt=0,14",2,2018
Memory Replay GANs: Learning to Generate New Categories without Forgetting,323,neurips,17,6,2023-06-15 17:55:10.169000,https://github.com/WuChenshen/MeRGAN,57,Memory replay gans: Learning to generate new categories without forgetting,"https://scholar.google.com/scholar?cluster=10386986757383440246&hl=en&as_sdt=0,5",2,2018
Insights on representational similarity in neural networks with canonical correlation,317,neurips,145,7,2023-06-15 17:55:10.360000,https://github.com/google/svcca,596,Insights on representational similarity in neural networks with canonical correlation,"https://scholar.google.com/scholar?cluster=15689105000424764079&hl=en&as_sdt=0,48",27,2018
"FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network",191,neurips,369,28,2023-06-15 17:55:10.552000,https://github.com/Microsoft/EdgeML,1453,"Fastgrnn: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network","https://scholar.google.com/scholar?cluster=14286601091173970187&hl=en&as_sdt=0,41",87,2018
Conditional Adversarial Domain Adaptation,1680,neurips,88,15,2023-06-15 17:55:10.742000,https://github.com/thuml/CDAN,374,Conditional adversarial domain adaptation,"https://scholar.google.com/scholar?cluster=951003799487024572&hl=en&as_sdt=0,5",11,2018
Bayesian Nonparametric Spectral Estimation,36,neurips,2,2,2023-06-15 17:55:10.933000,https://github.com/GAMES-UChile/BayesianSpectralEstimation,14,Bayesian nonparametric spectral estimation,"https://scholar.google.com/scholar?cluster=17785517224633397163&hl=en&as_sdt=0,5",4,2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks,1316,neurips,78,16,2023-06-15 17:55:11.123000,https://github.com/pokaxpoka/deep_Mahalanobis_detector,303,A simple unified framework for detecting out-of-distribution samples and adversarial attacks,"https://scholar.google.com/scholar?cluster=59561906500021733&hl=en&as_sdt=0,31",9,2018
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise,464,neurips,15,0,2023-06-15 17:55:11.314000,https://github.com/mmazeika/glc,87,Using trusted data to train deep networks on labels corrupted by severe noise,"https://scholar.google.com/scholar?cluster=3616817429291706463&hl=en&as_sdt=0,44",4,2018
Masking: A New Perspective of Noisy Supervision,208,neurips,7,0,2023-06-15 17:55:11.505000,https://github.com/bhanML/Masking,55,Masking: A new perspective of noisy supervision,"https://scholar.google.com/scholar?cluster=10612946092230113975&hl=en&as_sdt=0,32",5,2018
Found Graph Data and Planted Vertex Covers,9,neurips,1,0,2023-06-15 17:55:11.695000,https://github.com/arbenson/FGDnPVC,3,Found graph data and planted vertex covers,"https://scholar.google.com/scholar?cluster=3952614015987874962&hl=en&as_sdt=0,39",3,2018
Fast Estimation of Causal Interactions using Wold Processes,12,neurips,4,2,2023-06-15 17:55:11.886000,https://github.com/flaviovdf/granger-busca,6,Fast estimation of causal interactions using wold processes,"https://scholar.google.com/scholar?cluster=3436970798067835046&hl=en&as_sdt=0,44",3,2018
Reparameterization Gradient for Non-differentiable Models,25,neurips,1,0,2023-06-15 17:55:12.077000,https://github.com/wonyeol/reparam-nondiff,5,Reparameterization gradient for non-differentiable models,"https://scholar.google.com/scholar?cluster=15564293157719874680&hl=en&as_sdt=0,31",3,2018
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents,352,neurips,298,20,2023-06-15 17:55:12.267000,https://github.com/uber-research/deep-neuroevolution,1597,Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents,"https://scholar.google.com/scholar?cluster=9461747331584701646&hl=en&as_sdt=0,11",82,2018
Generalizing Tree Probability Estimation via Bayesian Networks,23,neurips,6,0,2023-06-15 17:55:12.458000,https://github.com/zcrabbit/sbn,8,Generalizing tree probability estimation via Bayesian networks,"https://scholar.google.com/scholar?cluster=17096075908350325992&hl=en&as_sdt=0,5",1,2018
SimplE Embedding for Link Prediction in Knowledge Graphs,661,neurips,36,1,2023-06-15 17:55:12.648000,https://github.com/Mehran-k/SimplE,134,Simple embedding for link prediction in knowledge graphs,"https://scholar.google.com/scholar?cluster=1390081697322675650&hl=en&as_sdt=0,5",9,2018
Statistical mechanics of low-rank tensor decomposition,16,neurips,0,0,2023-06-15 17:55:12.839000,https://github.com/ganguli-lab/tensorAMP,4,Statistical mechanics of low-rank tensor decomposition,"https://scholar.google.com/scholar?cluster=9594213569092054865&hl=en&as_sdt=0,1",4,2018
A Structured Prediction Approach for Label Ranking,30,neurips,2,0,2023-06-15 17:55:13.030000,https://github.com/akorba/Structured_Approach_Label_Ranking,6,A structured prediction approach for label ranking,"https://scholar.google.com/scholar?cluster=7075820179073932212&hl=en&as_sdt=0,41",3,2018
Sparsified SGD with Memory,594,neurips,11,1,2023-06-15 17:55:13.221000,https://github.com/epfml/sparsifiedSGD,50,Sparsified SGD with memory,"https://scholar.google.com/scholar?cluster=6832257024596167334&hl=en&as_sdt=0,36",9,2018
Model Agnostic Supervised Local Explanations,167,neurips,8,0,2023-06-15 17:55:13.411000,https://github.com/GDPlumb/MAPLE,26,Model agnostic supervised local explanations,"https://scholar.google.com/scholar?cluster=3090118674779699868&hl=en&as_sdt=0,23",3,2018
Probabilistic Matrix Factorization for Automated Machine Learning,126,neurips,13,4,2023-06-15 17:55:13.601000,https://github.com/rsheth80/pmf-automl,41,Probabilistic matrix factorization for automated machine learning,"https://scholar.google.com/scholar?cluster=6902330776298089199&hl=en&as_sdt=0,21",4,2018
Norm-Ranging LSH for Maximum Inner Product Search,47,neurips,10,0,2023-06-15 17:55:13.792000,https://github.com/xinyandai/similarity-search,18,Norm-ranging lsh for maximum inner product search,"https://scholar.google.com/scholar?cluster=4956999863940081632&hl=en&as_sdt=0,47",10,2018
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions,85,neurips,2,0,2023-06-15 17:55:13.983000,https://github.com/BorisMuzellec/EllipticalEmbeddings,9,Generalizing point embeddings using the wasserstein space of elliptical distributions,"https://scholar.google.com/scholar?cluster=3601826070675882278&hl=en&as_sdt=0,23",4,2018
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification,60,neurips,2,0,2023-06-15 17:55:14.173000,https://github.com/dmilios/dirichletGPC,13,Dirichlet-based gaussian processes for large-scale calibrated classification,"https://scholar.google.com/scholar?cluster=7488422957804807823&hl=en&as_sdt=0,36",2,2018
Latent Alignment and Variational Attention,138,neurips,60,2,2023-06-15 17:55:14.363000,https://github.com/harvardnlp/var-attn,324,Latent alignment and variational attention,"https://scholar.google.com/scholar?cluster=6335407498429393003&hl=en&as_sdt=0,37",23,2018
Infinite-Horizon Gaussian Processes,29,neurips,7,3,2023-06-15 17:55:14.554000,https://github.com/AaltoML/IHGP,28,Infinite-horizon Gaussian processes,"https://scholar.google.com/scholar?cluster=13722784833220822191&hl=en&as_sdt=0,5",6,2018
Constrained Graph Variational Autoencoders for Molecule Design,405,neurips,54,4,2023-06-15 17:55:14.744000,https://github.com/Microsoft/constrained-graph-variational-autoencoder,202,Constrained graph variational autoencoders for molecule design,"https://scholar.google.com/scholar?cluster=2838800553083041205&hl=en&as_sdt=0,23",11,2018
Hardware Conditioned Policies for Multi-Robot Transfer Learning,65,neurips,7,0,2023-06-15 17:55:14.935000,https://github.com/taochenshh/hcp,17,Hardware conditioned policies for multi-robot transfer learning,"https://scholar.google.com/scholar?cluster=11432360308578824406&hl=en&as_sdt=0,33",4,2018
Learning Disentangled Joint Continuous and Discrete Representations,203,neurips,65,1,2023-06-15 17:55:15.125000,https://github.com/Schlumberger/joint-vae,449,Learning disentangled joint continuous and discrete representations,"https://scholar.google.com/scholar?cluster=14996308996785863098&hl=en&as_sdt=0,10",21,2018
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples,158,neurips,6,1,2023-06-15 17:55:15.316000,https://github.com/AmIAttribute/AmI,29,Attacks meet interpretability: Attribute-steered detection of adversarial samples,"https://scholar.google.com/scholar?cluster=2985314933504776828&hl=en&as_sdt=0,5",1,2018
Differentiable MPC for End-to-end Planning and Control,286,neurips,42,4,2023-06-15 17:55:15.506000,https://github.com/locuslab/differentiable-mpc,157,Differentiable mpc for end-to-end planning and control,"https://scholar.google.com/scholar?cluster=14843462917652881335&hl=en&as_sdt=0,43",10,2018
Binary Classification from Positive-Confidence Data,58,neurips,6,0,2023-06-15 17:55:15.697000,https://github.com/takashiishida/pconf,50,Binary classification from positive-confidence data,"https://scholar.google.com/scholar?cluster=10725870998628923240&hl=en&as_sdt=0,33",7,2018
"Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs",492,neurips,39,1,2023-06-15 17:55:15.887000,https://github.com/timgaripov/dnn-mode-connectivity,217,"Loss surfaces, mode connectivity, and fast ensembling of dnns","https://scholar.google.com/scholar?cluster=7857512178594187445&hl=en&as_sdt=0,1",12,2018
A Unified View of Piecewise Linear Neural Network Verification,294,neurips,8,0,2023-06-15 17:55:16.078000,https://github.com/oval-group/PLNN-verification,33,A unified view of piecewise linear neural network verification,"https://scholar.google.com/scholar?cluster=5109084814333031747&hl=en&as_sdt=0,22",9,2018
Can We Gain More from Orthogonality Regularizations in Training Deep Networks?,284,neurips,28,0,2023-06-15 17:55:16.268000,https://github.com/nbansal90/Can-we-Gain-More-from-Orthogonality,113,Can we gain more from orthogonality regularizations in training deep networks?,"https://scholar.google.com/scholar?cluster=16253012284749788151&hl=en&as_sdt=0,33",9,2018
Training deep learning based denoisers without ground truth data,114,neurips,11,0,2023-06-15 17:55:16.459000,https://github.com/Shakarim94/Net-SURE,43,Training deep learning based denoisers without ground truth data,"https://scholar.google.com/scholar?cluster=10949844547317882495&hl=en&as_sdt=0,33",2,2018
Structural Causal Bandits: Where to Intervene?,74,neurips,10,0,2023-06-15 17:55:16.649000,https://github.com/sanghack81/SCMMAB-NIPS2018,16,Structural causal bandits: Where to intervene?,"https://scholar.google.com/scholar?cluster=4413359648093381122&hl=en&as_sdt=0,5",1,2018
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms,964,neurips,98,8,2023-06-15 17:55:16.840000,https://github.com/brain-research/realistic-ssl-evaluation,448,Realistic evaluation of deep semi-supervised learning algorithms,"https://scholar.google.com/scholar?cluster=15456844754123849487&hl=en&as_sdt=0,19",43,2018
Revisiting Decomposable Submodular Function Minimization with Incidence Relations,22,neurips,1,0,2023-06-15 17:55:17.031000,https://github.com/lipan00123/DSFM-with-incidence-relations,0,Revisiting decomposable submodular function minimization with incidence relations,"https://scholar.google.com/scholar?cluster=11168625649110015445&hl=en&as_sdt=0,25",1,2018
Scaling Gaussian Process Regression with Derivatives,79,neurips,8,4,2023-06-15 17:55:17.221000,https://github.com/ericlee0803/GP_Derivatives,31,Scaling Gaussian process regression with derivatives,"https://scholar.google.com/scholar?cluster=12933093226685125068&hl=en&as_sdt=0,33",11,2018
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification,327,neurips,80,10,2023-06-15 17:55:17.411000,https://github.com/yxgeee/FD-GAN,275,Fd-gan: Pose-guided feature distilling gan for robust person re-identification,"https://scholar.google.com/scholar?cluster=8848217033553196180&hl=en&as_sdt=0,1",8,2018
Graphical Generative Adversarial Networks,41,neurips,15,4,2023-06-15 17:55:17.602000,https://github.com/zhenxuan00/graphical-gan,71,Graphical generative adversarial networks,"https://scholar.google.com/scholar?cluster=13094733406106291079&hl=en&as_sdt=0,29",14,2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives,488,neurips,15,0,2023-06-15 17:55:17.792000,https://github.com/IBM/Contrastive-Explanation-Method,51,Explanations based on the missing: Towards contrastive explanations with pertinent negatives,"https://scholar.google.com/scholar?cluster=14566322531022731329&hl=en&as_sdt=0,39",13,2018
Context-aware Synthesis and Placement of Object Instances,94,neurips,10,6,2023-06-15 17:55:17.983000,https://github.com/NVlabs/Instance_Insertion,84,Context-aware synthesis and placement of object instances,"https://scholar.google.com/scholar?cluster=16175327312247199712&hl=en&as_sdt=0,31",17,2018
Group Equivariant Capsule Networks,119,neurips,9,5,2023-06-15 17:55:18.174000,https://github.com/mrjel/group_equivariant_capsules_pytorch,29,Group equivariant capsule networks,"https://scholar.google.com/scholar?cluster=11608023930229611825&hl=en&as_sdt=0,10",2,2018
MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval,5,neurips,2,0,2023-06-15 17:55:18.364000,https://github.com/epfl-lts2/mulan,1,Mulan: A blind and off-grid method for multichannel echo retrieval,"https://scholar.google.com/scholar?cluster=88608764706264858&hl=en&as_sdt=0,5",9,2018
Breaking the Activation Function Bottleneck through Adaptive Parameterization,12,neurips,5,1,2023-06-15 17:55:18.554000,https://github.com/flennerhag/alstm,25,Breaking the activation function bottleneck through adaptive parameterization,"https://scholar.google.com/scholar?cluster=707894120541881868&hl=en&as_sdt=0,5",2,2018
Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements,11,neurips,0,0,2023-06-15 17:55:18.745000,https://github.com/ankushmandal/topkapi,11,Topkapi: parallel and fast sketches for finding top-k frequent elements,"https://scholar.google.com/scholar?cluster=17308935081714564523&hl=en&as_sdt=0,26",2,2018
The Price of Fair PCA: One Extra dimension,118,neurips,15,1,2023-06-15 17:55:18.935000,https://github.com/samirasamadi/Fair-PCA,23,The price of fair pca: One extra dimension,"https://scholar.google.com/scholar?cluster=6814300972813312615&hl=en&as_sdt=0,30",4,2018
Orthogonally Decoupled Variational Gaussian Processes,43,neurips,1,0,2023-06-15 17:55:19.125000,https://github.com/hughsalimbeni/orth_decoupled_var_gps,12,Orthogonally decoupled variational Gaussian processes,"https://scholar.google.com/scholar?cluster=13926573353559028690&hl=en&as_sdt=0,47",4,2018
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation,22,neurips,0,0,2023-06-15 17:55:19.316000,https://github.com/shivapratap/AlgorithmicAssurance_NIPS2018,3,Algorithmic assurance: An active approach to algorithmic testing using bayesian optimisation,"https://scholar.google.com/scholar?cluster=6517267723562437007&hl=en&as_sdt=0,15",1,2018
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds,204,neurips,23,2,2023-06-15 17:55:19.508000,https://github.com/xchen-tamu/linear-lista-cpss,48,Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds,"https://scholar.google.com/scholar?cluster=8395828592719058096&hl=en&as_sdt=0,5",5,2018
Efficient Neural Network Robustness Certification with General Activation Functions,580,neurips,6,0,2023-06-15 17:55:19.699000,https://github.com/huanzhang12/CROWN-Robustness-Certification,13,Efficient neural network robustness certification with general activation functions,"https://scholar.google.com/scholar?cluster=6606953928208344058&hl=en&as_sdt=0,44",4,2018
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning,86,neurips,9,4,2023-06-15 17:55:19.889000,https://github.com/tylersco/adapted_deep_embeddings,26,Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning,"https://scholar.google.com/scholar?cluster=11224359097846918125&hl=en&as_sdt=0,14",4,2018
KONG: Kernels for ordered-neighborhood graphs,3,neurips,2,0,2023-06-15 17:55:20.080000,https://github.com/kokiche/KONG,8,KONG: Kernels for ordered-neighborhood graphs,"https://scholar.google.com/scholar?cluster=7783420986460591653&hl=en&as_sdt=0,6",2,2018
Glow: Generative Flow with Invertible 1x1 Convolutions,2412,neurips,509,64,2023-06-15 17:55:20.270000,https://github.com/openai/glow,3016,Glow: Generative flow with invertible 1x1 convolutions,"https://scholar.google.com/scholar?cluster=5834689841973227263&hl=en&as_sdt=0,5",212,2018
Efficient Projection onto the Perfect Phylogeny Model,4,neurips,1,0,2023-06-15 17:55:20.461000,https://github.com/bentoayr/Efficient-Projection-onto-the-Perfect-Phylogeny-Model,2,Efficient projection onto the perfect phylogeny model,"https://scholar.google.com/scholar?cluster=5821955687711188887&hl=en&as_sdt=0,5",2,2018
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient,54,neurips,2,0,2023-06-15 17:55:20.651000,https://github.com/aaronpmishkin/SLANG,8,Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient,"https://scholar.google.com/scholar?cluster=16145055537497825367&hl=en&as_sdt=0,47",4,2018
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds,29,neurips,2,0,2023-06-15 17:55:20.841000,https://github.com/boschresearch/PAC_GP,9,Learning gaussian processes by minimizing pac-bayesian generalization bounds,"https://scholar.google.com/scholar?cluster=10486427122061554310&hl=en&as_sdt=0,44",8,2018
Lipschitz regularity of deep neural networks: analysis and efficient estimation,369,neurips,14,3,2023-06-15 17:55:21.032000,https://github.com/avirmaux/lipEstimation,49,Lipschitz regularity of deep neural networks: analysis and efficient estimation,"https://scholar.google.com/scholar?cluster=16196721810320018514&hl=en&as_sdt=0,36",1,2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation,792,neurips,102,9,2023-06-15 17:55:21.222000,https://github.com/bowenliu16/rl_graph_generation,310,Graph convolutional policy network for goal-directed molecular graph generation,"https://scholar.google.com/scholar?cluster=15276529180320001334&hl=en&as_sdt=0,39",19,2018
Video-to-Video Synthesis,927,neurips,1195,104,2023-06-15 17:55:21.413000,https://github.com/NVIDIA/vid2vid,8266,Video-to-video synthesis,"https://scholar.google.com/scholar?cluster=3120460092236365926&hl=en&as_sdt=0,23",250,2018
Bandit Learning with Implicit Feedback,22,neurips,4,0,2023-06-15 17:55:21.604000,https://github.com/qy7171/ec_bandit,7,Bandit learning with implicit feedback,"https://scholar.google.com/scholar?cluster=11670456531413289871&hl=en&as_sdt=0,6",1,2018
Adversarial Regularizers in Inverse Problems,202,neurips,6,1,2023-06-15 17:55:21.794000,https://github.com/lunz-s/DeepAdverserialRegulariser,13,Adversarial regularizers in inverse problems,"https://scholar.google.com/scholar?cluster=3594915696133260277&hl=en&as_sdt=0,34",2,2018
Hyperbolic Neural Networks,411,neurips,26,3,2023-06-15 17:55:21.985000,https://github.com/dalab/hyperbolic_nn,162,Hyperbolic neural networks,"https://scholar.google.com/scholar?cluster=12122146629122312177&hl=en&as_sdt=0,31",14,2018
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks,492,neurips,31,9,2023-06-15 17:55:22.176000,https://github.com/hujie-frank/GENet,227,Gather-excite: Exploiting feature context in convolutional neural networks,"https://scholar.google.com/scholar?cluster=9719951211536151216&hl=en&as_sdt=0,5",19,2018
Active Learning for Non-Parametric Regression Using Purely Random Trees,21,neurips,3,0,2023-06-15 17:55:22.366000,https://github.com/jackrgoetz/Mondrian_Tree_AL,3,Active learning for non-parametric regression using purely random trees,"https://scholar.google.com/scholar?cluster=7681049792975239576&hl=en&as_sdt=0,44",4,2018
Image-to-image translation for cross-domain disentanglement,265,neurips,19,5,2023-06-15 17:55:22.557000,https://github.com/agonzgarc/cross-domain-disen,88,Image-to-image translation for cross-domain disentanglement,"https://scholar.google.com/scholar?cluster=7146735712017629088&hl=en&as_sdt=0,48",3,2018
Practical Methods for Graph Two-Sample Testing,36,neurips,2,0,2023-06-15 17:55:22.747000,https://github.com/gdebarghya/Network-TwoSampleTesting,5,Practical methods for graph two-sample testing,"https://scholar.google.com/scholar?cluster=3213877141900838189&hl=en&as_sdt=0,6",1,2018
Learning to Navigate in Cities Without a Map,279,neurips,56,4,2023-06-15 17:55:22.938000,https://github.com/deepmind/streetlearn,268,Learning to navigate in cities without a map,"https://scholar.google.com/scholar?cluster=9758707731169438744&hl=en&as_sdt=0,39",12,2018
Invertibility of Convolutional Generative Networks from Partial Measurements,79,neurips,2,1,2023-06-15 17:55:23.129000,https://github.com/fangchangma/invert-generative-networks,19,Invertibility of convolutional generative networks from partial measurements,"https://scholar.google.com/scholar?cluster=13691072756611951369&hl=en&as_sdt=0,19",4,2018
Towards Robust Detection of Adversarial Examples,184,neurips,11,0,2023-06-15 17:55:23.320000,https://github.com/P2333/Reverse-Cross-Entropy,41,Towards robust detection of adversarial examples,"https://scholar.google.com/scholar?cluster=12795339654045612460&hl=en&as_sdt=0,18",4,2018
Direct Estimation of Differences in Causal Graphs,24,neurips,0,0,2023-06-15 17:55:23.510000,https://github.com/csquires/dci,8,Direct estimation of differences in causal graphs,"https://scholar.google.com/scholar?cluster=6891353891081698977&hl=en&as_sdt=0,26",5,2018
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments,145,neurips,820,36,2023-06-15 17:55:23.701000,https://github.com/deepmind/open_spiel,3694,Actor-critic policy optimization in partially observable multiagent environments,"https://scholar.google.com/scholar?cluster=8096003745039146783&hl=en&as_sdt=0,34",106,2018
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems,305,neurips,428,52,2023-06-15 17:55:23.891000,https://github.com/deepmodeling/deepmd-kit,1144,End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems,"https://scholar.google.com/scholar?cluster=4009423108945551834&hl=en&as_sdt=0,41",49,2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning,501,neurips,111,5,2023-06-15 17:55:24.082000,https://github.com/xunzheng/notears,482,Dags with no tears: Continuous optimization for structure learning,"https://scholar.google.com/scholar?cluster=7128195536288105484&hl=en&as_sdt=0,36",21,2018
Connectionist Temporal Classification with Maximum Entropy Regularization,49,neurips,41,8,2023-06-15 17:55:24.273000,https://github.com/liuhu-bigeye/enctc.crnn,137,Connectionist temporal classification with maximum entropy regularization,"https://scholar.google.com/scholar?cluster=16455105685023612483&hl=en&as_sdt=0,5",10,2018
Are GANs Created Equal? A Large-Scale Study,994,neurips,322,16,2023-06-15 17:55:24.464000,https://github.com/google/compare_gan,1814,Are gans created equal? a large-scale study,"https://scholar.google.com/scholar?cluster=3229217754457345915&hl=en&as_sdt=0,5",52,2018
FRAGE: Frequency-Agnostic Word Representation,149,neurips,21,6,2023-06-15 17:55:24.655000,https://github.com/ChengyueGongR/FrequencyAgnostic,117,Frage: Frequency-agnostic word representation,"https://scholar.google.com/scholar?cluster=899516517229807927&hl=en&as_sdt=0,31",6,2018
Variational Memory Encoder-Decoder,37,neurips,5,0,2023-06-15 17:55:24.845000,https://github.com/thaihungle/VMED,18,Variational memory encoder-decoder,"https://scholar.google.com/scholar?cluster=16470131384989674730&hl=en&as_sdt=0,10",4,2018
Data-Efficient Hierarchical Reinforcement Learning,690,neurips,46276,1206,2023-06-15 17:55:25.036000,https://github.com/tensorflow/models,75922,Data-efficient hierarchical reinforcement learning,"https://scholar.google.com/scholar?cluster=8228365515476642671&hl=en&as_sdt=0,11",2774,2018
Removing the Feature Correlation Effect of Multiplicative Noise,8,neurips,1,0,2023-06-15 17:55:25.226000,https://github.com/zj10/NCMN,3,Removing the feature correlation effect of multiplicative noise,"https://scholar.google.com/scholar?cluster=17402472050771179089&hl=en&as_sdt=0,5",1,2018
Efficient Loss-Based Decoding on Graphs for Extreme Classification,12,neurips,4,0,2023-06-15 17:55:25.417000,https://github.com/ievron/wltls,4,Efficient loss-based decoding on graphs for extreme classification,"https://scholar.google.com/scholar?cluster=17119928599826946784&hl=en&as_sdt=0,41",2,2018
Scalable methods for 8-bit training of neural networks,284,neurips,56,10,2023-06-15 17:55:25.607000,https://github.com/eladhoffer/quantized.pytorch,210,Scalable methods for 8-bit training of neural networks,"https://scholar.google.com/scholar?cluster=6261172322646700444&hl=en&as_sdt=0,10",13,2018
Step Size Matters in Deep Learning,26,neurips,1,0,2023-06-15 17:55:25.798000,https://github.com/nar-k/NIPS-2018,3,Step size matters in deep learning,"https://scholar.google.com/scholar?cluster=5460214845816514152&hl=en&as_sdt=0,47",1,2018
Dirichlet belief networks for topic structure learning,29,neurips,4,2,2023-06-15 17:55:25.989000,https://github.com/ethanhezhao/DirBN,7,Dirichlet belief networks for topic structure learning,"https://scholar.google.com/scholar?cluster=13908644537239897303&hl=en&as_sdt=0,47",2,2018
HOUDINI: Lifelong Learning as Program Synthesis,68,neurips,5,0,2023-06-15 17:55:26.180000,https://github.com/capergroup/houdini,45,Houdini: Lifelong learning as program synthesis,"https://scholar.google.com/scholar?cluster=10841457222027435818&hl=en&as_sdt=0,33",6,2018
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks,39,neurips,4,1,2023-06-15 17:55:26.371000,https://github.com/flatironinstitute/mantis,10,Manifold-tiling localized receptive fields are optimal in similarity-preserving neural networks,"https://scholar.google.com/scholar?cluster=1758414387739465296&hl=en&as_sdt=0,47",3,2018
Embedding Logical Queries on Knowledge Graphs,228,neurips,39,9,2023-06-15 17:55:26.562000,https://github.com/williamleif/graphqembed,116,Embedding logical queries on knowledge graphs,"https://scholar.google.com/scholar?cluster=9948805019620970484&hl=en&as_sdt=0,5",8,2018
Parsimonious Bayesian deep networks,7,neurips,2,0,2023-06-15 17:55:26.752000,https://github.com/mingyuanzhou/PBDN,3,Parsimonious Bayesian deep networks,"https://scholar.google.com/scholar?cluster=14376157659087127451&hl=en&as_sdt=0,5",5,2018
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion,289,neurips,46276,1206,2023-06-15 17:55:26.943000,https://github.com/tensorflow/models,75922,Sample-efficient reinforcement learning with stochastic ensemble value expansion,"https://scholar.google.com/scholar?cluster=12106658410656872341&hl=en&as_sdt=0,5",2774,2018
Neural Nearest Neighbors Networks,292,neurips,44,17,2023-06-15 17:55:27.134000,https://github.com/visinf/n3net,276,Neural nearest neighbors networks,"https://scholar.google.com/scholar?cluster=11963067599142958734&hl=en&as_sdt=0,10",15,2018
Neural Architecture Search with Bayesian Optimisation and Optimal Transport,546,neurips,27,5,2023-06-15 17:55:27.325000,https://github.com/kirthevasank/nasbot,128,Neural architecture search with bayesian optimisation and optimal transport,"https://scholar.google.com/scholar?cluster=7308576573219301832&hl=en&as_sdt=0,11",12,2018
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN,68,neurips,10,0,2023-06-15 17:55:27.526000,https://github.com/maciejzieba/binGAN,36,Bingan: Learning compact binary descriptors with a regularized gan,"https://scholar.google.com/scholar?cluster=7540991992898429437&hl=en&as_sdt=0,23",7,2018
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing,124,neurips,71,4,2023-06-15 17:55:27.717000,https://github.com/crazydonkey200/neural-symbolic-machines,371,Memory augmented policy optimization for program synthesis and semantic parsing,"https://scholar.google.com/scholar?cluster=4398387474099067788&hl=en&as_sdt=0,5",26,2018
LF-Net: Learning Local Features from Images,445,neurips,67,13,2023-06-15 17:55:27.908000,https://github.com/vcg-uvic/lf-net-release,300,LF-Net: Learning local features from images,"https://scholar.google.com/scholar?cluster=8243342192916977654&hl=en&as_sdt=0,5",19,2018
PointCNN: Convolution On X-Transformed Points,2077,neurips,359,59,2023-06-15 17:55:28.099000,https://github.com/yangyanli/PointCNN,1305,Pointcnn: Convolution on x-transformed points,"https://scholar.google.com/scholar?cluster=9461711858418183791&hl=en&as_sdt=0,47",56,2018
Assessing Generative Models via Precision and Recall,373,neurips,10,5,2023-06-15 17:55:28.289000,https://github.com/msmsajjadi/precision-recall-distributions,89,Assessing generative models via precision and recall,"https://scholar.google.com/scholar?cluster=651893942780229&hl=en&as_sdt=0,3",2,2018
Improved Network Robustness with Adversary Critic,13,neurips,0,0,2023-06-15 17:55:28.479000,https://github.com/aam-at/adversary_critic,13,Improved network robustness with adversary critic,"https://scholar.google.com/scholar?cluster=4193325299886417643&hl=en&as_sdt=0,47",4,2018
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators,25,neurips,1,0,2023-06-15 17:55:28.670000,https://github.com/keisuke198619/metricNLDS,1,Metric on nonlinear dynamical systems with perron-frobenius operators,"https://scholar.google.com/scholar?cluster=9736849801126744369&hl=en&as_sdt=0,24",2,2018
Non-Local Recurrent Network for Image Restoration,536,neurips,39,0,2023-06-15 17:55:28.861000,https://github.com/Ding-Liu/NLRN,169,Non-local recurrent network for image restoration,"https://scholar.google.com/scholar?cluster=17713021931965385894&hl=en&as_sdt=0,11",14,2018
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning,11,neurips,1,1,2023-06-15 17:55:29.051000,https://github.com/hsvgbkhgbv/TACTHMC,7,Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning,"https://scholar.google.com/scholar?cluster=1359920802371030920&hl=en&as_sdt=0,22",3,2018
A Stein variational Newton method,114,neurips,3,0,2023-06-15 17:55:29.242000,https://github.com/gianlucadetommaso/Stein-variational-samplers,21,A Stein variational Newton method,"https://scholar.google.com/scholar?cluster=2381223671647654052&hl=en&as_sdt=0,5",4,2018
Compositional Plan Vectors,12,neurips,0,14,2023-06-15 23:42:32.928000,https://github.com/cdevin/cpv,8,Compositional plan vectors,"https://scholar.google.com/scholar?cluster=15635463865993301870&hl=en&as_sdt=0,5",4,2019
Learning to Propagate for Graph Meta-Learning,90,neurips,3,2,2023-06-15 23:42:33.114000,https://github.com/liulu112601/Gated-Propagation-Net,36,Learning to propagate for graph meta-learning,"https://scholar.google.com/scholar?cluster=3473165000863905721&hl=en&as_sdt=0,5",2,2019
Multi-resolution Multi-task Gaussian Processes,33,neurips,3,0,2023-06-15 23:42:33.297000,https://github.com/ohamelijnck/multi_res_gps,6,Multi-resolution multi-task Gaussian processes,"https://scholar.google.com/scholar?cluster=5029064741200470600&hl=en&as_sdt=0,26",1,2019
Deep Equilibrium Models,452,neurips,75,5,2023-06-15 23:42:33.479000,https://github.com/locuslab/deq,650,Deep equilibrium models,"https://scholar.google.com/scholar?cluster=659851965041196662&hl=en&as_sdt=0,5",20,2019
Exact Gaussian Processes on a Million Data Points,205,neurips,501,318,2023-06-15 23:42:33.662000,https://github.com/cornellius-gp/gpytorch,3140,Exact Gaussian processes on a million data points,"https://scholar.google.com/scholar?cluster=4013716764327710087&hl=en&as_sdt=0,29",55,2019
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization,14,neurips,1,2,2023-06-15 23:42:33.844000,https://github.com/sorooshafiee/Optimistic_Likelihoods,3,Calculating optimistic likelihoods using (geodesically) convex optimization,"https://scholar.google.com/scholar?cluster=5806305643748445691&hl=en&as_sdt=0,14",1,2019
Improved Precision and Recall Metric for Assessing Generative Models,355,neurips,15,0,2023-06-15 23:42:34.026000,https://github.com/kynkaat/improved-precision-and-recall-metric,126,Improved precision and recall metric for assessing generative models,"https://scholar.google.com/scholar?cluster=16244569923752023320&hl=en&as_sdt=0,33",4,2019
Zero-Shot Semantic Segmentation,166,neurips,23,6,2023-06-15 23:42:34.208000,https://github.com/valeoai/ZS3,170,Zero-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=9122033339368914969&hl=en&as_sdt=0,49",14,2019
Hyperspherical Prototype Networks,75,neurips,6,2,2023-06-15 23:42:34.389000,https://github.com/psmmettes/hpn,59,Hyperspherical prototype networks,"https://scholar.google.com/scholar?cluster=15240435433337095231&hl=en&as_sdt=0,47",3,2019
Lower Bounds on Adversarial Robustness from Optimal Transport,82,neurips,0,0,2023-06-15 23:42:34.572000,https://github.com/inspire-group/robustness-via-transport,12,Lower bounds on adversarial robustness from optimal transport,"https://scholar.google.com/scholar?cluster=2678310467137454397&hl=en&as_sdt=0,41",4,2019
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution,33,neurips,1,0,2023-06-15 23:42:34.753000,https://github.com/qingqu06/MCS-BD,8,A nonconvex approach for exact and efficient multichannel sparse blind deconvolution,"https://scholar.google.com/scholar?cluster=16270892070562641285&hl=en&as_sdt=0,48",2,2019
Generalization of Reinforcement Learners with Working and Episodic Memory,49,neurips,16,1,2023-06-15 23:42:34.935000,https://github.com/deepmind/dm_memorytasks,222,Generalization of reinforcement learners with working and episodic memory,"https://scholar.google.com/scholar?cluster=15492128596340349153&hl=en&as_sdt=0,5",13,2019
DTWNet: a Dynamic Time Warping Network,67,neurips,24,0,2023-06-15 23:42:35.117000,https://github.com/TideDancer/DTWNet,61,Dtwnet: a dynamic time warping network,"https://scholar.google.com/scholar?cluster=12755791538559814955&hl=en&as_sdt=0,5",4,2019
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries,3,neurips,0,0,2023-06-15 23:42:35.300000,https://github.com/scarlett-nus/er_edge_det,1,Learning erdos-renyi random graphs via edge detecting queries,"https://scholar.google.com/scholar?cluster=10593108232555201387&hl=en&as_sdt=0,33",1,2019
Cormorant: Covariant Molecular Neural Networks,320,neurips,11,4,2023-06-15 23:42:35.482000,https://github.com/risilab/cormorant,51,Cormorant: Covariant molecular neural networks,"https://scholar.google.com/scholar?cluster=8775328101914516140&hl=en&as_sdt=0,6",6,2019
Explicit Explore-Exploit Algorithms in Continuous State Spaces,25,neurips,1,1,2023-06-15 23:42:35.663000,https://github.com/mbhenaff/neural-e3,6,Explicit explore-exploit algorithms in continuous state spaces,"https://scholar.google.com/scholar?cluster=12048053736281470251&hl=en&as_sdt=0,43",3,2019
Spherical Text Embedding,100,neurips,28,1,2023-06-15 23:42:35.845000,https://github.com/yumeng5/Spherical-Text-Embedding,175,Spherical text embedding,"https://scholar.google.com/scholar?cluster=12918153204372090641&hl=en&as_sdt=0,5",7,2019
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates,108,neurips,0,0,2023-06-15 23:42:36.028000,https://github.com/jnegrea/neurips2019-5904-code,0,Information-theoretic generalization bounds for SGLD via data-dependent estimates,"https://scholar.google.com/scholar?cluster=7753094016128603941&hl=en&as_sdt=0,36",2,2019
Efficient Algorithms for Smooth Minimax Optimization,161,neurips,1,0,2023-06-15 23:42:36.210000,https://github.com/POLane16/DIAG,2,Efficient algorithms for smooth minimax optimization,"https://scholar.google.com/scholar?cluster=16329029546814043430&hl=en&as_sdt=0,10",1,2019
Uniform convergence may be unable to explain generalization in deep learning,203,neurips,3,0,2023-06-15 23:42:36.391000,https://github.com/locuslab/uniform-convergence-NeurIPS19,10,Uniform convergence may be unable to explain generalization in deep learning,"https://scholar.google.com/scholar?cluster=863649597305754781&hl=en&as_sdt=0,5",5,2019
Robust exploration in linear quadratic reinforcement learning,30,neurips,1,0,2023-06-15 23:42:36.574000,https://github.com/umenberger/robust-exploration,3,Robust exploration in linear quadratic reinforcement learning,"https://scholar.google.com/scholar?cluster=2367192655687750423&hl=en&as_sdt=0,5",2,2019
Meta-Surrogate Benchmarking for Hyperparameter Optimization,39,neurips,122,42,2023-06-15 23:42:36.756000,https://github.com/amzn/emukit,518,Meta-surrogate benchmarking for hyperparameter optimization,"https://scholar.google.com/scholar?cluster=11453320688261024074&hl=en&as_sdt=0,44",17,2019
Bayesian Optimization under Heavy-tailed Payoffs,18,neurips,1,0,2023-06-15 23:42:36.938000,https://github.com/sayakrc/Bayesian-Optimization-under-Heavy-tailed-Payoffs,2,Bayesian optimization under heavy-tailed payoffs,"https://scholar.google.com/scholar?cluster=13505569785706603618&hl=en&as_sdt=0,23",1,2019
Meta-Learning with Implicit Gradients,611,neurips,7,3,2023-06-15 23:42:37.120000,https://github.com/aravindr93/imaml_dev,42,Meta-learning with implicit gradients,"https://scholar.google.com/scholar?cluster=13369476722285367510&hl=en&as_sdt=0,5",6,2019
Differentially Private Markov Chain Monte Carlo,20,neurips,1,0,2023-06-15 23:42:37.303000,https://github.com/DPBayes/DP-MCMC-NeurIPS2019,2,Differentially private markov chain monte carlo,"https://scholar.google.com/scholar?cluster=918464932035758284&hl=en&as_sdt=0,34",4,2019
Universal Boosting Variational Inference,25,neurips,1,2,2023-06-15 23:42:37.486000,https://github.com/trevorcampbell/ubvi,5,Universal boosting variational inference,"https://scholar.google.com/scholar?cluster=8765801192922699610&hl=en&as_sdt=0,5",1,2019
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning,119,neurips,15,6,2023-06-15 23:42:37.668000,https://github.com/yalidu/liir,53,Liir: Learning individual intrinsic reward in multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=17772634861741004001&hl=en&as_sdt=0,11",2,2019
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits,9,neurips,0,0,2023-06-15 23:42:37.850000,https://github.com/wenhao-z/Bayes_factor_Opposite_neuron,0,A normative theory for causal inference and Bayes factor computation in neural circuits,"https://scholar.google.com/scholar?cluster=17602894246019062673&hl=en&as_sdt=0,5",1,2019
The Geometry of Deep Networks: Power Diagram Subdivision,38,neurips,1,0,2023-06-15 23:42:38.033000,https://github.com/RandallBalestriero/PowerDiagram,1,The geometry of deep networks: Power diagram subdivision,"https://scholar.google.com/scholar?cluster=3949701883941421755&hl=en&as_sdt=0,5",3,2019
Semi-Parametric Efficient Policy Learning with Continuous Actions,42,neurips,0,0,2023-06-15 23:42:38.215000,https://github.com/vsyrgkanis/policy_learning_continuous_actions,1,Semi-parametric efficient policy learning with continuous actions,"https://scholar.google.com/scholar?cluster=4715242630767195643&hl=en&as_sdt=0,47",2,2019
Learning Stable Deep Dynamics Models,143,neurips,10,2,2023-06-15 23:42:38.397000,https://github.com/locuslab/stable_dynamics,25,Learning stable deep dynamics models,"https://scholar.google.com/scholar?cluster=15884383241607994844&hl=en&as_sdt=0,26",2,2019
Beyond the Single Neuron Convex Barrier for Neural Network Certification,140,neurips,99,12,2023-06-15 23:42:38.579000,https://github.com/eth-sri/eran,284,Beyond the single neuron convex barrier for neural network certification,"https://scholar.google.com/scholar?cluster=17997567581832300594&hl=en&as_sdt=0,11",22,2019
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models,166,neurips,33,2,2023-06-15 23:42:38.761000,https://github.com/iffsid/mmvae,142,Variational mixture-of-experts autoencoders for multi-modal deep generative models,"https://scholar.google.com/scholar?cluster=204166380229744591&hl=en&as_sdt=0,5",8,2019
Language as an Abstraction for Hierarchical Deep Reinforcement Learning,148,neurips,12,4,2023-06-15 23:42:38.944000,https://github.com/google-research/clevr_robot_env,118,Language as an abstraction for hierarchical deep reinforcement learning,"https://scholar.google.com/scholar?cluster=13558761030433152437&hl=en&as_sdt=0,33",7,2019
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes,131,neurips,10,0,2023-06-15 23:42:39.126000,https://github.com/mbohlkeschneider/gluon-ts,43,High-dimensional multivariate forecasting with low-rank gaussian copula processes,"https://scholar.google.com/scholar?cluster=15568852272532937940&hl=en&as_sdt=0,29",1,2019
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization,3,neurips,1,0,2023-06-15 23:42:39.308000,https://github.com/framinmansour/Learning-Macroscopic-Brain-Connectomes-via-Group-Sparse-Factorization,6,Learning macroscopic brain connectomes via group-sparse factorization,"https://scholar.google.com/scholar?cluster=18281061878272336341&hl=en&as_sdt=0,5",2,2019
Combinatorial Inference against Label Noise,19,neurips,0,1,2023-06-15 23:42:39.490000,https://github.com/snow12345/Combinatorial_Classification,7,Combinatorial inference against label noise,"https://scholar.google.com/scholar?cluster=10313449809360280189&hl=en&as_sdt=0,5",1,2019
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data,8,neurips,5,1,2023-06-15 23:42:39.672000,https://github.com/highan911/FLRML,6,Fast low-rank metric learning for large-scale and high-dimensional data,"https://scholar.google.com/scholar?cluster=5081716944652547266&hl=en&as_sdt=0,33",1,2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent,772,neurips,227,58,2023-06-15 23:42:39.854000,https://github.com/google/neural-tangents,2023,Wide neural networks of any depth evolve as linear models under gradient descent,"https://scholar.google.com/scholar?cluster=10271588959901500441&hl=en&as_sdt=0,5",64,2019
Retrosynthesis Prediction with Conditional Graph Logic Network,124,neurips,22,6,2023-06-15 23:42:40.037000,https://github.com/Hanjun-Dai/GLN,99,Retrosynthesis prediction with conditional graph logic network,"https://scholar.google.com/scholar?cluster=13973073530348784019&hl=en&as_sdt=0,5",10,2019
Efficient Pure Exploration in Adaptive Round model,13,neurips,0,0,2023-06-15 23:42:40.219000,https://github.com/jmshi123/mab-nips-2019,0,Efficient pure exploration in adaptive round model,"https://scholar.google.com/scholar?cluster=15910693782133163407&hl=en&as_sdt=0,5",2,2019
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction,8,neurips,1,3,2023-06-15 23:42:40.400000,https://github.com/alaflaquiere/learn-spatial-structure,1,Unsupervised emergence of egocentric spatial structure from sensorimotor prediction,"https://scholar.google.com/scholar?cluster=14146114987912922308&hl=en&as_sdt=0,31",2,2019
Generalized Off-Policy Actor-Critic,44,neurips,658,6,2023-06-15 23:42:40.584000,https://github.com/ShangtongZhang/DeepRL,2943,Generalized off-policy actor-critic,"https://scholar.google.com/scholar?cluster=9029293262524916308&hl=en&as_sdt=0,33",93,2019
"Average Individual Fairness: Algorithms, Generalization and Experiments",78,neurips,0,0,2023-06-15 23:42:40.767000,https://github.com/SaeedSharifiMa/AIF,0,"Average individual fairness: Algorithms, generalization and experiments","https://scholar.google.com/scholar?cluster=8157096146249952889&hl=en&as_sdt=0,5",2,2019
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse,12,neurips,2,0,2023-06-15 23:42:40.950000,https://github.com/berenslab/abc-ribbon,2,Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse,"https://scholar.google.com/scholar?cluster=17222924363509946962&hl=en&as_sdt=0,5",4,2019
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation,80,neurips,0,0,2023-06-15 23:42:41.132000,https://github.com/cwein3/jacobian-reg,0,Data-dependent sample complexity of deep neural networks via lipschitz augmentation,"https://scholar.google.com/scholar?cluster=17001639970968112177&hl=en&as_sdt=0,5",2,2019
Semi-supervisedly Co-embedding Attributed Networks,27,neurips,0,0,2023-06-15 23:42:41.314000,https://github.com/mengzaiqiao/SCAN,30,Semi-supervisedly co-embedding attributed networks,"https://scholar.google.com/scholar?cluster=14232143209027006977&hl=en&as_sdt=0,29",3,2019
Adaptive Auxiliary Task Weighting for Reinforcement Learning,82,neurips,1,0,2023-06-15 23:42:41.510000,https://github.com/Xingyu-Lin/auxiliary-tasks-rl,20,Adaptive auxiliary task weighting for reinforcement learning,"https://scholar.google.com/scholar?cluster=6568043272475560239&hl=en&as_sdt=0,10",3,2019
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders,127,neurips,34,5,2023-06-15 23:42:41.692000,https://github.com/emilemathieu/pvae,114,Continuous hierarchical representations with poincaré variational auto-encoders,"https://scholar.google.com/scholar?cluster=4743584755071980119&hl=en&as_sdt=0,39",6,2019
Training Image Estimators without Image Ground Truth,18,neurips,2,0,2023-06-15 23:42:41.874000,https://github.com/likesum/unsupimg,12,Training image estimators without image ground truth,"https://scholar.google.com/scholar?cluster=8370564258628427561&hl=en&as_sdt=0,5",4,2019
Minimizers of the Empirical Risk and Risk Monotonicity,21,neurips,0,0,2023-06-15 23:42:42.057000,https://github.com/tomviering/RiskMonotonicity,1,Minimizers of the empirical risk and risk monotonicity,"https://scholar.google.com/scholar?cluster=13614749018190091572&hl=en&as_sdt=0,5",1,2019
The Label Complexity of Active Learning from Observational Data,8,neurips,0,0,2023-06-15 23:42:42.239000,https://github.com/yyysbysb/al_obs_neurips19,0,The label complexity of active learning from observational data,"https://scholar.google.com/scholar?cluster=11282037010196502845&hl=en&as_sdt=0,5",1,2019
Learning Fairness in Multi-Agent Systems,43,neurips,9,0,2023-06-15 23:42:42.421000,https://github.com/PKU-AI-Edge/FEN,34,Learning fairness in multi-agent systems,"https://scholar.google.com/scholar?cluster=2510823275080690195&hl=en&as_sdt=0,6",2,2019
On Robustness to Adversarial Examples and Polynomial Optimization,32,neurips,0,0,2023-06-15 23:42:42.602000,https://github.com/abhrodutta/advrobust,0,On robustness to adversarial examples and polynomial optimization,"https://scholar.google.com/scholar?cluster=14449715261251259195&hl=en&as_sdt=0,39",1,2019
In-Place Zero-Space Memory Protection for CNN,18,neurips,2,0,2023-06-15 23:42:42.784000,https://github.com/guanh01/wot,2,In-place zero-space memory protection for cnn,"https://scholar.google.com/scholar?cluster=7089788483672559096&hl=en&as_sdt=0,15",2,2019
"Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask",327,neurips,28,2,2023-06-15 23:42:42.965000,https://github.com/uber-research/deconstructing-lottery-tickets,137,"Deconstructing lottery tickets: Zeros, signs, and the supermask","https://scholar.google.com/scholar?cluster=6213271169293396055&hl=en&as_sdt=0,36",7,2019
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes,179,neurips,22,1,2023-06-15 23:42:43.147000,https://github.com/cambridge-mlg/cnaps,152,Fast and flexible multi-task classification using conditional neural adaptive processes,"https://scholar.google.com/scholar?cluster=6556255070381758438&hl=en&as_sdt=0,44",11,2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning,601,neurips,73,9,2023-06-15 23:42:43.329000,https://github.com/wjmaddox/swa_gaussian,387,A simple baseline for bayesian uncertainty in deep learning,"https://scholar.google.com/scholar?cluster=4938182174332558509&hl=en&as_sdt=0,43",12,2019
CPM-Nets: Cross Partial Multi-View Networks,71,neurips,26,2,2023-06-15 23:42:43.517000,https://github.com/hanmenghan/CPM_Nets,72,CPM-Nets: Cross partial multi-view networks,"https://scholar.google.com/scholar?cluster=3047426886148116831&hl=en&as_sdt=0,33",3,2019
Efficiently avoiding saddle points with zero order methods: No gradients required,18,neurips,2,0,2023-06-15 23:42:43.699000,https://github.com/lamflokas/zero-order,3,Efficiently avoiding saddle points with zero order methods: No gradients required,"https://scholar.google.com/scholar?cluster=13601784096237697106&hl=en&as_sdt=0,33",2,2019
Learning metrics for persistence-based summaries and applications for graph classification,93,neurips,1,1,2023-06-15 23:42:43.881000,https://github.com/topology474/WKPI,11,Learning metrics for persistence-based summaries and applications for graph classification,"https://scholar.google.com/scholar?cluster=9051382955304665692&hl=en&as_sdt=0,33",1,2019
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph,69,neurips,9,7,2023-06-15 23:42:44.063000,https://github.com/yikang-li/PasteGAN,51,Pastegan: A semi-parametric method to generate image from scene graph,"https://scholar.google.com/scholar?cluster=13530635324497263822&hl=en&as_sdt=0,5",1,2019
Learning Local Search Heuristics for Boolean Satisfiability,89,neurips,7,1,2023-06-15 23:42:44.245000,https://github.com/emreyolcu/sat,29,Learning local search heuristics for boolean satisfiability,"https://scholar.google.com/scholar?cluster=13065026334789781574&hl=en&as_sdt=0,38",3,2019
Learning to Perform Local Rewriting for Combinatorial Optimization,231,neurips,48,8,2023-06-15 23:42:44.430000,https://github.com/facebookresearch/neural-rewriter,138,Learning to perform local rewriting for combinatorial optimization,"https://scholar.google.com/scholar?cluster=13941022610350989164&hl=en&as_sdt=0,20",7,2019
Learning Representations for Time Series Clustering,129,neurips,21,8,2023-06-15 23:42:44.612000,https://github.com/qianlima-lab/DTCR,69,Learning representations for time series clustering,"https://scholar.google.com/scholar?cluster=8145184496367809324&hl=en&as_sdt=0,4",9,2019
Joint-task Self-supervised Learning for Temporal Correspondence,116,neurips,23,1,2023-06-15 23:42:44.795000,https://github.com/Liusifei/UVC,172,Joint-task self-supervised learning for temporal correspondence,"https://scholar.google.com/scholar?cluster=15162867613361199730&hl=en&as_sdt=0,31",13,2019
On Distributed Averaging for Stochastic k-PCA,8,neurips,0,0,2023-06-15 23:42:44.976000,https://github.com/maheshakya/dist-averaging-k-pca,2,On distributed averaging for stochastic k-PCA,"https://scholar.google.com/scholar?cluster=3460811999232599777&hl=en&as_sdt=0,5",3,2019
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control,73,neurips,13,9,2023-06-15 23:42:45.159000,https://github.com/saizhang0218/VBC,41,Efficient communication in multi-agent reinforcement learning via variance based control,"https://scholar.google.com/scholar?cluster=14090873804037155766&hl=en&as_sdt=0,33",4,2019
A Bayesian Theory of Conformity in Collective Decision Making,9,neurips,0,0,2023-06-15 23:42:45.341000,https://github.com/kooosha/BayesianConformity,1,A Bayesian theory of conformity in collective decision making,"https://scholar.google.com/scholar?cluster=7455154068754976194&hl=en&as_sdt=0,14",1,2019
Poisson-Randomized Gamma Dynamical Systems,21,neurips,2,0,2023-06-15 23:42:45.523000,https://github.com/aschein/PRGDS,7,Poisson-randomized gamma dynamical systems,"https://scholar.google.com/scholar?cluster=6917148610185425748&hl=en&as_sdt=0,5",2,2019
Sequence Modeling with Unconstrained Generation Order,18,neurips,4,4,2023-06-15 23:42:45.705000,https://github.com/TIXFeniks/neurips2019_intrus,15,Sequence modeling with unconstrained generation order,"https://scholar.google.com/scholar?cluster=11928975685128979284&hl=en&as_sdt=0,10",3,2019
Online Continual Learning with Maximal Interfered Retrieval,83,neurips,17,7,2023-06-15 23:42:45.886000,https://github.com/optimass/Maximally_Interfered_Retrieval,81,Online class-incremental continual learning with adversarial shapley value,"https://scholar.google.com/scholar?cluster=13286994926038359819&hl=en&as_sdt=0,36",8,2019
Deep Generalized Method of Moments for Instrumental Variable Analysis,96,neurips,6,0,2023-06-15 23:42:46.069000,https://github.com/CausalML/DeepGMM,30,Deep generalized method of moments for instrumental variable analysis,"https://scholar.google.com/scholar?cluster=2190218199983415707&hl=en&as_sdt=0,33",5,2019
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders,26,neurips,3,0,2023-06-15 23:42:46.251000,https://github.com/tagas/vcae,6,Copulas as high-dimensional generative models: Vine copula autoencoders,"https://scholar.google.com/scholar?cluster=7223084287803967462&hl=en&as_sdt=0,33",1,2019
Implicit Semantic Data Augmentation for Deep Networks,126,neurips,91,7,2023-06-15 23:42:46.434000,https://github.com/blackfeather-wang/ISDA-for-Deep-Networks,558,Implicit semantic data augmentation for deep networks,"https://scholar.google.com/scholar?cluster=7550212963296230236&hl=en&as_sdt=0,5",15,2019
q-means: A quantum algorithm for unsupervised machine learning,143,neurips,2,1,2023-06-15 23:42:46.616000,https://github.com/JonasLandman/quantum_kmeans_NeurIPS_2019,6,q-means: A quantum algorithm for unsupervised machine learning,"https://scholar.google.com/scholar?cluster=6188393801436319062&hl=en&as_sdt=0,47",1,2019
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression,14,neurips,3,0,2023-06-15 23:42:46.798000,https://github.com/gerrili1996/DRLR_NIPS2019_exp,12,A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression,"https://scholar.google.com/scholar?cluster=14059374198558269929&hl=en&as_sdt=0,13",2,2019
Robust Attribution Regularization,58,neurips,0,2,2023-06-15 23:42:46.981000,https://github.com/jfc43/robust-attribution-regularization,15,Robust attribution regularization,"https://scholar.google.com/scholar?cluster=9772102979248482022&hl=en&as_sdt=0,33",2,2019
Kernel Instrumental Variable Regression,122,neurips,1,0,2023-06-15 23:42:47.163000,https://github.com/r4hu1-5in9h/KIV,7,Kernel instrumental variable regression,"https://scholar.google.com/scholar?cluster=14048410024611042671&hl=en&as_sdt=0,33",1,2019
Hindsight Credit Assignment,63,neurips,1,0,2023-06-15 23:42:47.345000,https://github.com/hca-neurips2019/hca,8,Hindsight credit assignment,"https://scholar.google.com/scholar?cluster=4046462463580411762&hl=en&as_sdt=0,33",2,2019
Zero-shot Learning via Simultaneous Generating and Learning,45,neurips,1,0,2023-06-15 23:42:47.551000,https://github.com/bogus2000/zero-shot_SGAL,2,Zero-shot learning via simultaneous generating and learning,"https://scholar.google.com/scholar?cluster=4888611816499728878&hl=en&as_sdt=0,14",4,2019
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder,34,neurips,1,1,2023-06-15 23:42:47.737000,https://github.com/GuyLor/direct_vae,14,Direct Optimization through for Discrete Variational Auto-Encoder,"https://scholar.google.com/scholar?cluster=9304709167594459468&hl=en&as_sdt=0,33",3,2019
Ouroboros: On Accelerating Training of Transformer-Based Language Models,5,neurips,1,1,2023-06-15 23:42:47.919000,https://github.com/LaraQianYang/Ouroboros,10,Ouroboros: On accelerating training of transformer-based language models,"https://scholar.google.com/scholar?cluster=5857133674460297105&hl=en&as_sdt=0,33",2,2019
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently,2,neurips,0,0,2023-06-15 23:42:48.100000,https://github.com/XiaoLiu-git/Push-Pull-feedback-for-NIPS2019,2,Push-pull feedback implements hierarchical information retrieval efficiently,"https://scholar.google.com/scholar?cluster=9412623594007001051&hl=en&as_sdt=0,31",1,2019
Calibration tests in multi-class classification: A unifying framework,70,neurips,4,0,2023-06-15 23:42:48.283000,https://github.com/devmotion/CalibrationPaper,15,Calibration tests in multi-class classification: A unifying framework,"https://scholar.google.com/scholar?cluster=3801848561463868777&hl=en&as_sdt=0,5",2,2019
Globally Optimal Learning for Structured Elliptical Losses,4,neurips,0,0,2023-06-15 23:42:48.465000,https://github.com/yowald/elliptical-losses,0,Globally optimal learning for structured elliptical losses,"https://scholar.google.com/scholar?cluster=13004269244782934257&hl=en&as_sdt=0,32",2,2019
MixMatch: A Holistic Approach to Semi-Supervised Learning,2316,neurips,162,2,2023-06-15 23:42:48.648000,https://github.com/google-research/mixmatch,1107,Mixmatch: A holistic approach to semi-supervised learning,"https://scholar.google.com/scholar?cluster=8843329865264835946&hl=en&as_sdt=0,29",26,2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks,69,neurips,3,0,2023-06-15 23:42:48.830000,https://github.com/ColinQiyangLi/LConvNet,32,Preventing gradient attenuation in lipschitz constrained convolutional networks,"https://scholar.google.com/scholar?cluster=16988033014976745098&hl=en&as_sdt=0,33",8,2019
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder,60,neurips,10,1,2023-06-15 23:42:49.012000,https://github.com/kingfengji/DeepConfuse,15,Learning to confuse: generating training time adversarial data with auto-encoder,"https://scholar.google.com/scholar?cluster=8039257054825778707&hl=en&as_sdt=0,33",2,2019
Attentive State-Space Modeling of Disease Progression,78,neurips,1,1,2023-06-15 23:42:49.194000,https://github.com/ahmedmalaa/attentive-state-space-models,5,Attentive state-space modeling of disease progression,"https://scholar.google.com/scholar?cluster=16630755121870037288&hl=en&as_sdt=0,33",1,2019
On two ways to use determinantal point processes for Monte Carlo integration,19,neurips,47,3,2023-06-15 23:42:49.376000,https://github.com/guilgautier/DPPy,204,On two ways to use determinantal point processes for Monte Carlo integration,"https://scholar.google.com/scholar?cluster=12801077756584329210&hl=en&as_sdt=0,44",16,2019
Controllable Text-to-Image Generation,248,neurips,35,9,2023-06-15 23:42:49.558000,https://github.com/mrlibw/ControlGAN,154,Controllable text-to-image generation,"https://scholar.google.com/scholar?cluster=18438617826827121407&hl=en&as_sdt=0,3",5,2019
Exploring Algorithmic Fairness in Robust Graph Covering Problems,44,neurips,0,0,2023-06-15 23:42:49.740000,https://github.com/Aida-Rahmattalabi/Fair-and-Robust-Graph-Covering-Problem,0,Exploring algorithmic fairness in robust graph covering problems,"https://scholar.google.com/scholar?cluster=12434116312128115468&hl=en&as_sdt=0,21",2,2019
Reducing the variance in online optimization by transporting past gradients,17,neurips,4,0,2023-06-15 23:42:49.922000,https://github.com/seba-1511/igt.pth,19,Reducing the variance in online optimization by transporting past gradients,"https://scholar.google.com/scholar?cluster=11851078121224648167&hl=en&as_sdt=0,22",2,2019
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces,11,neurips,0,0,2023-06-15 23:42:50.104000,https://github.com/BenyaminHaghi/DRNN-NeurIPS2019,3,Deep multi-state dynamic recurrent neural networks operating on wavelet based neural features for robust brain machine interfaces,"https://scholar.google.com/scholar?cluster=3157207408817715516&hl=en&as_sdt=0,5",2,2019
Graph Normalizing Flows,120,neurips,9,1,2023-06-15 23:42:50.289000,https://github.com/jliu/graph-normalizing-flows,51,Graph normalizing flows,"https://scholar.google.com/scholar?cluster=6217003823506794566&hl=en&as_sdt=0,44",3,2019
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction,27,neurips,3,2,2023-06-15 23:42:50.471000,https://github.com/tinyRattar/CSMRI_0325,31,Cascaded dilated dense network with two-step data consistency for MRI reconstruction,"https://scholar.google.com/scholar?cluster=8948167935740989245&hl=en&as_sdt=0,5",1,2019
Likelihood Ratios for Out-of-Distribution Detection,520,neurips,7320,1025,2023-06-15 23:42:50.653000,https://github.com/google-research/google-research,29776,Likelihood ratios for out-of-distribution detection,"https://scholar.google.com/scholar?cluster=8139743879647518819&hl=en&as_sdt=0,5",727,2019
Root Mean Square Layer Normalization,61,neurips,8,1,2023-06-15 23:42:50.836000,https://github.com/bzhangGo/rmsnorm,85,Root mean square layer normalization,"https://scholar.google.com/scholar?cluster=14510401956062153654&hl=en&as_sdt=0,44",4,2019
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs,230,neurips,29,1,2023-06-15 23:42:51.018000,https://github.com/malllabiisc/HyperGCN,149,Hypergcn: A new method for training graph convolutional networks on hypergraphs,"https://scholar.google.com/scholar?cluster=15969550418562746882&hl=en&as_sdt=0,5",4,2019
Asymptotics for Sketching in Least Squares Regression,46,neurips,0,0,2023-06-15 23:42:51.200000,https://github.com/liusf15/Sketching-lr,6,Asymptotics for sketching in least squares regression,"https://scholar.google.com/scholar?cluster=15974284881212026829&hl=en&as_sdt=0,5",2,2019
TAB-VCR: Tags and Attributes based VCR Baselines,18,neurips,8,1,2023-06-15 23:42:51.382000,https://github.com/Deanplayerljx/tab-vcr,19,TAB-VCR: tags and attributes based VCR baselines,"https://scholar.google.com/scholar?cluster=9340006550107070175&hl=en&as_sdt=0,43",3,2019
Assessing Social and Intersectional Biases in Contextualized Word Representations,157,neurips,2,0,2023-06-15 23:42:51.564000,https://github.com/tanyichern/social-biases-contextualized,4,Assessing social and intersectional biases in contextualized word representations,"https://scholar.google.com/scholar?cluster=434026761341591486&hl=en&as_sdt=0,38",1,2019
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery,7,neurips,0,0,2023-06-15 23:42:51.746000,https://github.com/dingchenwei/Likelihood-free_OICA,9,Likelihood-free overcomplete ICA and applications in causal discovery,"https://scholar.google.com/scholar?cluster=11860404397315313047&hl=en&as_sdt=0,33",1,2019
MaCow: Masked Convolutional Generative Flow,48,neurips,4,1,2023-06-15 23:42:51.928000,https://github.com/XuezheMax/macow,58,Macow: Masked convolutional generative flow,"https://scholar.google.com/scholar?cluster=149053927575210131&hl=en&as_sdt=0,31",4,2019
Batched Multi-armed Bandits Problem,101,neurips,1,0,2023-06-15 23:42:52.110000,https://github.com/Mathegineer/batched-bandit,3,Batched multi-armed bandits problem,"https://scholar.google.com/scholar?cluster=1369955008472544839&hl=en&as_sdt=0,5",1,2019
Causal Regularization,33,neurips,1,0,2023-06-15 23:42:52.293000,https://github.com/janzing/janzing.github.io,4,Causal regularization,"https://scholar.google.com/scholar?cluster=6604566561905490847&hl=en&as_sdt=0,10",0,2019
Augmented Neural ODEs,445,neurips,84,10,2023-06-15 23:42:52.474000,https://github.com/EmilienDupont/augmented-neural-odes,487,Augmented neural odes,"https://scholar.google.com/scholar?cluster=2463018982232972510&hl=en&as_sdt=0,5",19,2019
ANODEV2: A Coupled Neural ODE Framework,74,neurips,19,4,2023-06-15 23:42:52.656000,https://github.com/amirgholami/anode,99,ANODEV2: A coupled neural ODE framework,"https://scholar.google.com/scholar?cluster=18212332066465500294&hl=en&as_sdt=0,5",7,2019
Learning Neural Networks with Adaptive Regularization,16,neurips,14,0,2023-06-15 23:42:52.839000,https://github.com/yaohungt/Adaptive-Regularization-Neural-Network,67,Learning neural networks with adaptive regularization,"https://scholar.google.com/scholar?cluster=5481205132880543162&hl=en&as_sdt=0,14",5,2019
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels,112,neurips,44,0,2023-06-15 23:42:53.027000,https://github.com/yihanjiang/turboae,68,Turbo autoencoder: Deep learning based channel codes for point-to-point communication channels,"https://scholar.google.com/scholar?cluster=17000412546845490197&hl=en&as_sdt=0,30",9,2019
DetNAS: Backbone Search for Object Detection,217,neurips,49,1,2023-06-15 23:42:53.209000,https://github.com/megvii-model/DetNAS,288,Detnas: Backbone search for object detection,"https://scholar.google.com/scholar?cluster=17156640731829045371&hl=en&as_sdt=0,3",15,2019
Diffusion Improves Graph Learning,426,neurips,35,0,2023-06-15 23:42:53.391000,https://github.com/klicperajo/gdc,212,Diffusion improves graph learning,"https://scholar.google.com/scholar?cluster=17335287554708427599&hl=en&as_sdt=0,5",3,2019
"Inverting Deep Generative models, One layer at a time",49,neurips,3,0,2023-06-15 23:42:53.574000,https://github.com/cecilialeiqi/InvertGAN_LP,6,"Inverting deep generative models, one layer at a time","https://scholar.google.com/scholar?cluster=11354932647596357536&hl=en&as_sdt=0,33",2,2019
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks,196,neurips,5,0,2023-06-15 23:42:53.756000,https://github.com/Hadisalman/robust-verify-benchmark,39,A convex relaxation barrier to tight robustness verification of neural networks,"https://scholar.google.com/scholar?cluster=6023655920144066290&hl=en&as_sdt=0,5",3,2019
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods,272,neurips,0,0,2023-06-15 23:42:53.938000,https://github.com/optimization-for-data-driven-science/FairFashionMNIST,3,Solving a class of non-convex min-max games using iterative first order methods,"https://scholar.google.com/scholar?cluster=17358134548745942568&hl=en&as_sdt=0,5",3,2019
Modeling Tabular data using Conditional GAN,593,neurips,236,41,2023-06-15 23:42:54.120000,https://github.com/DAI-Lab/CTGAN,902,Modeling tabular data using conditional gan,"https://scholar.google.com/scholar?cluster=3578506996923518478&hl=en&as_sdt=0,5",22,2019
"Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates",155,neurips,22,7,2023-06-15 23:42:54.303000,https://github.com/IssamLaradji/sls,113,"Painless stochastic gradient: Interpolation, line-search, and convergence rates","https://scholar.google.com/scholar?cluster=14034515731155354848&hl=en&as_sdt=0,5",8,2019
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies,67,neurips,2,0,2023-06-15 23:42:54.491000,https://github.com/NMerlis/TabulaRL,2,Tight regret bounds for model-based reinforcement learning with greedy policies,"https://scholar.google.com/scholar?cluster=10045062126055715763&hl=en&as_sdt=0,5",0,2019
Neural Lyapunov Control,204,neurips,24,4,2023-06-15 23:42:54.672000,https://github.com/YaChienChang/Neural-Lyapunov-Control,93,Neural lyapunov control,"https://scholar.google.com/scholar?cluster=8520646851972056742&hl=en&as_sdt=0,5",4,2019
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization,11,neurips,1,0,2023-06-15 23:42:54.855000,https://github.com/adidevraj/SVRPDA,1,Stochastic variance reduced primal dual algorithms for empirical composition optimization,"https://scholar.google.com/scholar?cluster=14019914477826286322&hl=en&as_sdt=0,7",1,2019
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis,25,neurips,0,0,2023-06-15 23:42:55.037000,https://github.com/yos1up/data-dependence-of-plateau,2,Data-dependence of plateau phenomenon in learning with neural network---Statistical mechanical analysis,"https://scholar.google.com/scholar?cluster=9048066171797706784&hl=en&as_sdt=0,33",3,2019
Differentiable Cloth Simulation for Inverse Problems,119,neurips,15,7,2023-06-15 23:42:55.219000,https://github.com/williamljb/DifferentiableCloth,62,Differentiable cloth simulation for inverse problems,"https://scholar.google.com/scholar?cluster=6530342369806505197&hl=en&as_sdt=0,21",4,2019
Region-specific Diffeomorphic Metric Mapping,38,neurips,29,1,2023-06-15 23:42:55.402000,https://github.com/uncbiag/registration,245,Region-specific diffeomorphic metric mapping,"https://scholar.google.com/scholar?cluster=4638584861181072263&hl=en&as_sdt=0,47",16,2019
Domain Generalization via Model-Agnostic Learning of Semantic Features,506,neurips,19,5,2023-06-15 23:42:55.584000,https://github.com/biomedia-mira/masf,138,Domain generalization via model-agnostic learning of semantic features,"https://scholar.google.com/scholar?cluster=3778888251228243033&hl=en&as_sdt=0,36",7,2019
Unconstrained Monotonic Neural Networks,145,neurips,14,1,2023-06-15 23:42:55.766000,https://github.com/AWehenkel/UMNN,90,Unconstrained monotonic neural networks,"https://scholar.google.com/scholar?cluster=199577294502605803&hl=en&as_sdt=0,15",3,2019
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets,9,neurips,0,0,2023-06-15 23:42:55.949000,https://github.com/dkumor/instrumental-cutsets,0,Efficient identification in linear structural causal models with instrumental cutsets,"https://scholar.google.com/scholar?cluster=3388344391383563829&hl=en&as_sdt=0,33",2,2019
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.,39,neurips,195,7,2023-06-15 23:42:56.131000,https://github.com/kuleshov/audio-super-res,937,Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.,"https://scholar.google.com/scholar?cluster=329745740006359011&hl=en&as_sdt=0,44",23,2019
Inducing brain-relevant bias in natural language processing models,63,neurips,6,0,2023-06-15 23:42:56.314000,https://github.com/danrsc/bert_brain_neurips_2019,13,Inducing brain-relevant bias in natural language processing models,"https://scholar.google.com/scholar?cluster=8126421380617072393&hl=en&as_sdt=0,5",3,2019
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies,27,neurips,11,3,2023-06-15 23:42:56.496000,https://github.com/KamyarGh/rl_swiss,55,Smile: Scalable meta inverse reinforcement learning through context-conditional policies,"https://scholar.google.com/scholar?cluster=9166968138900222&hl=en&as_sdt=0,34",2,2019
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks,49,neurips,4,1,2023-06-15 23:42:56.678000,https://github.com/pfnet-research/einconv,36,Exploring unexplored tensor network decompositions for convolutional neural networks,"https://scholar.google.com/scholar?cluster=7698176316630164925&hl=en&as_sdt=0,5",18,2019
Interval timing in deep reinforcement learning agents,15,neurips,1383,59,2023-06-15 23:42:56.860000,https://github.com/deepmind/lab,6878,Interval timing in deep reinforcement learning agents,"https://scholar.google.com/scholar?cluster=7474977642715586787&hl=en&as_sdt=0,47",471,2019
Uncertainty-based Continual Learning with Adaptive Regularization,119,neurips,8,1,2023-06-15 23:42:57.041000,https://github.com/csm9493/UCL,30,Uncertainty-based continual learning with adaptive regularization,"https://scholar.google.com/scholar?cluster=12251011644241284133&hl=en&as_sdt=0,8",3,2019
Implicit Posterior Variational Inference for Deep Gaussian Processes,37,neurips,2,0,2023-06-15 23:42:57.223000,https://github.com/HeroKillerEver/ipvi-dgp,4,Implicit posterior variational inference for deep Gaussian processes,"https://scholar.google.com/scholar?cluster=9226734796788465308&hl=en&as_sdt=0,5",2,2019
Are Sixteen Heads Really Better than One?,654,neurips,13,3,2023-06-15 23:42:57.406000,https://github.com/pmichel31415/are-16-heads-really-better-than-1,151,Are sixteen heads really better than one?,"https://scholar.google.com/scholar?cluster=10123248687041820762&hl=en&as_sdt=0,33",6,2019
Model Compression with Adversarial Robustness: A Unified Optimization Framework,117,neurips,10,2,2023-06-15 23:42:57.587000,https://github.com/shupenggui/ATMC,45,Model compression with adversarial robustness: A unified optimization framework,"https://scholar.google.com/scholar?cluster=13117140860952320078&hl=en&as_sdt=0,23",5,2019
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks,78,neurips,2,0,2023-06-15 23:42:57.769000,https://github.com/ZiangYan/subspace-attack.pytorch,9,Subspace attack: Exploiting promising subspaces for query-efficient black-box attacks,"https://scholar.google.com/scholar?cluster=15048956358112658396&hl=en&as_sdt=0,41",4,2019
Combinatorial Bayesian Optimization using the Graph Cartesian Product,68,neurips,18,8,2023-06-15 23:42:57.951000,https://github.com/QUVA-Lab/COMBO,39,Combinatorial bayesian optimization using the graph cartesian product,"https://scholar.google.com/scholar?cluster=17490775000583948305&hl=en&as_sdt=0,5",8,2019
Sample Adaptive MCMC,6,neurips,0,0,2023-06-15 23:42:58.134000,https://github.com/michaelhzhu/SampleAdaptiveMCMC,0,Sample adaptive mcmc,"https://scholar.google.com/scholar?cluster=2679459716559547614&hl=en&as_sdt=0,33",3,2019
Tree-Sliced Variants of Wasserstein Distances,64,neurips,2,2,2023-06-15 23:42:58.316000,https://github.com/lttam/TreeWasserstein,12,Tree-sliced variants of Wasserstein distances,"https://scholar.google.com/scholar?cluster=11585923409514731345&hl=en&as_sdt=0,36",3,2019
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems,3,neurips,1,0,2023-06-15 23:42:58.498000,https://github.com/kaushalpaneri/ode2scm,3,Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems,"https://scholar.google.com/scholar?cluster=3048623518283550163&hl=en&as_sdt=0,5",1,2019
Topology-Preserving Deep Image Segmentation,166,neurips,18,10,2023-06-15 23:42:58.680000,https://github.com/HuXiaoling/TopoLoss,104,Topology-preserving deep image segmentation,"https://scholar.google.com/scholar?cluster=16336319447146727941&hl=en&as_sdt=0,5",5,2019
Progressive Augmentation of GANs,18,neurips,1,0,2023-06-15 23:42:58.862000,https://github.com/boschresearch/PA-GAN,6,Progressive augmentation of gans,"https://scholar.google.com/scholar?cluster=202132054535931802&hl=en&as_sdt=0,31",4,2019
Online sampling from log-concave distributions,6,neurips,2,0,2023-06-15 23:42:59.044000,https://github.com/holdenlee/Online_Sampling,0,Online sampling from log-concave distributions,"https://scholar.google.com/scholar?cluster=1144827139395736431&hl=en&as_sdt=0,5",4,2019
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer,3,neurips,0,0,2023-06-15 23:42:59.226000,https://github.com/joshuaas/GBDSP-NeurIPS19,5,Generalized block-diagonal structure pursuit: Learning soft latent task assignment against negative transfer,"https://scholar.google.com/scholar?cluster=3170413548219724478&hl=en&as_sdt=0,33",2,2019
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems,26,neurips,1,0,2023-06-15 23:42:59.408000,https://github.com/yhjung88/ThompsonSamplinginRestlessBandits,4,Regret bounds for thompson sampling in episodic restless bandit problems,"https://scholar.google.com/scholar?cluster=2292837516141377796&hl=en&as_sdt=0,5",1,2019
Adaptive Sequence Submodularity,27,neurips,0,0,2023-06-15 23:42:59.590000,https://github.com/ehsankazemi/adaptiveSubseq,5,Adaptive sequence submodularity,"https://scholar.google.com/scholar?cluster=11662805676922738881&hl=en&as_sdt=0,5",1,2019
"N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules",117,neurips,8,0,2023-06-15 23:42:59.772000,https://github.com/chao1224/n_gram_graph,30,"N-gram graph: Simple unsupervised representation for graphs, with applications to molecules","https://scholar.google.com/scholar?cluster=10555688337090524490&hl=en&as_sdt=0,37",3,2019
The spiked matrix model with generative priors,44,neurips,1,0,2023-06-15 23:42:59.954000,https://github.com/sphinxteam/StructuredPrior_demo,3,The spiked matrix model with generative priors,"https://scholar.google.com/scholar?cluster=598500019720272007&hl=en&as_sdt=0,33",5,2019
"The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares",111,neurips,116,0,2023-06-15 23:43:00.138000,https://github.com/D-X-Y/ResNeXt-DenseNet,608,"The step decay schedule: A near optimal, geometrically decaying learning rate procedure for least squares","https://scholar.google.com/scholar?cluster=18119082696067324871&hl=en&as_sdt=0,5",19,2019
Understanding and Improving Layer Normalization,171,neurips,0,3,2023-06-15 23:43:00.320000,https://github.com/lancopku/AdaNorm,39,Understanding and improving layer normalization,"https://scholar.google.com/scholar?cluster=12686324462743591705&hl=en&as_sdt=0,5",7,2019
Generative Modeling by Estimating Gradients of the Data Distribution,1107,neurips,76,5,2023-06-15 23:43:00.503000,https://github.com/ermongroup/ncsn,514,Generative modeling by estimating gradients of the data distribution,"https://scholar.google.com/scholar?cluster=7819543055117584506&hl=en&as_sdt=0,5",9,2019
Balancing Efficiency and Fairness in On-Demand Ridesourcing,47,neurips,3,0,2023-06-15 23:43:00.685000,https://github.com/zxok365/On-Demand-Ridesourcing-Project,4,Balancing efficiency and fairness in on-demand ridesourcing,"https://scholar.google.com/scholar?cluster=7775414618361693698&hl=en&as_sdt=0,5",2,2019
A coupled autoencoder approach for multi-modal analysis of cell types,26,neurips,1,0,2023-06-15 23:43:00.867000,https://github.com/AllenInstitute/coupledAE,6,A coupled autoencoder approach for multi-modal analysis of cell types,"https://scholar.google.com/scholar?cluster=4156171046829362168&hl=en&as_sdt=0,10",6,2019
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables,55,neurips,8,6,2023-06-15 23:43:01.049000,https://github.com/ermongroup/MetaIRL,60,Meta-inverse reinforcement learning with probabilistic context variables,"https://scholar.google.com/scholar?cluster=5700441467138799438&hl=en&as_sdt=0,44",10,2019
Practical and Consistent Estimation of f-Divergences,37,neurips,7320,1025,2023-06-15 23:43:01.231000,https://github.com/google-research/google-research,29776,Practical and consistent estimation of f-divergences,"https://scholar.google.com/scholar?cluster=11789682867268248535&hl=en&as_sdt=0,36",727,2019
Policy Poisoning in Batch Reinforcement Learning and Control,83,neurips,3,0,2023-06-15 23:43:01.415000,https://github.com/myzwisc/PPRL_NeurIPS19,5,Policy poisoning in batch reinforcement learning and control,"https://scholar.google.com/scholar?cluster=7958681038301936389&hl=en&as_sdt=0,5",1,2019
R2D2: Reliable and Repeatable Detector and Descriptor,145,neurips,78,15,2023-06-15 23:43:01.600000,https://github.com/naver/r2d2,399,R2d2: Reliable and repeatable detector and descriptor,"https://scholar.google.com/scholar?cluster=3698474168660752568&hl=en&as_sdt=0,11",25,2019
First Order Motion Model for Image Animation,544,neurips,3084,287,2023-06-15 23:43:01.782000,https://github.com/AliaksandrSiarohin/first-order-model,13547,First order motion model for image animation,"https://scholar.google.com/scholar?cluster=8970624957269493610&hl=en&as_sdt=0,5",352,2019
Scalable inference of topic evolution via models for latent geometric structures,12,neurips,0,0,2023-06-15 23:43:01.964000,https://github.com/moonfolk/SDDM,3,Scalable inference of topic evolution via models for latent geometric structures,"https://scholar.google.com/scholar?cluster=14180440036747609592&hl=en&as_sdt=0,5",2,2019
Anti-efficient encoding in emergent communication,73,neurips,98,7,2023-06-15 23:43:02.147000,https://github.com/facebookresearch/EGG,261,Anti-efficient encoding in emergent communication,"https://scholar.google.com/scholar?cluster=434185138707911239&hl=en&as_sdt=0,41",16,2019
Improving Black-box Adversarial Attacks with a Transfer-based Prior,209,neurips,10,4,2023-06-15 23:43:02.346000,https://github.com/thu-ml/Prior-Guided-RGF,35,Improving black-box adversarial attacks with a transfer-based prior,"https://scholar.google.com/scholar?cluster=327803698641685395&hl=en&as_sdt=0,38",7,2019
REM: From Structural Entropy to Community Structure Deception,38,neurips,0,1,2023-06-15 23:43:02.528000,https://github.com/CommunityDeception/CommunityDeceptor,0,REM: From structural entropy to community structure deception,"https://scholar.google.com/scholar?cluster=9942215555170717160&hl=en&as_sdt=0,10",1,2019
Unsupervised Object Segmentation by Redrawing,122,neurips,40,1,2023-06-15 23:43:02.711000,https://github.com/mickaelChen/ReDO,175,Unsupervised object segmentation by redrawing,"https://scholar.google.com/scholar?cluster=3034099820799167647&hl=en&as_sdt=0,5",9,2019
The Implicit Bias of AdaGrad on Separable Data,10,neurips,0,0,2023-06-15 23:43:02.894000,https://github.com/qianqian513/Implicit-bias-Adagrad,0,The implicit bias of adagrad on separable data,"https://scholar.google.com/scholar?cluster=8719652805953776322&hl=en&as_sdt=0,5",1,2019
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI,2,neurips,1,0,2023-06-15 23:43:03.076000,https://github.com/qianqianxu010/NeurIPS2019-iSplitLBI,1,iSplit LBI: Individualized partial ranking with ties via split LBI,"https://scholar.google.com/scholar?cluster=2046333522679278867&hl=en&as_sdt=0,21",1,2019
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation,136,neurips,24,5,2023-06-15 23:43:03.258000,https://github.com/canqin001/PointDAN,125,Pointdan: A multi-scale 3d domain adaption network for point cloud representation,"https://scholar.google.com/scholar?cluster=4237979119463438115&hl=en&as_sdt=0,44",14,2019
Certified Adversarial Robustness with Additive Noise,264,neurips,4,1,2023-06-15 23:43:03.440000,https://github.com/Bai-Li/STN-Code,20,Certified adversarial robustness with additive noise,"https://scholar.google.com/scholar?cluster=15944556675714796056&hl=en&as_sdt=0,33",2,2019
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer,17,neurips,0,0,2023-06-15 23:43:03.622000,https://github.com/theonlybars/neurips-2019-rppa,0,Optimal pricing in repeated posted-price auctions with different patience of the seller and the buyer,"https://scholar.google.com/scholar?cluster=4438568951333221100&hl=en&as_sdt=0,37",1,2019
Stand-Alone Self-Attention in Vision Models,897,neurips,7320,1025,2023-06-15 23:43:03.804000,https://github.com/google-research/google-research,29776,Stand-alone self-attention in vision models,"https://scholar.google.com/scholar?cluster=16072663067784939588&hl=en&as_sdt=0,5",727,2019
Debiased Bayesian inference for average treatment effects,12,neurips,2,0,2023-06-15 23:43:03.986000,https://github.com/kolyanray/Bayesian-Causal-Inference,1,Debiased Bayesian inference for average treatment effects,"https://scholar.google.com/scholar?cluster=3807772267363050118&hl=en&as_sdt=0,5",1,2019
Explicit Disentanglement of Appearance and Perspective in Generative Models,39,neurips,5,1,2023-06-15 23:43:04.168000,https://github.com/SkafteNicki/unsuper,7,Explicit disentanglement of appearance and perspective in generative models,"https://scholar.google.com/scholar?cluster=10895888132618213021&hl=en&as_sdt=0,10",0,2019
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices,42,neurips,171,1,2023-06-15 23:43:04.351000,https://github.com/snorkel-team/snorkel-tutorials,352,Slice-based learning: A programming model for residual learning in critical data slices,"https://scholar.google.com/scholar?cluster=1884557173665882878&hl=en&as_sdt=0,14",22,2019
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees,11,neurips,0,0,2023-06-15 23:43:04.533000,https://github.com/ruqizhang/poisson-gibbs,0,Poisson-minibatching for gibbs sampling with convergence rate guarantees,"https://scholar.google.com/scholar?cluster=8342800199415035207&hl=en&as_sdt=0,44",3,2019
Thompson Sampling for Multinomial Logit Contextual Bandits,36,neurips,0,0,2023-06-15 23:43:04.715000,https://github.com/minhwanoh/Thompson-sampling-for-MNL-contextual-bandits,0,Thompson sampling for multinomial logit contextual bandits,"https://scholar.google.com/scholar?cluster=3730407973811497775&hl=en&as_sdt=0,47",1,2019
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments,52,neurips,11,0,2023-06-15 23:43:04.897000,https://github.com/Caselles/NeurIPS19-SBDRL,35,Symmetry-based disentangled representation learning requires interaction with environments,"https://scholar.google.com/scholar?cluster=742614888975626574&hl=en&as_sdt=0,38",5,2019
Mining GOLD Samples for Conditional GANs,12,neurips,5,0,2023-06-15 23:43:05.079000,https://github.com/sangwoomo/gold,16,Mining GOLD samples for conditional GANs,"https://scholar.google.com/scholar?cluster=13194436655250832310&hl=en&as_sdt=0,43",2,2019
Implicit Generation and Modeling with Energy Based Models,226,neurips,61,2,2023-06-15 23:43:05.261000,https://github.com/openai/ebm_code_release,311,Implicit generation and modeling with energy based models,"https://scholar.google.com/scholar?cluster=4613962658885230569&hl=en&as_sdt=0,39",7,2019
Evaluating Protein Transfer Learning with TAPE,516,neurips,134,26,2023-06-15 23:43:05.444000,https://github.com/songlab-cal/tape,559,Evaluating protein transfer learning with TAPE,"https://scholar.google.com/scholar?cluster=2465375203234748072&hl=en&as_sdt=0,47",22,2019
Recurrent Space-time Graph Neural Networks,32,neurips,5,0,2023-06-15 23:43:05.626000,https://github.com/IuliaDuta/RSTG,39,Recurrent space-time graph neural networks,"https://scholar.google.com/scholar?cluster=8909911889342573482&hl=en&as_sdt=0,21",6,2019
Policy Continuation with Hindsight Inverse Dynamics,27,neurips,0,0,2023-06-15 23:43:05.808000,https://github.com/2Groza/PCHID_code,14,Policy continuation with hindsight inverse dynamics,"https://scholar.google.com/scholar?cluster=18153731156196581430&hl=en&as_sdt=0,5",2,2019
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off,12,neurips,1,0,2023-06-15 23:43:05.990000,https://github.com/yanivbl6/quantized_meanfield,13,A mean field theory of quantized deep networks: The quantization-depth trade-off,"https://scholar.google.com/scholar?cluster=9411987115140184550&hl=en&as_sdt=0,33",2,2019
Function-Space Distributions over Kernels,33,neurips,7,0,2023-06-15 23:43:06.172000,https://github.com/wjmaddox/spectralgp,29,Function-space distributions over kernels,"https://scholar.google.com/scholar?cluster=12057901025111797760&hl=en&as_sdt=0,10",4,2019
Fully Neural Network based Model for General Temporal Point Processes,106,neurips,16,1,2023-06-15 23:43:06.354000,https://github.com/omitakahiro/NeuralNetworkPointProcess,51,Fully neural network based model for general temporal point processes,"https://scholar.google.com/scholar?cluster=2876413970836324639&hl=en&as_sdt=0,32",6,2019
Improving Textual Network Learning with Variational Homophilic Embeddings,13,neurips,0,1,2023-06-15 23:43:06.537000,https://github.com/Wenlin-Wang/VHE19,2,Improving textual network learning with variational homophilic embeddings,"https://scholar.google.com/scholar?cluster=11511162412153376997&hl=en&as_sdt=0,47",2,2019
"Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting",247,neurips,44,5,2023-06-15 23:43:06.719000,https://github.com/rajatsen91/deepglo,160,"Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting","https://scholar.google.com/scholar?cluster=13798952634467747016&hl=en&as_sdt=0,28",10,2019
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso,17,neurips,3,5,2023-06-15 23:43:06.903000,https://github.com/QB3/CLaR,9,Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso,"https://scholar.google.com/scholar?cluster=4147865524251608502&hl=en&as_sdt=0,5",5,2019
PAC-Bayes under potentially heavy tails,25,neurips,0,0,2023-06-15 23:43:07.085000,https://github.com/feedbackward/1dim,1,PAC-Bayes under potentially heavy tails,"https://scholar.google.com/scholar?cluster=8266455462422665081&hl=en&as_sdt=0,33",2,2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers,404,neurips,36,0,2023-06-15 23:43:07.267000,https://github.com/Hadisalman/smoothing-adversarial,211,Provably robust deep learning via adversarially trained smoothed classifiers,"https://scholar.google.com/scholar?cluster=9920393851690535434&hl=en&as_sdt=0,48",9,2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty,146,neurips,31,3,2023-06-15 23:43:07.450000,https://github.com/d909b/cxplain,113,Cxplain: Causal explanations for model interpretation under uncertainty,"https://scholar.google.com/scholar?cluster=1657473688091727017&hl=en&as_sdt=0,5",8,2019
"Compacting, Picking and Growing for Unforgetting Continual Learning",180,neurips,22,6,2023-06-15 23:43:07.632000,https://github.com/ivclab/CPG,115,"Compacting, picking and growing for unforgetting continual learning","https://scholar.google.com/scholar?cluster=4980143563579080366&hl=en&as_sdt=0,18",9,2019
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments,36,neurips,614,301,2023-06-15 23:43:07.814000,https://github.com/Microsoft/EconML,3002,Machine learning estimation of heterogeneous treatment effects with instruments,"https://scholar.google.com/scholar?cluster=4151014229440412539&hl=en&as_sdt=0,19",70,2019
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning,25,neurips,128,12,2023-06-15 23:43:07.996000,https://github.com/TorchCraft/TorchCraftAI,640,A structured prediction approach for generalization in cooperative multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=7420014982047754701&hl=en&as_sdt=0,5",49,2019
On Fenchel Mini-Max Learning,21,neurips,1,0,2023-06-15 23:43:08.178000,https://github.com/chenyang-tao/FML,3,On fenchel mini-max learning,"https://scholar.google.com/scholar?cluster=17698432686807766794&hl=en&as_sdt=0,5",2,2019
Optimizing Generalized Rate Metrics with Three Players,22,neurips,7320,1025,2023-06-15 23:43:08.360000,https://github.com/google-research/google-research,29776,Optimizing generalized rate metrics with three players,"https://scholar.google.com/scholar?cluster=5386000896654989772&hl=en&as_sdt=0,5",727,2019
Stability of Graph Scattering Transforms,62,neurips,4,0,2023-06-15 23:43:08.543000,https://github.com/alelab-upenn/graph-scattering-transforms,27,Stability of graph scattering transforms,"https://scholar.google.com/scholar?cluster=1026238758085282246&hl=en&as_sdt=0,32",2,2019
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation,110,neurips,16,3,2023-06-15 23:43:08.726000,https://github.com/IBM/bLVNet-TAM,53,More is less: Learning efficient video representations by big-little network and depthwise temporal aggregation,"https://scholar.google.com/scholar?cluster=955029637361553625&hl=en&as_sdt=0,5",9,2019
PAC-Bayes Un-Expected Bernstein Inequality,32,neurips,0,0,2023-06-15 23:43:08.909000,https://github.com/bguedj/PAC-Bayesian-Un-Expected-Bernstein-Inequality,6,PAC-Bayes un-expected Bernstein inequality,"https://scholar.google.com/scholar?cluster=7074764130481002753&hl=en&as_sdt=0,14",5,2019
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback,16,neurips,1,0,2023-06-15 23:43:09.092000,https://github.com/arunv3rma/NeurIPS-2019,2,Censored semi-bandits: A framework for resource allocation with censored feedback,"https://scholar.google.com/scholar?cluster=15760111358296803544&hl=en&as_sdt=0,5",1,2019
Defending Against Neural Fake News,688,neurips,218,39,2023-06-15 23:43:09.274000,https://github.com/rowanz/grover,879,Defending against neural fake news,"https://scholar.google.com/scholar?cluster=5656807327286323509&hl=en&as_sdt=0,5",36,2019
Faster Boosting with Smaller Memory,7,neurips,4,2,2023-06-15 23:43:09.457000,https://github.com/arapat/sparrow,21,Faster boosting with smaller memory,"https://scholar.google.com/scholar?cluster=10204358402782261121&hl=en&as_sdt=0,5",3,2019
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation,91,neurips,2,0,2023-06-15 23:43:09.639000,https://github.com/hwang595/DETOX,15,DETOX: A redundancy-based framework for faster and more robust gradient aggregation,"https://scholar.google.com/scholar?cluster=6276765982452512417&hl=en&as_sdt=0,5",3,2019
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition,37,neurips,4,0,2023-06-15 23:43:09.822000,https://github.com/apple2373/MetaIRNet,28,Meta-reinforced synthetic data for one-shot fine-grained visual recognition,"https://scholar.google.com/scholar?cluster=4113151338341724063&hl=en&as_sdt=0,5",2,2019
PHYRE: A New Benchmark for Physical Reasoning,95,neurips,62,22,2023-06-15 23:43:10.004000,https://github.com/facebookresearch/phyre,421,Phyre: A new benchmark for physical reasoning,"https://scholar.google.com/scholar?cluster=9555658528231205655&hl=en&as_sdt=0,5",19,2019
Provably robust boosted decision stumps and trees against adversarial attacks,55,neurips,11,0,2023-06-15 23:43:10.186000,https://github.com/max-andr/provably-robust-boosting,47,Provably robust boosted decision stumps and trees against adversarial attacks,"https://scholar.google.com/scholar?cluster=6608146364863001507&hl=en&as_sdt=0,5",5,2019
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response,9,neurips,1,2,2023-06-15 23:43:10.368000,https://github.com/mlzxzhou/keras-gnm,2,Graph-based semi-supervised learning with non-ignorable non-response,"https://scholar.google.com/scholar?cluster=6776605979147432576&hl=en&as_sdt=0,22",3,2019
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series,579,neurips,119,6,2023-06-15 23:43:10.551000,https://github.com/YuliaRubanova/latent_ode,429,Latent ordinary differential equations for irregularly-sampled time series,"https://scholar.google.com/scholar?cluster=4522947842501588842&hl=en&as_sdt=0,5",20,2019
On the Correctness and Sample Complexity of Inverse Reinforcement Learning,13,neurips,1,0,2023-06-15 23:43:10.733000,https://github.com/akomandu/L1SVMIRL,2,On the correctness and sample complexity of inverse reinforcement learning,"https://scholar.google.com/scholar?cluster=5503249221034094355&hl=en&as_sdt=0,5",2,2019
A New Distribution on the Simplex with Auto-Encoding Applications,3,neurips,2,6,2023-06-15 23:43:10.915000,https://github.com/astirn/MV-Kumaraswamy,9,A new distribution on the simplex with auto-encoding applications,"https://scholar.google.com/scholar?cluster=3624843939474502459&hl=en&as_sdt=0,5",1,2019
Model Selection for Contextual Bandits,75,neurips,11,1,2023-06-15 23:43:11.097000,https://github.com/akshaykr/oracle_cb,28,Model selection for contextual bandits,"https://scholar.google.com/scholar?cluster=604693572400865214&hl=en&as_sdt=0,5",6,2019
FreeAnchor: Learning to Match Anchors for Visual Object Detection,306,neurips,113,15,2023-06-15 23:43:11.280000,https://github.com/zhangxiaosong18/FreeAnchor,670,Freeanchor: Learning to match anchors for visual object detection,"https://scholar.google.com/scholar?cluster=8989326398890700545&hl=en&as_sdt=0,5",21,2019
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,1343,neurips,286,72,2023-06-15 23:43:11.462000,https://github.com/nyu-mll/jiant,1526,Superglue: A stickier benchmark for general-purpose language understanding systems,"https://scholar.google.com/scholar?cluster=12169300718787849246&hl=en&as_sdt=0,5",47,2019
Glyce: Glyph-vectors for Chinese Character Representations,155,neurips,73,31,2023-06-15 23:43:11.646000,https://github.com/ShannonAI/glyce,400,Glyce: Glyph-vectors for chinese character representations,"https://scholar.google.com/scholar?cluster=12813244310394658475&hl=en&as_sdt=0,50",12,2019
General E(2)-Equivariant Steerable CNNs,319,neurips,69,6,2023-06-15 23:43:11.828000,https://github.com/QUVA-Lab/e2cnn,511,General e (2)-equivariant steerable cnns,"https://scholar.google.com/scholar?cluster=11235150486117594383&hl=en&as_sdt=0,14",18,2019
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets,67,neurips,0,0,2023-06-15 23:43:12.010000,https://github.com/RohithKuditipudi/mode-connectivity,0,Explaining landscape connectivity of low-cost solutions for multilayer nets,"https://scholar.google.com/scholar?cluster=11853008675817475458&hl=en&as_sdt=0,33",1,2019
Limitations of the empirical Fisher approximation for natural gradient descent,139,neurips,5,0,2023-06-15 23:43:12.192000,https://github.com/fkunstner/limitations-empirical-fisher,42,Limitations of the empirical Fisher approximation for natural gradient descent,"https://scholar.google.com/scholar?cluster=7342864390936584496&hl=en&as_sdt=0,33",5,2019
"Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression",31,neurips,1,0,2023-06-15 23:43:12.374000,https://github.com/utoronto-theory/pIRLS,8,"Fast, provably convergent irls algorithm for p-norm linear regression","https://scholar.google.com/scholar?cluster=4351185537881682779&hl=en&as_sdt=0,36",4,2019
A Model to Search for Synthesizable Molecules,84,neurips,23,11,2023-06-15 23:43:12.556000,https://github.com/john-bradshaw/molecule-chef,73,A model to search for synthesizable molecules,"https://scholar.google.com/scholar?cluster=11917452358715261450&hl=en&as_sdt=0,33",5,2019
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness,23,neurips,3,0,2023-06-15 23:43:12.738000,https://github.com/xiaozhanguva/Measure-Concentration,7,Empirically measuring concentration: Fundamental limits on intrinsic robustness,"https://scholar.google.com/scholar?cluster=2460203345511372640&hl=en&as_sdt=0,5",2,2019
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries,24,neurips,3,0,2023-06-15 23:43:12.921000,https://github.com/uvavision/DrillDown,11,Drill-down: Interactive retrieval of complex scenes using natural language queries,"https://scholar.google.com/scholar?cluster=15992977486578029861&hl=en&as_sdt=0,33",7,2019
Fast and Accurate Least-Mean-Squares Solvers,62,neurips,11,0,2023-06-15 23:43:13.103000,https://github.com/ibramjub/Fast-and-Accurate-Least-Mean-Squares-Solvers,72,Fast and accurate least-mean-squares solvers,"https://scholar.google.com/scholar?cluster=11022765373503234984&hl=en&as_sdt=0,44",4,2019
Graph Agreement Models for Semi-Supervised Learning,55,neurips,193,1,2023-06-15 23:43:13.285000,https://github.com/tensorflow/neural-structured-learning,967,Graph agreement models for semi-supervised learning,"https://scholar.google.com/scholar?cluster=17001131817438418296&hl=en&as_sdt=0,5",48,2019
A Kernel Loss for Solving the Bellman Equation,59,neurips,0,1,2023-06-15 23:43:13.467000,https://github.com/lewisKit/Kernel-Bellman-Loss,0,A kernel loss for solving the bellman equation,"https://scholar.google.com/scholar?cluster=2203690645569443989&hl=en&as_sdt=0,25",2,2019
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling,12,neurips,1,0,2023-06-15 23:43:13.657000,https://github.com/gbc16/AGEM,3,Agem: Solving linear inverse problems via deep priors and sampling,"https://scholar.google.com/scholar?cluster=5796954409607252223&hl=en&as_sdt=0,5",1,2019
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning,3,neurips,0,0,2023-06-15 23:43:13.839000,https://github.com/hci-unihd/Probabilistic_Watershed,8,Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning,"https://scholar.google.com/scholar?cluster=9919550398432186214&hl=en&as_sdt=0,5",2,2019
Learning Robust Options by Conditional Value at Risk Optimization,19,neurips,1,0,2023-06-15 23:43:14.030000,https://github.com/TakuyaHiraoka/Learning-Robust-Options-by-Conditional-Value-at-Risk-Optimization,9,Learning robust options by conditional value at risk optimization,"https://scholar.google.com/scholar?cluster=14168282705388373415&hl=en&as_sdt=0,45",3,2019
A Generic Acceleration Framework for Stochastic Composite Optimization,38,neurips,1,0,2023-06-15 23:43:14.212000,https://github.com/KuluAndrej/NIPS-2019-code,1,A generic acceleration framework for stochastic composite optimization,"https://scholar.google.com/scholar?cluster=10947919871280582095&hl=en&as_sdt=0,5",2,2019
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation,141,neurips,47,8,2023-06-15 23:43:14.395000,https://github.com/RunzheYang/MORL,189,A generalized algorithm for multi-objective reinforcement learning and policy adaptation,"https://scholar.google.com/scholar?cluster=7721047641895252765&hl=en&as_sdt=0,33",8,2019
Communication trade-offs for Local-SGD with large step size,40,neurips,1,0,2023-06-15 23:43:14.577000,https://github.com/kishinmh/Local-SGD,2,Communication trade-offs for local-sgd with large step size,"https://scholar.google.com/scholar?cluster=16743369759814373109&hl=en&as_sdt=0,5",1,2019
Towards modular and programmable architecture search,28,neurips,15,4,2023-06-15 23:43:14.759000,https://github.com/negrinho/deep_architect,121,Towards modular and programmable architecture search,"https://scholar.google.com/scholar?cluster=6733031206160413504&hl=en&as_sdt=0,5",12,2019
Large-scale optimal transport map estimation using projection pursuit,35,neurips,4,0,2023-06-15 23:43:14.942000,https://github.com/ChengzijunAixiaoli/PPMM,13,Large-scale optimal transport map estimation using projection pursuit,"https://scholar.google.com/scholar?cluster=5340124406367691762&hl=en&as_sdt=0,18",1,2019
Understanding Attention and Generalization in Graph Neural Networks,205,neurips,49,1,2023-06-15 23:43:15.124000,https://github.com/bknyaz/graph_attention_pool,263,Understanding attention and generalization in graph neural networks,"https://scholar.google.com/scholar?cluster=9139711807100164053&hl=en&as_sdt=0,39",8,2019
Twin Auxilary Classifiers GAN,64,neurips,13,3,2023-06-15 23:43:15.307000,https://github.com/batmanlab/twin_ac,47,Twin auxilary classifiers gan,"https://scholar.google.com/scholar?cluster=6377027598993488889&hl=en&as_sdt=0,23",1,2019
Online Prediction of Switching Graph Labelings with Cluster Specialists,3,neurips,0,0,2023-06-15 23:43:15.489000,https://github.com/jamesro/cluster-specialists,0,Online prediction of switching graph labelings with cluster specialists,"https://scholar.google.com/scholar?cluster=7730779833279774550&hl=en&as_sdt=0,47",2,2019
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters,127,neurips,0,1,2023-06-15 23:43:15.670000,https://github.com/xxshdw/auto_prune,6,Autoprune: Automatic network pruning by regularizing auxiliary parameters,"https://scholar.google.com/scholar?cluster=11406488290397197193&hl=en&as_sdt=0,5",0,2019
Understanding the Role of Momentum in Stochastic Gradient Methods,72,neurips,3,0,2023-06-15 23:43:15.852000,https://github.com/Kipok/understanding-momentum,14,Understanding the role of momentum in stochastic gradient methods,"https://scholar.google.com/scholar?cluster=10334362605827292159&hl=en&as_sdt=0,5",2,2019
DAC: The Double Actor-Critic Architecture for Learning Options,47,neurips,658,6,2023-06-15 23:43:16.035000,https://github.com/ShangtongZhang/DeepRL,2943,DAC: The double actor-critic architecture for learning options,"https://scholar.google.com/scholar?cluster=6317609422653411407&hl=en&as_sdt=0,43",93,2019
Learning from Label Proportions with Generative Adversarial Networks,26,neurips,1,1,2023-06-15 23:43:16.217000,https://github.com/liujiabin008/LLP-GAN,8,Learning from label proportions with generative adversarial networks,"https://scholar.google.com/scholar?cluster=12276305081354929369&hl=en&as_sdt=0,5",3,2019
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation,22,neurips,1,0,2023-06-15 23:43:16.400000,https://github.com/TURuibo/Neuropathic-Pain-Diagnosis-Simulator,7,Neuropathic pain diagnosis simulator for causal discovery algorithm evaluation,"https://scholar.google.com/scholar?cluster=3595858583853803295&hl=en&as_sdt=0,5",3,2019
Budgeted Reinforcement Learning in Continuous State Space,18,neurips,127,29,2023-06-15 23:43:16.581000,https://github.com/eleurent/rl-agents,455,Budgeted reinforcement learning in continuous state space,"https://scholar.google.com/scholar?cluster=1156851409573476480&hl=en&as_sdt=0,33",20,2019
Parameter elimination in particle Gibbs sampling,11,neurips,1,0,2023-06-15 23:43:16.764000,https://github.com/uu-sml/neurips2019-parameter-elimination,5,Parameter elimination in particle Gibbs sampling,"https://scholar.google.com/scholar?cluster=11832975274278617749&hl=en&as_sdt=0,50",7,2019
Understanding Sparse JL for Feature Hashing,22,neurips,0,0,2023-06-15 23:43:16.946000,https://github.com/mjagadeesan/sparsejl-featurehashing,4,Understanding sparse JL for feature hashing,"https://scholar.google.com/scholar?cluster=13523913285751839530&hl=en&as_sdt=0,10",1,2019
Planning in entropy-regularized Markov decision processes and games,18,neurips,1,0,2023-06-15 23:43:17.128000,https://github.com/omardrwch/smoothcruiser-check,1,Planning in entropy-regularized Markov decision processes and games,"https://scholar.google.com/scholar?cluster=18118594423877089336&hl=en&as_sdt=0,19",2,2019
Dynamic Local Regret for Non-convex Online Forecasting,10,neurips,2,0,2023-06-15 23:43:17.310000,https://github.com/Timbasa/Dynamic_Local_Regret_for_Non-convex_Online_Forecasting_NeurIPS2019,8,Dynamic local regret for non-convex online forecasting,"https://scholar.google.com/scholar?cluster=6302409167507525072&hl=en&as_sdt=0,5",4,2019
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation,93,neurips,10,3,2023-06-15 23:43:17.492000,https://github.com/felixykliu/NAOMI,46,Naomi: Non-autoregressive multiresolution sequence imputation,"https://scholar.google.com/scholar?cluster=5654873960381975776&hl=en&as_sdt=0,5",2,2019
Conformalized Quantile Regression,299,neurips,38,4,2023-06-15 23:43:17.674000,https://github.com/yromano/cqr,170,Conformalized quantile regression,"https://scholar.google.com/scholar?cluster=5581207407270823451&hl=en&as_sdt=0,5",8,2019
MarginGAN: Adversarial Training in Semi-Supervised Learning,36,neurips,2,0,2023-06-15 23:43:17.857000,https://github.com/xdu-DJhao/MarginGAN,9,MarginGAN: adversarial training in semi-supervised learning,"https://scholar.google.com/scholar?cluster=6031857058045286818&hl=en&as_sdt=0,5",1,2019
Cold Case: The Lost MNIST Digits,101,neurips,32,0,2023-06-15 23:43:18.040000,https://github.com/facebookresearch/qmnist,231,Cold case: The lost mnist digits,"https://scholar.google.com/scholar?cluster=9918380668226002925&hl=en&as_sdt=0,5",12,2019
RUBi: Reducing Unimodal Biases for Visual Question Answering,267,neurips,15,3,2023-06-15 23:43:18.222000,https://github.com/cdancette/rubi.bootstrap.pytorch,57,Rubi: Reducing unimodal biases for visual question answering,"https://scholar.google.com/scholar?cluster=3200511868750352559&hl=en&as_sdt=0,49",5,2019
Learning Sample-Specific Models with Low-Rank Personalized Regression,12,neurips,2,1,2023-06-15 23:43:18.404000,https://github.com/blengerich/personalized_regression,15,Learning sample-specific models with low-rank personalized regression,"https://scholar.google.com/scholar?cluster=9544461235321687427&hl=en&as_sdt=0,43",6,2019
"Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration",23,neurips,0,1,2023-06-15 23:43:18.586000,https://github.com/drgrhm/alg_config,1,"Procrastinating with confidence: Near-optimal, anytime, adaptive algorithm configuration","https://scholar.google.com/scholar?cluster=12402924190582219171&hl=en&as_sdt=0,5",1,2019
Unsupervised Scalable Representation Learning for Multivariate Time Series,236,neurips,84,0,2023-06-15 23:43:18.769000,https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries,340,Unsupervised scalable representation learning for multivariate time series,"https://scholar.google.com/scholar?cluster=1013253939456705166&hl=en&as_sdt=0,31",17,2019
Total Least Squares Regression in Input Sparsity Time,8,neurips,6,0,2023-06-15 23:43:18.951000,https://github.com/yangxinuw/total_least_squares_code,4,Total least squares regression in input sparsity time,"https://scholar.google.com/scholar?cluster=976445259821829575&hl=en&as_sdt=0,5",1,2019
Bayesian Learning of Sum-Product Networks,34,neurips,6,3,2023-06-15 23:43:19.134000,https://github.com/trappmartin/BayesianSumProductNetworks,12,Bayesian learning of sum-product networks,"https://scholar.google.com/scholar?cluster=10871336632487264585&hl=en&as_sdt=0,5",3,2019
Discriminative Topic Modeling with Logistic LDA,18,neurips,5,0,2023-06-15 23:43:19.316000,https://github.com/lucastheis/logistic_lda,18,Discriminative topic modeling with logistic LDA,"https://scholar.google.com/scholar?cluster=8692276849254224947&hl=en&as_sdt=0,33",2,2019
Disentangling Influence: Using disentangled representations to audit model predictions,20,neurips,1,0,2023-06-15 23:43:19.498000,https://github.com/charliemarx/disentangling-influence,4,Disentangling influence: Using disentangled representations to audit model predictions,"https://scholar.google.com/scholar?cluster=800319645349031007&hl=en&as_sdt=0,5",1,2019
Deep Structured Prediction for Facial Landmark Detection,24,neurips,5,0,2023-06-15 23:43:19.680000,https://github.com/lisha-chen/Deep-structured-facial-landmark-detection,18,Deep structured prediction for facial landmark detection,"https://scholar.google.com/scholar?cluster=18147202694366911205&hl=en&as_sdt=0,32",3,2019
Mutually Regressive Point Processes,16,neurips,0,0,2023-06-15 23:43:19.862000,https://github.com/ifiaposto/Mutually-Regressive-Point-Processes,4,Mutually regressive point processes,"https://scholar.google.com/scholar?cluster=9562149540635904941&hl=en&as_sdt=0,5",2,2019
Demystifying Black-box Models with Symbolic Metamodels,73,neurips,22,1,2023-06-15 23:43:20.045000,https://github.com/ahmedmalaa/Symbolic-Metamodeling,43,Demystifying black-box models with symbolic metamodels,"https://scholar.google.com/scholar?cluster=4982738822209753358&hl=en&as_sdt=0,33",5,2019
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data,76,neurips,6,2,2023-06-15 23:43:20.227000,https://github.com/qianlou/SHE,22,She: A fast and accurate deep neural network for encrypted data,"https://scholar.google.com/scholar?cluster=13256787420791403235&hl=en&as_sdt=0,5",1,2019
Competitive Gradient Descent,95,neurips,2,0,2023-06-15 23:43:20.409000,https://github.com/f-t-s/CGD,22,Competitive gradient descent,"https://scholar.google.com/scholar?cluster=16079761912267834651&hl=en&as_sdt=0,5",4,2019
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration,25,neurips,3,1,2023-06-15 23:43:20.610000,https://github.com/nyummvc/Arbicon-Net,15,Arbicon-net: Arbitrary continuous geometric transformation networks for image registration,"https://scholar.google.com/scholar?cluster=7779525469156132489&hl=en&as_sdt=0,33",4,2019
Point-Voxel CNN for Efficient 3D Deep Learning,456,neurips,126,0,2023-06-15 23:43:20.799000,https://github.com/mit-han-lab/pvcnn,556,Point-voxel cnn for efficient 3d deep learning,"https://scholar.google.com/scholar?cluster=10002989291325329256&hl=en&as_sdt=0,48",24,2019
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization,68,neurips,7,0,2023-06-15 23:43:20.982000,https://github.com/KaidiXu/ZO-AdaMM,22,Zo-adamm: Zeroth-order adaptive momentum method for black-box optimization,"https://scholar.google.com/scholar?cluster=410761263442584539&hl=en&as_sdt=0,33",2,2019
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging,162,neurips,54,3,2023-06-15 23:43:21.164000,https://github.com/perslev/U-Time,201,U-time: A fully convolutional network for time series segmentation applied to sleep staging,"https://scholar.google.com/scholar?cluster=8255933860376596525&hl=en&as_sdt=0,21",8,2019
Meta-Curvature,106,neurips,1,2,2023-06-15 23:43:21.346000,https://github.com/silverbottlep/meta_curvature,9,Meta-curvature,"https://scholar.google.com/scholar?cluster=8144207372117342162&hl=en&as_sdt=0,5",4,2019
Exploration via Hindsight Goal Generation,57,neurips,8,0,2023-06-15 23:43:21.533000,https://github.com/Stilwell-Git/Hindsight-Goal-Generation,24,Exploration via hindsight goal generation,"https://scholar.google.com/scholar?cluster=15515347371168435712&hl=en&as_sdt=0,33",3,2019
VIREL: A Variational Inference Framework for Reinforcement Learning,40,neurips,5,1,2023-06-15 23:43:21.724000,https://github.com/AnujMahajanOxf/VIREL,15,Virel: A variational inference framework for reinforcement learning,"https://scholar.google.com/scholar?cluster=3837224869714850766&hl=en&as_sdt=0,33",2,2019
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration,31,neurips,3,0,2023-06-15 23:43:21.907000,https://github.com/jliang993/A3DMM,8,Trajectory of alternating direction method of multipliers and adaptive acceleration,"https://scholar.google.com/scholar?cluster=8586222791731543519&hl=en&as_sdt=0,26",2,2019
Focused Quantization for Sparse CNNs,25,neurips,20,4,2023-06-15 23:43:22.095000,https://github.com/deep-fry/mayo,109,Focused quantization for sparse CNNs,"https://scholar.google.com/scholar?cluster=5764391117070924493&hl=en&as_sdt=0,10",11,2019
Knowledge Extraction with No Observable Data,75,neurips,10,0,2023-06-15 23:43:22.277000,https://github.com/snudatalab/KegNet,37,Knowledge extraction with no observable data,"https://scholar.google.com/scholar?cluster=3775105952512776839&hl=en&as_sdt=0,5",4,2019
Global Guarantees for Blind Demodulation with Generative Priors,28,neurips,0,0,2023-06-15 23:43:22.460000,https://github.com/babhrujoshi/Blind_demod_gen_prior,2,Global guarantees for blind demodulation with generative priors,"https://scholar.google.com/scholar?cluster=49698119456763508&hl=en&as_sdt=0,32",1,2019
Neural Jump Stochastic Differential Equations,182,neurips,17,0,2023-06-15 23:43:22.642000,https://github.com/000Justin000/torchdiffeq,45,Neural jump stochastic differential equations,"https://scholar.google.com/scholar?cluster=14697126289882105767&hl=en&as_sdt=0,33",4,2019
Learning about an exponential amount of conditional distributions,26,neurips,0,0,2023-06-15 23:43:22.824000,https://github.com/IshmaelBelghazi/learning_an_exponential_amount_of_conditional_distributions,0,Learning about an exponential amount of conditional distributions,"https://scholar.google.com/scholar?cluster=15393166666132601264&hl=en&as_sdt=0,48",2,2019
Multi-mapping Image-to-Image Translation via Learning Disentanglement,92,neurips,15,1,2023-06-15 23:43:23.007000,https://github.com/Xiaoming-Yu/DMIT,110,Multi-mapping image-to-image translation via learning disentanglement,"https://scholar.google.com/scholar?cluster=18213035425048385822&hl=en&as_sdt=0,5",15,2019
Explicitly disentangling image content from translation and rotation with spatial-VAE,71,neurips,18,2,2023-06-15 23:43:23.189000,https://github.com/tbepler/spatial-VAE,55,Explicitly disentangling image content from translation and rotation with spatial-VAE,"https://scholar.google.com/scholar?cluster=6574810273867158367&hl=en&as_sdt=0,14",7,2019
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies,149,neurips,1,0,2023-06-15 23:43:23.372000,https://github.com/ykasten/Convergence-Rate-NN-Different-Frequencies,6,The convergence rate of neural networks for learned functions of different frequencies,"https://scholar.google.com/scholar?cluster=12179223750271364799&hl=en&as_sdt=0,23",5,2019
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem,125,neurips,1,0,2023-06-15 23:43:23.554000,https://github.com/gomena/statistical_bounds_entropic_OT,1,Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem,"https://scholar.google.com/scholar?cluster=6105913006833342284&hl=en&as_sdt=0,44",1,2019
A Game Theoretic Approach to Class-wise Selective Rationalization,47,neurips,3,5,2023-06-15 23:43:23.736000,https://github.com/code-terminator/classwise_rationale,12,A game theoretic approach to class-wise selective rationalization,"https://scholar.google.com/scholar?cluster=8292388829309690879&hl=en&as_sdt=0,21",4,2019
Variational Bayesian Decision-making for Continuous Utilities,18,neurips,1,0,2023-06-15 23:43:23.920000,https://github.com/tkusmierczyk/lcvi,4,Variational Bayesian decision-making for continuous utilities,"https://scholar.google.com/scholar?cluster=2997507484784259646&hl=en&as_sdt=0,5",2,2019
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning,204,neurips,7320,1025,2023-06-15 23:43:24.103000,https://github.com/google-research/google-research,29776,Search on the replay buffer: Bridging planning and reinforcement learning,"https://scholar.google.com/scholar?cluster=17777579381680460522&hl=en&as_sdt=0,5",727,2019
Transductive Zero-Shot Learning with Visual Structure Constraint,72,neurips,9,1,2023-06-15 23:43:24.285000,https://github.com/raywzy/VSC,42,Transductive zero-shot learning with visual structure constraint,"https://scholar.google.com/scholar?cluster=14823968865961413196&hl=en&as_sdt=0,10",2,2019
Implicit Regularization for Optimal Sparse Recovery,63,neurips,1,0,2023-06-15 23:43:24.467000,https://github.com/TomasVaskevicius/implicit_sparsity_neurips2019,3,Implicit regularization for optimal sparse recovery,"https://scholar.google.com/scholar?cluster=6600835910528488334&hl=en&as_sdt=0,5",3,2019
Residual Flows for Invertible Generative Modeling,279,neurips,44,3,2023-06-15 23:43:24.649000,https://github.com/rtqichen/residual-flows,251,Residual flows for invertible generative modeling,"https://scholar.google.com/scholar?cluster=13099094504334344711&hl=en&as_sdt=0,5",12,2019
Adversarial Training and Robustness for Multiple Perturbations,309,neurips,8,16,2023-06-15 23:43:24.831000,https://github.com/ftramer/MultiRobustness,46,Adversarial training and robustness for multiple perturbations,"https://scholar.google.com/scholar?cluster=6630235695392252264&hl=en&as_sdt=0,22",2,2019
Stein Variational Gradient Descent With Matrix-Valued Kernels,54,neurips,1,0,2023-06-15 23:43:25.013000,https://github.com/dilinwang820/matrix_svgd,8,Stein variational gradient descent with matrix-valued kernels,"https://scholar.google.com/scholar?cluster=6300168546020464188&hl=en&as_sdt=0,10",3,2019
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes,140,neurips,22,1,2023-06-15 23:43:25.195000,https://github.com/thegregyang/GP4A,220,Wide feedforward or recurrent neural networks of any architecture are gaussian processes,"https://scholar.google.com/scholar?cluster=13759507907397409226&hl=en&as_sdt=0,5",9,2019
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums,37,neurips,2,0,2023-06-15 23:43:25.378000,https://github.com/HadrienHx/ADFS_NeurIPS,1,An accelerated decentralized stochastic proximal algorithm for finite sums,"https://scholar.google.com/scholar?cluster=748231720425736301&hl=en&as_sdt=0,5",1,2019
Data Cleansing for Models Trained with SGD,51,neurips,6,1,2023-06-15 23:43:25.560000,https://github.com/sato9hara/sgd-influence,48,Data cleansing for models trained with SGD,"https://scholar.google.com/scholar?cluster=11335556309506749393&hl=en&as_sdt=0,5",2,2019
Generating Diverse High-Fidelity Images with VQ-VAE-2,1024,neurips,1360,34,2023-06-15 23:43:25.742000,https://github.com/deepmind/sonnet,9571,Generating diverse high-fidelity images with vq-vae-2,"https://scholar.google.com/scholar?cluster=7339215229612384474&hl=en&as_sdt=0,5",425,2019
When to Trust Your Model: Model-Based Policy Optimization,611,neurips,77,18,2023-06-15 23:43:25.924000,https://github.com/JannerM/mbpo,416,When to trust your model: Model-based policy optimization,"https://scholar.google.com/scholar?cluster=4248859125840907707&hl=en&as_sdt=0,39",10,2019
On Making Stochastic Classifiers Deterministic,23,neurips,7320,1025,2023-06-15 23:43:26.106000,https://github.com/google-research/google-research,29776,On making stochastic classifiers deterministic,"https://scholar.google.com/scholar?cluster=9514965586959557733&hl=en&as_sdt=0,39",727,2019
Blind Super-Resolution Kernel Estimation using an Internal-GAN,323,neurips,73,38,2023-06-15 23:43:26.288000,https://github.com/sefibk/KernelGAN,312,Blind super-resolution kernel estimation using an internal-gan,"https://scholar.google.com/scholar?cluster=248352425941813595&hl=en&as_sdt=0,5",8,2019
Learning to Learn By Self-Critique,66,neurips,7,3,2023-06-15 23:43:26.471000,https://github.com/AntreasAntoniou/Learning_to_Learn_via_Self-Critique,44,Learning to learn by self-critique,"https://scholar.google.com/scholar?cluster=1091119097992623438&hl=en&as_sdt=0,33",6,2019
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses,33,neurips,0,0,2023-06-15 23:43:26.652000,https://github.com/umarteau/Newton-Method-for-GSC-losses-,3,Globally convergent newton methods for ill-conditioned generalized self-concordant losses,"https://scholar.google.com/scholar?cluster=4570728101284672632&hl=en&as_sdt=0,3",3,2019
Is Deeper Better only when Shallow is Good?,37,neurips,0,0,2023-06-15 23:43:26.835000,https://github.com/emalach/IsDeeperBetter,0,Is deeper better only when shallow is good?,"https://scholar.google.com/scholar?cluster=8541069837961005267&hl=en&as_sdt=0,44",1,2019
The Thermodynamic Variational Objective,36,neurips,0,0,2023-06-15 23:43:27.017000,https://github.com/vmasrani/tvo,0,The thermodynamic variational objective,"https://scholar.google.com/scholar?cluster=8303803537398982071&hl=en&as_sdt=0,5",0,2019
Sampling Sketches for Concave Sublinear Functions of Frequencies,6,neurips,0,0,2023-06-15 23:43:27.199000,https://github.com/ofirgeri/concave-sublinear-sampling,0,Sampling sketches for concave sublinear functions of frequencies,"https://scholar.google.com/scholar?cluster=5473104206629301806&hl=en&as_sdt=0,4",1,2019
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss,962,neurips,111,12,2023-06-15 23:43:27.381000,https://github.com/kaidic/LDAM-DRW,569,Learning imbalanced datasets with label-distribution-aware margin loss,"https://scholar.google.com/scholar?cluster=14488921758498385858&hl=en&as_sdt=0,5",15,2019
Multivariate Triangular Quantile Maps for Novelty Detection,18,neurips,3,5,2023-06-15 23:43:27.564000,https://github.com/GinGinWang/MTQ,7,Multivariate triangular quantile maps for novelty detection,"https://scholar.google.com/scholar?cluster=7987123893251995250&hl=en&as_sdt=0,5",3,2019
Gradient-based Adaptive Markov Chain Monte Carlo,23,neurips,5,0,2023-06-15 23:43:27.746000,https://github.com/mtitsias/gadMCMC,21,Gradient-based adaptive markov chain monte carlo,"https://scholar.google.com/scholar?cluster=13990086497515936909&hl=en&as_sdt=0,18",3,2019
Online Forecasting of Total-Variation-bounded Sequences,34,neurips,0,0,2023-06-15 23:43:27.928000,https://github.com/yuxiangw/tv_online,3,Online forecasting of total-variation-bounded sequences,"https://scholar.google.com/scholar?cluster=1207130136020942361&hl=en&as_sdt=0,33",2,2019
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video,386,neurips,52,15,2023-06-15 23:43:28.111000,https://github.com/JiawangBian/sc_depth_pl,293,Unsupervised scale-consistent depth and ego-motion learning from monocular video,"https://scholar.google.com/scholar?cluster=1362055635586007597&hl=en&as_sdt=0,5",10,2019
Variational Denoising Network: Toward Blind Noise Modeling and Removal,242,neurips,45,3,2023-06-15 23:43:28.292000,https://github.com/zsyOAOA/VDNet,194,Variational denoising network: Toward blind noise modeling and removal,"https://scholar.google.com/scholar?cluster=18313022457936123811&hl=en&as_sdt=0,5",3,2019
Multi-task Learning for Aggregated Data using Gaussian Processes,25,neurips,1,0,2023-06-15 23:43:28.475000,https://github.com/frb-yousefi/aggregated-multitask-gp,10,Multi-task learning for aggregated data using Gaussian processes,"https://scholar.google.com/scholar?cluster=9068915187307088687&hl=en&as_sdt=0,33",2,2019
Efficient characterization of electrically evoked responses for neural interfaces,5,neurips,0,0,2023-06-15 23:43:28.657000,https://github.com/Chichilnisky-Lab/shah-neurips-2019,3,Efficient characterization of electrically evoked responses for neural interfaces,"https://scholar.google.com/scholar?cluster=18237433847770575510&hl=en&as_sdt=0,5",8,2019
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels,197,neurips,17,1,2023-06-15 23:43:28.839000,https://github.com/KangchengHou/gntk,94,Graph neural tangent kernel: Fusing graph neural networks with graph kernels,"https://scholar.google.com/scholar?cluster=7700085274406978551&hl=en&as_sdt=0,23",6,2019
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces,40,neurips,3,0,2023-06-15 23:43:29.021000,https://github.com/wangbx66/differentially-private-q-learning,10,Privacy-preserving q-learning with functional noise in continuous spaces,"https://scholar.google.com/scholar?cluster=253585098814477836&hl=en&as_sdt=0,5",2,2019
Learning Data Manipulation for Augmentation and Weighting,106,neurips,18,5,2023-06-15 23:43:29.204000,https://github.com/tanyuqian/learning-data-manipulation,107,Learning data manipulation for augmentation and weighting,"https://scholar.google.com/scholar?cluster=8112277645678768477&hl=en&as_sdt=0,11",6,2019
Levenshtein Transformer,307,neurips,5869,1030,2023-06-15 23:43:29.386000,https://github.com/pytorch/fairseq,26461,Levenshtein transformer,"https://scholar.google.com/scholar?cluster=6969695107747166842&hl=en&as_sdt=0,5",411,2019
Learning Perceptual Inference by Contrasting,82,neurips,3,0,2023-06-15 23:43:29.568000,https://github.com/WellyZhang/CoPINet,26,Learning perceptual inference by contrasting,"https://scholar.google.com/scholar?cluster=6429330194267685212&hl=en&as_sdt=0,39",3,2019
Image Captioning: Transforming Objects into Words,375,neurips,46,13,2023-06-15 23:43:29.751000,https://github.com/yahoo/object_relation_transformer,167,Image captioning: Transforming objects into words,"https://scholar.google.com/scholar?cluster=10363318255496251924&hl=en&as_sdt=0,15",8,2019
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis,707,neurips,209,30,2023-06-15 23:43:29.933000,https://github.com/descriptinc/melgan-neurips,844,Melgan: Generative adversarial networks for conditional waveform synthesis,"https://scholar.google.com/scholar?cluster=3316540057684655113&hl=en&as_sdt=0,11",60,2019
Deliberative Explanations: visualizing network insecurities,9,neurips,2,0,2023-06-15 23:43:30.115000,https://github.com/peiwang062/Deliberative-explanation,2,Deliberative explanations: visualizing network insecurities,"https://scholar.google.com/scholar?cluster=7324304608131052861&hl=en&as_sdt=0,33",2,2019
Uncoupled Regression from Pairwise Comparison Data,10,neurips,0,0,2023-06-15 23:43:30.298000,https://github.com/liyuan9988/UncoupledRegressionComparison,4,Uncoupled regression from pairwise comparison data,"https://scholar.google.com/scholar?cluster=11084220127934527031&hl=en&as_sdt=0,5",1,2019
Pareto Multi-Task Learning,198,neurips,27,3,2023-06-15 23:43:30.480000,https://github.com/Xi-L/ParetoMTL,94,Pareto multi-task learning,"https://scholar.google.com/scholar?cluster=4838439418899055055&hl=en&as_sdt=0,5",1,2019
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos,162,neurips,14,2,2023-06-15 23:43:30.663000,https://github.com/yytzsy/SCDM,67,Semantic conditioned dynamic modulation for temporal sentence grounding in videos,"https://scholar.google.com/scholar?cluster=4012702168222045313&hl=en&as_sdt=0,14",3,2019
A Domain Agnostic Measure for Monitoring and Evaluating GANs,37,neurips,0,1,2023-06-15 23:43:30.845000,https://github.com/pgrnar/DualityGap,5,A domain agnostic measure for monitoring and evaluating GANs,"https://scholar.google.com/scholar?cluster=15032346685874617570&hl=en&as_sdt=0,47",6,2019
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data,30,neurips,3,1,2023-06-15 23:43:31.027000,https://github.com/snel-repo/lfads-cd,6,Enabling hyperparameter optimization in sequential autoencoders for spiking neural data,"https://scholar.google.com/scholar?cluster=1905318586909285690&hl=en&as_sdt=0,5",3,2019
Grid Saliency for Context Explanations of Semantic Segmentation,29,neurips,1,3,2023-06-15 23:43:31.210000,https://github.com/boschresearch/GridSaliency-ToyDatasetGen,10,Grid saliency for context explanations of semantic segmentation,"https://scholar.google.com/scholar?cluster=17400150270584494273&hl=en&as_sdt=0,5",5,2019
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products,62,neurips,17,3,2023-06-15 23:43:31.391000,https://github.com/Tharun24/MACH,45,Extreme classification in log memory using count-min sketch: A case study of amazon search with 50m products,"https://scholar.google.com/scholar?cluster=2998064929794090427&hl=en&as_sdt=0,14",6,2019
Selecting the independent coordinates of manifolds with large aspect ratios,11,neurips,0,0,2023-06-15 23:43:31.573000,https://github.com/yuchaz/independent_coordinate_search,1,Selecting the independent coordinates of manifolds with large aspect ratios,"https://scholar.google.com/scholar?cluster=6960980108691938580&hl=en&as_sdt=0,46",3,2019
DM2C: Deep Mixed-Modal Clustering,26,neurips,1,2,2023-06-15 23:43:31.756000,https://github.com/jiangyangby/DM2C,11,Dm2c: Deep mixed-modal clustering,"https://scholar.google.com/scholar?cluster=4258988165212066839&hl=en&as_sdt=0,5",2,2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates,19,neurips,1,0,2023-06-15 23:43:31.938000,https://github.com/adil-salim/SPLA,0,Stochastic proximal langevin algorithm: Potential splitting and nonasymptotic rates,"https://scholar.google.com/scholar?cluster=8964049524700423512&hl=en&as_sdt=0,47",1,2019
Fast AutoAugment,531,neurips,197,27,2023-06-15 23:43:32.121000,https://github.com/kakaobrain/fast-autoaugment,1558,Fast autoaugment,"https://scholar.google.com/scholar?cluster=1889800553508296252&hl=en&as_sdt=0,5",40,2019
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI,7,neurips,0,0,2023-06-15 23:43:32.303000,https://github.com/taotu/VBLDS_Connectivity_EEG_fMRI,8,A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI,"https://scholar.google.com/scholar?cluster=11225170596996891049&hl=en&as_sdt=0,39",1,2019
Efficient Forward Architecture Search,38,neurips,22,1,2023-06-15 23:43:32.486000,https://github.com/microsoft/petridishnn,110,Efficient forward architecture search,"https://scholar.google.com/scholar?cluster=28350854017058625&hl=en&as_sdt=0,14",14,2019
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network,84,neurips,12,1,2023-06-15 23:43:32.669000,https://github.com/demonzyj56/E3Outlier,38,Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network,"https://scholar.google.com/scholar?cluster=5342789458391186972&hl=en&as_sdt=0,47",4,2019
"Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games",54,neurips,1,0,2023-06-15 23:43:32.851000,https://github.com/lamflokas/cycles,0,"Poincaré recurrence, cycles and spurious equilibria in gradient-descent-ascent for non-convex non-concave zero-sum games","https://scholar.google.com/scholar?cluster=14231094102989983281&hl=en&as_sdt=0,14",2,2019
End-to-End Learning on 3D Protein Structure for Interface Prediction,80,neurips,13,5,2023-06-15 23:43:33.034000,https://github.com/drorlab/DIPS,60,End-to-end learning on 3d protein structure for interface prediction,"https://scholar.google.com/scholar?cluster=11547606784412884634&hl=en&as_sdt=0,10",16,2019
Scalable Global Optimization via Local Bayesian Optimization,254,neurips,33,4,2023-06-15 23:43:33.216000,https://github.com/uber-research/TuRBO,138,Scalable global optimization via local bayesian optimization,"https://scholar.google.com/scholar?cluster=4068527578266186377&hl=en&as_sdt=0,23",7,2019
Positional Normalization,76,neurips,16,1,2023-06-15 23:43:33.406000,https://github.com/Boyiliee/PONO,146,Positional normalization,"https://scholar.google.com/scholar?cluster=10490893363553766514&hl=en&as_sdt=0,5",9,2019
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model,38,neurips,55,38,2023-06-15 23:43:33.589000,https://github.com/pyprob/pyprob,386,Efficient probabilistic inference in the quest for physics beyond the standard model,"https://scholar.google.com/scholar?cluster=375356109416148493&hl=en&as_sdt=0,33",36,2019
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs,4,neurips,11,0,2023-06-15 23:43:33.771000,https://github.com/stanis-morozov/prodige,47,Beyond vector spaces: Compact data representation as differentiable weighted graphs,"https://scholar.google.com/scholar?cluster=3714868262045801223&hl=en&as_sdt=0,5",5,2019
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation,232,neurips,20,10,2023-06-15 23:43:33.954000,https://github.com/RogerZhangzz/CAG_UDA,135,Category anchor-guided unsupervised domain adaptation for semantic segmentation,"https://scholar.google.com/scholar?cluster=5741374386417443357&hl=en&as_sdt=0,39",5,2019
Novel positional encodings to enable tree-based transformers,108,neurips,49,10,2023-06-15 23:43:34.136000,https://github.com/microsoft/icecaps,283,Novel positional encodings to enable tree-based transformers,"https://scholar.google.com/scholar?cluster=8745417942122294740&hl=en&as_sdt=0,5",31,2019
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching,132,neurips,10,1,2023-06-15 23:43:34.319000,https://github.com/HongtengXu/s-gwl,31,Scalable gromov-wasserstein learning for graph partitioning and matching,"https://scholar.google.com/scholar?cluster=17818306347293669263&hl=en&as_sdt=0,5",2,2019
Deep Set Prediction Networks,92,neurips,17,2,2023-06-15 23:43:34.502000,https://github.com/Cyanogenoid/dspn,97,Deep set prediction networks,"https://scholar.google.com/scholar?cluster=1113560646792223618&hl=en&as_sdt=0,33",5,2019
A unified theory for the origin of grid cells through the lens of pattern formation,61,neurips,14,2,2023-06-15 23:43:34.684000,https://github.com/ganguli-lab/grid-pattern-formation,38,A unified theory for the origin of grid cells through the lens of pattern formation,"https://scholar.google.com/scholar?cluster=14776833330125536661&hl=en&as_sdt=0,11",19,2019
Functional Adversarial Attacks,153,neurips,6,1,2023-06-15 23:43:34.867000,https://github.com/cassidylaidlaw/ReColorAdv,31,Functional adversarial attacks,"https://scholar.google.com/scholar?cluster=1676214359814686616&hl=en&as_sdt=0,7",2,2019
Memory-oriented Decoder for Light Field Salient Object Detection,80,neurips,1,0,2023-06-15 23:43:35.049000,https://github.com/OIPLab-DUT/MoLF,5,Memory-oriented decoder for light field salient object detection,"https://scholar.google.com/scholar?cluster=6967318587141659814&hl=en&as_sdt=0,5",1,2019
Learning from Trajectories via Subgoal Discovery,33,neurips,2,1,2023-06-15 23:43:35.231000,https://github.com/sujoyp/subgoal-discovery,12,Learning from trajectories via subgoal discovery,"https://scholar.google.com/scholar?cluster=16236425199036856550&hl=en&as_sdt=0,36",2,2019
Unsupervised State Representation Learning in Atari,219,neurips,50,10,2023-06-15 23:43:35.414000,https://github.com/mila-iqia/atari-representation-learning,226,Unsupervised state representation learning in atari,"https://scholar.google.com/scholar?cluster=6441557733735697646&hl=en&as_sdt=0,39",16,2019
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning,9,neurips,0,0,2023-06-15 23:43:35.596000,https://github.com/oxwhirl/loaded-dice,8,Loaded DiCE: Trading off bias and variance in any-order score function gradient estimators for reinforcement learning,"https://scholar.google.com/scholar?cluster=12610147229310871912&hl=en&as_sdt=0,10",5,2019
Meta Learning with Relational Information for Short Sequences,15,neurips,1,0,2023-06-15 23:43:35.779000,https://github.com/HMJiangGatech/harmless,4,Meta learning with relational information for short sequences,"https://scholar.google.com/scholar?cluster=15009113702516018640&hl=en&as_sdt=0,44",3,2019
Kernel quadrature with DPPs,36,neurips,0,0,2023-06-15 23:43:35.961000,https://github.com/AyoubBelhadji/DPPKQ,0,Kernel quadrature with DPPs,"https://scholar.google.com/scholar?cluster=93716723923556238&hl=en&as_sdt=0,5",2,2019
A Debiased MDI Feature Importance Measure for Random Forests,70,neurips,0,1,2023-06-15 23:43:36.144000,https://github.com/shifwang/paper-debiased-feature-importance,3,A debiased MDI feature importance measure for random forests,"https://scholar.google.com/scholar?cluster=6510754319433333481&hl=en&as_sdt=0,5",2,2019
MintNet: Building Invertible Neural Networks with Masked Convolutions,57,neurips,7,4,2023-06-15 23:43:36.326000,https://github.com/ermongroup/mintnet,37,Mintnet: Building invertible neural networks with masked convolutions,"https://scholar.google.com/scholar?cluster=14647518229327139613&hl=en&as_sdt=0,33",6,2019
Learning Temporal Pose Estimation from Sparsely-Labeled Videos,57,neurips,15,8,2023-06-15 23:43:36.508000,https://github.com/facebookresearch/PoseWarper,121,Learning temporal pose estimation from sparsely-labeled videos,"https://scholar.google.com/scholar?cluster=1801466269510518613&hl=en&as_sdt=0,5",8,2019
On the equivalence between graph isomorphism testing and function approximation with GNNs,209,neurips,5,1,2023-06-15 23:43:36.691000,https://github.com/leichen2018/Ring-GNN,12,On the equivalence between graph isomorphism testing and function approximation with gnns,"https://scholar.google.com/scholar?cluster=12691711476883209&hl=en&as_sdt=0,14",3,2019
Information Competing Process for Learning Diversified Representations,14,neurips,0,1,2023-06-15 23:43:36.873000,https://github.com/hujiecpp/InformationCompetingProcess,17,Information competing process for learning diversified representations,"https://scholar.google.com/scholar?cluster=4705195957612955232&hl=en&as_sdt=0,33",3,2019
On Relating Explanations and Adversarial Examples,104,neurips,0,0,2023-06-15 23:43:37.056000,https://github.com/alexeyignatiev/xpce-duality,3,On relating explanations and adversarial examples,"https://scholar.google.com/scholar?cluster=13118428482617248562&hl=en&as_sdt=0,5",2,2019
Greedy Sampling for Approximate Clustering in the Presence of Outliers,18,neurips,1,0,2023-06-15 23:43:37.238000,https://github.com/Sharvaree/KMeans_Experiments,1,Greedy sampling for approximate clustering in the presence of outliers,"https://scholar.google.com/scholar?cluster=18078709320029715659&hl=en&as_sdt=0,10",3,2019
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology,116,neurips,4,1,2023-06-15 23:43:37.421000,https://github.com/nimadehmamy/Understanding-GCN,38,Understanding the representation power of graph neural networks in learning graph topology,"https://scholar.google.com/scholar?cluster=4481929579927594598&hl=en&as_sdt=0,5",5,2019
Single-Model Uncertainties for Deep Learning,198,neurips,15,0,2023-06-15 23:43:37.604000,https://github.com/facebookresearch/SingleModelUncertainty,60,Single-model uncertainties for deep learning,"https://scholar.google.com/scholar?cluster=12778462309465279243&hl=en&as_sdt=0,5",5,2019
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric,61,neurips,0,0,2023-06-15 23:43:37.787000,https://github.com/CausalML/xauc,4,The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric,"https://scholar.google.com/scholar?cluster=12656617424346800106&hl=en&as_sdt=0,18",3,2019
Wasserstein Weisfeiler-Lehman Graph Kernels,164,neurips,15,3,2023-06-15 23:43:37.970000,https://github.com/BorgwardtLab/WWL,67,Wasserstein weisfeiler-lehman graph kernels,"https://scholar.google.com/scholar?cluster=6976031050358812991&hl=en&as_sdt=0,5",6,2019
DATA: Differentiable ArchiTecture Approximation,45,neurips,0,1,2023-06-15 23:43:38.153000,https://github.com/XinbangZhang/DATA-NAS,11,Data: Differentiable architecture approximation,"https://scholar.google.com/scholar?cluster=17466991062887960112&hl=en&as_sdt=0,39",4,2019
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods,33,neurips,3,1,2023-06-15 23:43:38.335000,https://github.com/supratikp/HOOF,17,Fast efficient hyperparameter tuning for policy gradient methods,"https://scholar.google.com/scholar?cluster=18256524196894232759&hl=en&as_sdt=0,5",3,2019
Fast Structured Decoding for Sequence Models,96,neurips,0,0,2023-06-15 23:43:38.517000,https://github.com/Edward-Sun/structured-nart,14,Fast structured decoding for sequence models,"https://scholar.google.com/scholar?cluster=2109712873142708905&hl=en&as_sdt=0,5",6,2019
Guided Similarity Separation for Image Retrieval,39,neurips,7,4,2023-06-15 23:43:38.700000,https://github.com/layer6ai-labs/GSS,65,Guided similarity separation for image retrieval,"https://scholar.google.com/scholar?cluster=12527388362392990303&hl=en&as_sdt=0,3",7,2019
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks,156,neurips,18,0,2023-06-15 23:43:38.882000,https://github.com/kamwoh/DeepIPR,63,Rethinking deep neural network ownership verification: Embedding passports to defeat ambiguity attacks,"https://scholar.google.com/scholar?cluster=5775759195048878084&hl=en&as_sdt=0,5",2,2019
Addressing Failure Prediction by Learning Model Confidence,198,neurips,30,0,2023-06-15 23:43:39.065000,https://github.com/valeoai/ConfidNet,149,Addressing failure prediction by learning model confidence,"https://scholar.google.com/scholar?cluster=2867131902793640249&hl=en&as_sdt=0,33",7,2019
Communication-efficient Distributed SGD with Sketching,146,neurips,8,1,2023-06-15 23:43:39.248000,https://github.com/dhroth/sketchedsgd,26,Communication-efficient distributed SGD with sketching,"https://scholar.google.com/scholar?cluster=16388029036104596741&hl=en&as_sdt=0,5",4,2019
Exponential Family Estimation via Adversarial Dynamics Embedding,44,neurips,3,0,2023-06-15 23:43:39.439000,https://github.com/lzzcd001/ade-code,13,Exponential family estimation via adversarial dynamics embedding,"https://scholar.google.com/scholar?cluster=9361110386553111889&hl=en&as_sdt=0,5",4,2019
Towards Automatic Concept-based Explanations,400,neurips,37,8,2023-06-15 23:43:39.622000,https://github.com/amiratag/ACE,140,Towards automatic concept-based explanations,"https://scholar.google.com/scholar?cluster=16711649168989026855&hl=en&as_sdt=0,33",8,2019
Defending Neural Backdoors via Generative Distribution Modeling,118,neurips,3,0,2023-06-15 23:43:39.804000,https://github.com/superrrpotato/Defending-Neural-Backdoors-via-Generative-Distribution-Modeling,30,Defending neural backdoors via generative distribution modeling,"https://scholar.google.com/scholar?cluster=9257022899586805044&hl=en&as_sdt=0,33",4,2019
Offline Contextual Bayesian Optimization,25,neurips,2,1,2023-06-15 23:43:39.986000,https://github.com/fusion-ml/OCBO,8,Offline contextual bayesian optimization,"https://scholar.google.com/scholar?cluster=14250666700551486212&hl=en&as_sdt=0,5",6,2019
Uncertainty on Asynchronous Time Event Prediction,26,neurips,4,0,2023-06-15 23:43:40.169000,https://github.com/sharpenb/Uncertainty-Event-Prediction,18,Uncertainty on asynchronous time event prediction,"https://scholar.google.com/scholar?cluster=1453508021322991763&hl=en&as_sdt=0,14",1,2019
Hierarchical Decision Making by Generating and Following Natural Language Instructions,51,neurips,31,2,2023-06-15 23:43:40.354000,https://github.com/facebookresearch/minirts,154,Hierarchical decision making by generating and following natural language instructions,"https://scholar.google.com/scholar?cluster=12924202693815963&hl=en&as_sdt=0,33",11,2019
Structured Prediction with Projection Oracles,19,neurips,2,0,2023-06-15 23:43:40.537000,https://github.com/mblondel/projection-losses,25,Structured prediction with projection oracles,"https://scholar.google.com/scholar?cluster=16227835173432942621&hl=en&as_sdt=0,5",3,2019
Sobolev Independence Criterion,3,neurips,11,1,2023-06-15 23:43:40.719000,https://github.com/IBM/SIC,12,Sobolev independence criterion,"https://scholar.google.com/scholar?cluster=10351062325018710141&hl=en&as_sdt=0,33",11,2019
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions,42,neurips,0,0,2023-06-15 23:43:40.903000,https://github.com/aswilson07/ARGD,2,Accelerating rescaled gradient descent: Fast optimization of smooth functions,"https://scholar.google.com/scholar?cluster=3984857145166519117&hl=en&as_sdt=0,5",2,2019
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases,6,neurips,0,0,2023-06-15 23:43:41.085000,https://github.com/xiyangl3/adp-estimator,8,Minimax optimal estimation of approximate differential privacy on neighboring databases,"https://scholar.google.com/scholar?cluster=11105669156455896509&hl=en&as_sdt=0,3",2,2019
Neural Spline Flows,450,neurips,42,5,2023-06-15 23:43:41.269000,https://github.com/bayesiains/nsf,222,Neural spline flows,"https://scholar.google.com/scholar?cluster=8875670325745695973&hl=en&as_sdt=0,44",12,2019
Embedding Symbolic Knowledge into Deep Networks,76,neurips,10,3,2023-06-15 23:43:41.451000,https://github.com/ZiweiXU/LENSR,32,Embedding symbolic knowledge into deep networks,"https://scholar.google.com/scholar?cluster=14720048438970687985&hl=en&as_sdt=0,32",4,2019
Partitioning Structure Learning for Segmented Linear Regression Trees,2,neurips,1,0,2023-06-15 23:43:41.635000,https://github.com/xy-zheng/Segmented-Linear-Regression-Tree,7,Partitioning structure learning for segmented linear regression trees,"https://scholar.google.com/scholar?cluster=4768423146676252730&hl=en&as_sdt=0,5",3,2019
Sparse Variational Inference: Bayesian Coresets from Scratch,34,neurips,30,1,2023-06-15 23:43:41.817000,https://github.com/trevorcampbell/bayesian-coresets,124,Sparse variational inference: Bayesian coresets from scratch,"https://scholar.google.com/scholar?cluster=5409952380755212195&hl=en&as_sdt=0,5",8,2019
Policy Evaluation with Latent Confounders via Optimal Balance,17,neurips,0,1,2023-06-15 23:43:41.999000,https://github.com/CausalML/LatentConfounderBalancing,3,Policy evaluation with latent confounders via optimal balance,"https://scholar.google.com/scholar?cluster=18178264878955055838&hl=en&as_sdt=0,31",2,2019
Dancing to Music,164,neurips,80,16,2023-06-15 23:43:42.182000,https://github.com/NVlabs/Dance2Music,505,Dancing to music,"https://scholar.google.com/scholar?cluster=16920371227688956404&hl=en&as_sdt=0,5",45,2019
Direct Estimation of Differential Functional Graphical Models,10,neurips,0,0,2023-06-15 23:43:42.369000,https://github.com/boxinz17/FuDGE,1,Direct estimation of differential functional graphical models,"https://scholar.google.com/scholar?cluster=6229188529111598684&hl=en&as_sdt=0,33",3,2019
Backpropagation-Friendly Eigendecomposition,43,neurips,11,3,2023-06-15 23:43:42.555000,https://github.com/WeiWangTrento/Power-Iteration-SVD,69,Backpropagation-friendly eigendecomposition,"https://scholar.google.com/scholar?cluster=6440185494888261188&hl=en&as_sdt=0,34",4,2019
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness,114,neurips,17,2,2023-06-15 23:43:42.738000,https://github.com/KaosEngineer/PriorNetworks,51,Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness,"https://scholar.google.com/scholar?cluster=11591831502126572935&hl=en&as_sdt=0,5",4,2019
Adversarial Fisher Vectors for Unsupervised Representation Learning,10,neurips,19,1,2023-06-15 23:43:42.920000,https://github.com/apple/ml-afv,44,Adversarial fisher vectors for unsupervised representation learning,"https://scholar.google.com/scholar?cluster=6777850722350187062&hl=en&as_sdt=0,5",17,2019
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks,44,neurips,3,1,2023-06-15 23:43:43.102000,https://github.com/gletarte/dichotomize-and-generalize,5,Dichotomize and generalize: PAC-Bayesian binary activated deep neural networks,"https://scholar.google.com/scholar?cluster=12097211268555349606&hl=en&as_sdt=0,47",6,2019
Approximate Feature Collisions in Neural Nets,5,neurips,0,0,2023-06-15 23:43:43.284000,https://github.com/zth667/Approximate-Feature-Collisions-in-Neural-Nets,2,Approximate feature collisions in neural nets,"https://scholar.google.com/scholar?cluster=15639259790406372634&hl=en&as_sdt=0,33",2,2019
Characterizing Bias in Classifiers using Generative Models,36,neurips,0,0,2023-06-15 23:43:43.467000,https://github.com/danmcduff/characterizingBias,1,Characterizing bias in classifiers using generative models,"https://scholar.google.com/scholar?cluster=9354789485596756896&hl=en&as_sdt=0,5",1,2019
Coresets for Archetypal Analysis,15,neurips,0,0,2023-06-15 23:43:43.649000,https://github.com/smair/archetypalanalysis-coreset,4,Coresets for archetypal analysis,"https://scholar.google.com/scholar?cluster=7109457079600306157&hl=en&as_sdt=0,5",2,2019
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection,68,neurips,0,1,2023-06-15 23:43:43.832000,https://github.com/xgu1/DTM,2,Statistical analysis of nearest neighbor methods for anomaly detection,"https://scholar.google.com/scholar?cluster=18002734610348809299&hl=en&as_sdt=0,7",1,2019
Full-Gradient Representation for Neural Network Visualization,174,neurips,28,4,2023-06-15 23:43:44.014000,https://github.com/idiap/fullgrad-saliency,182,Full-gradient representation for neural network visualization,"https://scholar.google.com/scholar?cluster=14256731466962538010&hl=en&as_sdt=0,5",7,2019
Learnable Tree Filter for Structure-preserving Feature Transform,33,neurips,13,7,2023-06-15 23:43:44.196000,https://github.com/StevenGrove/TreeFilter-Torch,138,Learnable tree filter for structure-preserving feature transform,"https://scholar.google.com/scholar?cluster=7316153313719053190&hl=en&as_sdt=0,5",10,2019
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback,96,neurips,2,0,2023-06-15 23:43:44.378000,https://github.com/ZiyueHuang/dist-ef-sgdm,2,Communication-efficient distributed blockwise momentum SGD with error-feedback,"https://scholar.google.com/scholar?cluster=15177903812893243410&hl=en&as_sdt=0,19",3,2019
Coresets for Clustering with Fairness Constraints,88,neurips,0,9,2023-06-15 23:43:44.564000,https://github.com/sfjiang1990/Coresets-for-Clustering-with-Fairness-Constraints,1,Coresets for clustering with fairness constraints,"https://scholar.google.com/scholar?cluster=13757547833601117696&hl=en&as_sdt=0,5",1,2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle,364,neurips,30,1,2023-06-15 23:43:44.757000,https://github.com/a1600012888/YOPO-You-Only-Propagate-Once,173,You only propagate once: Accelerating adversarial training via maximal principle,"https://scholar.google.com/scholar?cluster=8806301774024240187&hl=en&as_sdt=0,5",8,2019
Chasing Ghosts: Instruction Following as Bayesian State Tracking,59,neurips,4,2,2023-06-15 23:43:44.939000,https://github.com/batra-mlp-lab/vln-chasing-ghosts,9,Chasing ghosts: Instruction following as bayesian state tracking,"https://scholar.google.com/scholar?cluster=11914100459452617998&hl=en&as_sdt=0,5",4,2019
Rethinking the CSC Model for Natural Images,67,neurips,14,2,2023-06-15 23:43:45.121000,https://github.com/drorsimon/CSCNet,28,Rethinking the CSC model for natural images,"https://scholar.google.com/scholar?cluster=8975540082038473364&hl=en&as_sdt=0,36",3,2019
Max-value Entropy Search for Multi-Objective Bayesian Optimization,95,neurips,3,0,2023-06-15 23:43:45.303000,https://github.com/belakaria/MESMO,16,Max-value entropy search for multi-objective bayesian optimization,"https://scholar.google.com/scholar?cluster=12951400276169505128&hl=en&as_sdt=0,5",2,2019
Categorized Bandits,13,neurips,1,0,2023-06-15 23:43:45.486000,https://github.com/mjedor/categorized-bandits,2,Categorized bandits,"https://scholar.google.com/scholar?cluster=1278360218254462409&hl=en&as_sdt=0,5",1,2019
Curriculum-guided Hindsight Experience Replay,113,neurips,10,2,2023-06-15 23:43:45.669000,https://github.com/mengf1/CHER,51,Curriculum-guided hindsight experience replay,"https://scholar.google.com/scholar?cluster=13835477089044998151&hl=en&as_sdt=0,5",4,2019
Random Path Selection for Continual Learning,166,neurips,12,4,2023-06-15 23:43:45.852000,https://github.com/brjathu/RPSnet,50,Random path selection for continual learning,"https://scholar.google.com/scholar?cluster=13661319739032626866&hl=en&as_sdt=0,14",2,2019
On Single Source Robustness in Deep Fusion Models,23,neurips,7,2,2023-06-15 23:43:46.034000,https://github.com/twankim/avod_ssn,11,On single source robustness in deep fusion models,"https://scholar.google.com/scholar?cluster=9475508091147138361&hl=en&as_sdt=0,5",3,2019
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift,239,neurips,16,7,2023-06-15 23:43:46.216000,https://github.com/steverab/failing-loudly,90,Failing loudly: An empirical study of methods for detecting dataset shift,"https://scholar.google.com/scholar?cluster=17114748058005960595&hl=en&as_sdt=0,5",3,2019
Shadowing Properties of Optimization Algorithms,14,neurips,0,0,2023-06-15 23:43:46.399000,https://github.com/aorvieto/shadowing,0,Shadowing properties of optimization algorithms,"https://scholar.google.com/scholar?cluster=16930734437470236077&hl=en&as_sdt=0,5",1,2019
Bayesian Batch Active Learning as Sparse Subset Approximation,104,neurips,13,1,2023-06-15 23:43:46.582000,https://github.com/rpinsler/active-bayesian-coresets,35,Bayesian batch active learning as sparse subset approximation,"https://scholar.google.com/scholar?cluster=9791556257184579641&hl=en&as_sdt=0,33",3,2019
Putting An End to End-to-End: Gradient-Isolated Learning of Representations,99,neurips,35,0,2023-06-15 23:43:46.764000,https://github.com/loeweX/Greedy_InfoMax,275,Putting an end to end-to-end: Gradient-isolated learning of representations,"https://scholar.google.com/scholar?cluster=3627926315320048762&hl=en&as_sdt=0,5",17,2019
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains,26,neurips,0,0,2023-06-15 23:43:46.946000,https://github.com/leaf-ai/muir,0,Modular universal reparameterization: Deep multi-task learning across diverse domains,"https://scholar.google.com/scholar?cluster=5453919109038030817&hl=en&as_sdt=0,44",4,2019
Decentralized Cooperative Stochastic Bandits,76,neurips,1,0,2023-06-15 23:43:47.129000,https://github.com/damaru2/decentralized-bandits,4,Decentralized cooperative stochastic bandits,"https://scholar.google.com/scholar?cluster=1662602703149301964&hl=en&as_sdt=0,33",1,2019
Powerset Convolutional Neural Networks,16,neurips,2,0,2023-06-15 23:43:47.312000,https://github.com/chrislybaer/Powerset-CNN,10,Powerset convolutional neural networks,"https://scholar.google.com/scholar?cluster=8655459443031428222&hl=en&as_sdt=0,36",1,2019
"Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning",64,neurips,0,0,2023-06-15 23:43:47.494000,https://github.com/SuReLI/rats-experiments,3,"Non-stationary Markov decision processes, a worst-case approach using model-based reinforcement learning","https://scholar.google.com/scholar?cluster=6196292218607210922&hl=en&as_sdt=0,5",3,2019
Optimal Decision Tree with Noisy Outcomes,9,neurips,1,0,2023-06-15 23:43:47.676000,https://github.com/sjia1/ODT-with-noisy-outcomes,0,Optimal decision tree with noisy outcomes,"https://scholar.google.com/scholar?cluster=13675004134696566292&hl=en&as_sdt=0,33",1,2019
Continual Unsupervised Representation Learning,211,neurips,2436,170,2023-06-15 23:43:47.859000,https://github.com/deepmind/deepmind-research,11902,Continual unsupervised representation learning,"https://scholar.google.com/scholar?cluster=16358329377631529922&hl=en&as_sdt=0,14",336,2019
Multiple Futures Prediction,279,neurips,27,5,2023-06-15 23:43:48.042000,https://github.com/apple/ml-multiple-futures-prediction,115,Multiple futures prediction,"https://scholar.google.com/scholar?cluster=13314964675169531830&hl=en&as_sdt=0,5",19,2019
Multiview Aggregation for Learning Category-Specific Shape Reconstruction,32,neurips,7,2,2023-06-15 23:43:48.224000,https://github.com/drsrinathsridhar/xnocs,35,Multiview aggregation for learning category-specific shape reconstruction,"https://scholar.google.com/scholar?cluster=6464092641166867923&hl=en&as_sdt=0,5",6,2019
Reinforcement Learning with Convex Constraints,85,neurips,8,0,2023-06-15 23:43:48.407000,https://github.com/xkianteb/ApproPO,13,Reinforcement learning with convex constraints,"https://scholar.google.com/scholar?cluster=17753055761505168493&hl=en&as_sdt=0,5",3,2019
Learning Hawkes Processes from a handful of events,29,neurips,8,1,2023-06-15 23:43:48.589000,https://github.com/trouleau/var-hawkes,7,Learning hawkes processes from a handful of events,"https://scholar.google.com/scholar?cluster=4846579627142993040&hl=en&as_sdt=0,3",1,2019
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation,68,neurips,25,14,2023-06-15 23:43:48.771000,https://github.com/nrgeup/controllable-text-attribute-transfer,130,Controllable unsupervised text attribute transfer via editing entangled latent representation,"https://scholar.google.com/scholar?cluster=6509221759724074439&hl=en&as_sdt=0,19",7,2019
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller,64,neurips,5,1,2023-06-15 23:43:48.954000,https://github.com/pathak22/hierarchical-imitation,54,Third-person visual imitation learning via decoupled hierarchical controller,"https://scholar.google.com/scholar?cluster=1152601165924877882&hl=en&as_sdt=0,25",7,2019
Connective Cognition Network for Directional Visual Commonsense Reasoning,30,neurips,7,3,2023-06-15 23:43:49.136000,https://github.com/AmingWu/CCN,15,Connective cognition network for directional visual commonsense reasoning,"https://scholar.google.com/scholar?cluster=10868299947293549232&hl=en&as_sdt=0,3",3,2019
Discriminator optimal transport,45,neurips,3,0,2023-06-15 23:43:49.319000,https://github.com/AkinoriTanaka-phys/DOT,13,Discriminator optimal transport,"https://scholar.google.com/scholar?cluster=18026540846498142859&hl=en&as_sdt=0,5",5,2019
Sequential Experimental Design for Transductive Linear Bandits,86,neurips,0,0,2023-06-15 23:43:49.501000,https://github.com/fiezt/Transductive-Linear-Bandit-Code,2,Sequential experimental design for transductive linear bandits,"https://scholar.google.com/scholar?cluster=12964128858664596570&hl=en&as_sdt=0,47",1,2019
End to end learning and optimization on graphs,68,neurips,19,2,2023-06-15 23:43:49.684000,https://github.com/bwilder0/clusternet,77,End to end learning and optimization on graphs,"https://scholar.google.com/scholar?cluster=2313073977352706710&hl=en&as_sdt=0,5",5,2019
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration,224,neurips,2,0,2023-06-15 23:43:49.867000,https://github.com/dirichletcal/dirichletcal.github.io,6,Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration,"https://scholar.google.com/scholar?cluster=8575384251894434874&hl=en&as_sdt=0,31",4,2019
Curvilinear Distance Metric Learning,21,neurips,0,0,2023-06-15 23:43:50.049000,https://github.com/functioncs/CDML,0,Curvilinear distance metric learning,"https://scholar.google.com/scholar?cluster=11833570111330207293&hl=en&as_sdt=0,36",1,2019
Sampling Networks and Aggregate Simulation for Online POMDP Planning,2,neurips,0,0,2023-06-15 23:43:50.232000,https://github.com/hcui01/SNAP,3,Sampling networks and aggregate simulation for online pomdp planning,"https://scholar.google.com/scholar?cluster=12398643923867979827&hl=en&as_sdt=0,5",3,2019
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences,104,neurips,30,2,2023-06-15 23:43:50.414000,https://github.com/google/bi-tempered-loss,142,Robust bi-tempered logistic loss based on bregman divergences,"https://scholar.google.com/scholar?cluster=4731664592680946460&hl=en&as_sdt=0,5",10,2019
Noise-tolerant fair classification,60,neurips,1,0,2023-06-15 23:43:50.596000,https://github.com/AIasd/noise_fairlearn,5,Noise-tolerant fair classification,"https://scholar.google.com/scholar?cluster=11272640623843823996&hl=en&as_sdt=0,39",4,2019
Saccader: Improving Accuracy of Hard Attention Models for Vision,64,neurips,7320,1025,2023-06-15 23:43:50.789000,https://github.com/google-research/google-research,29776,Saccader: Improving accuracy of hard attention models for vision,"https://scholar.google.com/scholar?cluster=6992264138718311127&hl=en&as_sdt=0,18",727,2019
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution,30,neurips,21,2,2023-06-15 23:43:50.971000,https://github.com/zhou13/neurvps,150,Neurvps: Neural vanishing point scanning via conic convolution,"https://scholar.google.com/scholar?cluster=3031823208555509253&hl=en&as_sdt=0,30",10,2019
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression,14,neurips,1,0,2023-06-15 23:43:51.157000,https://github.com/noc-lab/Select-Optimal-Decisions-via-DRO-KNN,5,Selecting optimal decisions via distributionally robust nearest-neighbor regression,"https://scholar.google.com/scholar?cluster=16020986183708685814&hl=en&as_sdt=0,5",2,2019
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations,47,neurips,10,2,2023-06-15 23:43:51.340000,https://github.com/fly519/ELGS,50,Exploiting local and global structure for point cloud semantic segmentation with contextual point representations,"https://scholar.google.com/scholar?cluster=17515136600424535326&hl=en&as_sdt=0,5",4,2019
Heterogeneous Graph Learning for Visual Commonsense Reasoning,41,neurips,14,4,2023-06-15 23:43:51.522000,https://github.com/yuweijiang/HGL-pytorch,46,Heterogeneous graph learning for visual commonsense reasoning,"https://scholar.google.com/scholar?cluster=1264363257779833283&hl=en&as_sdt=0,47",7,2019
Memory Efficient Adaptive Optimization,36,neurips,7320,1025,2023-06-15 23:43:51.705000,https://github.com/google-research/google-research,29776,Memory efficient adaptive optimization,"https://scholar.google.com/scholar?cluster=4548335888639667869&hl=en&as_sdt=0,33",727,2019
Conformal Prediction Under Covariate Shift,165,neurips,46,10,2023-06-15 23:43:51.888000,https://github.com/ryantibs/conformal,177,Conformal prediction under covariate shift,"https://scholar.google.com/scholar?cluster=6789636313624066732&hl=en&as_sdt=0,3",17,2019
Adapting Neural Networks for the Estimation of Treatment Effects,221,neurips,44,2,2023-06-15 23:43:52.071000,https://github.com/claudiashi57/dragonnet,190,Adapting neural networks for the estimation of treatment effects,"https://scholar.google.com/scholar?cluster=3867091808295935282&hl=en&as_sdt=0,5",8,2019
Optimal Sampling and Clustering in the Stochastic Block Model,5,neurips,0,0,2023-06-15 23:43:52.253000,https://github.com/fbsqkd/StochasticBlockModel,0,Optimal sampling and clustering in the stochastic block model,"https://scholar.google.com/scholar?cluster=16411279302020087962&hl=en&as_sdt=0,1",1,2019
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time,13,neurips,2,0,2023-06-15 23:43:52.435000,https://github.com/LUMII-Syslab/shuffle-exchange,9,Neural shuffle-exchange networks-sequence processing in o (n log n) time,"https://scholar.google.com/scholar?cluster=16640163416880839372&hl=en&as_sdt=0,33",13,2019
Markov Random Fields for Collaborative Filtering,22,neurips,2,1,2023-06-15 23:43:52.617000,https://github.com/hasteck/MRF_NeurIPS_2019,19,Markov random fields for collaborative filtering,"https://scholar.google.com/scholar?cluster=17117745531500946052&hl=en&as_sdt=0,21",2,2019
Structured Graph Learning Via Laplacian Spectral Constraints,47,neurips,0,0,2023-06-15 23:43:52.800000,https://github.com/dppalomar/spectralGraphTopology,0,Structured graph learning via Laplacian spectral constraints,"https://scholar.google.com/scholar?cluster=8868297779776898800&hl=en&as_sdt=0,5",0,2019
"Lookahead Optimizer: k steps forward, 1 step back",581,neurips,27,0,2023-06-15 23:43:52.982000,https://github.com/michaelrzhang/lookahead,217,"Lookahead optimizer: k steps forward, 1 step back","https://scholar.google.com/scholar?cluster=2599504418931364355&hl=en&as_sdt=0,5",9,2019
Finding Friend and Foe in Multi-Agent Games,37,neurips,5,7,2023-06-15 23:43:53.165000,https://github.com/Detry322/DeepRole,27,Finding friend and foe in multi-agent games,"https://scholar.google.com/scholar?cluster=16486277193316870849&hl=en&as_sdt=0,33",1,2019
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks,191,neurips,16,5,2023-06-15 23:43:53.348000,https://github.com/acbull/LADIES,73,Layer-dependent importance sampling for training deep and large graph convolutional networks,"https://scholar.google.com/scholar?cluster=8927879978865662944&hl=en&as_sdt=0,37",6,2019
Self-Supervised Generalisation with Meta Auxiliary Learning,125,neurips,28,0,2023-06-15 23:43:53.530000,https://github.com/lorenmt/maxl,161,Self-supervised generalisation with meta auxiliary learning,"https://scholar.google.com/scholar?cluster=18242502085163121025&hl=en&as_sdt=0,44",7,2019
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum,60,neurips,23,0,2023-06-15 23:43:53.713000,https://github.com/apple/ml-data-parameters,70,Data parameters: A new family of parameters for learning a differentiable curriculum,"https://scholar.google.com/scholar?cluster=6678746522052000465&hl=en&as_sdt=0,23",16,2019
One-Shot Object Detection with Co-Attention and Co-Excitation,144,neurips,77,21,2023-06-15 23:43:53.895000,https://github.com/timy90022/One-Shot-Object-Detection,399,One-shot object detection with co-attention and co-excitation,"https://scholar.google.com/scholar?cluster=5705545859762971669&hl=en&as_sdt=0,5",16,2019
Are Anchor Points Really Indispensable in Label-Noise Learning?,238,neurips,19,1,2023-06-15 23:43:54.077000,https://github.com/xiaoboxia/T-Revision,89,Are anchor points really indispensable in label-noise learning?,"https://scholar.google.com/scholar?cluster=13091313467127090506&hl=en&as_sdt=0,43",6,2019
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models,62,neurips,6,2,2023-06-15 23:43:54.260000,https://github.com/ArchipLab-LinfengZhang/pytorch-scalable-neural-networks,23,Scan: A scalable neural networks framework towards compact and efficient models,"https://scholar.google.com/scholar?cluster=5724917370843261685&hl=en&as_sdt=0,5",4,2019
Smoothing Structured Decomposable Circuits,17,neurips,1,0,2023-06-15 23:43:54.443000,https://github.com/AndyShih12/SSDC,6,Smoothing structured decomposable circuits,"https://scholar.google.com/scholar?cluster=13215158158274197353&hl=en&as_sdt=0,5",1,2019
Bayesian Joint Estimation of Multiple Graphical Models,22,neurips,0,0,2023-06-15 23:43:54.626000,https://github.com/xinming104/GemBag,1,Bayesian joint estimation of multiple graphical models,"https://scholar.google.com/scholar?cluster=7666126448972292722&hl=en&as_sdt=0,21",1,2019
Maximum Mean Discrepancy Gradient Flow,98,neurips,3,1,2023-06-15 23:43:54.808000,https://github.com/MichaelArbel/MMD-gradient-flow,6,Maximum mean discrepancy gradient flow,"https://scholar.google.com/scholar?cluster=613411100718118562&hl=en&as_sdt=0,3",2,2019
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies,154,neurips,0,2,2023-06-15 23:43:54.990000,https://github.com/xbpeng/mcp,10,Mcp: Learning composable hierarchical control with multiplicative compositional policies,"https://scholar.google.com/scholar?cluster=12493399866748517630&hl=en&as_sdt=0,5",13,2019
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks,122,neurips,30,17,2023-06-15 23:43:55.173000,https://github.com/abr/neurips2019,196,Legendre memory units: Continuous-time representation in recurrent neural networks,"https://scholar.google.com/scholar?cluster=12694102422873016624&hl=en&as_sdt=0,18",23,2019
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning,393,neurips,52,1,2023-06-15 23:43:55.355000,https://github.com/BlackHC/BatchBALD,206,Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning,"https://scholar.google.com/scholar?cluster=4637860255101712227&hl=en&as_sdt=0,5",9,2019
Screening Sinkhorn Algorithm for Regularized Optimal Transport,46,neurips,1,0,2023-06-15 23:43:55.538000,https://github.com/mzalaya/screenkhorn,10,Screening sinkhorn algorithm for regularized optimal transport,"https://scholar.google.com/scholar?cluster=6847300346799995877&hl=en&as_sdt=0,33",4,2019
Learning Deep Bilinear Transformation for Fine-grained Image Representation,127,neurips,18,5,2023-06-15 23:43:55.721000,https://github.com/researchmm/DBTNet,103,Learning deep bilinear transformation for fine-grained image representation,"https://scholar.google.com/scholar?cluster=5630007169775604434&hl=en&as_sdt=0,5",7,2019
Learning Compositional Neural Programs with Recursive Tree Search and Planning,39,neurips,15,5,2023-06-15 23:43:55.904000,https://github.com/instadeepai/AlphaNPI,75,Learning compositional neural programs with recursive tree search and planning,"https://scholar.google.com/scholar?cluster=2128386923909223198&hl=en&as_sdt=0,5",9,2019
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation,5,neurips,0,0,2023-06-15 23:43:56.086000,https://github.com/samuela/e-stops,4,Mo'states mo'problems: Emergency stop mechanisms from observation,"https://scholar.google.com/scholar?cluster=17441120353657662710&hl=en&as_sdt=0,18",4,2019
Kernelized Bayesian Softmax for Text Generation,3,neurips,3,0,2023-06-15 23:43:56.268000,https://github.com/NingMiao/KerBS,16,Kernelized bayesian softmax for text generation,"https://scholar.google.com/scholar?cluster=9263000748514336745&hl=en&as_sdt=0,5",4,2019
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization,45,neurips,3,1,2023-06-15 23:43:56.451000,https://github.com/RixonC/DINGO,5,DINGO: Distributed Newton-type method for gradient-norm optimization,"https://scholar.google.com/scholar?cluster=9185133392864435818&hl=en&as_sdt=0,47",1,2019
Object landmark discovery through unsupervised adaptation,14,neurips,6,2,2023-06-15 23:43:56.634000,https://github.com/ESanchezLozano/SAIC-Unsupervised-landmark-detection-NeurIPS2019,29,Object landmark discovery through unsupervised adaptation,"https://scholar.google.com/scholar?cluster=9031992010757447609&hl=en&as_sdt=0,5",3,2019
Block Coordinate Regularization by Denoising,67,neurips,7,0,2023-06-15 23:43:56.816000,https://github.com/wustl-cig/bcred,9,Block coordinate regularization by denoising,"https://scholar.google.com/scholar?cluster=1292618094762945559&hl=en&as_sdt=0,22",5,2019
Visual Concept-Metaconcept Learning,56,neurips,7,5,2023-06-15 23:43:57,https://github.com/Glaciohound/VCML,46,Visual concept-metaconcept learning,"https://scholar.google.com/scholar?cluster=11769852551616538355&hl=en&as_sdt=0,44",3,2019
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection,52,neurips,6,0,2023-06-15 23:43:57.182000,https://github.com/vlkniaz/MAGritte,22,The point where reality meets fantasy: Mixed adversarial generators for image splice detection,"https://scholar.google.com/scholar?cluster=12749511868838155616&hl=en&as_sdt=0,5",2,2019
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering,34,neurips,0,0,2023-06-15 23:43:57.365000,https://github.com/sushrutk/robust_sparse_mean_estimation,1,Outlier-robust high-dimensional sparse estimation via iterative filtering,"https://scholar.google.com/scholar?cluster=16618101230479779573&hl=en&as_sdt=0,44",2,2019
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks,132,neurips,25,0,2023-06-15 23:43:57.558000,https://github.com/cagatayyildiz/ODE2VAE,104,ODE2VAE: Deep generative second order ODEs with Bayesian neural networks,"https://scholar.google.com/scholar?cluster=12216088615598559688&hl=en&as_sdt=0,26",7,2019
Cross-Domain Transferability of Adversarial Perturbations,96,neurips,10,0,2023-06-15 23:43:57.740000,https://github.com/Muzammal-Naseer/Cross-domain-perturbations,46,Cross-domain transferability of adversarial perturbations,"https://scholar.google.com/scholar?cluster=7007287740429606925&hl=en&as_sdt=0,5",2,2019
Recovering Bandits,36,neurips,0,1,2023-06-15 23:43:57.923000,https://github.com/ciarapb/recovering_bandits,0,Recovering bandits,"https://scholar.google.com/scholar?cluster=11910471401388332238&hl=en&as_sdt=0,5",1,2019
A neurally plausible model for online recognition and postdiction in a dynamical environment,8,neurips,1,0,2023-06-15 23:43:58.106000,https://github.com/kevin-w-li/ddc_ssm,0,A neurally plausible model for online recognition and postdiction in a dynamical environment,"https://scholar.google.com/scholar?cluster=7571256273335094735&hl=en&as_sdt=0,5",1,2019
Importance Resampling for Off-policy Prediction,33,neurips,2,1,2023-06-15 23:43:58.288000,https://github.com/mkschleg/Resampling.jl,5,Importance resampling for off-policy prediction,"https://scholar.google.com/scholar?cluster=5157617091632613396&hl=en&as_sdt=0,5",3,2019
A Condition Number for Joint Optimization of Cycle-Consistent Networks,15,neurips,1,0,2023-06-15 23:43:58.470000,https://github.com/huangqx/NeurIPS19_Cycle,9,A condition number for joint optimization of cycle-consistent networks,"https://scholar.google.com/scholar?cluster=542481175348685009&hl=en&as_sdt=0,5",3,2019
A Graph Theoretic Additive Approximation of Optimal Transport,29,neurips,4,0,2023-06-15 23:43:58.653000,https://github.com/nathaniellahn/CombinatorialOptimalTransport,6,A graph theoretic additive approximation of optimal transport,"https://scholar.google.com/scholar?cluster=18196599913919395149&hl=en&as_sdt=0,5",2,2019
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking,3,neurips,1,0,2023-06-15 23:43:58.837000,https://github.com/sumeetsk/maxgap_bandit,0,Maxgap bandit: Adaptive algorithms for approximate ranking,"https://scholar.google.com/scholar?cluster=3849563562528294694&hl=en&as_sdt=0,33",3,2019
Exact Rate-Distortion in Autoencoders via Echo Noise,15,neurips,4,0,2023-06-15 23:43:59.019000,https://github.com/brekelma/echo,17,Exact rate-distortion in autoencoders via echo noise,"https://scholar.google.com/scholar?cluster=14670314259355602028&hl=en&as_sdt=0,33",3,2019
Bridging Machine Learning and Logical Reasoning by Abductive Learning,100,neurips,22,1,2023-06-15 23:43:59.202000,https://github.com/AbductiveLearning/ABL-HED,86,Bridging machine learning and logical reasoning by abductive learning,"https://scholar.google.com/scholar?cluster=1518342375288126288&hl=en&as_sdt=0,33",5,2019
Input-Output Equivalence of Unitary and Contractive RNNs,3,neurips,0,0,2023-06-15 23:43:59.386000,https://github.com/melikaemami/URNN,0,Input-output equivalence of unitary and contractive rnns,"https://scholar.google.com/scholar?cluster=4797724807389043789&hl=en&as_sdt=0,46",1,2019
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization,98,neurips,16,6,2023-06-15 23:43:59.583000,https://github.com/plumerai/rethinking-bnn-optimization,65,Latent weights do not exist: Rethinking binarized neural network optimization,"https://scholar.google.com/scholar?cluster=1826223927355185182&hl=en&as_sdt=0,5",10,2019
Differentiable Convex Optimization Layers,402,neurips,138,43,2023-06-15 23:43:59.766000,https://github.com/cvxgrp/cvxpylayers,1544,Differentiable convex optimization layers,"https://scholar.google.com/scholar?cluster=4803367516747588003&hl=en&as_sdt=0,5",55,2019
Graph Transformer Networks,583,neurips,148,13,2023-06-15 23:43:59.948000,https://github.com/seongjunyun/Graph_Transformer_Networks,772,Graph transformer networks,"https://scholar.google.com/scholar?cluster=10432505779472613736&hl=en&as_sdt=0,41",12,2019
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics,55,neurips,4,2,2023-06-15 23:44:00.131000,https://github.com/nnRNN/nnRNN_release,22,Non-normal recurrent neural network (nnrnn): learning long time dependencies while improving expressivity with transient dynamics,"https://scholar.google.com/scholar?cluster=8175788544476265366&hl=en&as_sdt=0,33",6,2019
Large Memory Layers with Product Keys,102,neurips,474,127,2023-06-15 23:44:00.314000,https://github.com/facebookresearch/XLM,2768,Large memory layers with product keys,"https://scholar.google.com/scholar?cluster=8134570978766877507&hl=en&as_sdt=0,33",56,2019
Computing Full Conformal Prediction Set with Approximate Homotopy,13,neurips,1,0,2023-06-15 23:44:00.497000,https://github.com/EugeneNdiaye/homotopy_conformal_prediction,4,Computing full conformal prediction set with approximate homotopy,"https://scholar.google.com/scholar?cluster=6957582506372918225&hl=en&as_sdt=0,31",4,2019
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification,191,neurips,39,4,2023-06-15 23:44:00.680000,https://github.com/yourh/AttentionXML,228,Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification,"https://scholar.google.com/scholar?cluster=17044546851648678548&hl=en&as_sdt=0,39",5,2019
Policy Learning for Fairness in Ranking,174,neurips,7,1,2023-06-15 23:44:00.863000,https://github.com/ashudeep/Fair-PGRank,19,Policy learning for fairness in ranking,"https://scholar.google.com/scholar?cluster=11031156669451093289&hl=en&as_sdt=0,33",2,2019
Integer Discrete Flows and Lossless Compression,118,neurips,18,2,2023-06-15 23:44:01.045000,https://github.com/jornpeters/integer_discrete_flows,94,Integer discrete flows and lossless compression,"https://scholar.google.com/scholar?cluster=4833991710159138834&hl=en&as_sdt=0,36",5,2019
Reconciling λ-Returns with Experience Replay,31,neurips,5,1,2023-06-15 23:44:01.228000,https://github.com/brett-daley/dqn-lambda,21,Reconciling λ-returns with experience replay,"https://scholar.google.com/scholar?cluster=3382445004313688129&hl=en&as_sdt=0,5",3,2019
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm,64,neurips,3,1,2023-06-15 23:44:01.410000,https://github.com/GiulsLu/Sinkhorn-Barycenters,20,Sinkhorn barycenters with free support via frank-wolfe algorithm,"https://scholar.google.com/scholar?cluster=8683927727496830804&hl=en&as_sdt=0,33",4,2019
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations,105,neurips,14,7,2023-06-15 23:44:01.602000,https://github.com/fenglinliu98/MIA,63,Aligning visual regions and textual concepts for semantic-grounded image representations,"https://scholar.google.com/scholar?cluster=2159125820163720413&hl=en&as_sdt=0,34",6,2019
Network Pruning via Transformable Architecture Search,230,neurips,279,13,2023-06-15 23:44:01.785000,https://github.com/D-X-Y/NAS-Projects,1494,Network pruning via transformable architecture search,"https://scholar.google.com/scholar?cluster=10081161153623762444&hl=en&as_sdt=0,5",45,2019
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives,27,neurips,1,0,2023-06-15 23:44:01.972000,https://github.com/wangchimit/mdp_q,0,Regret minimization for reinforcement learning with vectorial feedback and complex objectives,"https://scholar.google.com/scholar?cluster=15554596298446464048&hl=en&as_sdt=0,33",1,2019
Selective Sampling-based Scalable Sparse Subspace Clustering,40,neurips,6,0,2023-06-15 23:44:02.155000,https://github.com/smatsus/S5C,10,Selective sampling-based scalable sparse subspace clustering,"https://scholar.google.com/scholar?cluster=18109014271440966557&hl=en&as_sdt=0,47",4,2019
On the Expressive Power of Deep Polynomial Neural Networks,56,neurips,2,0,2023-06-15 23:44:02.338000,https://github.com/mtrager/polynomial_networks,7,On the expressive power of deep polynomial neural networks,"https://scholar.google.com/scholar?cluster=3267335187204945062&hl=en&as_sdt=0,23",3,2019
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos,60,neurips,15,6,2023-06-15 23:44:02.520000,https://github.com/ebatty/behavenet,48,BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos,"https://scholar.google.com/scholar?cluster=8465940907490752518&hl=en&as_sdt=0,10",8,2019
Accurate Layerwise Interpretable Competence Estimation,4,neurips,0,0,2023-06-15 23:44:02.703000,https://github.com/vickraj/ALICE,1,Accurate layerwise interpretable competence estimation,"https://scholar.google.com/scholar?cluster=6989485963144950384&hl=en&as_sdt=0,33",2,2019
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning,26,neurips,7,0,2023-06-15 23:44:02.886000,https://github.com/facebookresearch/gala,19,Gossip-based actor-learner architectures for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=7339058488760519540&hl=en&as_sdt=0,33",6,2019
Fast and Accurate Stochastic Gradient Estimation,29,neurips,4,1,2023-06-15 23:44:03.068000,https://github.com/keroro824/LGD,11,Fast and accurate stochastic gradient estimation,"https://scholar.google.com/scholar?cluster=14355182698351055018&hl=en&as_sdt=0,47",5,2019
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling,8,neurips,5,1,2023-06-15 23:44:03.250000,https://github.com/zhangzx-sjtu/LANTERN-NeurIPS-2019,10,Learning latent process from high-dimensional event sequences via efficient sampling,"https://scholar.google.com/scholar?cluster=1725612638929468853&hl=en&as_sdt=0,5",3,2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty,705,neurips,30,3,2023-06-15 23:44:03.433000,https://github.com/hendrycks/ss-ood,256,Using self-supervised learning can improve model robustness and uncertainty,"https://scholar.google.com/scholar?cluster=1993204184412498694&hl=en&as_sdt=0,10",7,2019
Space and Time Efficient Kernel Density Estimation in High Dimensions,50,neurips,1,0,2023-06-15 23:44:03.615000,https://github.com/talwagner/efficient_kde,20,Space and time efficient kernel density estimation in high dimensions,"https://scholar.google.com/scholar?cluster=2039472517470504550&hl=en&as_sdt=0,48",2,2019
Scalable Deep Generative Relational Model with High-Order Node Dependence,11,neurips,0,0,2023-06-15 23:44:03.798000,https://github.com/xuhuifan/SDREM,0,Scalable deep generative relational model with high-order node dependence,"https://scholar.google.com/scholar?cluster=17019622805732134469&hl=en&as_sdt=0,22",1,2019
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing,25,neurips,2,0,2023-06-15 23:44:03.980000,https://github.com/woodyx218/SLOPE_AMP,0,Algorithmic analysis and statistical estimation of slope via approximate message passing,"https://scholar.google.com/scholar?cluster=6840575355552883689&hl=en&as_sdt=0,14",2,2019
Multi-objects Generation with Amortized Structural Regularization,19,neurips,0,0,2023-06-15 23:44:04.163000,https://github.com/taufikxu/MOG-ASR,4,Multi-objects generation with amortized structural regularization,"https://scholar.google.com/scholar?cluster=2034846002804376958&hl=en&as_sdt=0,10",2,2019
Learning Distributions Generated by One-Layer ReLU Networks,20,neurips,0,0,2023-06-15 23:44:04.346000,https://github.com/wushanshan/densityEstimation,0,Learning distributions generated by one-layer ReLU networks,"https://scholar.google.com/scholar?cluster=12692430709826670328&hl=en&as_sdt=0,5",2,2019
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules,125,neurips,22,0,2023-06-15 23:44:04.528000,https://github.com/atomistic-machine-learning/G-SchNet,113,Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules,"https://scholar.google.com/scholar?cluster=10125464243837657094&hl=en&as_sdt=0,33",6,2019
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection,85,neurips,4,0,2023-06-15 23:44:04.711000,https://github.com/twistedcubic/que-outlier-detection,25,Quantum entropy scoring for fast robust mean estimation and improved outlier detection,"https://scholar.google.com/scholar?cluster=841892307545276376&hl=en&as_sdt=0,33",6,2019
Distributed Low-rank Matrix Factorization With Exact Consensus,13,neurips,0,0,2023-06-15 23:44:04.893000,https://github.com/xinshuoyang/DGD-LOCAL,0,Distributed low-rank matrix factorization with exact consensus,"https://scholar.google.com/scholar?cluster=5520219022394816483&hl=en&as_sdt=0,33",2,2019
Tensor Monte Carlo: Particle Methods for the GPU era,7,neurips,0,0,2023-06-15 23:44:05.076000,https://github.com/anonymous-78913/tmc-anon,1,Tensor Monte Carlo: particle methods for the GPU era,"https://scholar.google.com/scholar?cluster=16439696992538487592&hl=en&as_sdt=0,33",1,2019
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders,20,neurips,1,0,2023-06-15 23:44:05.258000,https://github.com/zhaozb08/MixPL-SPO,2,Learning mixtures of plackett-luce models from structured partial orders,"https://scholar.google.com/scholar?cluster=878719631991386250&hl=en&as_sdt=0,31",2,2019
Combining Generative and Discriminative Models for Hybrid Inference,43,neurips,4,0,2023-06-15 23:44:05.440000,https://github.com/vgsatorras/hybrid-inference,19,Combining generative and discriminative models for hybrid inference,"https://scholar.google.com/scholar?cluster=7519572566693624028&hl=en&as_sdt=0,9",3,2019
Region Mutual Information Loss for Semantic Segmentation,82,neurips,38,10,2023-06-15 23:44:05.623000,https://github.com/ZJULearning/RMI,257,Region mutual information loss for semantic segmentation,"https://scholar.google.com/scholar?cluster=686312133608642503&hl=en&as_sdt=0,33",10,2019
Variational Graph Recurrent Neural Networks,135,neurips,31,3,2023-06-15 23:44:05.805000,https://github.com/VGraphRNN/VGRNN,99,Variational graph recurrent neural networks,"https://scholar.google.com/scholar?cluster=8245745974174027367&hl=en&as_sdt=0,23",1,2019
Stochastic Bandits with Context Distributions,14,neurips,2,0,2023-06-15 23:44:05.988000,https://github.com/jkirschner42/ContextDistributions,0,Stochastic bandits with context distributions,"https://scholar.google.com/scholar?cluster=4525359308545283091&hl=en&as_sdt=0,47",2,2019
Geometry-Aware Neural Rendering,18,neurips,2,1,2023-06-15 23:44:06.170000,https://github.com/josh-tobin/egqn-datasets,12,Geometry-aware neural rendering,"https://scholar.google.com/scholar?cluster=13975640074645602977&hl=en&as_sdt=0,14",6,2019
Training Language GANs from Scratch,64,neurips,2436,170,2023-06-15 23:44:06.352000,https://github.com/deepmind/deepmind-research,11902,Training language gans from scratch,"https://scholar.google.com/scholar?cluster=8355933578151916965&hl=en&as_sdt=0,33",336,2019
On the (In)fidelity and Sensitivity of Explanations,265,neurips,5,1,2023-06-15 23:44:06.535000,https://github.com/chihkuanyeh/saliency_evaluation,18,On the (in) fidelity and sensitivity of explanations,"https://scholar.google.com/scholar?cluster=14868848543196386114&hl=en&as_sdt=0,49",5,2019
Foundations of Comparison-Based Hierarchical Clustering,27,neurips,0,0,2023-06-15 23:44:06.717000,https://github.com/mperrot/ComparisonHC,7,Foundations of comparison-based hierarchical clustering,"https://scholar.google.com/scholar?cluster=13988948004234767193&hl=en&as_sdt=0,33",1,2019
Neural Similarity Learning,22,neurips,5,0,2023-06-15 23:44:06.903000,https://github.com/wy1iu/NSL,33,Neural similarity learning,"https://scholar.google.com/scholar?cluster=1329367267940574099&hl=en&as_sdt=0,21",11,2019
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities,10,neurips,0,0,2023-06-15 23:44:07.085000,https://github.com/weiiew28/Least-Squares-EM,0,Global convergence of least squares EM for demixing two log-concave densities,"https://scholar.google.com/scholar?cluster=12181077309043150371&hl=en&as_sdt=0,33",2,2019
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise,39,neurips,0,0,2023-06-15 23:44:07.268000,https://github.com/umutsimsekli/sgd_first_exit_time,2,First exit time analysis of stochastic gradient descent under heavy-tailed gradient noise,"https://scholar.google.com/scholar?cluster=11205216308770275792&hl=en&as_sdt=0,33",1,2019
Hyper-Graph-Network Decoders for Block Codes,50,neurips,6,0,2023-06-15 23:44:07.450000,https://github.com/facebookresearch/HyperNetworkDecoder,18,Hyper-graph-network decoders for block codes,"https://scholar.google.com/scholar?cluster=7373030351038141081&hl=en&as_sdt=0,14",5,2019
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models,51,neurips,1,0,2023-06-15 23:44:07.632000,https://github.com/wushanshan/GraphLearn,3,Sparse logistic regression learns all discrete pairwise graphical models,"https://scholar.google.com/scholar?cluster=18105640724379310504&hl=en&as_sdt=0,33",3,2019
Coordinated hippocampal-entorhinal replay as structural inference,13,neurips,0,0,2023-06-15 23:44:07.814000,https://github.com/talfanevans/Coordinated_replay_for_structural_inference_NeurIPS_2019,0,Coordinated hippocampal-entorhinal replay as structural inference,"https://scholar.google.com/scholar?cluster=2193106409668155739&hl=en&as_sdt=0,44",1,2019
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition,117,neurips,13,2,2023-06-15 23:44:07.997000,https://github.com/vt-vl-lab/SDN,81,Why can't i dance in the mall? learning to mitigate scene bias in action recognition,"https://scholar.google.com/scholar?cluster=7980230470759828284&hl=en&as_sdt=0,25",4,2019
Invert to Learn to Invert,73,neurips,12,0,2023-06-15 23:44:08.183000,https://github.com/pputzky/invertible_rim,35,Invert to learn to invert,"https://scholar.google.com/scholar?cluster=5749756993506538119&hl=en&as_sdt=0,11",3,2019
Metamers of neural networks reveal divergence from human perceptual systems,45,neurips,3,0,2023-06-15 23:44:08.377000,https://github.com/jenellefeather/model_metamers,4,Metamers of neural networks reveal divergence from human perceptual systems,"https://scholar.google.com/scholar?cluster=11487383284090666509&hl=en&as_sdt=0,14",1,2019
Optimal Sparse Decision Trees,151,neurips,9,5,2023-06-15 23:44:08.574000,https://github.com/xiyanghu/OSDT,87,Optimal sparse decision trees,"https://scholar.google.com/scholar?cluster=2250336388738514433&hl=en&as_sdt=0,5",6,2019
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling,9,neurips,15,0,2023-06-15 23:44:08.757000,https://github.com/microsoft/Optimal-Freshness-Crawl-Scheduling,34,Staying up to date with online content changes using reinforcement learning for scheduling,"https://scholar.google.com/scholar?cluster=817478686075207663&hl=en&as_sdt=0,33",12,2019
This Looks Like That: Deep Learning for Interpretable Image Recognition,763,neurips,104,17,2023-06-15 23:44:08.939000,https://github.com/cfchen-duke/ProtoPNet,280,This looks like that: deep learning for interpretable image recognition,"https://scholar.google.com/scholar?cluster=13319230358009390187&hl=en&as_sdt=0,5",9,2019
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning,7,neurips,6,0,2023-06-15 23:44:09.121000,https://github.com/kakao/DAFT,32,Learning dynamics of attention: Human prior for interpretable machine reasoning,"https://scholar.google.com/scholar?cluster=5091360286215129323&hl=en&as_sdt=0,3",10,2019
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes,50,neurips,5,7,2023-06-15 23:44:09.303000,https://github.com/revbucket/geometric-certificates,40,Provable certificates for adversarial examples: Fitting a ball in the union of polytopes,"https://scholar.google.com/scholar?cluster=12220982508626507839&hl=en&as_sdt=0,33",4,2019
Fast Parallel Algorithms for Statistical Subset Selection Problems,8,neurips,0,0,2023-06-15 23:44:09.486000,https://github.com/robo-sq/dash,1,Fast parallel algorithms for statistical subset selection problems,"https://scholar.google.com/scholar?cluster=1744974239462418532&hl=en&as_sdt=0,34",2,2019
On Lazy Training in Differentiable Programming,646,neurips,1,0,2023-06-15 23:44:09.680000,https://github.com/edouardoyallon/lazy-training-CNN,11,On lazy training in differentiable programming,"https://scholar.google.com/scholar?cluster=7609132224233862548&hl=en&as_sdt=0,47",2,2019
Estimating Convergence of Markov chains with L-Lag Couplings,39,neurips,0,0,2023-06-15 23:44:09.862000,https://github.com/niloyb/LlagCouplings,8,Estimating convergence of Markov chains with L-lag couplings,"https://scholar.google.com/scholar?cluster=7664057036629656695&hl=en&as_sdt=0,5",3,2019
Neural Multisensory Scene Inference,7,neurips,0,0,2023-06-15 23:44:10.045000,https://github.com/lim0606/pytorch-generative-multisensory-network,2,Neural multisensory scene inference,"https://scholar.google.com/scholar?cluster=12739272795826190598&hl=en&as_sdt=0,5",5,2019
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions,6,neurips,3,0,2023-06-15 23:44:10.228000,https://github.com/MatteoT90/WibergianLearning,6,Fixing implicit derivatives: Trust-region based learning of continuous energy functions,"https://scholar.google.com/scholar?cluster=14745296383921099164&hl=en&as_sdt=0,31",5,2019
Correlation Clustering with Adaptive Similarity Queries,14,neurips,2,0,2023-06-15 23:44:10.411000,https://github.com/AP15/NeurIPS_2019,0,Correlation clustering with adaptive similarity queries,"https://scholar.google.com/scholar?cluster=7825046887341981145&hl=en&as_sdt=0,5",1,2019
Ease-of-Teaching and Language Structure from Emergent Communication,77,neurips,0,0,2023-06-15 23:44:10.594000,https://github.com/FushanLi/Ease-of-teaching-and-language-structure,4,Ease-of-teaching and language structure from emergent communication,"https://scholar.google.com/scholar?cluster=9879290810297308236&hl=en&as_sdt=0,5",1,2019
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition,50,neurips,0,0,2023-06-15 23:44:10.776000,https://github.com/rrogers386/DPComposition,0,Practical differentially private top-k selection with pay-what-you-get composition,"https://scholar.google.com/scholar?cluster=13096075295123378083&hl=en&as_sdt=0,10",3,2019
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking,22,neurips,25,0,2023-06-15 23:44:10.959000,https://github.com/yu-lab-vt/muSSP,92,muSSP: Efficient min-cost flow algorithm for multi-object tracking,"https://scholar.google.com/scholar?cluster=5651751699578908397&hl=en&as_sdt=0,36",5,2019
Invertible Convolutional Flow,37,neurips,0,1,2023-06-15 23:44:11.142000,https://github.com/Karami-m/Invertible-Convolutional-Flow,2,Invertible convolutional flow,"https://scholar.google.com/scholar?cluster=13011222781620393889&hl=en&as_sdt=0,33",2,2019
Neural Relational Inference with Fast Modular Meta-learning,53,neurips,11,2,2023-06-15 23:44:11.324000,https://github.com/FerranAlet/modular-metalearning,72,Neural relational inference with fast modular meta-learning,"https://scholar.google.com/scholar?cluster=10911682641501224691&hl=en&as_sdt=0,5",5,2019
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis,159,neurips,16,5,2023-06-15 23:44:11.507000,https://github.com/xh-liu/CC-FPSE,120,Learning to predict layout-to-image conditional convolutions for semantic image synthesis,"https://scholar.google.com/scholar?cluster=10050858944004797007&hl=en&as_sdt=0,33",9,2019
Approximate Inference Turns Deep Networks into Gaussian Processes,80,neurips,4,0,2023-06-15 23:44:11.689000,https://github.com/team-approx-bayes/dnn2gp,45,Approximate inference turns deep networks into gaussian processes,"https://scholar.google.com/scholar?cluster=7367896344754763984&hl=en&as_sdt=0,33",2,2019
SGD on Neural Networks Learns Functions of Increasing Complexity,106,neurips,2,0,2023-06-15 23:44:11.873000,https://github.com/anoneurips2019/SGD-learns-functions-of-increasing-complexity,2,Sgd on neural networks learns functions of increasing complexity,"https://scholar.google.com/scholar?cluster=7545613427429088321&hl=en&as_sdt=0,33",0,2019
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation,19,neurips,1,1,2023-06-15 23:44:12.056000,https://github.com/sorooshafiee/Nonparam_Likelihood,3,Optimistic distributionally robust optimization for nonparametric likelihood approximation,"https://scholar.google.com/scholar?cluster=4678014914038724524&hl=en&as_sdt=0,39",1,2019
Don't take it lightly: Phasing optical random projections with unknown operators,10,neurips,1,0,2023-06-15 23:44:12.239000,https://github.com/swing-research/opu_phase,5,Don't take it lightly: Phasing optical random projections with unknown operators,"https://scholar.google.com/scholar?cluster=17218217643749402266&hl=en&as_sdt=0,24",3,2019
Visualizing the PHATE of Neural Networks,22,neurips,9,1,2023-06-15 23:44:12.421000,https://github.com/scottgigante/m-phate,54,Visualizing the phate of neural networks,"https://scholar.google.com/scholar?cluster=10386490094735886479&hl=en&as_sdt=0,33",7,2019
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks,201,neurips,28,11,2023-06-15 23:44:12.603000,https://github.com/youzhonghui/gate-decorator-pruning,187,Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks,"https://scholar.google.com/scholar?cluster=364370970700584447&hl=en&as_sdt=0,5",10,2019
"Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights",13,neurips,0,0,2023-06-15 23:44:12.785000,https://github.com/mariajahja/kf-sf-flu-nowcasting,8,"Kalman filter, sensor fusion, and constrained regression: Equivalences and insights","https://scholar.google.com/scholar?cluster=15806795739922263365&hl=en&as_sdt=0,5",2,2019
Practical Deep Learning with Bayesian Principles,202,neurips,22,5,2023-06-15 23:44:12.968000,https://github.com/team-approx-bayes/dl-with-bayes,238,Practical deep learning with Bayesian principles,"https://scholar.google.com/scholar?cluster=13355160445802491048&hl=en&as_sdt=0,5",14,2019
Deep Active Learning with a Neural Architecture Search,39,neurips,1,0,2023-06-15 23:44:13.151000,https://github.com/geifmany/Active-inas,6,Deep active learning with a neural architecture search,"https://scholar.google.com/scholar?cluster=17316861516778734731&hl=en&as_sdt=0,33",2,2019
Quality Aware Generative Adversarial Networks,28,neurips,3,0,2023-06-15 23:44:13.333000,https://github.com/lfovia/QAGANS,20,Quality aware generative adversarial networks,"https://scholar.google.com/scholar?cluster=7569135651621693544&hl=en&as_sdt=0,33",1,2019
Control What You Can: Intrinsically Motivated Task-Planning Agent,25,neurips,1,0,2023-06-15 23:44:13.516000,https://github.com/s-bl/cwyc,4,Control what you can: Intrinsically motivated task-planning agent,"https://scholar.google.com/scholar?cluster=4748849991670858589&hl=en&as_sdt=0,47",2,2019
Momentum-Based Variance Reduction in Non-Convex SGD,259,neurips,7320,1025,2023-06-15 23:44:13.699000,https://github.com/google-research/google-research,29776,Momentum-based variance reduction in non-convex sgd,"https://scholar.google.com/scholar?cluster=15315656138665062900&hl=en&as_sdt=0,31",727,2019
Adversarial Self-Defense for Cycle-Consistent GANs,35,neurips,2,0,2023-06-15 23:44:13.883000,https://github.com/dbash/pix2pix_cyclegan_guess_noise,10,Adversarial self-defense for cycle-consistent GANs,"https://scholar.google.com/scholar?cluster=2846733163024685583&hl=en&as_sdt=0,33",2,2019
Ultrametric Fitting by Gradient Descent,25,neurips,3,0,2023-06-15 23:44:14.067000,https://github.com/PerretB/ultrametric-fitting,8,Ultrametric fitting by gradient descent,"https://scholar.google.com/scholar?cluster=1064532168086709457&hl=en&as_sdt=0,11",2,2019
Expressive power of tensor-network factorizations for probabilistic modeling,90,neurips,9,0,2023-06-15 23:44:14.249000,https://github.com/glivan/tensor_networks_for_probabilistic_modeling,28,Expressive power of tensor-network factorizations for probabilistic modeling,"https://scholar.google.com/scholar?cluster=973997541769819292&hl=en&as_sdt=0,33",5,2019
Machine Teaching of Active Sequential Learners,24,neurips,2,0,2023-06-15 23:44:14.432000,https://github.com/AaltoPML/machine-teaching-of-active-sequential-learners,9,Machine teaching of active sequential learners,"https://scholar.google.com/scholar?cluster=16295600938122436621&hl=en&as_sdt=0,5",12,2019
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs,41,neurips,2,0,2023-06-15 23:44:14.615000,https://github.com/marekpetrik/craam2,4,Beyond confidence regions: Tight Bayesian ambiguity sets for robust MDPs,"https://scholar.google.com/scholar?cluster=2675496836563141950&hl=en&as_sdt=0,5",1,2019
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization,111,neurips,6,2,2023-06-15 23:44:14.797000,https://github.com/shiyujiao/SAFA,34,Spatial-aware feature aggregation for image based cross-view geo-localization,"https://scholar.google.com/scholar?cluster=9193879788898998402&hl=en&as_sdt=0,14",1,2019
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification,68,neurips,0,0,2023-06-15 23:44:14.980000,https://github.com/lucaoneto/NIPS2019_Fairness,0,Leveraging labeled and unlabeled data for consistent fair binary classification,"https://scholar.google.com/scholar?cluster=2612431805502429071&hl=en&as_sdt=0,33",1,2019
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels,12,neurips,7320,1025,2023-06-15 23:44:15.162000,https://github.com/google-research/google-research,29776,Tight dimensionality reduction for sketching low degree polynomial kernels,"https://scholar.google.com/scholar?cluster=2891379264114413860&hl=en&as_sdt=0,15",727,2019
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning,26,neurips,4,0,2023-06-15 23:44:15.345000,https://github.com/shiwj16/raa-drl,9,Regularized Anderson acceleration for off-policy deep reinforcement learning,"https://scholar.google.com/scholar?cluster=7454070558612114755&hl=en&as_sdt=0,33",2,2019
Kernel Stein Tests for Multiple Model Comparison,11,neurips,2,0,2023-06-15 23:44:15.527000,https://github.com/jenninglim/model-comparison-test,5,Kernel stein tests for multiple model comparison,"https://scholar.google.com/scholar?cluster=8758698782174042861&hl=en&as_sdt=0,5",4,2019
Explanations can be manipulated and geometry is to blame,245,neurips,10,2,2023-06-15 23:44:15.710000,https://github.com/pankessel/explanations_can_be_manipulated,31,Explanations can be manipulated and geometry is to blame,"https://scholar.google.com/scholar?cluster=14180570023451576122&hl=en&as_sdt=0,33",1,2019
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks,34,neurips,3,1,2023-06-15 23:44:15.893000,https://github.com/ayaabdelsalam91/Input-Cell-Attention,12,Input-cell attention reduces vanishing saliency of recurrent neural networks,"https://scholar.google.com/scholar?cluster=4632327933243562924&hl=en&as_sdt=0,33",3,2019
Paradoxes in Fair Machine Learning,31,neurips,0,0,2023-06-15 23:44:16.075000,https://github.com/pgoelz/equalized,3,Paradoxes in fair machine learning,"https://scholar.google.com/scholar?cluster=18338740097234946174&hl=en&as_sdt=0,36",3,2019
Volumetric Correspondence Networks for Optical Flow,183,neurips,23,5,2023-06-15 23:44:16.258000,https://github.com/gengshan-y/VCN,147,Volumetric correspondence networks for optical flow,"https://scholar.google.com/scholar?cluster=16527531324179353765&hl=en&as_sdt=0,10",6,2019
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks,228,neurips,3,0,2023-06-15 23:44:16.441000,https://github.com/cwein3/large-lr-code,6,Towards explaining the regularization effect of initial large learning rate in training neural networks,"https://scholar.google.com/scholar?cluster=6617722188304549370&hl=en&as_sdt=0,36",2,2019
Multi-marginal Wasserstein GAN,76,neurips,10,0,2023-06-15 23:44:16.623000,https://github.com/caojiezhang/MWGAN,51,Multi-marginal wasserstein gan,"https://scholar.google.com/scholar?cluster=10067080185740979237&hl=en&as_sdt=0,33",1,2019
"PyTorch: An Imperative Style, High-Performance Deep Learning Library",28946,neurips,18601,12172,2023-06-15 23:44:16.806000,https://github.com/pytorch/pytorch,67867,"Pytorch: An imperative style, high-performance deep learning library","https://scholar.google.com/scholar?cluster=3528934790668989119&hl=en&as_sdt=0,5",1649,2019
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons,15,neurips,0,0,2023-06-15 23:44:16.989000,https://github.com/WenboRen/ranking-from-noisy-comparisons,2,On sample complexity upper and lower bounds for exact ranking from noisy comparisons,"https://scholar.google.com/scholar?cluster=17371903028090691021&hl=en&as_sdt=0,41",2,2019
NAT: Neural Architecture Transformer for Accurate and Compact Architectures,77,neurips,12,0,2023-06-15 23:44:17.171000,https://github.com/guoyongcs/NAT,57,Nat: Neural architecture transformer for accurate and compact architectures,"https://scholar.google.com/scholar?cluster=2412256637570332418&hl=en&as_sdt=0,5",3,2019
Learning to Self-Train for Semi-Supervised Few-Shot Classification,262,neurips,11,9,2023-06-15 23:44:17.354000,https://github.com/xinzheli1217/learning-to-self-train,89,Learning to self-train for semi-supervised few-shot classification,"https://scholar.google.com/scholar?cluster=7879404109068143287&hl=en&as_sdt=0,5",8,2019
Stochastic Frank-Wolfe for Composite Convex Minimization,20,neurips,2,0,2023-06-15 23:44:17.545000,https://github.com/alpyurtsever/SHCGM,2,Stochastic Frank-Wolfe for composite convex minimization,"https://scholar.google.com/scholar?cluster=9717935113633697368&hl=en&as_sdt=0,33",1,2019
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes,8,neurips,1,0,2023-06-15 23:44:17.728000,https://github.com/modestbayes/LFGP_NeurIPS,4,Modeling dynamic functional connectivity with latent factor Gaussian processes,"https://scholar.google.com/scholar?cluster=14525732227397762230&hl=en&as_sdt=0,33",4,2019
ETNet: Error Transition Network for Arbitrary Style Transfer,20,neurips,5,2,2023-06-15 23:44:17.911000,https://github.com/zhijieW94/ETNet,77,Etnet: Error transition network for arbitrary style transfer,"https://scholar.google.com/scholar?cluster=11291490385512424160&hl=en&as_sdt=0,33",8,2019
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model,35,neurips,14,1,2023-06-15 23:44:18.094000,https://github.com/microsoft/Icebreaker,42,Icebreaker: Element-wise efficient information acquisition with a bayesian deep latent gaussian model,"https://scholar.google.com/scholar?cluster=1836550825324169638&hl=en&as_sdt=0,5",8,2019
Post training 4-bit quantization of convolutional networks for rapid-deployment,427,neurips,57,13,2023-06-15 23:44:18.276000,https://github.com/submission2019/cnn-quantization,210,Post training 4-bit quantization of convolutional networks for rapid-deployment,"https://scholar.google.com/scholar?cluster=4498286641114478762&hl=en&as_sdt=0,36",8,2019
Implicit Regularization in Deep Matrix Factorization,359,neurips,12,1,2023-06-15 23:44:18.460000,https://github.com/roosephu/deep_matrix_factorization,29,Implicit regularization in deep matrix factorization,"https://scholar.google.com/scholar?cluster=10227179810482169638&hl=en&as_sdt=0,47",3,2019
Limitations of Lazy Training of Two-layers Neural Network,110,neurips,0,0,2023-06-15 23:44:18.643000,https://github.com/bGhorbani/Lazy-Training-Neural-Nets,1,Limitations of lazy training of two-layers neural network,"https://scholar.google.com/scholar?cluster=6757542555979455345&hl=en&as_sdt=0,14",3,2019
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning,39,neurips,0,0,2023-06-15 23:44:18.825000,https://github.com/fmaxgarcia/Meta-MDP,8,A meta-MDP approach to exploration for lifelong reinforcement learning,"https://scholar.google.com/scholar?cluster=12587192655885814669&hl=en&as_sdt=0,5",3,2019
Learning by Abstraction: The Neural State Machine,223,neurips,124,15,2023-06-15 23:44:19.008000,https://github.com/stanfordnlp/mac-network,482,Learning by abstraction: The neural state machine,"https://scholar.google.com/scholar?cluster=7361406080192630148&hl=en&as_sdt=0,6",32,2019
Unified Language Model Pre-training for Natural Language Understanding and Generation,1224,neurips,1867,362,2023-06-15 23:44:19.190000,https://github.com/microsoft/unilm,12770,Unified language model pre-training for natural language understanding and generation,"https://scholar.google.com/scholar?cluster=2361521774652423867&hl=en&as_sdt=0,5",260,2019
Metric Learning for Adversarial Robustness,156,neurips,8,0,2023-06-15 23:44:19.372000,https://github.com/columbia/Metric_Learning_Adversarial_Robustness,48,Metric learning for adversarial robustness,"https://scholar.google.com/scholar?cluster=12602705747887433697&hl=en&as_sdt=0,37",9,2019
Fine-grained Optimization of Deep Neural Networks,2,neurips,0,0,2023-06-15 23:44:19.555000,https://github.com/meteozay/fg-sgd,1,Fine-grained optimization of deep neural networks,"https://scholar.google.com/scholar?cluster=17242393399395222917&hl=en&as_sdt=0,31",1,2019
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity,86,neurips,16,3,2023-06-15 23:44:19.737000,https://github.com/pathak22/modular-assemblies,106,Learning to control self-assembling morphologies: a study of generalization via modularity,"https://scholar.google.com/scholar?cluster=6230712298907925889&hl=en&as_sdt=0,39",7,2019
Alleviating Label Switching with Optimal Transport,5,neurips,1,0,2023-06-15 23:44:19.920000,https://github.com/pierremon/label-switching,1,Alleviating label switching with optimal transport,"https://scholar.google.com/scholar?cluster=1201213527784885312&hl=en&as_sdt=0,10",1,2019
Fisher Efficient Inference of Intractable Models,11,neurips,1,0,2023-06-15 23:44:20.103000,https://github.com/anewgithubname/Stein-Density-Ratio-Estimation,9,Fisher efficient inference of intractable models,"https://scholar.google.com/scholar?cluster=13168405321313545565&hl=en&as_sdt=0,44",3,2019
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction,21,neurips,1,0,2023-06-15 23:44:20.285000,https://github.com/knowzou/SRVR,6,Stochastic gradient Hamiltonian Monte Carlo methods with recursive variance reduction,"https://scholar.google.com/scholar?cluster=11585981262585149330&hl=en&as_sdt=0,21",2,2019
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction,22,neurips,2,2,2023-06-15 23:44:20.468000,https://github.com/ErikGartner/actor,11,Domes to drones: Self-supervised active triangulation for 3d human pose reconstruction,"https://scholar.google.com/scholar?cluster=592377778107181309&hl=en&as_sdt=0,5",3,2019
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits,94,neurips,1,0,2023-06-15 23:44:20.651000,https://github.com/eboursier/sic-mmab,3,SIC-MMAB: synchronisation involves communication in multiplayer multi-armed bandits,"https://scholar.google.com/scholar?cluster=15702315682738134157&hl=en&as_sdt=0,47",2,2019
A Step Toward Quantifying Independently Reproducible Machine Learning Research,92,neurips,5,0,2023-06-15 23:44:20.834000,https://github.com/EdwardRaff/Quantifying-Independently-Reproducible-ML,74,A step toward quantifying independently reproducible machine learning research,"https://scholar.google.com/scholar?cluster=3230939669723958133&hl=en&as_sdt=0,44",6,2019
Latent distance estimation for random geometric graphs,21,neurips,0,0,2023-06-15 23:44:21.017000,https://github.com/ErnestoArayaValdivia/NeurIPS_Code,0,Latent distance estimation for random geometric graphs,"https://scholar.google.com/scholar?cluster=2780885878815062825&hl=en&as_sdt=0,23",1,2019
On the Inductive Bias of Neural Tangent Kernels,205,neurips,3,1,2023-06-15 23:44:21.200000,https://github.com/albietz/ckn_kernel,13,On the inductive bias of neural tangent kernels,"https://scholar.google.com/scholar?cluster=4267008353441249556&hl=en&as_sdt=0,5",2,2019
Rethinking Kernel Methods for Node Representation Learning on Graphs,20,neurips,7,3,2023-06-15 23:44:21.383000,https://github.com/bluer555/KernelGCN,32,Rethinking kernel methods for node representation learning on graphs,"https://scholar.google.com/scholar?cluster=3909779312042974366&hl=en&as_sdt=0,5",4,2019
Input Similarity from the Neural Network Perspective,40,neurips,3,0,2023-06-15 23:44:21.580000,https://github.com/Lydorn/netsimilarity,26,Input similarity from the neural network perspective,"https://scholar.google.com/scholar?cluster=3029405318289332183&hl=en&as_sdt=0,5",3,2019
Transfer Learning via Minimizing the Performance Gap Between Domains,40,neurips,2,0,2023-06-15 23:44:21.762000,https://github.com/bwang-ml/gapBoost,7,Transfer learning via minimizing the performance gap between domains,"https://scholar.google.com/scholar?cluster=15708830539707170384&hl=en&as_sdt=0,44",3,2019
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning,103,neurips,3,0,2023-06-15 23:44:21.946000,https://github.com/thuml/Batch-Spectral-Shrinkage,21,Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning,"https://scholar.google.com/scholar?cluster=787372724003726768&hl=en&as_sdt=0,5",3,2019
Efficiently Learning Fourier Sparse Set Functions,14,neurips,0,0,2023-06-15 23:44:22.128000,https://github.com/andisheh94/Efficiently-Learning-Fourier-Sparse-Set-Functions,2,Efficiently learning Fourier sparse set functions,"https://scholar.google.com/scholar?cluster=1775522679298532934&hl=en&as_sdt=0,34",1,2019
Goal-conditioned Imitation Learning,151,neurips,9,5,2023-06-15 23:44:22.310000,https://github.com/dingyiming0427/goalgail,60,Goal-conditioned imitation learning,"https://scholar.google.com/scholar?cluster=9705309728838214557&hl=en&as_sdt=0,5",3,2019
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing,13,neurips,3,0,2023-06-15 23:44:22.493000,https://github.com/flodinl/neurips-1bCS,0,Superset technique for approximate recovery in one-bit compressed sensing,"https://scholar.google.com/scholar?cluster=5088393971521119646&hl=en&as_sdt=0,31",1,2019
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance,51,neurips,0,0,2023-06-15 23:44:22.676000,https://github.com/kimiandj/min_swe,2,Asymptotic guarantees for learning generative models with the sliced-Wasserstein distance,"https://scholar.google.com/scholar?cluster=10949056232611348982&hl=en&as_sdt=0,34",2,2019
Learning Nonsymmetric Determinantal Point Processes,41,neurips,5,6,2023-06-15 23:44:22.859000,https://github.com/cgartrel/nonsymmetric-DPP-learning,20,Learning nonsymmetric determinantal point processes,"https://scholar.google.com/scholar?cluster=958215336299287859&hl=en&as_sdt=0,5",3,2019
Quantum Embedding of Knowledge for Reasoning,35,neurips,11,4,2023-06-15 23:44:23.042000,https://github.com/IBM/e2r,22,Quantum embedding of knowledge for reasoning,"https://scholar.google.com/scholar?cluster=11321153699952712196&hl=en&as_sdt=0,33",10,2019
Online Normalization for Training Neural Networks,40,neurips,19,2,2023-06-15 23:44:23.224000,https://github.com/cerebras/online-normalization,74,Online normalization for training neural networks,"https://scholar.google.com/scholar?cluster=2495221729297962361&hl=en&as_sdt=0,5",7,2019
Equitable Stable Matchings in Quadratic Time,8,neurips,2,0,2023-06-15 23:44:23.407000,https://github.com/ntzia/stable-marriage,1,Equitable stable matchings in quadratic time,"https://scholar.google.com/scholar?cluster=5357034451332937688&hl=en&as_sdt=0,34",2,2019
Making AI Forget You: Data Deletion in Machine Learning,209,neurips,4,1,2023-06-15 23:44:23.590000,https://github.com/tginart/deletion-efficient-kmeans,22,Making ai forget you: Data deletion in machine learning,"https://scholar.google.com/scholar?cluster=11624023015366681673&hl=en&as_sdt=0,5",4,2019
A New Defense Against Adversarial Images: Turning a Weakness into a Strength,103,neurips,10,0,2023-06-15 23:44:23.773000,https://github.com/s-huu/TurningWeaknessIntoStrength,36,A new defense against adversarial images: Turning a weakness into a strength,"https://scholar.google.com/scholar?cluster=11699672055738649895&hl=en&as_sdt=0,47",5,2019
Divergence-Augmented Policy Optimization,9,neurips,4,0,2023-06-15 23:44:23.955000,https://github.com/lns/dapo,36,Divergence-augmented policy optimization,"https://scholar.google.com/scholar?cluster=6823081176814326206&hl=en&as_sdt=0,33",3,2019
Gaussian-Based Pooling for Convolutional Neural Networks,12,neurips,6,3,2023-06-15 23:44:24.138000,https://github.com/tk1980/GaussianPooling,29,Gaussian-based pooling for convolutional neural networks,"https://scholar.google.com/scholar?cluster=2033748482757846351&hl=en&as_sdt=0,5",4,2019
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks,140,neurips,8,1,2023-06-15 23:44:24.320000,https://github.com/PwnerHarry/Stronger_GCN,50,Break the ceiling: Stronger multi-scale deep graph convolutional networks,"https://scholar.google.com/scholar?cluster=12919950599365272311&hl=en&as_sdt=0,7",8,2019
Bayesian Optimization with Unknown Search Space,27,neurips,2,0,2023-06-15 23:44:24.505000,https://github.com/HuongHa12/BO_unknown_searchspace,7,Bayesian optimization with unknown search space,"https://scholar.google.com/scholar?cluster=201571103459263814&hl=en&as_sdt=0,5",4,2019
Towards closing the gap between the theory and practice of SVRG,18,neurips,9,0,2023-06-15 23:44:24.688000,https://github.com/gowerrobert/StochOpt.jl,15,Towards closing the gap between the theory and practice of SVRG,"https://scholar.google.com/scholar?cluster=9351168021961753833&hl=en&as_sdt=0,5",2,2019
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening,44,neurips,3,1,2023-06-15 23:44:24.871000,https://github.com/Gecia/A-Unifying-Framework-for-Spectrum-Preserving-Graph-Sparsification-and-Coarsening,12,A unifying framework for spectrum-preserving graph sparsification and coarsening,"https://scholar.google.com/scholar?cluster=9934336085644961398&hl=en&as_sdt=0,47",2,2019
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks,67,neurips,4,1,2023-06-15 23:44:25.054000,https://github.com/Gunjan108/robust-ecoc,7,Error correcting output codes improve probability estimation and adversarial robustness of deep neural networks,"https://scholar.google.com/scholar?cluster=1032446707741176052&hl=en&as_sdt=0,23",4,2019
KerGM: Kernelized Graph Matching,35,neurips,2,2,2023-06-15 23:44:25.237000,https://github.com/ZhenZhang19920330/KerGM_Code,5,Kergm: Kernelized graph matching,"https://scholar.google.com/scholar?cluster=15872566588066107290&hl=en&as_sdt=0,33",1,2019
Robustness Verification of Tree-based Models,66,neurips,6,1,2023-06-15 23:44:25.419000,https://github.com/chenhongge/treeVerification,21,Robustness verification of tree-based models,"https://scholar.google.com/scholar?cluster=17206407102957374936&hl=en&as_sdt=0,5",2,2019
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition,73,neurips,18,9,2023-06-15 23:44:25.601000,https://github.com/nxsEdson/MLCR,62,Multi-label co-regularization for semi-supervised facial action unit recognition,"https://scholar.google.com/scholar?cluster=3209132207260506033&hl=en&as_sdt=0,33",4,2019
A Primal Dual Formulation For Deep Learning With Constraints,73,neurips,9,1,2023-06-15 23:44:25.784000,https://github.com/dair-iitd/dl-with-constraints,20,A primal dual formulation for deep learning with constraints,"https://scholar.google.com/scholar?cluster=4452120867924401058&hl=en&as_sdt=0,7",5,2019
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections,234,neurips,7320,1025,2023-06-15 23:44:25.967000,https://github.com/google-research/google-research,29776,Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections,"https://scholar.google.com/scholar?cluster=6244624580827104740&hl=en&as_sdt=0,14",727,2019
Intrinsic dimension of data representations in deep neural networks,158,neurips,11,5,2023-06-15 23:44:26.150000,https://github.com/ansuini/IntrinsicDimDeep,62,Intrinsic dimension of data representations in deep neural networks,"https://scholar.google.com/scholar?cluster=16544809050270363310&hl=en&as_sdt=0,32",4,2019
Program Synthesis and Semantic Parsing with Learned Code Idioms,61,neurips,13,7,2023-06-15 23:44:26.333000,https://github.com/rshin/seq2struct,22,Program synthesis and semantic parsing with learned code idioms,"https://scholar.google.com/scholar?cluster=15175412131165659282&hl=en&as_sdt=0,5",7,2019
Data-driven Estimation of Sinusoid Frequencies,37,neurips,18,0,2023-06-15 23:44:26.516000,https://github.com/sreyas-mohan/DeepFreq,33,Data-driven estimation of sinusoid frequencies,"https://scholar.google.com/scholar?cluster=11417596009919386835&hl=en&as_sdt=0,34",7,2019
Discovering Neural Wirings,89,neurips,18,5,2023-06-15 23:44:26.699000,https://github.com/allenai/dnw,139,Discovering neural wirings,"https://scholar.google.com/scholar?cluster=10495394185221457754&hl=en&as_sdt=0,5",8,2019
Fixing the train-test resolution discrepancy,486,neurips,158,5,2023-06-15 23:44:26.882000,https://github.com/facebookresearch/FixRes,999,Fixing the train-test resolution discrepancy,"https://scholar.google.com/scholar?cluster=13066354109775166927&hl=en&as_sdt=0,34",25,2019
Quadratic Video Interpolation,147,neurips,8,1,2023-06-15 23:44:27.070000,https://github.com/xuxy09/QVI,35,Quadratic video interpolation,"https://scholar.google.com/scholar?cluster=6119382903996871763&hl=en&as_sdt=0,31",1,2019
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game,57,neurips,6,3,2023-06-15 23:44:27.253000,https://github.com/tntrung/msgan,35,Self-supervised gan: Analysis and improvement with multi-class minimax game,"https://scholar.google.com/scholar?cluster=11060271873858333561&hl=en&as_sdt=0,5",6,2019
Learning step sizes for unfolded sparse coding,49,neurips,2,0,2023-06-15 23:44:27.437000,https://github.com/tomMoral/adopty,17,Learning step sizes for unfolded sparse coding,"https://scholar.google.com/scholar?cluster=1728368642998167227&hl=en&as_sdt=0,5",2,2019
Efficient Graph Generation with Graph Recurrent Attention Networks,243,neurips,88,10,2023-06-15 23:44:27.620000,https://github.com/lrjconan/GRAN,412,Efficient graph generation with graph recurrent attention networks,"https://scholar.google.com/scholar?cluster=12112068708355431361&hl=en&as_sdt=0,48",10,2019
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds,243,neurips,86,53,2023-06-15 23:44:27.802000,https://github.com/Yang7879/3D-BoNet,364,Learning object bounding boxes for 3D instance segmentation on point clouds,"https://scholar.google.com/scholar?cluster=7864241319192905246&hl=en&as_sdt=0,5",11,2019
Re-examination of the Role of Latent Variables in Sequence Modeling,6,neurips,1,0,2023-06-15 23:44:27.986000,https://github.com/zihangdai/reexamine-srnn,11,Re-examination of the role of latent variables in sequence modeling,"https://scholar.google.com/scholar?cluster=10487801182247175380&hl=en&as_sdt=0,5",5,2019
Consistency-based Semi-supervised Learning for Object detection,280,neurips,2,0,2023-06-15 23:44:28.169000,https://github.com/soo89/CSD-RFCN,31,Consistency-based semi-supervised learning for object detection,"https://scholar.google.com/scholar?cluster=920250286184474856&hl=en&as_sdt=0,5",1,2019
Exact Combinatorial Optimization with Graph Convolutional Neural Networks,310,neurips,81,8,2023-06-15 23:44:28.352000,https://github.com/ds4dm/learn2branch,291,Exact combinatorial optimization with graph convolutional neural networks,"https://scholar.google.com/scholar?cluster=7938246232558949476&hl=en&as_sdt=0,31",15,2019
"Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge",90,neurips,0,3,2023-06-15 23:44:28.535000,https://github.com/qiaott/LeicaGAN,35,"Learn, imagine and create: Text-to-image generation from prior knowledge","https://scholar.google.com/scholar?cluster=9101617990451691231&hl=en&as_sdt=0,33",1,2019
Compiler Auto-Vectorization with Imitation Learning,36,neurips,0,1,2023-06-15 23:44:28.718000,https://github.com/ithemal/vemal,0,Compiler auto-vectorization with imitation learning,"https://scholar.google.com/scholar?cluster=638281280745878872&hl=en&as_sdt=0,5",7,2019
"Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations",233,neurips,4,0,2023-06-15 23:44:28.901000,https://github.com/karakusc/horovod,2,"Qsparse-local-SGD: Distributed SGD with quantization, sparsification and local computations","https://scholar.google.com/scholar?cluster=16825277147142982104&hl=en&as_sdt=0,11",1,2019
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data,8,neurips,0,0,2023-06-15 23:44:29.084000,https://github.com/cunningham-lab/drs,1,Deep random splines for point process intensity estimation of neural population data,"https://scholar.google.com/scholar?cluster=14731396798621089484&hl=en&as_sdt=0,5",4,2019
Fast Decomposable Submodular Function Minimization using Constrained Total Variation,2,neurips,0,0,2023-06-15 23:44:29.268000,https://github.com/seshkumar/FasterSFMCode,0,Fast decomposable submodular function minimization using constrained total variation,"https://scholar.google.com/scholar?cluster=15441163684816125674&hl=en&as_sdt=0,33",2,2019
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies,52,neurips,2,0,2023-06-15 23:44:29.450000,https://github.com/BaoWangMath/EnResNet,19,Resnets ensemble via the feynman-kac formalism to improve natural and robust accuracies,"https://scholar.google.com/scholar?cluster=12953232414825992406&hl=en&as_sdt=0,5",2,2019
Learning elementary structures for 3D shape generation and matching,148,neurips,17,8,2023-06-15 23:44:29.632000,https://github.com/TheoDEPRELLE/AtlasNetV2,127,Learning elementary structures for 3d shape generation and matching,"https://scholar.google.com/scholar?cluster=2109791820647305873&hl=en&as_sdt=0,5",6,2019
Cross-Modal Learning with Adversarial Samples,26,neurips,1,0,2023-06-15 23:44:29.816000,https://github.com/ChaoLi1991/CMLA,8,Cross-modal learning with adversarial samples,"https://scholar.google.com/scholar?cluster=5714855065686823334&hl=en&as_sdt=0,5",3,2019
Learning Disentangled Representation for Robust Person Re-identification,67,neurips,20,1,2023-06-15 23:44:29.999000,https://github.com/cvlab-yonsei/projects,98,Learning disentangled representation for robust person re-identification,"https://scholar.google.com/scholar?cluster=7569542638892703397&hl=en&as_sdt=0,41",9,2019
Learning Deterministic Weighted Automata with Queries and Counterexamples,40,neurips,5,0,2023-06-15 23:44:30.182000,https://github.com/tech-srl/weighted_lstar,14,Learning deterministic weighted automata with queries and counterexamples,"https://scholar.google.com/scholar?cluster=4739515655099675842&hl=en&as_sdt=0,22",7,2019
Making the Cut: A Bandit-based Approach to Tiered Interviewing,9,neurips,1,0,2023-06-15 23:44:30.364000,https://github.com/principledhiring/TieredHiring,1,Making the cut: A bandit-based approach to tiered interviewing,"https://scholar.google.com/scholar?cluster=14271111588210565240&hl=en&as_sdt=0,5",2,2019
Manifold-regression to predict from MEG/EEG brain signals without source modeling,42,neurips,5,1,2023-06-15 23:44:30.547000,https://github.com/DavidSabbagh/NeurIPS19_manifold-regression-meeg,7,Manifold-regression to predict from MEG/EEG brain signals without source modeling,"https://scholar.google.com/scholar?cluster=2440641167368670167&hl=en&as_sdt=0,5",5,2019
Reflection Separation using a Pair of Unpolarized and Polarized Images,38,neurips,0,0,2023-06-15 23:44:30.730000,https://github.com/YouweiLyu/reflection_separation_with_un-polarized_images,26,Reflection separation using a pair of unpolarized and polarized images,"https://scholar.google.com/scholar?cluster=9587343905338547609&hl=en&as_sdt=0,50",3,2019
Co-Generation with GANs using AIS based HMC,3,neurips,1,0,2023-06-15 23:44:30.913000,https://github.com/AilsaF/cogen_by_ais,4,Co-Generation with GANs using AIS based HMC,"https://scholar.google.com/scholar?cluster=17978444032860345049&hl=en&as_sdt=0,5",5,2019
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds,22,neurips,1,0,2023-06-15 23:44:31.095000,https://github.com/CausalML/interventions-disparate-impact-responders,11,Assessing disparate impact of personalized interventions: identifiability and bounds,"https://scholar.google.com/scholar?cluster=11034607316544592064&hl=en&as_sdt=0,44",3,2019
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution,105,neurips,19,8,2023-06-15 23:44:31.278000,https://github.com/thangvubk/Cascade-RPN,178,Cascade rpn: Delving into high-quality region proposal network with adaptive convolution,"https://scholar.google.com/scholar?cluster=15521446702742963426&hl=en&as_sdt=0,5",12,2019
Variational Bayesian Optimal Experimental Design,87,neurips,4,4,2023-06-15 23:44:31.461000,https://github.com/ae-foster/pyro,8,Variational Bayesian optimal experimental design,"https://scholar.google.com/scholar?cluster=15336498043401775880&hl=en&as_sdt=0,33",6,2019
Flexible Modeling of Diversity with Strongly Log-Concave Distributions,11,neurips,0,0,2023-06-15 23:44:31.644000,https://github.com/joshr17/slc_sampling,2,Flexible modeling of diversity with strongly log-concave distributions,"https://scholar.google.com/scholar?cluster=6837075722751509721&hl=en&as_sdt=0,47",1,2019
Neural Machine Translation with Soft Prototype,16,neurips,4,1,2023-06-15 23:44:31.827000,https://github.com/ywang07/nmt_soft_prototype,8,Neural machine translation with soft prototype,"https://scholar.google.com/scholar?cluster=10440836540964517084&hl=en&as_sdt=0,5",1,2019
Doubly-Robust Lasso Bandit,49,neurips,0,1,2023-06-15 23:44:32.009000,https://github.com/gisoo1989/Doubly-Robust-Lasso-Bandit,6,Doubly-robust lasso bandit,"https://scholar.google.com/scholar?cluster=9511761143101036812&hl=en&as_sdt=0,5",2,2019
"Ask not what AI can do, but what AI should do: Towards a framework of task delegability",46,neurips,2,0,2023-06-15 23:44:32.192000,https://github.com/delegability/data,6,"Ask not what AI can do, but what AI should do: Towards a framework of task delegability","https://scholar.google.com/scholar?cluster=7940339125070572458&hl=en&as_sdt=0,10",0,2019
Offline Contextual Bandits with High Probability Fairness Guarantees,41,neurips,5,0,2023-06-15 23:44:32.374000,https://github.com/sgiguere/RobinHood-NeurIPS-2019,9,Offline contextual bandits with high probability fairness guarantees,"https://scholar.google.com/scholar?cluster=16399125850375524530&hl=en&as_sdt=0,5",1,2019
LCA: Loss Change Allocation for Neural Network Training,23,neurips,15,2,2023-06-15 23:44:32.557000,https://github.com/uber-research/loss-change-allocation,60,Lca: Loss change allocation for neural network training,"https://scholar.google.com/scholar?cluster=7222081194296604811&hl=en&as_sdt=0,5",7,2019
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians,21,neurips,5,0,2023-06-15 23:44:32.739000,https://github.com/BBVA/UMAL,13,Modelling heterogeneous distributions with an uncountable mixture of asymmetric laplacians,"https://scholar.google.com/scholar?cluster=15284501616973353353&hl=en&as_sdt=0,32",7,2019
GNNExplainer: Generating Explanations for Graph Neural Networks,679,neurips,154,26,2023-06-15 23:44:32.922000,https://github.com/RexYing/gnn-model-explainer,696,Gnnexplainer: Generating explanations for graph neural networks,"https://scholar.google.com/scholar?cluster=3833160255595095003&hl=en&as_sdt=0,5",20,2019
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption,48,neurips,1,0,2023-06-15 23:44:33.111000,https://github.com/georgehc/mnar_mc,11,Missing not at random in matrix completion: The effectiveness of estimating missingness probabilities under a low nuclear norm assumption,"https://scholar.google.com/scholar?cluster=17667220203657585647&hl=en&as_sdt=0,31",1,2019
Unsupervised learning of object structure and dynamics from videos,115,neurips,7320,1025,2023-06-15 23:44:33.294000,https://github.com/google-research/google-research,29776,Unsupervised learning of object structure and dynamics from videos,"https://scholar.google.com/scholar?cluster=988438959666734889&hl=en&as_sdt=0,5",727,2019
Cross-channel Communication Networks,24,neurips,3,2,2023-06-15 23:44:33.476000,https://github.com/jwyang/C3net,41,Cross-channel communication networks,"https://scholar.google.com/scholar?cluster=11079957716276939436&hl=en&as_sdt=0,5",5,2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training,210,neurips,11,1,2023-06-15 23:44:33.659000,https://github.com/Haichao-Zhang/FeatureScatter,68,Defense against adversarial attacks using feature scattering-based adversarial training,"https://scholar.google.com/scholar?cluster=16771461599702512011&hl=en&as_sdt=0,14",3,2019
Differentiable Ranking and Sorting using Optimal Transport,120,neurips,7320,1025,2023-06-15 23:44:33.841000,https://github.com/google-research/google-research,29776,Differentiable ranking and sorting using optimal transport,"https://scholar.google.com/scholar?cluster=18275340937640599555&hl=en&as_sdt=0,5",727,2019
Ordered Memory,18,neurips,8,1,2023-06-15 23:44:34.024000,https://github.com/yikangshen/Ordered-Memory,27,Ordered memory,"https://scholar.google.com/scholar?cluster=4062085826792189610&hl=en&as_sdt=0,16",4,2019
Initialization of ReLUs for Dynamical Isometry,19,neurips,0,0,2023-06-15 23:44:34.208000,https://github.com/alinadubatovka/information_propagation,0,Initialization of relus for dynamical isometry,"https://scholar.google.com/scholar?cluster=13000692432916595956&hl=en&as_sdt=0,5",1,2019
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset,99,neurips,2,2,2023-06-15 23:44:34.393000,https://github.com/rr-learning/disentanglement_dataset,68,On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset,"https://scholar.google.com/scholar?cluster=17192262335599006209&hl=en&as_sdt=0,5",7,2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization,200,neurips,25,0,2023-06-15 23:44:34.586000,https://github.com/epfml/powersgd,111,PowerSGD: Practical low-rank gradient compression for distributed optimization,"https://scholar.google.com/scholar?cluster=7612273195837769494&hl=en&as_sdt=0,26",10,2019
CNN^{2}: Viewpoint Generalization via a Binocular Vision,4,neurips,2,1,2023-06-15 23:44:34.768000,https://github.com/wdchenxyz/CNN2,11,CNN^{2}: Viewpoint Generalization via a Binocular Vision,"https://scholar.google.com/scholar?cluster=2893562313504828451&hl=en&as_sdt=0,5",3,2019
Unsupervised Learning of Object Keypoints for Perception and Control,155,neurips,2436,170,2023-06-15 23:44:34.951000,https://github.com/deepmind/deepmind-research,11902,Unsupervised learning of object keypoints for perception and control,"https://scholar.google.com/scholar?cluster=8034421720358293108&hl=en&as_sdt=0,5",336,2019
The Functional Neural Process,436,neurips,9,1,2023-06-15 23:44:35.134000,https://github.com/AMLab-Amsterdam/FNP,44,Neural processes,"https://scholar.google.com/scholar?cluster=10874092191257343841&hl=en&as_sdt=0,5",5,2019
Convergent Policy Optimization for Safe Reinforcement Learning,83,neurips,3,0,2023-06-15 23:44:35.317000,https://github.com/ming93/Safe_reinforcement_learning,10,Convergent policy optimization for safe reinforcement learning,"https://scholar.google.com/scholar?cluster=72038626648458848&hl=en&as_sdt=0,5",1,2019
Diffeomorphic Temporal Alignment Nets,25,neurips,13,1,2023-06-15 23:44:35.500000,https://github.com/BGU-CS-VIL/dtan,63,Diffeomorphic temporal alignment nets,"https://scholar.google.com/scholar?cluster=7364466321504417673&hl=en&as_sdt=0,5",7,2019
Multi-source Domain Adaptation for Semantic Segmentation,149,neurips,26,6,2023-06-15 23:44:35.683000,https://github.com/Luodian/MADAN,159,Multi-source domain adaptation for semantic segmentation,"https://scholar.google.com/scholar?cluster=3870807745428440093&hl=en&as_sdt=0,43",9,2019
Spectral Modification of Graphs for Improved Spectral Clustering,5,neurips,1,0,2023-06-15 23:44:35.866000,https://github.com/ikoutis/spectral-modification,2,Spectral modification of graphs for improved spectral clustering,"https://scholar.google.com/scholar?cluster=16104250235411376820&hl=en&as_sdt=0,10",2,2019
On Exact Computation with an Infinitely Wide Neural Net,705,neurips,11,0,2023-06-15 23:44:36.050000,https://github.com/ruosongwang/CNTK,106,On exact computation with an infinitely wide neural net,"https://scholar.google.com/scholar?cluster=9266929152941012357&hl=en&as_sdt=0,36",5,2019
Amortized Bethe Free Energy Minimization for Learning MRFs,14,neurips,2,2,2023-06-15 23:44:36.233000,https://github.com/swiseman/bethe-min,7,Amortized bethe free energy minimization for learning mrfs,"https://scholar.google.com/scholar?cluster=6385669697520989717&hl=en&as_sdt=0,5",4,2019
XLNet: Generalized Autoregressive Pretraining for Language Understanding,7092,neurips,1181,189,2023-06-15 23:44:36.416000,https://github.com/zihangdai/xlnet,6046,Xlnet: Generalized autoregressive pretraining for language understanding,"https://scholar.google.com/scholar?cluster=14487406216105917109&hl=en&as_sdt=0,5",172,2019
Conditional Independence Testing using Generative Adversarial Networks,33,neurips,6,0,2023-06-15 23:44:36.599000,https://github.com/alexisbellot/GCIT,10,Conditional independence testing using generative adversarial networks,"https://scholar.google.com/scholar?cluster=1653461036135291846&hl=en&as_sdt=0,22",2,2019
A Tensorized Transformer for Language Modeling,123,neurips,13,2,2023-06-15 23:44:36.781000,https://github.com/szhangtju/The-compression-of-Transformer,52,A tensorized transformer for language modeling,"https://scholar.google.com/scholar?cluster=10172100217073548450&hl=en&as_sdt=0,5",2,2019
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components,27,neurips,3,0,2023-06-15 23:44:36.963000,https://github.com/saralajew/cbc_networks,12,Classification-by-components: Probabilistic modeling of reasoning over a set of components,"https://scholar.google.com/scholar?cluster=12691103404451941071&hl=en&as_sdt=0,5",4,2019
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs,27,neurips,5,0,2023-06-15 23:44:37.146000,https://github.com/RonanFR/UCRL,25,Exploration bonus for regret minimization in discrete and continuous average reward mdps,"https://scholar.google.com/scholar?cluster=18182504260715903341&hl=en&as_sdt=0,11",5,2019
A neurally plausible model learns successor representations in partially observable environments,35,neurips,0,0,2023-06-15 23:44:37.329000,https://github.com/evertes/distributional_SF,7,A neurally plausible model learns successor representations in partially observable environments,"https://scholar.google.com/scholar?cluster=12108010444355822428&hl=en&as_sdt=0,5",1,2019
Cost Effective Active Search,137,neurips,3,0,2023-06-15 23:44:37.512000,https://github.com/shalijiang/efficient_nonmyopic_active_search,7,Diagnosing the search cost effect: Waiting time and the moderating impact of prior category knowledge,"https://scholar.google.com/scholar?cluster=707526541184886734&hl=en&as_sdt=0,11",2,2019
Inherent Weight Normalization in Stochastic Neural Networks,5,neurips,1,1,2023-06-15 23:44:37.694000,https://github.com/nmi-lab/neural_sampling_machines,10,Inherent weight normalization in stochastic neural networks,"https://scholar.google.com/scholar?cluster=17503775532323120531&hl=en&as_sdt=0,7",3,2019
Discrete Flows: Invertible Generative Models of Discrete Data,94,neurips,79,73,2023-06-15 23:44:37.878000,https://github.com/google/edward2,644,Discrete flows: Invertible generative models of discrete data,"https://scholar.google.com/scholar?cluster=2184666710327025867&hl=en&as_sdt=0,14",20,2019
Disentangled behavioural representations,19,neurips,3,6,2023-06-15 23:44:38.060000,https://github.com/adezfouli/rnn_hypercoder,3,Disentangled behavioural representations,"https://scholar.google.com/scholar?cluster=17586763103868560368&hl=en&as_sdt=0,5",2,2019
A Flexible Generative Framework for Graph-based Semi-supervised Learning,59,neurips,6,0,2023-06-15 23:44:38.243000,https://github.com/jiaqima/G3NN,15,A flexible generative framework for graph-based semi-supervised learning,"https://scholar.google.com/scholar?cluster=4643725798321580132&hl=en&as_sdt=0,36",4,2019
Online-Within-Online Meta-Learning,51,neurips,0,0,2023-06-15 23:44:38.426000,https://github.com/dstamos/Adversarial-LTL,4,Online-within-online meta-learning,"https://scholar.google.com/scholar?cluster=1456705708848816283&hl=en&as_sdt=0,40",1,2019
"Adversarial Examples Are Not Bugs, They Are Features",1456,neurips,157,25,2023-06-15 23:44:38.608000,https://github.com/MadryLab/robustness,799,"Adversarial examples are not bugs, they are features","https://scholar.google.com/scholar?cluster=9505899875968209772&hl=en&as_sdt=0,5",17,2019
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion,21,neurips,4,1,2023-06-15 23:44:38.791000,https://github.com/choyingw/CFCNet,35,Deep rgb-d canonical correlation analysis for sparse depth completion,"https://scholar.google.com/scholar?cluster=3860279258380832246&hl=en&as_sdt=0,21",3,2019
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck,128,neurips,16,1,2023-06-15 23:44:38.974000,https://github.com/microsoft/IBAC-SNI,47,Generalization in reinforcement learning with selective noise injection and information bottleneck,"https://scholar.google.com/scholar?cluster=4207867939848358393&hl=en&as_sdt=0,33",5,2019
Untangling in Invariant Speech Recognition,13,neurips,19,3,2023-06-15 23:44:39.157000,https://github.com/schung039/neural_manifolds_replicaMFT,43,Untangling in invariant speech recognition,"https://scholar.google.com/scholar?cluster=17300601145385623429&hl=en&as_sdt=0,33",3,2019
Certifiable Robustness to Graph Perturbations,92,neurips,7,1,2023-06-15 23:44:39.340000,https://github.com/abojchevski/graph_cert,12,Certifiable robustness to graph perturbations,"https://scholar.google.com/scholar?cluster=1934279975175671629&hl=en&as_sdt=0,5",2,2019
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks,19,neurips,1,0,2023-06-15 23:44:39.522000,https://github.com/jdlafferty/surfing,2,Surfing: Iterative optimization over incrementally trained deep networks,"https://scholar.google.com/scholar?cluster=6290748342679623443&hl=en&as_sdt=0,5",1,2019
Rates of Convergence for Large-scale Nearest Neighbor Classification,13,neurips,0,0,2023-06-15 23:44:39.705000,https://github.com/duanjiexin/bigNN,0,Rates of convergence for large-scale nearest neighbor classification,"https://scholar.google.com/scholar?cluster=2730299591243322106&hl=en&as_sdt=0,5",1,2019
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning,81,neurips,0,0,2023-06-15 23:44:39.888000,https://github.com/harsh6gpt1/adaptivetwotimeRL,0,Finite-time performance bounds and adaptive learning rate selection for two time-scale reinforcement learning,"https://scholar.google.com/scholar?cluster=14282611510273442808&hl=en&as_sdt=0,14",2,2019
Pseudo-Extended Markov chain Monte Carlo,14,neurips,1,0,2023-06-15 23:44:40.071000,https://github.com/chris-nemeth/pseudo-extended-mcmc-code,3,Pseudo-extended Markov chain Monte Carlo,"https://scholar.google.com/scholar?cluster=13706683704662205943&hl=en&as_sdt=0,39",5,2019
Hierarchical Optimal Transport for Multimodal Distribution Alignment,50,neurips,2,2,2023-06-15 23:44:40.254000,https://github.com/nerdslab/HiWA,29,Hierarchical optimal transport for multimodal distribution alignment,"https://scholar.google.com/scholar?cluster=14796992753189496904&hl=en&as_sdt=0,5",5,2019
Self-Routing Capsule Networks,79,neurips,12,1,2023-06-15 23:44:40.437000,https://github.com/coder3000/SR-CapsNet,41,Self-routing capsule networks,"https://scholar.google.com/scholar?cluster=15113384032510098808&hl=en&as_sdt=0,34",3,2019
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation,68,neurips,10,0,2023-06-15 23:44:40.619000,https://github.com/JianGuanTHU/IRecGAN,46,A model-based reinforcement learning with adversarial training for online recommendation,"https://scholar.google.com/scholar?cluster=4137106166024493753&hl=en&as_sdt=0,5",3,2019
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation,194,neurips,2,0,2023-06-15 23:44:40.802000,https://github.com/vuoristo/MMAML,18,Multimodal model-agnostic meta-learning via task-aware modulation,"https://scholar.google.com/scholar?cluster=16689078801386417335&hl=en&as_sdt=0,5",5,2019
Predicting the Politics of an Image Using Webly Supervised Data,21,neurips,175,21,2023-06-15 23:44:40.984000,https://github.com/dragnet-org/dragnet,1142,Predicting the politics of an image using webly supervised data,"https://scholar.google.com/scholar?cluster=861412190222908721&hl=en&as_sdt=0,5",133,2019
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets,39,neurips,1,4,2023-06-15 23:44:41.167000,https://github.com/victorcampos7/weightnorm-init,12,How to initialize your network? robust initialization for weightnorm & resnets,"https://scholar.google.com/scholar?cluster=13685873871964389208&hl=en&as_sdt=0,16",2,2019
Code Generation as a Dual Task of Code Summarization,148,neurips,7,4,2023-06-15 23:44:41.349000,https://github.com/Bolin0215/CSCGDual,17,Code generation as a dual task of code summarization,"https://scholar.google.com/scholar?cluster=14746121163037489756&hl=en&as_sdt=0,34",1,2019
Gradient based sample selection for online continual learning,474,neurips,12,14,2023-06-15 23:44:41.532000,https://github.com/rahafaljundi/Gradient-based-Sample-Selection,60,Gradient based sample selection for online continual learning,"https://scholar.google.com/scholar?cluster=14210983833434346363&hl=en&as_sdt=0,22",5,2019
Conditional Structure Generation through Graph Variational Generative Adversarial Nets,84,neurips,12,5,2023-06-15 23:44:41.714000,https://github.com/KelestZ/CondGen,48,Conditional structure generation through graph variational generative adversarial nets,"https://scholar.google.com/scholar?cluster=11602636019851588443&hl=en&as_sdt=0,5",4,2019
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting,560,neurips,62,7,2023-06-15 23:44:41.896000,https://github.com/xjtushujun/meta-weight-net,253,Meta-weight-net: Learning an explicit mapping for sample weighting,"https://scholar.google.com/scholar?cluster=17581878029469126945&hl=en&as_sdt=0,5",7,2019
Thompson Sampling with Information Relaxation Penalties,7,neurips,0,0,2023-06-15 23:44:42.079000,https://github.com/mskyt88/info-relax-sampling,0,Thompson sampling with information relaxation penalties,"https://scholar.google.com/scholar?cluster=10829250718917489138&hl=en&as_sdt=0,5",1,2019
Constraint-based Causal Structure Learning with Consistent Separating Sets,15,neurips,0,0,2023-06-15 23:44:42.262000,https://github.com/honghaoli42/consistent_pcalg,0,Constraint-based causal structure learning with consistent separating sets,"https://scholar.google.com/scholar?cluster=7060466264779924892&hl=en&as_sdt=0,39",2,2019
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update,65,neurips,3,0,2023-06-15 23:44:42.445000,https://github.com/suyoung-lee/Episodic-Backward-Update,16,Sample-efficient deep reinforcement learning via episodic backward update,"https://scholar.google.com/scholar?cluster=4339423520544824474&hl=en&as_sdt=0,31",1,2019
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior,143,neurips,14,11,2023-06-15 23:44:42.628000,https://github.com/chengchunhsu/WSIS_BBTP,93,Weakly supervised instance segmentation using the bounding box tightness prior,"https://scholar.google.com/scholar?cluster=16279253940935119442&hl=en&as_sdt=0,5",9,2019
Copula-like Variational Inference,4,neurips,1,0,2023-06-15 23:44:42.810000,https://github.com/marcelah/copula-like-vi,2,Copula-like variational inference,"https://scholar.google.com/scholar?cluster=11673032824816999908&hl=en&as_sdt=0,33",1,2019
Towards Hardware-Aware Tractable Learning of Probabilistic Models,7,neurips,2,0,2023-06-15 23:44:42.998000,https://github.com/laurago894/HwAwareProb,5,Towards hardware-aware tractable learning of probabilistic models,"https://scholar.google.com/scholar?cluster=3228255888644610265&hl=en&as_sdt=0,5",3,2019
Incremental Few-Shot Learning with Attention Attractor Networks,167,neurips,27,8,2023-06-15 23:44:43.181000,https://github.com/renmengye/inc-few-shot-attractor-public,115,Incremental few-shot learning with attention attractor networks,"https://scholar.google.com/scholar?cluster=13601757233344695275&hl=en&as_sdt=0,5",8,2019
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations,54,neurips,2,0,2023-06-15 23:44:43.363000,https://github.com/JerryLingjieMei/ADEPT-Model-Release,18,Modeling expectation violation in intuitive physics with coarse probabilistic object representations,"https://scholar.google.com/scholar?cluster=13697103313826084802&hl=en&as_sdt=0,33",10,2019
Efficient Convex Relaxations for Streaming PCA,4,neurips,0,0,2023-06-15 23:44:43.546000,https://github.com/tmarino2/Streaming_PCA,1,Efficient convex relaxations for streaming PCA,"https://scholar.google.com/scholar?cluster=4848852433077315561&hl=en&as_sdt=0,11",2,2019
Deep Model Transferability from Attribution Maps,48,neurips,4,1,2023-06-15 23:44:43.729000,https://github.com/zju-vipa/TransferbilityFromAttributionMaps,19,Deep model transferability from attribution maps,"https://scholar.google.com/scholar?cluster=4823918589598291923&hl=en&as_sdt=0,29",5,2019
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging,8,neurips,2,1,2023-06-15 23:44:43.912000,https://github.com/imagingofthings/DeepWave,7,Deepwave: a recurrent neural-network for real-time acoustic imaging,"https://scholar.google.com/scholar?cluster=8909154303117580680&hl=en&as_sdt=0,33",3,2019
Meta Architecture Search,36,neurips,1,1,2023-06-15 23:44:44.095000,https://github.com/ashaw596/meta_architecture_search,21,Meta architecture search,"https://scholar.google.com/scholar?cluster=11889304968518770704&hl=en&as_sdt=0,14",3,2019
Graph Structured Prediction Energy Networks,11,neurips,1,2,2023-06-15 23:44:44.277000,https://github.com/cgraber/GSPEN,8,Graph structured prediction energy networks,"https://scholar.google.com/scholar?cluster=4956777384539332368&hl=en&as_sdt=0,5",3,2019
Universal Invariant and Equivariant Graph Neural Networks,216,neurips,4,1,2023-06-15 23:44:44.460000,https://github.com/nkeriven/univgnn,8,Universal invariant and equivariant graph neural networks,"https://scholar.google.com/scholar?cluster=9485621363684643376&hl=en&as_sdt=0,5",3,2019
PIDForest: Anomaly Detection via Partial Identification,20,neurips,6,11,2023-06-15 23:44:44.642000,https://github.com/vatsalsharan/pidforest,25,Pidforest: anomaly detection via partial identification,"https://scholar.google.com/scholar?cluster=16154054441639592175&hl=en&as_sdt=0,33",3,2019
Face Reconstruction from Voice using Generative Adversarial Networks,43,neurips,32,4,2023-06-15 23:44:44.824000,https://github.com/cmu-mlsp/reconstructing_faces_from_voices,171,Face reconstruction from voice using generative adversarial networks,"https://scholar.google.com/scholar?cluster=2028677097849623866&hl=en&as_sdt=0,15",13,2019
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning,28,neurips,10,1,2023-06-15 23:44:45.007000,https://github.com/microsoft/logrl,26,Using a logarithmic mapping to enable lower discount factors in reinforcement learning,"https://scholar.google.com/scholar?cluster=13664515477486389545&hl=en&as_sdt=0,5",8,2019
PRNet: Self-Supervised Learning for Partial-to-Partial Registration,263,neurips,27,7,2023-06-15 23:44:45.190000,https://github.com/WangYueFt/prnet,104,Prnet: Self-supervised learning for partial-to-partial registration,"https://scholar.google.com/scholar?cluster=2200668442123135001&hl=en&as_sdt=0,5",7,2019
Learning to Optimize in Swarms,46,neurips,10,0,2023-06-15 23:44:45.372000,https://github.com/Shen-Lab/LOIS,14,Learning to optimize in swarms,"https://scholar.google.com/scholar?cluster=14460959149503655029&hl=en&as_sdt=0,5",4,2019
A Little Is Enough: Circumventing Defenses For Distributed Learning,245,neurips,6,2,2023-06-15 23:44:45.555000,https://github.com/moranant/attacking_distributing_learning,19,A little is enough: Circumventing defenses for distributed learning,"https://scholar.google.com/scholar?cluster=5802076485972034054&hl=en&as_sdt=0,10",2,2019
Statistical Model Aggregation via Parameter Matching,28,neurips,5,0,2023-06-15 23:44:45.738000,https://github.com/IBM/SPAHM,6,Statistical model aggregation via parameter matching,"https://scholar.google.com/scholar?cluster=4576666574864292124&hl=en&as_sdt=0,19",10,2019
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees,1,neurips,1,0,2023-06-15 23:44:45.923000,https://github.com/Muhammad-Osama/uncertainty_spatial_point_process,0,Prediction of spatial point processes: regularized method with out-of-sample guarantees,"https://scholar.google.com/scholar?cluster=14802758782588566999&hl=en&as_sdt=0,33",0,2019
STREETS: A Novel Camera Network Dataset for Traffic Flow,22,neurips,3,5,2023-06-15 23:44:46.106000,https://github.com/corey-snyder/STREETS,28,Streets: A novel camera network dataset for traffic flow,"https://scholar.google.com/scholar?cluster=12192449479723633961&hl=en&as_sdt=0,5",3,2019
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions,54,neurips,1,0,2023-06-15 23:44:46.289000,https://github.com/cpempire/pSVN,2,Projected Stein variational Newton: A fast and scalable Bayesian inference method in high dimensions,"https://scholar.google.com/scholar?cluster=5374015985674763908&hl=en&as_sdt=0,5",1,2019
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction,54,neurips,0,0,2023-06-15 23:44:46.473000,https://github.com/ganguli-lab/deep-retina-reduction,2,From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction,"https://scholar.google.com/scholar?cluster=12641169667609562982&hl=en&as_sdt=0,5",16,2019
Abstract Reasoning with Distracting Features,55,neurips,4,1,2023-06-15 23:44:46.655000,https://github.com/zkcys001/distracting_feature,25,Abstract reasoning with distracting features,"https://scholar.google.com/scholar?cluster=12802844100612242645&hl=en&as_sdt=0,10",3,2019
Deep Scale-spaces: Equivariance Over Scale,126,neurips,5,1,2023-06-15 23:44:46.838000,https://github.com/deworrall92/deep-scale-spaces,21,Deep scale-spaces: Equivariance over scale,"https://scholar.google.com/scholar?cluster=5786613009740480936&hl=en&as_sdt=0,5",3,2019
Generalized Sliced Wasserstein Distances,202,neurips,11,0,2023-06-15 23:44:47.022000,https://github.com/kimiandj/gsw,31,Generalized sliced wasserstein distances,"https://scholar.google.com/scholar?cluster=16864660898326164591&hl=en&as_sdt=0,10",2,2019
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference,11,neurips,0,0,2023-06-15 23:44:47.205000,https://github.com/colehurwitz/vae_spike_localization,2,Scalable spike source localization in extracellular recordings using amortized variational inference,"https://scholar.google.com/scholar?cluster=7317962235981887067&hl=en&as_sdt=0,5",0,2019
A General Framework for Symmetric Property Estimation,10,neurips,0,0,2023-06-15 23:44:47.388000,https://github.com/shiragur/CodeForPseudoPML,0,A general framework for symmetric property estimation,"https://scholar.google.com/scholar?cluster=17182237778285852187&hl=en&as_sdt=0,5",2,2019
CondConv: Conditionally Parameterized Convolutions for Efficient Inference,384,neurips,1790,294,2023-06-15 23:44:47.596000,https://github.com/tensorflow/tpu,5127,Condconv: Conditionally parameterized convolutions for efficient inference,"https://scholar.google.com/scholar?cluster=12029837360807310242&hl=en&as_sdt=0,5",369,2019
Towards a Zero-One Law for Column Subset Selection,26,neurips,0,0,2023-06-15 23:44:47.779000,https://github.com/zpl7840/general_loss_column_subset_selection,0,Towards a zero-one law for column subset selection,"https://scholar.google.com/scholar?cluster=5184402617939346172&hl=en&as_sdt=0,5",1,2019
Nonzero-sum Adversarial Hypothesis Testing Games,13,neurips,0,0,2023-06-15 23:44:47.962000,https://github.com/sarath1789/ahtg_neurips2019,0,Nonzero-sum adversarial hypothesis testing games,"https://scholar.google.com/scholar?cluster=2106859842031488052&hl=en&as_sdt=0,6",1,2019
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks,143,neurips,5,2,2023-06-15 23:44:48.144000,https://github.com/DingXiaoH/GSM-SGD,40,Global sparse momentum sgd for pruning very deep neural networks,"https://scholar.google.com/scholar?cluster=17035988967323546960&hl=en&as_sdt=0,48",5,2019
Quantum Wasserstein Generative Adversarial Networks,66,neurips,10,1,2023-06-15 23:44:48.328000,https://github.com/yiminghwang/qWGAN,45,Quantum wasserstein generative adversarial networks,"https://scholar.google.com/scholar?cluster=13035971902912722342&hl=en&as_sdt=0,5",5,2019
Deep Learning without Weight Transport,105,neurips,4,1,2023-06-15 23:44:48.511000,https://github.com/makrout/Deep-Learning-without-Weight-Transport,30,Deep learning without weight transport,"https://scholar.google.com/scholar?cluster=16021016757478630175&hl=en&as_sdt=0,34",3,2019
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks,107,neurips,0,0,2023-06-15 23:44:48.693000,https://github.com/GauthierGidel/Implicit-Regularization-of-Discrete-Gradient-Dynamics-in-Linear-Neural-Networks,0,Implicit regularization of discrete gradient dynamics in linear neural networks,"https://scholar.google.com/scholar?cluster=3335747216116083173&hl=en&as_sdt=0,14",2,2019
Generative Models for Graph-Based Protein Design,271,neurips,46,7,2023-06-15 23:44:48.876000,https://github.com/jingraham/neurips19-graph-protein-design,186,Generative models for graph-based protein design,"https://scholar.google.com/scholar?cluster=8179315795887115217&hl=en&as_sdt=0,47",7,2019
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks,104,neurips,2,0,2023-06-15 23:44:49.060000,https://github.com/stonezwr/ST-RSBP,11,Spike-train level backpropagation for training deep recurrent spiking neural networks,"https://scholar.google.com/scholar?cluster=15180879194749277106&hl=en&as_sdt=0,33",1,2019
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity,31,neurips,1,10,2023-06-15 23:44:49.243000,https://github.com/ariaaay/NeuralTaskonomy,15,Neural taskonomy: Inferring the similarity of task-derived representations from brain activity,"https://scholar.google.com/scholar?cluster=2336960988937785366&hl=en&as_sdt=0,33",2,2019
Adaptive Gradient-Based Meta-Learning Methods,269,neurips,1,0,2023-06-15 23:44:49.426000,https://github.com/mkhodak/ARUBA,10,Adaptive gradient-based meta-learning methods,"https://scholar.google.com/scholar?cluster=12829613586326997125&hl=en&as_sdt=0,5",1,2019
Compositional generalization through meta sequence-to-sequence learning,153,neurips,1,0,2023-06-15 23:44:49.612000,https://github.com/brendenlake/meta_seq2seq,44,Compositional generalization through meta sequence-to-sequence learning,"https://scholar.google.com/scholar?cluster=10650832284960970235&hl=en&as_sdt=0,39",8,2019
Meta-Learning Representations for Continual Learning,258,neurips,27,4,2023-06-15 23:44:49.795000,https://github.com/Khurramjaved96/mrcl,181,Meta-learning representations for continual learning,"https://scholar.google.com/scholar?cluster=8778557720740141982&hl=en&as_sdt=0,5",7,2019
A Composable Specification Language for Reinforcement Learning Tasks,66,neurips,4,0,2023-06-15 23:44:49.978000,https://github.com/keyshor/spectrl_tool,10,A composable specification language for reinforcement learning tasks,"https://scholar.google.com/scholar?cluster=11872644767058709693&hl=en&as_sdt=0,5",1,2019
On the Utility of Learning about Humans for Human-AI Coordination,190,neurips,98,4,2023-06-15 23:44:50.160000,https://github.com/HumanCompatibleAI/overcooked_ai,468,On the utility of learning about humans for human-ai coordination,"https://scholar.google.com/scholar?cluster=17425854259950271984&hl=en&as_sdt=0,11",16,2019
Park: An Open Platform for Learning-Augmented Computer Systems,71,neurips,43,17,2023-06-15 23:44:50.343000,https://github.com/park-project/park,189,Park: An open platform for learning-augmented computer systems,"https://scholar.google.com/scholar?cluster=11372767626321679465&hl=en&as_sdt=0,33",13,2019
Compression with Flows via Local Bits-Back Coding,47,neurips,5,2,2023-06-15 23:44:50.527000,https://github.com/hojonathanho/localbitsback,35,Compression with flows via local bits-back coding,"https://scholar.google.com/scholar?cluster=9614859180156738260&hl=en&as_sdt=0,5",4,2019
On Adversarial Mixup Resynthesis,54,neurips,3,1,2023-06-15 23:44:50.709000,https://github.com/christopher-beckham/amr,32,On adversarial mixup resynthesis,"https://scholar.google.com/scholar?cluster=3310014081611030550&hl=en&as_sdt=0,34",4,2019
Certifying Geometric Robustness of Neural Networks,100,neurips,5,6,2023-06-15 23:44:50.893000,https://github.com/eth-sri/deepg,15,Certifying geometric robustness of neural networks,"https://scholar.google.com/scholar?cluster=9475017515216465786&hl=en&as_sdt=0,14",7,2019
MAVEN: Multi-Agent Variational Exploration,268,neurips,21,4,2023-06-15 23:44:51.076000,https://github.com/AnujMahajanOxf/MAVEN,49,Maven: Multi-agent variational exploration,"https://scholar.google.com/scholar?cluster=3641019168324212820&hl=en&as_sdt=0,5",6,2019
The continuous Bernoulli: fixing a pervasive error in variational autoencoders,71,neurips,7,0,2023-06-15 23:44:51.260000,https://github.com/cunningham-lab/cb,31,The continuous Bernoulli: fixing a pervasive error in variational autoencoders,"https://scholar.google.com/scholar?cluster=13640532786864289225&hl=en&as_sdt=0,5",4,2019
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters,17,neurips,2,0,2023-06-15 23:44:51.443000,https://github.com/albertometelli/wql,8,Propagating uncertainty in reinforcement learning via wasserstein barycenters,"https://scholar.google.com/scholar?cluster=2109934115378775122&hl=en&as_sdt=0,21",2,2019
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters,27,neurips,3,1,2023-06-15 23:44:51.633000,https://github.com/wokas36/DFNets,10,DFNets: Spectral CNNs for graphs with feedback-looped filters,"https://scholar.google.com/scholar?cluster=6084314422726553090&hl=en&as_sdt=0,23",4,2019
Multiclass Learning from Contradictions,14,neurips,1,0,2023-06-15 23:44:51.816000,https://github.com/LGE-ARC-AdvancedAI/MU-SVM,1,Multiclass learning from contradictions,"https://scholar.google.com/scholar?cluster=5633591904420775490&hl=en&as_sdt=0,47",1,2019
Multi-relational Poincaré Graph Embeddings,250,neurips,30,1,2023-06-15 23:44:52,https://github.com/ibalazevic/multirelational-poincare,150,Multi-relational poincaré graph embeddings,"https://scholar.google.com/scholar?cluster=9000210112086695185&hl=en&as_sdt=0,5",7,2019
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization,38,neurips,7,4,2023-06-15 23:44:52.183000,https://github.com/csyhhu/MetaQuant,51,Metaquant: Learning to quantize by learning to penetrate non-differentiable quantization,"https://scholar.google.com/scholar?cluster=15883471795192638746&hl=en&as_sdt=0,5",5,2019
Normalization Helps Training of Quantized LSTM,39,neurips,7,2,2023-06-15 23:44:52.366000,https://github.com/houlu369/Normalized-Quantized-LSTM,27,Normalization helps training of quantized LSTM,"https://scholar.google.com/scholar?cluster=11640994388027903274&hl=en&as_sdt=0,5",1,2019
Multi-Agent Common Knowledge Reinforcement Learning,75,neurips,7,1,2023-06-15 23:44:52.550000,https://github.com/schroederdewitt/mackrl,31,Multi-agent common knowledge reinforcement learning,"https://scholar.google.com/scholar?cluster=6084747952815676289&hl=en&as_sdt=0,5",2,2019
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections,26,neurips,1,0,2023-06-15 23:44:52.733000,https://github.com/BorisMuzellec/SubspaceOT,1,Subspace detours: Building transport plans that are optimal on subspace projections,"https://scholar.google.com/scholar?cluster=2998304691038707291&hl=en&as_sdt=0,14",2,2019
The Broad Optimality of Profile Maximum Likelihood,26,neurips,0,0,2023-06-15 23:44:52.916000,https://github.com/ucsdyi/PML,1,The broad optimality of profile maximum likelihood,"https://scholar.google.com/scholar?cluster=4063268634884804791&hl=en&as_sdt=0,5",0,2019
Efficient online learning with kernels for adversarial large scale problems,15,neurips,0,0,2023-06-15 23:44:53.103000,https://github.com/Remjez/kernel-online-learning,0,Efficient online learning with kernels for adversarial large scale problems,"https://scholar.google.com/scholar?cluster=424533984591498235&hl=en&as_sdt=0,44",1,2019
On the Downstream Performance of Compressed Word Embeddings,23,neurips,4,0,2023-06-15 23:44:53.298000,https://github.com/HazyResearch/smallfry,18,On the downstream performance of compressed word embeddings,"https://scholar.google.com/scholar?cluster=10444272090155128399&hl=en&as_sdt=0,5",12,2019
Primal-Dual Block Generalized Frank-Wolfe,11,neurips,0,0,2023-06-15 23:44:53.481000,https://github.com/CarlsonZhuo/primal_dual_frank_wolfe,1,Primal-dual block generalized frank-wolfe,"https://scholar.google.com/scholar?cluster=4125673740157136146&hl=en&as_sdt=0,48",3,2019
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models,37,neurips,2,0,2023-06-15 23:44:53.679000,https://github.com/sphinxteam/spiked_matrix-tensor_T0,2,Who is afraid of big bad minima? analysis of gradient-flow in spiked matrix-tensor models,"https://scholar.google.com/scholar?cluster=5757410499012237876&hl=en&as_sdt=0,5",4,2019
Differential Privacy Has Disparate Impact on Model Accuracy,324,neurips,11,0,2023-06-15 23:44:53.862000,https://github.com/ebagdasa/differential-privacy-vs-fairness,31,Differential privacy has disparate impact on model accuracy,"https://scholar.google.com/scholar?cluster=4704572033718664713&hl=en&as_sdt=0,44",2,2019
Fair Algorithms for Clustering,202,neurips,5,12,2023-06-15 23:44:54.045000,https://github.com/nicolasjulioflores/fair_algorithms_for_clustering,10,Fair algorithms for clustering,"https://scholar.google.com/scholar?cluster=15890260769740780525&hl=en&as_sdt=0,34",3,2019
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers,17,neurips,236,513,2023-06-15 23:44:54.228000,https://github.com/zenodo/zenodo,793,The cells out of sample (coos) dataset and benchmarks for measuring out-of-sample generalization of image classifiers,"https://scholar.google.com/scholar?cluster=664729084222698681&hl=en&as_sdt=0,47",42,2019
On Tractable Computation of Expected Predictions,40,neurips,2,0,2023-06-15 23:44:54.411000,https://github.com/UCLA-StarAI/mc2,9,On tractable computation of expected predictions,"https://scholar.google.com/scholar?cluster=7393033356171648134&hl=en&as_sdt=0,5",4,2019
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering,20,neurips,0,1,2023-06-15 23:44:54.602000,https://github.com/Biwei-Huang/Specific-and-Shared-Causal-Relation-Modeling-and-Mechanism-Based-Clustering,2,Specific and shared causal relation modeling and mechanism-based clustering,"https://scholar.google.com/scholar?cluster=16092569721082830349&hl=en&as_sdt=0,5",1,2019
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks,154,neurips,13,0,2023-06-15 23:44:54.786000,https://github.com/thuml/TransNorm,73,Transferable normalization: Towards improving transferability of deep neural networks,"https://scholar.google.com/scholar?cluster=9221290800687054760&hl=en&as_sdt=0,5",4,2019
Semi-Implicit Graph Variational Auto-Encoders,86,neurips,9,2,2023-06-15 23:44:54.969000,https://github.com/sigvae/SIGraphVAE,22,Semi-implicit graph variational auto-encoders,"https://scholar.google.com/scholar?cluster=10588276767934139650&hl=en&as_sdt=0,34",1,2019
GOT: An Optimal Transport framework for Graph comparison,77,neurips,4,0,2023-06-15 23:44:55.153000,https://github.com/Hermina/GOT,28,GOT: an optimal transport framework for graph comparison,"https://scholar.google.com/scholar?cluster=17969024179191140070&hl=en&as_sdt=0,5",3,2019
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss,34,neurips,0,0,2023-06-15 23:44:55.335000,https://github.com/JunYan65/DRCR_NIPS2019_Code,0,Multivariate distributionally robust convex regression under absolute error loss,"https://scholar.google.com/scholar?cluster=7458836863119383276&hl=en&as_sdt=0,44",1,2019
A Benchmark for Interpretability Methods in Deep Neural Networks,469,neurips,7320,1025,2023-06-15 23:44:55.519000,https://github.com/google-research/google-research,29776,A benchmark for interpretability methods in deep neural networks,"https://scholar.google.com/scholar?cluster=1845943296865459984&hl=en&as_sdt=0,33",727,2019
Zero-shot Knowledge Transfer via Adversarial Belief Matching,162,neurips,18,1,2023-06-15 23:44:55.702000,https://github.com/polo5/ZeroShotKnowledgeTransfer,122,Zero-shot knowledge transfer via adversarial belief matching,"https://scholar.google.com/scholar?cluster=14084992756090695507&hl=en&as_sdt=0,5",5,2019
Discrete Object Generation with Reversible Inductive Construction,27,neurips,4,1,2023-06-15 23:44:55.884000,https://github.com/PrincetonLIPS/reversible-inductive-construction,29,Discrete object generation with reversible inductive construction,"https://scholar.google.com/scholar?cluster=13201286911892635677&hl=en&as_sdt=0,5",3,2019
Adaptively Aligned Image Captioning via Adaptive Attention Time,56,neurips,15,4,2023-06-15 23:44:56.067000,https://github.com/husthuaan/AAT,47,Adaptively aligned image captioning via adaptive attention time,"https://scholar.google.com/scholar?cluster=6529707515477430169&hl=en&as_sdt=0,5",5,2019
Fully Dynamic Consistent Facility Location,31,neurips,0,0,2023-06-15 23:44:56.250000,https://github.com/NikosParotsidis/Fully-dynamic_facility_location-NeurIPS2019,4,Fully dynamic consistent facility location,"https://scholar.google.com/scholar?cluster=4359801201128958247&hl=en&as_sdt=0,5",1,2019
"Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method",37,neurips,6,0,2023-06-16 15:09:49.371000,https://github.com/BensonRen/BDIMNNA,17,"Benchmarking deep inverse models over time, and the neural-adjoint method","https://scholar.google.com/scholar?cluster=10303492890298321577&hl=en&as_sdt=0,6",3,2020
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift,24,neurips,0,0,2023-06-16 15:09:49.585000,https://github.com/MasaKat0/OPE_CS,1,Off-policy evaluation and learning for external validity under a covariate shift,"https://scholar.google.com/scholar?cluster=11932511552912814820&hl=en&as_sdt=0,5",4,2020
Neural Methods for Point-wise Dependency Estimation,22,neurips,2,0,2023-06-16 15:09:49.777000,https://github.com/yaohungt/Pointwise_Dependency_Neural_Estimation,17,Neural methods for point-wise dependency estimation,"https://scholar.google.com/scholar?cluster=3025466449129186225&hl=en&as_sdt=0,5",4,2020
Fast and Flexible Temporal Point Processes with Triangular Maps,18,neurips,7,0,2023-06-16 15:09:49.969000,https://github.com/shchur/triangular-tpp,23,Fast and flexible temporal point processes with triangular maps,"https://scholar.google.com/scholar?cluster=7206682078029107173&hl=en&as_sdt=0,5",2,2020
Backpropagating Linearly Improves Transferability of Adversarial Examples,58,neurips,5,4,2023-06-16 15:09:50.161000,https://github.com/qizhangli/linbp-attack,39,Backpropagating linearly improves transferability of adversarial examples,"https://scholar.google.com/scholar?cluster=1816302577038884057&hl=en&as_sdt=0,5",1,2020
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering,6,neurips,2,0,2023-06-16 15:09:50.353000,https://github.com/SoftWiser-group/CFDebug,6,Trading personalization for accuracy: Data debugging in collaborative filtering,"https://scholar.google.com/scholar?cluster=12342608985408864603&hl=en&as_sdt=0,5",7,2020
Cascaded Text Generation with Markov Transformers,11,neurips,8,0,2023-06-16 15:09:50.545000,https://github.com/harvardnlp/cascaded-generation,123,Cascaded text generation with markov transformers,"https://scholar.google.com/scholar?cluster=12660981170581586406&hl=en&as_sdt=0,3",12,2020
Deep reconstruction of strange attractors from time series,33,neurips,28,0,2023-06-16 15:09:50.737000,https://github.com/williamgilpin/fnn,111,Deep reconstruction of strange attractors from time series,"https://scholar.google.com/scholar?cluster=13942188603498560541&hl=en&as_sdt=0,5",9,2020
Reciprocal Adversarial Learning via Characteristic Functions,4,neurips,2,0,2023-06-16 15:09:50.929000,https://github.com/ShengxiLi/rcf_gan,10,Reciprocal adversarial learning via characteristic functions,"https://scholar.google.com/scholar?cluster=4107964100222082951&hl=en&as_sdt=0,5",2,2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach,107,neurips,4,13,2023-06-16 15:09:51.120000,https://github.com/amirhk/recourse,28,Algorithmic recourse under imperfect causal knowledge: a probabilistic approach,"https://scholar.google.com/scholar?cluster=4986898048327715369&hl=en&as_sdt=0,48",5,2020
Minimax Classification with 0-1 Loss and Performance Guarantees,16,neurips,1,0,2023-06-16 15:09:51.312000,https://github.com/MachineLearningBCAM/Minimax-risk-classifiers-NeurIPS-2020,3,Minimax classification with 0-1 loss and performance guarantees,"https://scholar.google.com/scholar?cluster=3844746042992599379&hl=en&as_sdt=0,5",1,2020
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization,23,neurips,7,0,2023-06-16 15:09:51.504000,https://github.com/nnaisense/MAGE,29,How to learn a useful critic? Model-based action-gradient-estimator policy optimization,"https://scholar.google.com/scholar?cluster=12964689647322845845&hl=en&as_sdt=0,32",8,2020
Coresets for Regressions with Panel Data,12,neurips,0,0,2023-06-16 15:09:51.695000,https://github.com/huanglx12/Coresets-for-regressions-with-panel-data,0,Coresets for regressions with panel data,"https://scholar.google.com/scholar?cluster=9096294393329532403&hl=en&as_sdt=0,39",1,2020
Achieving Equalized Odds by Resampling Sensitive Attributes,23,neurips,4,0,2023-06-16 15:09:51.887000,https://github.com/yromano/fair_dummies,3,Achieving equalized odds by resampling sensitive attributes,"https://scholar.google.com/scholar?cluster=6396740997740111580&hl=en&as_sdt=0,39",3,2020
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates,52,neurips,6,1,2023-06-16 15:09:52.079000,https://github.com/wenhaol/PrSBC,10,Multi-robot collision avoidance under uncertainty with probabilistic safety barrier certificates,"https://scholar.google.com/scholar?cluster=16415958684564416079&hl=en&as_sdt=0,10",1,2020
Hard Shape-Constrained Kernel Machines,24,neurips,1,0,2023-06-16 15:09:52.271000,https://github.com/PCAubin/Hard-Shape-Constraints-for-Kernels,2,Hard shape-constrained kernel machines,"https://scholar.google.com/scholar?cluster=5312947070123746678&hl=en&as_sdt=0,5",1,2020
A Closer Look at the Training Strategy for Modern Meta-Learning,22,neurips,0,0,2023-06-16 15:09:52.463000,https://github.com/jiaxinchen666/meta-theory,9,A closer look at the training strategy for modern meta-learning,"https://scholar.google.com/scholar?cluster=1508062348687769372&hl=en&as_sdt=0,36",1,2020
Flows for simultaneous manifold learning and density estimation,104,neurips,22,2,2023-06-16 15:09:52.655000,https://github.com/johannbrehmer/manifold-flow,217,Flows for simultaneous manifold learning and density estimation,"https://scholar.google.com/scholar?cluster=12827214460848825511&hl=en&as_sdt=0,39",8,2020
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee,29,neurips,2,0,2023-06-16 15:09:52.847000,https://github.com/JinchengBai/sparse-variational-bnn,4,Efficient variational inference for sparse deep learning with theoretical guarantee,"https://scholar.google.com/scholar?cluster=10814248748579550273&hl=en&as_sdt=0,19",1,2020
One-bit Supervision for Image Classification,7,neurips,0,0,2023-06-16 15:09:53.048000,https://github.com/huhengtong/one-bit-supervision,6,One-bit supervision for image classification,"https://scholar.google.com/scholar?cluster=8819536365045393695&hl=en&as_sdt=0,5",1,2020
What is being transferred in transfer learning? ,265,neurips,11,0,2023-06-16 15:09:53.240000,https://github.com/google-research/understanding-transfer-learning,40,What is being transferred in transfer learning?,"https://scholar.google.com/scholar?cluster=13447249673581194617&hl=en&as_sdt=0,5",7,2020
Neural Networks with Recurrent Generative Feedback,30,neurips,8,0,2023-06-16 15:09:53.458000,https://github.com/yjhuangcd/CNNF,19,Neural networks with recurrent generative feedback,"https://scholar.google.com/scholar?cluster=18302025610474575461&hl=en&as_sdt=0,25",3,2020
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction,59,neurips,11,0,2023-06-16 15:09:53.651000,https://github.com/JinheonBaek/GEN,51,Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction,"https://scholar.google.com/scholar?cluster=1470927861111828133&hl=en&as_sdt=0,6",2,2020
Neuron Merging: Compensating for Pruned Neurons,21,neurips,11,4,2023-06-16 15:09:53.843000,https://github.com/friendshipkim/neuron-merging,35,Neuron merging: Compensating for pruned neurons,"https://scholar.google.com/scholar?cluster=8238161891344439767&hl=en&as_sdt=0,31",4,2020
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence,1977,neurips,162,17,2023-06-16 15:09:54.035000,https://github.com/google-research/fixmatch,990,Fixmatch: Simplifying semi-supervised learning with consistency and confidence,"https://scholar.google.com/scholar?cluster=8436393078669287497&hl=en&as_sdt=0,34",19,2020
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing,66,neurips,7,1,2023-06-16 15:09:54.226000,https://github.com/google-research/tf-opt,31,Reinforcement learning with combinatorial actions: An application to vehicle routing,"https://scholar.google.com/scholar?cluster=10633025590595233619&hl=en&as_sdt=0,43",10,2020
Causal Intervention for Weakly-Supervised Semantic Segmentation,242,neurips,28,12,2023-06-16 15:09:54.442000,https://github.com/ZHANGDONG-NJUST/CONTA,178,Causal intervention for weakly-supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=6645460811692278989&hl=en&as_sdt=0,33",5,2020
Debugging Tests for Model Explanations,126,neurips,2,0,2023-06-16 15:09:54.635000,https://github.com/adebayoj/explaindebug,3,Debugging tests for model explanations,"https://scholar.google.com/scholar?cluster=15051438141959870127&hl=en&as_sdt=0,5",3,2020
 Robust compressed sensing using generative models ,25,neurips,1,0,2023-06-16 15:09:54.826000,https://github.com/ajiljalal/csgm-robust-neurips,8,Robust compressed sensing using generative models,"https://scholar.google.com/scholar?cluster=11462485595148288562&hl=en&as_sdt=0,5",2,2020
Adapting Neural Architectures Between Domains,23,neurips,1,1,2023-06-16 15:09:55.017000,https://github.com/liyxi/AdaptNAS,7,Adapting neural architectures between domains,"https://scholar.google.com/scholar?cluster=15474765041948411848&hl=en&as_sdt=0,10",1,2020
Learning Guidance Rewards with Trajectory-space Smoothing,22,neurips,1,1,2023-06-16 15:09:55.211000,https://github.com/tgangwani/GuidanceRewards,10,Learning guidance rewards with trajectory-space smoothing,"https://scholar.google.com/scholar?cluster=16129997703943948282&hl=en&as_sdt=0,33",3,2020
Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding,29,neurips,0,0,2023-06-16 15:09:55.404000,https://github.com/rsonthal/TreeRep,21,Tree! i am no tree! i am a low dimensional hyperbolic embedding,"https://scholar.google.com/scholar?cluster=18232158800489906399&hl=en&as_sdt=0,5",3,2020
Deep Structural Causal Models for Tractable Counterfactual Inference,118,neurips,49,7,2023-06-16 15:09:55.596000,https://github.com/biomedia-mira/deepscm,224,Deep structural causal models for tractable counterfactual inference,"https://scholar.google.com/scholar?cluster=9027210436245269282&hl=en&as_sdt=0,18",9,2020
Convolutional Generation of Textured 3D Meshes,39,neurips,17,5,2023-06-16 15:09:55.790000,https://github.com/dariopavllo/convmesh,107,Convolutional generation of textured 3d meshes,"https://scholar.google.com/scholar?cluster=10601781187163028035&hl=en&as_sdt=0,5",5,2020
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks,18,neurips,1,0,2023-06-16 15:09:55.982000,https://github.com/cjf00000/StatQuant,21,A statistical framework for low-bitwidth training of deep neural networks,"https://scholar.google.com/scholar?cluster=11151412933346353614&hl=en&as_sdt=0,34",1,2020
Better Set Representations For Relational Reasoning,14,neurips,2,1,2023-06-16 15:09:56.175000,https://github.com/CUVL/SSLR,29,Better set representations for relational reasoning,"https://scholar.google.com/scholar?cluster=6489896145456654265&hl=en&as_sdt=0,5",6,2020
Primal-Dual Mesh Convolutional Neural Networks,75,neurips,18,10,2023-06-16 15:09:56.368000,https://github.com/MIT-SPARK/PD-MeshNet,97,Primal-dual mesh convolutional neural networks,"https://scholar.google.com/scholar?cluster=12375851352098825949&hl=en&as_sdt=0,5",7,2020
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning,33,neurips,0,0,2023-06-16 15:09:56.583000,https://github.com/dGiulia/ConditionalMetaLearning,2,The advantage of conditional meta-learning for biased regularization and fine tuning,"https://scholar.google.com/scholar?cluster=2418967028251018198&hl=en&as_sdt=0,10",2,2020
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks,47,neurips,5,1,2023-06-16 15:09:56.777000,https://github.com/tsingqguo/ABBA,27,Watch out! motion is blurring the vision of your deep neural networks,"https://scholar.google.com/scholar?cluster=15773966474412221565&hl=en&as_sdt=0,33",2,2020
Bayesian Deep Ensembles via the Neural Tangent Kernel,74,neurips,6,0,2023-06-16 15:09:56.970000,https://github.com/bobby-he/bayesian-ntk,21,Bayesian deep ensembles via the neural tangent kernel,"https://scholar.google.com/scholar?cluster=10890964373773286236&hl=en&as_sdt=0,5",3,2020
Adaptive Sampling for Stochastic Risk-Averse Learning,52,neurips,1,0,2023-06-16 15:09:57.163000,https://github.com/sebascuri/adacvar,6,Adaptive sampling for stochastic risk-averse learning,"https://scholar.google.com/scholar?cluster=10094126690067053033&hl=en&as_sdt=0,5",2,2020
Taming Discrete Integration via the Boon of Dimensionality,6,neurips,1,1,2023-06-16 15:09:57.356000,https://github.com/meelgroup/deweight,0,Taming discrete integration via the boon of dimensionality,"https://scholar.google.com/scholar?cluster=17208976171354395854&hl=en&as_sdt=0,36",3,2020
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond,150,neurips,47,15,2023-06-16 15:09:57.549000,https://github.com/KaidiXu/auto_LiRPA,208,Automatic perturbation analysis for scalable certified robustness and beyond,"https://scholar.google.com/scholar?cluster=346708359742349242&hl=en&as_sdt=0,33",8,2020
Conservative Q-Learning for Offline Reinforcement Learning,854,neurips,61,16,2023-06-16 15:09:57.742000,https://github.com/aviralkumar2907/CQL,305,Conservative q-learning for offline reinforcement learning,"https://scholar.google.com/scholar?cluster=7056274634823343559&hl=en&as_sdt=0,5",6,2020
Ensembling geophysical models with Bayesian Neural Networks,18,neurips,2,0,2023-06-16 15:09:57.935000,https://github.com/Ushnish-Sengupta/Model-Ensembler,9,Ensembling geophysical models with Bayesian neural networks,"https://scholar.google.com/scholar?cluster=12898556235367158665&hl=en&as_sdt=0,5",2,2020
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation,24,neurips,24,10,2023-06-16 15:09:58.132000,https://github.com/lyxok1/STM-Training,106,Delving into the cyclic mechanism in semi-supervised video object segmentation,"https://scholar.google.com/scholar?cluster=15310731299697520994&hl=en&as_sdt=0,25",6,2020
Understanding Deep Architecture with Reasoning Layer,12,neurips,0,0,2023-06-16 15:09:58.324000,https://github.com/xinshi-chen/Deep-Architecture-With-Reasoning-Layer,5,Understanding deep architecture with reasoning layer,"https://scholar.google.com/scholar?cluster=8179923820884933954&hl=en&as_sdt=0,33",1,2020
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming,3,neurips,0,0,2023-06-16 15:09:58.516000,https://github.com/joehuchette/reserve-price-optimization,2,Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming,"https://scholar.google.com/scholar?cluster=13519296265519190525&hl=en&as_sdt=0,47",1,2020
Learning to search efficiently for causally near-optimal treatments,7,neurips,1,0,2023-06-16 15:09:58.708000,https://github.com/Healthy-AI/TreatmentExploration,1,Learning to search efficiently for causally near-optimal treatments,"https://scholar.google.com/scholar?cluster=11107205422193494167&hl=en&as_sdt=0,5",1,2020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts,80,neurips,8,0,2023-06-16 15:09:58.899000,https://github.com/sharpenb/Posterior-Network,59,Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts,"https://scholar.google.com/scholar?cluster=13793786839752857625&hl=en&as_sdt=0,5",2,2020
A causal view of compositional zero-shot recognition,76,neurips,2,2,2023-06-16 15:09:59.091000,https://github.com/nv-research-israel/causal_comp,27,A causal view of compositional zero-shot recognition,"https://scholar.google.com/scholar?cluster=2543173389101020482&hl=en&as_sdt=0,10",6,2020
HiPPO: Recurrent Memory with Optimal Polynomial Projections,76,neurips,18,1,2023-06-16 15:09:59.284000,https://github.com/HazyResearch/hippo-code,92,Hippo: Recurrent memory with optimal polynomial projections,"https://scholar.google.com/scholar?cluster=10897171960502189367&hl=en&as_sdt=0,33",20,2020
Auto Learning Attention,24,neurips,3,1,2023-06-16 15:09:59.476000,https://github.com/btma48/AutoLA,21,Auto learning attention,"https://scholar.google.com/scholar?cluster=4640609275657710063&hl=en&as_sdt=0,36",4,2020
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect,291,neurips,67,25,2023-06-16 15:09:59.669000,https://github.com/KaihuaTang/Long-Tailed-Recognition.pytorch,531,Long-tailed classification by keeping the good and removing the bad momentum causal effect,"https://scholar.google.com/scholar?cluster=11307578533103322862&hl=en&as_sdt=0,44",12,2020
Deep Archimedean Copulas,12,neurips,2,1,2023-06-16 15:09:59.862000,https://github.com/lingchunkai/ACNet,8,Deep archimedean copulas,"https://scholar.google.com/scholar?cluster=453186630159063437&hl=en&as_sdt=0,5",1,2020
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization,67,neurips,5,2,2023-06-16 15:10:00.056000,https://github.com/facebookresearch/alebo,35,Re-examining linear embeddings for high-dimensional Bayesian optimization,"https://scholar.google.com/scholar?cluster=7963529277112461610&hl=en&as_sdt=0,5",10,2020
Neural Networks Fail to Learn Periodic Functions and How to Fix It,46,neurips,1,0,2023-06-16 15:10:00.250000,https://github.com/AdenosHermes/NeurIPS_2020_Snake,25,Neural networks fail to learn periodic functions and how to fix it,"https://scholar.google.com/scholar?cluster=16056803791186814907&hl=en&as_sdt=0,5",5,2020
Distribution Matching for Crowd Counting,166,neurips,50,14,2023-06-16 15:10:00.593000,https://github.com/cvlab-stonybrook/DM-Count,185,Distribution matching for crowd counting,"https://scholar.google.com/scholar?cluster=14310555288407205229&hl=en&as_sdt=0,5",8,2020
Correspondence learning via linearly-invariant embedding,40,neurips,3,1,2023-06-16 15:10:00.786000,https://github.com/riccardomarin/Diff-FMaps,10,Correspondence learning via linearly-invariant embedding,"https://scholar.google.com/scholar?cluster=6342234500198528456&hl=en&as_sdt=0,20",2,2020
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning,146,neurips,70,3,2023-06-16 15:10:00.982000,https://github.com/zcajiayin/L2D,193,Learning to dispatch for job shop scheduling via deep reinforcement learning,"https://scholar.google.com/scholar?cluster=17946575832024706335&hl=en&as_sdt=0,44",1,2020
 On Adaptive Attacks to Adversarial Example Defenses,615,neurips,12,2,2023-06-16 15:10:01.176000,https://github.com/wielandbrendel/adaptive_attacks_paper,78,On adaptive attacks to adversarial example defenses,"https://scholar.google.com/scholar?cluster=5574467727525147588&hl=en&as_sdt=0,35",8,2020
Ultrahyperbolic Representation Learning,11,neurips,0,0,2023-06-16 15:10:01.368000,https://github.com/MarcTLaw/UltrahyperbolicRepresentation,11,Ultrahyperbolic representation learning,"https://scholar.google.com/scholar?cluster=15522026458695881889&hl=en&as_sdt=0,5",6,2020
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms,17,neurips,2,0,2023-06-16 15:10:01.561000,https://github.com/khashayarkhv/many-armed-bandit,4,Unreasonable effectiveness of greedy algorithms in multi-armed bandit with many arms,"https://scholar.google.com/scholar?cluster=1593756484114132419&hl=en&as_sdt=0,5",1,2020
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction,20,neurips,5,0,2023-06-16 15:10:01.754000,https://github.com/JannerM/gamma-models,38,gamma-models: Generative temporal difference learning for infinite-horizon prediction,"https://scholar.google.com/scholar?cluster=16924243578285273048&hl=en&as_sdt=0,4",8,2020
Efficient Exact Verification of Binarized Neural Networks,40,neurips,3,0,2023-06-16 15:10:01.946000,https://github.com/jia-kai/eevbnn,10,Efficient exact verification of binarized neural networks,"https://scholar.google.com/scholar?cluster=3950117023454899474&hl=en&as_sdt=0,16",3,2020
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback,35,neurips,4,2,2023-06-16 15:10:02.138000,https://github.com/RyanWangZf/CVIB-Rec,23,Information theoretic counterfactual learning from missing-not-at-random feedback,"https://scholar.google.com/scholar?cluster=2026070403857564388&hl=en&as_sdt=0,47",3,2020
Language Models are Few-Shot Learners,11121,neurips,2202,3,2023-06-16 15:10:02.330000,https://github.com/openai/gpt-3,15171,Language models are few-shot learners,"https://scholar.google.com/scholar?cluster=15953747982133883426&hl=en&as_sdt=0,41",881,2020
MomentumRNN: Integrating Momentum into Recurrent Neural Networks,24,neurips,6,2,2023-06-16 15:10:02.522000,https://github.com/minhtannguyen/MomentumRNN,16,Momentumrnn: Integrating momentum into recurrent neural networks,"https://scholar.google.com/scholar?cluster=9149151218987275930&hl=en&as_sdt=0,10",1,2020
Projected Stein Variational Gradient Descent,42,neurips,1,1,2023-06-16 15:10:02.714000,https://github.com/cpempire/pSVGD,7,Projected Stein variational gradient descent,"https://scholar.google.com/scholar?cluster=11787408032214941846&hl=en&as_sdt=0,5",2,2020
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks,24,neurips,1,14,2023-06-16 15:10:02.907000,https://github.com/z-fabian/transfer_lowerbounds_arXiv,2,Minimax lower bounds for transfer learning with linear and one-hidden layer neural networks,"https://scholar.google.com/scholar?cluster=8519029442558083621&hl=en&as_sdt=0,3",2,2020
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks,354,neurips,61,11,2023-06-16 15:10:03.098000,https://github.com/FabianFuchsML/se3-transformer-public,388,Se (3)-transformers: 3d roto-translation equivariant attention networks,"https://scholar.google.com/scholar?cluster=7114881113669802193&hl=en&as_sdt=0,9",15,2020
On the equivalence of molecular graph convolution and molecular wave function with poor basis set,10,neurips,39,0,2023-06-16 15:10:03.292000,https://github.com/masashitsubaki/QuantumDeepField_molecule,164,On the equivalence of molecular graph convolution and molecular wave function with poor basis set,"https://scholar.google.com/scholar?cluster=15706248090993034000&hl=en&as_sdt=0,5",3,2020
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model,20,neurips,11,7,2023-06-16 15:10:03.484000,https://github.com/SteffenCzolbe/PerceptualSimilarity,90,A loss function for generative neural networks based on watson's perceptual model,"https://scholar.google.com/scholar?cluster=2642015369120549708&hl=en&as_sdt=0,33",4,2020
Adversarial Robustness of Supervised Sparse Coding,16,neurips,4,0,2023-06-16 15:10:03.681000,https://github.com/Sulam-Group/Adversarial-Robust-Supervised-Sparse-Coding,2,Adversarial robustness of supervised sparse coding,"https://scholar.google.com/scholar?cluster=7092140439598020620&hl=en&as_sdt=0,34",3,2020
Network Diffusions via Neural Mean-Field Dynamics,5,neurips,1,0,2023-06-16 15:10:03.874000,https://github.com/ShushanHe/neural-mf,4,Network diffusions via neural mean-field dynamics,"https://scholar.google.com/scholar?cluster=17188160558828611858&hl=en&as_sdt=0,33",1,2020
Rethinking pooling in graph neural networks,78,neurips,11,3,2023-06-16 15:10:04.066000,https://github.com/AaltoPML/Rethinking-pooling-in-GNNs,53,Rethinking pooling in graph neural networks,"https://scholar.google.com/scholar?cluster=7929818342253962258&hl=en&as_sdt=0,39",7,2020
Rescuing neural spike train models from bad MLE,3,neurips,3,0,2023-06-16 15:10:04.261000,https://github.com/diegoarri91/mmd-glm,6,Rescuing neural spike train models from bad MLE,"https://scholar.google.com/scholar?cluster=3646921033072503899&hl=en&as_sdt=0,33",3,2020
Deep Imitation Learning for Bimanual Robotic Manipulation,24,neurips,4,2,2023-06-16 15:10:04.453000,https://github.com/Rose-STL-Lab/HDR-IL,27,Deep imitation learning for bimanual robotic manipulation,"https://scholar.google.com/scholar?cluster=3337481646096729028&hl=en&as_sdt=0,5",4,2020
Stationary Activations for Uncertainty Calibration in Deep Learning,15,neurips,5,0,2023-06-16 15:10:04.645000,https://github.com/AaltoML/stationary-activations,9,Stationary activations for uncertainty calibration in deep learning,"https://scholar.google.com/scholar?cluster=13291548217087481879&hl=en&as_sdt=0,33",3,2020
On Power Laws in Deep Ensembles,32,neurips,3,0,2023-06-16 15:10:04.837000,https://github.com/nadiinchi/power_laws_deep_ensembles,2,On power laws in deep ensembles,"https://scholar.google.com/scholar?cluster=14597524051325855513&hl=en&as_sdt=0,18",2,2020
Practical Quasi-Newton Methods for Training Deep Neural Networks,63,neurips,8,0,2023-06-16 15:10:05.029000,https://github.com/renyiryry/kbfgs_neurips2020_public,17,Practical quasi-newton methods for training deep neural networks,"https://scholar.google.com/scholar?cluster=16186200424986740304&hl=en&as_sdt=0,5",1,2020
Consistent feature selection for analytic deep neural networks,13,neurips,2,0,2023-06-16 15:10:05.222000,https://github.com/vucdinh/alg-net,2,Consistent feature selection for analytic deep neural networks,"https://scholar.google.com/scholar?cluster=4872208848076144978&hl=en&as_sdt=0,33",3,2020
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification,89,neurips,31,2,2023-06-16 15:10:05.414000,https://github.com/blackfeather-wang/GFNet-Pytorch,177,Glance and focus: a dynamic approach to reducing spatial redundancy in image classification,"https://scholar.google.com/scholar?cluster=229727098340388548&hl=en&as_sdt=0,33",5,2020
Information Maximization for Few-Shot Learning,125,neurips,18,3,2023-06-16 15:10:05.606000,https://github.com/mboudiaf/TIM,110,Information maximization for few-shot learning,"https://scholar.google.com/scholar?cluster=11018359707721193758&hl=en&as_sdt=0,6",6,2020
Bayesian Robust Optimization for Imitation Learning,26,neurips,0,0,2023-06-16 15:10:05.798000,https://github.com/dsbrown1331/broil,3,Bayesian robust optimization for imitation learning,"https://scholar.google.com/scholar?cluster=974540193771601354&hl=en&as_sdt=0,31",3,2020
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance,418,neurips,83,9,2023-06-16 15:10:05.990000,https://github.com/lioryariv/idr,586,Multiview neural surface reconstruction by disentangling geometry and appearance,"https://scholar.google.com/scholar?cluster=6952139627795921381&hl=en&as_sdt=0,5",16,2020
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation,24,neurips,3,7,2023-06-16 15:10:06.182000,https://github.com/isapome/BrainProp,14,Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation,"https://scholar.google.com/scholar?cluster=8542738772122027975&hl=en&as_sdt=0,47",2,2020
Structured Prediction for Conditional Meta-Learning,11,neurips,1,0,2023-06-16 15:10:06.374000,https://github.com/RuohanW/Tasml,6,Structured prediction for conditional meta-learning,"https://scholar.google.com/scholar?cluster=6688833579162281826&hl=en&as_sdt=0,38",3,2020
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient,67,neurips,1,0,2023-06-16 15:10:06.567000,https://github.com/acnagle/optimal-lottery-tickets,3,Optimal lottery tickets via subset sum: Logarithmic over-parameterization is sufficient,"https://scholar.google.com/scholar?cluster=8996425038613953094&hl=en&as_sdt=0,44",1,2020
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes,283,neurips,927,137,2023-06-16 15:10:06.760000,https://github.com/facebookresearch/mmf,5242,The hateful memes challenge: Detecting hate speech in multimodal memes,"https://scholar.google.com/scholar?cluster=17728666238988121395&hl=en&as_sdt=0,33",117,2020
Identifying Learning Rules From Neural Network Observables,16,neurips,2,0,2023-06-16 15:10:06.952000,https://github.com/neuroailab/lr-identify,12,Identifying learning rules from neural network observables,"https://scholar.google.com/scholar?cluster=12719991320138828348&hl=en&as_sdt=0,33",6,2020
Improving Policy-Constrained Kidney Exchange via Pre-Screening,3,neurips,0,0,2023-06-16 15:10:07.144000,https://github.com/duncanmcelfresh/kpd-edge-query,0,Improving policy-constrained kidney exchange via pre-screening,"https://scholar.google.com/scholar?cluster=11750267690088441504&hl=en&as_sdt=0,22",3,2020
Dual Instrumental Variable Regression,63,neurips,1,0,2023-06-16 15:10:07.336000,https://github.com/krikamol/DualIV-NeurIPS2020,1,Dual instrumental variable regression,"https://scholar.google.com/scholar?cluster=7206130195065971102&hl=en&as_sdt=0,25",4,2020
Interventional Few-Shot Learning,159,neurips,22,11,2023-06-16 15:10:07.528000,https://github.com/yue-zhongqi/ifsl,152,Interventional few-shot learning,"https://scholar.google.com/scholar?cluster=6986077950904335953&hl=en&as_sdt=0,23",7,2020
ShiftAddNet: A Hardware-Inspired Deep Network,53,neurips,16,5,2023-06-16 15:10:07.720000,https://github.com/RICE-EIC/ShiftAddNet,60,Shiftaddnet: A hardware-inspired deep network,"https://scholar.google.com/scholar?cluster=11143869055965605135&hl=en&as_sdt=0,33",3,2020
Network-to-Network Translation with Conditional Invertible Neural Networks,34,neurips,19,6,2023-06-16 15:10:07.912000,https://github.com/CompVis/net2net,212,Network-to-network translation with conditional invertible neural networks,"https://scholar.google.com/scholar?cluster=10385399504485528967&hl=en&as_sdt=0,5",13,2020
Model-based Policy Optimization with Unsupervised Model Adaptation,21,neurips,0,1,2023-06-16 15:10:08.103000,https://github.com/RockySJ/ampo,13,Model-based policy optimization with unsupervised model adaptation,"https://scholar.google.com/scholar?cluster=6711842689847231868&hl=en&as_sdt=0,15",4,2020
Geometric All-way Boolean Tensor Decomposition,3,neurips,0,0,2023-06-16 15:10:08.295000,https://github.com/clwan/GETF,1,Geometric all-way boolean tensor decomposition,"https://scholar.google.com/scholar?cluster=8557467909142317065&hl=en&as_sdt=0,25",1,2020
Hold me tight! Influence of discriminative features on deep network boundaries,41,neurips,1,0,2023-06-16 15:10:08.488000,https://github.com/LTS4/hold-me-tight,21,Hold me tight! Influence of discriminative features on deep network boundaries,"https://scholar.google.com/scholar?cluster=7593820950200684211&hl=en&as_sdt=0,7",5,2020
Adversarial Self-Supervised Contrastive Learning,169,neurips,17,5,2023-06-16 15:10:08.680000,https://github.com/Kim-Minseon/RoCL,161,Adversarial self-supervised contrastive learning,"https://scholar.google.com/scholar?cluster=13558288573789113152&hl=en&as_sdt=0,1",10,2020
Learning to summarize with human feedback,326,neurips,127,6,2023-06-16 15:10:08.872000,https://github.com/openai/summarize-from-feedback,785,Learning to summarize with human feedback,"https://scholar.google.com/scholar?cluster=14483287577780422045&hl=en&as_sdt=0,5",127,2020
"Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks",3,neurips,1,0,2023-06-16 15:10:09.064000,https://github.com/FrostHan/HetFFN-,0,"Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks","https://scholar.google.com/scholar?cluster=818462062864224649&hl=en&as_sdt=0,44",2,2020
Learning Dynamic Belief Graphs to Generalize on Text-Based Games,84,neurips,11,1,2023-06-16 15:10:09.259000,https://github.com/xingdi-eric-yuan/GATA-public,33,Learning dynamic belief graphs to generalize on text-based games,"https://scholar.google.com/scholar?cluster=15134168610189625143&hl=en&as_sdt=0,10",4,2020
Triple descent and the two kinds of overfitting: where & why do they appear?,66,neurips,3,0,2023-06-16 15:10:09.452000,https://github.com/sdascoli/triple-descent-paper,7,Triple descent and the two kinds of overfitting: Where & why do they appear?,"https://scholar.google.com/scholar?cluster=16515586708066009664&hl=en&as_sdt=0,33",4,2020
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization,81,neurips,9,5,2023-06-16 15:10:09.644000,https://github.com/wyf0912/LDDG,54,Domain generalization for medical imaging classification with linear-dependency regularization,"https://scholar.google.com/scholar?cluster=7705964353868024891&hl=en&as_sdt=0,50",2,2020
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?,16,neurips,2,0,2023-06-16 15:10:09.836000,https://github.com/GuoqiangWoodrowWu/MLC-theory,5,Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?,"https://scholar.google.com/scholar?cluster=986985610325694720&hl=en&as_sdt=0,14",1,2020
Adaptive Gradient Quantization for Data-Parallel SGD,47,neurips,5,0,2023-06-16 15:10:10.028000,https://github.com/tabrizian/learning-to-quantize,20,Adaptive gradient quantization for data-parallel sgd,"https://scholar.google.com/scholar?cluster=1571526277141139654&hl=en&as_sdt=0,5",5,2020
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies,50,neurips,5,1,2023-06-16 15:10:10.220000,https://github.com/itaigat/removing-bias-in-multi-modal-classifiers,25,Removing bias in multi-modal classifiers: Regularization by maximizing functional entropies,"https://scholar.google.com/scholar?cluster=11041773532262485134&hl=en&as_sdt=0,5",1,2020
Audeo: Audio Generation for a Silent Performance Video,30,neurips,2,0,2023-06-16 15:10:10.412000,https://github.com/shlizee/Audeo,20,Audeo: Audio generation for a silent performance video,"https://scholar.google.com/scholar?cluster=13879342907781591680&hl=en&as_sdt=0,34",1,2020
Community detection using fast low-cardinality semidefinite programming
,3,neurips,1,0,2023-06-16 15:10:10.604000,https://github.com/locuslab/sdp_clustering,12,Community detection using fast low-cardinality semidefinite programming,"https://scholar.google.com/scholar?cluster=7593232396131727716&hl=en&as_sdt=0,33",5,2020
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement,85,neurips,13,0,2023-06-16 15:10:10.796000,https://github.com/xmlyqing00/AFB-URR,83,Video object segmentation with adaptive feature bank and uncertain-region refinement,"https://scholar.google.com/scholar?cluster=13323746974008516937&hl=en&as_sdt=0,33",3,2020
Inferring learning rules from animal decision-making,18,neurips,0,0,2023-06-16 15:10:10.990000,https://github.com/pillowlab/psytrack_learning,8,Inferring learning rules from animal decision-making,"https://scholar.google.com/scholar?cluster=6158593368508995675&hl=en&as_sdt=0,5",8,2020
Input-Aware Dynamic Backdoor Attack,195,neurips,2,3,2023-06-16 15:10:11.183000,https://github.com/VinAIResearch/input-aware-backdoor-attack-release,9,Input-aware dynamic backdoor attack,"https://scholar.google.com/scholar?cluster=2116699235703044974&hl=en&as_sdt=0,33",2,2020
Cross-Scale Internal Graph Neural Network for Image Super-Resolution,109,neurips,36,9,2023-06-16 15:10:11.376000,https://github.com/sczhou/IGNN,289,Cross-scale internal graph neural network for image super-resolution,"https://scholar.google.com/scholar?cluster=10605222671754393608&hl=en&as_sdt=0,5",17,2020
Restoring Negative Information in Few-Shot Object Detection,46,neurips,8,5,2023-06-16 15:10:11.568000,https://github.com/yang-yk/NP-RepMet,27,Restoring negative information in few-shot object detection,"https://scholar.google.com/scholar?cluster=13837106915985694250&hl=en&as_sdt=0,5",3,2020
Robust Correction of Sampling Bias using Cumulative Distribution Functions,6,neurips,0,0,2023-06-16 15:10:11.760000,https://github.com/honeybijan/NeurIPS2020,1,Robust correction of sampling bias using cumulative distribution functions,"https://scholar.google.com/scholar?cluster=14960787625732407840&hl=en&as_sdt=0,44",1,2020
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation,88,neurips,11,1,2023-06-16 15:10:11.952000,https://github.com/kgl-prml/Pixel-Level-Cycle-Association,86,Pixel-level cycle association: A new perspective for domain adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=15898877851209488916&hl=en&as_sdt=0,5",15,2020
Classification with Valid and Adaptive Coverage,108,neurips,6,0,2023-06-16 15:10:12.144000,https://github.com/msesia/arc,24,Classification with valid and adaptive coverage,"https://scholar.google.com/scholar?cluster=6435727128447832809&hl=en&as_sdt=0,37",2,2020
Diverse Image Captioning with Context-Object Split Latent Spaces,26,neurips,7,2,2023-06-16 15:10:12.335000,https://github.com/visinf/cos-cvae,35,Diverse image captioning with context-object split latent spaces,"https://scholar.google.com/scholar?cluster=8685721581290827769&hl=en&as_sdt=0,33",2,2020
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts,22,neurips,1,0,2023-06-16 15:10:12.529000,https://github.com/mryab/learning-at-home,42,Towards crowdsourced training of large neural networks using decentralized mixture-of-experts,"https://scholar.google.com/scholar?cluster=1517172184249734814&hl=en&as_sdt=0,44",5,2020
Bidirectional Convolutional Poisson Gamma Dynamical Systems,3,neurips,0,0,2023-06-16 15:10:12.721000,https://github.com/BoChenGroup/BCPGDS,0,Bidirectional convolutional Poisson gamma dynamical systems,"https://scholar.google.com/scholar?cluster=2617893906496182541&hl=en&as_sdt=0,33",1,2020
Deep Reinforcement and InfoMax Learning,77,neurips,4,0,2023-06-16 15:10:12.912000,https://github.com/bmazoure/DRIML,10,Deep reinforcement and infomax learning,"https://scholar.google.com/scholar?cluster=18204322956274436351&hl=en&as_sdt=0,39",3,2020
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow,11,neurips,0,1,2023-06-16 15:10:13.105000,https://github.com/didriknielsen/pixelcnn_flow,18,Closing the dequantization gap: Pixelcnn as a single-layer flow,"https://scholar.google.com/scholar?cluster=9793552037748729432&hl=en&as_sdt=0,5",6,2020
All Word Embeddings from One Embedding,12,neurips,3,0,2023-06-16 15:10:13.297000,https://github.com/takase/alone_seq2seq,26,All word embeddings from one embedding,"https://scholar.google.com/scholar?cluster=16025202978450671106&hl=en&as_sdt=0,21",5,2020
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks,9,neurips,0,0,2023-06-16 15:10:13.489000,https://github.com/wmyw96/convex-cnn-tf,0,How to characterize the landscape of overparameterized convolutional neural networks,"https://scholar.google.com/scholar?cluster=16949672964324049904&hl=en&as_sdt=0,5",1,2020
Adaptive Discretization for Model-Based Reinforcement Learning,19,neurips,2,0,2023-06-16 15:10:13.680000,https://github.com/seanrsinclair/AdaptiveQLearning,1,Adaptive discretization for model-based reinforcement learning,"https://scholar.google.com/scholar?cluster=16783221082226799&hl=en&as_sdt=0,46",1,2020
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching,39,neurips,13,4,2023-06-16 15:10:13.872000,https://github.com/binaryai/CodeCMR,43,Codecmr: Cross-modal retrieval for function-level binary source code matching,"https://scholar.google.com/scholar?cluster=8935328746274345549&hl=en&as_sdt=0,19",4,2020
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks,30,neurips,2,1,2023-06-16 15:10:14.065000,https://github.com/skypea/DAG_No_Fear,10,DAGs with No Fears: A closer look at continuous optimization for learning Bayesian networks,"https://scholar.google.com/scholar?cluster=1019446956575519869&hl=en&as_sdt=0,50",3,2020
Teaching a GAN What Not to Learn,14,neurips,6,1,2023-06-16 15:10:14.257000,https://github.com/DarthSid95/RumiGANs,29,Teaching a gan what not to learn,"https://scholar.google.com/scholar?cluster=5006743411241941329&hl=en&as_sdt=0,3",2,2020
Rethinking Learnable Tree Filter for Generic Feature Transform,13,neurips,9,7,2023-06-16 15:10:14.459000,https://github.com/StevenGrove/LearnableTreeFilterV2,89,Rethinking learnable tree filter for generic feature transform,"https://scholar.google.com/scholar?cluster=18019390806247170102&hl=en&as_sdt=0,33",2,2020
Self-Supervised Relational Reasoning for Representation Learning,43,neurips,24,0,2023-06-16 15:10:14.652000,https://github.com/mpatacchiola/self-supervised-relational-reasoning,136,Self-supervised relational reasoning for representation learning,"https://scholar.google.com/scholar?cluster=4065282984130236161&hl=en&as_sdt=0,44",7,2020
Sufficient dimension reduction for classification using principal optimal transport direction,12,neurips,2,0,2023-06-16 15:10:14.844000,https://github.com/ChengzijunAixiaoli/POTD,4,Sufficient dimension reduction for classification using principal optimal transport direction,"https://scholar.google.com/scholar?cluster=9453699128109678882&hl=en&as_sdt=0,5",1,2020
Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine,11,neurips,2,0,2023-06-16 15:10:15.039000,https://github.com/gerrili1996/Incremental_DRSVM,0,Fast epigraphical projection-based incremental algorithms for Wasserstein distributionally robust support vector machine,"https://scholar.google.com/scholar?cluster=17557069801985892953&hl=en&as_sdt=0,33",2,2020
Adaptive Reduced Rank Regression,14,neurips,5,0,2023-06-16 15:10:15.255000,https://github.com/Qiong-WU/ARRR_code,29,Adaptive reduced rank regression,"https://scholar.google.com/scholar?cluster=833219182915456157&hl=en&as_sdt=0,48",2,2020
Learning Loss for Test-Time Augmentation,50,neurips,2,2,2023-06-16 15:10:15.466000,https://github.com/bayesgroup/gps-augment,35,Learning loss for test-time augmentation,"https://scholar.google.com/scholar?cluster=11423734549303606224&hl=en&as_sdt=0,30",12,2020
Balanced Meta-Softmax for Long-Tailed Visual Recognition,238,neurips,10,0,2023-06-16 15:10:15.661000,https://github.com/jiawei-ren/BalancedMetaSoftmax,66,Balanced meta-softmax for long-tailed visual recognition,"https://scholar.google.com/scholar?cluster=6313928950899865573&hl=en&as_sdt=0,5",6,2020
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning,78,neurips,2,0,2023-06-16 15:10:15.854000,https://github.com/ElisevanderPol/mdp-homomorphic-networks,22,Mdp homomorphic networks: Group symmetries in reinforcement learning,"https://scholar.google.com/scholar?cluster=3290101781386627154&hl=en&as_sdt=0,5",2,2020
Object Goal Navigation using Goal-Oriented Semantic Exploration,259,neurips,44,6,2023-06-16 15:10:16.047000,https://github.com/devendrachaplot/Object-Goal-Navigation,169,Object goal navigation using goal-oriented semantic exploration,"https://scholar.google.com/scholar?cluster=2452364222221336490&hl=en&as_sdt=0,5",5,2020
Efficient semidefinite-programming-based inference for binary and multi-class MRFs,3,neurips,0,0,2023-06-16 15:10:16.241000,https://github.com/locuslab/sdp_mrf,3,Efficient semidefinite-programming-based inference for binary and multi-class MRFs,"https://scholar.google.com/scholar?cluster=795899549396489666&hl=en&as_sdt=0,33",6,2020
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing,123,neurips,15,9,2023-06-16 15:10:16.467000,https://github.com/laiguokun/Funnel-Transformer,197,Funnel-transformer: Filtering out sequential redundancy for efficient language processing,"https://scholar.google.com/scholar?cluster=13758108828249747636&hl=en&as_sdt=0,33",11,2020
Semantic Visual Navigation by Watching YouTube Videos,48,neurips,0,19,2023-06-16 15:10:16.660000,https://github.com/MatthewChang/video-dqn,22,Semantic visual navigation by watching youtube videos,"https://scholar.google.com/scholar?cluster=5339143065575935853&hl=en&as_sdt=0,39",2,2020
Learning Differential Equations that are Easy to Solve,76,neurips,31,4,2023-06-16 15:10:16.853000,https://github.com/jacobjinkelly/easy-neural-ode,245,Learning differential equations that are easy to solve,"https://scholar.google.com/scholar?cluster=17384297955183349294&hl=en&as_sdt=0,44",10,2020
Influence-Augmented Online Planning for Complex Environments,7,neurips,1,0,2023-06-16 15:10:17.045000,https://github.com/INFLUENCEorg/IAOP,3,Influence-augmented online planning for complex environments,"https://scholar.google.com/scholar?cluster=11045895327185763569&hl=en&as_sdt=0,5",3,2020
Probabilistic Time Series Forecasting with Shape and Temporal Diversity,21,neurips,16,2,2023-06-16 15:10:17.237000,https://github.com/vincent-leguen/STRIPE,74,Probabilistic time series forecasting with shape and temporal diversity,"https://scholar.google.com/scholar?cluster=1337249375985233521&hl=en&as_sdt=0,30",3,2020
Continual Deep Learning by Functional Regularisation of Memorable Past,82,neurips,4,1,2023-06-16 15:10:17.443000,https://github.com/team-approx-bayes/fromp,37,Continual deep learning by functional regularisation of memorable past,"https://scholar.google.com/scholar?cluster=10115135321591353527&hl=en&as_sdt=0,33",2,2020
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning,162,neurips,23,1,2023-06-16 15:10:17.637000,https://github.com/snap-stanford/distance-encoding,173,Distance encoding: Design provably more powerful neural networks for graph representation learning,"https://scholar.google.com/scholar?cluster=6342884862045270520&hl=en&as_sdt=0,5",8,2020
Fast Fourier Convolution,137,neurips,28,8,2023-06-16 15:10:17.829000,https://github.com/pkumivision/FFC,237,Fast fourier convolution,"https://scholar.google.com/scholar?cluster=2160547042943986472&hl=en&as_sdt=0,33",4,2020
Learning Structured Distributions From Untrusted Batches: Faster and Simpler,12,neurips,0,0,2023-06-16 15:10:18.022000,https://github.com/secanth/federated,1,Learning structured distributions from untrusted batches: Faster and simpler,"https://scholar.google.com/scholar?cluster=8991328284889449701&hl=en&as_sdt=0,33",1,2020
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks,49,neurips,7,0,2023-06-16 15:10:18.215000,https://github.com/ermongroup/ODS,50,Diversity can be transferred: Output diversification for white-and black-box attacks,"https://scholar.google.com/scholar?cluster=13509573931669660487&hl=en&as_sdt=0,47",8,2020
Efficient Low Rank Gaussian Variational Inference for Neural Networks,19,neurips,1,1,2023-06-16 15:10:18.408000,https://github.com/marctom/elrgvi,2,Efficient low rank gaussian variational inference for neural networks,"https://scholar.google.com/scholar?cluster=9190851527291082244&hl=en&as_sdt=0,5",2,2020
Probabilistic Circuits for Variational Inference in Discrete Graphical Models,19,neurips,0,0,2023-06-16 15:10:18.600000,https://github.com/AndyShih12/SPN_Variational_Inference,14,Probabilistic circuits for variational inference in discrete graphical models,"https://scholar.google.com/scholar?cluster=8548433346916922000&hl=en&as_sdt=0,32",2,2020
Labelling unlabelled videos from scratch with multi-modal self-supervision,108,neurips,14,4,2023-06-16 15:10:18.792000,https://github.com/facebookresearch/selavi,108,Labelling unlabelled videos from scratch with multi-modal self-supervision,"https://scholar.google.com/scholar?cluster=6374132588879486685&hl=en&as_sdt=0,5",12,2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization,405,neurips,34,6,2023-06-16 15:10:18.984000,https://github.com/izmailovpavel/understandingbdl,215,Bayesian deep learning and a probabilistic perspective of generalization,"https://scholar.google.com/scholar?cluster=13252502369933124881&hl=en&as_sdt=0,5",6,2020
Unsupervised Learning of Object Landmarks via Self-Training Correspondence,10,neurips,7,0,2023-06-16 15:10:19.176000,https://github.com/malldimi1/UnsupervisedLandmarks,22,Unsupervised learning of object landmarks via self-training correspondence,"https://scholar.google.com/scholar?cluster=5849709549192485830&hl=en&as_sdt=0,10",1,2020
Generative View Synthesis: From Single-view Semantics to Novel-view Images,13,neurips,1,0,2023-06-16 15:10:19.382000,https://github.com/tedyhabtegebrial/gvsnet,20,Generative view synthesis: From single-view semantics to novel-view images,"https://scholar.google.com/scholar?cluster=6878036878351382558&hl=en&as_sdt=0,5",5,2020
Deep Variational Instance Segmentation,8,neurips,4,5,2023-06-16 15:10:19.576000,https://github.com/jia2lin3yuan1/2020-instanceSeg,25,Deep variational instance segmentation,"https://scholar.google.com/scholar?cluster=16407992024714041715&hl=en&as_sdt=0,5",4,2020
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence,25,neurips,11,4,2023-06-16 15:10:19.769000,https://github.com/liuf1990/Implicit_Dense_Correspondence,55,Learning implicit functions for topology-varying dense 3d shape correspondence,"https://scholar.google.com/scholar?cluster=13134506600576574791&hl=en&as_sdt=0,10",9,2020
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems,12,neurips,1,0,2023-06-16 15:10:19.961000,https://github.com/flowersteam/holmes,5,Hierarchically organized latent modules for exploratory search in morphogenetic systems,"https://scholar.google.com/scholar?cluster=8455312043752460622&hl=en&as_sdt=0,5",1,2020
Probabilistic Orientation Estimation with Matrix Fisher Distributions,28,neurips,2,1,2023-06-16 15:10:20.152000,https://github.com/Davmo049/Public_prob_orientation_estimation_with_matrix_fisher_distributions,20,Probabilistic orientation estimation with matrix fisher distributions,"https://scholar.google.com/scholar?cluster=13738889246738372199&hl=en&as_sdt=0,36",3,2020
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons,3,neurips,0,0,2023-06-16 15:10:20.345000,https://github.com/Pehlevan-Group/BalancedEIMinimax,3,Minimax dynamics of optimally balanced spiking networks of excitatory and inhibitory neurons,"https://scholar.google.com/scholar?cluster=13427818061137891735&hl=en&as_sdt=0,5",4,2020
Towards Deeper Graph Neural Networks with Differentiable Group Normalization,125,neurips,4,1,2023-06-16 15:10:20.537000,https://github.com/Kaixiong-Zhou/DGN,32,Towards deeper graph neural networks with differentiable group normalization,"https://scholar.google.com/scholar?cluster=15936617451529189150&hl=en&as_sdt=0,34",2,2020
Stochastic Optimization for Performative Prediction,66,neurips,4,1,2023-06-16 15:10:20.729000,https://github.com/zykls/performative-prediction,20,Stochastic optimization for performative prediction,"https://scholar.google.com/scholar?cluster=17793048602767737159&hl=en&as_sdt=0,5",3,2020
Domain Adaptation as a Problem of Inference on Graphical Models,44,neurips,3,0,2023-06-16 15:10:20.921000,https://github.com/mgong2/DA_Infer,26,Domain adaptation as a problem of inference on graphical models,"https://scholar.google.com/scholar?cluster=15196795471254372547&hl=en&as_sdt=0,44",2,2020
HOI Analysis: Integrating and Decomposing Human-Object Interaction,58,neurips,46,2,2023-06-16 15:10:21.114000,https://github.com/DirtyHarryLYL/HAKE-Action-Torch,201,Hoi analysis: Integrating and decomposing human-object interaction,"https://scholar.google.com/scholar?cluster=1869809068174176654&hl=en&as_sdt=0,45",11,2020
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering,11,neurips,1,0,2023-06-16 15:10:21.305000,https://github.com/MengLiuPurdue/SLQ,2,Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering,"https://scholar.googleusercontent.com/scholar?q=cache:qcklNVm80uwJ:scholar.google.com/+Strongly+local+p-norm-cut+algorithms+for+semi-supervised+learning+and+local+graph+clustering&hl=en&as_sdt=0,33",3,2020
Deep Direct Likelihood Knockoffs,16,neurips,4,0,2023-06-16 15:10:21.497000,https://github.com/rajesh-lab/ddlk,7,Deep direct likelihood knockoffs,"https://scholar.google.com/scholar?cluster=6129032431811553962&hl=en&as_sdt=0,33",5,2020
Meta-Neighborhoods,10,neurips,3,0,2023-06-16 15:10:21.688000,https://github.com/lupalab/Meta-Neighborhoods,7,Meta-neighborhoods,"https://scholar.google.com/scholar?cluster=2219636310662669974&hl=en&as_sdt=0,1",2,2020
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons,8,neurips,1,0,2023-06-16 15:10:21.880000,https://github.com/gmahuas/2stepGLM,1,A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons,"https://scholar.google.com/scholar?cluster=14549343330955673745&hl=en&as_sdt=0,14",1,2020
Feature Importance Ranking for Deep Learning,66,neurips,14,2,2023-06-16 15:10:22.072000,https://github.com/maksym33/FeatureImportanceDL,31,Feature importance ranking for deep learning,"https://scholar.google.com/scholar?cluster=10291349468278084866&hl=en&as_sdt=0,14",3,2020
"Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks",40,neurips,1,0,2023-06-16 15:10:22.265000,https://github.com/umutsimsekli/Hausdorff-Dimension-and-Generalization,2,"Hausdorff dimension, heavy tails, and generalization in neural networks","https://scholar.google.com/scholar?cluster=8886979563776893274&hl=en&as_sdt=0,5",3,2020
Learning Physical Constraints with Neural Projections,24,neurips,2,1,2023-06-16 15:10:22.482000,https://github.com/y-sq/neural_proj,15,Learning physical constraints with neural projections,"https://scholar.google.com/scholar?cluster=1914156028148083261&hl=en&as_sdt=0,33",2,2020
Robust Optimization for Fairness with Noisy Protected Groups,85,neurips,2,1,2023-06-16 15:10:22.675000,https://github.com/wenshuoguo/robust-fairness-code,6,Robust optimization for fairness with noisy protected groups,"https://scholar.google.com/scholar?cluster=5111841011798470081&hl=en&as_sdt=0,5",1,2020
Noise-Contrastive Estimation for Multivariate Point Processes,14,neurips,2,0,2023-06-16 15:10:22.867000,https://github.com/HMEIatJHU/nce-mpp,15,Noise-contrastive estimation for multivariate point processes,"https://scholar.google.com/scholar?cluster=10618761970260910492&hl=en&as_sdt=0,5",3,2020
Multiscale Deep Equilibrium Models,139,neurips,29,0,2023-06-16 15:10:23.060000,https://github.com/locuslab/mdeq,222,Multiscale deep equilibrium models,"https://scholar.google.com/scholar?cluster=9858453803735938369&hl=en&as_sdt=0,5",13,2020
Sparse Graphical Memory for Robust Planning,41,neurips,8,1,2023-06-16 15:10:23.265000,https://github.com/scottemmons/sgm,28,Sparse graphical memory for robust planning,"https://scholar.google.com/scholar?cluster=14782939310889640294&hl=en&as_sdt=0,36",6,2020
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction,13,neurips,1,0,2023-06-16 15:10:23.461000,https://github.com/otiliastr/brain_task_effect,6,Modeling task effects on meaning representation in the brain via zero-shot meg prediction,"https://scholar.google.com/scholar?cluster=342859305245260360&hl=en&as_sdt=0,23",3,2020
Robust Quantization: One Model to Rule Them All,51,neurips,7,5,2023-06-16 15:10:23.653000,https://github.com/moranshkolnik/RobustQuantization,32,Robust quantization: One model to rule them all,"https://scholar.google.com/scholar?cluster=1861034670227893783&hl=en&as_sdt=0,5",6,2020
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming,68,neurips,23,2,2023-06-16 15:10:23.846000,https://github.com/deepmind/jax_verify,126,Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming,"https://scholar.google.com/scholar?cluster=415562155937952325&hl=en&as_sdt=0,43",8,2020
Federated Accelerated Stochastic Gradient Descent,108,neurips,1,0,2023-06-16 15:10:24.038000,https://github.com/hongliny/FedAc-NeurIPS20,12,Federated accelerated stochastic gradient descent,"https://scholar.google.com/scholar?cluster=17827059715585826187&hl=en&as_sdt=0,44",2,2020
An analytic theory of shallow networks dynamics for hinge loss classification,13,neurips,0,0,2023-06-16 15:10:24.230000,https://github.com/phiandark/DynHingeLoss,0,An analytic theory of shallow networks dynamics for hinge loss classification,"https://scholar.google.com/scholar?cluster=9155304244259608841&hl=en&as_sdt=0,41",1,2020
Learning to Orient Surfaces by Self-supervised Spherical CNNs,27,neurips,3,1,2023-06-16 15:10:24.422000,https://github.com/CVLAB-Unibo/compass,15,Learning to orient surfaces by self-supervised spherical cnns,"https://scholar.google.com/scholar?cluster=13771145081900249763&hl=en&as_sdt=0,24",9,2020
Parabolic Approximation Line Search for DNNs,12,neurips,3,1,2023-06-16 15:10:24.615000,https://github.com/cogsys-tuebingen/PAL,20,Parabolic approximation line search for dnns,"https://scholar.google.com/scholar?cluster=15049615666059175813&hl=en&as_sdt=0,11",8,2020
Generative causal explanations of black-box classifiers,49,neurips,10,0,2023-06-16 15:10:24.808000,https://github.com/siplab-gt/generative-causal-explanations,25,Generative causal explanations of black-box classifiers,"https://scholar.google.com/scholar?cluster=11533502889457597902&hl=en&as_sdt=0,34",5,2020
Sub-sampling for Efficient Non-Parametric Bandit Exploration,13,neurips,3,0,2023-06-16 15:10:25.002000,https://github.com/DBaudry/Sub-Sampling-Dueling-Algorithms-Neurips20,10,Sub-sampling for efficient non-parametric bandit exploration,"https://scholar.google.com/scholar?cluster=15996804451950962772&hl=en&as_sdt=0,10",1,2020
Learning under Model Misspecification: Applications to Variational and Ensemble methods,52,neurips,2,0,2023-06-16 15:10:25.195000,https://github.com/PGM-Lab/PAC2BAYES,9,Learning under model misspecification: Applications to variational and ensemble methods,"https://scholar.google.com/scholar?cluster=12176489635076115022&hl=en&as_sdt=0,33",7,2020
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles,71,neurips,13,0,2023-06-16 15:10:25.386000,https://github.com/zjysteven/DVERGE,54,DVERGE: diversifying vulnerabilities for enhanced robust generation of ensembles,"https://scholar.google.com/scholar?cluster=15783762290980425990&hl=en&as_sdt=0,5",1,2020
Latent World Models For Intrinsically Motivated Exploration,19,neurips,2,0,2023-06-16 15:10:25.579000,https://github.com/htdt/lwm,18,Latent world models for intrinsically motivated exploration,"https://scholar.google.com/scholar?cluster=5814046916674904026&hl=en&as_sdt=0,22",3,2020
Training Generative Adversarial Networks by Solving Ordinary Differential Equations,25,neurips,2436,170,2023-06-16 15:10:25.771000,https://github.com/deepmind/deepmind-research,11902,Training generative adversarial networks by solving ordinary differential equations,"https://scholar.google.com/scholar?cluster=5086997410208993615&hl=en&as_sdt=0,5",336,2020
Learning of Discrete Graphical Models with Neural Networks,6,neurips,0,0,2023-06-16 15:10:25.963000,https://github.com/lanl-ansi/NeurISE,0,Learning of discrete graphical models with neural networks,"https://scholar.google.com/scholar?cluster=17603472038483944187&hl=en&as_sdt=0,23",5,2020
RepPoints v2: Verification Meets Regression for Object Detection,86,neurips,49,14,2023-06-16 15:10:26.154000,https://github.com/Scalsol/RepPointsV2,294,Reppoints v2: Verification meets regression for object detection,"https://scholar.google.com/scholar?cluster=14843700105251392523&hl=en&as_sdt=0,47",10,2020
Unfolding the Alternating Optimization for Blind Super Resolution,150,neurips,39,6,2023-06-16 15:10:26.346000,https://github.com/greatlog/DAN,204,Unfolding the alternating optimization for blind super resolution,"https://scholar.google.com/scholar?cluster=16834542650773066132&hl=en&as_sdt=0,10",5,2020
Entrywise convergence of iterative methods for eigenproblems,2,neurips,0,0,2023-06-16 15:10:26.539000,https://github.com/VHarisop/entrywise-convergence,0,Entrywise convergence of iterative methods for eigenproblems,"https://scholar.google.com/scholar?cluster=4848039311509999194&hl=en&as_sdt=0,5",3,2020
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views,35,neurips,5,1,2023-06-16 15:10:26.731000,https://github.com/NanboLi/MulMON,16,Learning object-centric representations of multi-object scenes from multiple views,"https://scholar.google.com/scholar?cluster=5931711459859272834&hl=en&as_sdt=0,5",3,2020
Self-supervised Co-Training for Video Representation Learning,322,neurips,32,4,2023-06-16 15:10:26.923000,https://github.com/TengdaHan/CoCLR,274,Self-supervised co-training for video representation learning,"https://scholar.google.com/scholar?cluster=11310050495628333190&hl=en&as_sdt=0,5",13,2020
Gradient Estimation with Stochastic Softmax Tricks,53,neurips,5,2,2023-06-16 15:10:27.115000,https://github.com/choidami/sst,48,Gradient estimation with stochastic softmax tricks,"https://scholar.google.com/scholar?cluster=3158119995430472666&hl=en&as_sdt=0,38",2,2020
Meta-Learning Requires Meta-Augmentation,65,neurips,7320,1025,2023-06-16 15:10:27.307000,https://github.com/google-research/google-research,29776,Meta-learning requires meta-augmentation,"https://scholar.google.com/scholar?cluster=14551438470205957966&hl=en&as_sdt=0,5",727,2020
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting,20,neurips,5,3,2023-06-16 15:10:27.499000,https://github.com/Holmeswww/PPOGAN,24,Improving gan training with probability ratio clipping and sample reweighting,"https://scholar.google.com/scholar?cluster=1603102881023302087&hl=en&as_sdt=0,5",3,2020
On Testing of Samplers,7,neurips,1,4,2023-06-16 15:10:27.691000,https://github.com/meelgroup/barbarik,11,On testing of samplers,"https://scholar.google.com/scholar?cluster=5212652190142141590&hl=en&as_sdt=0,31",5,2020
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective,4,neurips,0,0,2023-06-16 15:10:27.883000,https://github.com/ntienvu/tvo_gp_bandit,1,Gaussian process bandit optimization of the thermodynamic variational objective,"https://scholar.google.com/scholar?cluster=4199760950647121080&hl=en&as_sdt=0,5",2,2020
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers,503,neurips,1867,362,2023-06-16 15:10:28.076000,https://github.com/microsoft/unilm,12770,Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers,"https://scholar.google.com/scholar?cluster=14860866195704248914&hl=en&as_sdt=0,6",260,2020
Woodbury Transformations for Deep Generative Flows,12,neurips,0,0,2023-06-16 15:10:28.271000,https://github.com/yolu1055/WoodburyTransformations,2,Woodbury transformations for deep generative flows,"https://scholar.google.com/scholar?cluster=5314675607084921976&hl=en&as_sdt=0,5",4,2020
Graph Contrastive Learning with Augmentations,864,neurips,90,27,2023-06-16 15:10:28.466000,https://github.com/Shen-Lab/GraphCL,434,Graph contrastive learning with augmentations,"https://scholar.google.com/scholar?cluster=9963871328827947371&hl=en&as_sdt=0,33",9,2020
Gradient Surgery for Multi-Task Learning,483,neurips,37,12,2023-06-16 15:10:28.658000,https://github.com/tianheyu927/PCGrad,255,Gradient surgery for multi-task learning,"https://scholar.google.com/scholar?cluster=15639381935804051305&hl=en&as_sdt=0,5",18,2020
Bayesian Probabilistic Numerical Integration with Tree-Based Models,2,neurips,1,0,2023-06-16 15:10:28.850000,https://github.com/ImperialCollegeLondon/BART-Int,7,Bayesian probabilistic numerical integration with tree-based models,"https://scholar.google.com/scholar?cluster=17070012166494581814&hl=en&as_sdt=0,5",3,2020
Graph Meta Learning via Local Subgraphs,99,neurips,28,3,2023-06-16 15:10:29.042000,https://github.com/mims-harvard/g-meta,105,Graph meta learning via local subgraphs,"https://scholar.google.com/scholar?cluster=12205589678815319348&hl=en&as_sdt=0,33",6,2020
Stochastic Deep Gaussian Processes over Graphs,12,neurips,4,2,2023-06-16 15:10:29.239000,https://github.com/naiqili/DGPG,21,Stochastic deep gaussian processes over graphs,"https://scholar.google.com/scholar?cluster=12355545301307730680&hl=en&as_sdt=0,5",1,2020
Evaluating Attribution for Graph Neural Networks,73,neurips,16,2,2023-06-16 15:10:29.431000,https://github.com/google-research/graph-attribution,65,Evaluating attribution for graph neural networks,"https://scholar.google.com/scholar?cluster=8947730950192198028&hl=en&as_sdt=0,5",7,2020
Neuron Shapley: Discovering the Responsible Neurons,73,neurips,4,1,2023-06-16 15:10:29.623000,https://github.com/amiratag/neuronshapley,21,Neuron shapley: Discovering the responsible neurons,"https://scholar.google.com/scholar?cluster=17071194082042236550&hl=en&as_sdt=0,5",3,2020
Stochastic Normalizing Flows,93,neurips,9,1,2023-06-16 15:10:29.816000,https://github.com/noegroup/stochastic_normalizing_flows,56,Stochastic normalizing flows,"https://scholar.google.com/scholar?cluster=16849056708118710462&hl=en&as_sdt=0,22",5,2020
Revisiting Parameter Sharing for Automatic Neural Channel Number Search,26,neurips,7,0,2023-06-16 15:10:30.008000,https://github.com/haolibai/APS-channel-search,20,Revisiting parameter sharing for automatic neural channel number search,"https://scholar.google.com/scholar?cluster=13186156999876305193&hl=en&as_sdt=0,1",3,2020
Differentially-Private Federated Linear Bandits,78,neurips,3,1,2023-06-16 15:10:30.201000,https://github.com/abhimanyudubey/private_federated_linear_bandits,3,Differentially-private federated linear bandits,"https://scholar.google.com/scholar?cluster=10188063075897991616&hl=en&as_sdt=0,5",1,2020
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking,31,neurips,10,18,2023-06-16 15:10:30.394000,https://github.com/paninski-lab/deepgraphpose,29,Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking,"https://scholar.google.com/scholar?cluster=3453822722256675361&hl=en&as_sdt=0,44",7,2020
Sparse Symplectically Integrated Neural Networks,20,neurips,1,0,2023-06-16 15:10:30.586000,https://github.com/dandip/ssinn,8,Sparse symplectically integrated neural networks,"https://scholar.google.com/scholar?cluster=14798517979957496479&hl=en&as_sdt=0,15",2,2020
Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision,16,neurips,2,0,2023-06-16 15:10:30.778000,https://github.com/nicolaihaeni/corn,14,Continuous object representation networks: novel view synthesis without target view supervision,"https://scholar.google.com/scholar?cluster=765897047698290451&hl=en&as_sdt=0,5",2,2020
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence,41,neurips,4,2,2023-06-16 15:10:30.970000,https://github.com/thomassutter/mmjsd,13,Multimodal generative learning utilizing jensen-shannon-divergence,"https://scholar.google.com/scholar?cluster=17836611088871038657&hl=en&as_sdt=0,5",1,2020
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers,134,neurips,23,0,2023-06-16 15:10:31.162000,https://github.com/tum-pbs/Solver-in-the-Loop,122,Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers,"https://scholar.google.com/scholar?cluster=2286766760551989039&hl=en&as_sdt=0,32",6,2020
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield,4,neurips,1,0,2023-06-16 15:10:31.356000,https://github.com/pesaranlab/neuro_cbs,6,Optimal adaptive electrode selection to maximize simultaneously recorded neuron yield,"https://scholar.google.com/scholar?cluster=3860529084779056549&hl=en&as_sdt=0,10",3,2020
Neurosymbolic Reinforcement Learning with Formally Verified Exploration,50,neurips,2,5,2023-06-16 15:10:31.548000,https://github.com/gavlegoat/safe-learning,13,Neurosymbolic reinforcement learning with formally verified exploration,"https://scholar.google.com/scholar?cluster=16428305531344128935&hl=en&as_sdt=0,5",2,2020
On 1/n neural representation and robustness,19,neurips,1,0,2023-06-16 15:10:31.742000,https://github.com/josuenassar/power_law,7,On 1/n neural representation and robustness,"https://scholar.google.com/scholar?cluster=14612770369819484609&hl=en&as_sdt=0,14",3,2020
Boundary thickness and robustness in learning models,27,neurips,1,0,2023-06-16 15:10:31.934000,https://github.com/nsfzyzz/boundary_thickness,17,Boundary thickness and robustness in learning models,"https://scholar.google.com/scholar?cluster=7743383416741324781&hl=en&as_sdt=0,14",2,2020
Demixed shared component analysis of neural population data from multiple brain areas,0,neurips,1,1,2023-06-16 15:10:32.126000,https://github.com/yu-takagi/dSCA,10,Demixed shared component analysis of neural population data from multiple brain areas,"https://scholar.google.com/scholar?cluster=14678847289626964830&hl=en&as_sdt=0,34",3,2020
Learning Kernel Tests Without Data Splitting,14,neurips,2,0,2023-06-16 15:10:32.320000,https://github.com/MPI-IS/tests-wo-splitting,5,Learning kernel tests without data splitting,"https://scholar.google.com/scholar?cluster=12039043020526096218&hl=en&as_sdt=0,14",3,2020
Unsupervised Data Augmentation for Consistency Training,1590,neurips,313,71,2023-06-16 15:10:32.512000,https://github.com/google-research/uda,2122,Unsupervised data augmentation for consistency training,"https://scholar.google.com/scholar?cluster=12880251999793471515&hl=en&as_sdt=0,22",44,2020
Pruning neural networks without any data by iteratively conserving synaptic flow,337,neurips,42,4,2023-06-16 15:10:32.704000,https://github.com/ganguli-lab/Synaptic-Flow,190,Pruning neural networks without any data by iteratively conserving synaptic flow,"https://scholar.google.com/scholar?cluster=1210718401723821316&hl=en&as_sdt=0,39",27,2020
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations,20,neurips,0,0,2023-06-16 15:10:32.896000,https://github.com/core-robotics-lab/personalized_neural_trees,3,Interpretable and personalized apprenticeship scheduling: Learning interpretable scheduling policies from heterogeneous user demonstrations,"https://scholar.google.com/scholar?cluster=14212646123511615039&hl=en&as_sdt=0,33",4,2020
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes,29,neurips,1,0,2023-06-16 15:10:33.088000,https://github.com/mxu34/mbrl-gpmm,25,Task-agnostic online reinforcement learning with an infinite mixture of gaussian processes,"https://scholar.google.com/scholar?cluster=1015317596809472337&hl=en&as_sdt=0,44",5,2020
Benchmarking Deep Learning Interpretability in Time Series Predictions,101,neurips,16,4,2023-06-16 15:10:33.280000,https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark,73,Benchmarking deep learning interpretability in time series predictions,"https://scholar.google.com/scholar?cluster=15559999759803172954&hl=en&as_sdt=0,33",4,2020
Federated Principal Component Analysis,34,neurips,6,0,2023-06-16 15:10:33.486000,https://github.com/andylamp/federated_pca,33,Federated principal component analysis,"https://scholar.google.com/scholar?cluster=5556638479744885012&hl=en&as_sdt=0,33",3,2020
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks,26,neurips,2,0,2023-06-16 15:10:33.678000,https://github.com/alevine0/patchSmoothing,15,(De) Randomized smoothing for certifiable defense against patch attacks,"https://scholar.google.com/scholar?cluster=7126332887163750199&hl=en&as_sdt=0,14",2,2020
SMYRF - Efficient Attention using Asymmetric Clustering,24,neurips,5,0,2023-06-16 15:10:33.870000,https://github.com/giannisdaras/smyrf,47,Smyrf-efficient attention using asymmetric clustering,"https://scholar.google.com/scholar?cluster=3416137016272222933&hl=en&as_sdt=0,33",3,2020
Neutralizing Self-Selection Bias in Sampling for Sortition,23,neurips,0,0,2023-06-16 15:10:34.063000,https://github.com/pgoelz/endtoend,0,Neutralizing self-selection bias in sampling for sortition,"https://scholar.google.com/scholar?cluster=12253485634374856447&hl=en&as_sdt=0,36",3,2020
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning,9,neurips,1,4,2023-06-16 15:10:34.286000,https://github.com/chandar-lab/LoCA,4,The LoCA regret: a consistent metric to evaluate model-based behavior in reinforcement learning,"https://scholar.google.com/scholar?cluster=1039496506051846849&hl=en&as_sdt=0,5",4,2020
Bootstrapping neural processes,19,neurips,5,1,2023-06-16 15:10:34.490000,https://github.com/juho-lee/bnp,24,Bootstrapping neural processes,"https://scholar.google.com/scholar?cluster=10569982778154807572&hl=en&as_sdt=0,14",2,2020
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs,55,neurips,13,2,2023-06-16 15:10:34.683000,https://github.com/Stalence/erdos_neu,27,Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs,"https://scholar.google.com/scholar?cluster=6718013845786623075&hl=en&as_sdt=0,5",3,2020
Neural Controlled Differential Equations for Irregular Time Series,255,neurips,67,3,2023-06-16 15:10:34.875000,https://github.com/patrick-kidger/NeuralCDE,528,Neural controlled differential equations for irregular time series,"https://scholar.google.com/scholar?cluster=1622654869428402760&hl=en&as_sdt=0,5",18,2020
Probabilistic Linear Solvers for Machine Learning,13,neurips,0,0,2023-06-16 15:10:35.068000,https://github.com/JonathanWenger/probabilistic-linear-solvers-for-ml,3,Probabilistic linear solvers for machine learning,"https://scholar.google.com/scholar?cluster=1672427431265786249&hl=en&as_sdt=0,34",0,2020
Multipole Graph Neural Operator for Parametric Partial Differential Equations,188,neurips,64,5,2023-06-16 15:10:35.280000,https://github.com/zongyi-li/graph-pde,188,Multipole graph neural operator for parametric partial differential equations,"https://scholar.google.com/scholar?cluster=13318009799245280479&hl=en&as_sdt=0,31",14,2020
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images,143,neurips,6,2,2023-06-16 15:10:35.480000,https://github.com/thunguyenphuoc/BlockGAN,42,Blockgan: Learning 3d object-aware scene representations from unlabelled images,"https://scholar.google.com/scholar?cluster=10671381446972867942&hl=en&as_sdt=0,21",2,2020
Towards Interpretable Natural Language Understanding with Explanations as Latent Variables,22,neurips,3,1,2023-06-16 15:10:35.673000,https://github.com/JamesHujy/ELV,20,Towards interpretable natural language understanding with explanations as latent variables,"https://scholar.google.com/scholar?cluster=922494767816650498&hl=en&as_sdt=0,33",2,2020
The Mean-Squared Error of Double Q-Learning,10,neurips,1,0,2023-06-16 15:10:35.865000,https://github.com/wentaoweng/The-Mean-Squared-Error-of-Double-Q-Learning,2,The mean-squared error of double Q-learning,"https://scholar.google.com/scholar?cluster=12658517305740432001&hl=en&as_sdt=0,47",1,2020
Denoising Diffusion Probabilistic Models,2458,neurips,212,17,2023-06-16 15:10:36.058000,https://github.com/hojonathanho/diffusion,2132,Denoising diffusion probabilistic models,"https://scholar.google.com/scholar?cluster=622631041436591387&hl=en&as_sdt=0,21",20,2020
Barking up the right tree: an approach to search over molecule synthesis DAGs,41,neurips,6,2,2023-06-16 15:10:36.250000,https://github.com/john-bradshaw/synthesis-dags,42,Barking up the right tree: an approach to search over molecule synthesis dags,"https://scholar.google.com/scholar?cluster=13448331198377833406&hl=en&as_sdt=0,33",1,2020
Bandit Samplers for Training Graph Neural Networks,32,neurips,2,2,2023-06-16 15:10:36.488000,https://github.com/xavierzw/ogb-geniepath-bs,3,Bandit samplers for training graph neural networks,"https://scholar.google.com/scholar?cluster=1856670325879954633&hl=en&as_sdt=0,33",1,2020
Sampling from a k-DPP without looking at all items,21,neurips,47,3,2023-06-16 15:10:36.681000,https://github.com/guilgautier/DPPy,204,Sampling from a k-DPP without looking at all items,"https://scholar.google.com/scholar?cluster=13828986995980178437&hl=en&as_sdt=0,10",16,2020
Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence,27,neurips,3,0,2023-06-16 15:10:36.874000,https://github.com/BorgwardtLab/fMRI_Cubical_Persistence,13,Uncovering the topology of time-varying fMRI data using cubical persistence,"https://scholar.google.com/scholar?cluster=11461528831299808646&hl=en&as_sdt=0,44",7,2020
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection,49,neurips,3,2,2023-06-16 15:10:37.067000,https://github.com/rmcong/CoADNet_NeurIPS20,18,CoADNet: Collaborative aggregation-and-distribution networks for co-salient object detection,"https://scholar.google.com/scholar?cluster=8678285635240455625&hl=en&as_sdt=0,50",4,2020
"Regularized linear autoencoders recover the principal components, eventually",21,neurips,1,1,2023-06-16 15:10:37.274000,https://github.com/XuchanBao/linear-ae,14,"Regularized linear autoencoders recover the principal components, eventually","https://scholar.google.com/scholar?cluster=12136029486551136178&hl=en&as_sdt=0,26",2,2020
UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection,24,neurips,4,5,2023-06-16 15:10:37.474000,https://github.com/shenyunhang/UWSOD,20,UWSOD: Toward fully-supervised-level capacity weakly supervised object detection,"https://scholar.google.com/scholar?cluster=9107656569803100242&hl=en&as_sdt=0,5",3,2020
Curriculum learning for multilevel budgeted combinatorial problems,5,neurips,0,0,2023-06-16 15:10:37.666000,https://github.com/AdelNabli/MCN,3,Curriculum learning for multilevel budgeted combinatorial problems,"https://scholar.google.com/scholar?cluster=1657047408095143576&hl=en&as_sdt=0,39",2,2020
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data,26,neurips,3,0,2023-06-16 15:10:37.858000,https://github.com/AudeSportisse/PPCA_MNAR,1,Estimation and imputation in probabilistic principal component analysis with missing not at random data,"https://scholar.google.com/scholar?cluster=2864178808450174600&hl=en&as_sdt=0,5",0,2020
Correlation Robust Influence Maximization,1,neurips,0,1,2023-06-16 15:10:38.051000,https://github.com/justanothergithubber/corr-im,7,Correlation robust influence maximization,"https://scholar.google.com/scholar?cluster=5585956565434768987&hl=en&as_sdt=0,5",2,2020
Neuronal Gaussian Process Regression,914,neurips,0,0,2023-06-16 15:10:38.243000,https://github.com/j-friedrich/neuronalGPR,2,Deep neural networks as gaussian processes,"https://scholar.google.com/scholar?cluster=6709509064500094656&hl=en&as_sdt=0,7",1,2020
Implicit Distributional Reinforcement Learning,10,neurips,3,1,2023-06-16 15:10:38.434000,https://github.com/zhougroup/IDAC,8,Implicit distributional reinforcement learning,"https://scholar.google.com/scholar?cluster=15829252829546371290&hl=en&as_sdt=0,5",2,2020
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE,35,neurips,4,0,2023-06-16 15:10:38.626000,https://github.com/zhd96/pi-vae,30,Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE,"https://scholar.google.com/scholar?cluster=619618171802037739&hl=en&as_sdt=0,44",2,2020
Interior Point Solving for LP-based prediction+optimisation,48,neurips,9,2,2023-06-16 15:10:38.819000,https://github.com/JayMan91/NeurIPSIntopt,14,Interior point solving for lp-based prediction+ optimisation,"https://scholar.google.com/scholar?cluster=1533126665853318342&hl=en&as_sdt=0,33",2,2020
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks,25,neurips,0,0,2023-06-16 15:10:39.011000,https://github.com/romanpogodin/plausible-kernelized-bottleneck,5,Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks,"https://scholar.google.com/scholar?cluster=18100053392278816994&hl=en&as_sdt=0,43",3,2020
Understanding the Role of Training Regimes in Continual Learning,123,neurips,11,6,2023-06-16 15:10:39.202000,https://github.com/imirzadeh/stable-continual-learning,71,Understanding the role of training regimes in continual learning,"https://scholar.google.com/scholar?cluster=13304877207545088213&hl=en&as_sdt=0,14",6,2020
Training Stronger Baselines for Learning to Optimize,30,neurips,7,1,2023-06-16 15:10:39.395000,https://github.com/VITA-Group/L2O-Training-Techniques,25,Training stronger baselines for learning to optimize,"https://scholar.google.com/scholar?cluster=16835534737946083220&hl=en&as_sdt=0,31",2,2020
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss,35,neurips,3,1,2023-06-16 15:10:39.589000,https://github.com/yuruntian/HyNet,54,Hynet: Learning local descriptor with hybrid similarity measure and triplet loss,"https://scholar.google.com/scholar?cluster=4475373303721859759&hl=en&as_sdt=0,10",5,2020
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free,28,neurips,9,0,2023-06-16 15:10:39.791000,https://github.com/VITA-Group/Once-for-All-Adversarial-Training,40,Once-for-all adversarial training: In-situ tradeoff between robustness and accuracy for free,"https://scholar.google.com/scholar?cluster=18012050461458046931&hl=en&as_sdt=0,5",9,2020
Rotated Binary Neural Network,96,neurips,19,1,2023-06-16 15:10:40.004000,https://github.com/lmbxmu/RBNN,75,Rotated binary neural network,"https://scholar.google.com/scholar?cluster=9922290527765380994&hl=en&as_sdt=0,43",7,2020
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian,10,neurips,1,0,2023-06-16 15:10:40.197000,https://github.com/lorenzodallamico/CoDeBetHe.jl,3,Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian,"https://scholar.google.com/scholar?cluster=926942398566929130&hl=en&as_sdt=0,5",1,2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness,242,neurips,178,119,2023-06-16 15:10:40.389000,https://github.com/google/uncertainty-baselines,1242,Simple and principled uncertainty estimation with deterministic deep learning via distance awareness,"https://scholar.google.com/scholar?cluster=7900448883391646024&hl=en&as_sdt=0,36",20,2020
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment,6,neurips,0,0,2023-06-16 15:10:40.582000,https://github.com/TavorB/adaptiveSpectral,0,Adaptive learning of rank-one models for efficient pairwise sequence alignment,"https://scholar.google.com/scholar?cluster=669545011100513558&hl=en&as_sdt=0,46",3,2020
Hierarchical nucleation in deep neural networks,15,neurips,2,0,2023-06-16 15:10:40.774000,https://github.com/diegodoimo/hierarchical_nucleation,6,Hierarchical nucleation in deep neural networks,"https://scholar.google.com/scholar?cluster=12500887125921827469&hl=en&as_sdt=0,5",3,2020
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains,1009,neurips,106,12,2023-06-16 15:10:40.966000,https://github.com/tancik/fourier-feature-networks,1030,Fourier features let networks learn high frequency functions in low dimensional domains,"https://scholar.google.com/scholar?cluster=14572159759264088577&hl=en&as_sdt=0,10",21,2020
Graph Geometry Interaction Learning,56,neurips,6,4,2023-06-16 15:10:41.159000,https://github.com/CheriseZhu/GIL,40,Graph geometry interaction learning,"https://scholar.google.com/scholar?cluster=4238397629187106403&hl=en&as_sdt=0,5",3,2020
Differentiable Augmentation for Data-Efficient GAN Training,393,neurips,171,23,2023-06-16 15:10:41.350000,https://github.com/mit-han-lab/data-efficient-gans,1192,Differentiable augmentation for data-efficient gan training,"https://scholar.google.com/scholar?cluster=6801864056016037549&hl=en&as_sdt=0,33",20,2020
Heuristic Domain Adaptation,28,neurips,9,0,2023-06-16 15:10:41.543000,https://github.com/cuishuhao/HDA,56,Heuristic domain adaptation,"https://scholar.google.com/scholar?cluster=5256770897520044696&hl=en&as_sdt=0,6",1,2020
Learning Certified Individually Fair Representations,60,neurips,2,0,2023-06-16 15:10:41.734000,https://github.com/eth-sri/lcifr,23,Learning certified individually fair representations,"https://scholar.google.com/scholar?cluster=5926392332798964524&hl=en&as_sdt=0,5",10,2020
Automatic Curriculum Learning through Value Disagreement,65,neurips,11,2,2023-06-16 15:10:41.926000,https://github.com/zzyunzhi/vds,24,Automatic curriculum learning through value disagreement,"https://scholar.google.com/scholar?cluster=6154929220771761601&hl=en&as_sdt=0,47",2,2020
The NetHack Learning Environment,94,neurips,102,16,2023-06-16 15:10:42.118000,https://github.com/facebookresearch/nle,870,The nethack learning environment,"https://scholar.google.com/scholar?cluster=11088505534192632756&hl=en&as_sdt=0,23",29,2020
Language and Visual Entity Relationship Graph for Agent Navigation,76,neurips,4,0,2023-06-16 15:10:42.309000,https://github.com/YicongHong/Entity-Graph-VLN,37,Language and visual entity relationship graph for agent navigation,"https://scholar.google.com/scholar?cluster=6555828545880639427&hl=en&as_sdt=0,33",3,2020
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping,23,neurips,11,2,2023-06-16 15:10:42.501000,https://github.com/CherBass/ICAM,50,ICAM: interpretable classification via disentangled representations and feature attribution mapping,"https://scholar.google.com/scholar?cluster=2236890371359287899&hl=en&as_sdt=0,32",4,2020
Boosting Adversarial Training with Hypersphere Embedding,101,neurips,13,1,2023-06-16 15:10:42.693000,https://github.com/ShawnXYang/AT_HE,31,Boosting adversarial training with hypersphere embedding,"https://scholar.google.com/scholar?cluster=9611585396722104249&hl=en&as_sdt=0,36",3,2020
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs,406,neurips,15,1,2023-06-16 15:10:42.885000,https://github.com/GemsLab/H2GCN,77,Beyond homophily in graph neural networks: Current limitations and effective designs,"https://scholar.google.com/scholar?cluster=13096699314940165476&hl=en&as_sdt=0,5",4,2020
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent,10,neurips,1,0,2023-06-16 15:10:43.077000,https://github.com/sskoul/ID2216,4,Efficient online learning of optimal rankings: Dimensionality reduction via gradient descent,"https://scholar.google.com/scholar?cluster=17654222550374080796&hl=en&as_sdt=0,48",1,2020
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification,39,neurips,102,34,2023-06-16 15:10:43.269000,https://github.com/VLL-HD/FrEIA,663,Training normalizing flows with the information bottleneck for competitive generative classification,"https://scholar.google.com/scholar?cluster=7085738876441578622&hl=en&as_sdt=0,49",20,2020
Deep Statistical Solvers,14,neurips,3,1,2023-06-16 15:10:43.466000,https://github.com/bdonon/DeepStatisticalSolvers,4,Deep statistical solvers,"https://scholar.google.com/scholar?cluster=5761359200414766377&hl=en&as_sdt=0,5",1,2020
Distributionally Robust Parametric Maximum Likelihood Estimation,9,neurips,0,0,2023-06-16 15:10:43.658000,https://github.com/angelosgeorghiou/DR-Parametric-MLE,2,Distributionally robust parametric maximum likelihood estimation,"https://scholar.google.com/scholar?cluster=11917985486247358648&hl=en&as_sdt=0,33",1,2020
Deep Transformation-Invariant Clustering,26,neurips,10,0,2023-06-16 15:10:43.850000,https://github.com/monniert/dti-clustering,67,Deep transformation-invariant clustering,"https://scholar.google.com/scholar?cluster=10717088515136058764&hl=en&as_sdt=0,5",3,2020
"Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree",17,neurips,0,0,2023-06-16 15:10:44.043000,https://github.com/functionadvanced/basis_pursuit_code,0,"Overfitting can be harmless for basis pursuit, but only to a degree","https://scholar.google.com/scholar?cluster=12884966030435629698&hl=en&as_sdt=0,5",2,2020
Improving Generalization in Reinforcement Learning with Mixture Regularization,64,neurips,8,1,2023-06-16 15:10:44.234000,https://github.com/kaixin96/mixreg,30,Improving generalization in reinforcement learning with mixture regularization,"https://scholar.google.com/scholar?cluster=3278230157932570215&hl=en&as_sdt=0,5",3,2020
Learning from Aggregate Observations,21,neurips,0,0,2023-06-16 15:10:44.427000,https://github.com/YivanZhang/lio,9,Learning from aggregate observations,"https://scholar.google.com/scholar?cluster=17146709459337763149&hl=en&as_sdt=0,33",2,2020
Subgraph Neural Networks,78,neurips,31,14,2023-06-16 15:10:44.619000,https://github.com/mims-harvard/SubGNN,155,Subgraph neural networks,"https://scholar.google.com/scholar?cluster=12519651667437268024&hl=en&as_sdt=0,44",9,2020
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search,260,neurips,146,45,2023-06-16 15:10:44.813000,https://github.com/jaywalnut310/glow-tts,554,Glow-tts: A generative flow for text-to-speech via monotonic alignment search,"https://scholar.google.com/scholar?cluster=4995990667849283087&hl=en&as_sdt=0,10",19,2020
Novelty Search in Representational Space for Sample Efficient Exploration,32,neurips,2,2,2023-06-16 15:10:45.008000,https://github.com/taodav/nsrs,11,Novelty search in representational space for sample efficient exploration,"https://scholar.google.com/scholar?cluster=15188964487009178721&hl=en&as_sdt=0,31",2,2020
Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice,25,neurips,0,0,2023-06-16 15:10:45.202000,https://github.com/ShufanWangBGM/OAfMSSRwMLA,1,Online algorithms for multi-shop ski rental with machine learned advice,"https://scholar.google.com/scholar?cluster=16607821741984068758&hl=en&as_sdt=0,11",1,2020
Learning Invariants through Soft Unification,7,neurips,1,0,2023-06-16 15:10:45.395000,https://github.com/nuric/softuni,4,Learning invariants through soft unification,"https://scholar.google.com/scholar?cluster=3931986727564031878&hl=en&as_sdt=0,5",1,2020
Variational Bayesian Monte Carlo with Noisy Likelihoods,26,neurips,2,0,2023-06-16 15:10:45.588000,https://github.com/lacerbi/infbench,3,Variational bayesian monte carlo with noisy likelihoods,"https://scholar.google.com/scholar?cluster=10498124267733273591&hl=en&as_sdt=0,5",5,2020
Adversarial Distributional Training for Robust Deep Learning,79,neurips,8,1,2023-06-16 15:10:45.781000,https://github.com/dongyp13/Adversarial-Distributional-Training,58,Adversarial distributional training for robust deep learning,"https://scholar.google.com/scholar?cluster=4714059054130702686&hl=en&as_sdt=0,5",1,2020
Greedy inference with structure-exploiting lazy maps,34,neurips,1,7,2023-06-16 15:10:45.973000,https://github.com/MichaelCBrennan/lazymaps,1,Greedy inference with structure-exploiting lazy maps,"https://scholar.google.com/scholar?cluster=12098930486710559887&hl=en&as_sdt=0,10",2,2020
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning,37,neurips,32,17,2023-06-16 15:10:46.166000,https://github.com/snuspl/nimble,239,Nimble: Lightweight and parallel gpu task scheduling for deep learning,"https://scholar.google.com/scholar?cluster=7176715468062683010&hl=en&as_sdt=0,47",10,2020
Finding the Homology of Decision Boundaries with Active Learning,13,neurips,0,0,2023-06-16 15:10:46.357000,https://github.com/wayne0908/Active-Learning-Homology,2,Finding the homology of decision boundaries with active learning,"https://scholar.google.com/scholar?cluster=16953441847668604826&hl=en&as_sdt=0,41",2,2020
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars,17,neurips,0,0,2023-06-16 15:10:46.550000,https://github.com/Zoesgithub/MNCE-RL,6,Reinforced molecular optimization with neighborhood-controlled grammars,"https://scholar.google.com/scholar?cluster=5211013872342896595&hl=en&as_sdt=0,5",2,2020
Certified Defense to Image Transformations via Randomized Smoothing,41,neurips,1,0,2023-06-16 15:10:46.742000,https://github.com/eth-sri/transformation-smoothing,3,Certified defense to image transformations via randomized smoothing,"https://scholar.google.com/scholar?cluster=9373644649920608208&hl=en&as_sdt=0,5",8,2020
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks,29,neurips,3,0,2023-06-16 15:10:46.934000,https://github.com/RobustGraph/RoboGraph,9,Certified robustness of graph convolution networks for graph classification under topological attacks,"https://scholar.google.com/scholar?cluster=8395286682706237378&hl=en&as_sdt=0,5",3,2020
Zero-Resource Knowledge-Grounded Dialogue Generation,44,neurips,9,6,2023-06-16 15:10:47.126000,https://github.com/nlpxucan/ZRKGC,36,Zero-resource knowledge-grounded dialogue generation,"https://scholar.google.com/scholar?cluster=6981655446810272506&hl=en&as_sdt=0,39",4,2020
Targeted Adversarial Perturbations for Monocular Depth Prediction,29,neurips,3,0,2023-06-16 15:10:47.319000,https://github.com/alexklwong/targeted-adversarial-perturbations-monocular-depth,12,Targeted adversarial perturbations for monocular depth prediction,"https://scholar.google.com/scholar?cluster=16134290645127049160&hl=en&as_sdt=0,34",3,2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties,6,neurips,1,0,2023-06-16 15:10:47.510000,https://github.com/boschresearch/Structured_DGP,3,Beyond the mean-field: Structured deep Gaussian processes improve the predictive uncertainties,"https://scholar.google.com/scholar?cluster=8221968686369534160&hl=en&as_sdt=0,5",4,2020
PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals,16,neurips,3,0,2023-06-16 15:10:47.702000,https://github.com/henrycharlesworth/PlanGAN,17,Plangan: Model-based planning with sparse rewards and multiple goals,"https://scholar.google.com/scholar?cluster=16931214957382065939&hl=en&as_sdt=0,33",1,2020
Bad Global Minima Exist and SGD Can Reach Them,57,neurips,2,0,2023-06-16 15:10:47.894000,https://github.com/chao1224/BadGlobalMinima,9,Bad global minima exist and sgd can reach them,"https://scholar.google.com/scholar?cluster=4377222193710581368&hl=en&as_sdt=0,33",1,2020
A Closer Look at Accuracy vs. Robustness,196,neurips,14,0,2023-06-16 15:10:48.086000,https://github.com/yangarbiter/robust-local-lipschitz,82,A closer look at accuracy vs. robustness,"https://scholar.google.com/scholar?cluster=13806860877256503450&hl=en&as_sdt=0,5",7,2020
Spin-Weighted Spherical CNNs,46,neurips,1,0,2023-06-16 15:10:48.286000,https://github.com/daniilidis-group/swscnn,21,Spin-weighted spherical cnns,"https://scholar.google.com/scholar?cluster=13743708889227032297&hl=en&as_sdt=0,11",8,2020
Baxter Permutation Process,10,neurips,1,0,2023-06-16 15:10:48.485000,https://github.com/nttcslab/baxter-permutation-process,6,Baxter permutation process,"https://scholar.google.com/scholar?cluster=290903901363151335&hl=en&as_sdt=0,5",5,2020
"Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation",36,neurips,744,214,2023-06-16 15:10:48.681000,https://github.com/awslabs/autogluon,5850,"Fast, accurate, and simple models for tabular data via augmented distillation","https://scholar.google.com/scholar?cluster=15277756439655303211&hl=en&as_sdt=0,47",91,2020
Approximate Cross-Validation for Structured Models,12,neurips,0,0,2023-06-16 15:10:48.876000,https://github.com/SoumyaTGhosh/structured-infinitesimal-jackknife,1,Approximate cross-validation for structured models,"https://scholar.google.com/scholar?cluster=5677418794939060287&hl=en&as_sdt=0,5",5,2020
"Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation",15,neurips,8,3,2023-06-16 15:10:49.069000,https://github.com/sajadn/Exemplar-VAE,65,"Exemplar vae: Linking generative models, nearest neighbor retrieval, and data augmentation","https://scholar.google.com/scholar?cluster=1402621202580730115&hl=en&as_sdt=0,34",3,2020
Debiased Contrastive Learning,340,neurips,33,3,2023-06-16 15:10:49.271000,https://github.com/chingyaoc/DCL,263,Debiased contrastive learning,"https://scholar.google.com/scholar?cluster=9278834174999362411&hl=en&as_sdt=0,5",8,2020
UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree,45,neurips,6,3,2023-06-16 15:10:49.495000,https://github.com/kacperkan/ucsgnet,32,UCSG-NET-unsupervised discovering of constructive solid geometry tree,"https://scholar.google.com/scholar?cluster=7447193649830937821&hl=en&as_sdt=0,11",1,2020
COT-GAN: Generating Sequential Data via Causal Optimal Transport,59,neurips,10,3,2023-06-16 15:10:49.688000,https://github.com/tianlinxu312/cot-gan,28,Cot-gan: Generating sequential data via causal optimal transport,"https://scholar.google.com/scholar?cluster=2786319985224529897&hl=en&as_sdt=0,5",0,2020
Understanding spiking networks through convex optimization,13,neurips,7,0,2023-06-16 15:10:49.881000,https://github.com/machenslab/spikes,14,Understanding spiking networks through convex optimization,"https://scholar.google.com/scholar?cluster=13728762608347936383&hl=en&as_sdt=0,5",1,2020
Large-Scale Methods for Distributionally Robust Optimization,105,neurips,6,1,2023-06-16 15:10:50.074000,https://github.com/daniellevy/fast-dro,43,Large-scale methods for distributionally robust optimization,"https://scholar.google.com/scholar?cluster=4841990441300957739&hl=en&as_sdt=0,18",4,2020
Adversarial Example Games,41,neurips,6,1,2023-06-16 15:10:50.269000,https://github.com/joeybose/Adversarial-Example-Games,24,Adversarial example games,"https://scholar.google.com/scholar?cluster=4037988847325628992&hl=en&as_sdt=0,5",4,2020
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts,20,neurips,0,0,2023-06-16 15:10:50.478000,https://github.com/leoozy/JointRD_Neurips2020,1,Residual distillation: Towards portable deep neural networks without shortcuts,"https://scholar.google.com/scholar?cluster=4325972833602775025&hl=en&as_sdt=0,5",1,2020
Further Analysis of Outlier Detection with Deep Generative Models,31,neurips,2,0,2023-06-16 15:10:50.670000,https://github.com/thu-ml/ood-dgm,8,Further analysis of outlier detection with deep generative models,"https://scholar.google.com/scholar?cluster=9058630234791749340&hl=en&as_sdt=0,5",8,2020
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning,12,neurips,2,0,2023-06-16 15:10:50.862000,https://github.com/Mehooz/BIRD_code,12,Bridging imagination and reality for model-based deep reinforcement learning,"https://scholar.google.com/scholar?cluster=1362648394458598270&hl=en&as_sdt=0,5",2,2020
Adversarial Learning for Robust Deep Clustering,56,neurips,2,2,2023-06-16 15:10:51.054000,https://github.com/xdxuyang/ALRDC,13,Adversarial learning for robust deep clustering,"https://scholar.google.com/scholar?cluster=10569924986373874642&hl=en&as_sdt=0,34",1,2020
Learning Mutational Semantics,4,neurips,2,0,2023-06-16 15:10:51.247000,https://github.com/brianhie/mutational-semantics-neurips2020,8,Learning mutational semantics,"https://scholar.google.com/scholar?cluster=4282139572655553972&hl=en&as_sdt=0,5",3,2020
Learning to Learn Variational Semantic Memory,14,neurips,2,3,2023-06-16 15:10:51.439000,https://github.com/YDU-uva/VSM,5,Learning to learn variational semantic memory,"https://scholar.google.com/scholar?cluster=6158298679245013068&hl=en&as_sdt=0,39",2,2020
Finer Metagenomic Reconstruction via Biodiversity Optimization,0,neurips,0,0,2023-06-16 15:10:51.630000,https://github.com/dkoslicki/MinimizeBiologicalDiversity,0,Finer metagenomic reconstruction via biodiversity optimization,"https://scholar.google.com/scholar?cluster=7553946638597085018&hl=en&as_sdt=0,44",2,2020
Self-Paced Deep Reinforcement Learning,30,neurips,2,3,2023-06-16 15:10:51.822000,https://github.com/psclklnk/spdl,25,Self-paced deep reinforcement learning,"https://scholar.google.com/scholar?cluster=12390741012444342538&hl=en&as_sdt=0,31",1,2020
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples,37,neurips,3,0,2023-06-16 15:10:52.014000,https://github.com/jayjaynandy/maximize-representation-gap,7,Towards maximizing the representation gap between in-domain & out-of-distribution examples,"https://scholar.google.com/scholar?cluster=9854712856118279269&hl=en&as_sdt=0,44",2,2020
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks,140,neurips,13,5,2023-06-16 15:10:52.206000,https://github.com/mims-harvard/GNNGuard,49,Gnnguard: Defending graph neural networks against adversarial attacks,"https://scholar.google.com/scholar?cluster=16210304984392782174&hl=en&as_sdt=0,5",5,2020
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction,67,neurips,17,16,2023-06-16 15:10:52.399000,https://github.com/simpleig/Geo-PIFu,107,Geo-pifu: Geometry and pixel aligned implicit functions for single-view human reconstruction,"https://scholar.google.com/scholar?cluster=413072927263183494&hl=en&as_sdt=0,5",9,2020
Optimal visual search based on a model of target detectability in natural images,11,neurips,0,0,2023-06-16 15:10:52.591000,https://github.com/rashidis/bio_based_detectability,2,Optimal visual search based on a model of target detectability in natural images,"https://scholar.google.com/scholar?cluster=5184014170685749857&hl=en&as_sdt=0,31",2,2020
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures,48,neurips,8,2,2023-06-16 15:10:52.784000,https://github.com/lightonai/dfa-scales-to-modern-deep-learning,72,Direct feedback alignment scales to modern deep learning tasks and architectures,"https://scholar.google.com/scholar?cluster=12044831412271008828&hl=en&as_sdt=0,47",10,2020
Bayesian Optimization for Iterative Learning,18,neurips,1,0,2023-06-16 15:10:52.976000,https://github.com/ntienvu/BOIL,6,Bayesian optimization for iterative learning,"https://scholar.google.com/scholar?cluster=10842170699487519102&hl=en&as_sdt=0,40",3,2020
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction,97,neurips,40,10,2023-06-16 15:10:53.169000,https://github.com/ryanchankh/mcr2,168,Learning diverse and discriminative representations via the principle of maximal coding rate reduction,"https://scholar.google.com/scholar?cluster=14992071413759250566&hl=en&as_sdt=0,32",7,2020
Learning Rich Rankings,9,neurips,0,0,2023-06-16 15:10:53.361000,https://github.com/arjunsesh/lrr-neurips,3,Learning rich rankings,"https://scholar.google.com/scholar?cluster=14598696558067197575&hl=en&as_sdt=0,5",2,2020
Color Visual Illusions: A Statistics-based Computational Model,5,neurips,1,0,2023-06-16 15:10:53.571000,https://github.com/eladhi/VI-Glow,2,Color visual illusions: A statistics-based computational model,"https://scholar.google.com/scholar?cluster=12501118116923270593&hl=en&as_sdt=0,29",1,2020
The Pitfalls of Simplicity Bias in Neural Networks,197,neurips,8,2,2023-06-16 15:10:53.802000,https://github.com/harshays/simplicitybiaspitfalls,32,The pitfalls of simplicity bias in neural networks,"https://scholar.google.com/scholar?cluster=13128598861891549872&hl=en&as_sdt=0,5",2,2020
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems,18,neurips,3,0,2023-06-16 15:10:53.995000,https://github.com/guaguakai/surrogate-optimization-learning,9,Automatically learning compact quality-aware surrogates for optimization problems,"https://scholar.google.com/scholar?cluster=914584928870458413&hl=en&as_sdt=0,44",2,2020
Empirical Likelihood for Contextual Bandits,9,neurips,1,0,2023-06-16 15:10:54.192000,https://github.com/pmineiro/elfcb,12,Empirical likelihood for contextual bandits,"https://scholar.google.com/scholar?cluster=2477802205256797409&hl=en&as_sdt=0,5",2,2020
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?,29,neurips,20,0,2023-06-16 15:10:54.385000,https://github.com/NVIDIA/GraphQSat,47,Can Q-learning with graph networks learn a generalizable branching heuristic for a SAT solver?,"https://scholar.google.com/scholar?cluster=14134895481476323546&hl=en&as_sdt=0,11",4,2020
Listening to Sounds of Silence for Speech Denoising,30,neurips,21,3,2023-06-16 15:10:54.577000,https://github.com/henryxrl/Listening-to-Sound-of-Silence-for-Speech-Denoising,41,Listening to sounds of silence for speech denoising,"https://scholar.google.com/scholar?cluster=15043544639901416404&hl=en&as_sdt=0,5",2,2020
BoxE: A Box Embedding Model for Knowledge Base Completion,111,neurips,4,0,2023-06-16 15:10:54.770000,https://github.com/ralphabb/BoxE,39,Boxe: A box embedding model for knowledge base completion,"https://scholar.google.com/scholar?cluster=10965427098747336243&hl=en&as_sdt=0,5",2,2020
Coherent Hierarchical Multi-Label Classification Networks,43,neurips,16,1,2023-06-16 15:10:54.962000,https://github.com/EGiunchiglia/C-HMCNN,64,Coherent hierarchical multi-label classification networks,"https://scholar.google.com/scholar?cluster=10722017253343281593&hl=en&as_sdt=0,11",4,2020
Federated Bayesian Optimization via Thompson Sampling,57,neurips,4,1,2023-06-16 15:10:55.155000,https://github.com/daizhongxiang/Federated_Bayesian_Optimization,16,Federated Bayesian optimization via Thompson sampling,"https://scholar.google.com/scholar?cluster=16578927726167332521&hl=en&as_sdt=0,43",1,2020
Neural Complexity Measures,178,neurips,0,0,2023-06-16 15:10:55.347000,https://github.com/yoonholee/neural-complexity,8,Architectural complexity measures of recurrent neural networks,"https://scholar.google.com/scholar?cluster=9430461092837132372&hl=en&as_sdt=0,14",2,2020
Self-Supervised Learning by Cross-Modal Audio-Video Clustering,346,neurips,10,0,2023-06-16 15:10:55.540000,https://github.com/HumamAlwassel/XDC,83,Self-supervised learning by cross-modal audio-video clustering,"https://scholar.google.com/scholar?cluster=7902775526850966872&hl=en&as_sdt=0,47",3,2020
Generalization Bound of Gradient Descent for Non-Convex Metric Learning,4,neurips,1,0,2023-06-16 15:10:55.733000,https://github.com/xyang6/SMILE,1,Generalization bound of gradient descent for non-convex metric learning,"https://scholar.google.com/scholar?cluster=2089980921102731438&hl=en&as_sdt=0,15",1,2020
GANSpace: Discovering Interpretable GAN Controls,610,neurips,248,27,2023-06-16 15:10:55.925000,https://github.com/harskish/ganspace,1731,Ganspace: Discovering interpretable gan controls,"https://scholar.google.com/scholar?cluster=1986716991541343890&hl=en&as_sdt=0,33",41,2020
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization,140,neurips,319,64,2023-06-16 15:10:56.118000,https://github.com/pytorch/botorch,2664,Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization,"https://scholar.google.com/scholar?cluster=8750355730430207793&hl=en&as_sdt=0,5",51,2020
Neuron-level Structured Pruning using Polarization Regularizer,81,neurips,11,7,2023-06-16 15:10:56.312000,https://github.com/polarizationpruning/PolarizationPruning,72,Neuron-level structured pruning using polarization regularizer,"https://scholar.google.com/scholar?cluster=11036870209312598760&hl=en&as_sdt=0,23",2,2020
Field-wise Learning for Multi-field Categorical Data,7,neurips,2,0,2023-06-16 15:10:56.504000,https://github.com/lzb5600/Field-wise-Learning,6,Field-wise learning for multi-field categorical data,"https://scholar.google.com/scholar?cluster=11839494695393533500&hl=en&as_sdt=0,10",2,2020
Continual Learning in Low-rank Orthogonal Subspaces,60,neurips,3,0,2023-06-16 15:10:56.697000,https://github.com/arslan-chaudhry/orthog_subspace,22,Continual learning in low-rank orthogonal subspaces,"https://scholar.google.com/scholar?cluster=6781823175035595745&hl=en&as_sdt=0,5",2,2020
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments,2291,neurips,262,36,2023-06-16 15:10:56.889000,https://github.com/facebookresearch/swav,1800,Unsupervised learning of visual features by contrasting cluster assignments,"https://scholar.google.com/scholar?cluster=13209348926291080860&hl=en&as_sdt=0,5",41,2020
Learning Deformable Tetrahedral Meshes for 3D Reconstruction,54,neurips,11,3,2023-06-16 15:10:57.080000,https://github.com/nv-tlabs/DefTet,117,Learning deformable tetrahedral meshes for 3d reconstruction,"https://scholar.google.com/scholar?cluster=6266590920859751769&hl=en&as_sdt=0,33",33,2020
Self-supervised learning through the eyes of a child,61,neurips,14,0,2023-06-16 15:10:57.276000,https://github.com/eminorhan/baby-vision,136,Self-supervised learning through the eyes of a child,"https://scholar.google.com/scholar?cluster=5608715260418451299&hl=en&as_sdt=0,39",7,2020
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning,20,neurips,4,1,2023-06-16 15:10:57.473000,https://github.com/taohan10200/USADTM,27,Unsupervised semantic aggregation and deformable template matching for semi-supervised learning,"https://scholar.google.com/scholar?cluster=9057482761003517439&hl=en&as_sdt=0,10",3,2020
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning,4,neurips,2,0,2023-06-16 15:10:57.667000,https://github.com/instadeepai/EGTA-NMARL,13,A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=18296902167525929783&hl=en&as_sdt=0,24",5,2020
Data Diversification: A Simple Strategy For Neural Machine Translation,64,neurips,6,2,2023-06-16 15:10:57.859000,https://github.com/nxphi47/data_diversification,23,Data diversification: A simple strategy for neural machine translation,"https://scholar.google.com/scholar?cluster=4075963785993246098&hl=en&as_sdt=0,33",2,2020
CoSE: Compositional Stroke Embeddings,25,neurips,6,0,2023-06-16 15:10:58.055000,https://github.com/eth-ait/cose,29,Cose: Compositional stroke embeddings,"https://scholar.google.com/scholar?cluster=17699683888953268299&hl=en&as_sdt=0,5",8,2020
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks,24,neurips,8,0,2023-06-16 15:10:58.248000,https://github.com/XuJing1022/HiT-MAC,22,Learning multi-agent coordination for enhancing target coverage in directional sensor networks,"https://scholar.google.com/scholar?cluster=3761066005883890135&hl=en&as_sdt=0,31",3,2020
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching,86,neurips,9,10,2023-06-16 15:10:58.441000,https://github.com/DTaoo/Discriminative-Sounding-Objects-Localization,52,Discriminative sounding objects localization via self-supervised audiovisual matching,"https://scholar.google.com/scholar?cluster=2914811188248897245&hl=en&as_sdt=0,43",4,2020
Learning Multi-Agent Communication through Structured Attentive Reasoning,24,neurips,8,2,2023-06-16 15:10:58.634000,https://github.com/caslab-vt/SARNet,21,Learning multi-agent communication through structured attentive reasoning,"https://scholar.google.com/scholar?cluster=17079361444341989269&hl=en&as_sdt=0,33",4,2020
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search,16,neurips,5,0,2023-06-16 15:10:58.826000,https://github.com/KyunghyunLee/aes-rl,15,An efficient asynchronous method for integrating evolutionary and gradient-based policy search,"https://scholar.google.com/scholar?cluster=16110755870648938289&hl=en&as_sdt=0,5",2,2020
MetaSDF: Meta-Learning Signed Distance Functions,149,neurips,16,2,2023-06-16 15:10:59.018000,https://github.com/shaohua0116/MultiDigitMNIST,80,Metasdf: Meta-learning signed distance functions,"https://scholar.google.com/scholar?cluster=14779381084072333819&hl=en&as_sdt=0,33",5,2020
Model-based Adversarial Meta-Reinforcement Learning,30,neurips,6,1,2023-06-16 15:10:59.210000,https://github.com/LinZichuan/AdMRL,31,Model-based adversarial meta-reinforcement learning,"https://scholar.google.com/scholar?cluster=13462874924828322027&hl=en&as_sdt=0,5",5,2020
Graph Policy Network for Transferable Active Learning on Graphs,36,neurips,9,1,2023-06-16 15:10:59.403000,https://github.com/ShengdingHu/GraphPolicyNetworkActiveLearning,37,Graph policy network for transferable active learning on graphs,"https://scholar.google.com/scholar?cluster=2017577530575623285&hl=en&as_sdt=0,34",2,2020
Towards a Better Global Loss Landscape of GANs,23,neurips,4,1,2023-06-16 15:10:59.596000,https://github.com/AilsaF/RS-GAN,27,Towards a better global loss landscape of GANs,"https://scholar.google.com/scholar?cluster=11884432475948197511&hl=en&as_sdt=0,5",3,2020
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning,198,neurips,25,5,2023-06-16 15:10:59.799000,https://github.com/oxwhirl/wqmix,92,Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=164177538324943983&hl=en&as_sdt=0,33",3,2020
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits,20,neurips,29,81,2023-06-16 15:10:59.992000,https://github.com/ThrunGroup/BanditPAM,597,Banditpam: Almost linear time k-medoids clustering via multi-armed bandits,"https://scholar.google.com/scholar?cluster=17391343875111249867&hl=en&as_sdt=0,28",8,2020
"UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging",68,neurips,9,8,2023-06-16 15:11:00.185000,https://github.com/ChaoningZhang/Universal-Deep-Hiding,76,"Udh: Universal deep hiding for steganography, watermarking, and light field messaging","https://scholar.google.com/scholar?cluster=10741692453903980438&hl=en&as_sdt=0,5",3,2020
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders,14,neurips,2,0,2023-06-16 15:11:00.377000,https://github.com/sisl/EvidentialSparsification,6,Evidential sparsification of multimodal latent spaces in conditional variational autoencoders,"https://scholar.google.com/scholar?cluster=15564375391911668745&hl=en&as_sdt=0,39",8,2020
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs,49,neurips,13,4,2023-06-16 15:11:00.570000,https://github.com/mlvlab/SELAR,50,Self-supervised auxiliary learning with meta-paths for heterogeneous graphs,"https://scholar.google.com/scholar?cluster=14092642603369641339&hl=en&as_sdt=0,5",5,2020
Can Graph Neural Networks Count Substructures?,180,neurips,3,1,2023-06-16 15:11:00.762000,https://github.com/leichen2018/GNN-Substructure-Counting,31,Can graph neural networks count substructures?,"https://scholar.google.com/scholar?cluster=15397526244877086732&hl=en&as_sdt=0,23",4,2020
Stable and expressive recurrent vision models,27,neurips,0,1,2023-06-16 15:11:00.954000,https://github.com/c-rbp/panoptic_segmentation,0,Stable and expressive recurrent vision models,"https://scholar.google.com/scholar?cluster=9835747249429440415&hl=en&as_sdt=0,39",2,2020
BRP-NAS: Prediction-based NAS using GCNs,128,neurips,10,1,2023-06-16 15:11:01.147000,https://github.com/thomasccp/eagle,55,Brp-nas: Prediction-based nas using gcns,"https://scholar.google.com/scholar?cluster=2963733122689341897&hl=en&as_sdt=0,36",6,2020
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport,53,neurips,6,1,2023-06-16 15:11:01.339000,https://github.com/marvin-eisenberger/deep-shells,35,Deep shells: Unsupervised shape correspondence with optimal transport,"https://scholar.google.com/scholar?cluster=7877199401266840564&hl=en&as_sdt=0,44",6,2020
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding,52,neurips,8,3,2023-06-16 15:11:01.531000,https://github.com/iboing/ISTA-NAS,29,Ista-nas: Efficient and consistent neural architecture search by sparse coding,"https://scholar.google.com/scholar?cluster=6611041012150582812&hl=en&as_sdt=0,5",3,2020
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D,25,neurips,1,0,2023-06-16 15:11:01.723000,https://github.com/princeton-vl/Rel3D,25,Rel3d: A minimally contrastive benchmark for grounding spatial relations in 3d,"https://scholar.google.com/scholar?cluster=2314911618125318907&hl=en&as_sdt=0,5",6,2020
Regularizing Black-box Models for Improved Interpretability,35,neurips,1,0,2023-06-16 15:11:01.915000,https://github.com/GDPlumb/ExpO,13,Regularizing black-box models for improved interpretability,"https://scholar.google.com/scholar?cluster=8791844934310569033&hl=en&as_sdt=0,18",3,2020
Semi-Supervised Neural Architecture Search,59,neurips,2,0,2023-06-16 15:11:02.109000,https://github.com/renqianluo/SemiNAS,23,Semi-supervised neural architecture search,"https://scholar.google.com/scholar?cluster=18063848254529842085&hl=en&as_sdt=0,43",2,2020
Consistency Regularization for Certified Robustness of Smoothed Classifiers,42,neurips,3,0,2023-06-16 15:11:02.303000,https://github.com/jh-jeong/smoothing-consistency,30,Consistency regularization for certified robustness of smoothed classifiers,"https://scholar.google.com/scholar?cluster=15871796108252532947&hl=en&as_sdt=0,5",2,2020
Make One-Shot Video Object Segmentation Efficient Again,30,neurips,5,2,2023-06-16 15:11:02.496000,https://github.com/dvl-tum/e-osvos,35,Make one-shot video object segmentation efficient again,"https://scholar.google.com/scholar?cluster=4861842359633775782&hl=en&as_sdt=0,44",5,2020
Depth Uncertainty in Neural Networks,71,neurips,11,2,2023-06-16 15:11:02.688000,https://github.com/cambridge-mlg/DUN,67,Depth uncertainty in neural networks,"https://scholar.google.com/scholar?cluster=8829822844552583626&hl=en&as_sdt=0,5",9,2020
Non-Euclidean Universal Approximation,50,neurips,0,0,2023-06-16 15:11:02.883000,https://github.com/AnastasisKratsios/NeurIPS2020_Non_Euclidean_Universal_Approximation_Example_DNN_Layer_Comparisons,2,Non-euclidean universal approximation,"https://scholar.google.com/scholar?cluster=154021427834857784&hl=en&as_sdt=0,5",1,2020
Constraining Variational Inference with Geometric Jensen-Shannon Divergence,18,neurips,2,0,2023-06-16 15:11:03.076000,https://github.com/jacobdeasy/geometric-js,16,Constraining variational inference with geometric jensen-shannon divergence,"https://scholar.google.com/scholar?cluster=7731569928986898287&hl=en&as_sdt=0,14",4,2020
Monotone operator equilibrium networks,84,neurips,4,0,2023-06-16 15:11:03.283000,https://github.com/locuslab/monotone_op_net,48,Monotone operator equilibrium networks,"https://scholar.google.com/scholar?cluster=17782936577976444731&hl=en&as_sdt=0,39",6,2020
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control,44,neurips,7,0,2023-06-16 15:11:03.498000,https://github.com/DesmondZhong/Lagrangian_caVAE,14,Unsupervised learning of lagrangian dynamics from images for prediction and control,"https://scholar.google.com/scholar?cluster=5340883116879003000&hl=en&as_sdt=0,5",1,2020
Learning Compositional Rules via Neural Program Synthesis,75,neurips,17,0,2023-06-16 15:11:03.692000,https://github.com/mtensor/rulesynthesis,53,Learning compositional rules via neural program synthesis,"https://scholar.google.com/scholar?cluster=3160670555314650508&hl=en&as_sdt=0,5",5,2020
Incorporating BERT into Parallel Sequence Decoding with Adapters,50,neurips,8,4,2023-06-16 15:11:03.884000,https://github.com/lemmonation/abnet,32,Incorporating bert into parallel sequence decoding with adapters,"https://scholar.google.com/scholar?cluster=5170178385408287500&hl=en&as_sdt=0,5",3,2020
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks,15,neurips,2,0,2023-06-16 15:11:04.077000,https://github.com/kazukiosawa/ngd_in_wide_nn,10,Understanding approximate fisher information for fast convergence of natural gradient descent in wide neural networks,"https://scholar.google.com/scholar?cluster=7137081707264639377&hl=en&as_sdt=0,34",1,2020
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error,26,neurips,1,0,2023-06-16 15:11:04.279000,https://github.com/nasiryahm/GAIT-prop,7,Gait-prop: A biologically plausible learning rule derived from backpropagation of error,"https://scholar.google.com/scholar?cluster=15875049954561764197&hl=en&as_sdt=0,3",3,2020
SCOP: Scientific Control for Reliable Neural Network Pruning,97,neurips,45,2,2023-06-16 15:11:04.487000,https://github.com/huawei-noah/Pruning,238,Scop: Scientific control for reliable neural network pruning,"https://scholar.google.com/scholar?cluster=10691651773549756733&hl=en&as_sdt=0,5",10,2020
Discovering conflicting groups in signed networks,15,neurips,2,0,2023-06-16 15:11:04.680000,https://github.com/rutzeng/SCG-NeurIPS2020,12,Discovering conflicting groups in signed networks,"https://scholar.google.com/scholar?cluster=16214693394380212585&hl=en&as_sdt=0,34",1,2020
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding,27,neurips,4,0,2023-06-16 15:11:04.874000,https://github.com/anishazaveri/austen_plots,20,Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding,"https://scholar.google.com/scholar?cluster=11433667847814374249&hl=en&as_sdt=0,50",4,2020
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions,0,neurips,0,1,2023-06-16 15:11:05.065000,https://github.com/matthewfaw/mixnmatch,1,Mix and match: an optimistic tree-search approach for learning models from mixture distributions,"https://scholar.google.com/scholar?cluster=9708198695831582690&hl=en&as_sdt=0,5",1,2020
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain,102,neurips,25,0,2023-06-16 15:11:05.258000,https://github.com/jsyoon0823/VIME,112,Vime: Extending the success of self-and semi-supervised learning to tabular domain,"https://scholar.google.com/scholar?cluster=6759722027373902233&hl=en&as_sdt=0,5",3,2020
Phase retrieval in high dimensions: Statistical and computational phase transitions,35,neurips,0,0,2023-06-16 15:11:05.450000,https://github.com/sphinxteam/PhaseRetrieval_demo,0,Phase retrieval in high dimensions: Statistical and computational phase transitions,"https://scholar.google.com/scholar?cluster=12300381021684314628&hl=en&as_sdt=0,36",5,2020
Soft Contrastive Learning for Visual Localization,13,neurips,1,1,2023-06-16 15:11:05.642000,https://github.com/janinethoma/soft_contrastive_learning,20,Soft contrastive learning for visual localization,"https://scholar.google.com/scholar?cluster=416644308863323258&hl=en&as_sdt=0,5",2,2020
Fine-Grained Dynamic Head for Object Detection,26,neurips,8,5,2023-06-16 15:11:05.835000,https://github.com/StevenGrove/DynamicHead,79,Fine-grained dynamic head for object detection,"https://scholar.google.com/scholar?cluster=17089335587335369004&hl=en&as_sdt=0,5",3,2020
Modeling and Optimization Trade-off in Meta-learning,24,neurips,1,0,2023-06-16 15:11:06.027000,https://github.com/intel-isl/MetaLearningTradeoffs,4,Modeling and optimization trade-off in meta-learning,"https://scholar.google.com/scholar?cluster=6968213922312284457&hl=en&as_sdt=0,5",9,2020
SnapBoost: A Heterogeneous Boosting Machine,6,neurips,2,0,2023-06-16 15:11:06.221000,https://github.com/IBM/snapboost-neurips,8,Snapboost: A heterogeneous boosting machine,"https://scholar.google.com/scholar?cluster=11504245933861702155&hl=en&as_sdt=0,10",5,2020
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces,44,neurips,1,0,2023-06-16 15:11:06.413000,https://github.com/hyenal/relate,31,RELATE: Physically plausible multi-object scene synthesis using structured latent spaces,"https://scholar.google.com/scholar?cluster=10197798109184209151&hl=en&as_sdt=0,5",4,2020
GreedyFool: Distortion-Aware Sparse Adversarial Attack,36,neurips,5,2,2023-06-16 15:11:06.606000,https://github.com/LightDXY/GreedyFool,29,Greedyfool: Distortion-aware sparse adversarial attack,"https://scholar.google.com/scholar?cluster=9173830500471022220&hl=en&as_sdt=0,36",1,2020
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data,40,neurips,6,0,2023-06-16 15:11:06.799000,https://github.com/microsoft/VAEM,12,VAEM: a deep generative model for heterogeneous mixed type data,"https://scholar.google.com/scholar?cluster=1707955127597658267&hl=en&as_sdt=0,5",4,2020
RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist,67,neurips,16,7,2023-06-16 15:11:06.991000,https://github.com/uta-smile/RetroXpert,48,Retroxpert: Decompose retrosynthesis prediction like a chemist,"https://scholar.google.com/scholar?cluster=1673974540890711426&hl=en&as_sdt=0,14",7,2020
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining,82,neurips,11,0,2023-06-16 15:11:07.184000,https://github.com/cambridge-mlg/weighted-retraining,30,Sample-efficient optimization in the latent space of deep generative models via weighted retraining,"https://scholar.google.com/scholar?cluster=6526315194994935478&hl=en&as_sdt=0,44",6,2020
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID,357,neurips,66,13,2023-06-16 15:11:07.376000,https://github.com/yxgeee/SpCL,294,Self-paced contrastive learning with hybrid memory for domain adaptive object re-id,"https://scholar.google.com/scholar?cluster=12125072561642183242&hl=en&as_sdt=0,3",7,2020
Winning the Lottery with Continuous Sparsification,86,neurips,5,1,2023-06-16 15:11:07.568000,https://github.com/lolemacs/continuous-sparsification,24,Winning the lottery with continuous sparsification,"https://scholar.google.com/scholar?cluster=6340697086981943139&hl=en&as_sdt=0,47",3,2020
Joints in Random Forests,27,neurips,5,3,2023-06-16 15:11:07.760000,https://github.com/AlCorreia/GeFs,29,Joints in random forests,"https://scholar.google.com/scholar?cluster=16339434295073356631&hl=en&as_sdt=0,11",3,2020
Compositional Generalization by Learning Analytical Expressions,12,neurips,58,10,2023-06-16 15:11:07.953000,https://github.com/microsoft/ContextualSP,310,Compositional generalization by learning analytical expressions,"https://scholar.google.com/scholar?cluster=14346875242399038266&hl=en&as_sdt=0,5",15,2020
JAX MD: A Framework for Differentiable Physics,100,neurips,155,64,2023-06-16 15:11:08.155000,https://github.com/google/jax-md,941,Jax md: a framework for differentiable physics,"https://scholar.google.com/scholar?cluster=10280332258260460086&hl=en&as_sdt=0,44",49,2020
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images,59,neurips,18,0,2023-06-16 15:11:08.348000,https://github.com/chenhsuanlin/signed-distance-SRN,114,Sdf-srn: Learning signed distance 3d object reconstruction from static images,"https://scholar.google.com/scholar?cluster=11067846104623774002&hl=en&as_sdt=0,47",4,2020
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures,6,neurips,1,0,2023-06-16 15:11:08.541000,https://github.com/JWoong148/MetaPerturb,13,Metaperturb: Transferable regularizer for heterogeneous tasks and architectures,"https://scholar.google.com/scholar?cluster=7151677939304906463&hl=en&as_sdt=0,23",2,2020
Learning to solve TV regularised problems with unrolled algorithms,10,neurips,3,0,2023-06-16 15:11:08.734000,https://github.com/hcherkaoui/carpet,10,Learning to solve TV regularised problems with unrolled algorithms,"https://scholar.google.com/scholar?cluster=7897340009151799054&hl=en&as_sdt=0,5",2,2020
Improving robustness against common corruptions by covariate shift adaptation,225,neurips,4,5,2023-06-16 15:11:08.927000,https://github.com/bethgelab/robustness,107,Improving robustness against common corruptions by covariate shift adaptation,"https://scholar.google.com/scholar?cluster=3624568905947100464&hl=en&as_sdt=0,5",16,2020
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning,14,neurips,1,0,2023-06-16 15:11:09.121000,https://github.com/srambhatla/TensorNOODL,1,Provable online CP/PARAFAC decomposition of a structured tensor via dictionary learning,"https://scholar.google.com/scholar?cluster=16029493978579736845&hl=en&as_sdt=0,36",1,2020
Look-ahead Meta Learning for Continual Learning,77,neurips,17,0,2023-06-16 15:11:09.315000,https://github.com/montrealrobotics/La-MAML,63,Look-ahead meta learning for continual learning,"https://scholar.google.com/scholar?cluster=17815879397506747892&hl=en&as_sdt=0,3",5,2020
A polynomial-time algorithm for learning nonparametric causal graphs,21,neurips,0,0,2023-06-16 15:11:09.521000,https://github.com/MingGao97/NPVAR,4,A polynomial-time algorithm for learning nonparametric causal graphs,"https://scholar.google.com/scholar?cluster=14706924750789311400&hl=en&as_sdt=0,47",3,2020
Proximal Mapping for Deep Regularization,1,neurips,1,0,2023-06-16 15:11:09.714000,https://github.com/learndeep2019/ProxNet,6,Proximal mapping for deep regularization,"https://scholar.google.com/scholar?cluster=7235863984702530816&hl=en&as_sdt=0,33",2,2020
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models,51,neurips,10,1,2023-06-16 15:11:09.912000,https://github.com/OATML/ucate,24,Identifying causal-effect inference failure with uncertainty-aware models,"https://scholar.google.com/scholar?cluster=15948587746481389105&hl=en&as_sdt=0,3",2,2020
Deep active inference agents using Monte-Carlo methods,58,neurips,10,2,2023-06-16 15:11:10.105000,https://github.com/zfountas/deep-active-inference-mc,56,Deep active inference agents using Monte-Carlo methods,"https://scholar.google.com/scholar?cluster=6913305558243551046&hl=en&as_sdt=0,33",4,2020
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations,13,neurips,0,0,2023-06-16 15:11:10.299000,https://github.com/aritchie9590/NDIGO,1,Consistent estimation of identifiable nonparametric mixture models from grouped observations,"https://scholar.google.com/scholar?cluster=17764309851292828713&hl=en&as_sdt=0,3",1,2020
In search of robust measures of generalization,58,neurips,5,0,2023-06-16 15:11:10.521000,https://github.com/nitarshan/robust-generalization-measures,27,In search of robust measures of generalization,"https://scholar.google.com/scholar?cluster=14875253410055834291&hl=en&as_sdt=0,5",4,2020
Softmax Deep Double Deterministic Policy Gradients,41,neurips,4,2,2023-06-16 15:11:10.714000,https://github.com/ling-pan/SD3,36,Softmax deep double deterministic policy gradients,"https://scholar.google.com/scholar?cluster=11974289959292119279&hl=en&as_sdt=0,34",2,2020
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity,19,neurips,8,0,2023-06-16 15:11:10.907000,https://github.com/deep-spin/sparse-marginalization-lvm,24,Efficient marginalization of discrete and structured latent variables via sparsity,"https://scholar.google.com/scholar?cluster=16514108199949566387&hl=en&as_sdt=0,5",4,2020
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs,11,neurips,2,1,2023-06-16 15:11:11.100000,https://github.com/yaxingwang/DeepI2I,25,Deepi2i: Enabling deep hierarchical image-to-image translation by transferring from gans,"https://scholar.google.com/scholar?cluster=13982918844141309648&hl=en&as_sdt=0,33",6,2020
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances,359,neurips,57,10,2023-06-16 15:11:11.293000,https://github.com/alinlab/CSI,254,Csi: Novelty detection via contrastive learning on distributionally shifted instances,"https://scholar.google.com/scholar?cluster=7033158044687417724&hl=en&as_sdt=0,5",7,2020
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning,86,neurips,11,0,2023-06-16 15:11:11.505000,https://github.com/mzho7212/LICA,47,Learning implicit credit assignment for cooperative multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=3915814748065478142&hl=en&as_sdt=0,44",1,2020
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning,10,neurips,3,0,2023-06-16 15:11:11.696000,https://github.com/VITA-Group/MATE,7,MATE: plugging in model awareness to task embedding for meta learning,"https://scholar.google.com/scholar?cluster=6157757074915250340&hl=en&as_sdt=0,33",2,2020
Predictive Information Accelerates Learning in RL,64,neurips,10,1,2023-06-16 15:11:11.889000,https://github.com/google-research/pisac,39,Predictive information accelerates learning in rl,"https://scholar.google.com/scholar?cluster=10907320326175710661&hl=en&as_sdt=0,10",8,2020
Counterexample-Guided Learning of Monotonic Neural Networks,38,neurips,7,2,2023-06-16 15:11:12.081000,https://github.com/AishwaryaSivaraman/COMET,16,Counterexample-guided learning of monotonic neural networks,"https://scholar.google.com/scholar?cluster=5391837593184408852&hl=en&as_sdt=0,5",5,2020
On the Trade-off between Adversarial and Backdoor Robustness,34,neurips,4,0,2023-06-16 15:11:12.282000,https://github.com/nthu-datalab/On.the.Trade-off.between.Adversarial.and.Backdoor.Robustness,16,On the trade-off between adversarial and backdoor robustness,"https://scholar.google.com/scholar?cluster=10900350868300129860&hl=en&as_sdt=0,5",4,2020
Implicit Graph Neural Networks,90,neurips,10,1,2023-06-16 15:11:12.496000,https://github.com/SwiftieH/IGNN,49,Implicit graph neural networks,"https://scholar.google.com/scholar?cluster=18159437078590406343&hl=en&as_sdt=0,33",2,2020
Rethinking Importance Weighting for Deep Learning under Distribution Shift,65,neurips,7,0,2023-06-16 15:11:12.689000,https://github.com/TongtongFANG/DIW,17,Rethinking importance weighting for deep learning under distribution shift,"https://scholar.google.com/scholar?cluster=14240629165004038270&hl=en&as_sdt=0,33",1,2020
Guiding Deep Molecular Optimization with Genetic Exploration,44,neurips,12,0,2023-06-16 15:11:12.881000,https://github.com/sungsoo-ahn/genetic-expert-guided-learning,19,Guiding deep molecular optimization with genetic exploration,"https://scholar.google.com/scholar?cluster=14089467275472583248&hl=en&as_sdt=0,5",2,2020
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks,134,neurips,23,8,2023-06-16 15:11:13.075000,https://github.com/stonezwr/TSSL-BP,55,Temporal spike sequence learning via backpropagation for deep spiking neural networks,"https://scholar.google.com/scholar?cluster=16845893280072286634&hl=en&as_sdt=0,10",2,2020
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation,69,neurips,15,3,2023-06-16 15:11:13.268000,https://github.com/verashira/TSPNet,97,Tspnet: Hierarchical feature learning via temporal semantic pyramid for sign language translation,"https://scholar.google.com/scholar?cluster=16139838619918263139&hl=en&as_sdt=0,34",7,2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning,117,neurips,7,0,2023-06-16 15:11:13.492000,https://github.com/wronnyhuang/metapoison,40,Metapoison: Practical general-purpose clean-label data poisoning,"https://scholar.google.com/scholar?cluster=12626791803327337128&hl=en&as_sdt=0,36",5,2020
Training Generative Adversarial Networks with Limited Data,1112,neurips,498,73,2023-06-16 15:11:13.686000,https://github.com/NVlabs/stylegan2-ada,1731,Training generative adversarial networks with limited data,"https://scholar.google.com/scholar?cluster=9063880872255850171&hl=en&as_sdt=0,1",37,2020
Deeply Learned Spectral Total Variation Decomposition,5,neurips,1,0,2023-06-16 15:11:13.878000,https://github.com/TamaraGrossmann/TVspecNET,3,Deeply learned spectral total variation decomposition,"https://scholar.google.com/scholar?cluster=7349648081709070834&hl=en&as_sdt=0,36",1,2020
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training,27,neurips,4,1,2023-06-16 15:11:14.071000,https://github.com/RICE-EIC/FracTrain,11,Fractrain: Fractionally squeezing bit savings both temporally and spatially for efficient dnn training,"https://scholar.google.com/scholar?cluster=1131091866886503352&hl=en&as_sdt=0,44",2,2020
Improving Neural Network Training in Low Dimensional Random Bases,11,neurips,1,0,2023-06-16 15:11:14.264000,https://github.com/graphcore-research/random-bases,13,Improving neural network training in low dimensional random bases,"https://scholar.google.com/scholar?cluster=13165492743270008587&hl=en&as_sdt=0,5",3,2020
Safe Reinforcement Learning via Curriculum Induction,68,neurips,8,1,2023-06-16 15:11:14.457000,https://github.com/zuzuba/CISR_NeurIPS20,19,Safe reinforcement learning via curriculum induction,"https://scholar.google.com/scholar?cluster=8445182531560403381&hl=en&as_sdt=0,47",2,2020
PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks,159,neurips,8,3,2023-06-16 15:11:14.649000,https://github.com/vunhatminh/PGMExplainer,54,Pgm-explainer: Probabilistic graphical model explanations for graph neural networks,"https://scholar.google.com/scholar?cluster=2699838992970724085&hl=en&as_sdt=0,4",2,2020
Few-Cost Salient Object Detection with Adversarial-Paced Learning,56,neurips,1,4,2023-06-16 15:11:14.841000,https://github.com/hb-stone/FC-SOD,16,Few-cost salient object detection with adversarial-paced learning,"https://scholar.google.com/scholar?cluster=18093471542867628559&hl=en&as_sdt=0,10",2,2020
Learning Black-Box Attackers with Transferable Priors and Query Feedback,45,neurips,4,1,2023-06-16 15:11:15.035000,https://github.com/TrustworthyDL/LeBA,28,Learning black-box attackers with transferable priors and query feedback,"https://scholar.google.com/scholar?cluster=6702320856145728902&hl=en&as_sdt=0,43",3,2020
Locally Differentially Private (Contextual) Bandits Learning,36,neurips,0,0,2023-06-16 15:11:15.227000,https://github.com/huang-research-group/LDPbandit2020,4,Locally differentially private (contextual) bandits learning,"https://scholar.google.com/scholar?cluster=7254373858969503567&hl=en&as_sdt=0,5",1,2020
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax,16,neurips,2,1,2023-06-16 15:11:15.421000,https://github.com/cunningham-lab/igr,24,Invertible gaussian reparameterization: Revisiting the gumbel-softmax,"https://scholar.google.com/scholar?cluster=4895882618721897785&hl=en&as_sdt=0,33",5,2020
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization,20,neurips,2,0,2023-06-16 15:11:15.614000,https://github.com/ubisoft/ubisoft-la-forge-ASAF,14,Adversarial soft advantage fitting: Imitation learning without policy optimization,"https://scholar.google.com/scholar?cluster=15547174239533139584&hl=en&as_sdt=0,37",5,2020
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space,43,neurips,2,1,2023-06-16 15:11:15.806000,https://github.com/AnTuo1998/AE-KD,21,Agree to disagree: Adaptive ensemble knowledge distillation in gradient space,"https://scholar.google.com/scholar?cluster=18027461890187573806&hl=en&as_sdt=0,5",2,2020
Matérn Gaussian Processes on Riemannian Manifolds,71,neurips,7,0,2023-06-16 15:11:15.999000,https://github.com/spbu-math-cs/Riemannian-Gaussian-Processes,22,Matérn Gaussian processes on Riemannian manifolds,"https://scholar.google.com/scholar?cluster=6279045067331501246&hl=en&as_sdt=0,11",7,2020
Improved Techniques for Training Score-Based Generative Models,385,neurips,47,4,2023-06-16 15:11:16.193000,https://github.com/ermongroup/ncsnv2,201,Improved techniques for training score-based generative models,"https://scholar.google.com/scholar?cluster=12852382198544252304&hl=en&as_sdt=0,36",14,2020
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations,2397,neurips,5869,1030,2023-06-16 15:11:16.385000,https://github.com/pytorch/fairseq,26463,wav2vec 2.0: A framework for self-supervised learning of speech representations,"https://scholar.google.com/scholar?cluster=17012233978100358310&hl=en&as_sdt=0,5",411,2020
"Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients",36,neurips,70,118,2023-06-16 15:11:16.577000,https://github.com/wsmoses/Enzyme,971,"Instead of rewriting foreign code for machine learning, automatically synthesize fast gradients","https://scholar.google.com/scholar?cluster=8551089294709765522&hl=en&as_sdt=0,47",37,2020
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?,64,neurips,9,0,2023-06-16 15:11:16.770000,https://github.com/MSU-MLSys-Lab/arch2vec,44,Does unsupervised architecture representation learning help neural architecture search?,"https://scholar.google.com/scholar?cluster=1242457712275613976&hl=en&as_sdt=0,32",5,2020
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments,52,neurips,0,0,2023-06-16 15:11:16.962000,https://github.com/theryanl/mitigating_manipulation_via_randomized_reviewer_assignment,1,Mitigating manipulation in peer review via randomized reviewer assignments,"https://scholar.google.com/scholar?cluster=8604310710998077908&hl=en&as_sdt=0,39",1,2020
Contrastive learning of global and local features for medical image segmentation with limited annotations,307,neurips,39,4,2023-06-16 15:11:17.155000,https://github.com/krishnabits001/domain_specific_cl,163,Contrastive learning of global and local features for medical image segmentation with limited annotations,"https://scholar.google.com/scholar?cluster=4824494533053964264&hl=en&as_sdt=0,23",2,2020
Self-Supervised Graph Transformer on Large-Scale Molecular Data,330,neurips,56,13,2023-06-16 15:11:17.347000,https://github.com/tencent-ailab/grover,257,Self-supervised graph transformer on large-scale molecular data,"https://scholar.google.com/scholar?cluster=697764344389876578&hl=en&as_sdt=0,33",4,2020
Generative Neurosymbolic Machines,45,neurips,4,0,2023-06-16 15:11:17.541000,https://github.com/JindongJiang/GNM,30,Generative neurosymbolic machines,"https://scholar.google.com/scholar?cluster=8665652977960746383&hl=en&as_sdt=0,5",3,2020
Efficient estimation of neural tuning during naturalistic behavior,9,neurips,1,0,2023-06-16 15:11:17.734000,https://github.com/BalzaniEdoardo/PGAM,1,Efficient estimation of neural tuning during naturalistic behavior,"https://scholar.google.com/scholar?cluster=3674318133407421247&hl=en&as_sdt=0,5",4,2020
High-recall causal discovery for autocorrelated time series with latent confounders,45,neurips,224,7,2023-06-16 15:11:17.927000,https://github.com/jakobrunge/tigramite,925,High-recall causal discovery for autocorrelated time series with latent confounders,"https://scholar.google.com/scholar?cluster=6795430234253215305&hl=en&as_sdt=0,46",37,2020
Joint Contrastive Learning with Infinite Possibilities,46,neurips,7,0,2023-06-16 15:11:18.120000,https://github.com/caiqi/Joint-Contrastive-Learning,41,Joint contrastive learning with infinite possibilities,"https://scholar.google.com/scholar?cluster=6409005295330808572&hl=en&as_sdt=0,11",1,2020
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows,67,neurips,34,9,2023-06-16 15:11:18.315000,https://github.com/didriknielsen/survae_flows,276,Survae flows: Surjections to bridge the gap between vaes and flows,"https://scholar.google.com/scholar?cluster=1881827871992475792&hl=en&as_sdt=0,5",28,2020
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes,2,neurips,0,0,2023-06-16 15:11:18.526000,https://github.com/hqminh/gp_sketch_nips,1,Revisiting the sample complexity of sparse spectrum approximation of gaussian processes,"https://scholar.google.com/scholar?cluster=8244493661366176703&hl=en&as_sdt=0,5",2,2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks,9,neurips,6,2,2023-06-16 15:11:18.721000,https://github.com/dtak/ocbnn-public,37,Incorporating interpretable output constraints in Bayesian neural networks,"https://scholar.google.com/scholar?cluster=18422416496972996615&hl=en&as_sdt=0,5",27,2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty,64,neurips,10,0,2023-06-16 15:11:18.922000,https://github.com/biomedia-mira/stochastic_segmentation_networks,58,Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty,"https://scholar.google.com/scholar?cluster=2760463474925616365&hl=en&as_sdt=0,15",5,2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA,45,neurips,14,5,2023-06-16 15:11:19.114000,https://github.com/ilkhem/icebeem,69,Ice-beem: Identifiable conditional energy-based deep models based on nonlinear ica,"https://scholar.google.com/scholar?cluster=384384070295711356&hl=en&as_sdt=0,5",2,2020
CogLTX: Applying BERT to Long Texts,90,neurips,45,15,2023-06-16 15:11:19.307000,https://github.com/Sleepychord/CogLTX,237,Cogltx: Applying bert to long texts,"https://scholar.google.com/scholar?cluster=18138927852402221262&hl=en&as_sdt=0,5",3,2020
Uncertainty Aware Semi-Supervised Learning on Graph Data,51,neurips,7,0,2023-06-16 15:11:19.500000,https://github.com/zxj32/uncertainty-GNN,31,Uncertainty aware semi-supervised learning on graph data,"https://scholar.google.com/scholar?cluster=4897163804428494443&hl=en&as_sdt=0,5",1,2020
ConvBERT: Improving BERT with Span-based Dynamic Convolution,118,neurips,52,5,2023-06-16 15:11:19.692000,https://github.com/yitu-opensource/ConvBert,239,Convbert: Improving bert with span-based dynamic convolution,"https://scholar.google.com/scholar?cluster=10192234385431493258&hl=en&as_sdt=0,41",9,2020
Practical No-box Adversarial Attacks against DNNs,34,neurips,3,1,2023-06-16 15:11:19.886000,https://github.com/qizhangli/nobox-attacks,28,Practical no-box adversarial attacks against dnns,"https://scholar.google.com/scholar?cluster=6838267970372396918&hl=en&as_sdt=0,5",2,2020
Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization,6,neurips,1,0,2023-06-16 15:11:20.078000,https://github.com/hassanmortagy/Walking-in-the-Shadow,1,Walking in the shadow: A new perspective on descent directions for constrained minimization,"https://scholar.google.com/scholar?cluster=1091839893594685655&hl=en&as_sdt=0,33",2,2020
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation,59,neurips,8,3,2023-06-16 15:11:20.271000,https://github.com/yogeshbalaji/robustOT,37,Robust optimal transport with applications in generative modeling and domain adaptation,"https://scholar.google.com/scholar?cluster=12381846774517697347&hl=en&as_sdt=0,21",1,2020
Autofocused oracles for model-based design,48,neurips,0,1,2023-06-16 15:11:20.479000,https://github.com/clarafy/autofocused_oracles,7,Autofocused oracles for model-based design,"https://scholar.google.com/scholar?cluster=6937579487208451262&hl=en&as_sdt=0,3",1,2020
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning,22,neurips,5,0,2023-06-16 15:11:20.671000,https://github.com/younggyoseo/trajectory_mcl,36,Trajectory-wise multiple choice learning for dynamics generalization in reinforcement learning,"https://scholar.google.com/scholar?cluster=648830007414407622&hl=en&as_sdt=0,33",3,2020
CompRess: Self-Supervised Learning by Compressing Representations,56,neurips,12,0,2023-06-16 15:11:20.863000,https://github.com/UMBCvision/CompReSS,73,Compress: Self-supervised learning by compressing representations,"https://scholar.google.com/scholar?cluster=6444771032611059422&hl=en&as_sdt=0,5",5,2020
Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN,16,neurips,3,1,2023-06-16 15:11:21.055000,https://github.com/duolala1/Reconstructing-Perceptive-Images-from-Brain-Activity-by-Shape-Semantic-GAN,13,Reconstructing perceptive images from brain activity by shape-semantic GAN,"https://scholar.google.com/scholar?cluster=18044755082798532975&hl=en&as_sdt=0,10",2,2020
A Spectral Energy Distance for Parallel Speech Synthesis,44,neurips,7320,1025,2023-06-16 15:11:21.248000,https://github.com/google-research/google-research,29776,A spectral energy distance for parallel speech synthesis,"https://scholar.google.com/scholar?cluster=9787276349444445830&hl=en&as_sdt=0,5",727,2020
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations,125,neurips,41,5,2023-06-16 15:11:21.441000,https://github.com/dicarlolab/vonenet,103,Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations,"https://scholar.google.com/scholar?cluster=14266709854899740173&hl=en&as_sdt=0,44",15,2020
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift,13,neurips,3,2,2023-06-16 15:11:21.633000,https://github.com/ZaydH/arbitrary_pu,12,Learning from positive and unlabeled data with arbitrary positive shift,"https://scholar.google.com/scholar?cluster=15991809969582276499&hl=en&as_sdt=0,34",4,2020
Deep Energy-based Modeling of Discrete-Time Physics,38,neurips,0,0,2023-06-16 15:11:21.826000,https://github.com/tksmatsubara/discrete-autograd,14,Deep energy-based modeling of discrete-time physics,"https://scholar.google.com/scholar?cluster=17442296376869037659&hl=en&as_sdt=0,31",2,2020
Self-Learning Transformations for Improving Gaze and Head Redirection,31,neurips,13,5,2023-06-16 15:11:22.019000,https://github.com/swook/faze_preprocess,36,Self-learning transformations for improving gaze and head redirection,"https://scholar.google.com/scholar?cluster=5970866983104779512&hl=en&as_sdt=0,22",3,2020
Language-Conditioned Imitation Learning for Robot Manipulation Tasks,73,neurips,18,4,2023-06-16 15:11:22.212000,https://github.com/ir-lab/LanguagePolicies,56,Language-conditioned imitation learning for robot manipulation tasks,"https://scholar.google.com/scholar?cluster=6592795961085192473&hl=en&as_sdt=0,26",4,2020
Node Embeddings and Exact Low-Rank Representations of Complex Networks,25,neurips,2,0,2023-06-16 15:11:22.405000,https://github.com/schariya/exact-embeddings,1,Node embeddings and exact low-rank representations of complex networks,"https://scholar.google.com/scholar?cluster=7942197443873251549&hl=en&as_sdt=0,33",3,2020
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications,66,neurips,820,36,2023-06-16 15:11:22.597000,https://github.com/deepmind/open_spiel,3697,Fictitious play for mean field games: Continuous time analysis and applications,"https://scholar.google.com/scholar?cluster=15909658431053288076&hl=en&as_sdt=0,6",106,2020
Interferobot: aligning an optical interferometer by a reinforcement learning agent,11,neurips,0,1,2023-06-16 15:11:22.790000,https://github.com/dmitrySorokin/interferobotProject,8,Interferobot: aligning an optical interferometer by a reinforcement learning agent,"https://scholar.google.com/scholar?cluster=2017472169343079097&hl=en&as_sdt=0,33",2,2020
Principal Neighbourhood Aggregation for Graph Nets,375,neurips,54,1,2023-06-16 15:11:22.982000,https://github.com/lukecavabarrett/pna,309,Principal neighbourhood aggregation for graph nets,"https://scholar.google.com/scholar?cluster=16853110833313152641&hl=en&as_sdt=0,33",5,2020
Instance Selection for GANs,34,neurips,4,3,2023-06-16 15:11:23.175000,https://github.com/uoguelph-mlrg/instance_selection_for_gans,42,Instance selection for gans,"https://scholar.google.com/scholar?cluster=17012682042599095713&hl=en&as_sdt=0,44",7,2020
Video Frame Interpolation without Temporal Priors,18,neurips,3,1,2023-06-16 15:11:23.367000,https://github.com/yjzhang96/UTI-VFI,31,Video frame interpolation without temporal priors,"https://scholar.google.com/scholar?cluster=2687678947317958892&hl=en&as_sdt=0,33",4,2020
Learning compositional functions via multiplicative weight updates,14,neurips,0,0,2023-06-16 15:11:23.564000,https://github.com/jxbz/madam,48,Learning compositional functions via multiplicative weight updates,"https://scholar.google.com/scholar?cluster=4109629922045417832&hl=en&as_sdt=0,5",6,2020
The interplay between randomness and structure during learning in RNNs,34,neurips,4,0,2023-06-16 15:11:23.756000,https://github.com/frschu/neurips_2020_interplay_randomness_structure,2,The interplay between randomness and structure during learning in RNNs,"https://scholar.google.com/scholar?cluster=12747185201874235106&hl=en&as_sdt=0,33",1,2020
Group Contextual Encoding for 3D Point Clouds,4,neurips,2,0,2023-06-16 15:11:23.949000,https://github.com/AsahiLiu/PointDetectron,17,Group contextual encoding for 3d point clouds,"https://scholar.google.com/scholar?cluster=18035326901258524486&hl=en&as_sdt=0,33",1,2020
Is normalization indispensable for training deep neural network? ,43,neurips,2,0,2023-06-16 15:11:24.141000,https://github.com/hukkai/rescaling,33,Is normalization indispensable for training deep neural network?,"https://scholar.google.com/scholar?cluster=13638844365029775861&hl=en&as_sdt=0,34",1,2020
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks,43,neurips,3,1,2023-06-16 15:11:24.333000,https://github.com/AmirooR/IntraOrderPreservingCalibration,11,Intra order-preserving functions for calibration of multi-class neural networks,"https://scholar.google.com/scholar?cluster=4818750365991041990&hl=en&as_sdt=0,33",3,2020
Linear Time Sinkhorn Divergences using Positive Features,16,neurips,4,0,2023-06-16 15:11:24.525000,https://github.com/meyerscetbon/LinearSinkhorn,16,Linear time Sinkhorn divergences using positive features,"https://scholar.google.com/scholar?cluster=5122167736110613142&hl=en&as_sdt=0,43",2,2020
VarGrad: A Low-Variance Gradient Estimator for Variational Inference,14,neurips,0,1,2023-06-16 15:11:24.717000,https://github.com/aboustati/vargrad,12,VarGrad: a low-variance gradient estimator for variational inference,"https://scholar.google.com/scholar?cluster=16870506199120747314&hl=en&as_sdt=0,33",4,2020
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction,6,neurips,0,2,2023-06-16 15:11:24.909000,https://github.com/WuLoli/CAECseq,2,A convolutional auto-encoder for haplotype assembly and viral quasispecies reconstruction,"https://scholar.google.com/scholar?cluster=627030552528297106&hl=en&as_sdt=0,33",1,2020
Adversarial Counterfactual Learning and Evaluation for Recommender System,24,neurips,8,0,2023-06-16 15:11:25.102000,https://github.com/StatsDLMathsRecomSys/Adversarial-Counterfactual-Learning-and-Evaluation-for-Recommender-System,21,Adversarial counterfactual learning and evaluation for recommender system,"https://scholar.google.com/scholar?cluster=8553459307205349621&hl=en&as_sdt=0,16",2,2020
Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control,16,neurips,4,0,2023-06-16 15:11:25.295000,https://github.com/giorgosmamakoukas/MemoryEfficientStableLDS,17,Memory-efficient learning of stable linear dynamical systems for prediction and control,"https://scholar.google.com/scholar?cluster=6270757032099202742&hl=en&as_sdt=0,33",2,2020
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder,53,neurips,26,10,2023-06-16 15:11:25.498000,https://github.com/microsoft/RelationNet2,209,Relationnet++: Bridging visual representations for object detection via transformer decoder,"https://scholar.google.com/scholar?cluster=2597487558534489609&hl=en&as_sdt=0,33",24,2020
Neurosymbolic Transformers for Multi-Agent Communication,18,neurips,2,0,2023-06-16 15:11:25.690000,https://github.com/jinala/multi-agent-neurosym-transformers,16,Neurosymbolic transformers for multi-agent communication,"https://scholar.google.com/scholar?cluster=4554423143327303574&hl=en&as_sdt=0,33",1,2020
Fairness in Streaming Submodular Maximization: Algorithms and Hardness,29,neurips,7320,1025,2023-06-16 15:11:25.882000,https://github.com/google-research/google-research,29776,Fairness in streaming submodular maximization: Algorithms and hardness,"https://scholar.google.com/scholar?cluster=762679963021898212&hl=en&as_sdt=0,11",727,2020
Smoothed Geometry for Robust Attribution,36,neurips,1,0,2023-06-16 15:11:26.075000,https://github.com/zifanw/smoothed_geometry,7,Smoothed geometry for robust attribution,"https://scholar.google.com/scholar?cluster=9573430737133381882&hl=en&as_sdt=0,5",1,2020
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms,20,neurips,0,0,2023-06-16 15:11:26.266000,https://github.com/saralajew/robust_NPCs,2,Fast adversarial robustness certification of nearest prototype classifiers for arbitrary seminorms,"https://scholar.google.com/scholar?cluster=5975974193120629268&hl=en&as_sdt=0,39",1,2020
Multi-agent active perception with prediction rewards,9,neurips,0,0,2023-06-16 15:11:26.459000,https://github.com/laurimi/multiagent-prediction-reward,9,Multi-agent active perception with prediction rewards,"https://scholar.google.com/scholar?cluster=16061069900902871568&hl=en&as_sdt=0,50",4,2020
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations,53,neurips,6,1,2023-06-16 15:11:26.650000,https://github.com/davrempe/caspr,66,Caspr: Learning canonical spatiotemporal point cloud representations,"https://scholar.google.com/scholar?cluster=2193354072785076555&hl=en&as_sdt=0,18",10,2020
Deep Automodulators,2,neurips,2,0,2023-06-16 15:11:26.842000,https://github.com/AaltoVision/automodulator,14,Deep automodulators,"https://scholar.google.com/scholar?cluster=15567958931014667232&hl=en&as_sdt=0,33",2,2020
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning,83,neurips,31,15,2023-06-16 15:11:27.035000,https://github.com/NVlabs/conv-tt-lstm,111,Convolutional tensor-train lstm for spatio-temporal learning,"https://scholar.google.com/scholar?cluster=14678206680499883821&hl=en&as_sdt=0,5",5,2020
The Potts-Ising model for discrete multivariate data,3,neurips,0,0,2023-06-16 15:11:27.227000,https://github.com/aaamini/pois_comparisons,1,The Potts-Ising model for discrete multivariate data,"https://scholar.google.com/scholar?cluster=6863880968599010032&hl=en&as_sdt=0,33",1,2020
"MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics",5,neurips,1,0,2023-06-16 15:11:27.419000,https://github.com/stephaneckstein/minmaxot,1,"Minmax methods for optimal transport and beyond: Regularization, approximation and numerics","https://scholar.google.com/scholar?cluster=13129304767916620268&hl=en&as_sdt=0,33",1,2020
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick,22,neurips,2,1,2023-06-16 15:11:27.611000,https://github.com/mmrezaee/VRTM,10,A discrete variational recurrent topic model without the reparametrization trick,"https://scholar.google.com/scholar?cluster=14340020828627005891&hl=en&as_sdt=0,33",2,2020
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces,8,neurips,0,0,2023-06-16 15:11:27.803000,https://github.com/akashsaha06/NeurIPS-2020,2,Learning with operator-valued kernels in reproducing kernel Krein spaces,"https://scholar.google.com/scholar?cluster=13474809885434499902&hl=en&as_sdt=0,32",1,2020
Learning Bounds for Risk-sensitive Learning,35,neurips,1,0,2023-06-16 15:11:27.996000,https://github.com/jaeho-lee/oce,5,Learning bounds for risk-sensitive learning,"https://scholar.google.com/scholar?cluster=14340544354224111780&hl=en&as_sdt=0,13",1,2020
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints,92,neurips,13,0,2023-06-16 15:11:28.189000,https://github.com/mfinzi/constrained-hamiltonian-neural-networks,87,Simplifying hamiltonian and lagrangian neural networks via explicit constraints,"https://scholar.google.com/scholar?cluster=2817099507045066025&hl=en&as_sdt=0,15",5,2020
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency,60,neurips,1,1,2023-06-16 15:11:28.382000,https://github.com/wichmann-lab/error-consistency,6,Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency,"https://scholar.google.com/scholar?cluster=13784841370093089337&hl=en&as_sdt=0,33",3,2020
RANet: Region Attention Network for Semantic Segmentation,23,neurips,3,0,2023-06-16 15:11:28.575000,https://github.com/dingguo1996/RANet,32,Ranet: Region attention network for semantic segmentation,"https://scholar.google.com/scholar?cluster=10094620109587185343&hl=en&as_sdt=0,41",3,2020
Learning sparse codes from compressed representations with biologically plausible local wiring constraints,2,neurips,0,0,2023-06-16 15:11:28.768000,https://github.com/siplab-gt/localized-sparse-coding,1,Learning sparse codes from compressed representations with biologically plausible local wiring constraints,"https://scholar.google.com/scholar?cluster=16665600177860294505&hl=en&as_sdt=0,10",2,2020
Directional Pruning of Deep Neural Networks,26,neurips,8,2,2023-06-16 15:11:28.960000,https://github.com/donlan2710/gRDA-Optimizer,40,Directional pruning of deep neural networks,"https://scholar.google.com/scholar?cluster=8389784571669099879&hl=en&as_sdt=0,5",3,2020
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity,10,neurips,4,0,2023-06-16 15:11:29.152000,https://github.com/L0SG/NanoFlow,64,Nanoflow: Scalable normalizing flows with sublinear parameter complexity,"https://scholar.google.com/scholar?cluster=2139954886810739910&hl=en&as_sdt=0,5",4,2020
Graph Cross Networks with Vertex Infomax Pooling,44,neurips,10,7,2023-06-16 15:11:29.345000,https://github.com/limaosen0/GXN,44,Graph cross networks with vertex infomax pooling,"https://scholar.google.com/scholar?cluster=12147623399962209676&hl=en&as_sdt=0,5",4,2020
MOPO: Model-based Offline Policy Optimization,454,neurips,40,9,2023-06-16 15:11:29.537000,https://github.com/tianheyu927/mopo,142,Mopo: Model-based offline policy optimization,"https://scholar.google.com/scholar?cluster=17944635357002581259&hl=en&as_sdt=0,47",8,2020
Building powerful and equivariant graph neural networks with structural message-passing,85,neurips,2,1,2023-06-16 15:11:29.729000,https://github.com/cvignac/SMP,21,Building powerful and equivariant graph neural networks with structural message-passing,"https://scholar.google.com/scholar?cluster=9701369192475707124&hl=en&as_sdt=0,5",4,2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning,54,neurips,7,2,2023-06-16 15:11:29.922000,https://github.com/sebascuri/hucrl,28,Efficient model-based reinforcement learning through optimistic policy search and planning,"https://scholar.google.com/scholar?cluster=13950651557612001480&hl=en&as_sdt=0,5",2,2020
Practical Low-Rank Communication Compression in Decentralized Deep Learning,26,neurips,1,0,2023-06-16 15:11:30.115000,https://github.com/epfml/powergossip,7,Practical low-rank communication compression in decentralized deep learning,"https://scholar.google.com/scholar?cluster=326168580277977318&hl=en&as_sdt=0,20",6,2020
3D Shape Reconstruction from Vision and Touch,27,neurips,13,0,2023-06-16 15:11:30.307000,https://github.com/facebookresearch/3D-Vision-and-Touch,59,3d shape reconstruction from vision and touch,"https://scholar.google.com/scholar?cluster=5012332327817595589&hl=en&as_sdt=0,39",10,2020
GradAug: A New Regularization Method for Deep Neural Networks,22,neurips,6,1,2023-06-16 15:11:30.521000,https://github.com/taoyang1122/GradAug,90,Gradaug: A new regularization method for deep neural networks,"https://scholar.google.com/scholar?cluster=6983882752578782153&hl=en&as_sdt=0,36",6,2020
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay,36,neurips,5,0,2023-06-16 15:11:30.714000,https://github.com/sfujim/LAP-PAL,27,An equivalence between loss functions and non-uniform sampling in experience replay,"https://scholar.google.com/scholar?cluster=7573921906024948700&hl=en&as_sdt=0,5",1,2020
Rational neural networks,40,neurips,4,1,2023-06-16 15:11:30.906000,https://github.com/NBoulle/RationalNets,18,Rational neural networks,"https://scholar.google.com/scholar?cluster=12116003355526084292&hl=en&as_sdt=0,5",5,2020
DISK: Learning local features with policy gradient,124,neurips,31,2,2023-06-16 15:11:31.098000,https://github.com/cvlab-epfl/disk,217,DISK: Learning local features with policy gradient,"https://scholar.google.com/scholar?cluster=3357995340662303301&hl=en&as_sdt=0,5",13,2020
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency,15,neurips,7,4,2023-06-16 15:11:31.303000,https://github.com/zhaofang0627/HPBTT,34,Human parsing based texture transfer from single image to 3D human via cross-view consistency,"https://scholar.google.com/scholar?cluster=1392260009532397096&hl=en&as_sdt=0,23",5,2020
Point process models for sequence detection in high-dimensional neural spike trains,19,neurips,14,13,2023-06-16 15:11:31.501000,https://github.com/lindermanlab/PPSeq.jl,54,Point process models for sequence detection in high-dimensional neural spike trains,"https://scholar.google.com/scholar?cluster=9563598193970283659&hl=en&as_sdt=0,5",5,2020
Meta-Consolidation for Continual Learning,41,neurips,6,2,2023-06-16 15:11:31.694000,https://github.com/JosephKJ/merlin,35,Meta-consolidation for continual learning,"https://scholar.google.com/scholar?cluster=15752256087440241118&hl=en&as_sdt=0,50",3,2020
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting,25,neurips,3,1,2023-06-16 15:11:31.887000,https://github.com/GRASP-ML/LPG-FTW,18,Lifelong policy gradient learning of factored policies for faster training without forgetting,"https://scholar.google.com/scholar?cluster=165730710114613899&hl=en&as_sdt=0,36",4,2020
Kernel Methods Through the Roof: Handling Billions of Points Efficiently,79,neurips,18,11,2023-06-16 15:11:32.080000,https://github.com/FalkonML/falkon,144,Kernel methods through the roof: handling billions of points efficiently,"https://scholar.google.com/scholar?cluster=3529879786066434320&hl=en&as_sdt=0,44",5,2020
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness,94,neurips,5,4,2023-06-16 15:11:32.272000,https://github.com/garyzhao/ME-ADA,44,Maximum-entropy adversarial data augmentation for improved generalization and robustness,"https://scholar.google.com/scholar?cluster=7615895385729702903&hl=en&as_sdt=0,33",5,2020
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler,37,neurips,23,2,2023-06-16 15:11:32.465000,https://github.com/ZhiningLiu1998/mesa,98,MESA: boost ensemble imbalanced learning with meta-sampler,"https://scholar.google.com/scholar?cluster=7795141053937994912&hl=en&as_sdt=0,44",6,2020
CoinPress: Practical Private Mean and Covariance Estimation,64,neurips,4,1,2023-06-16 15:11:32.657000,https://github.com/twistedcubic/coin-press,26,Coinpress: Practical private mean and covariance estimation,"https://scholar.google.com/scholar?cluster=13482839562115522238&hl=en&as_sdt=0,5",8,2020
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks,76,neurips,3,0,2023-06-16 15:11:32.850000,https://github.com/dms-net/scatteringGCN,18,Scattering gcn: Overcoming oversmoothness in graph convolutional networks,"https://scholar.google.com/scholar?cluster=18035755183666892660&hl=en&as_sdt=0,5",3,2020
Scalable Graph Neural Networks via Bidirectional Propagation,74,neurips,3,3,2023-06-16 15:11:33.043000,https://github.com/chennnM/GBP,22,Scalable graph neural networks via bidirectional propagation,"https://scholar.google.com/scholar?cluster=9080075378376168855&hl=en&as_sdt=0,5",1,2020
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning,101,neurips,12,1,2023-06-16 15:11:33.235000,https://github.com/bbuing9/DARP,60,Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning,"https://scholar.google.com/scholar?cluster=8038258359188951578&hl=en&as_sdt=0,6",4,2020
The Strong Screening Rule for SLOPE,11,neurips,0,0,2023-06-16 15:11:33.427000,https://github.com/jolars/slope-screening-code,0,The strong screening rule for SLOPE,"https://scholar.google.com/scholar?cluster=12320339008639900419&hl=en&as_sdt=0,39",2,2020
Efficient Generation of Structured Objects with Constrained Adversarial Networks,17,neurips,0,0,2023-06-16 15:11:33.620000,https://github.com/unitn-sml/CAN,6,Efficient generation of structured objects with constrained adversarial networks,"https://scholar.google.com/scholar?cluster=4567145751569316371&hl=en&as_sdt=0,47",7,2020
Learning Sparse Prototypes for Text Generation,16,neurips,2,1,2023-06-16 15:11:33.812000,https://github.com/jxhe/sparse-text-prototype,19,Learning sparse prototypes for text generation,"https://scholar.google.com/scholar?cluster=7964564098048473464&hl=en&as_sdt=0,5",2,2020
Implicit Rank-Minimizing Autoencoder,28,neurips,9,0,2023-06-16 15:11:34.004000,https://github.com/facebookresearch/irmae,45,Implicit rank-minimizing autoencoder,"https://scholar.google.com/scholar?cluster=13933352693018665516&hl=en&as_sdt=0,5",8,2020
Task-Oriented Feature Distillation,25,neurips,8,8,2023-06-16 15:11:34.198000,https://github.com/ArchipLab-LinfengZhang/Task-Oriented-Feature-Distillation,37,Task-oriented feature distillation,"https://scholar.google.com/scholar?cluster=4090442245139962638&hl=en&as_sdt=0,33",3,2020
When Do Neural Networks Outperform Kernel Methods?,116,neurips,0,0,2023-06-16 15:11:34.391000,https://github.com/bGhorbani/linearized_neural_networks,1,When do neural networks outperform kernel methods?,"https://scholar.google.com/scholar?cluster=9006100228205031604&hl=en&as_sdt=0,33",2,2020
A Variational Approach for Learning from Positive and Unlabeled Data,23,neurips,2,0,2023-06-16 15:11:34.584000,https://github.com/HC-Feynman/vpu,16,A variational approach for learning from positive and unlabeled data,"https://scholar.google.com/scholar?cluster=9825864282634047944&hl=en&as_sdt=0,33",1,2020
Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut,16,neurips,3,2,2023-06-16 15:11:34.777000,https://github.com/ShenfeiPei/KSUMS,7,Efficient Clustering Based On A Unified View Of -means And Ratio-cut,"https://scholar.google.com/scholar?cluster=8591169336968317824&hl=en&as_sdt=0,33",2,2020
Coresets via Bilevel Optimization for Continual Learning and Streaming,119,neurips,7,1,2023-06-16 15:11:34.970000,https://github.com/zalanborsos/bilevel_coresets,59,Coresets via bilevel optimization for continual learning and streaming,"https://scholar.google.com/scholar?cluster=8782040357228016957&hl=en&as_sdt=0,5",3,2020
Deep Evidential Regression,208,neurips,86,14,2023-06-16 15:11:35.162000,https://github.com/aamini/evidential-deep-learning,335,Deep evidential regression,"https://scholar.google.com/scholar?cluster=1290131026867107522&hl=en&as_sdt=0,5",17,2020
Bayesian Pseudocoresets,19,neurips,0,0,2023-06-16 15:11:35.355000,https://github.com/trevorcampbell/pseudocoresets-experiments,2,Bayesian pseudocoresets,"https://scholar.google.com/scholar?cluster=3191000793035049676&hl=en&as_sdt=0,5",1,2020
"See, Hear, Explore: Curiosity via Audio-Visual Association",40,neurips,3,0,2023-06-16 15:11:35.548000,https://github.com/vdean/audio-curiosity,22,"See, hear, explore: Curiosity via audio-visual association","https://scholar.google.com/scholar?cluster=3876755724987793251&hl=en&as_sdt=0,34",6,2020
A Biologically Plausible Neural Network for Slow Feature Analysis,12,neurips,3,0,2023-06-16 15:11:35.755000,https://github.com/flatironinstitute/bio-sfa,9,A biologically plausible neural network for slow feature analysis,"https://scholar.google.com/scholar?cluster=9129829239701859332&hl=en&as_sdt=0,33",5,2020
Learning Feature Sparse Principal Subspace,12,neurips,1,0,2023-06-16 15:11:35.947000,https://github.com/icety3/FSPCA,3,Learning feature sparse principal subspace,"https://scholar.google.com/scholar?cluster=14875636565166044035&hl=en&as_sdt=0,33",1,2020
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping,61,neurips,1,0,2023-06-16 15:11:36.141000,https://github.com/eduardgorbunov/accelerated_clipping,0,Stochastic optimization with heavy-tailed noise via accelerated gradient clipping,"https://scholar.google.com/scholar?cluster=13617610532050808796&hl=en&as_sdt=0,5",1,2020
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering,60,neurips,27,4,2023-06-16 15:11:36.335000,https://github.com/HazyResearch/HypHC,171,From trees to continuous embeddings and back: Hyperbolic hierarchical clustering,"https://scholar.google.com/scholar?cluster=14446389788474790006&hl=en&as_sdt=0,10",18,2020
A Randomized Algorithm to Reduce the Support of Discrete Measures,11,neurips,0,0,2023-06-16 15:11:36.528000,https://github.com/FraCose/Recombination_Random_Algos,1,A randomized algorithm to reduce the support of discrete measures,"https://scholar.google.com/scholar?cluster=4952355762729380364&hl=en&as_sdt=0,31",2,2020
Distributionally Robust Federated Averaging,81,neurips,32,4,2023-06-16 15:11:36.720000,https://github.com/MLOPTPSU/FedTorch,153,Distributionally robust federated averaging,"https://scholar.google.com/scholar?cluster=7220059045750454455&hl=en&as_sdt=0,38",5,2020
Supermasks in Superposition,161,neurips,19,8,2023-06-16 15:11:36.912000,https://github.com/RAIVNLab/supsup,105,Supermasks in superposition,"https://scholar.google.com/scholar?cluster=9249214660750910893&hl=en&as_sdt=0,47",9,2020
Learning to Incentivize Other Learning Agents,42,neurips,5,0,2023-06-16 15:11:37.106000,https://github.com/011235813/lio,21,Learning to incentivize other learning agents,"https://scholar.google.com/scholar?cluster=4917678019855172893&hl=en&as_sdt=0,10",3,2020
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation,53,neurips,5,0,2023-06-16 15:11:37.305000,https://github.com/jytime/DICL-Flow,147,Displacement-invariant matching cost learning for accurate optical flow estimation,"https://scholar.google.com/scholar?cluster=18219962578086154043&hl=en&as_sdt=0,5",2,2020
Calibrating Deep Neural Networks using Focal Loss,217,neurips,27,2,2023-06-16 15:11:37.502000,https://github.com/torrvision/focal_calibration,131,Calibrating deep neural networks using focal loss,"https://scholar.google.com/scholar?cluster=5652808911409049311&hl=en&as_sdt=0,5",8,2020
Optimizing Mode Connectivity via Neuron Alignment,27,neurips,0,1,2023-06-16 15:11:37.694000,https://github.com/IBM/NeuronAlignment,11,Optimizing mode connectivity via neuron alignment,"https://scholar.google.com/scholar?cluster=2446555805962125063&hl=en&as_sdt=0,19",7,2020
First Order Constrained Optimization in Policy Space,61,neurips,6,2,2023-06-16 15:11:37.886000,https://github.com/ymzhang01/focops,19,First order constrained optimization in policy space,"https://scholar.google.com/scholar?cluster=13576739471377341905&hl=en&as_sdt=0,10",1,2020
Learning Augmented Energy Minimization via Speed Scaling,47,neurips,0,13,2023-06-16 15:11:38.079000,https://github.com/andreasr27/LAS,1,Learning augmented energy minimization via speed scaling,"https://scholar.google.com/scholar?cluster=17308040311209580615&hl=en&as_sdt=0,33",1,2020
Neural Sparse Representation for Image Restoration,21,neurips,6,2,2023-06-16 15:11:38.272000,https://github.com/ychfan/nsr,27,Neural sparse representation for image restoration,"https://scholar.google.com/scholar?cluster=10878239147304379491&hl=en&as_sdt=0,33",3,2020
Certified Monotonic Neural Networks,51,neurips,4,0,2023-06-16 15:11:38.470000,https://github.com/gnobitab/CertifiedMonotonicNetwork,19,Certified monotonic neural networks,"https://scholar.google.com/scholar?cluster=10699232933677275869&hl=en&as_sdt=0,43",1,2020
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina,8,neurips,1,0,2023-06-16 15:11:38.662000,https://github.com/berenslab/bc_network,0,System identification with biophysical constraints: A circuit model of the inner retina,"https://scholar.google.com/scholar?cluster=5347063978169460934&hl=en&as_sdt=0,47",4,2020
Efficient Algorithms for Device Placement of DNN Graph Operators,37,neurips,12,0,2023-06-16 15:11:38.855000,https://github.com/msr-fiddle/dnn-partitioning,34,Efficient algorithms for device placement of dnn graph operators,"https://scholar.google.com/scholar?cluster=9495456628645575288&hl=en&as_sdt=0,11",3,2020
BOSS: Bayesian Optimization over String Spaces,54,neurips,4,2,2023-06-16 15:11:39.047000,https://github.com/henrymoss/BOSS,19,Boss: Bayesian optimization over string spaces,"https://scholar.google.com/scholar?cluster=5626895554294984605&hl=en&as_sdt=0,33",3,2020
Improved Analysis of Clipping Algorithms for Non-convex Optimization,27,neurips,2,0,2023-06-16 15:11:39.240000,https://github.com/zbh2047/clipping-algorithms,7,Improved analysis of clipping algorithms for non-convex optimization,"https://scholar.google.com/scholar?cluster=7794174474681522409&hl=en&as_sdt=0,5",1,2020
"A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection",34,neurips,17,4,2023-06-16 15:11:39.432000,https://github.com/kemaloksuz/aLRPLoss,132,"A ranking-based, balanced loss function unifying classification and localisation in object detection","https://scholar.google.com/scholar?cluster=17699742698226354325&hl=en&as_sdt=0,5",5,2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks,52,neurips,7,1,2023-06-16 15:11:39.625000,https://github.com/ginevracoal/robustBNNs,15,Robustness of bayesian neural networks to gradient-based attacks,"https://scholar.google.com/scholar?cluster=10011308363254706917&hl=en&as_sdt=0,24",2,2020
Sparse Weight Activation Training,45,neurips,6,2,2023-06-16 15:11:39.817000,https://github.com/AamirRaihan/SWAT,20,Sparse weight activation training,"https://scholar.google.com/scholar?cluster=13365043317939429653&hl=en&as_sdt=0,5",3,2020
Collapsing Bandits and Their Application to Public Health Intervention,45,neurips,2,0,2023-06-16 15:11:40.009000,https://github.com/AdityaMate/collapsing_bandits,9,Collapsing bandits and their application to public health intervention,"https://scholar.google.com/scholar?cluster=8570523626474094821&hl=en&as_sdt=0,10",2,2020
Neural Sparse Voxel Fields,577,neurips,89,30,2023-06-16 15:11:40.202000,https://github.com/facebookresearch/NSVF,710,Neural sparse voxel fields,"https://scholar.google.com/scholar?cluster=8122086353742917335&hl=en&as_sdt=0,31",60,2020
The Discrete Gaussian for Differential Privacy,145,neurips,15,1,2023-06-16 15:11:40.397000,https://github.com/IBM/discrete-gaussian-differential-privacy,52,The discrete gaussian for differential privacy,"https://scholar.google.com/scholar?cluster=15167325577394029097&hl=en&as_sdt=0,22",10,2020
Learning efficient task-dependent representations with synaptic plasticity,10,neurips,0,0,2023-06-16 15:11:40.591000,https://github.com/colinbredenberg/Efficient-Plasticity-Camera-Ready,0,Learning efficient task-dependent representations with synaptic plasticity,"https://scholar.google.com/scholar?cluster=9379444748985417987&hl=en&as_sdt=0,38",2,2020
Disentangling Human Error from Ground Truth in Segmentation of Medical Images,52,neurips,14,4,2023-06-16 15:11:40.784000,https://github.com/moucheng2017/Learn_Noisy_Labels_Medical_Images,58,Disentangling human error from ground truth in segmentation of medical images,"https://scholar.google.com/scholar?cluster=285062865281898576&hl=en&as_sdt=0,5",3,2020
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences,16,neurips,290,26,2023-06-16 15:11:40.978000,https://github.com/openai/multi-agent-emergence-environments,1469,Emergent reciprocity and team formation from randomized uncertain social preferences,"https://scholar.google.com/scholar?cluster=15635465066667628419&hl=en&as_sdt=0,10",167,2020
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity,16,neurips,1,0,2023-06-16 15:11:41.171000,https://github.com/HornHehhf/LANTK,6,Label-aware neural tangent kernel: Toward better generalization and local elasticity,"https://scholar.google.com/scholar?cluster=8612232995248267129&hl=en&as_sdt=0,34",2,2020
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows,31,neurips,1,4,2023-06-16 15:11:41.367000,https://github.com/hmdolatabadi/AdvFlow,39,Advflow: Inconspicuous black-box adversarial attacks using normalizing flows,"https://scholar.google.com/scholar?cluster=14447439050002958501&hl=en&as_sdt=0,5",3,2020
On the Expressiveness of Approximate Inference in Bayesian Neural Networks,84,neurips,0,0,2023-06-16 15:11:41.572000,https://github.com/cambridge-mlg/expressiveness-approx-bnns,11,On the expressiveness of approximate inference in bayesian neural networks,"https://scholar.google.com/scholar?cluster=5102786395821574554&hl=en&as_sdt=0,31",6,2020
"Dark Experience for General Continual Learning: a Strong, Simple Baseline",308,neurips,65,4,2023-06-16 15:11:41.765000,https://github.com/aimagelab/mammoth,346,"Dark experience for general continual learning: a strong, simple baseline","https://scholar.google.com/scholar?cluster=2597864278610919682&hl=en&as_sdt=0,5",10,2020
PLLay: Efficient Topological Layer based on Persistent Landscapes,38,neurips,3,0,2023-06-16 15:11:41.957000,https://github.com/jisuk1/pllay,15,Pllay: Efficient topological layer based on persistent landscapes,"https://scholar.google.com/scholar?cluster=11445863975926543932&hl=en&as_sdt=0,1",2,2020
Inductive Quantum Embedding,5,neurips,11,4,2023-06-16 15:11:42.150000,https://github.com/IBM/e2r,22,Inductive quantum embedding,"https://scholar.google.com/scholar?cluster=3314915984353001868&hl=en&as_sdt=0,36",10,2020
Understanding and Improving Fast Adversarial Training,186,neurips,11,0,2023-06-16 15:11:42.365000,https://github.com/tml-epfl/understanding-fast-adv-training,89,Understanding and improving fast adversarial training,"https://scholar.google.com/scholar?cluster=2088861284079495555&hl=en&as_sdt=0,5",5,2020
"Attack of the Tails: Yes, You Really Can Backdoor Federated Learning",297,neurips,9,3,2023-06-16 15:11:42.560000,https://github.com/ksreenivasan/OOD_Federated_Learning,41,"Attack of the tails: Yes, you really can backdoor federated learning","https://scholar.google.com/scholar?cluster=13414623177431802672&hl=en&as_sdt=0,36",2,2020
Domain Generalization via Entropy Regularization,144,neurips,8,4,2023-06-16 15:11:42.753000,https://github.com/sshan-zhao/DG_via_ER,52,Domain generalization via entropy regularization,"https://scholar.google.com/scholar?cluster=427952868050737540&hl=en&as_sdt=0,33",2,2020
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels,94,neurips,28,1,2023-06-16 15:11:42.946000,https://github.com/BayesWatch/deep-kernel-transfer,182,Bayesian meta-learning for the few-shot setting via deep kernels,"https://scholar.google.com/scholar?cluster=18214192068106676357&hl=en&as_sdt=0,23",14,2020
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding,21,neurips,2,0,2023-06-16 15:11:43.140000,https://github.com/gergely-flamich/relative-entropy-coding,14,Compressing images by encoding their latent representations with relative entropy coding,"https://scholar.google.com/scholar?cluster=12289205300644930057&hl=en&as_sdt=0,25",3,2020
An Efficient Adversarial Attack for Tree Ensembles,12,neurips,5,1,2023-06-16 15:11:43.344000,https://github.com/chong-z/tree-ensemble-attack,19,An efficient adversarial attack for tree ensembles,"https://scholar.google.com/scholar?cluster=609360742914199443&hl=en&as_sdt=0,44",0,2020
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations,24,neurips,5,0,2023-06-16 15:11:43.537000,https://github.com/ZijieH/LG-ODE,23,Learning continuous system dynamics from irregularly-sampled partial observations,"https://scholar.google.com/scholar?cluster=8858649239314376854&hl=en&as_sdt=0,14",2,2020
Robust Pre-Training by Adversarial Contrastive Learning,140,neurips,16,2,2023-06-16 15:11:43.739000,https://github.com/VITA-Group/Adversarial-Contrastive-Learning,99,Robust pre-training by adversarial contrastive learning,"https://scholar.google.com/scholar?cluster=16518369038810216082&hl=en&as_sdt=0,3",3,2020
When Counterpoint Meets Chinese Folk Melodies,5,neurips,2,1,2023-06-16 15:11:43.932000,https://github.com/nina124/FolkDuet,12,When counterpoint meets chinese folk melodies,"https://scholar.google.com/scholar?cluster=12963487339842203249&hl=en&as_sdt=0,3",2,2020
Universal Domain Adaptation through Self Supervision,196,neurips,19,3,2023-06-16 15:11:44.125000,https://github.com/VisionLearningGroup/DANCE,111,Universal domain adaptation through self supervision,"https://scholar.google.com/scholar?cluster=11345299015007987908&hl=en&as_sdt=0,31",4,2020
Stochastic Normalization,10,neurips,1,1,2023-06-16 15:11:44.332000,https://github.com/thuml/StochNorm,23,Stochastic normalization,"https://scholar.google.com/scholar?cluster=5318680963113509022&hl=en&as_sdt=0,33",6,2020
Constrained episodic reinforcement learning in concave-convex and knapsack settings,37,neurips,1,2,2023-06-16 15:11:44.545000,https://github.com/miryoosefi/ConRL,8,Constrained episodic reinforcement learning in concave-convex and knapsack settings,"https://scholar.google.com/scholar?cluster=14503128503676733543&hl=en&as_sdt=0,5",2,2020
Cross-validation Confidence Intervals for Test Error,23,neurips,0,0,2023-06-16 15:11:44.738000,https://github.com/alexandre-bayle/cvci,7,Cross-validation confidence intervals for test error,"https://scholar.google.com/scholar?cluster=15681064119655058632&hl=en&as_sdt=0,5",2,2020
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation,57,neurips,71,21,2023-06-16 15:11:44.931000,https://github.com/alexandre01/deepsvg,730,Deepsvg: A hierarchical generative network for vector graphics animation,"https://scholar.google.com/scholar?cluster=5374969560499371553&hl=en&as_sdt=0,5",21,2020
Bayesian Attention Modules,37,neurips,9,0,2023-06-16 15:11:45.123000,https://github.com/zhougroup/BAM,29,Bayesian attention modules,"https://scholar.google.com/scholar?cluster=5527896286369211202&hl=en&as_sdt=0,33",4,2020
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough,9,neurips,7,1,2023-06-16 15:11:45.316000,https://github.com/lushleaf/Network-Pruning-Greedy-Forward-Selection,20,Greedy optimization provably wins the lottery: Logarithmic number of winning tickets is enough,"https://scholar.google.com/scholar?cluster=8946682883342660892&hl=en&as_sdt=0,5",2,2020
Path Integral Based Convolution and Pooling for Graph Neural Networks,32,neurips,4,6,2023-06-16 15:11:45.520000,https://github.com/YuGuangWang/PAN,26,Path integral based convolution and pooling for graph neural networks,"https://scholar.google.com/scholar?cluster=14179965344392955374&hl=en&as_sdt=0,14",2,2020
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks,51,neurips,10,1,2023-06-16 15:11:45.713000,https://github.com/ioanabica/SCIGAN,19,Estimating the effects of continuous-valued interventions using generative adversarial networks,"https://scholar.google.com/scholar?cluster=6398203741512669443&hl=en&as_sdt=0,5",1,2020
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings,8,neurips,0,0,2023-06-16 15:11:45.905000,https://github.com/HeejongBong/ldfa,1,Latent dynamic factor analysis of high-dimensional neural recordings,"https://scholar.google.com/scholar?cluster=2397075989835657043&hl=en&as_sdt=0,5",1,2020
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning,34,neurips,9,3,2023-06-16 15:11:46.108000,https://github.com/NVlabs/Bongard-LOGO,47,Bongard-logo: A new benchmark for human-level concept learning and reasoning,"https://scholar.google.com/scholar?cluster=9164011458889391917&hl=en&as_sdt=0,33",13,2020
GAN Memory with No Forgetting,75,neurips,4,1,2023-06-16 15:11:46.307000,https://github.com/MiaoyunZhao/GANmemory_LifelongLearning,45,Gan memory with no forgetting,"https://scholar.google.com/scholar?cluster=13145134091678192364&hl=en&as_sdt=0,5",3,2020
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games,30,neurips,2,0,2023-06-16 15:11:46.532000,https://github.com/YunqiuXu/SHA-KG,9,Deep reinforcement learning with stacked hierarchical attention for text-based games,"https://scholar.google.com/scholar?cluster=10348481176946628089&hl=en&as_sdt=0,36",3,2020
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding,34,neurips,0,1,2023-06-16 15:11:46.724000,https://github.com/llan-ml/MetaTNE,9,Node classification on graphs with few-shot novel labels via meta transformed network embedding,"https://scholar.google.com/scholar?cluster=14402211114000422574&hl=en&as_sdt=0,33",1,2020
Relative gradient optimization of the Jacobian term in unsupervised deep learning,18,neurips,2,0,2023-06-16 15:11:46.916000,https://github.com/fissoreg/relative-gradient-jacobian,19,Relative gradient optimization of the jacobian term in unsupervised deep learning,"https://scholar.google.com/scholar?cluster=6730260623059884783&hl=en&as_sdt=0,5",3,2020
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds,4,neurips,0,6,2023-06-16 15:11:47.108000,https://github.com/vlievin/ovis,10,Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds,"https://scholar.google.com/scholar?cluster=14600314100540480653&hl=en&as_sdt=0,44",4,2020
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning,29,neurips,8,0,2023-06-16 15:11:47.310000,https://github.com/juliusberner/deep_kolmogorov,19,Numerically solving parametric families of high-dimensional Kolmogorov partial differential equations via deep learning,"https://scholar.google.com/scholar?cluster=1531427583443268400&hl=en&as_sdt=0,37",4,2020
AViD Dataset: Anonymized Videos from Diverse Countries,33,neurips,3,5,2023-06-16 15:11:47.520000,https://github.com/piergiaj/AViD,50,Avid dataset: Anonymized videos from diverse countries,"https://scholar.google.com/scholar?cluster=9859841321029436808&hl=en&as_sdt=0,48",6,2020
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning,30,neurips,2,4,2023-06-16 15:11:47.714000,https://github.com/delchiaro/RATT,16,Ratt: Recurrent attention to transient tasks for continual image captioning,"https://scholar.google.com/scholar?cluster=16302376296510206339&hl=en&as_sdt=0,10",4,2020
"Decisions, Counterfactual Explanations and Strategic Behavior",43,neurips,3,0,2023-06-16 15:11:47.906000,https://github.com/Networks-Learning/strategic-decisions,21,"Decisions, counterfactual explanations and strategic behavior","https://scholar.google.com/scholar?cluster=651816189513643853&hl=en&as_sdt=0,5",5,2020
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample,38,neurips,11,3,2023-06-16 15:11:48.099000,https://github.com/shirgur/hp-vae-gan,53,Hierarchical patch vae-gan: Generating diverse videos from a single sample,"https://scholar.google.com/scholar?cluster=1314368623752181451&hl=en&as_sdt=0,5",7,2020
Reservoir Computing meets Recurrent Kernels and Structured Transforms,18,neurips,3,0,2023-06-16 15:11:48.291000,https://github.com/rubenohana/Reservoir-computing-kernels,9,Reservoir computing meets recurrent kernels and structured transforms,"https://scholar.google.com/scholar?cluster=14374060195466396389&hl=en&as_sdt=0,44",3,2020
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection,91,neurips,19,25,2023-06-16 15:11:48.484000,https://github.com/DeLightCMU/CASD,82,Comprehensive attention self-distillation for weakly-supervised object detection,"https://scholar.google.com/scholar?cluster=10475927418265297768&hl=en&as_sdt=0,21",9,2020
MPNet: Masked and Permuted Pre-training for Language Understanding,373,neurips,29,8,2023-06-16 15:11:48.676000,https://github.com/microsoft/MPNet,258,Mpnet: Masked and permuted pre-training for language understanding,"https://scholar.google.com/scholar?cluster=4431403751836804866&hl=en&as_sdt=0,20",13,2020
Lipschitz-Certifiable Training with a Tight Outer Bound,36,neurips,1,0,2023-06-16 15:11:48.869000,https://github.com/sungyoon-lee/bcp,6,Lipschitz-certifiable training with a tight outer bound,"https://scholar.google.com/scholar?cluster=11149574436277547066&hl=en&as_sdt=0,5",2,2020
Conformal Symplectic and Relativistic Optimization,47,neurips,0,0,2023-06-16 15:11:49.060000,https://github.com/guisf/rgd,3,Conformal symplectic and relativistic optimization,"https://scholar.google.com/scholar?cluster=18020920739168612378&hl=en&as_sdt=0,21",1,2020
Inverting Gradients - How easy is it to break privacy in federated learning?,582,neurips,57,0,2023-06-16 15:11:49.253000,https://github.com/JonasGeiping/invertinggradients,194,Inverting gradients-how easy is it to break privacy in federated learning?,"https://scholar.google.com/scholar?cluster=18261025537787576960&hl=en&as_sdt=0,11",2,2020
Dynamic allocation of limited memory resources in reinforcement learning,3,neurips,1,0,2023-06-16 15:11:49.452000,https://github.com/nisheetpatel/DynamicResourceAllocator,5,Dynamic allocation of limited memory resources in reinforcement learning,"https://scholar.google.com/scholar?cluster=4741311554113692472&hl=en&as_sdt=0,21",2,2020
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation,13,neurips,6,1,2023-06-16 15:11:49.645000,https://github.com/IdeasLabUT/CHIP-Network-Model,7,CHIP: a Hawkes process model for continuous-time networks with scalable and consistent estimation,"https://scholar.google.com/scholar?cluster=11549730124527673623&hl=en&as_sdt=0,5",7,2020
Design Space for Graph Neural Networks,198,neurips,167,15,2023-06-16 15:11:49.837000,https://github.com/snap-stanford/graphgym,1396,Design space for graph neural networks,"https://scholar.google.com/scholar?cluster=11786181132461670181&hl=en&as_sdt=0,5",23,2020
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis,790,neurips,391,78,2023-06-16 15:11:50.030000,https://github.com/jik876/hifi-gan,1325,Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,"https://scholar.google.com/scholar?cluster=6605967141544813805&hl=en&as_sdt=0,33",31,2020
 Unbalanced Sobolev Descent ,7,neurips,3,1,2023-06-16 15:11:50.223000,https://github.com/IBM/USD,6,Unbalanced sobolev descent,"https://scholar.google.com/scholar?cluster=14494122772083038319&hl=en&as_sdt=0,36",7,2020
Identifying Mislabeled Data using the Area Under the Margin Ranking,145,neurips,16,9,2023-06-16 15:11:50.416000,https://github.com/asappresearch/aum,68,Identifying mislabeled data using the area under the margin ranking,"https://scholar.google.com/scholar?cluster=935651973392109362&hl=en&as_sdt=0,19",2,2020
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games,93,neurips,99,5,2023-06-16 15:11:50.610000,https://github.com/facebookresearch/rebel,554,Combining deep reinforcement learning and search for imperfect-information games,"https://scholar.google.com/scholar?cluster=4530917614847709299&hl=en&as_sdt=0,5",26,2020
High-Throughput Synchronous Deep RL,14,neurips,3,2,2023-06-16 15:11:50.803000,https://github.com/IouJenLiu/HTS-RL,18,High-throughput synchronous deep rl,"https://scholar.google.com/scholar?cluster=4006743594128174439&hl=en&as_sdt=0,21",4,2020
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables,13,neurips,2,0,2023-06-16 15:11:50.997000,https://github.com/StannisZhou/mixed_hmc,11,Mixed Hamiltonian Monte Carlo for mixed discrete and continuous variables,"https://scholar.google.com/scholar?cluster=2223840957645999633&hl=en&as_sdt=0,10",2,2020
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration,62,neurips,5,0,2023-06-16 15:11:51.190000,https://github.com/XLearning-SCU/2020-NeurIPS-CLEARER,16,Clearer: Multi-scale neural architecture search for image restoration,"https://scholar.google.com/scholar?cluster=3207659434560988619&hl=en&as_sdt=0,36",0,2020
Compositional Explanations of Neurons,89,neurips,10,1,2023-06-16 15:11:51.383000,https://github.com/jayelm/compexp,23,Compositional explanations of neurons,"https://scholar.google.com/scholar?cluster=15725346730266402738&hl=en&as_sdt=0,22",5,2020
Functional Regularization for Representation Learning: A Unified Theoretical Perspective,13,neurips,0,0,2023-06-16 15:11:51.576000,https://github.com/sid7954/functional-regularization,4,Functional regularization for representation learning: A unified theoretical perspective,"https://scholar.google.com/scholar?cluster=565293895434429828&hl=en&as_sdt=0,5",2,2020
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits,51,neurips,7,0,2023-06-16 15:11:51.768000,https://github.com/jparkerholder/PB2,21,Provably efficient online hyperparameter optimization with population-based bandits,"https://scholar.google.com/scholar?cluster=14437140412856434698&hl=en&as_sdt=0,10",1,2020
Understanding Global Feature Contributions With Additive Importance Measures,159,neurips,35,6,2023-06-16 15:11:51.962000,https://github.com/iancovert/sage,178,Understanding global feature contributions with additive importance measures,"https://scholar.google.com/scholar?cluster=15444878093984821600&hl=en&as_sdt=0,34",6,2020
Co-Tuning for Transfer Learning,53,neurips,4,0,2023-06-16 15:11:52.155000,https://github.com/thuml/CoTuning,37,Co-tuning for transfer learning,"https://scholar.google.com/scholar?cluster=14838654300858225214&hl=en&as_sdt=0,36",7,2020
Succinct and Robust Multi-Agent Communication With Temporal Message Control,30,neurips,9,2,2023-06-16 15:11:52.349000,https://github.com/saizhang0218/TMC,21,Succinct and robust multi-agent communication with temporal message control,"https://scholar.google.com/scholar?cluster=5673533236420067969&hl=en&as_sdt=0,31",2,2020
Big Bird: Transformers for Longer Sequences,1132,neurips,95,26,2023-06-16 15:11:52.542000,https://github.com/google-research/bigbird,510,Big bird: Transformers for longer sequences,"https://scholar.google.com/scholar?cluster=11654897857579035055&hl=en&as_sdt=0,5",12,2020
Neural Execution Engines: Learning to Execute Subroutines,32,neurips,1,0,2023-06-16 15:11:52.736000,https://github.com/Yujun-Yan/Neural-Execution-Engines,13,Neural execution engines: Learning to execute subroutines,"https://scholar.google.com/scholar?cluster=14967734265100608215&hl=en&as_sdt=0,39",3,2020
Random Reshuffling: Simple Analysis with Vast Improvements,76,neurips,3,0,2023-06-16 15:11:52.928000,https://github.com/konstmish/random_reshuffling,3,Random reshuffling: Simple analysis with vast improvements,"https://scholar.google.com/scholar?cluster=10792079397833408832&hl=en&as_sdt=0,5",2,2020
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors,46,neurips,7,7,2023-06-16 15:11:53.124000,https://github.com/orybkin/video-gcp,41,Long-horizon visual planning with goal-conditioned hierarchical predictors,"https://scholar.google.com/scholar?cluster=10633756524513419826&hl=en&as_sdt=0,5",5,2020
Dual-Resolution Correspondence Networks,75,neurips,8,2,2023-06-16 15:11:53.338000,https://github.com/ActiveVisionLab/DualRC-Net,51,Dual-resolution correspondence networks,"https://scholar.google.com/scholar?cluster=3029115928365838099&hl=en&as_sdt=0,5",6,2020
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification,7,neurips,0,0,2023-06-16 15:11:53.536000,https://github.com/LvWilliam/EWTH_Loss,10,The dilemma of trihard loss and an element-weighted trihard loss for person re-identification,"https://scholar.google.com/scholar?cluster=8305704582517734688&hl=en&as_sdt=0,14",2,2020
Towards Neural Programming Interfaces,4,neurips,6,2,2023-06-16 15:11:53.729000,https://github.com/DRAGNLabs/towards-neural-programming-interfaces,13,Towards neural programming interfaces,"https://scholar.google.com/scholar?cluster=12937220013331905850&hl=en&as_sdt=0,21",3,2020
Continuous Meta-Learning without Tasks,71,neurips,4,13,2023-06-16 15:11:53.922000,https://github.com/StanfordASL/moca,27,Continuous meta-learning without tasks,"https://scholar.google.com/scholar?cluster=3924794146291307550&hl=en&as_sdt=0,5",10,2020
Pruning Filter in Filter,67,neurips,32,1,2023-06-16 15:11:54.115000,https://github.com/fxmeng/Pruning-Filter-in-Filter,166,Pruning filter in filter,"https://scholar.google.com/scholar?cluster=8643629430951886343&hl=en&as_sdt=0,5",3,2020
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods,27,neurips,1,1,2023-06-16 15:11:54.319000,https://github.com/zwfightzw/Meta-Critic,9,Online meta-critic learning for off-policy actor-critic methods,"https://scholar.google.com/scholar?cluster=15413829867352499622&hl=en&as_sdt=0,26",2,2020
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations,37,neurips,18,2,2023-06-16 15:11:54.521000,https://github.com/yunshengtian/DGEMO,73,Diversity-guided multi-objective bayesian optimization with batch evaluations,"https://scholar.google.com/scholar?cluster=3042580278447313182&hl=en&as_sdt=0,5",5,2020
SOLOv2: Dynamic and Fast Instance Segmentation,492,neurips,299,122,2023-06-16 15:11:54.715000,https://github.com/WXinlong/SOLO,1594,Solov2: Dynamic and fast instance segmentation,"https://scholar.google.com/scholar?cluster=4993232610053036190&hl=en&as_sdt=0,22",33,2020
Continuous Regularized Wasserstein Barycenters,30,neurips,0,2,2023-06-16 15:11:54.909000,https://github.com/lingxiaoli94/CWB,10,Continuous regularized wasserstein barycenters,"https://scholar.google.com/scholar?cluster=7488197485560112624&hl=en&as_sdt=0,5",1,2020
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting,190,neurips,96,20,2023-06-16 15:11:55.103000,https://github.com/microsoft/StemGNN,360,Spectral temporal graph neural network for multivariate time-series forecasting,"https://scholar.google.com/scholar?cluster=8609729441168460418&hl=en&as_sdt=0,33",9,2020
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies,42,neurips,8,1,2023-06-16 15:11:55.296000,https://github.com/YuehuaZhu/ProxyGML,59,Fewer is more: A deep graph metric learning perspective using fewer proxies,"https://scholar.google.com/scholar?cluster=13172519934941641323&hl=en&as_sdt=0,5",2,2020
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting,432,neurips,71,4,2023-06-16 15:11:55.489000,https://github.com/LeiBAI/AGCRN,211,Adaptive graph convolutional recurrent network for traffic forecasting,"https://scholar.google.com/scholar?cluster=531500407384902218&hl=en&as_sdt=0,5",5,2020
Learning outside the Black-Box: The pursuit of interpretable models,17,neurips,7,1,2023-06-16 15:11:55.683000,https://github.com/JonathanCrabbe/Symbolic-Pursuit,14,Learning outside the black-box: The pursuit of interpretable models,"https://scholar.google.com/scholar?cluster=829655441463875439&hl=en&as_sdt=0,33",4,2020
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach,53,neurips,11,0,2023-06-16 15:11:55.881000,https://github.com/goldblum/AdversarialQuerying,46,Adversarially robust few-shot learning: A meta-learning approach,"https://scholar.google.com/scholar?cluster=15509526791894083783&hl=en&as_sdt=0,5",3,2020
Neural Anisotropy Directions,14,neurips,4,0,2023-06-16 15:11:56.082000,https://github.com/LTS4/neural-anisotropy-directions,16,Neural anisotropy directions,"https://scholar.google.com/scholar?cluster=13055612320165183651&hl=en&as_sdt=0,33",8,2020
Digraph Inception Convolutional Networks,50,neurips,7,3,2023-06-16 15:11:56.277000,https://github.com/flyingtango/DiGCN,35,Digraph inception convolutional networks,"https://scholar.google.com/scholar?cluster=3901637816715670823&hl=en&as_sdt=0,5",2,2020
Stochastic Stein Discrepancies,31,neurips,0,2,2023-06-16 15:11:56.469000,https://github.com/jgorham/stochastic_stein_discrepancy,0,Stochastic stein discrepancies,"https://scholar.google.com/scholar?cluster=9711426818450432498&hl=en&as_sdt=0,31",2,2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs,86,neurips,4,0,2023-06-16 15:11:56.663000,https://github.com/ignavierng/golem,25,On the role of sparsity and dag constraints for learning linear dags,"https://scholar.google.com/scholar?cluster=1555649342103707426&hl=en&as_sdt=0,39",1,2020
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search,53,neurips,168,24,2023-06-16 15:11:56.856000,https://github.com/microsoft/cream,1078,Cream of the crop: Distilling prioritized paths for one-shot neural architecture search,"https://scholar.google.com/scholar?cluster=11578986430039663904&hl=en&as_sdt=0,5",25,2020
Fair Multiple Decision Making Through Soft Interventions,9,neurips,3,0,2023-06-16 15:11:57.049000,https://github.com/yaoweihu/Fair-Multiple-Decision-Making,0,Fair multiple decision making through soft interventions,"https://scholar.google.com/scholar?cluster=11596139614836314222&hl=en&as_sdt=0,39",1,2020
Learning to Play No-Press Diplomacy with Best Response Policy Iteration,33,neurips,7,0,2023-06-16 15:11:57.244000,https://github.com/deepmind/diplomacy,31,Learning to play no-press diplomacy with best response policy iteration,"https://scholar.google.com/scholar?cluster=17288570672333951438&hl=en&as_sdt=0,21",4,2020
Inverse Learning of Symmetries,6,neurips,1,0,2023-06-16 15:11:57.436000,https://github.com/bmda-unibas/InverseLearningOfSymmetries,1,Inverse learning of symmetries,"https://scholar.google.com/scholar?cluster=11141520143943539280&hl=en&as_sdt=0,5",1,2020
Effective Diversity in Population Based Reinforcement Learning,108,neurips,8,1,2023-06-16 15:11:57.628000,https://github.com/jparkerholder/DvD_ES,39,Effective diversity in population based reinforcement learning,"https://scholar.google.com/scholar?cluster=13580562811176408122&hl=en&as_sdt=0,15",1,2020
Hybrid Models for Learning to Branch,70,neurips,10,3,2023-06-16 15:11:57.822000,https://github.com/pg2455/Hybrid-learn2branch,38,Hybrid models for learning to branch,"https://scholar.google.com/scholar?cluster=15951000887589486103&hl=en&as_sdt=0,5",3,2020
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression,90,neurips,4,0,2023-06-16 15:11:58.016000,https://github.com/IST-DASLab/WoodFisher,40,Woodfisher: Efficient second-order approximation for neural network compression,"https://scholar.google.com/scholar?cluster=10333842317237774040&hl=en&as_sdt=0,5",8,2020
Bi-level Score Matching for Learning Energy-based Latent Variable Models,12,neurips,2,0,2023-06-16 15:11:58.209000,https://github.com/baofff/BiSM,11,Bi-level score matching for learning energy-based latent variable models,"https://scholar.google.com/scholar?cluster=17042861642132917683&hl=en&as_sdt=0,15",1,2020
Decision trees as partitioning machines to characterize their generalization properties,10,neurips,0,0,2023-06-16 15:11:58.402000,https://github.com/jsleb333/paper-decision-trees-as-partitioning-machines,2,Decision trees as partitioning machines to characterize their generalization properties,"https://scholar.google.com/scholar?cluster=8941851754954952752&hl=en&as_sdt=0,5",2,2020
Learning to Prove Theorems by Learning to Generate Theorems,19,neurips,2,4,2023-06-16 15:11:58.595000,https://github.com/princeton-vl/MetaGen,22,Learning to prove theorems by learning to generate theorems,"https://scholar.google.com/scholar?cluster=6712350260601158611&hl=en&as_sdt=0,1",4,2020
3D Self-Supervised Methods for Medical Imaging,129,neurips,38,1,2023-06-16 15:11:58.788000,https://github.com/HealthML/self-supervised-3d-tasks,175,3d self-supervised methods for medical imaging,"https://scholar.google.com/scholar?cluster=9530893768928591494&hl=en&as_sdt=0,5",14,2020
 Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods ,15,neurips,0,0,2023-06-16 15:11:58.981000,https://github.com/LaurenceA/adabayes,1,Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods,"https://scholar.google.com/scholar?cluster=1727499068879761795&hl=en&as_sdt=0,5",2,2020
Worst-Case Analysis for Randomly Collected Data,3,neurips,2,0,2023-06-16 15:11:59.174000,https://github.com/justc2/worst-case-randomly-collected,3,Worst-case analysis for randomly collected data,"https://scholar.google.com/scholar?cluster=5223589641836641973&hl=en&as_sdt=0,32",1,2020
Byzantine Resilient Distributed Multi-Task Learning,7,neurips,3,0,2023-06-16 15:11:59.367000,https://github.com/JianiLi/resilientDistributedMTL,8,Byzantine resilient distributed multi-task learning,"https://scholar.google.com/scholar?cluster=2493973977655145797&hl=en&as_sdt=0,33",2,2020
Improving model calibration with accuracy versus uncertainty optimization,90,neurips,10,0,2023-06-16 15:11:59.559000,https://github.com/IntelLabs/AVUC,42,Improving model calibration with accuracy versus uncertainty optimization,"https://scholar.google.com/scholar?cluster=6764629857380442008&hl=en&as_sdt=0,5",10,2020
The Convolution Exponential and Generalized Sylvester Flows,25,neurips,3,0,2023-06-16 15:11:59.751000,https://github.com/ehoogeboom/convolution_exponential_and_sylvester,29,The convolution exponential and generalized sylvester flows,"https://scholar.google.com/scholar?cluster=17016423652429713457&hl=en&as_sdt=0,26",3,2020
The MAGICAL Benchmark for Robust Imitation,34,neurips,9,1,2023-06-16 15:11:59.945000,https://github.com/qxcv/magical,65,The magical benchmark for robust imitation,"https://scholar.google.com/scholar?cluster=1590548379851528188&hl=en&as_sdt=0,31",6,2020
X-CAL: Explicit Calibration for Survival Analysis,21,neurips,2,1,2023-06-16 15:12:00.138000,https://github.com/rajesh-lab/X-CAL,10,X-cal: Explicit calibration for survival analysis,"https://scholar.google.com/scholar?cluster=2990043349435495022&hl=en&as_sdt=0,5",4,2020
BERT Loses Patience: Fast and Robust Inference with Early Exit,153,neurips,6,3,2023-06-16 15:12:00.333000,https://github.com/JetRunner/PABEE,57,Bert loses patience: Fast and robust inference with early exit,"https://scholar.google.com/scholar?cluster=4686936952101505814&hl=en&as_sdt=0,33",5,2020
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning,65,neurips,5,1,2023-06-16 15:12:00.525000,https://github.com/lanyavik/BAIL,15,BAIL: Best-action imitation learning for batch deep reinforcement learning,"https://scholar.google.com/scholar?cluster=11856041909374113565&hl=en&as_sdt=0,5",2,2020
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes,19,neurips,0,0,2023-06-16 15:12:00.718000,https://github.com/hmhyau/rl-intention,5,What did you think would happen? explaining agent behaviour through intended outcomes,"https://scholar.google.com/scholar?cluster=11580344209780119679&hl=en&as_sdt=0,46",2,2020
What if Neural Networks had SVDs?,4,neurips,9,1,2023-06-16 15:12:00.911000,https://github.com/AlexanderMath/fasth,65,What if neural networks had SVDs?,"https://scholar.google.com/scholar?cluster=721216332172545219&hl=en&as_sdt=0,15",4,2020
CoMIR: Contrastive Multimodal Image Representation for Registration,44,neurips,10,4,2023-06-16 15:12:01.103000,https://github.com/MIDA-group/CoMIR,61,CoMIR: Contrastive multimodal image representation for registration,"https://scholar.google.com/scholar?cluster=5281972989603667847&hl=en&as_sdt=0,19",8,2020
How do fair decisions fare in long-term qualification?,46,neurips,1,0,2023-06-16 15:12:01.295000,https://github.com/TURuibo/long-term-impact-of-fairness-constraints,4,How do fair decisions fare in long-term qualification?,"https://scholar.google.com/scholar?cluster=6407521976837665673&hl=en&as_sdt=0,37",3,2020
Measuring Robustness to Natural Distribution Shifts in Image Classification,327,neurips,5,1,2023-06-16 15:12:01.488000,https://github.com/modestyachts/imagenet-testbed,92,Measuring robustness to natural distribution shifts in image classification,"https://scholar.google.com/scholar?cluster=3019171535172049328&hl=en&as_sdt=0,5",9,2020
Learning Optimal Representations with the Decodable Information Bottleneck,27,neurips,2,0,2023-06-16 15:12:01.681000,https://github.com/YannDubs/Mini_Decodable_Information_Bottleneck,8,Learning optimal representations with the decodable information bottleneck,"https://scholar.google.com/scholar?cluster=17923868091696998967&hl=en&as_sdt=0,47",2,2020
Neural Non-Rigid Tracking,30,neurips,35,3,2023-06-16 15:12:01.873000,https://github.com/DeformableFriends/NeuralTracking,172,Neural non-rigid tracking,"https://scholar.google.com/scholar?cluster=15233540047338923816&hl=en&as_sdt=0,5",6,2020
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection,43,neurips,3,1,2023-06-16 15:12:02.066000,https://github.com/blanclist/ICNet,27,Icnet: Intra-saliency correlation network for co-saliency detection,"https://scholar.google.com/scholar?cluster=7463846021499911806&hl=en&as_sdt=0,23",4,2020
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows,13,neurips,5,0,2023-06-16 15:12:02.259000,https://github.com/zcrabbit/vbpi-nf,5,Improved variational Bayesian phylogenetic inference with normalizing flows,"https://scholar.google.com/scholar?cluster=5113994271918913106&hl=en&as_sdt=0,15",1,2020
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients,349,neurips,109,6,2023-06-16 15:12:02.451000,https://github.com/juntang-zhuang/Adabelief-Optimizer,1021,Adabelief optimizer: Adapting stepsizes by the belief in observed gradients,"https://scholar.google.com/scholar?cluster=794903835077311857&hl=en&as_sdt=0,23",21,2020
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding,44,neurips,0,1,2023-06-16 15:12:02.645000,https://github.com/StanfordAI4HI/off_policy_confounding,3,Off-policy policy evaluation for sequential decisions under unobserved confounding,"https://scholar.google.com/scholar?cluster=7361110146120594119&hl=en&as_sdt=0,33",4,2020
Modern Hopfield Networks and Attention for Immune Repertoire Classification,68,neurips,20,2,2023-06-16 15:12:02.838000,https://github.com/ml-jku/DeepRC,93,Modern hopfield networks and attention for immune repertoire classification,"https://scholar.google.com/scholar?cluster=10816753582099343978&hl=en&as_sdt=0,33",10,2020
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers,29,neurips,14,0,2023-06-16 15:12:03.031000,https://github.com/MIT-SPARK/CertifiablyRobustPerception,91,One ring to rule them all: Certifiably robust geometric perception with outliers,"https://scholar.google.com/scholar?cluster=4069822237780378965&hl=en&as_sdt=0,33",9,2020
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks,13,neurips,4,0,2023-06-16 15:12:03.224000,https://github.com/delta2323/GB-GNN,12,Optimization and generalization analysis of transduction through gradient boosting and application to multi-scale graph neural networks,"https://scholar.google.com/scholar?cluster=4267488543735531510&hl=en&as_sdt=0,47",3,2020
Experimental design for MRI by greedy policy search,26,neurips,5,0,2023-06-16 15:12:03.417000,https://github.com/Timsey/pg_mri,20,Experimental design for MRI by greedy policy search,"https://scholar.google.com/scholar?cluster=15235565020311490673&hl=en&as_sdt=0,34",4,2020
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation,17,neurips,8,1,2023-06-16 15:12:03.611000,https://github.com/asonabend/ESRL,7,Expert-supervised reinforcement learning for offline policy learning and evaluation,"https://scholar.google.com/scholar?cluster=16131210561518100341&hl=en&as_sdt=0,5",4,2020
Time-Reversal Symmetric ODE Network,18,neurips,1,0,2023-06-16 15:12:03.804000,https://github.com/inhuh/trs-oden,6,Time-reversal symmetric ode network,"https://scholar.google.com/scholar?cluster=4037950341179248560&hl=en&as_sdt=0,5",2,2020
Fast Unbalanced Optimal Transport on a Tree,21,neurips,0,0,2023-06-16 15:12:03.998000,https://github.com/joisino/treegkr,11,Fast unbalanced optimal transport on a tree,"https://scholar.google.com/scholar?cluster=14959154682905354615&hl=en&as_sdt=0,5",3,2020
Handling Missing Data with Graph Representation Learning,90,neurips,27,7,2023-06-16 15:12:04.193000,https://github.com/maxiaoba/GRAPE,109,Handling missing data with graph representation learning,"https://scholar.google.com/scholar?cluster=3645976030445533910&hl=en&as_sdt=0,5",2,2020
Improving Auto-Augment via Augmentation-Wise Weight Sharing,28,neurips,10,1,2023-06-16 15:12:04.385000,https://github.com/Awesome-AutoAug-Algorithms/AWS-OHL-AutoAug,46,Improving auto-augment via augmentation-wise weight sharing,"https://scholar.google.com/scholar?cluster=7360656205039027560&hl=en&as_sdt=0,47",6,2020
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles,9,neurips,2,0,2023-06-16 15:12:04.577000,https://github.com/wznpub/MMA_Regularization,10,MMA regularization: Decorrelating weights of neural networks by maximizing the minimal angles,"https://scholar.google.com/scholar?cluster=5540242986881962415&hl=en&as_sdt=0,5",1,2020
HRN: A Holistic Approach to One Class Learning,29,neurips,4,1,2023-06-16 15:12:04.769000,https://github.com/morning-dews/HRN,15,Hrn: A holistic approach to one class learning,"https://scholar.google.com/scholar?cluster=6301247389765291961&hl=en&as_sdt=0,31",1,2020
Modeling Shared responses in Neuroimaging Studies through MultiView ICA,18,neurips,3,1,2023-06-16 15:12:04.962000,https://github.com/hugorichard/multiviewica,24,Modeling shared responses in neuroimaging studies through multiview ica,"https://scholar.google.com/scholar?cluster=367202636846206154&hl=en&as_sdt=0,5",3,2020
Efficient Learning of Generative Models via Finite-Difference Score Matching,30,neurips,3,6,2023-06-16 15:12:05.154000,https://github.com/taufikxu/FD-ScoreMatching,11,Efficient learning of generative models via finite-difference score matching,"https://scholar.google.com/scholar?cluster=378107545503683177&hl=en&as_sdt=0,22",3,2020
BayReL: Bayesian Relational Learning for Multi-omics Data Integration,6,neurips,2,0,2023-06-16 15:12:05.347000,https://github.com/ehsanhajiramezanali/BayReL,5,BayReL: Bayesian relational learning for multi-omics data integration,"https://scholar.google.com/scholar?cluster=8576961726337855853&hl=en&as_sdt=0,33",1,2020
Weakly Supervised Deep Functional Maps for Shape Matching,38,neurips,4,2,2023-06-16 15:12:05.539000,https://github.com/Not-IITian/Weakly-supervised-Functional-map,23,Weakly supervised deep functional maps for shape matching,"https://scholar.google.com/scholar?cluster=10860093597681931185&hl=en&as_sdt=0,44",4,2020
Rethinking the Value of Labels for Improving Class-Imbalanced Learning,258,neurips,113,4,2023-06-16 15:12:05.731000,https://github.com/YyzHarry/imbalanced-semi-self,690,Rethinking the value of labels for improving class-imbalanced learning,"https://scholar.google.com/scholar?cluster=272061710147272859&hl=en&as_sdt=0,5",14,2020
Provably Robust Metric Learning,4,neurips,1,0,2023-06-16 15:12:05.924000,https://github.com/wangwllu/provably_robust_metric_learning,9,Provably robust metric learning,"https://scholar.google.com/scholar?cluster=13877432189650792111&hl=en&as_sdt=0,3",1,2020
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings,218,neurips,28,2,2023-06-16 15:12:06.117000,https://github.com/hugochan/IDGL,189,Iterative deep graph learning for graph neural networks: Better and robust node embeddings,"https://scholar.google.com/scholar?cluster=9442254169180194337&hl=en&as_sdt=0,39",8,2020
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems,111,neurips,16,2,2023-06-16 15:12:06.310000,https://github.com/HazyResearch/hidden-stratification,52,No subclass left behind: Fine-grained robustness in coarse-grained classification problems,"https://scholar.google.com/scholar?cluster=10068670017880921815&hl=en&as_sdt=0,41",18,2020
Self-Adaptive Training: beyond Empirical Risk Minimization,134,neurips,23,0,2023-06-16 15:12:06.526000,https://github.com/LayneH/self-adaptive-training,122,Self-adaptive training: beyond empirical risk minimization,"https://scholar.google.com/scholar?cluster=8932486507160067341&hl=en&as_sdt=0,5",4,2020
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement,96,neurips,44,4,2023-06-16 15:12:06.719000,https://github.com/xin71/MTTS-CAN,129,Multi-task temporal shift attention networks for on-device contactless vitals measurement,"https://scholar.google.com/scholar?cluster=9152870442516577713&hl=en&as_sdt=0,14",7,2020
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation,3,neurips,3,2,2023-06-16 15:12:06.911000,https://github.com/MiuLab/TaylorGAN,31,TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation,"https://scholar.google.com/scholar?cluster=13902671358077823170&hl=en&as_sdt=0,34",9,2020
Dual-Free Stochastic Decentralized Optimization with Variance Reduction,26,neurips,0,0,2023-06-16 15:12:07.104000,https://github.com/HadrienHx/DVR_NeurIPS,1,Dual-free stochastic decentralized optimization with variance reduction,"https://scholar.google.com/scholar?cluster=10047292317729943616&hl=en&as_sdt=0,14",1,2020
Throughput-Optimal Topology Design for Cross-Silo Federated Learning,53,neurips,7,2,2023-06-16 15:12:07.298000,https://github.com/omarfoq/communication-in-cross-silo-fl,25,Throughput-optimal topology design for cross-silo federated learning,"https://scholar.google.com/scholar?cluster=8109752902275871461&hl=en&as_sdt=0,26",0,2020
Quantized Variational Inference,1,neurips,0,0,2023-06-16 15:12:07.492000,https://github.com/amirdib/quantized-variational-inference,1,Quantized variational inference,"https://scholar.google.com/scholar?cluster=8568625166316224952&hl=en&as_sdt=0,14",2,2020
Asymptotically Optimal Exact Minibatch Metropolis-Hastings,15,neurips,0,0,2023-06-16 15:12:07.685000,https://github.com/ruqizhang/tunamh,2,Asymptotically optimal exact minibatch metropolis-hastings,"https://scholar.google.com/scholar?cluster=3007609299912938607&hl=en&as_sdt=0,44",2,2020
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search,69,neurips,66,6,2023-06-16 15:12:07.886000,https://github.com/facebookresearch/LaMCTS,409,Learning search space partition for black-box optimization using monte carlo tree search,"https://scholar.google.com/scholar?cluster=9187963788424431133&hl=en&as_sdt=0,34",18,2020
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks,33,neurips,3,1,2023-06-16 15:12:08.096000,https://github.com/KyungsuKim42/ANTLR,15,Unifying activation-and timing-based learning rules for spiking neural networks,"https://scholar.google.com/scholar?cluster=10358457347608277973&hl=en&as_sdt=0,14",2,2020
Space-Time Correspondence as a Contrastive Random Walk,169,neurips,36,10,2023-06-16 15:12:08.288000,https://github.com/ajabri/videowalk,254,Space-time correspondence as a contrastive random walk,"https://scholar.google.com/scholar?cluster=9614996608688836578&hl=en&as_sdt=0,32",20,2020
An Efficient Framework for Clustered Federated Learning,368,neurips,22,1,2023-06-16 15:12:08.483000,https://github.com/jichan3751/ifca,77,An efficient framework for clustered federated learning,"https://scholar.google.com/scholar?cluster=351619806118785755&hl=en&as_sdt=0,34",2,2020
Autoencoders that don't overfit towards the Identity,40,neurips,4,1,2023-06-16 15:12:08.676000,https://github.com/hasteck/EDLAE_NeurIPS2020,11,Autoencoders that don't overfit towards the identity,"https://scholar.google.com/scholar?cluster=14138077155025649539&hl=en&as_sdt=0,35",1,2020
Parameterized Explainer for Graph Neural Network,258,neurips,13,2,2023-06-16 15:12:08.869000,https://github.com/flyingdoog/PGExplainer,102,Parameterized explainer for graph neural network,"https://scholar.google.com/scholar?cluster=17322495705735423565&hl=en&as_sdt=0,22",5,2020
Flexible mean field variational inference using mixtures of non-overlapping exponential families,3,neurips,0,0,2023-06-16 15:12:09.062000,https://github.com/jeffspence/non_overlapping_mixtures,1,Flexible mean field variational inference using mixtures of non-overlapping exponential families,"https://scholar.google.com/scholar?cluster=3380676252436682174&hl=en&as_sdt=0,33",1,2020
HYDRA: Pruning Adversarially Robust Neural Networks,142,neurips,19,2,2023-06-16 15:12:09.254000,https://github.com/inspire-group/compactness-robustness,85,Hydra: Pruning adversarially robust neural networks,"https://scholar.google.com/scholar?cluster=11257797302923322781&hl=en&as_sdt=0,5",6,2020
NVAE: A Deep Hierarchical Variational Autoencoder,524,neurips,148,27,2023-06-16 15:12:09.447000,https://github.com/NVlabs/NVAE,882,NVAE: A deep hierarchical variational autoencoder,"https://scholar.google.com/scholar?cluster=9419654938449434940&hl=en&as_sdt=0,41",17,2020
Learning Disentangled Representations and Group Structure of Dynamical Environments,30,neurips,3,4,2023-06-16 15:12:09.640000,https://github.com/IndustAI/learning-group-structure,13,Learning disentangled representations and group structure of dynamical environments,"https://scholar.google.com/scholar?cluster=1554847643319320473&hl=en&as_sdt=0,5",3,2020
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models,4,neurips,1,0,2023-06-16 15:12:09.836000,https://github.com/christa60/dynens,3,Wisdom of the ensemble: Improving consistency of deep learning models,"https://scholar.google.com/scholar?cluster=5672422435437063522&hl=en&as_sdt=0,34",1,2020
Universal Function Approximation on Graphs,5,neurips,1,0,2023-06-16 15:12:10.029000,https://github.com/bruel-gabrielsson/universal-function-approximation-on-graphs,10,Universal function approximation on graphs,"https://scholar.google.com/scholar?cluster=10884321580328108356&hl=en&as_sdt=0,21",1,2020
Accelerating Reinforcement Learning through GPU Atari Emulation,18,neurips,32,15,2023-06-16 15:12:10.222000,https://github.com/NVLABs/cule,216,Accelerating reinforcement learning through gpu atari emulation,"https://scholar.google.com/scholar?cluster=14852827801833804671&hl=en&as_sdt=0,5",20,2020
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs,43,neurips,5,2,2023-06-16 15:12:10.414000,https://github.com/dtak/mbrl-smdp-ode,27,Model-based reinforcement learning for semi-markov decision processes with neural odes,"https://scholar.google.com/scholar?cluster=6882030783154485592&hl=en&as_sdt=0,5",3,2020
Graph Stochastic Neural Networks for Semi-supervised Learning,28,neurips,6,1,2023-06-16 15:12:10.607000,https://github.com/GSNN/GSNN,17,Graph stochastic neural networks for semi-supervised learning,"https://scholar.google.com/scholar?cluster=12398431409964717174&hl=en&as_sdt=0,26",2,2020
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning,23,neurips,113,0,2023-06-16 15:12:10.799000,https://github.com/Thinklab-SJTU/ThinkMatch,714,Graduated assignment for joint multi-graph matching and clustering with application to unsupervised graph matching network learning,"https://scholar.google.com/scholar?cluster=15043532701197211063&hl=en&as_sdt=0,43",22,2020
Estimating Training Data Influence by Tracing Gradient Descent,145,neurips,14,4,2023-06-16 15:12:10.992000,https://github.com/frederick0329/TracIn,186,Estimating training data influence by tracing gradient descent,"https://scholar.google.com/scholar?cluster=1975203419691170892&hl=en&as_sdt=0,5",8,2020
Joint Policy Search for Multi-agent Collaboration with Imperfect Information,11,neurips,9,0,2023-06-16 15:12:11.187000,https://github.com/facebookresearch/jps,41,Joint policy search for multi-agent collaboration with imperfect information,"https://scholar.google.com/scholar?cluster=9814706809980127110&hl=en&as_sdt=0,5",6,2020
Learning Retrospective Knowledge with Reverse Reinforcement Learning,11,neurips,658,6,2023-06-16 15:12:11.380000,https://github.com/ShangtongZhang/DeepRL,2943,Learning retrospective knowledge with reverse reinforcement learning,"https://scholar.google.com/scholar?cluster=5697894321582614972&hl=en&as_sdt=0,34",93,2020
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data,8,neurips,1,2,2023-06-16 15:12:11.573000,https://github.com/mcogswell/dialog_without_dialog,5,Dialog without dialog data: Learning visual dialog agents from VQA data,"https://scholar.google.com/scholar?cluster=15836872788471162855&hl=en&as_sdt=0,33",2,2020
The Complete Lasso Tradeoff Diagram,7,neurips,0,0,2023-06-16 15:12:11.767000,https://github.com/HuaWang-wharton/CompleteLassoDiagram,0,The complete Lasso tradeoff diagram,"https://scholar.google.com/scholar?cluster=11441396913332504259&hl=en&as_sdt=0,5",0,2020
The Primal-Dual method for Learning Augmented Algorithms,77,neurips,0,0,2023-06-16 15:12:11.959000,https://github.com/etienne4/PDLA,3,The primal-dual method for learning augmented algorithms,"https://scholar.google.com/scholar?cluster=17410161354545999384&hl=en&as_sdt=0,5",1,2020
A Class of Algorithms for General Instrumental Variable Models,26,neurips,1,0,2023-06-16 15:12:12.156000,https://github.com/nikikilbertus/general-iv-models,13,A class of algorithms for general instrumental variable models,"https://scholar.google.com/scholar?cluster=6114438229492187489&hl=en&as_sdt=0,5",3,2020
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms,25,neurips,3,3,2023-06-16 15:12:12.354000,https://github.com/antoniobarbalau/black-box-ripper,26,Black-Box Ripper: Copying black-box models using generative evolutionary algorithms,"https://scholar.google.com/scholar?cluster=2038937056151338541&hl=en&as_sdt=0,5",2,2020
Bayesian Optimization of Risk Measures,34,neurips,3,0,2023-06-16 15:12:12.547000,https://github.com/saitcakmak/BoRisk,20,Bayesian optimization of risk measures,"https://scholar.google.com/scholar?cluster=11597649173870382888&hl=en&as_sdt=0,10",4,2020
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search,24,neurips,3,0,2023-06-16 15:12:12.743000,https://github.com/tarungog/torsionnet_paper_version,12,Torsionnet: A reinforcement learning approach to sequential conformer search,"https://scholar.google.com/scholar?cluster=15323026786978211130&hl=en&as_sdt=0,11",5,2020
GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis,490,neurips,69,8,2023-06-16 15:12:12.940000,https://github.com/autonomousvision/graf,376,Graf: Generative radiance fields for 3d-aware image synthesis,"https://scholar.google.com/scholar?cluster=6074305542312504170&hl=en&as_sdt=0,5",19,2020
A Simple Language Model for Task-Oriented Dialogue,341,neurips,76,24,2023-06-16 15:12:13.133000,https://github.com/salesforce/simpletod,217,A simple language model for task-oriented dialogue,"https://scholar.google.com/scholar?cluster=13901694758455015611&hl=en&as_sdt=0,43",13,2020
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval,10,neurips,0,0,2023-06-16 15:12:13.325000,https://github.com/fawuuu/mirror_spr,0,A continuous-time mirror descent approach to sparse phase retrieval,"https://scholar.google.com/scholar?cluster=17231492085001366085&hl=en&as_sdt=0,5",1,2020
Confidence sequences for sampling without replacement,16,neurips,0,0,2023-06-16 15:12:13.536000,https://github.com/wannabesmith/confseq_wor,4,Confidence sequences for sampling without replacement,"https://scholar.google.com/scholar?cluster=4371792767519028336&hl=en&as_sdt=0,33",2,2020
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge,75,neurips,1,1,2023-06-16 15:12:13.728000,https://github.com/alontalmor/TeachYourAI,44,Leap-of-thought: Teaching pre-trained models to systematically reason over implicit knowledge,"https://scholar.google.com/scholar?cluster=11221279526378822822&hl=en&as_sdt=0,33",6,2020
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games,49,neurips,16,2,2023-06-16 15:12:13.922000,https://github.com/JBLanier/pipeline-psro,37,Pipeline psro: A scalable approach for finding approximate nash equilibria in large games,"https://scholar.google.com/scholar?cluster=8078944900964563231&hl=en&as_sdt=0,5",4,2020
Latent Template Induction with Gumbel-CRFs,8,neurips,8,0,2023-06-16 15:12:14.115000,https://github.com/FranxYao/Gumbel-CRF,53,Latent template induction with Gumbel-CRFS,"https://scholar.google.com/scholar?cluster=11572320243625839339&hl=en&as_sdt=0,45",5,2020
Factorizable Graph Convolutional Networks,110,neurips,9,1,2023-06-16 15:12:14.307000,https://github.com/ihollywhy/FactorGCN.PyTorch,47,Factorizable graph convolutional networks,"https://scholar.google.com/scholar?cluster=8785212060536911333&hl=en&as_sdt=0,5",1,2020
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses,54,neurips,8,0,2023-06-16 15:12:14.500000,https://github.com/val-iisc/GAMA-GAT,23,Guided adversarial attack for evaluating and enhancing adversarial defenses,"https://scholar.google.com/scholar?cluster=8789193805515156711&hl=en&as_sdt=0,4",13,2020
A Study on Encodings for Neural Architecture Search,62,neurips,5,0,2023-06-16 15:12:14.692000,https://github.com/naszilla/nas-encodings,29,A study on encodings for neural architecture search,"https://scholar.google.com/scholar?cluster=10654503174667687184&hl=en&as_sdt=0,5",8,2020
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising,56,neurips,14,1,2023-06-16 15:12:14.885000,https://github.com/divelab/Noise2Same,61,Noise2Same: Optimizing a self-supervised bound for image denoising,"https://scholar.google.com/scholar?cluster=11034449771862821776&hl=en&as_sdt=0,47",4,2020
Early-Learning Regularization Prevents Memorization of Noisy Labels,304,neurips,28,5,2023-06-16 15:12:15.078000,https://github.com/shengliu66/ELR,249,Early-learning regularization prevents memorization of noisy labels,"https://scholar.google.com/scholar?cluster=3805522034549943304&hl=en&as_sdt=0,25",8,2020
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond,71,neurips,33,11,2023-06-16 15:12:15.271000,https://github.com/dvlab-research/Simple-SR,215,Lapar: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond,"https://scholar.google.com/scholar?cluster=5145084170737435928&hl=en&as_sdt=0,6",5,2020
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot,48,neurips,5,1,2023-06-16 15:12:15.464000,https://github.com/JingtongSu/sanity-checking-pruning,39,Sanity-checking pruning methods: Random tickets can win the jackpot,"https://scholar.google.com/scholar?cluster=2172629804299709441&hl=en&as_sdt=0,33",2,2020
Position-based Scaled Gradient for Model Quantization and Pruning,24,neurips,3,1,2023-06-16 15:12:15.658000,https://github.com/Jangho-Kim/PSG-pytorch,17,Position-based scaled gradient for model quantization and pruning,"https://scholar.google.com/scholar?cluster=1487663288303677561&hl=en&as_sdt=0,5",3,2020
Graph Information Bottleneck,105,neurips,25,1,2023-06-16 15:12:15.853000,https://github.com/snap-stanford/GIB,104,Graph information bottleneck,"https://scholar.google.com/scholar?cluster=11004655296553092045&hl=en&as_sdt=0,5",44,2020
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference,39,neurips,369,28,2023-06-16 15:12:16.052000,https://github.com/Microsoft/EdgeML,1453,RNNPool: Efficient non-linear pooling for RAM constrained inference,"https://scholar.google.com/scholar?cluster=9340951550254223370&hl=en&as_sdt=0,5",87,2020
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation,22,neurips,2,1,2023-06-16 15:12:16.246000,https://github.com/Jacobew/AutoPanoptic,19,Auto-panoptic: Cooperative multi-component architecture search for panoptic segmentation,"https://scholar.google.com/scholar?cluster=11947807024654626869&hl=en&as_sdt=0,6",2,2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks,162,neurips,13,1,2023-06-16 15:12:16.439000,https://github.com/chihkuanyeh/concept_exp,42,On completeness-aware concept-based explanations in deep neural networks,"https://scholar.google.com/scholar?cluster=1524554551065921155&hl=en&as_sdt=0,5",4,2020
Why Normalizing Flows Fail to Detect Out-of-Distribution Data,147,neurips,11,9,2023-06-16 15:12:16.632000,https://github.com/PolinaKirichenko/flows_ood,79,Why normalizing flows fail to detect out-of-distribution data,"https://scholar.google.com/scholar?cluster=2771286037773844242&hl=en&as_sdt=0,47",2,2020
Unsupervised Translation of Programming Languages,105,neurips,114,33,2023-06-16 15:12:16.825000,https://github.com/facebookresearch/CodeGen,540,Unsupervised translation of programming languages,"https://scholar.google.com/scholar?cluster=1104657131784756679&hl=en&as_sdt=0,21",31,2020
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation,76,neurips,12,1,2023-06-16 15:12:17.018000,https://github.com/RoyalVane/ASM,69,Adversarial style mining for one-shot unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=12682829350097829096&hl=en&as_sdt=0,5",5,2020
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder,120,neurips,6,2,2023-06-16 15:12:17.211000,https://github.com/XavierXiao/Likelihood-Regret,36,Likelihood regret: An out-of-distribution detection score for variational auto-encoder,"https://scholar.google.com/scholar?cluster=17961908496712601770&hl=en&as_sdt=0,5",4,2020
Meta-Learning through Hebbian Plasticity in Random Networks,61,neurips,20,0,2023-06-16 15:12:17.403000,https://github.com/enajx/HebbianMetaLearning,103,Meta-learning through hebbian plasticity in random networks,"https://scholar.google.com/scholar?cluster=14182623640516258528&hl=en&as_sdt=0,47",1,2020
Statistical and Topological Properties of Sliced Probability Divergences,44,neurips,2,0,2023-06-16 15:12:17.596000,https://github.com/kimiandj/sliced_div,0,Statistical and topological properties of sliced probability divergences,"https://scholar.google.com/scholar?cluster=12747887556426720635&hl=en&as_sdt=0,5",1,2020
Probabilistic Active Meta-Learning,28,neurips,5,0,2023-06-16 15:12:17.791000,https://github.com/jeankaddour/paml,15,Probabilistic active meta-learning,"https://scholar.google.com/scholar?cluster=10986627198228240905&hl=en&as_sdt=0,5",1,2020
Linearly Converging Error Compensated SGD,58,neurips,0,0,2023-06-16 15:12:17.986000,https://github.com/eduardgorbunov/ef_sigma_k,1,Linearly converging error compensated SGD,"https://scholar.google.com/scholar?cluster=9254067822190880000&hl=en&as_sdt=0,5",1,2020
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction,8,neurips,3,2,2023-06-16 15:12:18.178000,https://github.com/facebookresearch/c3dm,18,Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction,"https://scholar.google.com/scholar?cluster=11150205973301180781&hl=en&as_sdt=0,5",11,2020
The Cone of Silence: Speech Separation by Localization,35,neurips,20,3,2023-06-16 15:12:18.371000,https://github.com/vivjay30/Cone-of-Silence,127,The cone of silence: Speech separation by localization,"https://scholar.google.com/scholar?cluster=8905558076062704423&hl=en&as_sdt=0,33",11,2020
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds,12,neurips,4,0,2023-06-16 15:12:18.564000,https://github.com/NoemieJaquier/GaBOtorch,38,High-dimensional Bayesian optimization via nested Riemannian manifolds,"https://scholar.google.com/scholar?cluster=1646248372513417394&hl=en&as_sdt=0,5",2,2020
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula,17,neurips,0,0,2023-06-16 15:12:18.758000,https://github.com/yuxuanzhao2295/Matrix-Completion-with-Quantified-Uncertainty-through-Low-Rank-Gaussian-Copula,1,Matrix completion with quantified uncertainty through low rank gaussian copula,"https://scholar.google.com/scholar?cluster=18308777915678894427&hl=en&as_sdt=0,5",1,2020
Sparse and Continuous Attention Mechanisms,20,neurips,2,0,2023-06-16 15:12:18.950000,https://github.com/deep-spin/mcan-vqa-continuous-attention,20,Sparse and continuous attention mechanisms,"https://scholar.google.com/scholar?cluster=8098274056344502290&hl=en&as_sdt=0,5",4,2020
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection,415,neurips,70,25,2023-06-16 15:12:19.143000,https://github.com/implus/GFocal,546,Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection,"https://scholar.google.com/scholar?cluster=16305632232773240100&hl=en&as_sdt=0,18",14,2020
Learning by Minimizing the Sum of Ranked Range,12,neurips,1,0,2023-06-16 15:12:19.336000,https://github.com/discovershu/SoRR,10,Learning by minimizing the sum of ranked range,"https://scholar.google.com/scholar?cluster=5995735188540741359&hl=en&as_sdt=0,44",1,2020
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations,151,neurips,16,3,2023-06-16 15:12:19.570000,https://github.com/chenhongge/StateAdvDRL,90,Robust deep reinforcement learning against adversarial perturbations on state observations,"https://scholar.google.com/scholar?cluster=4468368848724952344&hl=en&as_sdt=0,23",5,2020
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features,61,neurips,3,1,2023-06-16 15:12:19.772000,https://github.com/boschresearch/hierarchical_anomaly_detection,40,Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features,"https://scholar.google.com/scholar?cluster=16029376900610177245&hl=en&as_sdt=0,39",5,2020
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment,10,neurips,0,0,2023-06-16 15:12:19.966000,https://github.com/MInner/lrmf,1,Log-likelihood ratio minimizing flows: Towards robust and quantifiable neural distribution alignment,"https://scholar.google.com/scholar?cluster=13503385515572497823&hl=en&as_sdt=0,34",1,2020
Implicit Regularization in Deep Learning May Not Be Explainable by Norms,108,neurips,2,1,2023-06-16 15:12:20.159000,https://github.com/noamrazin/imp_reg_dl_not_norms,7,Implicit regularization in deep learning may not be explainable by norms,"https://scholar.google.com/scholar?cluster=15094324317237150803&hl=en&as_sdt=0,44",3,2020
POMO: Policy Optimization with Multiple Optima for Reinforcement Learning,97,neurips,24,0,2023-06-16 15:12:20.352000,https://github.com/yd-kwon/POMO,96,Pomo: Policy optimization with multiple optima for reinforcement learning,"https://scholar.google.com/scholar?cluster=10640697018374796647&hl=en&as_sdt=0,5",2,2020
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor,21,neurips,6,0,2023-06-16 15:12:20.550000,https://github.com/ispc-lab/RSKDD-Net,34,Rskdd-net: Random sample-based keypoint detector and descriptor,"https://scholar.google.com/scholar?cluster=4142945676817836667&hl=en&as_sdt=0,33",2,2020
ContraGAN: Contrastive Learning for Conditional Image Generation,105,neurips,316,30,2023-06-16 15:12:20.744000,https://github.com/POSTECH-CVLab/PyTorch-StudioGAN,3190,Contragan: Contrastive learning for conditional image generation,"https://scholar.google.com/scholar?cluster=18317588262394095158&hl=en&as_sdt=0,44",52,2020
On the distance between two neural networks and the stability of learning,38,neurips,7,0,2023-06-16 15:12:20.937000,https://github.com/jxbz/fromage,116,On the distance between two neural networks and the stability of learning,"https://scholar.google.com/scholar?cluster=16791561363789203322&hl=en&as_sdt=0,5",6,2020
A Topological Filter for Learning with Label Noise,58,neurips,7,2,2023-06-16 15:12:21.129000,https://github.com/pxiangwu/TopoFilter,21,A topological filter for learning with label noise,"https://scholar.google.com/scholar?cluster=3115391967239595458&hl=en&as_sdt=0,47",3,2020
Personalized Federated Learning with Moreau Envelopes,454,neurips,81,3,2023-06-16 15:12:21.321000,https://github.com/CharlieDinh/pFedMe,243,Personalized federated learning with moreau envelopes,"https://scholar.google.com/scholar?cluster=17442117675158664178&hl=en&as_sdt=0,5",3,2020
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters,11,neurips,3,0,2023-06-16 15:12:21.546000,https://github.com/PrincetonLIPS/AHGP,20,Task-agnostic amortized inference of gaussian process hyperparameters,"https://scholar.google.com/scholar?cluster=12673972723308026781&hl=en&as_sdt=0,5",4,2020
Energy-based Out-of-distribution Detection,527,neurips,52,0,2023-06-16 15:12:21.740000,https://github.com/wetliu/energy_ood,326,Energy-based out-of-distribution detection,"https://scholar.google.com/scholar?cluster=6749168752375875068&hl=en&as_sdt=0,14",8,2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them,52,neurips,4,0,2023-06-16 15:12:21.932000,https://github.com/liuchen11/AdversaryLossLandscape,32,On the loss landscape of adversarial training: Identifying challenges and how to overcome them,"https://scholar.google.com/scholar?cluster=5092768704180341925&hl=en&as_sdt=0,50",2,2020
User-Dependent Neural Sequence Models for Continuous-Time Event Data,10,neurips,5,0,2023-06-16 15:12:22.126000,https://github.com/ajboyd2/vae_mpp,10,User-dependent neural sequence models for continuous-time event data,"https://scholar.google.com/scholar?cluster=18086639497022008062&hl=en&as_sdt=0,36",1,2020
Active Structure Learning of Causal DAGs via Directed Clique Trees,23,neurips,1,0,2023-06-16 15:12:22.320000,https://github.com/csquires/dct-policy,5,Active structure learning of causal DAGs via directed clique trees,"https://scholar.google.com/scholar?cluster=2190615991629114246&hl=en&as_sdt=0,5",3,2020
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs,51,neurips,1,0,2023-06-16 15:12:22.559000,https://github.com/nkeriven/random-graph-gnn,12,Convergence and stability of graph convolutional networks on large random graphs,"https://scholar.google.com/scholar?cluster=8332036655143866488&hl=en&as_sdt=0,33",1,2020
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization,408,neurips,319,64,2023-06-16 15:12:22.752000,https://github.com/pytorch/botorch,2663,BoTorch: A framework for efficient Monte-Carlo Bayesian optimization,"https://scholar.google.com/scholar?cluster=1764580662257780594&hl=en&as_sdt=0,5",51,2020
Reconsidering Generative Objectives For Counterfactual Reasoning,19,neurips,3,0,2023-06-16 15:12:22.945000,https://github.com/DannieLu/BV-NICE,10,Reconsidering generative objectives for counterfactual reasoning,"https://scholar.google.com/scholar?cluster=17354375508713844403&hl=en&as_sdt=0,14",3,2020
Quantile Propagation for Wasserstein-Approximate Gaussian Processes,2,neurips,1,4,2023-06-16 15:12:23.138000,https://github.com/RuiZhang2016/Quantile-Propagation-for-Wasserstein-Approximate-Gaussian-Processes,0,Quantile propagation for wasserstein-approximate gaussian processes,"https://scholar.google.com/scholar?cluster=12042120379690917891&hl=en&as_sdt=0,36",2,2020
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning,48,neurips,6,1,2023-06-16 15:12:23.368000,https://github.com/trzhang0116/HRAC,29,Generating adjacency-constrained subgoals in hierarchical reinforcement learning,"https://scholar.google.com/scholar?cluster=16065617713704817902&hl=en&as_sdt=0,5",2,2020
High-contrast “gaudy” images improve the training of deep neural network models of visual cortex,3,neurips,1,0,2023-06-16 15:12:23.592000,https://github.com/pillowlab/gaudy-images,5,High-contrast “gaudy” images improve the training of deep neural network models of visual cortex,"https://scholar.google.com/scholar?cluster=3979615738480690604&hl=en&as_sdt=0,11",8,2020
Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion,46,neurips,7,0,2023-06-16 15:12:23.785000,https://github.com/MIRALab-USTC/KGE-DURA,42,Duality-induced regularizer for tensor factorization based knowledge graph completion,"https://scholar.google.com/scholar?cluster=2035583007156508987&hl=en&as_sdt=0,10",3,2020
H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks,9,neurips,3,0,2023-06-16 15:12:23.980000,https://github.com/IGITUGraz/H-Mem,9,H-mem: Harnessing synaptic plasticity with hebbian memory networks,"https://scholar.google.com/scholar?cluster=16583441948206163184&hl=en&as_sdt=0,47",5,2020
Curriculum By Smoothing,42,neurips,4,1,2023-06-16 15:12:24.174000,https://github.com/pairlab/CBS,38,Curriculum by smoothing,"https://scholar.google.com/scholar?cluster=13722465389000493780&hl=en&as_sdt=0,33",4,2020
Fast Transformers with Clustered Attention,95,neurips,161,28,2023-06-16 15:12:24.367000,https://github.com/idiap/fast-transformers,1433,Fast transformers with clustered attention,"https://scholar.google.com/scholar?cluster=12028542204791594532&hl=en&as_sdt=0,1",27,2020
Strongly Incremental Constituency Parsing with Graph Neural Networks,20,neurips,6,0,2023-06-16 15:12:24.562000,https://github.com/princeton-vl/attach-juxtapose-parser,30,Strongly incremental constituency parsing with graph neural networks,"https://scholar.google.com/scholar?cluster=11445099204030608115&hl=en&as_sdt=0,22",5,2020
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians,19,neurips,9,0,2023-06-16 15:12:24.764000,https://github.com/pomonam/Self-Tuning-Networks,46,Delta-stn: Efficient bilevel optimization for neural networks using structured response jacobians,"https://scholar.google.com/scholar?cluster=4174355255756713694&hl=en&as_sdt=0,11",2,2020
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis,45,neurips,11,1,2023-06-16 15:12:24.957000,https://github.com/Khrylx/RFC,125,Residual force control for agile human behavior imitation and extended motion synthesis,"https://scholar.google.com/scholar?cluster=14507242578909021405&hl=en&as_sdt=0,5",9,2020
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings,99,neurips,4,0,2023-06-16 15:12:25.151000,https://github.com/chrsmrrs/sparsewl,18,Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings,"https://scholar.google.com/scholar?cluster=6518483326763541093&hl=en&as_sdt=0,47",1,2020
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion,16,neurips,2,0,2023-06-16 15:12:25.343000,https://github.com/maqqbu/MMSR,7,Adversarial crowdsourcing through robust rank-one matrix completion,"https://scholar.google.com/scholar?cluster=13042202879705481913&hl=en&as_sdt=0,5",1,2020
Learning Semantic-aware Normalization for Generative Adversarial Networks,8,neurips,3,1,2023-06-16 15:12:25.547000,https://github.com/researchmm/SariGAN,50,Learning semantic-aware normalization for generative adversarial networks,"https://scholar.google.com/scholar?cluster=9760501643800019907&hl=en&as_sdt=0,11",19,2020
Differentiable Causal Discovery from Interventional Data,88,neurips,9,0,2023-06-16 15:12:25.740000,https://github.com/slachapelle/dcdi,52,Differentiable causal discovery from interventional data,"https://scholar.google.com/scholar?cluster=3426161106232828380&hl=en&as_sdt=0,23",4,2020
Robust Persistence Diagrams using Reproducing Kernels,3,neurips,0,0,2023-06-16 15:12:25.934000,https://github.com/sidv23/robust-PDs,4,Robust persistence diagrams using reproducing kernels,"https://scholar.google.com/scholar?cluster=18368713409545563505&hl=en&as_sdt=0,47",3,2020
CrossTransformers: spatially-aware few-shot transfer,216,neurips,136,44,2023-06-16 15:12:26.127000,https://github.com/google-research/meta-dataset,698,Crosstransformers: spatially-aware few-shot transfer,"https://scholar.google.com/scholar?cluster=17678351520585842037&hl=en&as_sdt=0,5",24,2020
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology,39,neurips,19,3,2023-06-16 15:12:26.321000,https://github.com/MIT-AI-Accelerator/neurips-2020-sevir,55,Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology,"https://scholar.google.com/scholar?cluster=8777075661534579096&hl=en&as_sdt=0,5",9,2020
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization,12,neurips,3,0,2023-06-16 15:12:26.633000,https://github.com/facebookresearch/ContextualBO,13,High-dimensional contextual policy search with unknown context rewards using Bayesian optimization,"https://scholar.google.com/scholar?cluster=13229202016902486124&hl=en&as_sdt=0,36",7,2020
Model Fusion via Optimal Transport,83,neurips,15,4,2023-06-16 15:12:26.837000,https://github.com/sidak/otfusion,72,Model fusion via optimal transport,"https://scholar.google.com/scholar?cluster=4296035737617171484&hl=en&as_sdt=0,14",4,2020
Learning Individually Inferred Communication for Multi-Agent Cooperation,66,neurips,11,2,2023-06-16 15:12:27.030000,https://github.com/PKU-AI-Edge/I2C,31,Learning individually inferred communication for multi-agent cooperation,"https://scholar.google.com/scholar?cluster=1670323167364350618&hl=en&as_sdt=0,5",1,2020
Set2Graph: Learning Graphs From Sets,35,neurips,6,0,2023-06-16 15:12:27.224000,https://github.com/hadarser/SetToGraphPaper,19,Set2graph: Learning graphs from sets,"https://scholar.google.com/scholar?cluster=3992706616039043484&hl=en&as_sdt=0,47",1,2020
Graph Random Neural Networks for Semi-Supervised Learning on Graphs,228,neurips,37,8,2023-06-16 15:12:27.417000,https://github.com/Grand20/grand,182,Graph random neural networks for semi-supervised learning on graphs,"https://scholar.google.com/scholar?cluster=2995656499437981589&hl=en&as_sdt=0,11",3,2020
Gradient Boosted Normalizing Flows,5,neurips,3,4,2023-06-16 15:12:27.610000,https://github.com/robert-giaquinto/gradient-boosted-normalizing-flows,25,Gradient boosted normalizing flows,"https://scholar.google.com/scholar?cluster=952614259564825666&hl=en&as_sdt=0,10",3,2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs,1349,neurips,397,17,2023-06-16 15:12:27.804000,https://github.com/snap-stanford/ogb,1685,Open graph benchmark: Datasets for machine learning on graphs,"https://scholar.google.com/scholar?cluster=4143980941711296523&hl=en&as_sdt=0,44",42,2020
Texture Interpolation for Probing Visual Perception,13,neurips,0,0,2023-06-16 15:12:28.002000,https://github.com/JonathanVacher/texture-interpolation,4,Texture interpolation for probing visual perception,"https://scholar.google.com/scholar?cluster=7728700650682598427&hl=en&as_sdt=0,5",1,2020
Hierarchical Neural Architecture Search for Deep Stereo Matching,216,neurips,50,13,2023-06-16 15:12:28.196000,https://github.com/XuelianCheng/LEAStereo,246,Hierarchical neural architecture search for deep stereo matching,"https://scholar.google.com/scholar?cluster=16363724602040348057&hl=en&as_sdt=0,43",4,2020
Auditing Differentially Private Machine Learning: How Private is Private SGD?,114,neurips,4,2,2023-06-16 15:12:28.389000,https://github.com/jagielski/auditing-dpsgd,26,Auditing differentially private machine learning: How private is private sgd?,"https://scholar.google.com/scholar?cluster=281241057337328648&hl=en&as_sdt=0,33",3,2020
Measuring Systematic Generalization in Neural Proof Generation with Transformers,41,neurips,0,0,2023-06-16 15:12:28.584000,https://github.com/NicolasAG/SGinPG,8,Measuring systematic generalization in neural proof generation with transformers,"https://scholar.google.com/scholar?cluster=8849018836826676230&hl=en&as_sdt=0,33",2,2020
Big Self-Supervised Models are Strong Semi-Supervised Learners,1567,neurips,570,69,2023-06-16 15:12:28.777000,https://github.com/google-research/simclr,3562,Big self-supervised models are strong semi-supervised learners,"https://scholar.google.com/scholar?cluster=18105628451996555050&hl=en&as_sdt=0,5",46,2020
 Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization,32,neurips,0,0,2023-06-16 15:12:28.971000,https://github.com/gpleiss/ciq_experiments,2,Fast matrix square roots with applications to Gaussian processes and Bayesian optimization,"https://scholar.google.com/scholar?cluster=4177384831232294846&hl=en&as_sdt=0,41",1,2020
Model Class Reliance for Random Forests,16,neurips,4,0,2023-06-16 15:12:29.166000,https://github.com/gavin-s-smith/mcrforest,4,Model class reliance for random forests,"https://scholar.google.com/scholar?cluster=4402509966168777669&hl=en&as_sdt=0,25",2,2020
Learning to Adapt to Evolving Domains,39,neurips,7,4,2023-06-16 15:12:29.390000,https://github.com/Liuhong99/EAML,25,Learning to adapt to evolving domains,"https://scholar.google.com/scholar?cluster=16226509627178633585&hl=en&as_sdt=0,44",3,2020
Synthesizing Tasks for Block-based Programming,7,neurips,2,0,2023-06-16 15:12:29.584000,https://github.com/adishs/neurips2020_synthesizing-tasks_code,1,Synthesizing tasks for block-based programming,"https://scholar.google.com/scholar?cluster=16452730924427259118&hl=en&as_sdt=0,21",1,2020
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks,25,neurips,3,1,2023-06-16 15:12:29.787000,https://github.com/klightz/Firefly,27,Firefly neural architecture descent: a general approach for growing neural networks,"https://scholar.google.com/scholar?cluster=13122447831516243168&hl=en&as_sdt=0,15",1,2020
Multiparameter Persistence Image for Topological Machine Learning,32,neurips,1,1,2023-06-16 15:12:29.986000,https://github.com/MathieuCarriere/multipers,10,Multiparameter persistence image for topological machine learning,"https://scholar.google.com/scholar?cluster=13550036092847919796&hl=en&as_sdt=0,5",3,2020
Matrix Inference and Estimation in Multi-Layer Models,7,neurips,2,0,2023-06-16 15:12:30.178000,https://github.com/parthe/ML-Mat-VAMP,0,Matrix inference and estimation in multi-layer models,"https://scholar.google.com/scholar?cluster=10959272077824888298&hl=en&as_sdt=0,39",1,2020
MeshSDF: Differentiable Iso-Surface Extraction,82,neurips,18,3,2023-06-16 15:12:30.371000,https://github.com/cvlab-epfl/MeshSDF,188,Meshsdf: Differentiable iso-surface extraction,"https://scholar.google.com/scholar?cluster=13067371230627821675&hl=en&as_sdt=0,33",10,2020
Variational Interaction Information Maximization for Cross-domain Disentanglement,22,neurips,5,0,2023-06-16 15:12:30.564000,https://github.com/gr8joo/IIAE,19,Variational interaction information maximization for cross-domain disentanglement,"https://scholar.google.com/scholar?cluster=13489781620394262850&hl=en&as_sdt=0,11",2,2020
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning,16,neurips,0,0,2023-06-16 15:12:30.757000,https://github.com/FlorenceFeng/StateDecoding,5,Provably efficient exploration for reinforcement learning using unsupervised learning,"https://scholar.google.com/scholar?cluster=15174934444919444347&hl=en&as_sdt=0,10",1,2020
Wasserstein Distances for Stereo Disparity Estimation,41,neurips,16,2,2023-06-16 15:12:30.950000,https://github.com/Div99/W-Stereo-Disp,94,Wasserstein distances for stereo disparity estimation,"https://scholar.google.com/scholar?cluster=10193193234465084361&hl=en&as_sdt=0,5",8,2020
Multi-agent Trajectory Prediction with Fuzzy Query Attention,16,neurips,7,0,2023-06-16 15:12:31.143000,https://github.com/nitinkamra1992/FQA,34,Multi-agent trajectory prediction with fuzzy query attention,"https://scholar.google.com/scholar?cluster=3202936941876716183&hl=en&as_sdt=0,5",2,2020
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping,3,neurips,0,0,2023-06-16 15:12:31.352000,https://github.com/Shashankaubaru/He-NMFGT,0,Multilabel classification by hierarchical partitioning and data-dependent grouping,"https://scholar.google.com/scholar?cluster=12279533609015464937&hl=en&as_sdt=0,5",1,2020
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data,21,neurips,4,12,2023-06-16 15:12:31.548000,https://github.com/tachukao/mgplvm-pytorch,21,Manifold GPLVMs for discovering non-Euclidean latent structure in neural data,"https://scholar.google.com/scholar?cluster=15482374417517923029&hl=en&as_sdt=0,23",5,2020
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning,134,neurips,55,13,2023-06-16 15:12:31.750000,https://github.com/gingsi/coot-videotext,259,Coot: Cooperative hierarchical transformer for video-text representation learning,"https://scholar.google.com/scholar?cluster=3723984631534803573&hl=en&as_sdt=0,5",8,2020
Passport-aware Normalization for Deep Model Protection,44,neurips,6,0,2023-06-16 15:12:31.944000,https://github.com/ZJZAC/Passport-aware-Normalization,16,Passport-aware normalization for deep model protection,"https://scholar.google.com/scholar?cluster=15269211415463725285&hl=en&as_sdt=0,5",1,2020
Learning One Representation to Optimize All Rewards,28,neurips,3,0,2023-06-16 16:05:14.922000,https://github.com/ahmed-touati/controllable_agent,24,Learning one representation to optimize all rewards,"https://scholar.google.com/scholar?cluster=9814375614256861048&hl=en&as_sdt=0,16",4,2021
Matrix factorisation and the interpretation of geodesic distance,7,neurips,3,0,2023-06-16 16:05:15.123000,https://github.com/anniegray52/graphs,2,Matrix factorisation and the interpretation of geodesic distance,"https://scholar.google.com/scholar?cluster=17304238490744462864&hl=en&as_sdt=0,14",1,2021
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks,71,neurips,8,0,2023-06-16 16:05:15.322000,https://github.com/hengruizhang98/CCA-SSG,51,From canonical correlation analysis to self-supervised graph neural networks,"https://scholar.google.com/scholar?cluster=7947998668914854789&hl=en&as_sdt=0,5",1,2021
BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain,5,neurips,0,1,2023-06-16 16:05:15.522000,https://github.com/ztluostat/bast,3,BAST: Bayesian additive regression spanning trees for complex constrained domain,"https://scholar.google.com/scholar?cluster=1588870201252653971&hl=en&as_sdt=0,5",1,2021
Hyperbolic Busemann Learning with Ideal Prototypes,8,neurips,4,0,2023-06-16 16:05:15.721000,https://github.com/minaghadimiatigh/hyperbolic-busemann-learning,20,Hyperbolic busemann learning with ideal prototypes,"https://scholar.google.com/scholar?cluster=16865019425945397467&hl=en&as_sdt=0,5",2,2021
ReAct: Out-of-distribution Detection With Rectified Activations,133,neurips,8,0,2023-06-16 16:05:15.920000,https://github.com/deeplearning-wisc/react,43,React: Out-of-distribution detection with rectified activations,"https://scholar.google.com/scholar?cluster=14758995866117688581&hl=en&as_sdt=0,47",3,2021
AugMax: Adversarial Composition of Random Augmentations for Robust Training,53,neurips,21,0,2023-06-16 16:05:16.118000,https://github.com/vita-group/augmax,118,Augmax: Adversarial composition of random augmentations for robust training,"https://scholar.google.com/scholar?cluster=405640925261784405&hl=en&as_sdt=0,22",7,2021
Habitat 2.0: Training Home Assistants to Rearrange their Habitat,205,neurips,378,170,2023-06-16 16:05:16.317000,https://github.com/facebookresearch/habitat-lab,1109,Habitat 2.0: Training home assistants to rearrange their habitat,"https://scholar.google.com/scholar?cluster=17501231246845502994&hl=en&as_sdt=0,31",43,2021
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods,9,neurips,0,0,2023-06-16 16:05:16.517000,https://github.com/artberryx/SAR,5,Time discretization-invariant safe action repetition for policy gradient methods,"https://scholar.google.com/scholar?cluster=557586246467545482&hl=en&as_sdt=0,50",2,2021
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection,12,neurips,5,14,2023-06-16 16:05:16.716000,https://github.com/shengtao96/centripetaltext,28,Centripetaltext: An efficient text instance representation for scene text detection,"https://scholar.google.com/scholar?cluster=9087656668537689317&hl=en&as_sdt=0,31",3,2021
DRIVE: One-bit Distributed Mean Estimation,18,neurips,0,0,2023-06-16 16:05:16.915000,https://github.com/amitport/drive-one-bit-distributed-mean-estimation,4,Drive: One-bit distributed mean estimation,"https://scholar.google.com/scholar?cluster=1334987039142805665&hl=en&as_sdt=0,5",3,2021
Local Explanation of Dialogue Response Generation,5,neurips,0,1,2023-06-16 16:05:17.114000,https://github.com/Pascalson/LERG,16,Local explanation of dialogue response generation,"https://scholar.google.com/scholar?cluster=3462316691671296408&hl=en&as_sdt=0,5",2,2021
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation,9,neurips,2,1,2023-06-16 16:05:17.313000,https://github.com/aaltoml/scalable-inference-in-sdes,10,Scalable inference in SDEs by direct matching of the Fokker–Planck–Kolmogorov equation,"https://scholar.google.com/scholar?cluster=7639024003048883297&hl=en&as_sdt=0,15",2,2021
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation,4,neurips,0,0,2023-06-16 16:05:17.511000,https://github.com/gkazunii/Legendre-tucker-rank-reduction,3,Fast tucker rank reduction for non-negative tensors using mean-field approximation,"https://scholar.google.com/scholar?cluster=3870932887094976119&hl=en&as_sdt=0,15",1,2021
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound,8,neurips,3,0,2023-06-16 16:05:17.710000,https://github.com/vzantedeschi/StocMV,6,Learning stochastic majority votes by minimizing a PAC-Bayes generalization bound,"https://scholar.google.com/scholar?cluster=13539537575801656514&hl=en&as_sdt=0,33",1,2021
Unique sparse decomposition of low rank matrices,1,neurips,1,0,2023-06-16 16:05:17.908000,https://github.com/Jindiande/Unique_Fac_of_Low_Rank,0,Unique sparse decomposition of low rank matrices,"https://scholar.google.com/scholar?cluster=11396351707745211823&hl=en&as_sdt=0,36",2,2021
Neighborhood Reconstructing Autoencoders,7,neurips,2,0,2023-06-16 16:05:18.107000,https://github.com/Gabe-YHLee/NRAE-public,25,Neighborhood reconstructing autoencoders,"https://scholar.google.com/scholar?cluster=17951945066721582980&hl=en&as_sdt=0,5",1,2021
TopicNet: Semantic Graph-Guided Topic Discovery,9,neurips,1,1,2023-06-16 16:05:18.306000,https://github.com/bochengroup/topicnet,4,Topicnet: Semantic graph-guided topic discovery,"https://scholar.google.com/scholar?cluster=8671863207015234727&hl=en&as_sdt=0,23",4,2021
(Almost) Free Incentivized Exploration from Decentralized Learning Agents,0,neurips,2,0,2023-06-16 16:05:18.505000,https://github.com/shengroup/observe_then_incentivize,0,(Almost) Free Incentivized Exploration from Decentralized Learning Agents,"https://scholar.google.com/scholar?cluster=8823665853849835723&hl=en&as_sdt=0,5",1,2021
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness,7,neurips,1,0,2023-06-16 16:05:18.704000,https://github.com/neu-spiral/hbar,14,Revisiting hilbert-schmidt information bottleneck for adversarial robustness,"https://scholar.google.com/scholar?cluster=17051533810140769652&hl=en&as_sdt=0,5",4,2021
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs,5,neurips,0,0,2023-06-16 16:05:18.903000,https://github.com/changwoo-lee/TLOHO,0,T-LoHo: A Bayesian regularization model for structured sparsity and smoothness on graphs,"https://scholar.google.com/scholar?cluster=38237968899205623&hl=en&as_sdt=0,36",1,2021
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming,28,neurips,0,0,2023-06-16 16:05:19.103000,https://github.com/CORE-Robotics-Lab/Utility-of-Explainable-AI-NeurIPS2021,0,The utility of explainable ai in ad hoc human-machine teaming,"https://scholar.google.com/scholar?cluster=14623218223463321908&hl=en&as_sdt=0,5",2,2021
Subgoal Search For Complex Reasoning Tasks,12,neurips,4,1,2023-06-16 16:05:19.301000,https://github.com/subgoal-search/subgoal-search,17,Subgoal search for complex reasoning tasks,"https://scholar.google.com/scholar?cluster=12867531461756557618&hl=en&as_sdt=0,14",2,2021
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision,9,neurips,1,0,2023-06-16 16:05:19.500000,https://github.com/hekj/landmark-rxr,8,Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision,"https://scholar.google.com/scholar?cluster=10123860778510964052&hl=en&as_sdt=0,5",1,2021
On the Importance of Gradients for Detecting Distributional Shifts in the Wild,108,neurips,5,2,2023-06-16 16:05:19.699000,https://github.com/deeplearning-wisc/gradnorm_ood,47,On the importance of gradients for detecting distributional shifts in the wild,"https://scholar.google.com/scholar?cluster=16248002193974452072&hl=en&as_sdt=0,15",2,2021
Do Different Tracking Tasks Require Different Appearance Models?,40,neurips,32,21,2023-06-16 16:05:19.898000,https://github.com/Zhongdao/UniTrack,315,Do different tracking tasks require different appearance models?,"https://scholar.google.com/scholar?cluster=5904945497934783289&hl=en&as_sdt=0,47",10,2021
Towards robust vision by multi-task learning on monkey visual cortex,24,neurips,1,0,2023-06-16 16:05:20.097000,https://github.com/sinzlab/neural_cotraining,11,Towards robust vision by multi-task learning on monkey visual cortex,"https://scholar.google.com/scholar?cluster=41919782116802759&hl=en&as_sdt=0,5",6,2021
Learning Domain Invariant Representations in Goal-conditioned Block MDPs,8,neurips,9,0,2023-06-16 16:05:20.296000,https://github.com/facebookresearch/icp-block-mdp,43,Learning domain invariant representations in goal-conditioned block mdps,"https://scholar.google.com/scholar?cluster=6609047029323084207&hl=en&as_sdt=0,5",8,2021
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning,14,neurips,0,0,2023-06-16 16:05:20.496000,https://github.com/ssethz/multi-perturbation-ed,5,Near-optimal multi-perturbation experimental design for causal structure learning,"https://scholar.google.com/scholar?cluster=15730591700962293964&hl=en&as_sdt=0,5",1,2021
Fuzzy Clustering with Similarity Queries,1,neurips,37,9,2023-06-16 16:05:20.696000,https://github.com/omadson/fuzzy-c-means,141,Fuzzy Clustering with Similarity Queries,"https://scholar.google.com/scholar?cluster=4880637304563204630&hl=en&as_sdt=0,15",2,2021
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL,15,neurips,2,0,2023-06-16 16:05:20.895000,https://github.com/khalednakhleh/NeurWIN,2,Neurwin: Neural whittle index network for restless bandits via deep rl,"https://scholar.google.com/scholar?cluster=15114473685997353079&hl=en&as_sdt=0,28",1,2021
Alias-Free Generative Adversarial Networks,760,neurips,939,151,2023-06-16 16:05:21.094000,https://github.com/NVlabs/stylegan3,5233,Alias-free generative adversarial networks,"https://scholar.google.com/scholar?cluster=17368705487922251039&hl=en&as_sdt=0,10",56,2021
Perturb-and-max-product: Sampling and learning in discrete energy-based models,3,neurips,1,0,2023-06-16 16:05:21.294000,https://github.com/vicariousinc/perturb_and_max_product,2,Perturb-and-max-product: Sampling and learning in discrete energy-based models,"https://scholar.google.com/scholar?cluster=9625174616528081623&hl=en&as_sdt=0,34",7,2021
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games,22,neurips,2,0,2023-06-16 16:05:21.494000,https://github.com/sjtu-marl/bd_rd_psro,12,Towards unifying behavioral and response diversity for open-ended learning in zero-sum games,"https://scholar.google.com/scholar?cluster=4169431602989656565&hl=en&as_sdt=0,47",2,2021
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples,16,neurips,1,1,2023-06-16 16:05:21.693000,https://github.com/sungyoon-lee/losslandscapematters,3,Towards better understanding of training certifiably robust models against adversarial examples,"https://scholar.google.com/scholar?cluster=17516226191552628723&hl=en&as_sdt=0,28",2,2021
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage,16,neurips,2,0,2023-06-16 16:05:21.893000,https://github.com/jdchang1/milo,13,Mitigating covariate shift in imitation learning via offline data with partial coverage,"https://scholar.google.com/scholar?cluster=16326608831095542308&hl=en&as_sdt=0,33",1,2021
Global Filter Networks for Image Classification,169,neurips,32,5,2023-06-16 16:05:22.092000,https://github.com/raoyongming/GFNet,310,Global filter networks for image classification,"https://scholar.google.com/scholar?cluster=17238210229818271657&hl=en&as_sdt=0,34",8,2021
CAFE: Catastrophic Data Leakage in Vertical Federated Learning,60,neurips,4,3,2023-06-16 16:05:22.291000,https://github.com/derafael/cafe,17,CAFE: Catastrophic data leakage in vertical federated learning,"https://scholar.google.com/scholar?cluster=8108405873195186105&hl=en&as_sdt=0,33",1,2021
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee,22,neurips,7,0,2023-06-16 16:05:22.490000,https://github.com/flint-xf-fan/Byzantine-Federeated-RL,43,Fault-tolerant federated reinforcement learning with theoretical guarantee,"https://scholar.google.com/scholar?cluster=16500433966124392053&hl=en&as_sdt=0,5",4,2021
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers,122,neurips,12,2,2023-06-16 16:05:22.689000,https://github.com/rabeehk/compacter,87,Compacter: Efficient low-rank hypercomplex adapter layers,"https://scholar.google.com/scholar?cluster=6044403345525594141&hl=en&as_sdt=0,33",6,2021
Distilling Image Classifiers in Object Detectors,5,neurips,3,1,2023-06-16 16:05:22.888000,https://github.com/NVlabs/DICOD,27,Distilling image classifiers in object detectors,"https://scholar.google.com/scholar?cluster=10848349490425744852&hl=en&as_sdt=0,33",6,2021
Subgroup Generalization and Fairness of Graph Neural Networks,41,neurips,4,1,2023-06-16 16:05:23.087000,https://github.com/theaperdeng/gnn-generalization-fairness,2,Subgroup generalization and fairness of graph neural networks,"https://scholar.google.com/scholar?cluster=15293693344501115614&hl=en&as_sdt=0,44",3,2021
Scaling Neural Tangent Kernels via Sketching and Random Features,15,neurips,2,0,2023-06-16 16:05:23.286000,https://github.com/insuhan/ntk-sketch-rf,8,Scaling neural tangent kernels via sketching and random features,"https://scholar.google.com/scholar?cluster=12022337721774352923&hl=en&as_sdt=0,6",1,2021
Long Short-Term Transformer for Online Action Detection,39,neurips,13,5,2023-06-16 16:05:23.485000,https://github.com/amazon-research/long-short-term-transformer,100,Long short-term transformer for online action detection,"https://scholar.google.com/scholar?cluster=3271205271757526851&hl=en&as_sdt=0,47",8,2021
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection,13,neurips,7,1,2023-06-16 16:05:23.685000,https://github.com/kobybibas/pnml_ood_detection,22,Single layer predictive normalized maximum likelihood for out-of-distribution detection,"https://scholar.google.com/scholar?cluster=3648486984737742004&hl=en&as_sdt=0,31",2,2021
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation,44,neurips,50,7,2023-06-16 16:05:23.884000,https://github.com/SysCV/pcan,342,Prototypical cross-attention networks for multiple object tracking and segmentation,"https://scholar.google.com/scholar?cluster=9943655597902986083&hl=en&as_sdt=0,3",10,2021
Learning Optimal Predictive Checklists,7,neurips,2,0,2023-06-16 16:05:24.084000,https://github.com/MLforHealth/predictive_checklists,5,Learning optimal predictive checklists,"https://scholar.google.com/scholar?cluster=17421241641568013154&hl=en&as_sdt=0,14",2,2021
Gradient Starvation: A Learning Proclivity in Neural Networks,130,neurips,7,1,2023-06-16 16:05:24.283000,https://github.com/mohammadpz/Gradient_Starvation,53,Gradient starvation: A learning proclivity in neural networks,"https://scholar.google.com/scholar?cluster=4980681547647500046&hl=en&as_sdt=0,33",5,2021
Offline Reinforcement Learning as One Big Sequence Modeling Problem,271,neurips,52,6,2023-06-16 16:05:24.482000,https://github.com/JannerM/trajectory-transformer,339,Offline reinforcement learning as one big sequence modeling problem,"https://scholar.google.com/scholar?cluster=4951503534992558310&hl=en&as_sdt=0,33",5,2021
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices,8,neurips,0,1,2023-06-16 16:05:24.681000,https://github.com/tarodz/shapeshifter,1,Shapeshifter: a parameter-efficient transformer using factorized reshaped matrices,"https://scholar.google.com/scholar?cluster=16541495741212848836&hl=en&as_sdt=0,10",1,2021
Regularized Softmax Deep Multi-Agent Q-Learning,12,neurips,2,3,2023-06-16 16:05:24.880000,https://github.com/ling-pan/res,19,Regularized softmax deep multi-agent Q-learning,"https://scholar.google.com/scholar?cluster=16754114336798964505&hl=en&as_sdt=0,5",2,2021
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling,3,neurips,3,1,2023-06-16 16:05:25.079000,https://github.com/tech-submissions/physics-aware-downsampling,7,Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling,"https://scholar.google.com/scholar?cluster=18263225743039070318&hl=en&as_sdt=0,33",1,2021
Systematic Generalization with Edge Transformers,11,neurips,3,0,2023-06-16 16:05:25.278000,https://github.com/bergen/edgetransformer,15,Systematic generalization with edge transformers,"https://scholar.google.com/scholar?cluster=4782172685835509964&hl=en&as_sdt=0,5",1,2021
Maximum Likelihood Training of Score-Based Diffusion Models,159,neurips,20,2,2023-06-16 16:05:25.478000,https://github.com/yang-song/score_flow,95,Maximum likelihood training of score-based diffusion models,"https://scholar.google.com/scholar?cluster=9322848153795569908&hl=en&as_sdt=0,5",7,2021
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond,9,neurips,2,3,2023-06-16 16:05:25.677000,https://github.com/netw0rkf10w/crf,23,Regularized frank-wolfe for dense crfs: Generalizing mean field and beyond,"https://scholar.google.com/scholar?cluster=7839337390537061151&hl=en&as_sdt=0,34",3,2021
Scalable Intervention Target Estimation in Linear Models,6,neurips,0,0,2023-06-16 16:05:25.876000,https://github.com/bvarici/intervention-estimation,0,Scalable intervention target estimation in linear models,"https://scholar.google.com/scholar?cluster=7424623310042885171&hl=en&as_sdt=0,10",2,2021
Play to Grade: Testing Coding Games as Classifying Markov Decision Process,6,neurips,4,0,2023-06-16 16:05:26.075000,https://github.com/windweller/play-to-grade,5,Play to grade: testing coding games as classifying Markov decision process,"https://scholar.google.com/scholar?cluster=2851413574453679120&hl=en&as_sdt=0,5",2,2021
Differentiable Unsupervised Feature Selection based on a Gated Laplacian,20,neurips,3,0,2023-06-16 16:05:26.275000,https://github.com/Ofirlin/DUFS,6,Differentiable unsupervised feature selection based on a gated laplacian,"https://scholar.google.com/scholar?cluster=12231819460372873074&hl=en&as_sdt=0,47",1,2021
Smooth Bilevel Programming for Sparse Regularization,9,neurips,0,0,2023-06-16 16:05:26.474000,https://github.com/gpeyre/2021-NonCvxPro,8,Smooth bilevel programming for sparse regularization,"https://scholar.google.com/scholar?cluster=1358361892892499297&hl=en&as_sdt=0,33",2,2021
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning,19,neurips,13,0,2023-06-16 16:05:26.673000,https://github.com/PwnerHarry/CP,51,A consciousness-inspired planning agent for model-based reinforcement learning,"https://scholar.google.com/scholar?cluster=1913167865279429468&hl=en&as_sdt=0,5",6,2021
Beltrami Flow and Neural Diffusion on Graphs,32,neurips,42,4,2023-06-16 16:05:26.872000,https://github.com/twitter-research/graph-neural-pde,254,Beltrami flow and neural diffusion on graphs,"https://scholar.google.com/scholar?cluster=11396329542224285473&hl=en&as_sdt=0,5",12,2021
"Think Big, Teach Small: Do Language Models Distil Occam’s Razor?",1,neurips,0,0,2023-06-16 16:05:27.072000,https://github.com/gonzalojaimovitch/think-big-teach-small,0,"Think Big, Teach Small: Do Language Models Distil Occam's Razor?","https://scholar.google.com/scholar?cluster=324406477696359661&hl=en&as_sdt=0,5",1,2021
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA,29,neurips,3,1,2023-06-16 16:05:27.271000,https://github.com/HHalva/snica,10,Disentangling identifiable features from noisy data with structured nonlinear ICA,"https://scholar.google.com/scholar?cluster=16776677318937402527&hl=en&as_sdt=0,21",3,2021
"Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems",15,neurips,3,0,2023-06-16 16:05:27.469000,https://github.com/davidxujiayang/cpnets,11,"Conditionally parameterized, discretization-aware neural networks for mesh-based modeling of physical systems","https://scholar.google.com/scholar?cluster=10149448284676300480&hl=en&as_sdt=0,33",1,2021
Adaptive Conformal Inference Under Distribution Shift,53,neurips,1,0,2023-06-16 16:05:27.670000,https://github.com/ISGibbs/AdaptiveConformal,1,Adaptive conformal inference under distribution shift,"https://scholar.google.com/scholar?cluster=263561861099566875&hl=en&as_sdt=0,14",2,2021
Periodic Activation Functions Induce Stationarity,13,neurips,2,0,2023-06-16 16:05:27.869000,https://github.com/aaltoml/periodicbnn,14,Periodic activation functions induce stationarity,"https://scholar.google.com/scholar?cluster=4217215713078286668&hl=en&as_sdt=0,41",1,2021
Revealing and Protecting Labels in Distributed Training,12,neurips,1,0,2023-06-16 16:05:28.068000,https://github.com/googleinterns/learning-bag-of-words,0,Revealing and protecting labels in distributed training,"https://scholar.google.com/scholar?cluster=3247990079527207067&hl=en&as_sdt=0,26",3,2021
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning,2,neurips,2,0,2023-06-16 16:05:28.267000,https://github.com/victordarvariu/solving-graph-pgg,3,Solving Graph-based Public Goods Games with Tree Search and Imitation Learning,"https://scholar.google.com/scholar?cluster=18441928551030825405&hl=en&as_sdt=0,39",1,2021
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization,50,neurips,5,2,2023-06-16 16:05:28.466000,https://github.com/GentleZhu/EGI,20,Transfer learning of graph neural networks with ego-graph information maximization,"https://scholar.google.com/scholar?cluster=5328682952509931138&hl=en&as_sdt=0,10",2,2021
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership,10,neurips,1,0,2023-06-16 16:05:28.665000,https://github.com/vita-group/no-stealing-lth,8,You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership,"https://scholar.google.com/scholar?cluster=15882101546967524183&hl=en&as_sdt=0,43",6,2021
End-to-End Weak Supervision,25,neurips,11,4,2023-06-16 16:05:28.864000,https://github.com/autonlab/weasel,142,End-to-end weak supervision,"https://scholar.google.com/scholar?cluster=10702508004213948659&hl=en&as_sdt=0,33",4,2021
Shift Invariance Can Reduce Adversarial Robustness,8,neurips,0,0,2023-06-16 16:05:29.064000,https://github.com/SongweiGe/shift-invariance-adv-robustness,1,Shift invariance can reduce adversarial robustness,"https://scholar.google.com/scholar?cluster=11307069539231769168&hl=en&as_sdt=0,33",2,2021
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics,1,neurips,0,0,2023-06-16 16:05:29.264000,https://github.com/ischubert/l2e,3,Learning to execute: Efficient learning of universal plan-conditioned policies in robotics,"https://scholar.google.com/scholar?cluster=16472016640815710448&hl=en&as_sdt=0,36",2,2021
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks,10,neurips,4,0,2023-06-16 16:05:29.464000,https://github.com/grayhong/self-diagnosing-gan,21,Self-diagnosing gan: Diagnosing underrepresented samples in generative adversarial networks,"https://scholar.google.com/scholar?cluster=934777026430658759&hl=en&as_sdt=0,10",2,2021
Efficient Truncated Linear Regression with Unknown Noise Variance,4,neurips,0,0,2023-06-16 16:05:29.668000,https://github.com/pstefanou12/truncated-regression-with-unknown-noise-variance-neurips-2021,1,Efficient truncated linear regression with unknown noise variance,"https://scholar.google.com/scholar?cluster=16700113284876080282&hl=en&as_sdt=0,47",1,2021
Breaking the Dilemma of Medical Image-to-image Translation,45,neurips,18,3,2023-06-16 16:05:29.884000,https://github.com/kid-liet/reg-gan,106,Breaking the dilemma of medical image-to-image translation,"https://scholar.google.com/scholar?cluster=16465540370988661764&hl=en&as_sdt=0,33",3,2021
Temporally Abstract Partial Models,5,neurips,1,0,2023-06-16 16:05:30.085000,https://github.com/deepmind/affordances_option_models,21,Temporally abstract partial models,"https://scholar.google.com/scholar?cluster=4581996143071889142&hl=en&as_sdt=0,5",4,2021
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence,53,neurips,7,1,2023-06-16 16:05:30.286000,https://github.com/ahoho/topics,40,Is automated topic model evaluation broken? the incoherence of coherence,"https://scholar.google.com/scholar?cluster=11755535918239308515&hl=en&as_sdt=0,33",5,2021
Do Input Gradients Highlight Discriminative Features? ,30,neurips,1,1,2023-06-16 16:05:30.486000,https://github.com/harshays/inputgradients,10,Do input gradients highlight discriminative features?,"https://scholar.google.com/scholar?cluster=11330786422400793960&hl=en&as_sdt=0,10",2,2021
Improving Conditional Coverage via Orthogonal Quantile Regression,16,neurips,1,0,2023-06-16 16:05:30.687000,https://github.com/Shai128/oqr,10,Improving conditional coverage via orthogonal quantile regression,"https://scholar.google.com/scholar?cluster=14048759357099673213&hl=en&as_sdt=0,44",1,2021
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations,24,neurips,2,2,2023-06-16 16:05:30.889000,https://github.com/sli057/Geo-TRAP,6,Adversarial attacks on black box video classifiers: Leveraging the power of geometric transformations,"https://scholar.google.com/scholar?cluster=2786816693505158644&hl=en&as_sdt=0,5",2,2021
"Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\""om Method",17,neurips,5,1,2023-06-16 16:05:31.090000,https://github.com/pkuzengqi/skyformer,48,"Skyformer: Remodel self-attention with gaussian kernel and nystr\"" om method","https://scholar.google.com/scholar?cluster=251222659359430658&hl=en&as_sdt=0,33",5,2021
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification,182,neurips,56,8,2023-06-16 16:05:31.290000,https://github.com/szc19990412/TransMIL,188,Transmil: Transformer based correlated multiple instance learning for whole slide image classification,"https://scholar.google.com/scholar?cluster=13608733975059322575&hl=en&as_sdt=0,5",4,2021
Multi-view Contrastive Graph Clustering,70,neurips,6,0,2023-06-16 16:05:31.493000,https://github.com/panern/mcgc,40,Multi-view contrastive graph clustering,"https://scholar.google.com/scholar?cluster=14221322770534641657&hl=en&as_sdt=0,5",1,2021
Inverse-Weighted Survival Games,5,neurips,0,0,2023-06-16 16:05:31.694000,https://github.com/rajesh-lab/inverse-weighted-survival-games,4,Inverse-weighted survival games,"https://scholar.google.com/scholar?cluster=6438123884467705284&hl=en&as_sdt=0,10",1,2021
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability,23,neurips,1,0,2023-06-16 16:05:31.894000,https://github.com/irom-lab/PAC-BUS,3,Generalization bounds for meta-learning via pac-bayes and uniform stability,"https://scholar.google.com/scholar?cluster=9536700152943509349&hl=en&as_sdt=0,44",8,2021
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement,43,neurips,319,64,2023-06-16 16:05:32.097000,https://github.com/pytorch/botorch,2663,Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement,"https://scholar.google.com/scholar?cluster=10095790416853214075&hl=en&as_sdt=0,47",51,2021
Explaining Hyperparameter Optimization via Partial Dependence Plots,25,neurips,0,1,2023-06-16 16:05:32.300000,https://github.com/slds-lmu/paper_2021_xautoml,2,Explaining hyperparameter optimization via partial dependence plots,"https://scholar.google.com/scholar?cluster=15140821706034992592&hl=en&as_sdt=0,5",12,2021
Representation Learning on Spatial Networks,9,neurips,2,1,2023-06-16 16:05:32.507000,https://github.com/rollingstonezz/sgmp_code,14,Representation learning on spatial networks,"https://scholar.google.com/scholar?cluster=18262507146784502070&hl=en&as_sdt=0,34",1,2021
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time,9,neurips,2,1,2023-06-16 16:05:32.717000,https://github.com/snel-repo/sbtt-demo,6,Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time,"https://scholar.google.com/scholar?cluster=116915416169272448&hl=en&as_sdt=0,33",3,2021
Preserved central model for faster bidirectional compression in distributed settings,22,neurips,0,0,2023-06-16 16:05:32.918000,https://github.com/philipco/mcm-bidirectional-compression,1,Preserved central model for faster bidirectional compression in distributed settings,"https://scholar.google.com/scholar?cluster=11324851301084839987&hl=en&as_sdt=0,5",1,2021
Luna: Linear Unified Nested Attention,64,neurips,15,1,2023-06-16 16:05:33.123000,https://github.com/XuezheMax/fairseq-apollo,94,Luna: Linear unified nested attention,"https://scholar.google.com/scholar?cluster=15945065740745831634&hl=en&as_sdt=0,33",6,2021
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias,6,neurips,9,0,2023-06-16 16:05:33.323000,https://github.com/IntelLabs/causality-lab,53,Iterative causal discovery in the possible presence of latent confounders and selection bias,"https://scholar.google.com/scholar?cluster=15917731379630778872&hl=en&as_sdt=0,38",10,2021
Associating Objects with Transformers for Video Object Segmentation,96,neurips,6,0,2023-06-16 16:05:33.522000,https://github.com/z-x-yang/AOT,91,Associating objects with transformers for video object segmentation,"https://scholar.google.com/scholar?cluster=3585510538357549856&hl=en&as_sdt=0,21",13,2021
Automatic Symmetry Discovery with Lie Algebra Convolutional Network,42,neurips,3,0,2023-06-16 16:05:33.720000,https://github.com/nimadehmamy/l-conv-code,38,Automatic symmetry discovery with lie algebra convolutional network,"https://scholar.google.com/scholar?cluster=14029131064477993418&hl=en&as_sdt=0,5",4,2021
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks,14,neurips,4,0,2023-06-16 16:05:33.920000,https://github.com/gmum/Zero-Time-Waste,12,Zero time waste: recycling predictions in early exit neural networks,"https://scholar.google.com/scholar?cluster=16788123232185667658&hl=en&as_sdt=0,44",5,2021
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation,26,neurips,2,1,2023-06-16 16:05:34.120000,https://github.com/tydpan/NorCal,27,On model calibration for long-tailed object detection and instance segmentation,"https://scholar.google.com/scholar?cluster=2480452967273135514&hl=en&as_sdt=0,11",2,2021
ReSSL: Relational Self-Supervised Learning with Weak Augmentation,50,neurips,8,1,2023-06-16 16:05:34.327000,https://github.com/KyleZheng1997/ReSSL,50,Ressl: Relational self-supervised learning with weak augmentation,"https://scholar.google.com/scholar?cluster=9030640366859568915&hl=en&as_sdt=0,10",3,2021
Learning to See by Looking at Noise,14,neurips,5,2,2023-06-16 16:05:34.527000,https://github.com/mbaradad/learning_with_noise,91,Learning to see by looking at noise,"https://scholar.google.com/scholar?cluster=17950334231348284249&hl=en&as_sdt=0,33",5,2021
Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN,14,neurips,21,13,2023-06-16 16:05:34.726000,https://github.com/xiezhy6/pasta-gan,76,Towards scalable unpaired virtual try-on via patch-routed spatially-adaptive GAN,"https://scholar.google.com/scholar?cluster=9712953587399366251&hl=en&as_sdt=0,34",3,2021
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models,52,neurips,2,0,2023-06-16 16:05:34.926000,https://github.com/oxai/intersectional_gpt2,9,Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models,"https://scholar.google.com/scholar?cluster=10610853007934037556&hl=en&as_sdt=0,10",8,2021
Weisfeiler and Lehman Go Cellular: CW Networks,121,neurips,20,0,2023-06-16 16:05:35.127000,https://github.com/twitter-research/cwn,124,Weisfeiler and lehman go cellular: Cw networks,"https://scholar.google.com/scholar?cluster=10604779220263542295&hl=en&as_sdt=0,33",7,2021
Learning Conjoint Attentions for Graph Neural Nets,15,neurips,0,0,2023-06-16 16:05:35.328000,https://github.com/he-tiantian/cats,5,Learning conjoint attentions for graph neural nets,"https://scholar.google.com/scholar?cluster=4054823873527255592&hl=en&as_sdt=0,18",2,2021
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution,29,neurips,8,1,2023-06-16 16:05:35.527000,https://github.com/mingsun-tse/assl,53,Aligned structured sparsity learning for efficient image super-resolution,"https://scholar.google.com/scholar?cluster=11894104122584992183&hl=en&as_sdt=0,5",8,2021
Lip to Speech Synthesis with Visual Context Attentional GAN,22,neurips,4,0,2023-06-16 16:05:35.726000,https://github.com/ms-dot-k/Visual-Context-Attentional-GAN,12,Lip to speech synthesis with visual context attentional GAN,"https://scholar.google.com/scholar?cluster=3002779332675732669&hl=en&as_sdt=0,5",1,2021
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning,6,neurips,1,0,2023-06-16 16:05:35.927000,https://github.com/kibeomkim/gace-gdan,24,Goal-aware cross-entropy for multi-target reinforcement learning,"https://scholar.google.com/scholar?cluster=16382968152534550618&hl=en&as_sdt=0,16",3,2021
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images,52,neurips,11,1,2023-06-16 16:05:36.129000,https://github.com/taconite/MetaAvatar-release,109,Metaavatar: Learning animatable clothed human models from few depth images,"https://scholar.google.com/scholar?cluster=16058062617951022189&hl=en&as_sdt=0,33",6,2021
Distributed Principal Component Analysis with Limited Communication,5,neurips,0,0,2023-06-16 16:05:36.329000,https://github.com/ist-daslab/qrgd,2,Distributed principal component analysis with limited communication,"https://scholar.google.com/scholar?cluster=16491167680974044307&hl=en&as_sdt=0,5",4,2021
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update,13,neurips,4,2,2023-06-16 16:05:36.529000,https://github.com/lessketching/newtonsketch,1,Newton-LESS: Sparsification without trade-offs for the sketched newton update,"https://scholar.google.com/scholar?cluster=8971361646067584316&hl=en&as_sdt=0,33",1,2021
Confident Anchor-Induced Multi-Source Free Domain Adaptation,34,neurips,1,1,2023-06-16 16:05:36.733000,https://github.com/learning-group123/caida,18,Confident anchor-induced multi-source free domain adaptation,"https://scholar.google.com/scholar?cluster=4891466716654628888&hl=en&as_sdt=0,26",2,2021
Word2Fun: Modelling Words as Functions for Diachronic Word Representation,1,neurips,0,1,2023-06-16 16:05:36.936000,https://github.com/wabyking/word2fun,10,Word2Fun: Modelling Words as Functions for Diachronic Word Representation,"https://scholar.google.com/scholar?cluster=14848701185772884980&hl=en&as_sdt=0,33",1,2021
Low-Rank Constraints for Fast Inference in Structured Models,10,neurips,0,1,2023-06-16 16:05:37.139000,https://github.com/justinchiu/low-rank-models,5,Low-rank constraints for fast inference in structured models,"https://scholar.google.com/scholar?cluster=15216352374611711176&hl=en&as_sdt=0,14",3,2021
Accumulative Poisoning Attacks on Real-time Data,11,neurips,1,0,2023-06-16 16:05:37.341000,https://github.com/ShawnXYang/AccumulativeAttack,17,Accumulative poisoning attacks on real-time data,"https://scholar.google.com/scholar?cluster=17018461129104727462&hl=en&as_sdt=0,44",2,2021
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators,28,neurips,5,1,2023-06-16 16:05:37.541000,https://github.com/ai-secure/g-pate,23,G-PATE: scalable differentially private data generator via private aggregation of teacher discriminators,"https://scholar.google.com/scholar?cluster=18094495377911036601&hl=en&as_sdt=0,5",2,2021
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning,8,neurips,3,1,2023-06-16 16:05:37.747000,https://github.com/alinlab/oreo,21,Object-aware regularization for addressing causal confusion in imitation learning,"https://scholar.google.com/scholar?cluster=11591778827238296891&hl=en&as_sdt=0,5",3,2021
Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks,37,neurips,0,0,2023-06-16 16:05:37.948000,https://github.com/cog-imperial/partitionedformulations_nn,2,Partition-based formulations for mixed-integer optimization of trained relu neural networks,"https://scholar.google.com/scholar?cluster=322600744726062077&hl=en&as_sdt=0,44",3,2021
"Hyperparameter Optimization Is Deceiving Us, and How to Stop It",12,neurips,1,0,2023-06-16 16:05:38.147000,https://github.com/pasta41/deception,0,"Hyperparameter optimization is deceiving us, and how to stop it","https://scholar.google.com/scholar?cluster=13676283395211391710&hl=en&as_sdt=0,14",3,2021
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning,8,neurips,1,0,2023-06-16 16:05:38.347000,https://github.com/kristian-georgiev/sgmrl,4,On the convergence theory of debiased model-agnostic meta-reinforcement learning,"https://scholar.google.com/scholar?cluster=4479200688561137043&hl=en&as_sdt=0,6",2,2021
3D Pose Transfer with Correspondence Learning and Mesh Refinement,12,neurips,4,0,2023-06-16 16:05:38.547000,https://github.com/chaoyuesong/3d-corenet,28,3D pose transfer with correspondence learning and mesh refinement,"https://scholar.google.com/scholar?cluster=16594397098263890471&hl=en&as_sdt=0,5",7,2021
Framing RNN as a kernel method: A neural ODE approach,13,neurips,3,0,2023-06-16 16:05:38.746000,https://github.com/afermanian/rnn-kernel,6,Framing RNN as a kernel method: A neural ODE approach,"https://scholar.google.com/scholar?cluster=12320309652310006031&hl=en&as_sdt=0,33",3,2021
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking,8,neurips,2,2,2023-06-16 16:05:38.947000,https://github.com/mcc-wh/csa,21,Contextual similarity aggregation with self-attention for visual re-ranking,"https://scholar.google.com/scholar?cluster=1731686966736408676&hl=en&as_sdt=0,1",2,2021
Can Information Flows Suggest Targets for Interventions in Neural Circuits?,2,neurips,1,0,2023-06-16 16:05:39.149000,https://github.com/praveenv253/ann-info-flow,0,Can information flows suggest targets for interventions in neural circuits?,"https://scholar.google.com/scholar?cluster=59078764435359692&hl=en&as_sdt=0,5",2,2021
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes,17,neurips,4,0,2023-06-16 16:05:39.348000,https://github.com/zhaozhiqian/synctwin-neurips-2021,5,Synctwin: Treatment effect estimation with longitudinal outcomes,"https://scholar.google.com/scholar?cluster=12038492275286203225&hl=en&as_sdt=0,44",4,2021
Unsupervised Motion Representation Learning with Capsule Autoencoders,12,neurips,0,0,2023-06-16 16:05:39.548000,https://github.com/ZiweiXU/CapsuleMotion,9,Unsupervised motion representation learning with capsule autoencoders,"https://scholar.google.com/scholar?cluster=14303399087955819091&hl=en&as_sdt=0,5",1,2021
Exploring Forensic Dental Identification with Deep Learning,4,neurips,1,1,2023-06-16 16:05:39.749000,https://github.com/liangyuandg/foid,4,Exploring forensic dental identification with deep learning,"https://scholar.google.com/scholar?cluster=10294284615303387635&hl=en&as_sdt=0,5",1,2021
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks,49,neurips,36,0,2023-06-16 16:05:39.948000,https://github.com/Future-Power-Networks/MAPDN,98,Multi-agent reinforcement learning for active voltage control on power distribution networks,"https://scholar.google.com/scholar?cluster=339266555786095875&hl=en&as_sdt=0,10",2,2021
Dangers of Bayesian Model Averaging under Covariate Shift,26,neurips,2,0,2023-06-16 16:05:40.148000,https://github.com/izmailovpavel/bnn_covariate_shift,28,Dangers of bayesian model averaging under covariate shift,"https://scholar.google.com/scholar?cluster=9253304407956386101&hl=en&as_sdt=0,5",3,2021
Towards Lower Bounds on the Depth of ReLU Neural Networks,13,neurips,0,0,2023-06-16 16:05:40.347000,https://github.com/ChristophHertrich/relu-mip-depth-bound,0,Towards lower bounds on the depth of ReLU neural networks,"https://scholar.google.com/scholar?cluster=4120327399657306898&hl=en&as_sdt=0,5",1,2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective,13,neurips,0,0,2023-06-16 16:05:40.548000,https://github.com/gpleiss/limits_of_large_width,5,The limitations of large width in neural networks: A deep Gaussian process perspective,"https://scholar.google.com/scholar?cluster=18411382208005468775&hl=en&as_sdt=0,47",1,2021
Exact marginal prior distributions of finite Bayesian neural networks,24,neurips,0,0,2023-06-16 16:05:40.751000,https://github.com/pehlevan-group/exactbayesiannetworkpriors,0,Exact marginal prior distributions of finite Bayesian neural networks,"https://scholar.google.com/scholar?cluster=8265985358387037900&hl=en&as_sdt=0,5",2,2021
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees,4,neurips,1,0,2023-06-16 16:05:40.978000,https://github.com/kjason/resnest,2,ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees,"https://scholar.google.com/scholar?cluster=9550021969422604022&hl=en&as_sdt=0,5",1,2021
Repulsive Deep Ensembles are Bayesian,44,neurips,5,0,2023-06-16 16:05:41.182000,https://github.com/ratschlab/repulsive_ensembles,13,Repulsive deep ensembles are bayesian,"https://scholar.google.com/scholar?cluster=4880325796914110864&hl=en&as_sdt=0,5",5,2021
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM),2,neurips,4,0,2023-06-16 16:05:41.383000,https://github.com/jayroxis/dam-pytorch,15,Learning compact representations of neural networks using discriminative masking (dam),"https://scholar.google.com/scholar?cluster=14512990192508822553&hl=en&as_sdt=0,44",2,2021
Neural Auto-Curricula in Two-Player Zero-Sum Games,20,neurips,3,0,2023-06-16 16:05:41.584000,https://github.com/waterhorse1/nac,21,Neural auto-curricula in two-player zero-sum games,"https://scholar.google.com/scholar?cluster=9201661815839550883&hl=en&as_sdt=0,5",2,2021
From global to local MDI variable importances for random forests and when they are Shapley values,7,neurips,0,0,2023-06-16 16:05:41.792000,https://github.com/asutera/local-mdi-importance,3,From global to local MDI variable importances for random forests and when they are Shapley values,"https://scholar.google.com/scholar?cluster=18342054531367511892&hl=en&as_sdt=0,5",1,2021
On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness,41,neurips,11,0,2023-06-16 16:05:42.038000,https://github.com/facebookresearch/augmentation-corruption,38,On interaction between augmentations and corruptions in natural corruption robustness,"https://scholar.google.com/scholar?cluster=440630592288573899&hl=en&as_sdt=0,10",7,2021
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data,37,neurips,5,2,2023-06-16 16:05:42.239000,https://github.com/asrafulashiq/dynamic-cdfsl,25,Dynamic distillation network for cross-domain few-shot recognition with unlabeled data,"https://scholar.google.com/scholar?cluster=9716577277370774605&hl=en&as_sdt=0,5",4,2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations,29,neurips,1,1,2023-06-16 16:05:42.450000,https://github.com/peterbhase/ExplanationSearch,15,The out-of-distribution problem in explainability and search methods for feature importance explanations,"https://scholar.google.com/scholar?cluster=11979193341973776256&hl=en&as_sdt=0,5",1,2021
Control Variates for Slate Off-Policy Evaluation,3,neurips,0,0,2023-06-16 16:05:42.653000,https://github.com/fernandoamat/slateope,3,Control variates for slate off-policy evaluation,"https://scholar.google.com/scholar?cluster=7057011324301771972&hl=en&as_sdt=0,5",1,2021
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation,50,neurips,32,3,2023-06-16 16:05:42.856000,https://github.com/nicklashansen/dmcontrol-generalization-benchmark,121,Stabilizing deep q-learning with convnets and vision transformers under data augmentation,"https://scholar.google.com/scholar?cluster=6794503273897899990&hl=en&as_sdt=0,26",4,2021
On Effective Scheduling of Model-based Reinforcement Learning,6,neurips,0,0,2023-06-16 16:05:43.056000,https://github.com/hanglai/autombpo,11,On effective scheduling of model-based reinforcement learning,"https://scholar.google.com/scholar?cluster=11128521607771619105&hl=en&as_sdt=0,5",1,2021
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience,5,neurips,1,0,2023-06-16 16:05:43.259000,https://github.com/eulerlab/rave,8,Removing inter-experimental variability from functional data in systems neuroscience,"https://scholar.google.com/scholar?cluster=6596108345516212065&hl=en&as_sdt=0,5",3,2021
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components,3,neurips,1,0,2023-06-16 16:05:43.459000,https://github.com/nveldt/SparseCardDSFM,1,Approximate decomposable submodular function minimization for cardinality-based components,"https://scholar.google.com/scholar?cluster=7765734626612115875&hl=en&as_sdt=0,5",2,2021
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution,5,neurips,0,1,2023-06-16 16:05:43.659000,https://github.com/ars22/meta-learning-eval-id-vs-ood,1,Two sides of meta-learning evaluation: In vs. out of distribution,"https://scholar.google.com/scholar?cluster=3248310209715009715&hl=en&as_sdt=0,5",3,2021
Debiased Visual Question Answering from Feature and Sample Perspectives,19,neurips,7,7,2023-06-16 16:05:43.860000,https://github.com/zhiquan-wen/d-vqa,20,Debiased visual question answering from feature and sample perspectives,"https://scholar.google.com/scholar?cluster=9092713122749845551&hl=en&as_sdt=0,5",0,2021
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness,7,neurips,3,2,2023-06-16 16:05:44.060000,https://github.com/jie-ren/a-unified-game-theoretic-interpretation-of-adversarial-robustness,18,Towards a unified game-theoretic view of adversarial perturbations and robustness,"https://scholar.google.com/scholar?cluster=10405183538906234310&hl=en&as_sdt=0,5",1,2021
On the Out-of-distribution Generalization of Probabilistic Image Modelling,21,neurips,0,0,2023-06-16 16:05:44.260000,https://github.com/zmtomorrow/nelloc,9,On the out-of-distribution generalization of probabilistic image modelling,"https://scholar.google.com/scholar?cluster=16600938628354788442&hl=en&as_sdt=0,5",1,2021
Information Directed Reward Learning for Reinforcement Learning,6,neurips,1,0,2023-06-16 16:05:44.460000,https://github.com/david-lindner/idrl,9,Information directed reward learning for reinforcement learning,"https://scholar.google.com/scholar?cluster=8772252576862267451&hl=en&as_sdt=0,47",4,2021
SSMF: Shifting Seasonal Matrix Factorization,2,neurips,2,0,2023-06-16 16:05:44.668000,https://github.com/kokikwbt/ssmf,10,Ssmf: Shifting seasonal matrix factorization,"https://scholar.google.com/scholar?cluster=11697569962161025412&hl=en&as_sdt=0,6",1,2021
Robust and differentially private mean estimation,40,neurips,0,0,2023-06-16 16:05:44.871000,https://github.com/xiyangl3/robust_dp,3,Robust and differentially private mean estimation,"https://scholar.google.com/scholar?cluster=4295339113216361062&hl=en&as_sdt=0,5",2,2021
Adaptable Agent Populations via a Generative Model of Policies,8,neurips,0,2,2023-06-16 16:05:45.071000,https://github.com/kennyderek/adap,11,Adaptable agent populations via a generative model of policies,"https://scholar.google.com/scholar?cluster=11064961923408119459&hl=en&as_sdt=0,5",2,2021
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity,11,neurips,3,3,2023-06-16 16:05:45.272000,https://github.com/bcmi/tramas-weak-shot-object-detection,50,Mixed supervised object detection by transferring mask prior and semantic similarity,"https://scholar.google.com/scholar?cluster=809819108668093612&hl=en&as_sdt=0,5",7,2021
IQ-Learn: Inverse soft-Q Learning for Imitation,43,neurips,26,5,2023-06-16 16:05:45.471000,https://github.com/Div99/IQ-Learn,135,Iq-learn: Inverse soft-q learning for imitation,"https://scholar.google.com/scholar?cluster=267480393884738505&hl=en&as_sdt=0,10",2,2021
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data,6,neurips,2,0,2023-06-16 16:05:45.672000,https://github.com/kaist-dmlab/taufe,7,Task-agnostic undesirable feature deactivation using out-of-distribution data,"https://scholar.google.com/scholar?cluster=15884866726240245144&hl=en&as_sdt=0,21",2,2021
Speedy Performance Estimation for Neural Architecture Search,19,neurips,0,1,2023-06-16 16:05:45.872000,https://github.com/rubinxin/TSE,8,Speedy performance estimation for neural architecture search,"https://scholar.google.com/scholar?cluster=12649354892939725087&hl=en&as_sdt=0,5",1,2021
Environment Generation for Zero-Shot Compositional Reinforcement Learning,19,neurips,7321,1026,2023-06-16 16:05:46.078000,https://github.com/google-research/google-research,29786,Environment generation for zero-shot compositional reinforcement learning,"https://scholar.google.com/scholar?cluster=4049956378759656568&hl=en&as_sdt=0,5",727,2021
Optimizing Conditional Value-At-Risk of Black-Box Functions,10,neurips,2,0,2023-06-16 16:05:46.278000,https://github.com/qphong/bayesopt-lv,1,Optimizing conditional value-at-risk of black-box functions,"https://scholar.google.com/scholar?cluster=1243167075412658030&hl=en&as_sdt=0,5",1,2021
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning,22,neurips,7,1,2023-06-16 16:05:46.479000,https://github.com/chongjiange/care,116,Revitalizing cnn attention via transformers in self-supervised visual representation learning,"https://scholar.google.com/scholar?cluster=11137326961804977691&hl=en&as_sdt=0,10",6,2021
Learning to Learn Graph Topologies,15,neurips,3,0,2023-06-16 16:05:46.680000,https://github.com/xpuoxford/l2g-neurips2021,18,Learning to learn graph topologies,"https://scholar.google.com/scholar?cluster=6887973786384581527&hl=en&as_sdt=0,5",2,2021
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks,18,neurips,9,0,2023-06-16 16:05:46.879000,https://github.com/rainorangelemon/gnn-motion-planning,61,Reducing collision checking for sampling-based motion planning using graph neural networks,"https://scholar.google.com/scholar?cluster=15148652525294899591&hl=en&as_sdt=0,5",5,2021
Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons,1,neurips,0,0,2023-06-16 16:05:47.079000,https://github.com/wenboren/multi-wise-ranking,0,Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons,"https://scholar.google.com/scholar?cluster=2656146730518033072&hl=en&as_sdt=0,33",1,2021
Efficient Bayesian network structure learning via local Markov boundary search,7,neurips,0,0,2023-06-16 16:05:47.279000,https://github.com/minggao97/tam,0,Efficient Bayesian network structure learning via local Markov boundary search,"https://scholar.google.com/scholar?cluster=9088410418328444112&hl=en&as_sdt=0,5",2,2021
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention,36,neurips,13,3,2023-06-16 16:05:47.479000,https://github.com/egyptdj/stagin,53,Learning dynamic graph representation of brain connectome with spatio-temporal attention,"https://scholar.google.com/scholar?cluster=4519412816058293652&hl=en&as_sdt=0,21",2,2021
Understanding the Generalization Benefit of Model Invariance from a Data Perspective,15,neurips,0,0,2023-06-16 16:05:47.679000,https://github.com/bangann/understanding-invariance,1,Understanding the generalization benefit of model invariance from a data perspective,"https://scholar.google.com/scholar?cluster=6413093922837759333&hl=en&as_sdt=0,29",2,2021
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?,20,neurips,2,1,2023-06-16 16:05:47.880000,https://github.com/dongxinshuai/rift-neurips2021,10,How should pre-trained language models be fine-tuned towards adversarial robustness?,"https://scholar.google.com/scholar?cluster=6181501372653861648&hl=en&as_sdt=0,36",5,2021
Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks,3,neurips,2,0,2023-06-16 16:05:48.080000,https://github.com/robert-lieck/rbn,12,Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks,"https://scholar.google.com/scholar?cluster=7508062902599380178&hl=en&as_sdt=0,5",1,2021
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration,17,neurips,5,0,2023-06-16 16:05:48.280000,https://github.com/gavinkerrigan/conf_matrix_and_calibration,7,Combining human predictions with model probabilities via confusion matrices and calibration,"https://scholar.google.com/scholar?cluster=12718168671257506616&hl=en&as_sdt=0,5",2,2021
Probabilistic Attention for Interactive Segmentation,2,neurips,9,0,2023-06-16 16:05:48.481000,https://github.com/apple/ml-probabilistic-attention,21,Probabilistic attention for interactive segmentation,"https://scholar.google.com/scholar?cluster=6574265597018003751&hl=en&as_sdt=0,47",6,2021
Pruning Randomly Initialized Neural Networks with Iterative Randomization,14,neurips,2,0,2023-06-16 16:05:48.680000,https://github.com/dchiji-ntt/iterand,9,Pruning randomly initialized neural networks with iterative randomization,"https://scholar.google.com/scholar?cluster=11749710093845056800&hl=en&as_sdt=0,7",2,2021
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization,13,neurips,1,0,2023-06-16 16:05:48.880000,https://github.com/baofff/stability_ho,2,Stability and generalization of bilevel programming in hyperparameter optimization,"https://scholar.google.com/scholar?cluster=3805382994554865062&hl=en&as_sdt=0,5",1,2021
Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection Strategies,21,neurips,8,0,2023-06-16 16:05:49.080000,https://github.com/Rondorf/BOReL,20,Offline Meta Reinforcement Learning--Identifiability Challenges and Effective Data Collection Strategies,"https://scholar.google.com/scholar?cluster=3592419884384460621&hl=en&as_sdt=0,5",3,2021
Flexible Option Learning,11,neurips,0,0,2023-06-16 16:05:49.280000,https://github.com/mklissa/moc,7,Flexible option learning,"https://scholar.google.com/scholar?cluster=1622137245379658654&hl=en&as_sdt=0,15",2,2021
Credit Assignment in Neural Networks through Deep Feedback Control,16,neurips,1,0,2023-06-16 16:05:49.480000,https://github.com/meulemansalex/deep_feedback_control,6,Credit assignment in neural networks through deep feedback control,"https://scholar.google.com/scholar?cluster=10215619151456904513&hl=en&as_sdt=0,33",2,2021
Neural Additive Models: Interpretable Machine Learning with Neural Nets,245,neurips,6,0,2023-06-16 16:05:49.680000,https://github.com/lemeln/nam,14,Neural additive models: Interpretable machine learning with neural nets,"https://scholar.google.com/scholar?cluster=14127065231811177587&hl=en&as_sdt=0,18",0,2021
Kernel Functional Optimisation,4,neurips,0,1,2023-06-16 16:05:49.880000,https://github.com/mailtoarunkumarav/kernelfunctionaloptimisation,2,Kernel functional optimisation,"https://scholar.google.com/scholar?cluster=9446899252048844733&hl=en&as_sdt=0,13",1,2021
Generalized Shape Metrics on Neural Representations,22,neurips,8,2,2023-06-16 16:05:50.082000,https://github.com/ahwillia/netrep,83,Generalized shape metrics on neural representations,"https://scholar.google.com/scholar?cluster=3294259291908791528&hl=en&as_sdt=0,39",3,2021
Towards Robust Bisimulation Metric Learning,20,neurips,2,0,2023-06-16 16:05:50.282000,https://github.com/metekemertas/RobustBisimulation,6,Towards robust bisimulation metric learning,"https://scholar.google.com/scholar?cluster=167387616529603590&hl=en&as_sdt=0,5",2,2021
Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning,22,neurips,1,0,2023-06-16 16:05:50.482000,https://github.com/EkdeepSLubana/BeyondBatchNorm,16,Beyond batchnorm: Towards a unified understanding of normalization in deep learning,"https://scholar.google.com/scholar?cluster=2227521021573022102&hl=en&as_sdt=0,31",3,2021
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training,5,neurips,1,0,2023-06-16 16:05:50.683000,https://github.com/npminh12/nn-clt,0,Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training,"https://scholar.google.com/scholar?cluster=17789162731650605846&hl=en&as_sdt=0,36",2,2021
Medical Dead-ends and Learning to Identify High-Risk States and Treatments,21,neurips,15,0,2023-06-16 16:05:50.882000,https://github.com/microsoft/med-deadend,43,Medical dead-ends and learning to identify high-risk states and treatments,"https://scholar.google.com/scholar?cluster=7718917214677411862&hl=en&as_sdt=0,34",6,2021
Batch Normalization Orthogonalizes Representations in Deep Random Networks,20,neurips,1,0,2023-06-16 16:05:51.080000,https://github.com/hadidaneshmand/batchnorm21,3,Batch normalization orthogonalizes representations in deep random networks,"https://scholar.google.com/scholar?cluster=8201984774954479451&hl=en&as_sdt=0,5",1,2021
Support vector machines and linear regression coincide with very high-dimensional features,15,neurips,0,0,2023-06-16 16:05:51.281000,https://github.com/scO0rpion/SVM-Proliferation-NIPS2021,1,Support vector machines and linear regression coincide with very high-dimensional features,"https://scholar.google.com/scholar?cluster=9835458066237043881&hl=en&as_sdt=0,5",2,2021
Offline RL Without Off-Policy Evaluation,67,neurips,1,4,2023-06-16 16:05:51.481000,https://github.com/davidbrandfonbrener/onestep-rl,27,Offline rl without off-policy evaluation,"https://scholar.google.com/scholar?cluster=16078097822784982755&hl=en&as_sdt=0,47",2,2021
Continuous vs. Discrete Optimization of Deep Neural Networks,17,neurips,0,0,2023-06-16 16:05:51.680000,https://github.com/elkabzo/cont_disc_opt_dnn,0,Continuous vs. discrete optimization of deep neural networks,"https://scholar.google.com/scholar?cluster=6909198963909680227&hl=en&as_sdt=0,5",3,2021
Can contrastive learning avoid shortcut solutions?,60,neurips,2,1,2023-06-16 16:05:51.884000,https://github.com/joshr17/IFM,45,Can contrastive learning avoid shortcut solutions?,"https://scholar.google.com/scholar?cluster=705841367969128558&hl=en&as_sdt=0,5",1,2021
Convex Polytope Trees,1,neurips,1,0,2023-06-16 16:05:52.115000,https://github.com/rezaarmand/Convex_Polytope_Trees,2,Convex Polytope Trees,"https://scholar.google.com/scholar?cluster=2959989804162360758&hl=en&as_sdt=0,44",1,2021
Noisy Recurrent Neural Networks,27,neurips,1,1,2023-06-16 16:05:52.315000,https://github.com/erichson/NoisyRNN,1,Noisy recurrent neural networks,"https://scholar.google.com/scholar?cluster=6463637827089951262&hl=en&as_sdt=0,33",2,2021
Matrix encoding networks for neural combinatorial optimization,20,neurips,10,1,2023-06-16 16:05:52.515000,https://github.com/yd-kwon/MatNet,45,Matrix encoding networks for neural combinatorial optimization,"https://scholar.google.com/scholar?cluster=13176466295428561186&hl=en&as_sdt=0,33",4,2021
Continuous Latent Process Flows,8,neurips,5,0,2023-06-16 16:05:52.716000,https://github.com/borealisai/continuous-latent-process-flows,8,Continuous latent process flows,"https://scholar.google.com/scholar?cluster=10696451274107290963&hl=en&as_sdt=0,45",2,2021
Dataset Distillation with Infinitely Wide Convolutional Networks,87,neurips,7321,1026,2023-06-16 16:05:52.916000,https://github.com/google-research/google-research,29786,Dataset distillation with infinitely wide convolutional networks,"https://scholar.google.com/scholar?cluster=5517336236766100405&hl=en&as_sdt=0,39",727,2021
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search,16,neurips,559,118,2023-06-16 16:05:53.117000,https://github.com/Microsoft/SPTAG,4539,Spann: Highly-efficient billion-scale approximate nearest neighborhood search,"https://scholar.google.com/scholar?cluster=17393178550199669476&hl=en&as_sdt=0,5",140,2021
Analysis of one-hidden-layer neural networks via the resolvent method,3,neurips,0,0,2023-06-16 16:05:53.317000,https://github.com/wirhabenzeit/nonlinearRMT,0,Analysis of one-hidden-layer neural networks via the resolvent method,"https://scholar.google.com/scholar?cluster=1141084690647947388&hl=en&as_sdt=0,43",1,2021
Grounding Spatio-Temporal Language with Transformers,10,neurips,0,0,2023-06-16 16:05:53.516000,https://github.com/flowersteam/spatio-temporal-language-transformers,8,Grounding spatio-temporal language with transformers,"https://scholar.google.com/scholar?cluster=7814702552809480292&hl=en&as_sdt=0,14",7,2021
Learning where to learn: Gradient sparsity in meta and continual learning,29,neurips,4,1,2023-06-16 16:05:53.716000,https://github.com/johswald/learning_where_to_learn,31,Learning where to learn: Gradient sparsity in meta and continual learning,"https://scholar.google.com/scholar?cluster=15647321533147892633&hl=en&as_sdt=0,5",2,2021
Domain Invariant Representation Learning with Domain Density Transformations,35,neurips,1,0,2023-06-16 16:05:53.917000,https://github.com/atuannguyen/dirt,9,Domain invariant representation learning with domain density transformations,"https://scholar.google.com/scholar?cluster=12877601023534457317&hl=en&as_sdt=0,33",1,2021
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning,10,neurips,5,2,2023-06-16 16:05:54.117000,https://github.com/microsoft/Playvirtual,14,Playvirtual: Augmenting cycle-consistent virtual trajectories for reinforcement learning,"https://scholar.google.com/scholar?cluster=13710133509096551909&hl=en&as_sdt=0,38",5,2021
Efficient Equivariant Network,12,neurips,1,0,2023-06-16 16:05:54.320000,https://github.com/LingshenHe/Efficient-Equivariant-Network,9,Efficient equivariant network,"https://scholar.google.com/scholar?cluster=547182555419234548&hl=en&as_sdt=0,33",2,2021
Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation,4,neurips,0,0,2023-06-16 16:05:54.519000,https://github.com/Kennethborup/self_distillation,15,Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation,"https://scholar.google.com/scholar?cluster=8987467992945921645&hl=en&as_sdt=0,5",4,2021
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition,21,neurips,22,9,2023-06-16 16:05:54.719000,https://github.com/lucaslie/torchprune,146,Compressing neural networks: Towards determining the optimal layer-wise decomposition,"https://scholar.google.com/scholar?cluster=11443977889418286525&hl=en&as_sdt=0,15",5,2021
Accurate Point Cloud Registration with Robust Optimal Transport,13,neurips,13,2,2023-06-16 16:05:54.919000,https://github.com/uncbiag/shapmagn,84,Accurate point cloud registration with robust optimal transport,"https://scholar.google.com/scholar?cluster=15753020473046072321&hl=en&as_sdt=0,14",6,2021
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions,9,neurips,0,0,2023-06-16 16:05:55.119000,https://github.com/zib-iol/fw-generalized-selfconcordant,0,Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions,"https://scholar.google.com/scholar?cluster=1532722627115622764&hl=en&as_sdt=0,48",1,2021
Automatic Data Augmentation for Generalization in Reinforcement Learning,38,neurips,19,1,2023-06-16 16:05:55.319000,https://github.com/rraileanu/auto-drac,97,Automatic data augmentation for generalization in reinforcement learning,"https://scholar.google.com/scholar?cluster=11787479877857738831&hl=en&as_sdt=0,50",6,2021
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration,17,neurips,6,0,2023-06-16 16:05:55.519000,https://github.com/inria-thoth/t3sc,18,A trainable spectral-spatial sparse coding model for hyperspectral image restoration,"https://scholar.google.com/scholar?cluster=14845341365243064096&hl=en&as_sdt=0,5",0,2021
MarioNette: Self-Supervised Sprite Learning,28,neurips,6,0,2023-06-16 16:05:55.718000,https://github.com/dmsm/MarioNette,29,Marionette: Self-supervised sprite learning,"https://scholar.google.com/scholar?cluster=4806143850107186086&hl=en&as_sdt=0,5",1,2021
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem,8,neurips,4890,2916,2023-06-16 16:05:55.917000,https://github.com/ray-project/ray,26189,RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem,"https://scholar.google.com/scholar?cluster=4240571206448451235&hl=en&as_sdt=0,4",450,2021
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction,5,neurips,0,0,2023-06-16 16:05:56.116000,https://github.com/nvlabs/bcts,2,Improve agents without retraining: Parallel tree search with off-policy correction,"https://scholar.google.com/scholar?cluster=15142203700069682566&hl=en&as_sdt=0,44",6,2021
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems,5,neurips,2,0,2023-06-16 16:05:56.316000,https://github.com/lcs2-iiitd/transevolve,10,Redesigning the transformer architecture with insights from multi-particle dynamical systems,"https://scholar.google.com/scholar?cluster=10864040246145849746&hl=en&as_sdt=0,5",3,2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks,50,neurips,1,0,2023-06-16 16:05:56.515000,https://github.com/HanxunH/RobustWRN,30,Exploring architectural ingredients of adversarially robust deep neural networks,"https://scholar.google.com/scholar?cluster=17017038540474728130&hl=en&as_sdt=0,5",1,2021
Center Smoothing: Certified Robustness for Networks with Structured Outputs,8,neurips,1,0,2023-06-16 16:05:56.714000,https://github.com/aounon/center-smoothing,4,Center smoothing: Certified robustness for networks with structured outputs,"https://scholar.google.com/scholar?cluster=6774778402376683053&hl=en&as_sdt=0,5",1,2021
"Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex",12,neurips,3,2,2023-06-16 16:05:56.913000,https://github.com/colinconwell/deepmousetrap,16,"Neural regression, representational similarity, model zoology & neural taskonomy at scale in rodent visual cortex","https://scholar.google.com/scholar?cluster=14703235667751909226&hl=en&as_sdt=0,39",1,2021
Parameter Inference with Bifurcation Diagrams,1,neurips,2,6,2023-06-16 16:05:57.113000,https://github.com/gszep/BifurcationInference.jl,26,Parameter Inference with Bifurcation Diagrams,"https://scholar.google.com/scholar?cluster=11587125408302818135&hl=en&as_sdt=0,20",3,2021
Similarity and Matching of Neural Network Representations,25,neurips,1,0,2023-06-16 16:05:57.312000,https://github.com/renyi-ai/drfrankenstein,9,Similarity and matching of neural network representations,"https://scholar.google.com/scholar?cluster=18028760850112175257&hl=en&as_sdt=0,5",4,2021
DOCTOR: A Simple Method for Detecting Misclassification Errors,19,neurips,3,2,2023-06-16 16:05:57.511000,https://github.com/doctor-public-submission/DOCTOR,19,Doctor: A simple method for detecting misclassification errors,"https://scholar.google.com/scholar?cluster=17068138253074503270&hl=en&as_sdt=0,34",2,2021
Contrastive Laplacian Eigenmaps,21,neurips,2,1,2023-06-16 16:05:57.711000,https://github.com/allenhaozhu/coles,18,Contrastive laplacian eigenmaps,"https://scholar.google.com/scholar?cluster=17149806302685325367&hl=en&as_sdt=0,5",1,2021
Shape Registration in the Time of Transformers,19,neurips,5,0,2023-06-16 16:05:57.910000,https://github.com/GiovanniTRA/transmatching,21,Shape registration in the time of transformers,"https://scholar.google.com/scholar?cluster=7252503647497259902&hl=en&as_sdt=0,5",4,2021
Dissecting the Diffusion Process in Linear Graph Convolutional Networks,29,neurips,4,1,2023-06-16 16:05:58.110000,https://github.com/yifeiwang77/dgc,12,Dissecting the diffusion process in linear graph convolutional networks,"https://scholar.google.com/scholar?cluster=953644699740016159&hl=en&as_sdt=0,10",1,2021
Dynamic Grained Encoder for Vision Transformers,12,neurips,2,2,2023-06-16 16:05:58.310000,https://github.com/stevengrove/vtpack,28,Dynamic grained encoder for vision transformers,"https://scholar.google.com/scholar?cluster=2925930572923827932&hl=en&as_sdt=0,1",1,2021
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning,28,neurips,0,0,2023-06-16 16:05:58.509000,https://github.com/nzw0301/Understanding-Negative-Samples,6,Understanding negative samples in instance discriminative self-supervised representation learning,"https://scholar.google.com/scholar?cluster=280361585391691198&hl=en&as_sdt=0,6",1,2021
On UMAP's True Loss Function,18,neurips,2,0,2023-06-16 16:05:58.709000,https://github.com/hci-unihd/UMAPs-true-loss,6,On UMAP's true loss function,"https://scholar.google.com/scholar?cluster=13625192232753067686&hl=en&as_sdt=0,39",1,2021
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data ,9,neurips,0,0,2023-06-16 16:05:58.908000,https://github.com/fengliu90/MetaTesting,5,Meta two-sample testing: Learning kernels for testing with limited data,"https://scholar.google.com/scholar?cluster=3537368320170973148&hl=en&as_sdt=0,11",1,2021
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE,2,neurips,0,0,2023-06-16 16:05:59.107000,https://github.com/aiqz/bype-vae,6,ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE,"https://scholar.google.com/scholar?cluster=11014089413900793097&hl=en&as_sdt=0,5",1,2021
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition,19,neurips,4,0,2023-06-16 16:05:59.307000,https://github.com/wustl-cig/pnp-recovery,7,Recovery analysis for plug-and-play priors using the restricted eigenvalue condition,"https://scholar.google.com/scholar?cluster=6589504408297538842&hl=en&as_sdt=0,34",3,2021
Group Equivariant Subsampling,10,neurips,1,0,2023-06-16 16:05:59.506000,https://github.com/jinxu06/gsubsampling,15,Group equivariant subsampling,"https://scholar.google.com/scholar?cluster=5738105186247068728&hl=en&as_sdt=0,5",2,2021
Data Sharing and Compression for Cooperative Networked Control,9,neurips,0,0,2023-06-16 16:05:59.705000,https://github.com/chengjiangnan/cooperative_networked_control,3,Data sharing and compression for cooperative networked control,"https://scholar.google.com/scholar?cluster=14181089307501854409&hl=en&as_sdt=0,3",1,2021
Hyperbolic Procrustes Analysis Using Riemannian Geometry,4,neurips,1,1,2023-06-16 16:05:59.904000,https://github.com/ronentalmonlab/hyperbolicprocrustesanalysis,2,Hyperbolic Procrustes Analysis Using Riemannian Geometry,"https://scholar.google.com/scholar?cluster=7536383024640437829&hl=en&as_sdt=0,47",1,2021
Improving Contrastive Learning on Imbalanced Data via Open-World Sampling,16,neurips,2,0,2023-06-16 16:06:00.103000,https://github.com/vita-group/mak,26,Improving contrastive learning on imbalanced data via open-world sampling,"https://scholar.google.com/scholar?cluster=3568757840495479770&hl=en&as_sdt=0,44",7,2021
Multi-Person 3D Motion Prediction with Multi-Range Transformers,16,neurips,5,1,2023-06-16 16:06:00.303000,https://github.com/jiashunwang/MRT,52,Multi-person 3D motion prediction with multi-range transformers,"https://scholar.google.com/scholar?cluster=10505346865379052907&hl=en&as_sdt=0,39",2,2021
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds,1,neurips,2,3,2023-06-16 16:06:00.502000,https://github.com/pearsonlab/bubblewrap,4,Bubblewrap: Online tiling and real-time flow prediction on neural manifolds,"https://scholar.google.com/scholar?cluster=10067153401508770550&hl=en&as_sdt=0,31",4,2021
Learning to Combine Per-Example Solutions for Neural Program Synthesis,5,neurips,2,0,2023-06-16 16:06:00.702000,https://github.com/shrivastavadisha/N-PEPS,18,Learning to combine per-example solutions for neural program synthesis,"https://scholar.google.com/scholar?cluster=1667137904448964441&hl=en&as_sdt=0,41",1,2021
On Success and Simplicity: A Second Look at Transferable Targeted Attacks,43,neurips,8,0,2023-06-16 16:06:00.901000,https://github.com/ZhengyuZhao/Targeted-Tansfer,38,On success and simplicity: A second look at transferable targeted attacks,"https://scholar.google.com/scholar?cluster=8748504809749727274&hl=en&as_sdt=0,5",1,2021
Learning Causal Semantic Representation for Out-of-Distribution Prediction,45,neurips,3,0,2023-06-16 16:06:01.101000,https://github.com/changliu00/causal-semantic-generative-model,62,Learning causal semantic representation for out-of-distribution prediction,"https://scholar.google.com/scholar?cluster=8202256397627886972&hl=en&as_sdt=0,31",2,2021
Conformal Time-series Forecasting,39,neurips,11,1,2023-06-16 16:06:01.300000,https://github.com/kamilest/conformal-rnn,45,Conformal time-series forecasting,"https://scholar.google.com/scholar?cluster=5073869937636714274&hl=en&as_sdt=0,3",3,2021
A 3D Generative Model for Structure-Based Drug Design,55,neurips,37,6,2023-06-16 16:06:01.499000,https://github.com/luost26/3d-generative-sbdd,139,A 3D generative model for structure-based drug design,"https://scholar.google.com/scholar?cluster=6836358933346454027&hl=en&as_sdt=0,5",15,2021
Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference,9,neurips,2,1,2023-06-16 16:06:01.702000,https://github.com/kennethwdk/pinet,14,Robust pose estimation in crowded scenes with direct pose-level inference,"https://scholar.google.com/scholar?cluster=9963375473361085203&hl=en&as_sdt=0,47",1,2021
Conformal Prediction using Conditional Histograms,24,neurips,2,1,2023-06-16 16:06:01.903000,https://github.com/msesia/chr,16,Conformal prediction using conditional histograms,"https://scholar.google.com/scholar?cluster=18022084762703462978&hl=en&as_sdt=0,5",1,2021
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization,4,neurips,0,0,2023-06-16 16:06:02.103000,https://github.com/rghosh92/n2n,0,Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization,"https://scholar.google.com/scholar?cluster=10271494152241252872&hl=en&as_sdt=0,5",2,2021
Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement,28,neurips,1,1,2023-06-16 16:06:02.304000,https://github.com/amingwu/svd-dictionary-enhancement,10,Generalized and discriminative few-shot object detection via SVD-dictionary enhancement,"https://scholar.google.com/scholar?cluster=5723968759372478905&hl=en&as_sdt=0,5",3,2021
Conditioning Sparse Variational Gaussian Processes for Online Decision-making,18,neurips,3,2,2023-06-16 16:06:02.506000,https://github.com/wjmaddox/online_vargp,19,Conditioning sparse variational gaussian processes for online decision-making,"https://scholar.google.com/scholar?cluster=4727485038673276351&hl=en&as_sdt=0,11",1,2021
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems,11,neurips,1,0,2023-06-16 16:06:02.705000,https://github.com/mkofinas/locs,20,Roto-translated local coordinate frames for interacting dynamical systems,"https://scholar.google.com/scholar?cluster=4389798723436017716&hl=en&as_sdt=0,5",4,2021
Retiring Adult: New Datasets for Fair Machine Learning,154,neurips,14,3,2023-06-16 16:06:02.910000,https://github.com/zykls/folktables,168,Retiring adult: New datasets for fair machine learning,"https://scholar.google.com/scholar?cluster=4475275989640781366&hl=en&as_sdt=0,5",6,2021
Cardinality constrained submodular maximization for random streams,6,neurips,0,0,2023-06-16 16:06:03.157000,https://github.com/where-is-paul/submodular-streaming,0,Cardinality constrained submodular maximization for random streams,"https://scholar.google.com/scholar?cluster=3566616688572088469&hl=en&as_sdt=0,48",1,2021
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation,15,neurips,2,1,2023-06-16 16:06:03.357000,https://github.com/jixuan-wang/grad2task,14,Grad2task: Improved few-shot text classification using gradients for task representation,"https://scholar.google.com/scholar?cluster=16326528771354336170&hl=en&as_sdt=0,10",3,2021
A variational approximate posterior for the deep Wishart process,1,neurips,6,2,2023-06-16 16:06:03.558000,https://github.com/LaurenceA/bayesfunc,12,A variational approximate posterior for the deep Wishart process,"https://scholar.google.com/scholar?cluster=12807465440035985886&hl=en&as_sdt=0,33",3,2021
Neural Tangent Kernel Maximum Mean Discrepancy,15,neurips,1,0,2023-06-16 16:06:03.757000,https://github.com/xycheng/NTK-MMD,2,Neural tangent kernel maximum mean discrepancy,"https://scholar.google.com/scholar?cluster=12192272068722232202&hl=en&as_sdt=0,43",1,2021
Subgraph Federated Learning with Missing Neighbor Generation,46,neurips,12,2,2023-06-16 16:06:03.957000,https://github.com/zkhku/fedsage,46,Subgraph federated learning with missing neighbor generation,"https://scholar.google.com/scholar?cluster=6545450769549258065&hl=en&as_sdt=0,34",2,2021
Sub-Linear Memory: How to Make Performers SLiM,13,neurips,7321,1026,2023-06-16 16:06:04.156000,https://github.com/google-research/google-research,29786,Sub-linear memory: How to make performers slim,"https://scholar.google.com/scholar?cluster=1235739226041970723&hl=en&as_sdt=0,22",727,2021
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization,9,neurips,4,0,2023-06-16 16:06:04.356000,https://github.com/devnkong/VQ-GNN,19,VQ-GNN: A universal framework to scale up graph neural networks using vector quantization,"https://scholar.google.com/scholar?cluster=7465359431482590053&hl=en&as_sdt=0,33",2,2021
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima,42,neurips,7,7,2023-06-16 16:06:04.559000,https://github.com/moukamisama/f2m,29,Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima,"https://scholar.google.com/scholar?cluster=13513065360011314265&hl=en&as_sdt=0,14",3,2021
"Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks",19,neurips,1,0,2023-06-16 16:06:04.758000,https://github.com/tolgabirdal/phdimgeneralization,17,"Intrinsic dimension, persistent homology and generalization in neural networks","https://scholar.google.com/scholar?cluster=6053095805266781547&hl=en&as_sdt=0,47",4,2021
GemNet: Universal Directional Graph Neural Networks for Molecules,95,neurips,23,0,2023-06-16 16:06:04.957000,https://github.com/TUM-DAML/gemnet_pytorch,139,Gemnet: Universal directional graph neural networks for molecules,"https://scholar.google.com/scholar?cluster=17365183675502729479&hl=en&as_sdt=0,34",4,2021
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning,9,neurips,3,0,2023-06-16 16:06:05.157000,https://github.com/mandt-lab/variational-beam-search,7,Detecting and adapting to irregular distribution shifts in bayesian online learning,"https://scholar.google.com/scholar?cluster=8682460145444593023&hl=en&as_sdt=0,11",3,2021
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions,12,neurips,8,0,2023-06-16 16:06:05.357000,https://github.com/mahuanaaa/monig,26,Trustworthy multimodal regression with mixture of normal-inverse gamma distributions,"https://scholar.google.com/scholar?cluster=4055725857500470289&hl=en&as_sdt=0,11",2,2021
Does Knowledge Distillation Really Work?,89,neurips,2,1,2023-06-16 16:06:05.556000,https://github.com/samuelstanton/gnosis,28,Does knowledge distillation really work?,"https://scholar.google.com/scholar?cluster=14465818591986091867&hl=en&as_sdt=0,15",5,2021
Teachable Reinforcement Learning via Advice Distillation,0,neurips,4,2,2023-06-16 16:06:05.756000,https://github.com/rll-research/teachable,14,Teachable Reinforcement Learning via Advice Distillation,"https://scholar.google.com/scholar?cluster=2130873946833920299&hl=en&as_sdt=0,5",1,2021
Antipodes of Label Differential Privacy: PATE and ALIBI,21,neurips,3,0,2023-06-16 16:06:05.959000,https://github.com/facebookresearch/label_dp_antipodes,22,Antipodes of label differential privacy: Pate and alibi,"https://scholar.google.com/scholar?cluster=8767021277999281936&hl=en&as_sdt=0,47",11,2021
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases,9,neurips,0,0,2023-06-16 16:06:06.158000,https://github.com/kreimanlab/VisualSearchAsymmetry,2,Visual search asymmetry: Deep nets and humans share similar inherent biases,"https://scholar.google.com/scholar?cluster=4659542011867306284&hl=en&as_sdt=0,5",4,2021
On the Universality of Graph Neural Networks on Large Random Graphs,18,neurips,1,0,2023-06-16 16:06:06.357000,https://github.com/nkeriven/random-graph-gnn,12,On the universality of graph neural networks on large random graphs,"https://scholar.google.com/scholar?cluster=16885293553955687964&hl=en&as_sdt=0,39",1,2021
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation,9,neurips,5,1,2023-06-16 16:06:06.560000,https://github.com/xingchenwan/grabnel,12,Adversarial attacks on graph classifiers via bayesian optimisation,"https://scholar.google.com/scholar?cluster=13672846858663173728&hl=en&as_sdt=0,39",2,2021
Do Wider Neural Networks Really Help Adversarial Robustness?,54,neurips,92,18,2023-06-16 16:06:06.759000,https://github.com/fra31/auto-attack,525,Do wider neural networks really help adversarial robustness?,"https://scholar.google.com/scholar?cluster=11340118178463211034&hl=en&as_sdt=0,36",9,2021
ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning,39,neurips,12,0,2023-06-16 16:06:06.960000,https://github.com/leehyuck/abc,27,Abc: Auxiliary balanced classifier for class-imbalanced semi-supervised learning,"https://scholar.google.com/scholar?cluster=866707790595664862&hl=en&as_sdt=0,5",2,2021
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery,25,neurips,3,1,2023-06-16 16:06:07.160000,https://github.com/ermongroup/bcd-nets,16,Bcd nets: Scalable variational approaches for bayesian causal discovery,"https://scholar.google.com/scholar?cluster=11629795294538646215&hl=en&as_sdt=0,5",7,2021
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks,4,neurips,2,1,2023-06-16 16:06:07.361000,https://github.com/bit-ml/dyreg-gnn,13,Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks,"https://scholar.google.com/scholar?cluster=2805094251623106386&hl=en&as_sdt=0,31",7,2021
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective,25,neurips,2,0,2023-06-16 16:06:07.561000,https://github.com/XuZhengzhuo/Prior-LT,18,Towards calibrated model for long-tailed visual recognition from prior perspective,"https://scholar.google.com/scholar?cluster=6176357525682720979&hl=en&as_sdt=0,44",1,2021
Learning to Draw: Emergent Communication through Sketching,8,neurips,1,0,2023-06-16 16:06:07.760000,https://github.com/Ddaniela13/LearningToDraw,20,Learning to draw: Emergent communication through sketching,"https://scholar.google.com/scholar?cluster=7936219275341815856&hl=en&as_sdt=0,47",2,2021
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose,11,neurips,3,0,2023-06-16 16:06:07.959000,https://github.com/angtian/neuralvs,20,Neural view synthesis and matching for semi-supervised few-shot learning of 3d pose,"https://scholar.google.com/scholar?cluster=7966798121022187733&hl=en&as_sdt=0,33",2,2021
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning,85,neurips,34,1,2023-06-16 16:06:08.158000,https://github.com/Princeton-SysML/GradAttack,163,Evaluating gradient inversion attacks and defenses in federated learning,"https://scholar.google.com/scholar?cluster=921667981702285218&hl=en&as_sdt=0,44",4,2021
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification,43,neurips,96,3,2023-06-16 16:06:08.358000,https://github.com/amzn/pecos,442,Fast multi-resolution transformer fine-tuning for extreme multi-label text classification,"https://scholar.google.com/scholar?cluster=3453341538236618558&hl=en&as_sdt=0,5",20,2021
HRFormer: High-Resolution Vision Transformer for Dense Predict,64,neurips,63,19,2023-06-16 16:06:08.558000,https://github.com/HRNet/HRFormer,423,Hrformer: High-resolution vision transformer for dense predict,"https://scholar.google.com/scholar?cluster=929504162912042332&hl=en&as_sdt=0,5",14,2021
Manifold Topology Divergence: a Framework for Comparing Data Manifolds. ,11,neurips,0,1,2023-06-16 16:06:08.757000,https://github.com/ilyatrofimov/mtopdiv,11,Manifold Topology Divergence: a Framework for Comparing Data Manifolds.,"https://scholar.google.com/scholar?cluster=17211466672120196882&hl=en&as_sdt=0,33",2,2021
Weak-shot Fine-grained Classification via Similarity Transfer,15,neurips,9,0,2023-06-16 16:06:08.957000,https://github.com/bcmi/SimTrans-Weak-Shot-Classification,60,Weak-shot fine-grained classification via similarity transfer,"https://scholar.google.com/scholar?cluster=9671426641005762258&hl=en&as_sdt=0,39",8,2021
Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders,3,neurips,0,0,2023-06-16 16:06:09.156000,https://github.com/boschresearch/gmm_dae,13,Shape your space: A gaussian mixture regularization approach to deterministic autoencoders,"https://scholar.google.com/scholar?cluster=4949577002012723077&hl=en&as_sdt=0,11",4,2021
Regret Bounds for Gaussian-Process Optimization in Large Domains,2,neurips,0,0,2023-06-16 16:06:09.356000,https://github.com/mwuethri/regret-bounds-for-gaussian-process-optimization-in-large-domains,0,Regret Bounds for Gaussian-Process Optimization in Large Domains,"https://scholar.google.com/scholar?cluster=13958128142984191002&hl=en&as_sdt=0,5",1,2021
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem,49,neurips,10,3,2023-06-16 16:06:09.555000,https://github.com/liangxinedu/neurolkh,36,NeuroLKH: Combining deep learning model with Lin-Kernighan-Helsgaun heuristic for solving the traveling salesman problem,"https://scholar.google.com/scholar?cluster=15742552904375770583&hl=en&as_sdt=0,3",1,2021
Meta-learning with an Adaptive Task Scheduler,20,neurips,1,0,2023-06-16 16:06:09.755000,https://github.com/huaxiuyao/ATS,17,Meta-learning with an adaptive task scheduler,"https://scholar.google.com/scholar?cluster=7034157580850953271&hl=en&as_sdt=0,33",1,2021
Edge Representation Learning with Hypergraphs,24,neurips,5,1,2023-06-16 16:06:09.954000,https://github.com/harryjo97/EHGNN,38,Edge representation learning with hypergraphs,"https://scholar.google.com/scholar?cluster=13386857555344208572&hl=en&as_sdt=0,10",2,2021
One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval,27,neurips,11,2,2023-06-16 16:06:10.155000,https://github.com/AkariAsai/CORA,63,One question answering model for many languages with cross-lingual dense passage retrieval,"https://scholar.google.com/scholar?cluster=2691643624683077982&hl=en&as_sdt=0,5",3,2021
LEADS: Learning Dynamical Systems that Generalize Across Environments,11,neurips,4,0,2023-06-16 16:06:10.354000,https://github.com/yuan-yin/leads,17,LEADS: Learning dynamical systems that generalize across environments,"https://scholar.google.com/scholar?cluster=14202840426672915694&hl=en&as_sdt=0,47",2,2021
Storchastic: A Framework for General Stochastic Automatic Differentiation,9,neurips,5,53,2023-06-16 16:06:10.554000,https://github.com/HEmile/storchastic,155,Storchastic: A framework for general stochastic automatic differentiation,"https://scholar.google.com/scholar?cluster=400914295796581713&hl=en&as_sdt=0,34",7,2021
Robustness of Graph Neural Networks at Scale,41,neurips,6,0,2023-06-16 16:06:10.757000,https://github.com/sigeisler/robustness_of_gnns_at_scale,20,Robustness of graph neural networks at scale,"https://scholar.google.com/scholar?cluster=2310809073193622200&hl=en&as_sdt=0,5",4,2021
Random Noise Defense Against Query-Based Black-Box Attacks,24,neurips,8,1,2023-06-16 16:06:10.959000,https://github.com/SCLBD/BlackboxBench,47,Random noise defense against query-based black-box attacks,"https://scholar.google.com/scholar?cluster=5823403933289238841&hl=en&as_sdt=0,33",2,2021
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL,16,neurips,9,0,2023-06-16 16:06:11.161000,https://github.com/dmirlab-group/sadga,30,Sadga: Structure-aware dual graph aggregation network for text-to-sql,"https://scholar.google.com/scholar?cluster=1414568396267987258&hl=en&as_sdt=0,5",4,2021
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers,42,neurips,2,0,2023-06-16 16:06:11.362000,https://github.com/IDSIA/recurrent-fwp,40,Going beyond linear transformers with recurrent fast weight programmers,"https://scholar.google.com/scholar?cluster=7454464025962811538&hl=en&as_sdt=0,44",10,2021
Proper Value Equivalence,22,neurips,0,0,2023-06-16 16:06:11.564000,https://github.com/chrisgrimm/proper_value_equivalence,5,Proper value equivalence,"https://scholar.google.com/scholar?cluster=9083466870698024082&hl=en&as_sdt=0,5",2,2021
Neural Scene Flow Prior,31,neurips,9,2,2023-06-16 16:06:11.765000,https://github.com/lilac-lee/neural_scene_flow_prior,99,Neural scene flow prior,"https://scholar.google.com/scholar?cluster=8188256741599180302&hl=en&as_sdt=0,5",9,2021
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift,38,neurips,5,2,2023-06-16 16:06:11.966000,https://github.com/automl/nes,26,Neural ensemble search for uncertainty estimation and dataset shift,"https://scholar.google.com/scholar?cluster=11225734588910887046&hl=en&as_sdt=0,5",11,2021
Finding Bipartite Components in Hypergraphs,2,neurips,0,0,2023-06-16 16:06:12.165000,https://github.com/pmacg/hypergraph-bipartite-components,5,Finding Bipartite Components in Hypergraphs,"https://scholar.google.com/scholar?cluster=6321982817275178738&hl=en&as_sdt=0,26",1,2021
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise,30,neurips,1,0,2023-06-16 16:06:12.399000,https://github.com/hongxin001/ODNL,16,Open-set label noise can improve robustness against inherent label noise,"https://scholar.google.com/scholar?cluster=18714998357358816&hl=en&as_sdt=0,33",1,2021
Relational Self-Attention: What's Missing in Attention for Video Understanding,16,neurips,6,4,2023-06-16 16:06:12.606000,https://github.com/KimManjin/RSA,45,Relational self-attention: What's missing in attention for video understanding,"https://scholar.google.com/scholar?cluster=11774709697468302185&hl=en&as_sdt=0,33",2,2021
Towards Enabling Meta-Learning from Target Models,1,neurips,0,0,2023-06-16 16:06:12.806000,https://github.com/njulus/ST,7,Towards enabling meta-learning from target models,"https://scholar.google.com/scholar?cluster=18110537945582791730&hl=en&as_sdt=0,39",1,2021
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement,64,neurips,18,2,2023-06-16 16:06:13.009000,https://github.com/applied-ai-lab/genesis,87,Genesis-v2: Inferring unordered object representations without iterative refinement,"https://scholar.google.com/scholar?cluster=5704050688122267837&hl=en&as_sdt=0,5",4,2021
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning,15,neurips,0,0,2023-06-16 16:06:13.210000,https://github.com/albertometelli/subgaussian-is,1,Subgaussian and differentiable importance sampling for off-policy evaluation and learning,"https://scholar.google.com/scholar?cluster=11613603668630448953&hl=en&as_sdt=0,21",1,2021
Fair Classification with Adversarial Perturbations,24,neurips,0,0,2023-06-16 16:06:13.409000,https://github.com/AnayMehrotra/Fair-classification-with-adversarial-perturbations,3,Fair classification with adversarial perturbations,"https://scholar.google.com/scholar?cluster=6990181264383347779&hl=en&as_sdt=0,44",1,2021
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces,27,neurips,1,0,2023-06-16 16:06:13.613000,https://github.com/aryandeshwal/ladder,11,Combining latent space and structured kernels for bayesian optimization over combinatorial spaces,"https://scholar.google.com/scholar?cluster=7142356730368207972&hl=en&as_sdt=0,5",2,2021
Gradual Domain Adaptation without Indexed Intermediate Domains,19,neurips,0,1,2023-06-16 16:06:13.813000,https://github.com/hongyouc/idol,3,Gradual domain adaptation without indexed intermediate domains,"https://scholar.google.com/scholar?cluster=6843477456336193628&hl=en&as_sdt=0,33",2,2021
Learning Markov State Abstractions for Deep Reinforcement Learning,16,neurips,4,0,2023-06-16 16:06:14.013000,https://github.com/camall3n/markov-state-abstractions,16,Learning markov state abstractions for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=17056908587988458528&hl=en&as_sdt=0,6",2,2021
Panoptic 3D Scene Reconstruction From a Single RGB Image,32,neurips,21,7,2023-06-16 16:06:14.212000,https://github.com/xheon/panoptic-reconstruction,151,Panoptic 3d scene reconstruction from a single rgb image,"https://scholar.google.com/scholar?cluster=12832750898530092236&hl=en&as_sdt=0,5",11,2021
Measuring Generalization with Optimal Transport,11,neurips,1,0,2023-06-16 16:06:14.412000,https://github.com/chingyaoc/kV-Margin,26,Measuring generalization with optimal transport,"https://scholar.google.com/scholar?cluster=6085733723572289031&hl=en&as_sdt=0,34",3,2021
Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses,21,neurips,0,0,2023-06-16 16:06:14.611000,https://github.com/huthlab/rep_structure,2,Low-dimensional structure in the space of language representations is reflected in brain responses,"https://scholar.google.com/scholar?cluster=10259223995030137805&hl=en&as_sdt=0,9",5,2021
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models,10,neurips,1,0,2023-06-16 16:06:14.810000,https://github.com/zlin7/lvd,12,Locally valid and discriminative prediction intervals for deep learning models,"https://scholar.google.com/scholar?cluster=11921032232010944367&hl=en&as_sdt=0,44",1,2021
Personalized Federated Learning With Gaussian Processes,44,neurips,7,0,2023-06-16 16:06:15.009000,https://github.com/IdanAchituve/pFedGP,25,Personalized federated learning with gaussian processes,"https://scholar.google.com/scholar?cluster=10986123828571573534&hl=en&as_sdt=0,31",1,2021
Implicit SVD for Graph Representation Learning,3,neurips,0,0,2023-06-16 16:06:15.209000,https://github.com/samihaija/isvd,16,Implicit SVD for Graph Representation Learning,"https://scholar.google.com/scholar?cluster=8383713992891185869&hl=en&as_sdt=0,33",2,2021
Offline Model-based Adaptable Policy Learning,16,neurips,3,0,2023-06-16 16:06:15.407000,https://github.com/xionghuichen/maple,17,Offline model-based adaptable policy learning,"https://scholar.google.com/scholar?cluster=4236652701971289768&hl=en&as_sdt=0,18",3,2021
Ensembling Graph Predictions for AMR Parsing,15,neurips,5,2,2023-06-16 16:06:15.610000,https://github.com/ibm/graph_ensemble_learning,34,Ensembling graph predictions for AMR parsing,"https://scholar.google.com/scholar?cluster=10642315014350686884&hl=en&as_sdt=0,44",13,2021
On the interplay between data structure and loss function in classification problems,12,neurips,0,0,2023-06-16 16:06:15.810000,https://github.com/sdascoli/data-structure,1,On the interplay between data structure and loss function in classification problems,"https://scholar.google.com/scholar?cluster=12068370246989147855&hl=en&as_sdt=0,23",2,2021
Mixture Proportion Estimation and PU Learning:A Modern Approach,23,neurips,3,1,2023-06-16 16:06:16.009000,https://github.com/acmi-lab/pu_learning,31,Mixture proportion estimation and pu learning: a modern approach,"https://scholar.google.com/scholar?cluster=16408997249461916765&hl=en&as_sdt=0,33",2,2021
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks,31,neurips,2,0,2023-06-16 16:06:16.208000,https://github.com/IST-DASLab/ACDC,18,Ac/dc: Alternating compressed/decompressed training of deep neural networks,"https://scholar.google.com/scholar?cluster=4491256831875771327&hl=en&as_sdt=0,5",6,2021
HyperSPNs: Compact and Expressive Probabilistic Circuits,7,neurips,0,0,2023-06-16 16:06:16.408000,https://github.com/andyshih12/hyperspn,10,HyperSPNs: compact and expressive probabilistic circuits,"https://scholar.google.com/scholar?cluster=13400910128328075358&hl=en&as_sdt=0,5",2,2021
Scaling Vision with Sparse Mixture of Experts,176,neurips,40,10,2023-06-16 16:06:16.607000,https://github.com/google-research/vmoe,319,Scaling vision with sparse mixture of experts,"https://scholar.google.com/scholar?cluster=1108172362434613333&hl=en&as_sdt=0,44",13,2021
Adversarial Intrinsic Motivation for Reinforcement Learning,13,neurips,0,0,2023-06-16 16:06:16.807000,https://github.com/iDurugkar/adversarial-intrinsic-motivation,3,Adversarial intrinsic motivation for reinforcement learning,"https://scholar.google.com/scholar?cluster=17506892387153258326&hl=en&as_sdt=0,44",1,2021
L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization,7,neurips,0,0,2023-06-16 16:06:17.006000,https://github.com/jeremiemelo/l2ight,14,L2ight: Enabling on-chip learning for optical neural networks via efficient in-situ subspace optimization,"https://scholar.google.com/scholar?cluster=12160624402740671006&hl=en&as_sdt=0,10",2,2021
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond,32,neurips,7,0,2023-06-16 16:06:17.206000,https://github.com/vis-opt-group/iaptt-gm,6,Towards gradient-based bilevel optimization with non-convex followers and beyond,"https://scholar.google.com/scholar?cluster=4742630241589008678&hl=en&as_sdt=0,5",1,2021
Multi-Facet Clustering Variational Autoencoders,10,neurips,8,1,2023-06-16 16:06:17.405000,https://github.com/FabianFalck/mfcvae,29,Multi-facet clustering variational autoencoders,"https://scholar.google.com/scholar?cluster=16117521834362890782&hl=en&as_sdt=0,5",5,2021
Searching the Search Space of Vision Transformer,19,neurips,167,24,2023-06-16 16:06:17.606000,https://github.com/microsoft/cream,1078,Searching the search space of vision transformer,"https://scholar.google.com/scholar?cluster=17171842121702147403&hl=en&as_sdt=0,44",25,2021
Inverse Problems Leveraging Pre-trained Contrastive Representations,6,neurips,2,0,2023-06-16 16:06:17.805000,https://github.com/sriram-ravula/contrastive-inversion,26,Inverse problems leveraging pre-trained contrastive representations,"https://scholar.google.com/scholar?cluster=13090230797997641705&hl=en&as_sdt=0,36",4,2021
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation,36,neurips,11,1,2023-06-16 16:06:18.005000,https://github.com/thibsej/unbalanced_gromov_wasserstein,32,The unbalanced gromov wasserstein distance: Conic formulation and relaxation,"https://scholar.google.com/scholar?cluster=4621301821355236560&hl=en&as_sdt=0,47",4,2021
Diffusion Models Beat GANs on Image Synthesis,1377,neurips,611,66,2023-06-16 16:06:18.204000,https://github.com/openai/guided-diffusion,4248,Diffusion models beat gans on image synthesis,"https://scholar.google.com/scholar?cluster=17982230494456470673&hl=en&as_sdt=0,31",122,2021
A Biased Graph Neural Network Sampler with Near-Optimal Regret,16,neurips,2,0,2023-06-16 16:06:18.404000,https://github.com/QingruZhang/Thanos,3,A biased graph neural network sampler with near-optimal regret,"https://scholar.google.com/scholar?cluster=10280015035200600286&hl=en&as_sdt=0,11",2,2021
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry,17,neurips,3,0,2023-06-16 16:06:18.603000,https://github.com/andyjm3/AI-vs-BW,3,On Riemannian optimization over positive definite matrices with the Bures-Wasserstein geometry,"https://scholar.google.com/scholar?cluster=2437471067279904808&hl=en&as_sdt=0,5",1,2021
Refining Language Models with Compositional Explanations,18,neurips,0,0,2023-06-16 16:06:18.802000,https://github.com/INK-USC/expl-refinement,13,Refining language models with compositional explanations,"https://scholar.google.com/scholar?cluster=5798502945545314166&hl=en&as_sdt=0,10",4,2021
What can linearized neural networks actually say about generalization?,18,neurips,1,0,2023-06-16 16:06:19.009000,https://github.com/gortizji/linearized-networks,13,What can linearized neural networks actually say about generalization?,"https://scholar.google.com/scholar?cluster=14899962507858942209&hl=en&as_sdt=0,33",3,2021
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning,67,neurips,54,18,2023-06-16 16:06:19.208000,https://github.com/facebookresearch/SEAL_OGB,178,Labeling trick: A theory of using graph neural networks for multi-node representation learning,"https://scholar.google.com/scholar?cluster=2266754779755324127&hl=en&as_sdt=0,5",10,2021
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients,20,neurips,0,0,2023-06-16 16:06:19.407000,https://github.com/lijunyi95/superadam,15,Super-adam: faster and universal framework of adaptive gradients,"https://scholar.google.com/scholar?cluster=14703252703783820284&hl=en&as_sdt=0,33",3,2021
Denoising Normalizing Flow,12,neurips,1,0,2023-06-16 16:06:19.607000,https://github.com/chrvt/denoising-normalizing-flow,18,Denoising normalizing flow,"https://scholar.google.com/scholar?cluster=17109941513992323915&hl=en&as_sdt=0,5",2,2021
Attention over Learned Object Embeddings Enables Complex Visual Reasoning,38,neurips,2436,170,2023-06-16 16:06:19.806000,https://github.com/deepmind/deepmind-research,11904,Attention over learned object embeddings enables complex visual reasoning,"https://scholar.google.com/scholar?cluster=127829313460149801&hl=en&as_sdt=0,33",336,2021
Differentiable Learning Under Triage,21,neurips,2,0,2023-06-16 16:06:20.005000,https://github.com/Networks-Learning/differentiable-learning-under-triage,3,Differentiable learning under triage,"https://scholar.google.com/scholar?cluster=3465216605112056644&hl=en&as_sdt=0,15",2,2021
An Image is Worth More Than a Thousand Words: Towards Disentanglement in The Wild,20,neurips,5,2,2023-06-16 16:06:20.205000,https://github.com/avivga/zerodim,18,An image is worth more than a thousand words: Towards disentanglement in the wild,"https://scholar.google.com/scholar?cluster=10161122564731884451&hl=en&as_sdt=0,44",1,2021
Efficient Statistical Assessment of Neural Network Corruption Robustness,5,neurips,0,0,2023-06-16 16:06:20.405000,https://github.com/karimtito/efficient-statistical,0,Efficient Statistical Assessment of Neural Network Corruption Robustness,"https://scholar.google.com/scholar?cluster=9015952957201151715&hl=en&as_sdt=0,23",1,2021
Realistic evaluation of transductive few-shot learning,17,neurips,2,0,2023-06-16 16:06:20.603000,https://github.com/oveilleux/realistic_transductive_few_shot,16,Realistic evaluation of transductive few-shot learning,"https://scholar.google.com/scholar?cluster=779657779998908467&hl=en&as_sdt=0,1",2,2021
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes,6,neurips,2,0,2023-06-16 16:06:20.803000,https://github.com/secure-ai-systems-group/qu-anti-zation,8,Qu-anti-zation: Exploiting quantization artifacts for achieving adversarial outcomes,"https://scholar.google.com/scholar?cluster=3502987218108347003&hl=en&as_sdt=0,47",1,2021
Integrating Tree Path in Transformer for Code Representation,23,neurips,0,0,2023-06-16 16:06:21.003000,https://github.com/awdhanpeng/tptrans,0,Integrating tree path in transformer for code representation,"https://scholar.google.com/scholar?cluster=12295099562232904052&hl=en&as_sdt=0,5",1,2021
Twins: Revisiting the Design of Spatial Attention in Vision Transformers,524,neurips,63,10,2023-06-16 16:06:21.202000,https://github.com/Meituan-AutoML/Twins,511,Twins: Revisiting the design of spatial attention in vision transformers,"https://scholar.google.com/scholar?cluster=5060121065165184210&hl=en&as_sdt=0,5",14,2021
Data-Efficient Instance Generation from Instance Discrimination,45,neurips,4,6,2023-06-16 16:06:21.402000,https://github.com/genforce/insgen,97,Data-efficient instance generation from instance discrimination,"https://scholar.google.com/scholar?cluster=1497192105347715658&hl=en&as_sdt=0,5",9,2021
Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games,1,neurips,0,0,2023-06-16 16:06:21.602000,https://github.com/nttcslab/diff-eq-comput-zdd,2,Differentiable equilibrium computation with decision diagrams for stackelberg models of combinatorial congestion games,"https://scholar.google.com/scholar?cluster=9969998986747783383&hl=en&as_sdt=0,22",2,2021
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System,8,neurips,2,0,2023-06-16 16:06:21.801000,https://github.com/rothkopflab/inverse-optimal-control,2,Inverse optimal control adapted to the noise characteristics of the human sensorimotor system,"https://scholar.google.com/scholar?cluster=5865855006238055136&hl=en&as_sdt=0,33",1,2021
Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment,5,neurips,1,2,2023-06-16 16:06:22.001000,https://github.com/CTI-VISION/Mirror-Sample,5,Reducing the covariate shift by mirror samples in cross domain alignment,"https://scholar.google.com/scholar?cluster=4872841041858237151&hl=en&as_sdt=0,23",1,2021
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning,8,neurips,10,3,2023-06-16 16:06:22.202000,https://github.com/jrwnter/pigvae,35,Permutation-invariant variational autoencoder for graph-level representation learning,"https://scholar.google.com/scholar?cluster=11891489203108750561&hl=en&as_sdt=0,5",3,2021
3DP3: 3D Scene Perception via Probabilistic Programming,24,neurips,3,0,2023-06-16 16:06:22.403000,https://github.com/probcomp/threedp3,10,3DP3: 3D scene perception via probabilistic programming,"https://scholar.google.com/scholar?cluster=6863695141270884118&hl=en&as_sdt=0,33",12,2021
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements,24,neurips,3,0,2023-06-16 16:06:22.602000,https://github.com/fjxmlzn/BSN,36,Why spectral normalization stabilizes gans: Analysis and improvements,"https://scholar.google.com/scholar?cluster=17254495230402208234&hl=en&as_sdt=0,5",1,2021
MADE: Exploration via Maximizing Deviation from Explored Regions,24,neurips,3,0,2023-06-16 16:06:22.802000,https://github.com/tianjunz/MADE,17,Made: Exploration via maximizing deviation from explored regions,"https://scholar.google.com/scholar?cluster=8010522815020070662&hl=en&as_sdt=0,5",4,2021
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation,586,neurips,504,187,2023-06-16 16:06:23.001000,https://github.com/salesforce/lavis,5506,Align before fuse: Vision and language representation learning with momentum distillation,"https://scholar.google.com/scholar?cluster=2949653561196582978&hl=en&as_sdt=0,20",75,2021
Variational Model Inversion Attacks,28,neurips,3,4,2023-06-16 16:06:23.201000,https://github.com/wangkua1/vmi,16,Variational model inversion attacks,"https://scholar.google.com/scholar?cluster=14139666548957095548&hl=en&as_sdt=0,29",3,2021
Graph Neural Networks with Adaptive Residual,21,neurips,4,2,2023-06-16 16:06:23.401000,https://github.com/lxiaorui/airgnn,15,Graph neural networks with adaptive residual,"https://scholar.google.com/scholar?cluster=15094075369662309997&hl=en&as_sdt=0,34",3,2021
TriBERT: Human-centric Audio-visual Representation Learning,4,neurips,2,3,2023-06-16 16:06:23.601000,https://github.com/ubc-vision/tribert,10,TriBERT: Human-centric Audio-visual Representation Learning,"https://scholar.google.com/scholar?cluster=8373124147207590076&hl=en&as_sdt=0,5",1,2021
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning,7,neurips,0,1,2023-06-16 16:06:23.801000,https://github.com/frt03/inference-based-rl,17,Co-adaptation of algorithmic and implementational innovations in inference-based deep reinforcement learning,"https://scholar.google.com/scholar?cluster=13585717862866911576&hl=en&as_sdt=0,5",0,2021
Can fMRI reveal the representation of syntactic structure in the brain?,15,neurips,2,0,2023-06-16 16:06:24.009000,https://github.com/anikethjr/brain_syntactic_representations,4,Can fMRI reveal the representation of syntactic structure in the brain?,"https://scholar.google.com/scholar?cluster=8612814511404914759&hl=en&as_sdt=0,5",4,2021
Robust Implicit Networks via Non-Euclidean Contractions,22,neurips,1,0,2023-06-16 16:06:24.213000,https://github.com/davydovalexander/non-euclidean_mon_op_net,0,Robust implicit networks via non-Euclidean contractions,"https://scholar.google.com/scholar?cluster=13884163203137511779&hl=en&as_sdt=0,21",1,2021
Efficient methods for Gaussian Markov random fields under sparse linear constraints,4,neurips,1,0,2023-06-16 16:06:24.414000,https://github.com/JonasWallin/CB,1,Efficient methods for Gaussian Markov random fields under sparse linear constraints,"https://scholar.google.com/scholar?cluster=8649010472840775906&hl=en&as_sdt=0,44",3,2021
On Provable Benefits of Depth in Training Graph Convolutional Networks,39,neurips,1,0,2023-06-16 16:06:24.614000,https://github.com/CongWeilin/DGCN,10,On provable benefits of depth in training graph convolutional networks,"https://scholar.google.com/scholar?cluster=12386140121969765106&hl=en&as_sdt=0,5",3,2021
Meta-Adaptive Nonlinear Control: Theory and Algorithms,13,neurips,11,0,2023-06-16 16:06:24.814000,https://github.com/GuanyaShi/Online-Meta-Adaptive-Control,38,Meta-adaptive nonlinear control: Theory and algorithms,"https://scholar.google.com/scholar?cluster=3468826703271927093&hl=en&as_sdt=0,5",3,2021
Compositional Reinforcement Learning from Logical Specifications,37,neurips,3,0,2023-06-16 16:06:25.021000,https://github.com/keyshor/dirl,11,Compositional reinforcement learning from logical specifications,"https://scholar.google.com/scholar?cluster=14766586595229560420&hl=en&as_sdt=0,22",1,2021
Credit Assignment Through Broadcasting a Global Error Vector,11,neurips,2,0,2023-06-16 16:06:25.221000,https://github.com/davidclark1/vectorizednets,2,Credit assignment through broadcasting a global error vector,"https://scholar.google.com/scholar?cluster=3727698490784134497&hl=en&as_sdt=0,5",1,2021
An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives,17,neurips,0,0,2023-06-16 16:06:25.422000,https://github.com/qiqi-helloworld/recover,10,An online method for a class of distributionally robust optimization with non-convex objectives,"https://scholar.google.com/scholar?cluster=5357983070298547802&hl=en&as_sdt=0,5",3,2021
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias,7,neurips,1,0,2023-06-16 16:06:25.621000,https://github.com/ehsan-mokhtarian/l-marvel,0,Recursive causal structure learning in the presence of latent variables and selection bias,"https://scholar.google.com/scholar?cluster=10465421088099872721&hl=en&as_sdt=0,14",1,2021
Spectral embedding for dynamic networks with stability guarantees,9,neurips,1,0,2023-06-16 16:06:25.821000,https://github.com/iggallagher/Dynamic-Network-Embedding,1,Spectral embedding for dynamic networks with stability guarantees,"https://scholar.google.com/scholar?cluster=15639417691972104804&hl=en&as_sdt=0,34",1,2021
Infinite Time Horizon Safety of Bayesian Neural Networks,9,neurips,1,0,2023-06-16 16:06:26.021000,https://github.com/mlech26l/bayesian_nn_safety,0,Infinite time horizon safety of Bayesian neural networks,"https://scholar.google.com/scholar?cluster=3317282080720132097&hl=en&as_sdt=0,33",2,2021
On the Estimation Bias in Double Q-Learning,4,neurips,0,2,2023-06-16 16:06:26.222000,https://github.com/stilwell-git/doubly-bounded-q-learning,1,On the Estimation Bias in Double Q-Learning,"https://scholar.google.com/scholar?cluster=6701423240345765419&hl=en&as_sdt=0,5",2,2021
Non-Gaussian Gaussian Processes for Few-Shot Regression,9,neurips,1,0,2023-06-16 16:06:26.421000,https://github.com/gmum/non-gaussian-gaussian-processes,17,Non-gaussian gaussian processes for few-shot regression,"https://scholar.google.com/scholar?cluster=13494016610404817418&hl=en&as_sdt=0,44",6,2021
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning,32,neurips,7,4,2023-06-16 16:06:26.621000,https://github.com/yiqinyang/icq,48,Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=3861157451473520917&hl=en&as_sdt=0,6",1,2021
K-Net: Towards Unified Image Segmentation,138,neurips,43,13,2023-06-16 16:06:26.828000,https://github.com/zwwwayne/k-net,442,K-net: Towards unified image segmentation,"https://scholar.google.com/scholar?cluster=9601688478354935911&hl=en&as_sdt=0,34",10,2021
Learning Collaborative Policies to Solve NP-hard Routing Problems,33,neurips,3,1,2023-06-16 16:06:27.027000,https://github.com/alstn12088/lcp,10,Learning collaborative policies to solve NP-hard routing problems,"https://scholar.google.com/scholar?cluster=6269783259343290144&hl=en&as_sdt=0,36",1,2021
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum,5,neurips,1,1,2023-06-16 16:06:27.227000,https://github.com/shuang-ao/co-pilot,1,CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum,"https://scholar.google.com/scholar?cluster=13913848066450327449&hl=en&as_sdt=0,22",1,2021
Kernel Identification Through Transformers,5,neurips,1,0,2023-06-16 16:06:27.427000,https://github.com/frgsimpson/kitt,8,Kernel identification through transformers,"https://scholar.google.com/scholar?cluster=17623460492368615234&hl=en&as_sdt=0,5",5,2021
Curriculum Design for Teaching via Demonstrations: Theory and Applications,6,neurips,1,0,2023-06-16 16:06:27.626000,https://github.com/adishs/neurips2021_curriculum-teaching-demonstrations_code,2,Curriculum Design for Teaching via Demonstrations: Theory and Applications,"https://scholar.google.com/scholar?cluster=15048435849390075589&hl=en&as_sdt=0,5",2,2021
Dynamic Causal Bayesian Optimization,13,neurips,9,0,2023-06-16 16:06:27.826000,https://github.com/neildhir/dcbo,24,Dynamic causal Bayesian optimization,"https://scholar.google.com/scholar?cluster=16636999477420016377&hl=en&as_sdt=0,5",1,2021
Equivariant Manifold Flows,8,neurips,0,0,2023-06-16 16:06:28.026000,https://github.com/cuai/equivariant-manifold-flows,7,Equivariant manifold flows,"https://scholar.google.com/scholar?cluster=13655183730986062647&hl=en&as_sdt=0,5",2,2021
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation,8,neurips,6,0,2023-06-16 16:06:28.225000,https://github.com/fangyanwen1106/RLANet,23,Recurrence along depth: Deep convolutional neural networks with recurrent layer aggregation,"https://scholar.google.com/scholar?cluster=4477865436853861704&hl=en&as_sdt=0,47",2,2021
Independent Prototype Propagation for Zero-Shot Compositionality,20,neurips,2,0,2023-06-16 16:06:28.425000,https://github.com/FrankRuis/ProtoProp,10,Independent prototype propagation for zero-shot compositionality,"https://scholar.google.com/scholar?cluster=13176465019073119909&hl=en&as_sdt=0,30",4,2021
Universal Graph Convolutional Networks,31,neurips,5,1,2023-06-16 16:06:28.624000,https://github.com/jindi-tju/U-GCN,15,Universal graph convolutional networks,"https://scholar.google.com/scholar?cluster=2138305562153632619&hl=en&as_sdt=0,5",1,2021
Adversarial Feature Desensitization,8,neurips,0,0,2023-06-16 16:06:28.824000,https://github.com/BashivanLab/afd,6,Adversarial feature desensitization,"https://scholar.google.com/scholar?cluster=435468338701140175&hl=en&as_sdt=0,36",1,2021
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition,66,neurips,12,3,2023-06-16 16:06:29.023000,https://github.com/cgtuebingen/Neural-PIL,80,Neural-pil: Neural pre-integrated lighting for reflectance decomposition,"https://scholar.google.com/scholar?cluster=3379298908758464795&hl=en&as_sdt=0,5",8,2021
Extracting Deformation-Aware Local Features by Learning to Deform,4,neurips,4,4,2023-06-16 16:06:29.223000,https://github.com/verlab/DEAL_NeurIPS_2021,24,Extracting deformation-aware local features by learning to deform,"https://scholar.google.com/scholar?cluster=14581155560161473029&hl=en&as_sdt=0,1",5,2021
Gradient-based Hyperparameter Optimization Over Long Horizons,5,neurips,0,0,2023-06-16 16:06:29.422000,https://github.com/polo5/fds,10,Gradient-based hyperparameter optimization over long horizons,"https://scholar.google.com/scholar?cluster=6997241772832263952&hl=en&as_sdt=0,5",1,2021
"The Causal-Neural Connection: Expressiveness, Learnability, and Inference",41,neurips,1,0,2023-06-16 16:06:29.622000,https://github.com/causalailab/neuralcausalmodels,6,"The causal-neural connection: Expressiveness, learnability, and inference","https://scholar.google.com/scholar?cluster=10952897351624704856&hl=en&as_sdt=0,5",1,2021
R-Drop: Regularized Dropout for Neural Networks,189,neurips,107,1,2023-06-16 16:06:29.821000,https://github.com/dropreg/R-Drop,816,R-drop: Regularized dropout for neural networks,"https://scholar.google.com/scholar?cluster=2475537860429813567&hl=en&as_sdt=0,47",5,2021
Diversity Enhanced Active Learning with Strictly Proper Scoring Rules,8,neurips,0,0,2023-06-16 16:06:30.021000,https://github.com/davidtw999/bemps,8,Diversity enhanced active learning with strictly proper scoring rules,"https://scholar.google.com/scholar?cluster=8484595844255881124&hl=en&as_sdt=0,41",1,2021
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning,21,neurips,10,1,2023-06-16 16:06:30.222000,https://github.com/clovaai/SSUL,51,SSUL: Semantic segmentation with unknown label for exemplar-based class-incremental learning,"https://scholar.google.com/scholar?cluster=2873857324904043175&hl=en&as_sdt=0,31",5,2021
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling,5,neurips,1,0,2023-06-16 16:06:30.421000,https://github.com/gregversteeg/esh_dynamics,31,Hamiltonian dynamics with non-newtonian momentum for rapid sampling,"https://scholar.google.com/scholar?cluster=8697297470988026011&hl=en&as_sdt=0,10",3,2021
Dynamic Normalization and Relay for Video Action Recognition,2,neurips,1,1,2023-06-16 16:06:30.621000,https://github.com/caidonkey/dnr,3,Dynamic normalization and relay for video action recognition,"https://scholar.google.com/scholar?cluster=17545308458532261547&hl=en&as_sdt=0,5",2,2021
True Few-Shot Learning with Language Models,168,neurips,11,1,2023-06-16 16:06:30.821000,https://github.com/ethanjperez/true_few_shot,138,True few-shot learning with language models,"https://scholar.google.com/scholar?cluster=1955924689354059509&hl=en&as_sdt=0,33",2,2021
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer,43,neurips,16,1,2023-06-16 16:06:31.021000,https://github.com/yining043/VRP-DACT,55,Learning to iteratively solve routing problems with dual-aspect collaborative transformer,"https://scholar.google.com/scholar?cluster=13083892741487844240&hl=en&as_sdt=0,43",2,2021
Learning interaction rules from multi-animal trajectories via augmented behavioral models,11,neurips,0,0,2023-06-16 16:06:31.220000,https://github.com/keisuke198619/abm,6,Learning interaction rules from multi-animal trajectories via augmented behavioral models,"https://scholar.google.com/scholar?cluster=13190745890031985835&hl=en&as_sdt=0,10",2,2021
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications,8,neurips,23,2,2023-06-16 16:06:31.420000,https://github.com/deepmind/jax_verify,126,Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications,"https://scholar.google.com/scholar?cluster=4180484968407632121&hl=en&as_sdt=0,33",8,2021
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure,5,neurips,0,0,2023-06-16 16:06:31.620000,https://github.com/avivros007/factored-mdp-with-unknown-structure,0,Oracle-efficient regret minimization in factored mdps with unknown structure,"https://scholar.google.com/scholar?cluster=10644518817824113787&hl=en&as_sdt=0,5",1,2021
Making the most of your day: online learning for optimal allocation of time,3,neurips,0,0,2023-06-16 16:06:31.820000,https://github.com/eboursier/making_most_of_your_time,1,Making the most of your day: online learning for optimal allocation of time,"https://scholar.google.com/scholar?cluster=391436083487229673&hl=en&as_sdt=0,8",1,2021
Continuous Doubly Constrained Batch Reinforcement Learning,16,neurips,1,0,2023-06-16 16:06:32.019000,https://github.com/amazon-research/cdc-batch-rl,8,Continuous doubly constrained batch reinforcement learning,"https://scholar.google.com/scholar?cluster=4821141646205094799&hl=en&as_sdt=0,10",2,2021
Score-based Generative Modeling in Latent Space,219,neurips,45,6,2023-06-16 16:06:32.219000,https://github.com/NVlabs/LSGM,280,Score-based generative modeling in latent space,"https://scholar.google.com/scholar?cluster=1591095957629218534&hl=en&as_sdt=0,5",8,2021
Deep Conditional Gaussian Mixture Model for Constrained Clustering,14,neurips,4,1,2023-06-16 16:06:32.419000,https://github.com/lauramanduchi/DC-GMM,21,Deep conditional gaussian mixture model for constrained clustering,"https://scholar.google.com/scholar?cluster=10567997347878882967&hl=en&as_sdt=0,33",1,2021
Bootstrap Your Object Detector via Mixed Training,5,neurips,6,3,2023-06-16 16:06:32.618000,https://github.com/mendelxu/mixtraining,55,Bootstrap your object detector via mixed training,"https://scholar.google.com/scholar?cluster=14330595085341601931&hl=en&as_sdt=0,5",11,2021
One Explanation is Not Enough: Structured Attention Graphs for Image Classification,7,neurips,3,0,2023-06-16 16:06:32.817000,https://github.com/viv92/structured-attention-graphs,23,One explanation is not enough: structured attention graphs for image classification,"https://scholar.google.com/scholar?cluster=2997308629773140284&hl=en&as_sdt=0,7",2,2021
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP,4,neurips,3,0,2023-06-16 16:06:33.018000,https://github.com/jsrozner/decrypt,8,Decrypting cryptic crosswords: Semantically complex wordplay puzzles as a target for nlp,"https://scholar.google.com/scholar?cluster=3859471810944880726&hl=en&as_sdt=0,47",3,2021
Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing,26,neurips,1,0,2023-06-16 16:06:33.217000,https://github.com/GenjiB/CM-Co-Occurrence-AVVP,3,Exploring cross-video and cross-modality signals for weakly-supervised audio-visual video parsing,"https://scholar.google.com/scholar?cluster=12267728961291796610&hl=en&as_sdt=0,44",1,2021
Dual Parameterization of Sparse Variational Gaussian Processes,15,neurips,1,0,2023-06-16 16:06:33.417000,https://github.com/AaltoML/t-SVGP,7,Dual parameterization of sparse variational Gaussian processes,"https://scholar.google.com/scholar?cluster=12813330382195057867&hl=en&as_sdt=0,11",1,2021
Hierarchical Skills for Efficient Exploration,16,neurips,5,1,2023-06-16 16:06:33.616000,https://github.com/facebookresearch/hsd3,44,Hierarchical skills for efficient exploration,"https://scholar.google.com/scholar?cluster=15461268367576192426&hl=en&as_sdt=0,11",8,2021
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models,5,neurips,2,0,2023-06-16 16:06:33.816000,https://github.com/sisl/evsoftmax,9,Evidential softmax for sparse multimodal distributions in deep generative models,"https://scholar.google.com/scholar?cluster=11285213067852338942&hl=en&as_sdt=0,15",9,2021
DeepGEM: Generalized Expectation-Maximization for Blind Inversion,7,neurips,2,0,2023-06-16 16:06:34.020000,https://github.com/angelafgao/DeepGEM,6,DeepGEM: Generalized expectation-maximization for blind inversion,"https://scholar.google.com/scholar?cluster=14209454194474907854&hl=en&as_sdt=0,9",3,2021
Learning to Generate Visual Questions with Noisy Supervision,6,neurips,0,0,2023-06-16 16:06:34.222000,https://github.com/alanswift/dh-gan,0,Learning to generate visual questions with noisy supervision,"https://scholar.google.com/scholar?cluster=5233322367909320124&hl=en&as_sdt=0,36",2,2021
Numerical Composition of Differential Privacy,58,neurips,9,4,2023-06-16 16:06:34.422000,https://github.com/microsoft/prv_accountant,44,Numerical composition of differential privacy,"https://scholar.google.com/scholar?cluster=2912362151232664509&hl=en&as_sdt=0,5",8,2021
Hyperparameter Tuning is All You Need for LISTA,8,neurips,6,1,2023-06-16 16:06:34.630000,https://github.com/vita-group/hyperlista,13,Hyperparameter tuning is all you need for lista,"https://scholar.google.com/scholar?cluster=4373381653773823100&hl=en&as_sdt=0,5",6,2021
Foundations of Symbolic Languages for Model Interpretability,11,neurips,0,0,2023-06-16 16:06:34.831000,https://github.com/angryseal/foil-prototype,1,Foundations of symbolic languages for model interpretability,"https://scholar.google.com/scholar?cluster=18188458823960639099&hl=en&as_sdt=0,44",3,2021
Impression learning: Online representation learning with synaptic plasticity,3,neurips,0,0,2023-06-16 16:06:35.031000,https://github.com/colinbredenberg/impression-learning-camera-ready,2,Impression learning: Online representation learning with synaptic plasticity,"https://scholar.google.com/scholar?cluster=18236139388730215945&hl=en&as_sdt=0,33",3,2021
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?,10,neurips,2,0,2023-06-16 16:06:35.231000,https://github.com/brendel-group/causal-understanding-via-visualizations,7,How Well do Feature Visualizations Support Causal Understanding of CNN Activations?,"https://scholar.google.com/scholar?cluster=18425728687494143861&hl=en&as_sdt=0,44",3,2021
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems,2,neurips,1,0,2023-06-16 16:06:35.431000,https://github.com/facebookresearch/self_defeating_improvements,4,Fixes that fail: Self-defeating improvements in machine-learning systems,"https://scholar.google.com/scholar?cluster=11324765281769913396&hl=en&as_sdt=0,15",8,2021
Coarse-to-fine Animal Pose and Shape Estimation,5,neurips,8,2,2023-06-16 16:06:35.631000,https://github.com/chaneyddtt/coarse-to-fine-3d-animal,26,Coarse-to-fine animal pose and shape estimation,"https://scholar.google.com/scholar?cluster=13174062854434293383&hl=en&as_sdt=0,5",2,2021
Meta-Learning Sparse Implicit Neural Representations,13,neurips,3,0,2023-06-16 16:06:35.832000,https://github.com/jaeho-lee/MetaSparseINR,45,Meta-learning sparse implicit neural representations,"https://scholar.google.com/scholar?cluster=15081844900772325837&hl=en&as_sdt=0,5",4,2021
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation,134,neurips,65,2,2023-06-16 16:06:36.032000,https://github.com/hkchengrex/STCN,480,Rethinking space-time networks with improved memory coverage for efficient video object segmentation,"https://scholar.google.com/scholar?cluster=972182322509240859&hl=en&as_sdt=0,11",8,2021
Towards Efficient and Effective Adversarial Training,28,neurips,1,1,2023-06-16 16:06:36.231000,https://github.com/val-iisc/nuat,15,Towards efficient and effective adversarial training,"https://scholar.google.com/scholar?cluster=11235823005919220194&hl=en&as_sdt=0,5",13,2021
Intriguing Properties of Contrastive Losses,115,neurips,570,69,2023-06-16 16:06:36.430000,https://github.com/google-research/simclr,3562,Intriguing properties of contrastive losses,"https://scholar.google.com/scholar?cluster=4366111052607966532&hl=en&as_sdt=0,11",46,2021
Detecting Moments and Highlights in Videos via Natural Language Queries,44,neurips,32,7,2023-06-16 16:06:36.629000,https://github.com/jayleicn/moment_detr,163,Detecting moments and highlights in videos via natural language queries,"https://scholar.google.com/scholar?cluster=2821905623322398755&hl=en&as_sdt=0,44",10,2021
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems,9,neurips,2,0,2023-06-16 16:06:36.829000,https://github.com/andrschl/stable-ndde,4,Learning stable deep dynamics models for partially observed or delayed dynamical systems,"https://scholar.google.com/scholar?cluster=9330759144731592211&hl=en&as_sdt=0,33",1,2021
An Uncertainty Principle is a Price of Privacy-Preserving Microdata,13,neurips,0,0,2023-06-16 16:06:37.030000,https://github.com/uscensusbureau/CostOfMicrodataNeurIPS2021,1,An uncertainty principle is a price of privacy-preserving microdata,"https://scholar.google.com/scholar?cluster=731929662689496666&hl=en&as_sdt=0,33",6,2021
Fairness in Ranking under Uncertainty,23,neurips,0,0,2023-06-16 16:06:37.229000,https://github.com/ashudeep/ranking-fairness-uncertainty,9,Fairness in ranking under uncertainty,"https://scholar.google.com/scholar?cluster=8766040345698032418&hl=en&as_sdt=0,5",2,2021
Generalized Proximal Policy Optimization with Sample Reuse,16,neurips,0,2,2023-06-16 16:06:37.428000,https://github.com/jqueeney/geppo,13,Generalized proximal policy optimization with sample reuse,"https://scholar.google.com/scholar?cluster=4171321851465762143&hl=en&as_sdt=0,10",1,2021
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data,20,neurips,7,1,2023-06-16 16:06:37.628000,https://github.com/zju-vipa/mosaickd,39,Mosaicking to distill: Knowledge distillation from out-of-domain data,"https://scholar.google.com/scholar?cluster=14692300996784513137&hl=en&as_sdt=0,5",4,2021
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection,17,neurips,3,0,2023-06-16 16:06:37.827000,https://github.com/jiwei0921/jsm,9,Joint semantic mining for weakly supervised RGB-d salient object detection,"https://scholar.google.com/scholar?cluster=6195993508190373693&hl=en&as_sdt=0,5",2,2021
Contrastive Learning for Neural Topic Model,17,neurips,3,3,2023-06-16 16:06:38.027000,https://github.com/nguyentthong/CLNTM,25,Contrastive learning for neural topic model,"https://scholar.google.com/scholar?cluster=10430438034264335741&hl=en&as_sdt=0,43",1,2021
ATISS: Autoregressive Transformers for Indoor Scene Synthesis,37,neurips,37,4,2023-06-16 16:06:38.230000,https://github.com/nv-tlabs/atiss,180,Atiss: Autoregressive transformers for indoor scene synthesis,"https://scholar.google.com/scholar?cluster=7663672356809385769&hl=en&as_sdt=0,33",16,2021
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks,4,neurips,4,0,2023-06-16 16:06:38.429000,https://github.com/hsndbk4/gdws,8,Generalized depthwise-separable convolutions for adversarially robust and efficient neural networks,"https://scholar.google.com/scholar?cluster=7917258653849335165&hl=en&as_sdt=0,22",1,2021
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers,1290,neurips,264,76,2023-06-16 16:06:38.629000,https://github.com/NVlabs/SegFormer,1684,SegFormer: Simple and efficient design for semantic segmentation with transformers,"https://scholar.google.com/scholar?cluster=11165298458048562314&hl=en&as_sdt=0,23",28,2021
Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning,15,neurips,3,0,2023-06-16 16:06:38.829000,https://github.com/compdyn/rl_grid_coarsen,4,Optimization-based algebraic multigrid coarsening using reinforcement learning,"https://scholar.google.com/scholar?cluster=17469824213122053869&hl=en&as_sdt=0,10",2,2021
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems,7,neurips,2,0,2023-06-16 16:06:39.029000,https://github.com/vita-group/dept,6,Delayed propagation transformer: A universal computation engine towards practical control in cyber-physical systems,"https://scholar.google.com/scholar?cluster=15971168398161981111&hl=en&as_sdt=0,29",6,2021
Explaining Latent Representations with a Corpus of Examples,12,neurips,8,0,2023-06-16 16:06:39.229000,https://github.com/jonathancrabbe/simplex,19,Explaining latent representations with a corpus of examples,"https://scholar.google.com/scholar?cluster=4017090788883976971&hl=en&as_sdt=0,22",3,2021
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks,12,neurips,0,0,2023-06-16 16:06:39.428000,https://github.com/neuroailab/mec,9,Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks,"https://scholar.google.com/scholar?cluster=9080982339262478119&hl=en&as_sdt=0,5",4,2021
FACMAC: Factored Multi-Agent Centralised Policy Gradients,89,neurips,26,8,2023-06-16 16:06:39.628000,https://github.com/schroederdewitt/multiagent_mujoco,250,Facmac: Factored multi-agent centralised policy gradients,"https://scholar.google.com/scholar?cluster=3516187907112505295&hl=en&as_sdt=0,47",9,2021
EDGE: Explaining Deep Reinforcement Learning Policies,15,neurips,0,1,2023-06-16 16:06:39.828000,https://github.com/henrygwb/edge,11,Edge: Explaining deep reinforcement learning policies,"https://scholar.google.com/scholar?cluster=10612065413768368207&hl=en&as_sdt=0,50",3,2021
Learning to Assimilate in Chaotic Dynamical Systems,5,neurips,0,0,2023-06-16 16:06:40.027000,https://github.com/mikemccabe210/amortizedassimilation,4,Learning to assimilate in chaotic dynamical systems,"https://scholar.google.com/scholar?cluster=4578338476402919317&hl=en&as_sdt=0,5",1,2021
Object-aware Contrastive Learning for Debiased Scene Representation,28,neurips,7,0,2023-06-16 16:06:40.227000,https://github.com/alinlab/object-aware-contrastive,43,Object-aware contrastive learning for debiased scene representation,"https://scholar.google.com/scholar?cluster=8671394054522107055&hl=en&as_sdt=0,11",5,2021
Evaluating Efficient Performance Estimators of Neural Architectures,36,neurips,27,13,2023-06-16 16:06:40.428000,https://github.com/walkerning/aw_nas,224,Evaluating efficient performance estimators of neural architectures,"https://scholar.google.com/scholar?cluster=12282663317439735649&hl=en&as_sdt=0,5",20,2021
How can classical multidimensional scaling go wrong?,1,neurips,0,0,2023-06-16 16:06:40.627000,https://github.com/rsonthal/Trace-cMDS,2,How can classical multidimensional scaling go wrong?,"https://scholar.google.com/scholar?cluster=2328869828786838167&hl=en&as_sdt=0,15",1,2021
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones,21,neurips,2,1,2023-06-16 16:06:40.828000,https://github.com/snap-stanford/ConE,17,Modeling heterogeneous hierarchies with relation-specific hyperbolic cones,"https://scholar.google.com/scholar?cluster=6519771613830294216&hl=en&as_sdt=0,39",4,2021
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality,20,neurips,4,0,2023-06-16 16:06:41.028000,https://github.com/Stanford-ILIAD/Confidence-Aware-Imitation-Learning,25,Confidence-aware imitation learning from demonstrations with varying optimality,"https://scholar.google.com/scholar?cluster=8104587392600832950&hl=en&as_sdt=0,22",5,2021
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections,20,neurips,0,0,2023-06-16 16:06:41.227000,https://github.com/kimiandj/fast_sw,2,Fast approximation of the sliced-Wasserstein distance using concentration of random projections,"https://scholar.google.com/scholar?cluster=16052307560028480988&hl=en&as_sdt=0,47",2,2021
Causal Navigation by Continuous-time Neural Networks,28,neurips,0,0,2023-06-16 16:06:41.427000,https://github.com/mit-drl/deepdrone-public,0,Causal navigation by continuous-time neural networks,"https://scholar.google.com/scholar?cluster=17904682122382854627&hl=en&as_sdt=0,23",4,2021
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers,121,neurips,27,8,2023-06-16 16:06:41.627000,https://github.com/facebookresearch/Motionformer,207,Keeping your eye on the ball: Trajectory attention in video transformers,"https://scholar.google.com/scholar?cluster=15297477857724176854&hl=en&as_sdt=0,47",11,2021
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning,8,neurips,3,0,2023-06-16 16:06:41.828000,https://github.com/xionghuichen/codas,6,Cross-modal domain adaptation for cost-efficient visual reinforcement learning,"https://scholar.google.com/scholar?cluster=2567086608461815937&hl=en&as_sdt=0,47",3,2021
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation,62,neurips,18,1,2023-06-16 16:06:42.028000,https://github.com/jiamings/d2c,105,D2c: Diffusion-decoding models for few-shot conditional generation,"https://scholar.google.com/scholar?cluster=10192213298820143142&hl=en&as_sdt=0,1",4,2021
Out-of-Distribution Generalization in Kernel Regression,7,neurips,1,0,2023-06-16 16:06:42.227000,https://github.com/pehlevan-group/kernel-ood-generalization,2,Out-of-distribution generalization in kernel regression,"https://scholar.google.com/scholar?cluster=569923176833535725&hl=en&as_sdt=0,5",1,2021
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective,27,neurips,6,0,2023-06-16 16:06:42.427000,https://github.com/jeremy313/fl-wbc,31,Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective,"https://scholar.google.com/scholar?cluster=828507894680614464&hl=en&as_sdt=0,44",1,2021
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote,7,neurips,0,1,2023-06-16 16:06:42.626000,https://github.com/stephanlorenzen/majorityvotebounds,12,Chebyshev-Cantelli PAC-Bayes-Bennett inequality for the weighted majority vote,"https://scholar.google.com/scholar?cluster=1599783900613292078&hl=en&as_sdt=0,23",2,2021
The Inductive Bias of Quantum Kernels,43,neurips,1,0,2023-06-16 16:06:42.826000,https://github.com/jmkuebler/quantumbias,3,The inductive bias of quantum kernels,"https://scholar.google.com/scholar?cluster=11262844044070115079&hl=en&as_sdt=0,5",2,2021
Pretraining Representations for Data-Efficient Reinforcement Learning,68,neurips,7,1,2023-06-16 16:06:43.026000,https://github.com/mila-iqia/SGI,46,Pretraining representations for data-efficient reinforcement learning,"https://scholar.google.com/scholar?cluster=14071558291421901639&hl=en&as_sdt=0,5",5,2021
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?,34,neurips,2,0,2023-06-16 16:06:43.225000,https://github.com/boone891214/sanity-check-LTH,5,Sanity checks for lottery tickets: Does your winning ticket really win the jackpot?,"https://scholar.google.com/scholar?cluster=205912691143067869&hl=en&as_sdt=0,32",2,2021
Understanding Interlocking Dynamics of Cooperative Rationalization,17,neurips,1,3,2023-06-16 16:06:43.426000,https://github.com/gorov/understanding_interlocking,1,Understanding interlocking dynamics of cooperative rationalization,"https://scholar.google.com/scholar?cluster=7420731120310310890&hl=en&as_sdt=0,5",1,2021
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning,20,neurips,0,1,2023-06-16 16:06:43.625000,https://github.com/jasonzhang929/BVFT_empirical_experiments,6,Towards hyperparameter-free policy selection for offline reinforcement learning,"https://scholar.google.com/scholar?cluster=9175248700275907762&hl=en&as_sdt=0,5",2,2021
"Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals",5,neurips,0,0,2023-06-16 16:06:43.831000,https://github.com/langliu95/divergence-frontier-bounds,0,"Divergence frontiers for generative models: Sample complexity, quantization effects, and frontier integrals","https://scholar.google.com/scholar?cluster=3503189319971497942&hl=en&as_sdt=0,5",1,2021
Consistency Regularization for Variational Auto-Encoders,34,neurips,2,0,2023-06-16 16:06:44.032000,https://github.com/sinhasam/crvae,2,Consistency regularization for variational auto-encoders,"https://scholar.google.com/scholar?cluster=15925452780992311811&hl=en&as_sdt=0,5",3,2021
Interactive Label Cleaning with Example-based Explanations,13,neurips,1,0,2023-06-16 16:06:44.233000,https://github.com/abonte/cincer,10,Interactive label cleaning with example-based explanations,"https://scholar.google.com/scholar?cluster=9096815047813990175&hl=en&as_sdt=0,33",4,2021
Glance-and-Gaze Vision Transformer,45,neurips,2,1,2023-06-16 16:06:44.432000,https://github.com/yucornetto/GG-Transformer,28,Glance-and-gaze vision transformer,"https://scholar.google.com/scholar?cluster=1431816651418361565&hl=en&as_sdt=0,14",7,2021
Self-Supervised GANs with Label Augmentation,11,neurips,3,0,2023-06-16 16:06:44.632000,https://github.com/houliangict/ssgan-la,19,Self-supervised gans with label augmentation,"https://scholar.google.com/scholar?cluster=14631812487211747492&hl=en&as_sdt=0,4",1,2021
Shape As Points: A Differentiable Poisson Solver,74,neurips,31,6,2023-06-16 16:06:44.833000,https://github.com/autonomousvision/shape_as_points,444,Shape as points: A differentiable poisson solver,"https://scholar.google.com/scholar?cluster=11152020817998179193&hl=en&as_sdt=0,5",23,2021
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks,20,neurips,0,0,2023-06-16 16:06:45.033000,https://github.com/rice-eic/robust-scratch-ticket,13,Drawing robust scratch tickets: Subnetworks with inborn robustness are found within randomly initialized networks,"https://scholar.google.com/scholar?cluster=1112960580717486938&hl=en&as_sdt=0,10",2,2021
Rectifying the Shortcut Learning of Background for Few-Shot Learning,47,neurips,16,0,2023-06-16 16:06:45.233000,https://github.com/Frankluox/FewShotCodeBase,84,Rectifying the shortcut learning of background for few-shot learning,"https://scholar.google.com/scholar?cluster=4412946575250832431&hl=en&as_sdt=0,20",4,2021
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning,10,neurips,0,0,2023-06-16 16:06:45.433000,https://github.com/uuujf/morl,2,Accommodating picky customers: Regret bound and exploration complexity for multi-objective reinforcement learning,"https://scholar.google.com/scholar?cluster=15706169705724363755&hl=en&as_sdt=0,39",1,2021
The Emergence of Objectness: Learning Zero-shot Segmentation from Videos,20,neurips,8,5,2023-06-16 16:06:45.632000,https://github.com/rt219/the-emergence-of-objectness,49,The emergence of objectness: Learning zero-shot segmentation from videos,"https://scholar.google.com/scholar?cluster=2619393052877495723&hl=en&as_sdt=0,5",6,2021
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference,20,neurips,0,1,2023-06-16 16:06:45.832000,https://github.com/ucla-starai/circuit-ops-atlas,4,A compositional atlas of tractable circuit operations for probabilistic inference,"https://scholar.google.com/scholar?cluster=1664691014930951801&hl=en&as_sdt=0,32",3,2021
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator,3,neurips,3,0,2023-06-16 16:06:46.033000,https://github.com/alekdimi/carms,1,CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator,"https://scholar.google.com/scholar?cluster=9402651328510741907&hl=en&as_sdt=0,3",1,2021
Representing Long-Range Context for Graph Neural Networks with Global Attention,70,neurips,17,5,2023-06-16 16:06:46.233000,https://github.com/ucbrise/graphtrans,86,Representing long-range context for graph neural networks with global attention,"https://scholar.google.com/scholar?cluster=4846274432308577518&hl=en&as_sdt=0,5",5,2021
Implicit Transformer Network for Screen Content Image Continuous Super-Resolution,15,neurips,7,3,2023-06-16 16:06:46.432000,https://github.com/codyshen0000/itsrn,40,Implicit transformer network for screen content image continuous super-resolution,"https://scholar.google.com/scholar?cluster=11245794845935575123&hl=en&as_sdt=0,10",8,2021
Channel Permutations for N:M Sparsity,17,neurips,1213,656,2023-06-16 16:06:46.632000,https://github.com/NVIDIA/apex,7297,Channel permutations for n: m sparsity,"https://scholar.google.com/scholar?cluster=11721196871022248200&hl=en&as_sdt=0,10",100,2021
Video Instance Segmentation using Inter-Frame Communication Transformers,73,neurips,13,3,2023-06-16 16:06:46.832000,https://github.com/sukjunhwang/IFC,85,Video instance segmentation using inter-frame communication transformers,"https://scholar.google.com/scholar?cluster=10954642986790215849&hl=en&as_sdt=0,44",5,2021
Progressive Coordinate Transforms for Monocular 3D Object Detection,44,neurips,10,6,2023-06-16 16:06:47.032000,https://github.com/amazon-research/progressive-coordinate-transforms,62,Progressive coordinate transforms for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=9147402197404882623&hl=en&as_sdt=0,26",4,2021
Structured Reordering for Modeling Latent Alignments in Sequence Transduction,17,neurips,19,0,2023-06-16 16:06:47.231000,https://github.com/berlino/tensor2struct-public,83,Structured reordering for modeling latent alignments in sequence transduction,"https://scholar.google.com/scholar?cluster=16621898977325649055&hl=en&as_sdt=0,11",6,2021
HNPE: Leveraging Global Parameters for Neural Posterior Estimation,2,neurips,2,1,2023-06-16 16:06:47.431000,https://github.com/plcrodrigues/hnpe,11,HNPE: Leveraging global parameters for neural posterior estimation,"https://scholar.google.com/scholar?cluster=65503754638557364&hl=en&as_sdt=0,10",6,2021
Alignment Attention by Matching Key and Query Distributions,5,neurips,1,0,2023-06-16 16:06:47.631000,https://github.com/szhang42/alignment_attention,6,Alignment attention by matching key and query distributions,"https://scholar.google.com/scholar?cluster=4032930998238119032&hl=en&as_sdt=0,34",2,2021
Settling the Variance of Multi-Agent Policy Gradients,24,neurips,7,3,2023-06-16 16:06:47.831000,https://github.com/morning9393/optimal-baseline-for-multi-agent-policy-gradients,24,Settling the variance of multi-agent policy gradients,"https://scholar.google.com/scholar?cluster=3289943660848512969&hl=en&as_sdt=0,14",1,2021
Controllable and Compositional Generation with Latent-Space Energy-Based Models,30,neurips,9,0,2023-06-16 16:06:48.031000,https://github.com/NVlabs/LACE,66,Controllable and compositional generation with latent-space energy-based models,"https://scholar.google.com/scholar?cluster=3651132171595385407&hl=en&as_sdt=0,15",4,2021
Reverse-Complement Equivariant Networks for DNA Sequences,8,neurips,1,0,2023-06-16 16:06:48.232000,https://github.com/vincentx15/equi-rc,9,Reverse-complement equivariant networks for DNA sequences,"https://scholar.google.com/scholar?cluster=10144921581759903612&hl=en&as_sdt=0,33",3,2021
Temporal-attentive Covariance Pooling Networks for Video Recognition,12,neurips,7,0,2023-06-16 16:06:48.432000,https://github.com/ZilinGao/Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition,23,Temporal-attentive covariance pooling networks for video recognition,"https://scholar.google.com/scholar?cluster=9201162908941511387&hl=en&as_sdt=0,5",1,2021
Marginalised Gaussian Processes with Nested Sampling,8,neurips,0,0,2023-06-16 16:06:48.632000,https://github.com/frgsimpson/nsampling,0,Marginalised gaussian processes with nested sampling,"https://scholar.google.com/scholar?cluster=17735373973978612966&hl=en&as_sdt=0,5",1,2021
Provably Faster Algorithms for Bilevel Optimization,71,neurips,4,0,2023-06-16 16:06:48.835000,https://github.com/JunjieYang97/MRVRBO,12,Provably faster algorithms for bilevel optimization,"https://scholar.google.com/scholar?cluster=9607977285586216355&hl=en&as_sdt=0,48",1,2021
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction,31,neurips,6,2,2023-06-16 16:06:49.035000,https://github.com/seongjunyun/neo_gnns,27,Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction,"https://scholar.google.com/scholar?cluster=2697789317033616944&hl=en&as_sdt=0,47",2,2021
Self-Supervised Multi-Object Tracking with Cross-input Consistency,11,neurips,2,5,2023-06-16 16:06:49.246000,https://github.com/favyen/uns20,14,Self-supervised multi-object tracking with cross-input consistency,"https://scholar.google.com/scholar?cluster=13432924091721167465&hl=en&as_sdt=0,5",1,2021
Tree in Tree: from Decision Trees to Decision Graphs,1,neurips,1,0,2023-06-16 16:06:49.456000,https://github.com/BingzhaoZhu/TnTDecisionGraph,10,Tree in Tree: from Decision Trees to Decision Graphs,"https://scholar.google.com/scholar?cluster=13675421190274880404&hl=en&as_sdt=0,33",1,2021
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles,57,neurips,40,7,2023-06-16 16:06:49.657000,https://github.com/PattanaikL/GeoMol,133,Geomol: Torsional geometric generation of molecular 3d conformer ensembles,"https://scholar.google.com/scholar?cluster=12713922106835404541&hl=en&as_sdt=0,33",7,2021
Implicit Semantic Response Alignment for Partial Domain Adaptation,5,neurips,1,0,2023-06-16 16:06:49.859000,https://github.com/implicit-seman-align/implicit-semantic-response-alignment,4,Implicit semantic response alignment for partial domain adaptation,"https://scholar.google.com/scholar?cluster=17586829602447359052&hl=en&as_sdt=0,5",1,2021
ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation,23,neurips,12,4,2023-06-16 16:06:50.105000,https://github.com/microsoft/UDA,83,ToAlign: task-oriented alignment for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=9142110376115272009&hl=en&as_sdt=0,5",7,2021
Safe Reinforcement Learning by Imagining the Near Future,23,neurips,6,1,2023-06-16 16:06:50.305000,https://github.com/gwthomas/safe-mbpo,29,Safe reinforcement learning by imagining the near future,"https://scholar.google.com/scholar?cluster=7090557022345376881&hl=en&as_sdt=0,5",2,2021
Towards Biologically Plausible Convolutional Networks,19,neurips,3,0,2023-06-16 16:06:50.506000,https://github.com/romanpogodin/towards-bio-plausible-conv,13,Towards biologically plausible convolutional networks,"https://scholar.google.com/scholar?cluster=17481866081274764684&hl=en&as_sdt=0,41",2,2021
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification,253,neurips,65,1,2023-06-16 16:06:50.706000,https://github.com/raoyongming/DynamicViT,474,Dynamicvit: Efficient vision transformers with dynamic token sparsification,"https://scholar.google.com/scholar?cluster=14185047449981394536&hl=en&as_sdt=0,5",11,2021
Learning Transferable Adversarial Perturbations,18,neurips,2,1,2023-06-16 16:06:50.944000,https://github.com/krishnakanthnakka/transferable_perturbations,21,Learning transferable adversarial perturbations,"https://scholar.google.com/scholar?cluster=13743701895740098488&hl=en&as_sdt=0,14",0,2021
PortaSpeech: Portable and High-Quality Generative Text-to-Speech,39,neurips,87,16,2023-06-16 16:06:51.144000,https://github.com/natspeech/natspeech,866,Portaspeech: Portable and high-quality generative text-to-speech,"https://scholar.google.com/scholar?cluster=4177501522773357655&hl=en&as_sdt=0,44",20,2021
Learning Treatment Effects in Panels with General Intervention Patterns,8,neurips,0,0,2023-06-16 16:06:51.344000,https://github.com/TianyiPeng/Causal-Inference-Code,0,Learning treatment effects in panels with general intervention patterns,"https://scholar.google.com/scholar?cluster=15798441898822677855&hl=en&as_sdt=0,36",2,2021
Lossy Compression for Lossless Prediction,39,neurips,7,1,2023-06-16 16:06:51.544000,https://github.com/YannDubs/lossyless,96,Lossy compression for lossless prediction,"https://scholar.google.com/scholar?cluster=767597494653209957&hl=en&as_sdt=0,39",8,2021
CCVS: Context-aware Controllable Video Synthesis,23,neurips,0,3,2023-06-16 16:06:51.745000,https://github.com/16lemoing/ccvs,19,Ccvs: context-aware controllable video synthesis,"https://scholar.google.com/scholar?cluster=4232968738296404748&hl=en&as_sdt=0,10",2,2021
Deep Extrapolation for Attribute-Enhanced Generation,11,neurips,11,2,2023-06-16 16:06:51.945000,https://github.com/salesforce/genhance,29,Deep extrapolation for attribute-enhanced generation,"https://scholar.google.com/scholar?cluster=14781609515979252520&hl=en&as_sdt=0,44",6,2021
Generalized DataWeighting via Class-Level Gradient Manipulation,11,neurips,3,0,2023-06-16 16:06:52.145000,https://github.com/ggchen1997/gdw-nips2021,18,Generalized dataweighting via class-level gradient manipulation,"https://scholar.google.com/scholar?cluster=4782284978839575069&hl=en&as_sdt=0,5",2,2021
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation,5,neurips,4,0,2023-06-16 16:06:52.346000,https://github.com/canqin001/efficient_graph_similarity_computation,27,Slow learning and fast inference: Efficient graph similarity computation via knowledge distillation,"https://scholar.google.com/scholar?cluster=7481527456044774037&hl=en&as_sdt=0,5",2,2021
Posterior Meta-Replay for Continual Learning,27,neurips,3,0,2023-06-16 16:06:52.545000,https://github.com/chrhenning/posterior_replay_cl,13,Posterior meta-replay for continual learning,"https://scholar.google.com/scholar?cluster=13065615771261410719&hl=en&as_sdt=0,31",1,2021
Optimizing Reusable Knowledge for Continual Learning via Metalearning,19,neurips,3,1,2023-06-16 16:06:52.745000,https://github.com/JuliousHurtado/meta-training-setup,9,Optimizing reusable knowledge for continual learning via metalearning,"https://scholar.google.com/scholar?cluster=17458269254540986123&hl=en&as_sdt=0,5",2,2021
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation,61,neurips,14,0,2023-06-16 16:06:52.946000,https://github.com/ivam-he/BernNet,38,Bernnet: Learning arbitrary graph spectral filters via bernstein approximation,"https://scholar.google.com/scholar?cluster=16143355753899412222&hl=en&as_sdt=0,3",2,2021
Co-evolution Transformer for Protein Contact Prediction,8,neurips,4,2,2023-06-16 16:06:53.146000,https://github.com/microsoft/proteinfolding,7,Co-evolution transformer for protein contact prediction,"https://scholar.google.com/scholar?cluster=13689461348782005120&hl=en&as_sdt=0,36",3,2021
Unsupervised Foreground Extraction via Deep Region Competition,17,neurips,3,0,2023-06-16 16:06:53.347000,https://github.com/yupeiyu98/drc,33,Unsupervised foreground extraction via deep region competition,"https://scholar.google.com/scholar?cluster=16513245695011473122&hl=en&as_sdt=0,15",2,2021
Class-Incremental Learning via Dual Augmentation,49,neurips,4,0,2023-06-16 16:06:53.546000,https://github.com/impression2805/il2a,21,Class-incremental learning via dual augmentation,"https://scholar.google.com/scholar?cluster=2287473140272807570&hl=en&as_sdt=0,5",1,2021
Credal Self-Supervised Learning,11,neurips,1,0,2023-06-16 16:06:53.747000,https://github.com/julilien/cssl,6,Credal self-supervised learning,"https://scholar.google.com/scholar?cluster=6910723304890074266&hl=en&as_sdt=0,33",2,2021
Spot the Difference: Detection of Topological Changes via Geometric Alignment,1,neurips,0,1,2023-06-16 16:06:53.947000,https://github.com/steffenczolbe/topologicalchangedetection,3,Spot the Difference: Detection of Topological Changes via Geometric Alignment,"https://scholar.google.com/scholar?cluster=5621165356317670171&hl=en&as_sdt=0,5",3,2021
A PAC-Bayes Analysis of Adversarial Robustness,8,neurips,0,0,2023-06-16 16:06:54.150000,https://github.com/paulviallard/neurips21-pb-robustness,4,A pac-bayes analysis of adversarial robustness,"https://scholar.google.com/scholar?cluster=4965785273710394143&hl=en&as_sdt=0,5",1,2021
Bayesian Optimization of Function Networks,22,neurips,3,0,2023-06-16 16:06:54.350000,https://github.com/raulastudillo06/bofn,4,Bayesian optimization of function networks,"https://scholar.google.com/scholar?cluster=4084524116407827795&hl=en&as_sdt=0,5",1,2021
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning,33,neurips,44,27,2023-06-16 16:06:54.549000,https://github.com/decile-team/cords,272,Retrieve: Coreset selection for efficient and robust semi-supervised learning,"https://scholar.google.com/scholar?cluster=6090246534903910907&hl=en&as_sdt=0,5",10,2021
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State,20,neurips,4,1,2023-06-16 16:06:54.749000,https://github.com/pkuxmq/ide-fsnn,25,Training feedback spiking neural networks by implicit differentiation on the equilibrium state,"https://scholar.google.com/scholar?cluster=6586041422303063440&hl=en&as_sdt=0,5",3,2021
Online Selective Classification with Limited Feedback,5,neurips,1,0,2023-06-16 16:06:54.952000,https://github.com/anilkagak2/online-selective-classification,2,Online selective classification with limited feedback,"https://scholar.google.com/scholar?cluster=15501560290765015507&hl=en&as_sdt=0,1",3,2021
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions,30,neurips,6,0,2023-06-16 16:06:55.153000,https://github.com/nec-research/tf-imle,68,Implicit MLE: backpropagating through discrete exponential family distributions,"https://scholar.google.com/scholar?cluster=5081288066118060759&hl=en&as_sdt=0,21",6,2021
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms,2,neurips,0,0,2023-06-16 16:06:55.354000,https://github.com/csy530216/pg-zoo,3,On the convergence of prior-guided zeroth-order optimization algorithms,"https://scholar.google.com/scholar?cluster=1225343765026705119&hl=en&as_sdt=0,5",2,2021
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective,6,neurips,3,0,2023-06-16 16:06:55.555000,https://github.com/smilesdzgk/gntm,9,Topic modeling revisited: A document graph-based neural network perspective,"https://scholar.google.com/scholar?cluster=13478795624326939129&hl=en&as_sdt=0,5",1,2021
Hard-Attention for Scalable Image Classification,18,neurips,2,0,2023-06-16 16:06:55.757000,https://github.com/Tpap/TNet,12,Hard-attention for scalable image classification,"https://scholar.google.com/scholar?cluster=12789329679837374584&hl=en&as_sdt=0,33",1,2021
"TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up",371,neurips,195,12,2023-06-16 16:06:55.958000,https://github.com/VITA-Group/TransGAN,1549,"Transgan: Two pure transformers can make one strong gan, and that can scale up","https://scholar.google.com/scholar?cluster=13264315013369292854&hl=en&as_sdt=0,5",32,2021
Characterizing the risk of fairwashing,11,neurips,1,0,2023-06-16 16:06:56.158000,https://github.com/aivodji/characterizing_fairwashing,0,Characterizing the risk of fairwashing,"https://scholar.google.com/scholar?cluster=16578546167532201637&hl=en&as_sdt=0,39",1,2021
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples,21,neurips,5,0,2023-06-16 16:06:56.360000,https://github.com/iamkanghyunchoi/qimera,24,Qimera: Data-free quantization with synthetic boundary supporting samples,"https://scholar.google.com/scholar?cluster=3050831061991737197&hl=en&as_sdt=0,5",3,2021
Adversarial Reweighting for Partial Domain Adaptation,9,neurips,1,0,2023-06-16 16:06:56.563000,https://github.com/xjtu-xgu/adversarial-reweighting-for-partial-domain-adaptation,16,Adversarial reweighting for partial domain adaptation,"https://scholar.google.com/scholar?cluster=285607461307195622&hl=en&as_sdt=0,36",1,2021
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information,22,neurips,0,0,2023-06-16 16:06:56.763000,https://github.com/IST-DASLab/M-FAC,11,M-fac: Efficient matrix-free approximations of second-order information,"https://scholar.google.com/scholar?cluster=17606620249219904066&hl=en&as_sdt=0,14",5,2021
Anti-Backdoor Learning: Training Clean Models on Poisoned Data,91,neurips,8,0,2023-06-16 16:06:56.963000,https://github.com/bboylyg/abl,61,Anti-backdoor learning: Training clean models on poisoned data,"https://scholar.google.com/scholar?cluster=8704631197528357914&hl=en&as_sdt=0,5",3,2021
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models,9,neurips,0,0,2023-06-16 16:06:57.164000,https://github.com/keunseokim91/lmpbt,1,Locally most powerful Bayesian test for out-of-distribution detection using deep generative models,"https://scholar.google.com/scholar?cluster=16483011761242448907&hl=en&as_sdt=0,10",1,2021
Robust Compressed Sensing MRI with Deep Generative Priors,98,neurips,11,2,2023-06-16 16:06:57.364000,https://github.com/utcsilab/csgm-mri-langevin,56,Robust compressed sensing mri with deep generative priors,"https://scholar.google.com/scholar?cluster=13822397892595830206&hl=en&as_sdt=0,45",4,2021
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions,6,neurips,2,0,2023-06-16 16:06:57.564000,https://github.com/JegZheng/CT-pytorch,11,Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions,"https://scholar.google.com/scholar?cluster=9036582230777928391&hl=en&as_sdt=0,6",2,2021
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models,22,neurips,3,1,2023-06-16 16:06:57.764000,https://github.com/zecevic-matej/ispn,6,Interventional sum-product networks: Causal inference with tractable probabilistic models,"https://scholar.google.com/scholar?cluster=6190649913010207640&hl=en&as_sdt=0,47",3,2021
PettingZoo: Gym for Multi-Agent Reinforcement Learning,145,neurips,303,18,2023-06-16 16:06:57.964000,https://github.com/Farama-Foundation/PettingZoo,1859,Pettingzoo: Gym for multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=13783223934701922919&hl=en&as_sdt=0,33",20,2021
Decision Transformer: Reinforcement Learning via Sequence Modeling,511,neurips,350,23,2023-06-16 16:06:58.164000,https://github.com/kzl/decision-transformer,1731,Decision transformer: Reinforcement learning via sequence modeling,"https://scholar.google.com/scholar?cluster=7704492432415173786&hl=en&as_sdt=0,5",25,2021
Probability Paths and the Structure of Predictions over Time,0,neurips,0,0,2023-06-16 16:06:58.364000,https://github.com/itsmrlin/probability-paths,1,Probability Paths and the Structure of Predictions over Time,"https://scholar.google.com/scholar?cluster=6857395478545683607&hl=en&as_sdt=0,19",1,2021
Automorphic Equivalence-aware Graph Neural Network,8,neurips,1,0,2023-06-16 16:06:58.564000,https://github.com/tsinghua-fib-lab/grape,3,Automorphic equivalence-aware graph neural network,"https://scholar.google.com/scholar?cluster=8149350483577160238&hl=en&as_sdt=0,22",1,2021
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems,11,neurips,0,0,2023-06-16 16:06:58.766000,https://github.com/ItaySafran/SGD_condition_number,0,Random shuffling beats sgd only after many epochs on ill-conditioned problems,"https://scholar.google.com/scholar?cluster=7972409612167103531&hl=en&as_sdt=0,5",1,2021
Efficient Neural Network Training via Forward and Backward Propagation Sparsification,22,neurips,0,0,2023-06-16 16:06:58.965000,https://github.com/x-zho14/VRPGE-Sparse-Training,4,Efficient neural network training via forward and backward propagation sparsification,"https://scholar.google.com/scholar?cluster=13227182771581567481&hl=en&as_sdt=0,47",2,2021
Large-Scale Wasserstein Gradient Flows,36,neurips,4,0,2023-06-16 16:06:59.165000,https://github.com/PetrMokrov/Large-Scale-Wasserstein-Gradient-Flows,23,Large-scale wasserstein gradient flows,"https://scholar.google.com/scholar?cluster=10744565130766307878&hl=en&as_sdt=0,7",4,2021
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings,6,neurips,3,1,2023-06-16 16:06:59.365000,https://github.com/hengruicai/djl,6,Deep jump learning for off-policy evaluation in continuous treatment settings,"https://scholar.google.com/scholar?cluster=6393386215888057987&hl=en&as_sdt=0,11",1,2021
Attention Approximates Sparse Distributed Memory,18,neurips,2,0,2023-06-16 16:06:59.565000,https://github.com/trentbrick/attention-approximates-sdm,17,Attention approximates sparse distributed memory,"https://scholar.google.com/scholar?cluster=18296333632073096000&hl=en&as_sdt=0,5",2,2021
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance,4,neurips,1,0,2023-06-16 16:06:59.764000,https://github.com/clinicalml/finding-decision-heterogeneity-regions,3,Finding regions of heterogeneity in decision-making via expected conditional covariance,"https://scholar.google.com/scholar?cluster=3846031101866356923&hl=en&as_sdt=0,39",5,2021
Identifying and Benchmarking Natural Out-of-Context Prediction Problems,4,neurips,0,0,2023-06-16 16:06:59.965000,https://github.com/dmadras/nooch,5,Identifying and benchmarking natural out-of-context prediction problems,"https://scholar.google.com/scholar?cluster=8053844208251353066&hl=en&as_sdt=0,47",1,2021
Overinterpretation reveals image classification model pathologies,34,neurips,6,3,2023-06-16 16:07:00.165000,https://github.com/gifford-lab/overinterpretation,18,Overinterpretation reveals image classification model pathologies,"https://scholar.google.com/scholar?cluster=15064589715025215072&hl=en&as_sdt=0,43",2,2021
Neural Circuit Synthesis from Specification Patterns,11,neurips,3,1,2023-06-16 16:07:00.365000,https://github.com/reactive-systems/ml2,3,Neural circuit synthesis from specification patterns,"https://scholar.google.com/scholar?cluster=14168342810209101010&hl=en&as_sdt=0,5",3,2021
Federated Multi-Task Learning under a Mixture of Distributions,114,neurips,25,0,2023-06-16 16:07:00.565000,https://github.com/omarfoq/fedem,116,Federated multi-task learning under a mixture of distributions,"https://scholar.google.com/scholar?cluster=7523531428975949915&hl=en&as_sdt=0,5",3,2021
ResT: An Efficient Transformer for Visual Recognition,121,neurips,27,10,2023-06-16 16:07:00.764000,https://github.com/wofmanaf/ResT,233,Rest: An efficient transformer for visual recognition,"https://scholar.google.com/scholar?cluster=16023950935157352535&hl=en&as_sdt=0,34",6,2021
Self-Supervised Learning with Kernel Dependence Maximization,35,neurips,1,0,2023-06-16 16:07:00.964000,https://github.com/deepmind/ssl_hsic,33,Self-supervised learning with kernel dependence maximization,"https://scholar.google.com/scholar?cluster=13912402342615870661&hl=en&as_sdt=0,47",3,2021
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception,9,neurips,0,0,2023-06-16 16:07:01.164000,https://github.com/chung-neuroai-lab/adversarial-manifolds,3,Neural population geometry reveals the role of stochasticity in robust perception,"https://scholar.google.com/scholar?cluster=8334152733875926312&hl=en&as_sdt=0,10",2,2021
Unsupervised Learning of Compositional Energy Concepts,33,neurips,8,2,2023-06-16 16:07:01.364000,https://github.com/yilundu/comet,48,Unsupervised learning of compositional energy concepts,"https://scholar.google.com/scholar?cluster=13193016976136899043&hl=en&as_sdt=0,19",2,2021
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach,6,neurips,1,0,2023-06-16 16:07:01.564000,https://github.com/aabbas90/COPS,14,Combinatorial optimization for panoptic segmentation: A fully differentiable approach,"https://scholar.google.com/scholar?cluster=1192610999668447759&hl=en&as_sdt=0,10",2,2021
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes ,8,neurips,1,2,2023-06-16 16:07:01.764000,https://github.com/nam630/acno_mdp,3,Reinforcement learning with state observation costs in action-contingent noiselessly observable markov decision processes,"https://scholar.google.com/scholar?cluster=7666336988392135584&hl=en&as_sdt=0,11",2,2021
Iterative Amortized Policy Optimization,19,neurips,0,1,2023-06-16 16:07:01.965000,https://github.com/joelouismarino/variational_rl,16,Iterative amortized policy optimization,"https://scholar.google.com/scholar?cluster=5877339606852616235&hl=en&as_sdt=0,8",3,2021
Nested Graph Neural Networks,69,neurips,10,0,2023-06-16 16:07:02.165000,https://github.com/muhanzhang/nestedgnn,45,Nested graph neural networks,"https://scholar.google.com/scholar?cluster=11431651511469545337&hl=en&as_sdt=0,14",1,2021
Multimodal and Multilingual Embeddings for Large-Scale Speech Mining,14,neurips,428,62,2023-06-16 16:07:02.365000,https://github.com/facebookresearch/LASER,3327,Multimodal and multilingual embeddings for large-scale speech mining,"https://scholar.google.com/scholar?cluster=2638068290174673175&hl=en&as_sdt=0,33",91,2021
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables,7,neurips,224,7,2023-06-16 16:07:02.565000,https://github.com/jakobrunge/tigramite,926,Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables,"https://scholar.google.com/scholar?cluster=4882214133614420018&hl=en&as_sdt=0,47",37,2021
A flow-based latent state generative model of neural population responses to natural images,9,neurips,5,0,2023-06-16 16:07:02.765000,https://github.com/sinzlab/bashiri-et-al-2021,5,A flow-based latent state generative model of neural population responses to natural images,"https://scholar.google.com/scholar?cluster=11678236070139425319&hl=en&as_sdt=0,5",4,2021
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction,84,neurips,11,0,2023-06-16 16:07:02.966000,https://github.com/zaixizhang/MGSSL,82,Motif-based graph self-supervised learning for molecular property prediction,"https://scholar.google.com/scholar?cluster=18172966297950947391&hl=en&as_sdt=0,41",2,2021
On Inductive Biases for Heterogeneous Treatment Effect Estimation,31,neurips,16,1,2023-06-16 16:07:03.166000,https://github.com/AliciaCurth/CATENets,80,On inductive biases for heterogeneous treatment effect estimation,"https://scholar.google.com/scholar?cluster=8065378932248670082&hl=en&as_sdt=0,33",1,2021
Adversarial Graph Augmentation to Improve Graph Contrastive Learning,129,neurips,4,1,2023-06-16 16:07:03.366000,https://github.com/susheels/adgcl,68,Adversarial graph augmentation to improve graph contrastive learning,"https://scholar.google.com/scholar?cluster=8871306304913199720&hl=en&as_sdt=0,5",2,2021
Contrastive Reinforcement Learning of Symbolic Reasoning Domains,8,neurips,3,1,2023-06-16 16:07:03.567000,https://github.com/gpoesia/socratic-tutor,6,Contrastive reinforcement learning of symbolic reasoning domains,"https://scholar.google.com/scholar?cluster=17064760670691302458&hl=en&as_sdt=0,34",3,2021
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework,5,neurips,1,0,2023-06-16 16:07:03.767000,https://github.com/tengteng95/spatial_ensemble,18,Spatial ensemble: a novel model smoothing mechanism for student-teacher framework,"https://scholar.google.com/scholar?cluster=16762456942955743613&hl=en&as_sdt=0,39",2,2021
Probabilistic Tensor Decomposition of Neural Population Spiking Activity,1,neurips,0,1,2023-06-16 16:07:03.966000,https://github.com/hugosou/vbgcp,6,Probabilistic tensor decomposition of neural population spiking activity,"https://scholar.google.com/scholar?cluster=10421872540300649808&hl=en&as_sdt=0,33",1,2021
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification,11,neurips,0,2,2023-06-16 16:07:04.177000,https://github.com/waltergerych/rbcc,1,Recurrent bayesian classifier chains for exact multi-label classification,"https://scholar.google.com/scholar?cluster=4029419628080987406&hl=en&as_sdt=0,5",1,2021
Adversarial Attack Generation Empowered by Min-Max Optimization,17,neurips,5,0,2023-06-16 16:07:04.420000,https://github.com/wangjksjtu/minmax-adv,13,Adversarial attack generation empowered by min-max optimization,"https://scholar.google.com/scholar?cluster=2026570449907320771&hl=en&as_sdt=0,10",2,2021
Safe Pontryagin Differentiable Programming,18,neurips,6,0,2023-06-16 16:07:04.639000,https://github.com/wanxinjin/Safe-PDP,52,Safe pontryagin differentiable programming,"https://scholar.google.com/scholar?cluster=9197004349873168467&hl=en&as_sdt=0,4",2,2021
Active 3D Shape Reconstruction from Vision and Touch,16,neurips,9,1,2023-06-16 16:07:04.839000,https://github.com/facebookresearch/Active-3D-Vision-and-Touch,19,Active 3D shape reconstruction from vision and touch,"https://scholar.google.com/scholar?cluster=15734454491754654805&hl=en&as_sdt=0,5",6,2021
"DualNet: Continual Learning, Fast and Slow",60,neurips,7,0,2023-06-16 16:07:05.039000,https://github.com/phquang/DualNet,47,"Dualnet: Continual learning, fast and slow","https://scholar.google.com/scholar?cluster=7928893258137916324&hl=en&as_sdt=0,5",2,2021
Deformable Butterfly: A Highly Structured and Sparse Linear Transform,6,neurips,1,1,2023-06-16 16:07:05.239000,https://github.com/ruilin0212/debut,11,Deformable butterfly: A highly structured and sparse linear transform,"https://scholar.google.com/scholar?cluster=2028959433486626192&hl=en&as_sdt=0,10",1,2021
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning,43,neurips,1,0,2023-06-16 16:07:05.439000,https://github.com/sangmichaelxie/pretraining_analysis,5,Why do pretrained language models help in downstream tasks? an analysis of head and prompt tuning,"https://scholar.google.com/scholar?cluster=9072064632949074229&hl=en&as_sdt=0,5",2,2021
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training,26,neurips,1,0,2023-06-16 16:07:05.639000,https://github.com/TLMichael/Delusive-Adversary,30,Better safe than sorry: Preventing delusive adversaries with adversarial training,"https://scholar.google.com/scholar?cluster=3520870120153676720&hl=en&as_sdt=0,18",2,2021
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations,34,neurips,11,0,2023-06-16 16:07:05.839000,https://github.com/wvangansbeke/Revisiting-Contrastive-SSL,85,Revisiting contrastive methods for unsupervised learning of visual representations,"https://scholar.google.com/scholar?cluster=735436327696041641&hl=en&as_sdt=0,5",6,2021
Diffusion Normalizing Flow,29,neurips,9,2,2023-06-16 16:07:06.039000,https://github.com/qsh-zh/DiffFlow,92,Diffusion normalizing flow,"https://scholar.google.com/scholar?cluster=14357142464181491088&hl=en&as_sdt=0,10",3,2021
Introspective Distillation for Robust Question Answering,23,neurips,2,2,2023-06-16 16:07:06.239000,https://github.com/yuleiniu/introd,13,Introspective distillation for robust question answering,"https://scholar.google.com/scholar?cluster=7828374703675153755&hl=en&as_sdt=0,23",1,2021
Adaptive Machine Unlearning,52,neurips,1,0,2023-06-16 16:07:06.439000,https://github.com/ChrisWaites/adaptive-machine-unlearning,14,Adaptive machine unlearning,"https://scholar.google.com/scholar?cluster=17284958947210206051&hl=en&as_sdt=0,5",2,2021
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training,35,neurips,12,3,2023-06-16 16:07:06.639000,https://github.com/zhuchen03/gradinit,127,Gradinit: Learning to initialize neural networks for stable and efficient training,"https://scholar.google.com/scholar?cluster=15488946872563904704&hl=en&as_sdt=0,33",4,2021
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs,4,neurips,2,1,2023-06-16 16:07:06.838000,https://github.com/iesl/geometric_graph_embedding,8,Capacity and bias of learned geometric embeddings for directed graphs,"https://scholar.google.com/scholar?cluster=16338786501738019143&hl=en&as_sdt=0,43",16,2021
Online Learning Of Neural Computations From Sparse Temporal Feedback,1,neurips,0,0,2023-06-16 16:07:07.039000,https://github.com/lukas-braun/learning-neural-computations,3,Online learning of neural computations from sparse temporal feedback,"https://scholar.google.com/scholar?cluster=6869448204792358463&hl=en&as_sdt=0,22",1,2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style,134,neurips,5,0,2023-06-16 16:07:07.240000,https://github.com/ysharma1126/ssl_identifiability,29,Self-supervised learning with data augmentations provably isolates content from style,"https://scholar.google.com/scholar?cluster=7917711258655478976&hl=en&as_sdt=0,5",2,2021
Instance-Conditional Knowledge Distillation for Object Detection,35,neurips,5,0,2023-06-16 16:07:07.440000,https://github.com/megvii-research/ICD,53,Instance-conditional knowledge distillation for object detection,"https://scholar.google.com/scholar?cluster=14282697853463699011&hl=en&as_sdt=0,41",5,2021
Multimodal Virtual Point 3D Detection,78,neurips,33,9,2023-06-16 16:07:07.640000,https://github.com/tianweiy/MVP,236,Multimodal virtual point 3d detection,"https://scholar.google.com/scholar?cluster=4582080155258437560&hl=en&as_sdt=0,5",5,2021
On Joint Learning for Solving Placement and Routing in Chip Design,23,neurips,31,8,2023-06-16 16:07:07.839000,https://github.com/thinklab-sjtu/eda-ai,125,On joint learning for solving placement and routing in chip design,"https://scholar.google.com/scholar?cluster=8601523056294216341&hl=en&as_sdt=0,21",6,2021
Learning with Algorithmic Supervision via Continuous Relaxations,17,neurips,4,1,2023-06-16 16:07:08.040000,https://github.com/felix-petersen/algovision,79,Learning with algorithmic supervision via continuous relaxations,"https://scholar.google.com/scholar?cluster=6447317346907557992&hl=en&as_sdt=0,26",2,2021
"DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras",102,neurips,184,64,2023-06-16 16:07:08.239000,https://github.com/princeton-vl/droid-slam,1192,"Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras","https://scholar.google.com/scholar?cluster=6382749367222033389&hl=en&as_sdt=0,5",43,2021
Few-Shot Object Detection via Association and DIscrimination,42,neurips,1,4,2023-06-16 16:07:08.441000,https://github.com/yhcao6/fadi,51,Few-shot object detection via association and discrimination,"https://scholar.google.com/scholar?cluster=251363499415465218&hl=en&as_sdt=0,5",4,2021
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning,48,neurips,7,1,2023-06-16 16:07:08.640000,https://github.com/shiming-chen/hsva,20,Hsva: Hierarchical semantic-visual adaptation for zero-shot learning,"https://scholar.google.com/scholar?cluster=1579442632617525911&hl=en&as_sdt=0,14",1,2021
Low-Rank Subspaces in GANs,35,neurips,4,2,2023-06-16 16:07:08.840000,https://github.com/zhujiapeng/LowRankGAN,114,Low-rank subspaces in gans,"https://scholar.google.com/scholar?cluster=12439105830629052103&hl=en&as_sdt=0,44",12,2021
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels,31,neurips,5,3,2023-06-16 16:07:09.041000,https://github.com/jizongFox/Self-paced-Contrastive-Learning,19,Self-paced contrastive learning for semi-supervised medical image segmentation with meta-labels,"https://scholar.google.com/scholar?cluster=410593789873583327&hl=en&as_sdt=0,39",2,2021
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems,8,neurips,1,0,2023-06-16 16:07:09.241000,https://github.com/jimmysmith1919/jslds_public,8,Reverse engineering recurrent neural networks with jacobian switching linear dynamical systems,"https://scholar.google.com/scholar?cluster=9142246519482264&hl=en&as_sdt=0,44",1,2021
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds,6,neurips,0,0,2023-06-16 16:07:09.442000,https://github.com/adampolak/dpm,1,Learning-augmented dynamic power management with multiple states via new ski rental bounds,"https://scholar.google.com/scholar?cluster=5423257059528807595&hl=en&as_sdt=0,5",2,2021
Large-Scale Unsupervised Object Discovery,28,neurips,2,1,2023-06-16 16:07:09.642000,https://github.com/huyvvo/LOD,19,Large-scale unsupervised object discovery,"https://scholar.google.com/scholar?cluster=15236204020494676594&hl=en&as_sdt=0,5",4,2021
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space,8,neurips,3,0,2023-06-16 16:07:09.842000,https://github.com/gorilla-lab-scut/ss-conv,30,Sparse steerable convolutions: An efficient learning of se (3)-equivariant features for estimation and tracking of object poses in 3d space,"https://scholar.google.com/scholar?cluster=7745307057738038854&hl=en&as_sdt=0,10",3,2021
On Linear Stability of SGD and Input-Smoothness of Neural Networks,23,neurips,2,1,2023-06-16 16:07:10.041000,https://github.com/ChaoMa93/Sobolev-Reg-of-SGD,6,On linear stability of sgd and input-smoothness of neural networks,"https://scholar.google.com/scholar?cluster=8707145438646691678&hl=en&as_sdt=0,20",1,2021
Joint inference and input optimization in equilibrium networks,8,neurips,0,1,2023-06-16 16:07:10.243000,https://github.com/locuslab/jiio-deq,8,Joint inference and input optimization in equilibrium networks,"https://scholar.google.com/scholar?cluster=16212650449337646631&hl=en&as_sdt=0,44",4,2021
A unified framework for bandit multiple testing,7,neurips,1,0,2023-06-16 16:07:10.443000,https://github.com/neilzxu/e_bmt,0,A unified framework for bandit multiple testing,"https://scholar.google.com/scholar?cluster=24267533719859223&hl=en&as_sdt=0,22",1,2021
Recovering Latent Causal Factor for Generalization to Distributional Shifts,18,neurips,4,1,2023-06-16 16:07:10.642000,https://github.com/wubotong/lacim,20,Recovering latent causal factor for generalization to distributional shifts,"https://scholar.google.com/scholar?cluster=13967586791355289063&hl=en&as_sdt=0,22",1,2021
Adversarial Neuron Pruning Purifies Backdoored Deep Models,84,neurips,10,1,2023-06-16 16:07:10.842000,https://github.com/csdongxian/anp_backdoor,36,Adversarial neuron pruning purifies backdoored deep models,"https://scholar.google.com/scholar?cluster=6050825940162092618&hl=en&as_sdt=0,5",2,2021
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection,18,neurips,4,2,2023-06-16 16:07:11.042000,https://github.com/caesarcai/lrpca,11,Learned robust pca: A scalable deep unfolding approach for high-dimensional outlier detection,"https://scholar.google.com/scholar?cluster=8055867094753250128&hl=en&as_sdt=0,14",2,2021
Dynamic Bottleneck for Robust Self-Supervised Exploration,13,neurips,1,0,2023-06-16 16:07:11.242000,https://github.com/baichenjia/db,4,Dynamic bottleneck for robust self-supervised exploration,"https://scholar.google.com/scholar?cluster=11409187468169077186&hl=en&as_sdt=0,5",2,2021
ProTo: Program-Guided Transformer for Program-Guided Tasks,21,neurips,1,0,2023-06-16 16:07:11.442000,https://github.com/sjtuytc/Neurips21-ProTo-Program-guided-Transformers-for-Program-guided-Tasks,20,Proto: Program-guided transformer for program-guided tasks,"https://scholar.google.com/scholar?cluster=17831895146124544328&hl=en&as_sdt=0,24",2,2021
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning,10,neurips,5,0,2023-06-16 16:07:11.642000,https://github.com/tianpeiyang/maptf_code,10,An efficient transfer learning framework for multiagent reinforcement learning,"https://scholar.google.com/scholar?cluster=982889218338734274&hl=en&as_sdt=0,34",3,2021
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform,11,neurips,1,0,2023-06-16 16:07:11.842000,https://github.com/Achillethin/NEO_non_equilibrium_sampling,0,Neo: Non equilibrium sampling on the orbits of a deterministic transform,"https://scholar.google.com/scholar?cluster=17961771076980989561&hl=en&as_sdt=0,5",1,2021
Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer,24,neurips,60,10,2023-06-16 16:07:12.043000,https://github.com/microsoft/mup,866,Tuning large neural networks via zero-shot hyperparameter transfer,"https://scholar.google.com/scholar?cluster=7493984337771588112&hl=en&as_sdt=0,36",26,2021
Differentiable Simulation of Soft Multi-body Systems,25,neurips,1,0,2023-06-16 16:07:12.243000,https://github.com/yilingqiao/diff_fem,33,Differentiable simulation of soft multi-body systems,"https://scholar.google.com/scholar?cluster=9841721368314533190&hl=en&as_sdt=0,5",5,2021
Good Classification Measures and How to Find Them,11,neurips,0,0,2023-06-16 16:07:12.443000,https://github.com/yandex-research/classification-measures,7,Good classification measures and how to find them,"https://scholar.google.com/scholar?cluster=11404788536905460119&hl=en&as_sdt=0,13",0,2021
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck,16,neurips,1,1,2023-06-16 16:07:12.644000,https://github.com/ByungKwanLee/Adversarial-Information-Bottleneck,41,Distilling robust and non-robust features in adversarial examples by information bottleneck,"https://scholar.google.com/scholar?cluster=5846557975157001548&hl=en&as_sdt=0,21",2,2021
A Prototype-Oriented Framework for Unsupervised Domain Adaptation,36,neurips,5,0,2023-06-16 16:07:12.844000,https://github.com/korawat-tanwisuth/proto_da,38,A prototype-oriented framework for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=13706347291358706428&hl=en&as_sdt=0,33",1,2021
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation,1,neurips,0,1,2023-06-16 16:07:13.044000,https://github.com/sunyinggilly/voten,2,Discerning decision-making process of deep neural networks with hierarchical voting transformation,"https://scholar.google.com/scholar?cluster=11689963681509960475&hl=en&as_sdt=0,5",1,2021
Risk-averse Heteroscedastic Bayesian Optimization,17,neurips,1,0,2023-06-16 16:07:13.244000,https://github.com/avidereta/risk-averse-hetero-bo,9,Risk-averse heteroscedastic bayesian optimization,"https://scholar.google.com/scholar?cluster=2848342155856072637&hl=en&as_sdt=0,10",3,2021
Invertible DenseNets with Concatenated LipSwish,11,neurips,3,0,2023-06-16 16:07:13.444000,https://github.com/yperugachidiaz/invertible_densenets,19,Invertible densenets with concatenated lipswish,"https://scholar.google.com/scholar?cluster=9347075628152014581&hl=en&as_sdt=0,10",1,2021
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning,10,neurips,1,0,2023-06-16 16:07:13.675000,https://github.com/akshaymehra24/LimitsOfUDA,6,Understanding the limits of unsupervised domain adaptation via data poisoning,"https://scholar.google.com/scholar?cluster=12714833644875201572&hl=en&as_sdt=0,36",3,2021
Scatterbrain: Unifying Sparse and Low-rank Attention,24,neurips,17,11,2023-06-16 16:07:13.876000,https://github.com/hazyresearch/scatterbrain,127,Scatterbrain: Unifying sparse and low-rank attention,"https://scholar.google.com/scholar?cluster=6782072706474039157&hl=en&as_sdt=0,5",22,2021
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial,10,neurips,0,1,2023-06-16 16:07:14.093000,https://github.com/ucsc-real/canlessbemore,2,Can less be more? when increasing-to-balancing label noise rates considered beneficial,"https://scholar.google.com/scholar?cluster=14641601547710945131&hl=en&as_sdt=0,14",2,2021
Projected GANs Converge Faster,90,neurips,94,19,2023-06-16 16:07:14.293000,https://github.com/autonomousvision/projected_gan,843,Projected gans converge faster,"https://scholar.google.com/scholar?cluster=3804763149823389605&hl=en&as_sdt=0,10",34,2021
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains,13,neurips,3,0,2023-06-16 16:07:14.493000,https://github.com/martius-lab/gatel0rd,18,Sparsely changing latent states for prediction and planning in partially observable domains,"https://scholar.google.com/scholar?cluster=9739662253385511838&hl=en&as_sdt=0,47",2,2021
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning,12,neurips,1,0,2023-06-16 16:07:14.693000,https://github.com/neeharperi/PreferenceNet,13,Preferencenet: Encoding human preferences in auction design with deep learning,"https://scholar.google.com/scholar?cluster=4524270007182675822&hl=en&as_sdt=0,5",4,2021
Large-Scale Learning with Fourier Features and Tensor Decompositions,2,neurips,0,0,2023-06-16 16:07:14.899000,https://github.com/fwesel/t-krr,5,Large-Scale Learning with Fourier Features and Tensor Decompositions,"https://scholar.google.com/scholar?cluster=12138145385303817860&hl=en&as_sdt=0,10",1,2021
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks,5,neurips,7,0,2023-06-16 16:07:15.105000,https://github.com/IBM/sau-explore,9,Deep bandits show-off: Simple and efficient exploration with deep networks,"https://scholar.google.com/scholar?cluster=401634264438416272&hl=en&as_sdt=0,5",5,2021
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning,15,neurips,0,0,2023-06-16 16:07:15.305000,https://github.com/aidefender/remern-remert,6,Regret minimization experience replay in off-policy reinforcement learning,"https://scholar.google.com/scholar?cluster=7565091361055493573&hl=en&as_sdt=0,33",3,2021
Relative Uncertainty Learning for Facial Expression Recognition,54,neurips,5,2,2023-06-16 16:07:15.505000,https://github.com/zyh-uaiaaaa/relative-uncertainty-learning,41,Relative uncertainty learning for facial expression recognition,"https://scholar.google.com/scholar?cluster=16134891885738614873&hl=en&as_sdt=0,15",1,2021
An Information-theoretic Approach to Distribution Shifts,7,neurips,1,0,2023-06-16 16:07:15.706000,https://github.com/mfederici/dsit,21,An information-theoretic approach to distribution shifts,"https://scholar.google.com/scholar?cluster=7250154476412020577&hl=en&as_sdt=0,32",1,2021
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors,12,neurips,1,0,2023-06-16 16:07:15.908000,https://github.com/liuzq09/PRI_SPCA,1,Towards sample-optimal compressive phase retrieval with sparse and generative priors,"https://scholar.google.com/scholar?cluster=14915702811632236465&hl=en&as_sdt=0,14",2,2021
Moser Flow: Divergence-based Generative Modeling on Manifolds,21,neurips,3,1,2023-06-16 16:07:16.109000,https://github.com/noamroze/moser_flow,13,Moser flow: Divergence-based generative modeling on manifolds,"https://scholar.google.com/scholar?cluster=17329804077540165882&hl=en&as_sdt=0,4",1,2021
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling,117,neurips,9,0,2023-06-16 16:07:16.308000,https://github.com/JTT94/diffusion_schrodinger_bridge,72,Diffusion Schrödinger bridge with applications to score-based generative modeling,"https://scholar.google.com/scholar?cluster=318258828713196441&hl=en&as_sdt=0,5",2,2021
Improving Transferability of Representations via Augmentation-Aware Self-Supervision,16,neurips,6,3,2023-06-16 16:07:16.508000,https://github.com/hankook/augself,41,Improving transferability of representations via augmentation-aware self-supervision,"https://scholar.google.com/scholar?cluster=14853889863955201608&hl=en&as_sdt=0,5",3,2021
Long-Short Transformer: Efficient Transformers for Language and Vision,64,neurips,34,0,2023-06-16 16:07:16.709000,https://github.com/NVIDIA/transformer-ls,211,Long-short transformer: Efficient transformers for language and vision,"https://scholar.google.com/scholar?cluster=9337214669127097563&hl=en&as_sdt=0,11",15,2021
Post-Training Sparsity-Aware Quantization,19,neurips,3,0,2023-06-16 16:07:16.909000,https://github.com/gilshm/sparq,27,Post-training sparsity-aware quantization,"https://scholar.google.com/scholar?cluster=9389319073093341225&hl=en&as_sdt=0,14",2,2021
Deconditional Downscaling with Gaussian Processes,12,neurips,0,0,2023-06-16 16:07:17.109000,https://github.com/shahineb/deconditional-downscaling,0,Deconditional downscaling with gaussian processes,"https://scholar.google.com/scholar?cluster=16062448146360267282&hl=en&as_sdt=0,5",1,2021
Per-Pixel Classification is Not All You Need for Semantic Segmentation,480,neurips,139,9,2023-06-16 16:07:17.308000,https://github.com/facebookresearch/MaskFormer,1137,Per-pixel classification is not all you need for semantic segmentation,"https://scholar.google.com/scholar?cluster=8508636578765152299&hl=en&as_sdt=0,38",22,2021
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data,5,neurips,0,0,2023-06-16 16:07:17.509000,https://github.com/ostadabbas/deep-markov-factor-analysis-dmfa-,2,Deep markov factor analysis: Towards concurrent temporal and spatial analysis of fmri data,"https://scholar.google.com/scholar?cluster=1299626113099194019&hl=en&as_sdt=0,5",1,2021
BooVAE: Boosting Approach for Continual Learning of VAE,9,neurips,0,0,2023-06-16 16:07:17.717000,https://github.com/AKuzina/BooVAE,9,BooVAE: Boosting approach for continual learning of VAE,"https://scholar.google.com/scholar?cluster=5333948931157317113&hl=en&as_sdt=0,5",3,2021
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL,6,neurips,0,0,2023-06-16 16:07:17.916000,https://github.com/wange011/offline-pessimistic,3,Pessimism meets invariance: Provably efficient offline mean-field multi-agent RL,"https://scholar.google.com/scholar?cluster=1776481233969285498&hl=en&as_sdt=0,5",2,2021
Emergent Communication of Generalizations,14,neurips,0,0,2023-06-16 16:07:18.116000,https://github.com/jayelm/emergent-generalization,11,Emergent communication of generalizations,"https://scholar.google.com/scholar?cluster=2371943873159023956&hl=en&as_sdt=0,39",2,2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification,25,neurips,12,2,2023-06-16 16:07:18.316000,https://github.com/stadlmax/Graph-Posterior-Network,32,Graph posterior network: Bayesian predictive uncertainty for node classification,"https://scholar.google.com/scholar?cluster=2435401315615096507&hl=en&as_sdt=0,5",4,2021
No-Press Diplomacy from Scratch,21,neurips,12,6,2023-06-16 16:07:18.516000,https://github.com/facebookresearch/diplomacy_searchbot,37,No-press diplomacy from scratch,"https://scholar.google.com/scholar?cluster=15217875294389573451&hl=en&as_sdt=0,21",11,2021
Learning latent causal graphs via mixture oracles,19,neurips,1,0,2023-06-16 16:07:18.716000,https://github.com/30bohdan/latent-dag,4,Learning latent causal graphs via mixture oracles,"https://scholar.google.com/scholar?cluster=1334743308550878246&hl=en&as_sdt=0,5",3,2021
Deep Contextual Video Compression,73,neurips,8,3,2023-06-16 16:07:18.919000,https://github.com/DeepMC-DCVC/DCVC,52,Deep contextual video compression,"https://scholar.google.com/scholar?cluster=7877485587962972033&hl=en&as_sdt=0,47",3,2021
On the Frequency Bias of Generative Models,27,neurips,2,0,2023-06-16 16:07:19.118000,https://github.com/autonomousvision/frequency_bias,36,On the frequency bias of generative models,"https://scholar.google.com/scholar?cluster=13416509158445417919&hl=en&as_sdt=0,14",9,2021
Learning curves of generic features maps for realistic datasets with a teacher-student model,47,neurips,3,0,2023-06-16 16:07:19.318000,https://github.com/IdePHICS/GCMProject,3,Learning curves of generic features maps for realistic datasets with a teacher-student model,"https://scholar.google.com/scholar?cluster=9914858716383445755&hl=en&as_sdt=0,31",2,2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices,10,neurips,2,1,2023-06-16 16:07:19.519000,https://github.com/yandex-research/moshpit-sgd,23,Moshpit SGD: Communication-efficient decentralized training on heterogeneous unreliable devices,"https://scholar.google.com/scholar?cluster=14221777398814396487&hl=en&as_sdt=0,18",2,2021
Self-Supervised Learning Disentangled Group Representation as Feature,30,neurips,8,1,2023-06-16 16:07:19.719000,https://github.com/Wangt-CN/IP-IRM,75,Self-supervised learning disentangled group representation as feature,"https://scholar.google.com/scholar?cluster=17449596620325468499&hl=en&as_sdt=0,14",4,2021
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning,2,neurips,0,0,2023-06-16 16:07:19.920000,https://github.com/INK-USC/SalKG,13,Salkg: Learning from knowledge graph explanations for commonsense reasoning,"https://scholar.google.com/scholar?cluster=6540484146067491013&hl=en&as_sdt=0,5",4,2021
Conformal Bayesian Computation,8,neurips,4,0,2023-06-16 16:07:20.121000,https://github.com/edfong/conformal_bayes,7,Conformal bayesian computation,"https://scholar.google.com/scholar?cluster=101153919231458556&hl=en&as_sdt=0,31",1,2021
Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices,7,neurips,1,0,2023-06-16 16:07:20.337000,https://github.com/fedelopez77/gyrospd,15,Vector-valued distance and gyrocalculus on the space of symmetric positive definite matrices,"https://scholar.google.com/scholar?cluster=12474792610041988042&hl=en&as_sdt=0,5",1,2021
Improved Transformer for High-Resolution GANs,48,neurips,7,4,2023-06-16 16:07:20.551000,https://github.com/google-research/hit-gan,85,Improved transformer for high-resolution gans,"https://scholar.google.com/scholar?cluster=13859013712282369139&hl=en&as_sdt=0,23",4,2021
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence,129,neurips,178,21,2023-06-16 16:07:20.752000,https://github.com/yangxue0827/RotationDetection,1013,Learning high-precision bounding box for rotated object detection via kullback-leibler divergence,"https://scholar.google.com/scholar?cluster=15795399494869889077&hl=en&as_sdt=0,47",21,2021
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling,276,neurips,175,13,2023-06-16 16:07:20.952000,https://github.com/torchssl/torchssl,1128,Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling,"https://scholar.google.com/scholar?cluster=3803828200228720306&hl=en&as_sdt=0,29",14,2021
Relative Flatness and Generalization,27,neurips,1,0,2023-06-16 16:07:21.152000,https://github.com/kampmichael/RelativeFlatnessAndGeneralization,5,Relative flatness and generalization,"https://scholar.google.com/scholar?cluster=16801711530011720213&hl=en&as_sdt=0,44",2,2021
Towards Multi-Grained Explainability for Graph Neural Networks,33,neurips,6,3,2023-06-16 16:07:21.352000,https://github.com/wuyxin/refine,54,Towards multi-grained explainability for graph neural networks,"https://scholar.google.com/scholar?cluster=17873571047667991497&hl=en&as_sdt=0,32",4,2021
Behavior From the Void: Unsupervised Active Pre-Training,96,neurips,46,16,2023-06-16 16:07:21.552000,https://github.com/rll-research/url_benchmark,290,Behavior from the void: Unsupervised active pre-training,"https://scholar.google.com/scholar?cluster=10900014046487526554&hl=en&as_sdt=0,44",7,2021
Neural Distance Embeddings for Biological Sequences,11,neurips,17,3,2023-06-16 16:07:21.752000,https://github.com/gcorso/neuroseed,63,Neural distance embeddings for biological sequences,"https://scholar.google.com/scholar?cluster=15398350307763828837&hl=en&as_sdt=0,5",2,2021
Fitting summary statistics of neural data with a differentiable spiking network simulator,5,neurips,0,0,2023-06-16 16:07:21.951000,https://github.com/epfl-lcn/pub-bellec-wang-2021-sample-and-measure,4,Fitting summary statistics of neural data with a differentiable spiking network simulator,"https://scholar.google.com/scholar?cluster=1562941632690587103&hl=en&as_sdt=0,21",6,2021
All Tokens Matter: Token Labeling for Training Better Vision Transformers,108,neurips,34,5,2023-06-16 16:07:22.152000,https://github.com/zihangJiang/TokenLabeling,401,All tokens matter: Token labeling for training better vision transformers,"https://scholar.google.com/scholar?cluster=2653381841404725442&hl=en&as_sdt=0,5",13,2021
Partition and Code: learning how to compress graphs,12,neurips,5,0,2023-06-16 16:07:22.352000,https://github.com/gbouritsas/PnC,22,Partition and Code: learning how to compress graphs,"https://scholar.google.com/scholar?cluster=850351115776622533&hl=en&as_sdt=0,5",1,2021
Online Variational Filtering and Parameter Learning,6,neurips,3,0,2023-06-16 16:07:22.552000,https://github.com/andrew-cr/online_var_fil,18,Online variational filtering and parameter learning,"https://scholar.google.com/scholar?cluster=17576462119960289899&hl=en&as_sdt=0,5",2,2021
Heavy Ball Neural Ordinary Differential Equations,33,neurips,2,1,2023-06-16 16:07:22.753000,https://github.com/hedixia/heavyballnode,12,Heavy ball neural ordinary differential equations,"https://scholar.google.com/scholar?cluster=7014300232807854100&hl=en&as_sdt=0,16",1,2021
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios,45,neurips,22,8,2023-06-16 16:07:22.953000,https://github.com/decile-team/distil,125,Similar: Submodular information measures based active learning in realistic scenarios,"https://scholar.google.com/scholar?cluster=1754543766939132313&hl=en&as_sdt=0,11",13,2021
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning,18,neurips,2,0,2023-06-16 16:07:23.160000,https://github.com/danruod/fs-dgpm,10,Flattening sharpness for dynamic gradient projection memory benefits continual learning,"https://scholar.google.com/scholar?cluster=11838604925888767745&hl=en&as_sdt=0,45",1,2021
Taxonomizing local versus global structure in neural network loss landscapes,9,neurips,8,0,2023-06-16 16:07:23.360000,https://github.com/nsfzyzz/loss_landscape_taxonomy,12,Taxonomizing local versus global structure in neural network loss landscapes,"https://scholar.google.com/scholar?cluster=3906442243364935062&hl=en&as_sdt=0,6",2,2021
Learning Models for Actionable Recourse,11,neurips,1,0,2023-06-16 16:07:23.560000,https://github.com/alexisjihyeross/adversarial_recourse,4,Learning models for actionable recourse,"https://scholar.google.com/scholar?cluster=328953936977118438&hl=en&as_sdt=0,5",2,2021
EIGNN: Efficient Infinite-Depth Graph Neural Networks,21,neurips,0,0,2023-06-16 16:07:23.761000,https://github.com/liu-jc/eignn,15,Eignn: Efficient infinite-depth graph neural networks,"https://scholar.google.com/scholar?cluster=8296752647036611693&hl=en&as_sdt=0,5",2,2021
Federated Graph Classification over Non-IID Graphs,53,neurips,6,1,2023-06-16 16:07:23.962000,https://github.com/Oxfordblue7/GCFL,30,Federated graph classification over non-iid graphs,"https://scholar.google.com/scholar?cluster=1741103138242476670&hl=en&as_sdt=0,5",1,2021
Conflict-Averse Gradient Descent for Multi-task learning,69,neurips,17,2,2023-06-16 16:07:24.164000,https://github.com/cranial-xix/cagrad,69,Conflict-averse gradient descent for multi-task learning,"https://scholar.google.com/scholar?cluster=8762775323920560478&hl=en&as_sdt=0,47",3,2021
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate,11,neurips,0,1,2023-06-16 16:07:24.367000,https://github.com/xingyuansun/amorsyn,3,Amortized synthesis of constrained configurations using a differentiable surrogate,"https://scholar.google.com/scholar?cluster=4270018087352708570&hl=en&as_sdt=0,5",1,2021
Revisiting Deep Learning Models for Tabular Data,189,neurips,19,0,2023-06-16 16:07:24.567000,https://github.com/Yura52/tabular-dl-revisiting-models,82,Revisiting deep learning models for tabular data,"https://scholar.google.com/scholar?cluster=9460438335911205282&hl=en&as_sdt=0,47",2,2021
SOPE: Spectrum of Off-Policy Estimators,5,neurips,0,0,2023-06-16 16:07:24.767000,https://github.com/pearl-utexas/sope,0,Sope: Spectrum of off-policy estimators,"https://scholar.google.com/scholar?cluster=16068674194932676119&hl=en&as_sdt=0,28",1,2021
Label-Imbalanced and Group-Sensitive Classification under Overparameterization,41,neurips,4,0,2023-06-16 16:07:24.967000,https://github.com/orparask/VS-Loss,11,Label-imbalanced and group-sensitive classification under overparameterization,"https://scholar.google.com/scholar?cluster=1004937566956517205&hl=en&as_sdt=0,5",1,2021
Functional Regularization for Reinforcement Learning via Learned Fourier Features,5,neurips,2,0,2023-06-16 16:07:25.166000,https://github.com/alexlioralexli/learned-fourier-features,13,Functional regularization for reinforcement learning via learned fourier features,"https://scholar.google.com/scholar?cluster=9954772889129922226&hl=en&as_sdt=0,5",1,2021
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity,26,neurips,0,0,2023-06-16 16:07:25.369000,https://github.com/hugobb/StochasticGamesOpt,2,Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity,"https://scholar.google.com/scholar?cluster=17964943870757537526&hl=en&as_sdt=0,11",1,2021
Adversarial Robustness with Non-uniform Perturbations,18,neurips,1,2,2023-06-16 16:07:25.569000,https://github.com/amazon-research/adversarial-robustness-with-nonuniform-perturbations,6,Adversarial robustness with non-uniform perturbations,"https://scholar.google.com/scholar?cluster=18104312853513215625&hl=en&as_sdt=0,3",1,2021
Container: Context Aggregation Networks,43,neurips,9,1,2023-06-16 16:07:25.770000,https://github.com/allenai/container,50,Container: Context aggregation network,"https://scholar.google.com/scholar?cluster=16108325035684916829&hl=en&as_sdt=0,33",7,2021
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs,53,neurips,5,0,2023-06-16 16:07:25.970000,https://github.com/miralab-ustc/qe-cone,37,Cone: Cone embeddings for multi-hop reasoning over knowledge graphs,"https://scholar.google.com/scholar?cluster=5827447506363072334&hl=en&as_sdt=0,14",2,2021
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time,6,neurips,2,1,2023-06-16 16:07:26.169000,https://github.com/anshuln/training-for-the-future,6,Training for the future: A simple gradient interpolation loss to generalize along time,"https://scholar.google.com/scholar?cluster=10591578379838672358&hl=en&as_sdt=0,11",2,2021
Agent Modelling under Partial Observability for Deep Reinforcement Learning,25,neurips,6,3,2023-06-16 16:07:26.370000,https://github.com/uoe-agents/LIAM,23,Agent modelling under partial observability for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=12719717848124946591&hl=en&as_sdt=0,5",4,2021
Conservative Offline Distributional Reinforcement Learning,39,neurips,5,0,2023-06-16 16:07:26.570000,https://github.com/JasonMa2016/CODAC,11,Conservative offline distributional reinforcement learning,"https://scholar.google.com/scholar?cluster=15495713229557963768&hl=en&as_sdt=0,5",2,2021
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks,2,neurips,1,2,2023-06-16 16:07:26.771000,https://github.com/machanic/tangentattack,15,Finding optimal tangent points for reducing distortions of hard-label attacks,"https://scholar.google.com/scholar?cluster=2435511386343410332&hl=en&as_sdt=0,3",2,2021
Scalable Diverse Model Selection for Accessible Transfer Learning,13,neurips,3,1,2023-06-16 16:07:26.971000,https://github.com/dbolya/parc,14,Scalable diverse model selection for accessible transfer learning,"https://scholar.google.com/scholar?cluster=3499493758042195459&hl=en&as_sdt=0,5",1,2021
Fine-Grained Zero-Shot Learning with DNA as Side Information,11,neurips,3,1,2023-06-16 16:07:27.171000,https://github.com/sbadirli/Fine-Grained-ZSL-with-DNA,7,Fine-grained zero-shot learning with dna as side information,"https://scholar.google.com/scholar?cluster=5909945614319311609&hl=en&as_sdt=0,5",1,2021
Scheduling jobs with stochastic holding costs,2,neurips,0,0,2023-06-16 16:07:27.371000,https://github.com/learning-to-schedule/learning-to-schedule,3,Scheduling jobs with stochastic holding costs,"https://scholar.google.com/scholar?cluster=2702725393229126199&hl=en&as_sdt=0,18",1,2021
Exploiting a Zoo of Checkpoints for Unseen Tasks,3,neurips,1,1,2023-06-16 16:07:27.571000,https://github.com/baidu-research/task_space,9,Exploiting a zoo of checkpoints for unseen tasks,"https://scholar.google.com/scholar?cluster=4042561766012812832&hl=en&as_sdt=0,36",2,2021
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach,9,neurips,10,0,2023-06-16 16:07:27.779000,https://github.com/qitianwu/FATE,18,Towards open-world feature extrapolation: An inductive graph learning approach,"https://scholar.google.com/scholar?cluster=5551821224850406446&hl=en&as_sdt=0,5",2,2021
Stochastic bandits with groups of similar arms.,4,neurips,0,0,2023-06-16 16:07:27.980000,https://github.com/fabienpesquerel/stochastic-bandits-with-groups-of-similar-arms-neurips-2021,0,Stochastic bandits with groups of similar arms.,"https://scholar.google.com/scholar?cluster=5844900148532938512&hl=en&as_sdt=0,5",2,2021
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds,6,neurips,1,0,2023-06-16 16:07:28.179000,https://github.com/samgregoost/Rethinking-CGANs,18,Rethinking conditional GAN training: An approach using geometrically structured latent manifolds,"https://scholar.google.com/scholar?cluster=11263236703274984853&hl=en&as_sdt=0,47",1,2021
Fast Axiomatic Attribution for Neural Networks,11,neurips,1,0,2023-06-16 16:07:28.379000,https://github.com/visinf/fast-axiomatic-attribution,13,Fast axiomatic attribution for neural networks,"https://scholar.google.com/scholar?cluster=1107042055315452910&hl=en&as_sdt=0,5",3,2021
Compressive Visual Representations,29,neurips,6,0,2023-06-16 16:07:28.578000,https://github.com/google-research/compressive-visual-representations,33,Compressive visual representations,"https://scholar.google.com/scholar?cluster=10272376959092579040&hl=en&as_sdt=0,5",6,2021
Grounding inductive biases in natural images: invariance stems from variations in data,11,neurips,2,0,2023-06-16 16:07:28.778000,https://github.com/facebookresearch/grounding-inductive-biases,15,Grounding inductive biases in natural images: invariance stems from variations in data,"https://scholar.google.com/scholar?cluster=1326104865279107103&hl=en&as_sdt=0,11",15,2021
Directed Graph Contrastive Learning,28,neurips,1,0,2023-06-16 16:07:28.978000,https://github.com/flyingtango/digcl,33,Directed graph contrastive learning,"https://scholar.google.com/scholar?cluster=8884605387451104351&hl=en&as_sdt=0,14",2,2021
Space-time Mixing Attention for Video Transformer,64,neurips,7,1,2023-06-16 16:07:29.179000,https://github.com/1adrianb/video-transformers,37,Space-time mixing attention for video transformer,"https://scholar.google.com/scholar?cluster=13067561196339094187&hl=en&as_sdt=0,5",3,2021
Only Train Once: A One-Shot Neural Network Training And Pruning Framework,36,neurips,27,3,2023-06-16 16:07:29.379000,https://github.com/tianyic/only_train_once,185,Only train once: A one-shot neural network training and pruning framework,"https://scholar.google.com/scholar?cluster=10322314510418461770&hl=en&as_sdt=0,38",9,2021
Referring Transformer: A One-step Approach to Multi-task Visual Grounding,43,neurips,0,2,2023-06-16 16:07:29.579000,https://github.com/ubc-vision/RefTR,49,Referring transformer: A one-step approach to multi-task visual grounding,"https://scholar.google.com/scholar?cluster=5171653636072617077&hl=en&as_sdt=0,47",2,2021
Decoupling the Depth and Scope of Graph Neural Networks,62,neurips,18,2,2023-06-16 16:07:29.779000,https://github.com/facebookresearch/shaDow_GNN,120,Decoupling the depth and scope of graph neural networks,"https://scholar.google.com/scholar?cluster=584581848788200255&hl=en&as_sdt=0,5",8,2021
Knowledge-Adaptation Priors,12,neurips,2,0,2023-06-16 16:07:29.980000,https://github.com/team-approx-bayes/kpriors,13,Knowledge-adaptation priors,"https://scholar.google.com/scholar?cluster=8988283742772208306&hl=en&as_sdt=0,21",3,2021
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting,6,neurips,5,0,2023-06-16 16:07:30.184000,https://github.com/AdityaLab/EpiFNP,8,When in doubt: Neural non-parametric uncertainty quantification for epidemic forecasting,"https://scholar.google.com/scholar?cluster=17960456956653217453&hl=en&as_sdt=0,26",3,2021
CogView: Mastering Text-to-Image Generation via Transformers,264,neurips,165,17,2023-06-16 16:07:30.389000,https://github.com/THUDM/CogView,1407,Cogview: Mastering text-to-image generation via transformers,"https://scholar.google.com/scholar?cluster=11027183169038977124&hl=en&as_sdt=0,5",54,2021
Algorithmic stability and generalization of an unsupervised feature selection algorithm,6,neurips,3,0,2023-06-16 16:07:30.590000,https://github.com/xinxingwu-uk/ufs,11,Algorithmic stability and generalization of an unsupervised feature selection algorithm,"https://scholar.google.com/scholar?cluster=4215442541318442618&hl=en&as_sdt=0,41",1,2021
Matching a Desired Causal State via Shift Interventions,9,neurips,1,0,2023-06-16 16:07:30.791000,https://github.com/uhlerlab/causal_mean_matching,5,Matching a desired causal state via shift interventions,"https://scholar.google.com/scholar?cluster=9050178103753857442&hl=en&as_sdt=0,33",1,2021
Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport,9,neurips,3,0,2023-06-16 16:07:30.991000,https://github.com/hsinyilin19/discriminator-constrained-optimal-transport-network,21,Unsupervised noise adaptive speech enhancement by discriminator-constrained optimal transport,"https://scholar.google.com/scholar?cluster=334924147335137151&hl=en&as_sdt=0,5",1,2021
Policy Learning Using Weak Supervision,4,neurips,1,0,2023-06-16 16:07:31.209000,https://github.com/wangjksjtu/peerpl,1,Policy learning using weak supervision,"https://scholar.google.com/scholar?cluster=16464632898961524841&hl=en&as_sdt=0,18",2,2021
Chasing Sparsity in Vision Transformers: An End-to-End Exploration,94,neurips,11,2,2023-06-16 16:07:31.409000,https://github.com/VITA-Group/SViTE,75,Chasing sparsity in vision transformers: An end-to-end exploration,"https://scholar.google.com/scholar?cluster=12875590970833854171&hl=en&as_sdt=0,44",14,2021
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis,44,neurips,19,3,2023-06-16 16:07:31.611000,https://github.com/xingangpan/shadegan,127,A shading-guided generative implicit model for shape-accurate 3d-aware image synthesis,"https://scholar.google.com/scholar?cluster=10440624240411040295&hl=en&as_sdt=0,3",15,2021
Row-clustering of a Point Process-valued Matrix,2,neurips,2,1,2023-06-16 16:07:31.811000,https://github.com/lihaoyin/mmmpp,0,Row-clustering of a Point Process-valued Matrix,"https://scholar.googleusercontent.com/scholar?q=cache:psVjPBSJPQwJ:scholar.google.com/+Row-clustering+of+a+Point+Process-valued+Matrix&hl=en&as_sdt=0,47",1,2021
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information,11,neurips,4,6,2023-06-16 16:07:32.011000,https://github.com/camp-explain-ai/inputiba,27,Fine-grained neural network explanation by identifying input features with predictive information,"https://scholar.google.com/scholar?cluster=13775392989196455147&hl=en&as_sdt=0,5",5,2021
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints,38,neurips,3,0,2023-06-16 16:07:32.213000,https://github.com/pralab/Fast-Minimum-Norm-FMN-Attack,19,Fast minimum-norm adversarial attacks through adaptive norm constraints,"https://scholar.google.com/scholar?cluster=1895780984587172600&hl=en&as_sdt=0,4",3,2021
Uncertainty Quantification and Deep Ensembles,66,neurips,1,1,2023-06-16 16:07:32.413000,https://github.com/rahulrahaman/Uncertainty-Quantification-and-Deep-Ensemble,5,Uncertainty quantification and deep ensembles,"https://scholar.google.com/scholar?cluster=429015783640365674&hl=en&as_sdt=0,8",1,2021
Directed Probabilistic Watershed,0,neurips,0,0,2023-06-16 16:07:32.614000,https://github.com/hci-unihd/directed_probabilistic_watershed,0,Directed Probabilistic Watershed,"https://scholar.google.com/scholar?cluster=2806900364390366919&hl=en&as_sdt=0,20",1,2021
Explicable Reward Design for Reinforcement Learning Agents,18,neurips,3,0,2023-06-16 16:07:32.814000,https://github.com/adishs/neurips2021_explicable-reward-design_code,3,Explicable reward design for reinforcement learning agents,"https://scholar.google.com/scholar?cluster=150260483940462803&hl=en&as_sdt=0,33",1,2021
A Minimalist Approach to Offline Reinforcement Learning,267,neurips,36,2,2023-06-16 16:07:33.016000,https://github.com/sfujim/TD3_BC,228,A minimalist approach to offline reinforcement learning,"https://scholar.google.com/scholar?cluster=1743052010402400643&hl=en&as_sdt=0,33",4,2021
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs,7,neurips,1,0,2023-06-16 16:07:33.217000,https://github.com/raulastudillo06/budgetedbo,7,Multi-step budgeted bayesian optimization with unknown evaluation costs,"https://scholar.google.com/scholar?cluster=11066965782601470103&hl=en&as_sdt=0,5",3,2021
$\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression,110,neurips,21,1,2023-06-16 16:07:33.417000,https://github.com/jacobi93/alpha-iou,154,-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression,"https://scholar.google.com/scholar?cluster=6960142602186458983&hl=en&as_sdt=0,5",5,2021
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient,24,neurips,18,8,2023-06-16 16:07:33.618000,https://github.com/google-research/FirstOrderLp.jl,77,Practical large-scale linear programming using primal-dual hybrid gradient,"https://scholar.google.com/scholar?cluster=15174638035980967431&hl=en&as_sdt=0,43",13,2021
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions,8,neurips,3,0,2023-06-16 16:07:33.819000,https://github.com/ignavierng/local-astar,10,Reliable causal discovery with improved exact search and weaker assumptions,"https://scholar.google.com/scholar?cluster=15393722733482596224&hl=en&as_sdt=0,5",3,2021
Node Dependent Local Smoothing for Scalable Graph Learning,19,neurips,1,2,2023-06-16 16:07:34.019000,https://github.com/zwt233/ndls,15,Node dependent local smoothing for scalable graph learning,"https://scholar.google.com/scholar?cluster=6608453490006216987&hl=en&as_sdt=0,33",2,2021
Across-animal odor decoding by probabilistic manifold alignment,1,neurips,1,0,2023-06-16 16:07:34.219000,https://github.com/pedroherrerovidal/amlds,4,Across-animal odor decoding by probabilistic manifold alignment,"https://scholar.google.com/scholar?cluster=14107653280115649019&hl=en&as_sdt=0,5",1,2021
Excess Capacity and Backdoor Poisoning,14,neurips,0,0,2023-06-16 16:07:34.419000,https://github.com/narenmanoj/mnist-adv-training,2,Excess capacity and backdoor poisoning,"https://scholar.google.com/scholar?cluster=13952393692022590215&hl=en&as_sdt=0,5",1,2021
BCORLE($\lambda$): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market,4,neurips,2,1,2023-06-16 16:07:34.618000,https://github.com/ZSCDumin/BCORLE,5,BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market,"https://scholar.google.com/scholar?cluster=9674170088897673060&hl=en&as_sdt=0,21",2,2021
Generic Neural Architecture Search via Regression,13,neurips,9,2,2023-06-16 16:07:34.818000,https://github.com/leeyeehoo/GenNAS,35,Generic neural architecture search via regression,"https://scholar.google.com/scholar?cluster=17264205069746943313&hl=en&as_sdt=0,5",3,2021
"Interesting Object, Curious Agent: Learning Task-Agnostic Exploration",27,neurips,4,2,2023-06-16 16:07:35.018000,https://github.com/sparisi/cbet,30,"Interesting object, curious agent: Learning task-agnostic exploration","https://scholar.google.com/scholar?cluster=17517132874362052805&hl=en&as_sdt=0,47",1,2021
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement,1,neurips,0,0,2023-06-16 16:07:35.218000,https://github.com/heyangqin/simigrad,1,SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement,"https://scholar.google.com/scholar?cluster=13956766250705409738&hl=en&as_sdt=0,33",0,2021
Implicit Regularization in Matrix Sensing via Mirror Descent,5,neurips,0,0,2023-06-16 16:07:35.419000,https://github.com/fawuuu/irmsmd,0,Implicit regularization in matrix sensing via mirror descent,"https://scholar.google.com/scholar?cluster=1552182046702461253&hl=en&as_sdt=0,3",1,2021
Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs,8,neurips,5,7,2023-06-16 16:07:35.618000,https://github.com/yujia-yan/skipping-the-frame-level,47,Skipping the frame-level: Event-based piano transcription with neural semi-crfs,"https://scholar.google.com/scholar?cluster=5485151064368059296&hl=en&as_sdt=0,47",6,2021
Deep Learning on a Data Diet: Finding Important Examples Early in Training,98,neurips,18,1,2023-06-16 16:07:35.817000,https://github.com/mansheej/data_diet,73,Deep learning on a data diet: Finding important examples early in training,"https://scholar.google.com/scholar?cluster=6692350500928309521&hl=en&as_sdt=0,29",4,2021
Auditing Black-Box Prediction Models for Data Minimization Compliance,7,neurips,0,0,2023-06-16 16:07:36.017000,https://github.com/rastegarpanah/data-minimization-auditor,3,Auditing black-box prediction models for data minimization compliance,"https://scholar.google.com/scholar?cluster=14874021960575881635&hl=en&as_sdt=0,5",2,2021
Meta Internal Learning,5,neurips,2,0,2023-06-16 16:07:36.218000,https://github.com/RaphaelBensTAU/MetaInternalLearning,11,Meta internal learning,"https://scholar.google.com/scholar?cluster=16305601992312989829&hl=en&as_sdt=0,43",2,2021
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis,30,neurips,5,1,2023-06-16 16:07:36.418000,https://github.com/sheldontsui/gof_neurips2021,100,Generative occupancy fields for 3d surface-aware image synthesis,"https://scholar.google.com/scholar?cluster=17796152118908275759&hl=en&as_sdt=0,47",14,2021
Local policy search with Bayesian optimization,8,neurips,6,0,2023-06-16 16:07:36.630000,https://github.com/sarmueller/gibo,6,Local policy search with Bayesian optimization,"https://scholar.google.com/scholar?cluster=12884901871071371472&hl=en&as_sdt=0,14",2,2021
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks,25,neurips,3,0,2023-06-16 16:07:36.830000,https://github.com/nm-sparsity/dominosearch,12,DominoSearch: Find layer-wise fine-grained N: M sparse schemes from dense neural networks,"https://scholar.google.com/scholar?cluster=12253443518394083686&hl=en&as_sdt=0,1",1,2021
Techniques for Symbol Grounding with SATNet,9,neurips,2,0,2023-06-16 16:07:37.030000,https://github.com/SeverTopan/SATNet,7,Techniques for symbol grounding with SATNet,"https://scholar.google.com/scholar?cluster=10654873214439307966&hl=en&as_sdt=0,33",2,2021
Object DGCNN: 3D Object Detection using Dynamic Graphs,46,neurips,116,47,2023-06-16 16:07:37.230000,https://github.com/wangyueft/detr3d,607,Object dgcnn: 3d object detection using dynamic graphs,"https://scholar.google.com/scholar?cluster=4400840303049250796&hl=en&as_sdt=0,38",20,2021
Safe Policy Optimization with Local Generalized Linear Function Approximations,2,neurips,2,0,2023-06-16 16:07:37.431000,https://github.com/akifumi-wachi-4/spolf,6,Safe Policy Optimization with Local Generalized Linear Function Approximations,"https://scholar.google.com/scholar?cluster=5085292587764280618&hl=en&as_sdt=0,11",2,2021
The balancing principle for parameter choice in distance-regularized domain adaptation,2,neurips,1,1,2023-06-16 16:07:37.632000,https://github.com/xpitfire/bpda,5,The balancing principle for parameter choice in distance-regularized domain adaptation,"https://scholar.google.com/scholar?cluster=7370752937301100335&hl=en&as_sdt=0,33",4,2021
Gaussian Kernel Mixture Network for Single Image Defocus Deblurring,10,neurips,5,3,2023-06-16 16:07:37.832000,https://github.com/cszcwu/gkmnet,21,Gaussian kernel mixture network for single image defocus deblurring,"https://scholar.google.com/scholar?cluster=12551867425600364926&hl=en&as_sdt=0,5",1,2021
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge,21,neurips,2,0,2023-06-16 16:07:38.033000,https://github.com/boone891214/mest,15,Mest: Accurate and fast memory-economic sparse training framework on the edge,"https://scholar.google.com/scholar?cluster=4772832212685237675&hl=en&as_sdt=0,44",1,2021
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods,93,neurips,16,4,2023-06-16 16:07:38.233000,https://github.com/cuai/non-homophily-large-scale,78,Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods,"https://scholar.google.com/scholar?cluster=580916846840497144&hl=en&as_sdt=0,33",5,2021
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example,17,neurips,27,3,2023-06-16 16:07:38.433000,https://github.com/galgreshler/Catch-A-Waveform,139,Catch-a-waveform: Learning to generate audio from a single short example,"https://scholar.google.com/scholar?cluster=16318229752393122559&hl=en&as_sdt=0,5",4,2021
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective,24,neurips,9,1,2023-06-16 16:07:38.633000,https://github.com/VITA-Group/Ultra-Data-Efficient-GAN-Training,79,Data-efficient gan training beyond (just) augmentations: A lottery ticket perspective,"https://scholar.google.com/scholar?cluster=2933094985071684054&hl=en&as_sdt=0,32",14,2021
When Are Solutions Connected in Deep Networks?,1057,neurips,0,0,2023-06-16 16:07:38.834000,https://github.com/modeconnectivity/modeconnectivity,1,Shortcut learning in deep neural networks,"https://scholar.google.com/scholar?cluster=8900616021122454496&hl=en&as_sdt=0,5",1,2021
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation,12,neurips,4,1,2023-06-16 16:07:39.033000,https://github.com/haoang97/tohan,9,TOHAN: A one-step approach towards few-shot hypothesis adaptation,"https://scholar.google.com/scholar?cluster=3362363617253826009&hl=en&as_sdt=0,34",1,2021
Learning Graph Cellular Automata,16,neurips,10,0,2023-06-16 16:07:39.234000,https://github.com/danielegrattarola/gnca,40,Learning graph cellular automata,"https://scholar.google.com/scholar?cluster=4711762577281942253&hl=en&as_sdt=0,5",3,2021
Efficient Online Estimation of Causal Effects by Deciding What to Observe,8,neurips,0,0,2023-06-16 16:07:39.434000,https://github.com/acmi-lab/online-moment-selection,6,Efficient online estimation of causal effects by deciding what to observe,"https://scholar.google.com/scholar?cluster=200941432468169658&hl=en&as_sdt=0,5",2,2021
Variational Multi-Task Learning with Gumbel-Softmax Priors,11,neurips,1,0,2023-06-16 16:07:39.634000,https://github.com/autumn9999/vmtl,8,Variational multi-task learning with Gumbel-softmax priors,"https://scholar.google.com/scholar?cluster=979168555779336414&hl=en&as_sdt=0,11",2,2021
Accelerating Quadratic Optimization with Reinforcement Learning,16,neurips,15,0,2023-06-16 16:07:39.834000,https://github.com/berkeleyautomation/rlqp,75,Accelerating quadratic optimization with reinforcement learning,"https://scholar.google.com/scholar?cluster=3276389589139369906&hl=en&as_sdt=0,26",10,2021
Deep Residual Learning in Spiking Neural Networks,134,neurips,15,9,2023-06-16 16:07:40.034000,https://github.com/fangwei123456/Spike-Element-Wise-ResNet,78,Deep residual learning in spiking neural networks,"https://scholar.google.com/scholar?cluster=13799567303335562143&hl=en&as_sdt=0,5",3,2021
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation,10,neurips,4,1,2023-06-16 16:07:40.234000,https://github.com/zhengzx-nlp/reder,13,Duplex sequence-to-sequence learning for reversible machine translation,"https://scholar.google.com/scholar?cluster=7004295426093526403&hl=en&as_sdt=0,5",3,2021
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks,44,neurips,2,1,2023-06-16 16:07:40.434000,https://github.com/papers-submission/structured_transposable_masks,29,Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks,"https://scholar.google.com/scholar?cluster=17844164362787871979&hl=en&as_sdt=0,44",1,2021
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning,15,neurips,5,0,2023-06-16 16:07:40.635000,https://github.com/hangxu0304/DeepReduce,9,Deepreduce: A sparse-tensor communication framework for federated deep learning,"https://scholar.google.com/scholar?cluster=12891448574066341486&hl=en&as_sdt=0,47",1,2021
Exploiting Domain-Specific Features to Enhance Domain Generalization,44,neurips,2,1,2023-06-16 16:07:40.836000,https://github.com/vinairesearch/mdsdi,15,Exploiting domain-specific features to enhance domain generalization,"https://scholar.google.com/scholar?cluster=4543966632677300341&hl=en&as_sdt=0,5",0,2021
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer,6,neurips,3,0,2023-06-16 16:07:41.036000,https://github.com/ZidiXiu/CRT,11,Supercharging imbalanced data learning with energy-based contrastive representation transfer,"https://scholar.google.com/scholar?cluster=10778774199177050175&hl=en&as_sdt=0,5",1,2021
Disrupting Deep Uncertainty Estimation Without Harming Accuracy,4,neurips,1,0,2023-06-16 16:07:41.236000,https://github.com/IdoGalil/ACE,3,Disrupting deep uncertainty estimation without harming accuracy,"https://scholar.google.com/scholar?cluster=11133839384441962400&hl=en&as_sdt=0,33",2,2021
Task-Adaptive Neural Network Search with Meta-Contrastive Learning,4,neurips,6,0,2023-06-16 16:07:41.436000,https://github.com/wyjeong/tans,16,Task-adaptive neural network search with meta-contrastive learning,"https://scholar.google.com/scholar?cluster=11693856033014005643&hl=en&as_sdt=0,43",3,2021
Neural Flows: Efficient Alternative to Neural ODEs,22,neurips,13,2,2023-06-16 16:07:41.636000,https://github.com/mbilos/neural-flows-experiments,67,Neural flows: Efficient alternative to neural ODEs,"https://scholar.google.com/scholar?cluster=18217547123817497623&hl=en&as_sdt=0,39",3,2021
End-to-end reconstruction meets data-driven regularization for inverse problems,16,neurips,0,0,2023-06-16 16:07:41.836000,https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization,4,End-to-end reconstruction meets data-driven regularization for inverse problems,"https://scholar.google.com/scholar?cluster=16248522739800820583&hl=en&as_sdt=0,14",1,2021
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs,17,neurips,11,0,2023-06-16 16:07:42.037000,https://github.com/thinklab-sjtu/ppo-bihyb,73,A bi-level framework for learning to solve combinatorial optimization on graphs,"https://scholar.google.com/scholar?cluster=9298076485127002860&hl=en&as_sdt=0,10",3,2021
When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?,62,neurips,2,5,2023-06-16 16:07:42.237000,https://github.com/lijiefan/advcl,41,When does contrastive learning preserve adversarial robustness from pretraining to finetuning?,"https://scholar.google.com/scholar?cluster=3038595225265579627&hl=en&as_sdt=0,43",2,2021
Learning to Predict Trustworthiness with Steep Slope Loss,3,neurips,0,1,2023-06-16 16:07:42.437000,https://github.com/luoyan407/predict_trustworthiness,5,Learning to predict trustworthiness with steep slope loss,"https://scholar.google.com/scholar?cluster=8106650061212447650&hl=en&as_sdt=0,23",1,2021
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay,17,neurips,1,0,2023-06-16 16:07:42.637000,https://github.com/tipt0p/periodic_behavior_bn_wd,3,On the periodic behavior of neural network training with batch normalization and weight decay,"https://scholar.google.com/scholar?cluster=15045687956314005194&hl=en&as_sdt=0,5",2,2021
NeRV: Neural Representations for Videos,60,neurips,18,1,2023-06-16 16:07:42.837000,https://github.com/haochen-rye/nerv,234,Nerv: Neural representations for videos,"https://scholar.google.com/scholar?cluster=73059912539981135&hl=en&as_sdt=0,19",8,2021
Generative vs. Discriminative: Rethinking The Meta-Continual Learning,9,neurips,0,0,2023-06-16 16:07:43.037000,https://github.com/aminbana/gemcl,5,Generative vs. discriminative: Rethinking the meta-continual learning,"https://scholar.google.com/scholar?cluster=13601389422673314728&hl=en&as_sdt=0,5",2,2021
Rethinking Graph Transformers with Spectral Attention,156,neurips,31,1,2023-06-16 16:07:43.238000,https://github.com/DevinKreuzer/SAN,113,Rethinking graph transformers with spectral attention,"https://scholar.google.com/scholar?cluster=15947585912676378001&hl=en&as_sdt=0,10",6,2021
Perceptual Score: What Data Modalities Does Your Model Perceive?,12,neurips,1,0,2023-06-16 16:07:43.437000,https://github.com/itaigat/perceptual-score,8,Perceptual score: What data modalities does your model perceive?,"https://scholar.google.com/scholar?cluster=15852788555752209518&hl=en&as_sdt=0,5",1,2021
PiRank: Scalable Learning To Rank via Differentiable Sorting,8,neurips,8,3,2023-06-16 16:07:43.637000,https://github.com/ermongroup/pirank,58,Pirank: Scalable learning to rank via differentiable sorting,"https://scholar.google.com/scholar?cluster=8617942621344232575&hl=en&as_sdt=0,3",8,2021
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data,51,neurips,23,4,2023-06-16 16:07:43.837000,https://github.com/endlesssora/deceived,246,Deceive D: adaptive pseudo augmentation for GAN training with limited data,"https://scholar.google.com/scholar?cluster=4433178012946526426&hl=en&as_sdt=0,29",17,2021
Variational Diffusion Models,289,neurips,16,7,2023-06-16 16:07:44.037000,https://github.com/google-research/vdm,195,Variational diffusion models,"https://scholar.google.com/scholar?cluster=6024265554705485514&hl=en&as_sdt=0,33",4,2021
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition,17,neurips,133,24,2023-06-16 16:07:44.237000,https://github.com/microsoft/NeuralSpeech,1007,Fastcorrect: Fast error correction with edit alignment for automatic speech recognition,"https://scholar.google.com/scholar?cluster=5241252993966056956&hl=en&as_sdt=0,11",30,2021
Hierarchical Reinforcement Learning with Timed Subgoals,12,neurips,2,0,2023-06-16 16:07:44.436000,https://github.com/martius-lab/hits,24,Hierarchical reinforcement learning with timed subgoals,"https://scholar.google.com/scholar?cluster=15547085409137841678&hl=en&as_sdt=0,5",3,2021
SNIPS: Solving Noisy Inverse Problems Stochastically,64,neurips,4,0,2023-06-16 16:07:44.636000,https://github.com/bahjat-kawar/snips_torch,38,SNIPS: Solving noisy inverse problems stochastically,"https://scholar.google.com/scholar?cluster=4461341669386556106&hl=en&as_sdt=0,5",1,2021
Stateful ODE-Nets using Basis Function Expansions,9,neurips,6,1,2023-06-16 16:07:44.837000,https://github.com/afqueiruga/StatefulOdeNets,38,Stateful ode-nets using basis function expansions,"https://scholar.google.com/scholar?cluster=5210524906297832917&hl=en&as_sdt=0,47",7,2021
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?,73,neurips,3,4,2023-06-16 16:07:45.037000,https://github.com/vita-epfl/ttt-plus-plus,49,TTT++: When does self-supervised test-time training fail or thrive?,"https://scholar.google.com/scholar?cluster=3286823258483076490&hl=en&as_sdt=0,11",5,2021
 Boosted CVaR Classification,8,neurips,0,0,2023-06-16 16:07:45.238000,https://github.com/runtianz/boosted_cvar,4,Boosted cvar classification,"https://scholar.google.com/scholar?cluster=15164821511040155182&hl=en&as_sdt=0,5",2,2021
SOLQ: Segmenting Objects by Learning Queries,65,neurips,20,4,2023-06-16 16:07:45.437000,https://github.com/megvii-research/SOLQ,180,Solq: Segmenting objects by learning queries,"https://scholar.google.com/scholar?cluster=1852377411269249881&hl=en&as_sdt=0,5",10,2021
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models,24,neurips,4,0,2023-06-16 16:07:45.638000,https://github.com/Physics-aware-AI/DiffCoSim,21,Extending lagrangian and hamiltonian neural networks with differentiable contact models,"https://scholar.google.com/scholar?cluster=1516550074609182504&hl=en&as_sdt=0,15",1,2021
Few-Shot Segmentation via Cycle-Consistent Transformer,55,neurips,1,0,2023-06-16 16:07:45.838000,https://github.com/GengDavid/CyCTR,4,Few-shot segmentation via cycle-consistent transformer,"https://scholar.google.com/scholar?cluster=12634091315159410445&hl=en&as_sdt=0,39",2,2021
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks,58,neurips,3,0,2023-06-16 16:07:46.038000,https://github.com/karolismart/dropgnn,21,DropGNN: Random dropouts increase the expressiveness of graph neural networks,"https://scholar.google.com/scholar?cluster=6783529052723520360&hl=en&as_sdt=0,39",1,2021
Searching Parameterized AP Loss for Object Detection,1,neurips,3,0,2023-06-16 16:07:46.238000,https://github.com/fundamentalvision/parameterized-ap-loss,46,Searching parameterized AP loss for object detection,"https://scholar.google.com/scholar?cluster=99102542694531912&hl=en&as_sdt=0,33",2,2021
 NeuroMLR: Robust & Reliable Route Recommendation on Road Networks ,4,neurips,4,1,2023-06-16 16:07:46.440000,https://github.com/idea-iitd/neuromlr,9,NeuroMLR: Robust & Reliable Route Recommendation on Road Networks,"https://scholar.google.com/scholar?cluster=10547772011796748524&hl=en&as_sdt=0,5",1,2021
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning,65,neurips,8,6,2023-06-16 16:07:46.639000,https://github.com/hzhupku/semiseg-ael,112,Semi-supervised semantic segmentation via adaptive equalization learning,"https://scholar.google.com/scholar?cluster=11624791894491431600&hl=en&as_sdt=0,5",5,2021
Comprehensive Knowledge Distillation with Causal Intervention,20,neurips,2,0,2023-06-16 16:07:46.839000,https://github.com/xiang-deng-dl/cid,12,Comprehensive knowledge distillation with causal intervention,"https://scholar.google.com/scholar?cluster=2381368202143761298&hl=en&as_sdt=0,5",1,2021
Two steps to risk sensitivity,5,neurips,1,0,2023-06-16 16:07:47.040000,https://github.com/crgagne/twosteps_neurips2021,2,Two steps to risk sensitivity,"https://scholar.google.com/scholar?cluster=11403909575499559814&hl=en&as_sdt=0,10",2,2021
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization,4,neurips,1,0,2023-06-16 16:07:47.241000,https://github.com/ondrejbohdal/evograd,18,Evograd: Efficient gradient-based meta-learning and hyperparameter optimization,"https://scholar.google.com/scholar?cluster=6358521501110876720&hl=en&as_sdt=0,33",2,2021
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration,2,neurips,0,0,2023-06-16 16:07:47.441000,https://github.com/sylydya/sparse-deep-learning-a-new-framework-immuneto-local-traps-and-miscalibration,0,Sparse deep learning: A new framework immune to local traps and miscalibration,"https://scholar.google.com/scholar?cluster=9056246695961108406&hl=en&as_sdt=0,11",1,2021
NORESQA: A Framework for Speech Quality Assessment using Non-Matching References,20,neurips,10,2,2023-06-16 16:07:47.642000,https://github.com/facebookresearch/Noresqa,49,NORESQA: A framework for speech quality assessment using non-matching references,"https://scholar.google.com/scholar?cluster=7363609071396561507&hl=en&as_sdt=0,5",6,2021
AFEC: Active Forgetting of Negative Transfer in Continual Learning,19,neurips,1,1,2023-06-16 16:07:47.842000,https://github.com/lywang3081/AFEC,15,AFEC: Active forgetting of negative transfer in continual learning,"https://scholar.google.com/scholar?cluster=16155786595918509496&hl=en&as_sdt=0,11",1,2021
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization,6,neurips,3,0,2023-06-16 16:07:48.042000,https://github.com/shengroup/mpmab_beacon,0,Heterogeneous multi-player multi-armed bandits: Closing the gap and generalization,"https://scholar.google.com/scholar?cluster=4342595432442676512&hl=en&as_sdt=0,5",1,2021
SWAD: Domain Generalization by Seeking Flat Minima,142,neurips,16,0,2023-06-16 16:07:48.243000,https://github.com/khanrc/swad,124,Swad: Domain generalization by seeking flat minima,"https://scholar.google.com/scholar?cluster=17399407021631973298&hl=en&as_sdt=0,5",2,2021
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting,339,neurips,288,0,2023-06-16 16:07:48.443000,https://github.com/thuml/autoformer,1148,Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,"https://scholar.google.com/scholar?cluster=3122351390757400654&hl=en&as_sdt=0,22",13,2021
Predicting Event Memorability from Contextual Visual Semantics,1,neurips,0,0,2023-06-16 16:07:48.644000,https://github.com/ffzzy840304/predicting-event-memorability,0,Predicting Event Memorability from Contextual Visual Semantics,"https://scholar.google.com/scholar?cluster=12697030383321085621&hl=en&as_sdt=0,5",1,2021
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning,29,neurips,53,4,2023-06-16 16:07:48.844000,https://github.com/zixuanke/pycontinual,211,Achieving forgetting prevention and knowledge transfer in continual learning,"https://scholar.google.com/scholar?cluster=8575145504672099483&hl=en&as_sdt=0,5",5,2021
Combiner: Full Attention Transformer with Sparse Computation Cost,38,neurips,7321,1026,2023-06-16 16:07:49.043000,https://github.com/google-research/google-research,29786,Combiner: Full attention transformer with sparse computation cost,"https://scholar.google.com/scholar?cluster=397201754720393524&hl=en&as_sdt=0,5",727,2021
Geometry Processing with Neural Fields,35,neurips,18,0,2023-06-16 16:07:49.244000,https://github.com/stevenygd/nfgp,175,Geometry processing with neural fields,"https://scholar.google.com/scholar?cluster=9959525918645208605&hl=en&as_sdt=0,5",9,2021
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network,21,neurips,50,0,2023-06-16 16:07:49.444000,https://github.com/JusperLee/AFRCNN-For-Speech-Separation,120,Speech separation using an asynchronous fully recurrent convolutional neural network,"https://scholar.google.com/scholar?cluster=11722770519480068778&hl=en&as_sdt=0,5",5,2021
NAS-Bench-x11 and the Power of Learning Curves,15,neurips,4,2,2023-06-16 16:07:49.644000,https://github.com/automl/nas-bench-x11,17,Nas-bench-x11 and the power of learning curves,"https://scholar.google.com/scholar?cluster=13249979735452010353&hl=en&as_sdt=0,20",13,2021
Learning Disentangled Behavior Embeddings,8,neurips,0,0,2023-06-16 16:07:49.845000,https://github.com/mishne-lab/dbe-disentangled-behavior-embedding,13,Learning disentangled behavior embeddings,"https://scholar.google.com/scholar?cluster=15061877753853905670&hl=en&as_sdt=0,5",2,2021
Sparse Flows: Pruning Continuous-depth Models,12,neurips,22,9,2023-06-16 16:07:50.046000,https://github.com/lucaslie/torchprune,146,Sparse flows: Pruning continuous-depth models,"https://scholar.google.com/scholar?cluster=14652867200651009298&hl=en&as_sdt=0,5",5,2021
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks,59,neurips,14,0,2023-06-16 16:07:50.246000,https://github.com/BorealisAI/SLAPS-GNN,66,SLAPS: Self-supervision improves structure learning for graph neural networks,"https://scholar.google.com/scholar?cluster=13514640295473095313&hl=en&as_sdt=0,47",5,2021
Aligning Pretraining for Detection via Object-Level Contrastive Learning,74,neurips,19,16,2023-06-16 16:07:50.445000,https://github.com/hologerry/SoCo,156,Aligning pretraining for detection via object-level contrastive learning,"https://scholar.google.com/scholar?cluster=9757750069113028831&hl=en&as_sdt=0,44",7,2021
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization,7,neurips,0,0,2023-06-16 16:07:50.645000,https://github.com/travers-rhodes/jlonevae,1,Local Disentanglement in Variational Auto-Encoders Using Jacobian Regularization,"https://scholar.google.com/scholar?cluster=6881834482710680851&hl=en&as_sdt=0,5",1,2021
Encoding Spatial Distribution of Convolutional Features for Texture Representation,11,neurips,2,4,2023-06-16 16:07:50.847000,https://github.com/csfengli/fenet,10,Encoding spatial distribution of convolutional features for texture representation,"https://scholar.google.com/scholar?cluster=17445922379003065477&hl=en&as_sdt=0,43",1,2021
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds,34,neurips,3,1,2023-06-16 16:07:51.047000,https://github.com/yjhuangcd/local-lipschitz,18,Training certifiably robust neural networks with efficient local lipschitz bounds,"https://scholar.google.com/scholar?cluster=17265131367455074862&hl=en&as_sdt=0,43",3,2021
Counterexample Guided RL Policy Refinement Using Bayesian Optimization,4,neurips,0,1,2023-06-16 16:07:51.252000,https://github.com/britig/policy-refinement-bo,1,Counterexample guided RL policy refinement using bayesian optimization,"https://scholar.google.com/scholar?cluster=477353423111121794&hl=en&as_sdt=0,25",1,2021
A Variational Perspective on Diffusion-Based Generative Models and Score Matching,91,neurips,13,0,2023-06-16 16:07:51.454000,https://github.com/CW-Huang/sdeflow-light,99,A variational perspective on diffusion-based generative models and score matching,"https://scholar.google.com/scholar?cluster=11086576557599019726&hl=en&as_sdt=0,5",3,2021
Causal Influence Detection for Improving Efficiency in Reinforcement Learning,27,neurips,1,0,2023-06-16 16:07:51.656000,https://github.com/martius-lab/cid-in-rl,26,Causal influence detection for improving efficiency in reinforcement learning,"https://scholar.google.com/scholar?cluster=9354463069793604013&hl=en&as_sdt=0,5",4,2021
Cycle Self-Training for Domain Adaptation,70,neurips,4,3,2023-06-16 16:07:51.859000,https://github.com/Liuhong99/CST,39,Cycle self-training for domain adaptation,"https://scholar.google.com/scholar?cluster=18057534663552819958&hl=en&as_sdt=0,31",3,2021
Optimal Policies Tend To Seek Power,21,neurips,1,0,2023-06-16 16:07:52.059000,https://github.com/loganriggs/optimal-policies-tend-to-seek-power,0,Optimal policies tend to seek power,"https://scholar.google.com/scholar?cluster=2244318566147213779&hl=en&as_sdt=0,29",2,2021
"PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair",18,neurips,14,8,2023-06-16 16:07:52.261000,https://github.com/google-research/plur,86,"PLUR: A unifying, graph-based view of program learning, understanding, and repair","https://scholar.google.com/scholar?cluster=17073370459198177510&hl=en&as_sdt=0,14",11,2021
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining,122,neurips,13,2,2023-06-16 16:07:52.462000,https://github.com/microsoft/coco-lm,112,Coco-lm: Correcting and contrasting text sequences for language model pretraining,"https://scholar.google.com/scholar?cluster=4355255601645727108&hl=en&as_sdt=0,14",4,2021
XDO: A Double Oracle Algorithm for Extensive-Form Games,29,neurips,8,0,2023-06-16 16:07:52.672000,https://github.com/indylab/nxdo,27,XDO: A double oracle algorithm for extensive-form games,"https://scholar.google.com/scholar?cluster=14117190087630680195&hl=en&as_sdt=0,5",4,2021
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations,2,neurips,1,0,2023-06-16 16:07:52.880000,https://github.com/vihari/aaa,2,Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations,"https://scholar.google.com/scholar?cluster=10657209211783824075&hl=en&as_sdt=0,44",2,2021
Probabilistic Margins for Instance Reweighting in Adversarial Training,19,neurips,1,0,2023-06-16 16:07:53.081000,https://github.com/qizhouwang/mail,10,Probabilistic margins for instance reweighting in adversarial training,"https://scholar.google.com/scholar?cluster=6438754136382937945&hl=en&as_sdt=0,10",1,2021
The Difficulty of Passive Learning in Deep Reinforcement Learning,25,neurips,2436,170,2023-06-16 16:07:53.282000,https://github.com/deepmind/deepmind-research,11904,The difficulty of passive learning in deep reinforcement learning,"https://scholar.google.com/scholar?cluster=4514798007776798220&hl=en&as_sdt=0,6",336,2021
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models,8,neurips,0,1,2023-06-16 16:07:53.483000,https://github.com/echoyi/rps_lje,2,Representer point selection via local jacobian expansion for post-hoc classifier explanation of deep neural networks and ensemble models,"https://scholar.google.com/scholar?cluster=10184783151152200562&hl=en&as_sdt=0,5",3,2021
Editing a classifier by rewriting its prediction rules,33,neurips,7,0,2023-06-16 16:07:53.684000,https://github.com/madrylab/editingclassifiers,88,Editing a classifier by rewriting its prediction rules,"https://scholar.google.com/scholar?cluster=10393645433715100130&hl=en&as_sdt=0,5",6,2021
How Modular should Neural Module Networks Be for Systematic Generalization?,5,neurips,0,0,2023-06-16 16:07:53.885000,https://github.com/vanessadamario/understanding_reasoning,6,How Modular Should Neural Module Networks Be for Systematic Generalization?,"https://scholar.google.com/scholar?cluster=1661765216246697940&hl=en&as_sdt=0,5",2,2021
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization,4,neurips,1,0,2023-06-16 16:07:54.096000,https://github.com/dlej/adaptive-dropout,0,The flip side of the reweighted coin: duality of adaptive dropout and regularization,"https://scholar.google.com/scholar?cluster=7949218782652631707&hl=en&as_sdt=0,5",1,2021
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs,21,neurips,3,3,2023-06-16 16:07:54.300000,https://github.com/akirato/perm-gaussiankg,9,Probabilistic entity representation model for reasoning over knowledge graphs,"https://scholar.google.com/scholar?cluster=3279393825125769301&hl=en&as_sdt=0,5",1,2021
Black Box Probabilistic Numerics,2,neurips,0,0,2023-06-16 16:07:54.502000,https://github.com/oteym/bbpn,0,Black box probabilistic numerics,"https://scholar.google.com/scholar?cluster=11244542960585978883&hl=en&as_sdt=0,5",1,2021
Interpolation can hurt robust generalization even when there is no noise,8,neurips,0,0,2023-06-16 16:07:54.706000,https://github.com/michaelaerni/interpolation_robustness,1,Interpolation can hurt robust generalization even when there is no noise,"https://scholar.google.com/scholar?cluster=15775630453700777923&hl=en&as_sdt=0,5",2,2021
On the Equivalence between Neural Network and Support Vector Machine,8,neurips,3,0,2023-06-16 16:07:54.910000,https://github.com/leslie-ch/equiv-nn-svm,8,On the equivalence between neural network and support vector machine,"https://scholar.google.com/scholar?cluster=13784067833914528352&hl=en&as_sdt=0,5",2,2021
Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training,50,neurips,316,30,2023-06-16 16:07:55.125000,https://github.com/POSTECH-CVLab/PyTorch-StudioGAN,3190,Rebooting acgan: Auxiliary classifier gans with stable training,"https://scholar.google.com/scholar?cluster=15126723779815766107&hl=en&as_sdt=0,10",52,2021
Robust and Decomposable Average Precision for Image Retrieval,13,neurips,9,0,2023-06-16 16:07:55.326000,https://github.com/elias-ramzi/roadmap,70,Robust and decomposable average precision for image retrieval,"https://scholar.google.com/scholar?cluster=16259594709481566013&hl=en&as_sdt=0,5",4,2021
Spatio-Temporal Variational Gaussian Processes,15,neurips,1,1,2023-06-16 16:07:55.528000,https://github.com/aaltoml/spatio-temporal-gps,30,Spatio-temporal variational Gaussian processes,"https://scholar.google.com/scholar?cluster=5327408766327785744&hl=en&as_sdt=0,31",2,2021
Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes,2,neurips,1,0,2023-06-16 16:07:55.728000,https://github.com/AminKolarijani/ConjVI,0,Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes,"https://scholar.google.com/scholar?cluster=16725357238288679502&hl=en&as_sdt=0,47",1,2021
Adaptive Risk Minimization: Learning to Adapt to Domain Shift,78,neurips,24,3,2023-06-16 16:07:55.959000,https://github.com/henrikmarklund/arm,78,Adaptive risk minimization: Learning to adapt to domain shift,"https://scholar.google.com/scholar?cluster=6509702681777063562&hl=en&as_sdt=0,5",7,2021
Learning State Representations from Random Deep Action-conditional Predictions,3,neurips,0,0,2023-06-16 16:07:56.163000,https://github.com/Hwhitetooth/random_gvfs,3,Learning state representations from random deep action-conditional predictions,"https://scholar.google.com/scholar?cluster=15623109071018458033&hl=en&as_sdt=0,5",3,2021
Tracking People with 3D Representations,19,neurips,6,6,2023-06-16 16:07:56.363000,https://github.com/brjathu/T3DP,83,Tracking people with 3D representations,"https://scholar.google.com/scholar?cluster=18142751187854037322&hl=en&as_sdt=0,36",4,2021
Optimal Sketching for Trace Estimation,8,neurips,0,0,2023-06-16 16:07:56.564000,https://github.com/11hifish/OptSketchTraceEst,1,Optimal sketching for trace estimation,"https://scholar.google.com/scholar?cluster=1153169636268932836&hl=en&as_sdt=0,5",2,2021
Estimating Multi-cause Treatment Effects via Single-cause Perturbation,8,neurips,1,0,2023-06-16 16:07:56.764000,https://github.com/zhaozhiqian/single-cause-perturbation-neurips-2021,9,Estimating multi-cause treatment effects via single-cause perturbation,"https://scholar.google.com/scholar?cluster=15417661006229778320&hl=en&as_sdt=0,5",2,2021
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms,20,neurips,1,0,2023-06-16 16:07:56.964000,https://github.com/vanderschaarlab/miracle,16,Miracle: Causally-aware imputation via learning missing data mechanisms,"https://scholar.google.com/scholar?cluster=637656559224861079&hl=en&as_sdt=0,5",1,2021
Efficient Training of Visual Transformers with Small Datasets,86,neurips,11,1,2023-06-16 16:07:57.164000,https://github.com/yhlleo/VTs-Drloc,124,Efficient training of visual transformers with small datasets,"https://scholar.google.com/scholar?cluster=17891879498080154736&hl=en&as_sdt=0,5",3,2021
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration,54,neurips,8,1,2023-06-16 16:07:57.365000,https://github.com/haoyu94/coarse-to-fine-correspondences,79,Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration,"https://scholar.google.com/scholar?cluster=11101496447247741194&hl=en&as_sdt=0,5",7,2021
Partial success in closing the gap between human and machine vision,87,neurips,31,3,2023-06-16 16:07:57.565000,https://github.com/bethgelab/model-vs-human,286,Partial success in closing the gap between human and machine vision,"https://scholar.google.com/scholar?cluster=875131557547078483&hl=en&as_sdt=0,44",14,2021
"LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes",7,neurips,2,0,2023-06-16 16:07:57.765000,https://github.com/RAIVNLab/LLC,10,"Llc: Accurate, multi-purpose learnt low-dimensional binary codes","https://scholar.google.com/scholar?cluster=13039200529155817900&hl=en&as_sdt=0,26",7,2021
Well-tuned Simple Nets Excel on Tabular Datasets,65,neurips,12,2,2023-06-16 16:07:57.966000,https://github.com/releaunifreiburg/WellTunedSimpleNets,61,Well-tuned simple nets excel on tabular datasets,"https://scholar.google.com/scholar?cluster=3278110535551285021&hl=en&as_sdt=0,5",0,2021
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples,17,neurips,0,0,2023-06-16 16:07:58.166000,https://github.com/lehduong/poodle,15,Poodle: Improving few-shot learning via penalizing out-of-distribution samples,"https://scholar.google.com/scholar?cluster=3110608132459166392&hl=en&as_sdt=0,5",1,2021
Densely connected normalizing flows,27,neurips,9,0,2023-06-16 16:07:58.369000,https://github.com/matejgrcic/DenseFlow,32,Densely connected normalizing flows,"https://scholar.google.com/scholar?cluster=12123857522303227293&hl=en&as_sdt=0,32",4,2021
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing,8,neurips,0,0,2023-06-16 16:07:58.569000,https://github.com/thecharlieblake/snowflake,4,Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing,"https://scholar.google.com/scholar?cluster=12712996642787863150&hl=en&as_sdt=0,5",1,2021
VAST: Value Function Factorization with Variable Agent Sub-Teams,5,neurips,1,0,2023-06-16 16:07:58.769000,https://github.com/thomyphan/scalable-marl,4,Vast: Value function factorization with variable agent sub-teams,"https://scholar.google.com/scholar?cluster=15101436546519629155&hl=en&as_sdt=0,3",1,2021
Multiwavelet-based Operator Learning for Differential Equations,58,neurips,6,1,2023-06-16 16:07:58.969000,https://github.com/gaurav71531/mwt-operator,40,Multiwavelet-based operator learning for differential equations,"https://scholar.google.com/scholar?cluster=15278573285274207764&hl=en&as_sdt=0,5",1,2021
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning,13,neurips,6,1,2023-06-16 16:07:59.170000,https://github.com/aakashrkaku/intermdiate_layer_matter_ssl,39,Intermediate layers matter in momentum contrastive self supervised learning,"https://scholar.google.com/scholar?cluster=17990388829355645344&hl=en&as_sdt=0,36",2,2021
Learning Nonparametric Volterra Kernels with Gaussian Processes,2,neurips,2,0,2023-06-16 16:07:59.371000,https://github.com/magnusross/nvkm,1,Learning nonparametric Volterra kernels with Gaussian processes,"https://scholar.google.com/scholar?cluster=10898264461292575760&hl=en&as_sdt=0,33",3,2021
DiBS: Differentiable Bayesian Structure Learning,28,neurips,8,1,2023-06-16 16:07:59.571000,https://github.com/larslorch/dibs,35,Dibs: Differentiable bayesian structure learning,"https://scholar.google.com/scholar?cluster=4035014769080983661&hl=en&as_sdt=0,5",3,2021
Nonparametric estimation of continuous DPPs with kernel methods,2,neurips,0,0,2023-06-16 16:07:59.771000,https://github.com/mrfanuel/learningcontinuousdpps.jl,0,Nonparametric estimation of continuous DPPs with kernel methods,"https://scholar.google.com/scholar?cluster=4870049229735004027&hl=en&as_sdt=0,5",2,2021
FINE Samples for Learning with Noisy Labels,37,neurips,11,1,2023-06-16 16:07:59.972000,https://github.com/Kthyeon/FINE_official,28,Fine samples for learning with noisy labels,"https://scholar.google.com/scholar?cluster=5795819026441834181&hl=en&as_sdt=0,1",3,2021
Residual2Vec: Debiasing graph embedding with random graphs,8,neurips,1,1,2023-06-16 16:08:00.173000,https://github.com/skojaku/residual2vec,5,Residual2Vec: Debiasing graph embedding with random graphs,"https://scholar.google.com/scholar?cluster=741770936150407440&hl=en&as_sdt=0,48",3,2021
Training Neural Networks with Fixed Sparse Masks,47,neurips,1,1,2023-06-16 16:08:00.372000,https://github.com/varunnair18/fish,44,Training neural networks with fixed sparse masks,"https://scholar.google.com/scholar?cluster=16194905137327399007&hl=en&as_sdt=0,3",5,2021
Learning to Schedule Heuristics in Branch and Bound,27,neurips,0,0,2023-06-16 16:08:00.573000,https://github.com/antoniach/heuristic-scheduling,2,Learning to schedule heuristics in branch and bound,"https://scholar.google.com/scholar?cluster=5910831186806034579&hl=en&as_sdt=0,5",1,2021
On Training Implicit Models,31,neurips,0,0,2023-06-16 16:08:00.773000,https://github.com/gsunshine/phantom_grad,3,On training implicit models,"https://scholar.google.com/scholar?cluster=15707261069141178694&hl=en&as_sdt=0,33",1,2021
MLP-Mixer: An all-MLP Architecture for Vision,1181,neurips,976,108,2023-06-16 16:08:00.973000,https://github.com/google-research/vision_transformer,7383,Mlp-mixer: An all-mlp architecture for vision,"https://scholar.google.com/scholar?cluster=10553738615668616847&hl=en&as_sdt=0,10",83,2021
A Framework to Learn with Interpretation,19,neurips,0,0,2023-06-16 16:08:01.173000,https://github.com/jayneelparekh/flint,4,A framework to learn with interpretation,"https://scholar.google.com/scholar?cluster=4070242673228533811&hl=en&as_sdt=0,44",2,2021
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective,34,neurips,10,2,2023-06-16 16:08:01.376000,https://github.com/kamwoh/orthohash,86,One loss for all: Deep hashing with a single cosine similarity based learning objective,"https://scholar.google.com/scholar?cluster=2583147407697394986&hl=en&as_sdt=0,47",6,2021
Discovering and Achieving Goals via World Models,45,neurips,16,0,2023-06-16 16:08:01.577000,https://github.com/orybkin/lexa,76,Discovering and achieving goals via world models,"https://scholar.google.com/scholar?cluster=5829288564563555127&hl=en&as_sdt=0,33",5,2021
Understanding and Improving Early Stopping for Learning with Noisy Labels,68,neurips,4,0,2023-06-16 16:08:01.779000,https://github.com/tmllab/PES,21,Understanding and improving early stopping for learning with noisy labels,"https://scholar.google.com/scholar?cluster=15957250689455234622&hl=en&as_sdt=0,5",1,2021
On the Power of Edge Independent Graph Models,4,neurips,1,0,2023-06-16 16:08:01.985000,https://github.com/konsotirop/edge_independent_models,0,On the power of edge independent graph models,"https://scholar.google.com/scholar?cluster=18323628081237600189&hl=en&as_sdt=0,43",1,2021
"Understanding Adaptive, Multiscale Temporal Integration In Deep Speech Recognition Systems",2,neurips,2,0,2023-06-16 16:08:02.186000,https://github.com/naplab/pytci,5,"Understanding adaptive, multiscale temporal integration in deep speech recognition systems","https://scholar.google.com/scholar?cluster=12420066153878945080&hl=en&as_sdt=0,5",3,2021
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer,16,neurips,8,1,2023-06-16 16:08:02.395000,https://github.com/zinengtang/VidLanKD,56,Vidlankd: Improving language understanding via video-distilled knowledge transfer,"https://scholar.google.com/scholar?cluster=7463854148128804617&hl=en&as_sdt=0,5",4,2021
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess,14,neurips,0,0,2023-06-16 16:08:02.607000,https://github.com/csslab/behavioral-stylometry,13,Detecting individual decision-making style: Exploring behavioral stylometry in chess,"https://scholar.google.com/scholar?cluster=5114217380206270337&hl=en&as_sdt=0,10",4,2021
AutoGEL: An Automated Graph Neural Network with Explicit Link Information,19,neurips,1,0,2023-06-16 16:08:02.809000,https://github.com/zwangeo/autogel,8,Autogel: An automated graph neural network with explicit link information,"https://scholar.google.com/scholar?cluster=17230311752348468985&hl=en&as_sdt=0,5",2,2021
Recognizing Vector Graphics without Rasterization,6,neurips,12,2,2023-06-16 16:08:03.009000,https://github.com/microsoft/YOLaT-VectorGraphicsRecognition,59,Recognizing vector graphics without rasterization,"https://scholar.google.com/scholar?cluster=15241098815827282500&hl=en&as_sdt=0,5",7,2021
"On Episodes, Prototypical Networks, and Few-Shot Learning",45,neurips,4,1,2023-06-16 16:08:03.210000,https://github.com/fiveai/on-episodes-fsl,26,"On episodes, prototypical networks, and few-shot learning","https://scholar.google.com/scholar?cluster=7793453768259983774&hl=en&as_sdt=0,5",7,2021
CHIP: CHannel Independence-based Pruning for Compact Neural Networks,51,neurips,5,3,2023-06-16 16:08:03.411000,https://github.com/eclipsess/chip_neurips2021,18,Chip: Channel independence-based pruning for compact neural networks,"https://scholar.google.com/scholar?cluster=8136547128458704716&hl=en&as_sdt=0,33",3,2021
Active Offline Policy Selection,11,neurips,2,0,2023-06-16 16:08:03.611000,https://github.com/deepmind/active_ops,29,Active offline policy selection,"https://scholar.google.com/scholar?cluster=11479789843875532495&hl=en&as_sdt=0,5",5,2021
Information-theoretic generalization bounds for black-box learning algorithms,19,neurips,1,0,2023-06-16 16:08:03.812000,https://github.com/hrayrhar/f-cmi,3,Information-theoretic generalization bounds for black-box learning algorithms,"https://scholar.google.com/scholar?cluster=17028084888610967844&hl=en&as_sdt=0,5",5,2021
Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation,6,neurips,4,0,2023-06-16 16:08:04.038000,https://github.com/mingcv/ytmt-strategy,39,Trash or treasure? an interactive dual-stream strategy for single image reflection separation,"https://scholar.google.com/scholar?cluster=18395690050017455415&hl=en&as_sdt=0,5",2,2021
Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding,22,neurips,1,0,2023-06-16 16:08:04.239000,https://github.com/tewiSong/Rot-Pro,10,Rot-pro: Modeling transitivity by projection in knowledge graph embedding,"https://scholar.google.com/scholar?cluster=11215289012161533976&hl=en&as_sdt=0,33",1,2021
Modular Gaussian Processes for Transfer Learning,4,neurips,1,0,2023-06-16 16:08:04.439000,https://github.com/pmorenoz/modulargp,13,Modular Gaussian processes for transfer learning,"https://scholar.google.com/scholar?cluster=2796305591602379959&hl=en&as_sdt=0,33",2,2021
Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering,68,neurips,11,8,2023-06-16 16:08:04.639000,https://github.com/YoungJoongUNC/Neural_Human_Performer,112,Neural human performer: Learning generalizable radiance fields for human performance rendering,"https://scholar.google.com/scholar?cluster=7942977182226378581&hl=en&as_sdt=0,5",10,2021
Asymptotics of representation learning in finite Bayesian neural networks,21,neurips,1,0,2023-06-16 16:08:04.839000,https://github.com/pehlevan-group/finite-width-bayesian,2,Asymptotics of representation learning in finite Bayesian neural networks,"https://scholar.google.com/scholar?cluster=3625210166021367573&hl=en&as_sdt=0,33",2,2021
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?,20,neurips,2,1,2023-06-16 16:08:05.050000,https://github.com/dmirlab-group/dsan,19,Domain adaptation with invariant representation learning: What transformations to learn?,"https://scholar.google.com/scholar?cluster=10398669082966342781&hl=en&as_sdt=0,33",3,2021
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation,91,neurips,40,3,2023-06-16 16:08:05.252000,https://github.com/ermongroup/csdi,148,CSDI: Conditional score-based diffusion models for probabilistic time series imputation,"https://scholar.google.com/scholar?cluster=3890787205229522603&hl=en&as_sdt=0,5",8,2021
Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging,7,neurips,2,0,2023-06-16 16:08:05.452000,https://github.com/alihashemi-ai/dugh-neurips-2021,4,Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging,"https://scholar.google.com/scholar?cluster=6986870933699161127&hl=en&as_sdt=0,11",1,2021
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels,13,neurips,0,0,2023-06-16 16:08:05.653000,https://github.com/skarp/local-signal-adaptivity,1,Local signal adaptivity: Provable feature learning in neural networks beyond kernels,"https://scholar.google.com/scholar?cluster=5974588458999600841&hl=en&as_sdt=0,39",1,2021
Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding,12,neurips,87,8,2023-06-16 16:08:05.853000,https://github.com/brendenpetersen/deep-symbolic-optimization,374,Symbolic regression via deep reinforcement learning enhanced genetic programming seeding,"https://scholar.google.com/scholar?cluster=17727261586296192952&hl=en&as_sdt=0,5",12,2021
Choose a Transformer: Fourier or Galerkin,49,neurips,23,1,2023-06-16 16:08:06.054000,https://github.com/scaomath/galerkin-transformer,172,Choose a transformer: Fourier or galerkin,"https://scholar.google.com/scholar?cluster=8571374970772054230&hl=en&as_sdt=0,43",6,2021
Canonical Capsules: Self-Supervised Capsules in Canonical Pose,33,neurips,21,1,2023-06-16 16:08:06.254000,https://github.com/canonical-capsules/canonical-capsules,168,Canonical capsules: Self-supervised capsules in canonical pose,"https://scholar.google.com/scholar?cluster=8210427563278866334&hl=en&as_sdt=0,5",15,2021
Dynamics-regulated kinematic policy for egocentric pose estimation,27,neurips,5,0,2023-06-16 16:08:06.455000,https://github.com/KlabCMU/kin-poly,64,Dynamics-regulated kinematic policy for egocentric pose estimation,"https://scholar.google.com/scholar?cluster=3653129200622032279&hl=en&as_sdt=0,33",8,2021
Not All Low-Pass Filters are Robust in Graph Convolutional Networks,21,neurips,1,1,2023-06-16 16:08:06.655000,https://github.com/swiftieh/lfr,8,Not all low-pass filters are robust in graph convolutional networks,"https://scholar.google.com/scholar?cluster=931846674338665597&hl=en&as_sdt=0,47",2,2021
Counterfactual Maximum Likelihood Estimation for Training Deep Networks,3,neurips,1,1,2023-06-16 16:08:06.856000,https://github.com/WANGXinyiLinda/CMLE,9,Counterfactual Maximum Likelihood Estimation for Training Deep Networks,"https://scholar.google.com/scholar?cluster=12195718352241039351&hl=en&as_sdt=0,34",1,2021
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks,7,neurips,3,1,2023-06-16 16:08:07.056000,https://github.com/gsimchoni/lmmnn,15,Using random effects to account for high-cardinality categorical features and repeated measures in deep neural networks,"https://scholar.google.com/scholar?cluster=6823564351084758904&hl=en&as_sdt=0,3",2,2021
Learning the optimal Tikhonov regularizer for inverse problems,10,neurips,1,0,2023-06-16 16:08:07.256000,https://github.com/LearnTikhonov/Code,2,Learning the optimal Tikhonov regularizer for inverse problems,"https://scholar.google.com/scholar?cluster=4351597932105828079&hl=en&as_sdt=0,3",1,2021
NovelD: A Simple yet Effective Exploration Criterion,27,neurips,4,1,2023-06-16 16:08:07.456000,https://github.com/tianjunz/NovelD,32,Noveld: A simple yet effective exploration criterion,"https://scholar.google.com/scholar?cluster=5494596245419796169&hl=en&as_sdt=0,5",3,2021
Second-Order Neural ODE Optimizer,8,neurips,7,0,2023-06-16 16:08:07.657000,https://github.com/ghliu/snopt,40,Second-order neural ode optimizer,"https://scholar.google.com/scholar?cluster=440731558768338090&hl=en&as_sdt=0,26",2,2021
Dense Unsupervised Learning for Video Segmentation,15,neurips,21,2,2023-06-16 16:08:07.858000,https://github.com/visinf/dense-ulearn-vos,178,Dense unsupervised learning for video segmentation,"https://scholar.google.com/scholar?cluster=4698820805615701905&hl=en&as_sdt=0,5",8,2021
Charting and Navigating the Space of Solutions for Recurrent Neural Networks,8,neurips,1,0,2023-06-16 16:08:08.059000,https://github.com/eliaturner/space-of-solutions-rnn,0,Charting and navigating the space of solutions for recurrent neural networks,"https://scholar.google.com/scholar?cluster=1383134726251772649&hl=en&as_sdt=0,5",2,2021
Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes,2,neurips,0,0,2023-06-16 16:08:08.260000,https://github.com/jaimoondra/submodular-polytope-projections,0,Reusing combinatorial structure: Faster iterative projections over submodular base polytopes,"https://scholar.google.com/scholar?cluster=4313712568936757155&hl=en&as_sdt=0,39",2,2021
Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes,8,neurips,0,0,2023-06-16 16:08:08.461000,https://github.com/salusanga/alm-dnn,12,Constrained optimization to train neural networks on critical and under-represented classes,"https://scholar.google.com/scholar?cluster=6071197627058372251&hl=en&as_sdt=0,5",1,2021
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification,6,neurips,0,0,2023-06-16 16:08:08.661000,https://github.com/clreda/misspecified-top-m,0,Dealing with misspecification in fixed-confidence linear top-m identification,"https://scholar.google.com/scholar?cluster=17658923978445131586&hl=en&as_sdt=0,5",1,2021
Set Prediction in the Latent Space,2,neurips,0,0,2023-06-16 16:08:08.862000,https://github.com/phizaz/latent-set-prediction,5,Set prediction in the latent space,"https://scholar.google.com/scholar?cluster=7307560885637402716&hl=en&as_sdt=0,5",3,2021
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision,6,neurips,6,2,2023-06-16 16:08:09.062000,https://github.com/deepmind/dm_hamiltonian_dynamics_suite,28,Symetric: measuring the quality of learnt hamiltonian dynamics inferred from vision,"https://scholar.google.com/scholar?cluster=17033719678461609846&hl=en&as_sdt=0,15",5,2021
Learning with Holographic Reduced Representations,12,neurips,0,2,2023-06-16 16:08:09.262000,https://github.com/NeuromorphicComputationResearchProgram/Learning-with-Holographic-Reduced-Representations,15,Learning with holographic reduced representations,"https://scholar.google.com/scholar?cluster=17605710809418918656&hl=en&as_sdt=0,18",2,2021
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations,22,neurips,6,1,2023-06-16 16:08:09.463000,https://github.com/roosephu/crabs,12,Learning barrier certificates: Towards safe reinforcement learning with zero training-time violations,"https://scholar.google.com/scholar?cluster=14400533417780078206&hl=en&as_sdt=0,33",2,2021
On the Second-order Convergence Properties of Random Search Methods,3,neurips,0,0,2023-06-16 16:08:09.663000,https://github.com/adamsolomou/second-order-random-search,0,On the second-order convergence properties of random search methods,"https://scholar.google.com/scholar?cluster=13871613628804983300&hl=en&as_sdt=0,33",1,2021
A Max-Min Entropy Framework for Reinforcement Learning,5,neurips,0,0,2023-06-16 16:08:09.865000,https://github.com/seungyulhan/mme,3,A max-min entropy framework for reinforcement learning,"https://scholar.google.com/scholar?cluster=7183103060961218750&hl=en&as_sdt=0,50",1,2021
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods,21,neurips,4,0,2023-06-16 16:08:10.066000,https://github.com/desi-ivanova/idad,12,Implicit deep adaptive design: policy-based experimental design without likelihoods,"https://scholar.google.com/scholar?cluster=8438101725055656373&hl=en&as_sdt=0,33",1,2021
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis,19,neurips,0,0,2023-06-16 16:08:10.266000,https://github.com/livreq/meta-sgld,1,Generalization bounds for meta-learning: An information-theoretic analysis,"https://scholar.google.com/scholar?cluster=15486384152648151886&hl=en&as_sdt=0,5",1,2021
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits,2,neurips,0,0,2023-06-16 16:08:10.470000,https://github.com/bjoernhad/gcwidentification,1,Identification of the generalized Condorcet winner in multi-dueling bandits,"https://scholar.google.com/scholar?cluster=4702390528340000199&hl=en&as_sdt=0,38",1,2021
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch,12,neurips,1,0,2023-06-16 16:08:10.671000,https://github.com/lviano/robustmce_irl,3,Robust inverse reinforcement learning under transition dynamics mismatch,"https://scholar.google.com/scholar?cluster=6158260538019956069&hl=en&as_sdt=0,5",1,2021
Post-processing for Individual Fairness,32,neurips,1,0,2023-06-16 16:08:10.871000,https://github.com/felix-petersen/fairness-post-processing,5,Post-processing for individual fairness,"https://scholar.google.com/scholar?cluster=4902734240414782212&hl=en&as_sdt=0,33",1,2021
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization,26,neurips,11,6,2023-06-16 16:08:11.071000,https://github.com/VisionLearningGroup/OP_Match,45,Openmatch: Open-set semi-supervised learning with open-set consistency regularization,"https://scholar.google.com/scholar?cluster=2362582259050725811&hl=en&as_sdt=0,44",2,2021
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering,63,neurips,10,1,2023-06-16 16:08:11.271000,https://github.com/DevSinghSachan/emdr2,96,End-to-end training of multi-document reader and retriever for open-domain question answering,"https://scholar.google.com/scholar?cluster=6640291202097102131&hl=en&as_sdt=0,33",14,2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis,36,neurips,4,0,2023-06-16 16:08:11.471000,https://github.com/fel-thomas/Sobol-Attribution-Method,24,Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis,"https://scholar.google.com/scholar?cluster=18305760760422611286&hl=en&as_sdt=0,33",2,2021
PatchGame: Learning to Signal Mid-level Patches in Referential Games,3,neurips,2,0,2023-06-16 16:08:11.672000,https://github.com/kampta/patchgame,22,PatchGame: learning to signal mid-level patches in referential games,"https://scholar.google.com/scholar?cluster=15355548784664334020&hl=en&as_sdt=0,5",3,2021
Implicit Generative Copulas,9,neurips,1,0,2023-06-16 16:08:11.873000,https://github.com/timcjanke/igc,4,Implicit generative copulas,"https://scholar.google.com/scholar?cluster=9521615669512014539&hl=en&as_sdt=0,33",1,2021
Tensor Normal Training for Deep Learning Models,10,neurips,1,0,2023-06-16 16:08:12.075000,https://github.com/renyiryry/tnt_neurips_2021,4,Tensor normal training for deep learning models,"https://scholar.google.com/scholar?cluster=3326882924041786200&hl=en&as_sdt=0,47",2,2021
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning,25,neurips,1,2,2023-06-16 16:08:12.279000,https://github.com/cuis15/FCFL,14,Addressing algorithmic disparity and performance inconsistency in federated learning,"https://scholar.google.com/scholar?cluster=11506353861688134805&hl=en&as_sdt=0,26",1,2021
Morié Attack (MA): A New Potential Risk of Screen Photos,4,neurips,8,3,2023-06-16 16:08:12.483000,https://github.com/Dantong88/Moire_Attack,25,Morié attack (ma): A new potential risk of screen photos,"https://scholar.google.com/scholar?cluster=5204824031822855869&hl=en&as_sdt=0,50",1,2021
Lattice partition recovery with dyadic CART,3,neurips,0,0,2023-06-16 16:08:12.684000,https://github.com/hernanmp/partition_recovery,0,Lattice partition recovery with dyadic CART,"https://scholar.google.com/scholar?cluster=194222908834003497&hl=en&as_sdt=0,33",2,2021
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection,132,neurips,101,12,2023-06-16 16:08:12.886000,https://github.com/hustvl/YOLOS,716,You only look at one sequence: Rethinking transformer in vision through object detection,"https://scholar.google.com/scholar?cluster=8455459026871994587&hl=en&as_sdt=0,21",22,2021
Learning to delegate for large-scale vehicle routing,33,neurips,11,1,2023-06-16 16:08:13.086000,https://github.com/mit-wu-lab/learning-to-delegate,59,Learning to delegate for large-scale vehicle routing,"https://scholar.google.com/scholar?cluster=3486762460110339204&hl=en&as_sdt=0,33",2,2021
Towards Context-Agnostic Learning Using Synthetic Data,3,neurips,0,0,2023-06-16 16:08:13.286000,https://github.com/charlesjin/synthetic_data,0,Towards Context-Agnostic Learning Using Synthetic Data,"https://scholar.google.com/scholar?cluster=5766633238116465358&hl=en&as_sdt=0,38",2,2021
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers,3,neurips,0,0,2023-06-16 16:08:13.486000,https://github.com/blairbilodeau/neurips-2021,0,Minimax optimal quantile and semi-adversarial regret via root-logarithmic regularizers,"https://scholar.google.com/scholar?cluster=6590407016231039594&hl=en&as_sdt=0,33",1,2021
Sequence-to-Sequence Learning with Latent Neural Grammars,19,neurips,3,0,2023-06-16 16:08:13.686000,https://github.com/yoonkim/neural-qcfg,43,Sequence-to-sequence learning with latent neural grammars,"https://scholar.google.com/scholar?cluster=8101496336796731630&hl=en&as_sdt=0,44",5,2021
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition,19,neurips,1,1,2023-06-16 16:08:13.886000,https://github.com/gt-ripl/geometric-sensitivity-decomposition,18,A geometric perspective towards neural calibration via sensitivity decomposition,"https://scholar.google.com/scholar?cluster=11287329988480930857&hl=en&as_sdt=0,33",2,2021
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization,51,neurips,8,3,2023-06-16 16:08:14.086000,https://github.com/thesalon/fast-dpsgd,55,Enabling fast differentially private sgd via just-in-time compilation and vectorization,"https://scholar.google.com/scholar?cluster=3530716804480287020&hl=en&as_sdt=0,15",2,2021
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores,34,neurips,2,0,2023-06-16 16:08:14.288000,https://github.com/anguyen8/effectiveness-attribution-maps,16,The effectiveness of feature attribution methods and its correlation with automatic evaluation scores,"https://scholar.google.com/scholar?cluster=1502626993942622867&hl=en&as_sdt=0,33",3,2021
Coordinated Proximal Policy Optimization,13,neurips,1,0,2023-06-16 16:08:14.489000,https://github.com/ZifanWu/Coordinated-PPO,6,Coordinated proximal policy optimization,"https://scholar.google.com/scholar?cluster=3968189013521929332&hl=en&as_sdt=0,47",0,2021
Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning,30,neurips,6,1,2023-06-16 16:08:14.690000,https://github.com/grayhong/bias-contrastive-learning,20,Unbiased classification through bias-contrastive and bias-balanced learning,"https://scholar.google.com/scholar?cluster=9164048874502815433&hl=en&as_sdt=0,32",2,2021
Pragmatic Image Compression for Human-in-the-Loop Decision-Making,10,neurips,1,0,2023-06-16 16:08:14.890000,https://github.com/rddy/pico,10,Pragmatic Image Compression for Human-in-the-Loop Decision-Making,"https://scholar.google.com/scholar?cluster=14120252900286558336&hl=en&as_sdt=0,23",1,2021
Generalized Linear Bandits with Local Differential Privacy ,14,neurips,0,0,2023-06-16 16:08:15.091000,https://github.com/liangzp/LDP-Bandit,13,Generalized linear bandits with local differential privacy,"https://scholar.google.com/scholar?cluster=10585991561945031003&hl=en&as_sdt=0,11",2,2021
Characterizing possible failure modes in physics-informed neural networks,217,neurips,24,2,2023-06-16 16:08:15.291000,https://github.com/a1k12/characterizing-pinns-failure-modes,71,Characterizing possible failure modes in physics-informed neural networks,"https://scholar.google.com/scholar?cluster=269500818750259409&hl=en&as_sdt=0,10",5,2021
Artistic Style Transfer with Internal-external Learning and Contrastive Learning,47,neurips,5,2,2023-06-16 16:08:15.492000,https://github.com/halbertch/iecontraast,60,Artistic style transfer with internal-external learning and contrastive learning,"https://scholar.google.com/scholar?cluster=17574032712333265817&hl=en&as_sdt=0,47",3,2021
Fast Abductive Learning by Similarity-based Consistency Optimization,11,neurips,1,0,2023-06-16 16:08:15.694000,https://github.com/abductivelearning/ablsim,8,Fast abductive learning by similarity-based consistency optimization,"https://scholar.google.com/scholar?cluster=8539963460239876225&hl=en&as_sdt=0,5",2,2021
The Elastic Lottery Ticket Hypothesis,19,neurips,3,0,2023-06-16 16:08:15.894000,https://github.com/VITA-Group/ElasticLTH,10,The elastic lottery ticket hypothesis,"https://scholar.google.com/scholar?cluster=16545358675895401857&hl=en&as_sdt=0,33",9,2021
Joint Inference for Neural Network Depth and Dropout Regularization,5,neurips,0,0,2023-06-16 16:08:16.095000,https://github.com/MahdiGilany/Depth_and_Dropout,2,Joint inference for neural network depth and dropout regularization,"https://scholar.google.com/scholar?cluster=9001704603020268713&hl=en&as_sdt=0,33",1,2021
Improving Deep Learning Interpretability by Saliency Guided Training,31,neurips,2,0,2023-06-16 16:08:16.296000,https://github.com/ayaabdelsalam91/saliency_guided_training,8,Improving deep learning interpretability by saliency guided training,"https://scholar.google.com/scholar?cluster=17593389442039305805&hl=en&as_sdt=0,33",1,2021
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data,14,neurips,5,0,2023-06-16 16:08:16.496000,https://github.com/chl8856/survite,16,SurvITE: learning heterogeneous treatment effects from time-to-event data,"https://scholar.google.com/scholar?cluster=3737018677370322471&hl=en&as_sdt=0,5",1,2021
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings,11,neurips,0,0,2023-06-16 16:08:16.697000,https://github.com/lili-chen/SEER,21,Improving computational efficiency in visual reinforcement learning via stored embeddings,"https://scholar.google.com/scholar?cluster=3434130720798218429&hl=en&as_sdt=0,5",2,2021
Learning Generalized Gumbel-max Causal Mechanisms,10,neurips,7321,1026,2023-06-16 16:08:16.898000,https://github.com/google-research/google-research,29786,Learning generalized gumbel-max causal mechanisms,"https://scholar.google.com/scholar?cluster=5199832091407110116&hl=en&as_sdt=0,36",727,2021
Are Transformers more robust than CNNs? ,140,neurips,9,1,2023-06-16 16:08:17.098000,https://github.com/ytongbai/ViTs-vs-CNNs,157,Are transformers more robust than cnns?,"https://scholar.google.com/scholar?cluster=2316302132679082774&hl=en&as_sdt=0,33",13,2021
Automated Discovery of Adaptive Attacks on Adversarial Defenses,15,neurips,7,0,2023-06-16 16:08:17.299000,https://github.com/eth-sri/adaptive-auto-attack,23,Automated discovery of adaptive attacks on adversarial defenses,"https://scholar.google.com/scholar?cluster=238969790812050690&hl=en&as_sdt=0,5",5,2021
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media,14,neurips,0,0,2023-06-16 16:08:17.499000,https://github.com/meta-hg/metahg,10,Distilling meta knowledge on heterogeneous graph for illicit drug trafficker detection on social media,"https://scholar.google.com/scholar?cluster=16874907594472944579&hl=en&as_sdt=0,44",1,2021
Curriculum Disentangled Recommendation with Noisy Multi-feedback,20,neurips,3,0,2023-06-16 16:08:17.699000,https://github.com/forchchch/cdr,16,Curriculum disentangled recommendation with noisy multi-feedback,"https://scholar.google.com/scholar?cluster=13030142921653638499&hl=en&as_sdt=0,33",1,2021
Interpretable agent communication from scratch (with a generic visual processor emerging on the side),13,neurips,98,7,2023-06-16 16:08:17.900000,https://github.com/facebookresearch/EGG,261,Interpretable agent communication from scratch (with a generic visual processor emerging on the side),"https://scholar.google.com/scholar?cluster=11916940036915302991&hl=en&as_sdt=0,50",16,2021
MAU: A Motion-Aware Unit for Video Prediction and Beyond,27,neurips,8,1,2023-06-16 16:08:18.100000,https://github.com/ZhengChang467/MAU,23,Mau: A motion-aware unit for video prediction and beyond,"https://scholar.google.com/scholar?cluster=9016601602145736560&hl=en&as_sdt=0,43",2,2021
MagNet: A Neural Network for Directed Graphs,39,neurips,4,0,2023-06-16 16:08:18.301000,https://github.com/matthew-hirn/magnet,25,Magnet: A neural network for directed graphs,"https://scholar.google.com/scholar?cluster=14949439358621371423&hl=en&as_sdt=0,33",4,2021
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning,10,neurips,7,1,2023-06-16 16:08:18.502000,https://github.com/hayeonlee/help,48,Hardware-adaptive efficient latency prediction for nas via meta-learning,"https://scholar.google.com/scholar?cluster=1151236959431526951&hl=en&as_sdt=0,33",4,2021
Topological Relational Learning on Graphs,15,neurips,0,1,2023-06-16 16:08:18.702000,https://github.com/tri-gnn/tri-gnn,10,Topological relational learning on graphs,"https://scholar.google.com/scholar?cluster=11165869042107158625&hl=en&as_sdt=0,5",2,2021
Least Square Calibration for Peer Reviews,243,neurips,0,0,2023-06-16 16:08:18.902000,https://github.com/lab-sigma/lsc,1,Generalization based on least squares adjustment,"https://scholar.google.com/scholar?cluster=11630654823828571630&hl=en&as_sdt=0,22",0,2021
Scaling Up Exact Neural Network Compression by ReLU Stability,11,neurips,0,0,2023-06-16 16:08:19.103000,https://github.com/yuxwind/ExactCompression,7,Scaling up exact neural network compression by ReLU stability,"https://scholar.google.com/scholar?cluster=8701546882777093481&hl=en&as_sdt=0,15",1,2021
Passive attention in artificial neural networks predicts human visual selectivity,14,neurips,2,0,2023-06-16 16:08:19.317000,https://github.com/czhao39/neurips-attention,5,Passive attention in artificial neural networks predicts human visual selectivity,"https://scholar.google.com/scholar?cluster=2962365279533540728&hl=en&as_sdt=0,44",3,2021
Instance-Dependent Partial Label Learning,33,neurips,3,0,2023-06-16 16:08:19.519000,https://github.com/palm-ml/valen,22,Instance-dependent partial label learning,"https://scholar.google.com/scholar?cluster=15329270138955343757&hl=en&as_sdt=0,36",1,2021
Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification,15,neurips,2,1,2023-06-16 16:08:19.720000,https://github.com/NeurIPS2021Paper4075/SemiMonDEQ,0,Semialgebraic representation of monotone deep equilibrium models and applications to certification,"https://scholar.google.com/scholar?cluster=4954807623648783263&hl=en&as_sdt=0,16",1,2021
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction,405,neurips,168,64,2023-06-16 16:08:19.927000,https://github.com/Totoro97/NeuS,1077,Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction,"https://scholar.google.com/scholar?cluster=13663958172634895799&hl=en&as_sdt=0,33",22,2021
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture,9,neurips,3,0,2023-06-16 16:08:20.134000,https://github.com/suyoung-lee/ldm,15,Improving generalization in meta-rl with imaginary tasks from latent dynamics mixture,"https://scholar.google.com/scholar?cluster=7863235735756161058&hl=en&as_sdt=0,5",1,2021
Localization with Sampling-Argmax,5,neurips,6,5,2023-06-16 16:08:20.334000,https://github.com/Jeff-sjtu/sampling-argmax,80,Localization with sampling-argmax,"https://scholar.google.com/scholar?cluster=16900151620493971528&hl=en&as_sdt=0,33",7,2021
Improved Regularization and Robustness for Fine-tuning in Neural Networks,19,neurips,1,1,2023-06-16 16:08:20.535000,https://github.com/neu-statsml-research/regularized-self-labeling,24,Improved regularization and robustness for fine-tuning in neural networks,"https://scholar.google.com/scholar?cluster=14262652923694182167&hl=en&as_sdt=0,49",2,2021
BARTScore: Evaluating Generated Text as Text Generation,225,neurips,30,9,2023-06-16 16:08:20.735000,https://github.com/neulab/BARTScore,237,Bartscore: Evaluating generated text as text generation,"https://scholar.google.com/scholar?cluster=8096338858323282474&hl=en&as_sdt=0,33",6,2021
Robust Contrastive Learning Using Negative Samples with Diminished Semantics,42,neurips,8,0,2023-06-16 16:08:20.935000,https://github.com/SongweiGe/Contrastive-Learning-with-Non-Semantic-Negatives,40,Robust contrastive learning using negative samples with diminished semantics,"https://scholar.google.com/scholar?cluster=7490092898284708794&hl=en&as_sdt=0,33",2,2021
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation,84,neurips,67,8,2023-06-16 16:08:21.152000,https://github.com/bengioe/gflownet,457,Flow network based generative models for non-iterative diverse candidate generation,"https://scholar.google.com/scholar?cluster=8126213328674234815&hl=en&as_sdt=0,18",10,2021
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation,62,neurips,5,6,2023-06-16 16:08:21.353000,https://github.com/jbeomlee93/rib,81,Reducing information bottleneck for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=1609158517855836438&hl=en&as_sdt=0,33",3,2021
AC-GC: Lossy Activation Compression with Guaranteed Convergence,10,neurips,0,1,2023-06-16 16:08:21.553000,https://github.com/rdevans0/acgc,3,Ac-gc: Lossy activation compression with guaranteed convergence,"https://scholar.google.com/scholar?cluster=1264227773571406457&hl=en&as_sdt=0,47",1,2021
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks,6,neurips,0,0,2023-06-16 16:08:21.753000,https://github.com/ksandeshk/spatial-vs-robustness,0,Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks,"https://scholar.google.com/scholar?cluster=15810468543209230356&hl=en&as_sdt=0,44",1,2021
Universal Off-Policy Evaluation,33,neurips,1,1,2023-06-16 16:08:21.953000,https://github.com/yashchandak/UnO,3,Universal off-policy evaluation,"https://scholar.google.com/scholar?cluster=15687557673143979580&hl=en&as_sdt=0,5",2,2021
Efficiently Identifying Task Groupings for Multi-Task Learning,84,neurips,7321,1026,2023-06-16 16:08:22.154000,https://github.com/google-research/google-research,29786,Efficiently identifying task groupings for multi-task learning,"https://scholar.google.com/scholar?cluster=14971960796131955796&hl=en&as_sdt=0,14",727,2021
Instance-Conditioned GAN,67,neurips,72,11,2023-06-16 16:08:22.354000,https://github.com/facebookresearch/ic_gan,520,Instance-conditioned gan,"https://scholar.google.com/scholar?cluster=9688091502040853342&hl=en&as_sdt=0,33",20,2021
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales,6,neurips,0,0,2023-06-16 16:08:22.554000,https://github.com/compmem/deepsith,8,DeepSITH: Efficient learning via decomposition of what and when across time scales,"https://scholar.google.com/scholar?cluster=9839987193236490170&hl=en&as_sdt=0,13",6,2021
A Unified View of cGANs with and without Classifiers,6,neurips,2,0,2023-06-16 16:08:22.754000,https://github.com/sian-chen/pytorch-ecgan,24,A Unified View of cGANs with and without Classifiers,"https://scholar.google.com/scholar?cluster=7864400027799016217&hl=en&as_sdt=0,33",3,2021
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration,7,neurips,2,0,2023-06-16 16:08:22.954000,https://github.com/ssl-codelab/uota,6,Improving self-supervised learning with automated unsupervised outlier arbitration,"https://scholar.google.com/scholar?cluster=16964194655596276571&hl=en&as_sdt=0,5",1,2021
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss,7,neurips,2,0,2023-06-16 16:08:23.155000,https://github.com/michael-iuzzolino/CascadedNets,6,Improving anytime prediction with parallel cascaded networks and a temporal-difference loss,"https://scholar.google.com/scholar?cluster=14093037979980851402&hl=en&as_sdt=0,10",2,2021
Identifiable Generative models for Missing Not at Random Data Imputation,10,neurips,25,1,2023-06-16 16:08:23.356000,https://github.com/microsoft/project-azua,208,Identifiable generative models for missing not at random data imputation,"https://scholar.google.com/scholar?cluster=3807116109136589039&hl=en&as_sdt=0,33",11,2021
Local Hyper-Flow Diffusion,8,neurips,2,0,2023-06-16 16:08:23.558000,https://github.com/s-h-yang/HFD,2,Local hyper-flow diffusion,"https://scholar.google.com/scholar?cluster=15981181330230884559&hl=en&as_sdt=0,21",1,2021
Permuton-induced Chinese Restaurant Process,2,neurips,1,0,2023-06-16 16:08:23.766000,https://github.com/nttcslab/permuton-induced-crp,3,Permuton-induced Chinese restaurant process,"https://scholar.google.com/scholar?cluster=15342887541779236192&hl=en&as_sdt=0,33",3,2021
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error,1,neurips,2,0,2023-06-16 16:08:23.967000,https://github.com/justc2/worst-case-randomly-collected,3,Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error,"https://scholar.google.com/scholar?cluster=5134119309073898368&hl=en&as_sdt=0,33",1,2021
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game,38,neurips,1,0,2023-06-16 16:08:24.169000,https://github.com/scriddie/varsortability,12,Beware of the simulated dag! causal discovery benchmarks may be easy to game,"https://scholar.google.com/scholar?cluster=15056583277700690862&hl=en&as_sdt=0,33",4,2021
Robust Predictable Control,20,neurips,562,12,2023-06-16 16:08:24.370000,https://github.com/eleurent/highway-env,1849,Robust predictable control,"https://scholar.google.com/scholar?cluster=8057387371950805488&hl=en&as_sdt=0,33",23,2021
Unsupervised Speech Recognition,173,neurips,5878,1030,2023-06-16 16:08:24.572000,https://github.com/pytorch/fairseq,26479,Unsupervised speech recognition,"https://scholar.google.com/scholar?cluster=7092177079954747232&hl=en&as_sdt=0,14",411,2021
Online Learning and Control of Complex Dynamical Systems from Sensory Input,2,neurips,0,0,2023-06-16 16:08:24.773000,https://github.com/oumayb/online_dynamics_control,6,Online Learning and Control of Complex Dynamical Systems from Sensory Input,"https://scholar.google.com/scholar?cluster=1383948933204770647&hl=en&as_sdt=0,5",1,2021
Self-Supervised Bug Detection and Repair,56,neurips,19,6,2023-06-16 16:08:24.974000,https://github.com/microsoft/neurips21-self-supervised-bug-detection-and-repair,97,Self-supervised bug detection and repair,"https://scholar.google.com/scholar?cluster=7144327257575633372&hl=en&as_sdt=0,33",12,2021
Faster Neural Network Training with Approximate Tensor Operations,22,neurips,0,0,2023-06-16 16:08:25.179000,https://github.com/acsl-technion/approx,6,Faster neural network training with approximate tensor operations,"https://scholar.google.com/scholar?cluster=14033293774816161034&hl=en&as_sdt=0,38",1,2021
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis,6,neurips,8,2,2023-06-16 16:08:25.380000,https://github.com/KellyYutongHe/satellite-pixel-synthesis-pytorch,24,Spatial-temporal super-resolution of satellite imagery via conditional pixel synthesis,"https://scholar.google.com/scholar?cluster=15319459420045526884&hl=en&as_sdt=0,33",5,2021
Garment4D: Garment Reconstruction from Point Cloud Sequences,9,neurips,17,4,2023-06-16 16:08:25.580000,https://github.com/hongfz16/garment4d,121,Garment4d: Garment reconstruction from point cloud sequences,"https://scholar.google.com/scholar?cluster=2204817169651451344&hl=en&as_sdt=0,33",6,2021
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data,28,neurips,6,3,2023-06-16 16:08:25.780000,https://github.com/gentlezhu/shift-robust-gnns,45,Shift-robust gnns: Overcoming the limitations of localized graph training data,"https://scholar.google.com/scholar?cluster=13890659734687981736&hl=en&as_sdt=0,33",2,2021
RIM: Reliable Influence-based Active Learning on Graphs,3,neurips,2,0,2023-06-16 16:08:25.980000,https://github.com/zwt233/rim,4,Rim: Reliable influence-based active learning on graphs,"https://scholar.google.com/scholar?cluster=5200896252753882608&hl=en&as_sdt=0,14",2,2021
Dynamical Wasserstein Barycenters for Time-series Modeling,4,neurips,1,0,2023-06-16 16:08:26.181000,https://github.com/kevin-c-cheng/dynamicalwassbarycenters_gaussian,9,Dynamical Wasserstein barycenters for time-series modeling,"https://scholar.google.com/scholar?cluster=14561701553240392595&hl=en&as_sdt=0,11",1,2021
RelaySum for Decentralized Deep Learning on Heterogeneous Data,33,neurips,2,0,2023-06-16 16:08:26.381000,https://github.com/epfml/relaysgd,6,Relaysum for decentralized deep learning on heterogeneous data,"https://scholar.google.com/scholar?cluster=13522675478671696276&hl=en&as_sdt=0,33",6,2021
Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs,14,neurips,6,0,2023-06-16 16:08:26.583000,https://github.com/jw9730/hot,46,Transformers generalize deepsets and can be extended to graphs & hypergraphs,"https://scholar.google.com/scholar?cluster=4459735355491111784&hl=en&as_sdt=0,33",1,2021
Encoding Robustness to Image Style via Adversarial Feature Perturbations,5,neurips,2,0,2023-06-16 16:08:26.783000,https://github.com/azshue/AdvBN,9,Encoding robustness to image style via adversarial feature perturbations,"https://scholar.google.com/scholar?cluster=6403103949061103720&hl=en&as_sdt=0,5",1,2021
"Natural continual learning: success is a journey, not (just) a destination",25,neurips,1,0,2023-06-16 16:08:26.984000,https://github.com/tachukao/ncl,7,"Natural continual learning: success is a journey, not (just) a destination","https://scholar.google.com/scholar?cluster=14888388153938453691&hl=en&as_sdt=0,33",2,2021
Unsupervised Part Discovery from Contrastive Reconstruction,33,neurips,6,3,2023-06-16 16:08:27.184000,https://github.com/subhc/unsup-parts,59,Unsupervised part discovery from contrastive reconstruction,"https://scholar.google.com/scholar?cluster=5041027842313790381&hl=en&as_sdt=0,33",6,2021
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning,26,neurips,2,2,2023-06-16 16:08:27.385000,https://github.com/guochengqian/assanet,30,Assanet: An anisotropic separable set abstraction for efficient point cloud representation learning,"https://scholar.google.com/scholar?cluster=14172357416366632432&hl=en&as_sdt=0,33",6,2021
Fair Sequential Selection Using Supervised Learning Models,5,neurips,0,0,2023-06-16 16:08:27.585000,https://github.com/m0hammadmahdi/neurips2021_fair-sequential-selection-using-supervised-learning-models,0,Fair sequential selection using supervised learning models,"https://scholar.google.com/scholar?cluster=1562219194987270101&hl=en&as_sdt=0,3",1,2021
Towards Sample-efficient Overparameterized Meta-learning,16,neurips,0,0,2023-06-16 16:08:27.786000,https://github.com/sunyue93/rep-learning,0,Towards sample-efficient overparameterized meta-learning,"https://scholar.google.com/scholar?cluster=7770324416491946595&hl=en&as_sdt=0,41",2,2021
"Independent mechanism analysis, a new concept?",38,neurips,5,1,2023-06-16 16:08:27.986000,https://github.com/lgresele/independent-mechanism-analysis,19,"Independent mechanism analysis, a new concept?","https://scholar.google.com/scholar?cluster=3071675767973388187&hl=en&as_sdt=0,33",2,2021
Robustness via Uncertainty-aware Cycle Consistency,9,neurips,4,0,2023-06-16 16:08:28.186000,https://github.com/explainableml/uncertaintyawarecycleconsistency,21,Robustness via uncertainty-aware cycle consistency,"https://scholar.google.com/scholar?cluster=6383754569439233889&hl=en&as_sdt=0,36",5,2021
CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method,3,neurips,0,0,2023-06-16 16:08:28.387000,https://github.com/dooseokjeong/cbp,1,CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method,"https://scholar.google.com/scholar?cluster=7208237735280582675&hl=en&as_sdt=0,6",1,2021
Implicit Sparse Regularization: The Impact of Depth and Early Stopping,12,neurips,0,0,2023-06-16 16:08:28.587000,https://github.com/jiangyuan2li/implicit-sparse-regularization,1,Implicit sparse regularization: The impact of depth and early stopping,"https://scholar.google.com/scholar?cluster=4712253773396003910&hl=en&as_sdt=0,33",3,2021
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning,24,neurips,4,1,2023-06-16 16:08:28.787000,https://github.com/junsu-kim97/higl,27,Landmark-guided subgoal generation in hierarchical reinforcement learning,"https://scholar.google.com/scholar?cluster=12842225468737823551&hl=en&as_sdt=0,33",2,2021
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations,15,neurips,0,0,2023-06-16 16:08:28.988000,https://github.com/conglu1997/nppac,7,On pathologies in KL-regularized reinforcement learning from expert demonstrations,"https://scholar.google.com/scholar?cluster=13346980739265186497&hl=en&as_sdt=0,31",2,2021
Conditional Generation Using Polynomial Expansions,9,neurips,0,0,2023-06-16 16:08:29.189000,https://github.com/grigorisg9gr/polynomial_nets_for_conditional_generation,6,Conditional generation using polynomial expansions,"https://scholar.google.com/scholar?cluster=2570209794956894506&hl=en&as_sdt=0,33",2,2021
Adaptive Online Packing-guided Search for POMDPs,6,neurips,3,0,2023-06-16 16:08:29.390000,https://github.com/lamda-pomdp/adaops.jl,9,Adaptive Online Packing-guided Search for POMDPs,"https://scholar.google.com/scholar?cluster=1368812390956957164&hl=en&as_sdt=0,47",2,2021
End-to-end Multi-modal Video Temporal Grounding,19,neurips,0,2,2023-06-16 16:08:29.596000,https://github.com/wenz116/drft,17,End-to-end multi-modal video temporal grounding,"https://scholar.google.com/scholar?cluster=12383012058423217562&hl=en&as_sdt=0,33",5,2021
How Powerful are Performance Predictors in Neural Architecture Search?,70,neurips,94,29,2023-06-16 16:08:29.797000,https://github.com/automl/NASLib,402,How powerful are performance predictors in neural architecture search?,"https://scholar.google.com/scholar?cluster=14402357540412302091&hl=en&as_sdt=0,5",14,2021
Stylized Dialogue Generation with Multi-Pass Dual Learning,7,neurips,0,3,2023-06-16 16:08:30.004000,https://github.com/codebaseli/mpdl,3,Stylized dialogue generation with multi-pass dual learning,"https://scholar.google.com/scholar?cluster=11118854969470052027&hl=en&as_sdt=0,21",1,2021
Entropy-based adaptive Hamiltonian Monte Carlo,4,neurips,0,0,2023-06-16 16:08:30.206000,https://github.com/marcelah/entropy_adaptive_hmc,1,Entropy-based adaptive hamiltonian monte carlo,"https://scholar.google.com/scholar?cluster=3200582858390152415&hl=en&as_sdt=0,48",1,2021
Continual World: A Robotic Benchmark For Continual Reinforcement Learning,35,neurips,11,5,2023-06-16 16:08:30.407000,https://github.com/awarelab/continual_world,54,Continual world: A robotic benchmark for continual reinforcement learning,"https://scholar.google.com/scholar?cluster=1195122932828127100&hl=en&as_sdt=0,5",3,2021
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias,181,neurips,15,3,2023-06-16 16:08:30.607000,https://github.com/Annbless/ViTAE,104,Vitae: Vision transformer advanced by exploring intrinsic inductive bias,"https://scholar.google.com/scholar?cluster=14266701726231961165&hl=en&as_sdt=0,25",8,2021
Open Rule Induction,4,neurips,3,0,2023-06-16 16:08:30.808000,https://github.com/chenxran/orion,18,Open rule induction,"https://scholar.google.com/scholar?cluster=18275159905566382663&hl=en&as_sdt=0,50",1,2021
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme,17,neurips,6,2,2023-06-16 16:08:31.009000,https://github.com/sjleo/gcc,34,Revisiting discriminator in GAN compression: A generator-discriminator cooperative compression scheme,"https://scholar.google.com/scholar?cluster=14200424528838121517&hl=en&as_sdt=0,33",3,2021
Topographic VAEs learn Equivariant Capsules,16,neurips,14,2,2023-06-16 16:08:31.210000,https://github.com/akandykeller/topographicvae,72,Topographic vaes learn equivariant capsules,"https://scholar.google.com/scholar?cluster=4234338937076957460&hl=en&as_sdt=0,32",3,2021
MobILE: Model-Based Imitation Learning From Observation Alone,16,neurips,2,1,2023-06-16 16:08:31.412000,https://github.com/rahulkidambi/mobile-neurips2021,6,Mobile: Model-based imitation learning from observation alone,"https://scholar.google.com/scholar?cluster=8914369701297657795&hl=en&as_sdt=0,5",2,2021
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling,7,neurips,2,0,2023-06-16 16:08:31.613000,https://github.com/ruiqigao/grid-cell-path,40,On path integration of grid cells: group representation and isotropic scaling,"https://scholar.google.com/scholar?cluster=12036851998836312234&hl=en&as_sdt=0,44",2,2021
Making a (Counterfactual) Difference One Rationale at a Time,3,neurips,1,1,2023-06-16 16:08:31.814000,https://github.com/mlplyler/cfs_for_rationales,5,Making a (Counterfactual) Difference One Rationale at a Time,"https://scholar.google.com/scholar?cluster=641729738996559860&hl=en&as_sdt=0,5",1,2021
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds,29,neurips,4,5,2023-06-16 16:08:32.015000,https://github.com/fpthink/v2b,33,3D Siamese voxel-to-BEV tracker for sparse point clouds,"https://scholar.google.com/scholar?cluster=3916550808113986620&hl=en&as_sdt=0,5",3,2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning,43,neurips,35,1,2023-06-16 16:08:32.215000,https://github.com/OATML/Non-Parametric-Transformers,370,Self-attention between datapoints: Going beyond individual input-output pairs in deep learning,"https://scholar.google.com/scholar?cluster=1349347196741730102&hl=en&as_sdt=0,5",9,2021
On Contrastive Representations of Stochastic Processes,9,neurips,1,0,2023-06-16 16:08:32.416000,https://github.com/ae-foster/cresp,11,On contrastive representations of stochastic processes,"https://scholar.google.com/scholar?cluster=14134769068028722426&hl=en&as_sdt=0,33",3,2021
"Scalars are universal: Equivariant machine learning, structured like classical physics",52,neurips,5,0,2023-06-16 16:08:32.617000,https://github.com/weichiyao/scalaremlp,14,"Scalars are universal: Equivariant machine learning, structured like classical physics","https://scholar.google.com/scholar?cluster=15130731993267157989&hl=en&as_sdt=0,33",2,2021
Unsupervised Object-Level Representation Learning from Scene Images,41,neurips,5,4,2023-06-16 16:08:32.818000,https://github.com/jiahao000/orl,56,Unsupervised object-level representation learning from scene images,"https://scholar.google.com/scholar?cluster=11947642466448713378&hl=en&as_sdt=0,43",1,2021
Stronger NAS with Weaker Predictors,16,neurips,6,1,2023-06-16 16:08:33.018000,https://github.com/VITA-Group/WeakNAS,21,Stronger nas with weaker predictors,"https://scholar.google.com/scholar?cluster=7907486067931275084&hl=en&as_sdt=0,33",10,2021
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training,17,neurips,5,0,2023-06-16 16:08:33.218000,https://github.com/shengliu66/ConvNorm,26,Convolutional normalization: Improving deep convolutional network robustness and training,"https://scholar.google.com/scholar?cluster=2251331511068092550&hl=en&as_sdt=0,33",2,2021
On the Expected Complexity of Maxout Networks,5,neurips,0,0,2023-06-16 16:08:33.418000,https://github.com/hanna-tseran/maxout_complexity,0,On the expected complexity of maxout networks,"https://scholar.google.com/scholar?cluster=17674952708371009223&hl=en&as_sdt=0,5",1,2021
Can multi-label classification networks know what they don’t know?,53,neurips,4,4,2023-06-16 16:08:33.620000,https://github.com/deeplearning-wisc/multi-label-ood,31,Can multi-label classification networks know what they don't know?,"https://scholar.google.com/scholar?cluster=7813141666624240186&hl=en&as_sdt=0,19",1,2021
Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion,11,neurips,15,3,2023-06-16 16:08:33.820000,https://github.com/wutong16/density_aware_chamfer_distance,112,Balanced chamfer distance as a comprehensive metric for point cloud completion,"https://scholar.google.com/scholar?cluster=15226228858005494931&hl=en&as_sdt=0,36",6,2021
Gradient-based Editing of Memory Examples for Online Task-free Continual Learning,45,neurips,1,16,2023-06-16 16:08:34.021000,https://github.com/INK-USC/GMED,14,Gradient-based editing of memory examples for online task-free continual learning,"https://scholar.google.com/scholar?cluster=5596453218256135917&hl=en&as_sdt=0,5",7,2021
Clockwork Variational Autoencoders,31,neurips,8,2,2023-06-16 16:08:34.222000,https://github.com/vaibhavsaxena11/cwvae,40,Clockwork variational autoencoders,"https://scholar.google.com/scholar?cluster=16734321734301883406&hl=en&as_sdt=0,44",2,2021
Language models enable zero-shot prediction of the effects of mutations on protein function,156,neurips,419,54,2023-06-16 16:08:34.422000,https://github.com/facebookresearch/esm,2083,Language models enable zero-shot prediction of the effects of mutations on protein function,"https://scholar.google.com/scholar?cluster=7905832058791782023&hl=en&as_sdt=0,33",58,2021
Deep Reinforcement Learning at the Edge of the Statistical Precipice,230,neurips,37,1,2023-06-16 16:08:34.622000,https://github.com/google-research/rliable,590,Deep reinforcement learning at the edge of the statistical precipice,"https://scholar.google.com/scholar?cluster=2097182699708093297&hl=en&as_sdt=0,19",11,2021
Mind the Gap: Assessing Temporal Generalization in Neural Language Models,35,neurips,2436,170,2023-06-16 16:08:34.822000,https://github.com/deepmind/deepmind-research,11904,Mind the gap: Assessing temporal generalization in neural language models,"https://scholar.google.com/scholar?cluster=5752613093594915014&hl=en&as_sdt=0,5",336,2021
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks,14,neurips,0,0,2023-06-16 16:08:35.025000,https://github.com/mbarsbey/sgd_comp_gen,1,Heavy tails in SGD and compressibility of overparametrized neural networks,"https://scholar.google.com/scholar?cluster=2571177896093593726&hl=en&as_sdt=0,5",2,2021
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation,79,neurips,8,1,2023-06-16 16:08:35.226000,https://github.com/albert0147/sfda_neighbors,56,Exploiting the intrinsic neighborhood structure for source-free domain adaptation,"https://scholar.google.com/scholar?cluster=10860760915812805775&hl=en&as_sdt=0,5",2,2021
Learning with Noisy Correspondence for Cross-modal Matching,33,neurips,3,2,2023-06-16 16:08:35.427000,https://github.com/XLearning-SCU/2021-NeurIPS-NCR,37,Learning with noisy correspondence for cross-modal matching,"https://scholar.google.com/scholar?cluster=15452038367398862205&hl=en&as_sdt=0,7",2,2021
Parameter Prediction for Unseen Deep Architectures,33,neurips,58,3,2023-06-16 16:08:35.628000,https://github.com/facebookresearch/ppuda,473,Parameter prediction for unseen deep architectures,"https://scholar.google.com/scholar?cluster=5856024017848216222&hl=en&as_sdt=0,5",19,2021
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction,89,neurips,24,5,2023-06-16 16:08:35.828000,https://github.com/DeepGraphLearning/NBFNet,152,Neural bellman-ford networks: A general graph neural network framework for link prediction,"https://scholar.google.com/scholar?cluster=1918122330889670479&hl=en&as_sdt=0,5",6,2021
CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction,8,neurips,0,0,2023-06-16 16:08:36.029000,https://github.com/lebrat/CorticalFlow,4,Corticalflow: a diffeomorphic mesh transformer network for cortical surface reconstruction,"https://scholar.google.com/scholar?cluster=15767727877386818542&hl=en&as_sdt=0,5",1,2021
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression,10,neurips,4,0,2023-06-16 16:08:36.234000,https://github.com/google-research/sloe-logistic,27,SLOE: A faster method for statistical inference in high-dimensional logistic regression,"https://scholar.google.com/scholar?cluster=1558840668295453842&hl=en&as_sdt=0,5",4,2021
ELLA: Exploration through Learned Language Abstraction,21,neurips,2,0,2023-06-16 16:08:36.440000,https://github.com/Stanford-ILIAD/ELLA,17,Ella: Exploration through learned language abstraction,"https://scholar.google.com/scholar?cluster=1927255777603103026&hl=en&as_sdt=0,36",5,2021
Learning Distilled Collaboration Graph for Multi-Agent Perception,56,neurips,18,2,2023-06-16 16:08:36.640000,https://github.com/ai4ce/DiscoNet,109,Learning distilled collaboration graph for multi-agent perception,"https://scholar.google.com/scholar?cluster=14200311259933317556&hl=en&as_sdt=0,3",5,2021
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments,18,neurips,3,2,2023-06-16 16:08:36.842000,https://github.com/yycdavid/program-synthesis-guided-rl,15,Program synthesis guided reinforcement learning for partially observed environments,"https://scholar.google.com/scholar?cluster=14513934498714825801&hl=en&as_sdt=0,3",1,2021
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation,24,neurips,54,6,2023-06-16 16:08:37.043000,https://github.com/onion-liu/BlendGAN,481,Blendgan: Implicitly gan blending for arbitrary stylized face generation,"https://scholar.google.com/scholar?cluster=4524216348356707290&hl=en&as_sdt=0,41",24,2021
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models,1,neurips,2,0,2023-06-16 16:08:37.247000,https://github.com/lenarttreven/dgm,4,Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models,"https://scholar.google.com/scholar?cluster=7201902897927895371&hl=en&as_sdt=0,5",3,2021
Adjusting for Autocorrelated Errors in Neural Networks for Time Series,14,neurips,24,0,2023-06-16 16:08:37.447000,https://github.com/Daikon-Sun/AdjustAutocorrelation,55,Adjusting for autocorrelated errors in neural networks for time series,"https://scholar.google.com/scholar?cluster=6601944845381484010&hl=en&as_sdt=0,19",5,2021
A Geometric Analysis of Neural Collapse with Unconstrained Features,61,neurips,7,2,2023-06-16 16:08:37.653000,https://github.com/tding1/Neural-Collapse,39,A geometric analysis of neural collapse with unconstrained features,"https://scholar.google.com/scholar?cluster=4057119112941072069&hl=en&as_sdt=0,33",3,2021
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild,61,neurips,31,2,2023-06-16 16:08:37.854000,https://github.com/jasonyzhang/ners,263,NeRS: neural reflectance surfaces for sparse-view 3D reconstruction in the wild,"https://scholar.google.com/scholar?cluster=14745401126644120046&hl=en&as_sdt=0,10",12,2021
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning,32,neurips,0,1,2023-06-16 16:08:38.055000,https://github.com/vanint/core-tuning,20,Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning,"https://scholar.google.com/scholar?cluster=9361339446003315812&hl=en&as_sdt=0,5",2,2021
Topology-Imbalance Learning for Semi-Supervised Node Classification,33,neurips,7,3,2023-06-16 16:08:38.256000,https://github.com/victorchen96/renode,46,Topology-imbalance learning for semi-supervised node classification,"https://scholar.google.com/scholar?cluster=13925259727682632154&hl=en&as_sdt=0,1",2,2021
Gradient Inversion with Generative Image Prior,50,neurips,3,1,2023-06-16 16:08:38.457000,https://github.com/ml-postech/gradient-inversion-generative-image-prior,24,Gradient inversion with generative image prior,"https://scholar.google.com/scholar?cluster=17804052682569498638&hl=en&as_sdt=0,21",3,2021
Autobahn: Automorphism-based Graph Neural Nets,28,neurips,2,0,2023-06-16 16:08:38.658000,https://github.com/risilab/Autobahn,26,Autobahn: Automorphism-based graph neural nets,"https://scholar.google.com/scholar?cluster=15296065143551246227&hl=en&as_sdt=0,5",5,2021
Data Augmentation Can Improve Robustness,101,neurips,2436,170,2023-06-16 16:08:38.858000,https://github.com/deepmind/deepmind-research,11904,Data augmentation can improve robustness,"https://scholar.google.com/scholar?cluster=12512503752375350271&hl=en&as_sdt=0,33",336,2021
Deep Explicit Duration Switching Models for Time Series,13,neurips,4,0,2023-06-16 16:08:39.059000,https://github.com/abdulfatir/REDSDS,14,Deep explicit duration switching models for time series,"https://scholar.google.com/scholar?cluster=14557842774333633153&hl=en&as_sdt=0,5",2,2021
Shared Independent Component Analysis for Multi-Subject Neuroimaging,6,neurips,0,0,2023-06-16 16:08:39.260000,https://github.com/hugorichard/shica,9,Shared Independent Component Analysis for Multi-Subject Neuroimaging,"https://scholar.google.com/scholar?cluster=7343578052852866167&hl=en&as_sdt=0,44",3,2021
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects,6,neurips,12,1,2023-06-16 16:08:39.462000,https://github.com/rozumden/ShapeFromBlur,107,Shape from blur: Recovering textured 3d shape and motion of fast moving objects,"https://scholar.google.com/scholar?cluster=13910484849922207382&hl=en&as_sdt=0,33",4,2021
Residual Pathway Priors for Soft Equivariance Constraints,16,neurips,0,2,2023-06-16 16:08:39.663000,https://github.com/mfinzi/residual-pathway-priors,14,Residual pathway priors for soft equivariance constraints,"https://scholar.google.com/scholar?cluster=14878562091868847850&hl=en&as_sdt=0,33",2,2021
Learning Large Neighborhood Search Policy for Integer Programming,12,neurips,3,1,2023-06-16 16:08:39.868000,https://github.com/wxy1427/learn-lns-policy,14,Learning large neighborhood search policy for integer programming,"https://scholar.google.com/scholar?cluster=16588835717125760391&hl=en&as_sdt=0,33",2,2021
Provable Representation Learning for Imitation with Contrastive Fourier Features,26,neurips,7321,1026,2023-06-16 16:08:40.068000,https://github.com/google-research/google-research,29786,Provable representation learning for imitation with contrastive fourier features,"https://scholar.google.com/scholar?cluster=8157207826137904117&hl=en&as_sdt=0,26",727,2021
Counterfactual Explanations in Sequential Decision Making Under Uncertainty,18,neurips,3,0,2023-06-16 16:08:40.269000,https://github.com/networks-learning/counterfactual-explanations-mdp,10,Counterfactual explanations in sequential decision making under uncertainty,"https://scholar.google.com/scholar?cluster=12617016944988481192&hl=en&as_sdt=0,10",2,2021
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness,17,neurips,3,0,2023-06-16 16:08:40.470000,https://github.com/jh-jeong/smoothmix,18,Smoothmix: Training confidence-calibrated smoothed classifiers for certified robustness,"https://scholar.google.com/scholar?cluster=2235240635330767821&hl=en&as_sdt=0,36",1,2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks,8,neurips,0,0,2023-06-16 16:08:40.673000,https://github.com/boschresearch/meta-rs,7,Meta-learning the search distribution of black-box random search based adversarial attacks,"https://scholar.google.com/scholar?cluster=12889184094217530949&hl=en&as_sdt=0,5",5,2021
Rectangular Flows for Manifold Learning,24,neurips,1,1,2023-06-16 16:08:40.874000,https://github.com/layer6ai-labs/rectangular-flows,6,Rectangular flows for manifold learning,"https://scholar.google.com/scholar?cluster=10070884240732208071&hl=en&as_sdt=0,8",4,2021
On the Generative Utility of Cyclic Conditionals,1,neurips,7,1,2023-06-16 16:08:41.079000,https://github.com/changliu00/cygen,44,On the generative utility of cyclic conditionals,"https://scholar.google.com/scholar?cluster=16459389015391413710&hl=en&as_sdt=0,23",9,2021
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels,37,neurips,1,0,2023-06-16 16:08:41.280000,https://github.com/erikenglesson/gjs,16,Generalized jensen-shannon divergence loss for learning with noisy labels,"https://scholar.google.com/scholar?cluster=14179695996413180324&hl=en&as_sdt=0,14",1,2021
Continual Learning via Local Module Composition,31,neurips,3,0,2023-06-16 16:08:41.480000,https://github.com/oleksost/lmc,21,Continual learning via local module composition,"https://scholar.google.com/scholar?cluster=7775292558659449750&hl=en&as_sdt=0,18",1,2021
Adversarial Examples Make Strong Poisons,53,neurips,9,0,2023-06-16 16:08:41.681000,https://github.com/lhfowl/adversarial_poisons,38,Adversarial examples make strong poisons,"https://scholar.google.com/scholar?cluster=14707000567139585913&hl=en&as_sdt=0,32",1,2021
Coresets for Decision Trees of Signals,12,neurips,1,0,2023-06-16 16:08:41.883000,https://github.com/ernestosanches/decision-trees-coreset,3,Coresets for decision trees of signals,"https://scholar.google.com/scholar?cluster=8121919874821938952&hl=en&as_sdt=0,31",1,2021
Local plasticity rules can learn deep representations using self-supervised contrastive predictions,27,neurips,2,0,2023-06-16 16:08:42.084000,https://github.com/EPFL-LCN/pub-illing2021-neurips,17,Local plasticity rules can learn deep representations using self-supervised contrastive predictions,"https://scholar.google.com/scholar?cluster=8723626128481871858&hl=en&as_sdt=0,37",6,2021
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning,10,neurips,1,1,2023-06-16 16:08:42.286000,https://github.com/VivienCabannes/partial_labelling,9,Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning,"https://scholar.google.com/scholar?cluster=14486030124977090010&hl=en&as_sdt=0,10",1,2021
Unlabeled Principal Component Analysis,6,neurips,1,0,2023-06-16 16:08:42.486000,https://github.com/yaoyzh/Unlabeled_PCA_NeurIPS2021,1,Unlabeled principal component analysis,"https://scholar.google.com/scholar?cluster=13930442209235067345&hl=en&as_sdt=0,1",1,2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data,12,neurips,1,0,2023-06-16 16:08:42.687000,https://github.com/oatml/causal-bald,13,Causal-bald: Deep bayesian active learning of outcomes to infer treatment-effects from observational data,"https://scholar.google.com/scholar?cluster=14293468675130337012&hl=en&as_sdt=0,14",0,2021
Scalable Rule-Based Representation Learning for Interpretable Classification,29,neurips,13,2,2023-06-16 16:08:42.888000,https://github.com/12wang3/rrl,65,Scalable rule-based representation learning for interpretable classification,"https://scholar.google.com/scholar?cluster=4256640870246033381&hl=en&as_sdt=0,10",4,2021
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection,10,neurips,2,2,2023-06-16 16:08:43.090000,https://github.com/dongnana777/bridging-non-co-occurrence,7,Bridging non co-occurrence with unlabeled in-the-wild data for incremental object detection,"https://scholar.google.com/scholar?cluster=9835506167976269253&hl=en&as_sdt=0,33",2,2021
Generating Datasets of 3D Garments with Sewing Patterns,10,neurips,13,0,2023-06-16 16:08:43.290000,https://github.com/maria-korosteleva/garment-pattern-generator,85,Generating datasets of 3d garments with sewing patterns,"https://scholar.google.com/scholar?cluster=15013601898375662673&hl=en&as_sdt=0,5",4,2021
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation,23,neurips,11,2,2023-06-16 16:08:43.491000,https://github.com/stanfordmimi/skm-tea,56,Skm-tea: A dataset for accelerated mri reconstruction with dense image labels for quantitative clinical evaluation,"https://scholar.google.com/scholar?cluster=14148193139570714789&hl=en&as_sdt=0,33",4,2021
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee,7,neurips,2,0,2023-06-16 16:08:43.693000,https://github.com/ds3lab/feebee,4,Evaluating Bayes error estimators on real-world datasets with FeeBee,"https://scholar.google.com/scholar?cluster=2464591974383238873&hl=en&as_sdt=0,5",7,2021
PASS: An ImageNet replacement for self-supervised pretraining without humans,23,neurips,17,2,2023-06-16 16:08:43.895000,https://github.com/yukimasano/PASS,254,Pass: An imagenet replacement for self-supervised pretraining without humans,"https://scholar.google.com/scholar?cluster=16947555364895475194&hl=en&as_sdt=0,44",6,2021
URLB: Unsupervised Reinforcement Learning Benchmark,61,neurips,46,16,2023-06-16 16:08:44.096000,https://github.com/rll-research/url_benchmark,290,URLB: Unsupervised reinforcement learning benchmark,"https://scholar.google.com/scholar?cluster=12980539145906444225&hl=en&as_sdt=0,33",7,2021
An Empirical Study of Graph Contrastive Learning,68,neurips,81,12,2023-06-16 16:08:44.296000,https://github.com/GraphCL/PyGCL,675,An empirical study of graph contrastive learning,"https://scholar.google.com/scholar?cluster=6611245938611321529&hl=en&as_sdt=0,5",8,2021
Chest ImaGenome Dataset for Clinical Reasoning,15,neurips,0,0,2023-06-16 16:08:44.496000,https://github.com/LourentzouTBD/ChestImaGenomeChangeDetection,1,Chest ImaGenome dataset for clinical reasoning,"https://scholar.google.com/scholar?cluster=3704746853609253199&hl=en&as_sdt=0,33",1,2021
WRENCH: A Comprehensive Benchmark for Weak Supervision,61,neurips,27,7,2023-06-16 16:08:44.697000,https://github.com/jieyuz2/wrench,194,Wrench: A comprehensive benchmark for weak supervision,"https://scholar.google.com/scholar?cluster=16182721416857685898&hl=en&as_sdt=0,33",6,2021
A Dataset for Answering Time-Sensitive Questions,17,neurips,5,2,2023-06-16 16:08:44.897000,https://github.com/wenhuchen/time-sensitive-qa,42,A dataset for answering time-sensitive questions,"https://scholar.google.com/scholar?cluster=9316987576931607453&hl=en&as_sdt=0,38",1,2021
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation,9,neurips,1,1,2023-06-16 16:08:45.097000,https://github.com/XanderJC/medkit-learn,22,The medkit-learn (ing) environment: Medical decision modelling through simulation,"https://scholar.google.com/scholar?cluster=17528200629661115793&hl=en&as_sdt=0,5",3,2021
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics,15,neurips,13,0,2023-06-16 16:08:45.298000,https://github.com/charan223/FairDeepLearning,31,Benchmarking bias mitigation algorithms in representation learning through fairness metrics,"https://scholar.google.com/scholar?cluster=6740709092795107376&hl=en&as_sdt=0,10",5,2021
Datasets for Online Controlled Experiments,3,neurips,0,0,2023-06-16 16:08:45.499000,https://github.com/liuchbryan/oce-dataset,4,Datasets for online controlled experiments,"https://scholar.google.com/scholar?cluster=408266997951009779&hl=en&as_sdt=0,47",3,2021
The CLEAR Benchmark: Continual LEArning on Real-World Imagery,36,neurips,4,0,2023-06-16 16:08:45.700000,https://github.com/linzhiqiu/continual-learning,13,The clear benchmark: Continual learning on real-world imagery,"https://scholar.google.com/scholar?cluster=17993292222696601191&hl=en&as_sdt=0,20",4,2021
ReaSCAN: Compositional Reasoning in Language Grounding,7,neurips,3,0,2023-06-16 16:08:45.903000,https://github.com/frankaging/Reason-SCAN,17,ReaSCAN: Compositional reasoning in language grounding,"https://scholar.google.com/scholar?cluster=7096206809179384730&hl=en&as_sdt=0,5",5,2021
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions,16,neurips,1,0,2023-06-16 16:08:46.104000,https://github.com/newbeeyoung/video-corruption-robustness,16,Benchmarking the robustness of spatial-temporal models against corruptions,"https://scholar.google.com/scholar?cluster=13559459758592949977&hl=en&as_sdt=0,33",3,2021
GraphGT: Machine Learning Datasets for Graph Generation and Transformation,32,neurips,7,2,2023-06-16 16:08:46.304000,https://github.com/yuanqidu/graphgt,51,Graphgt: Machine learning datasets for graph generation and transformation,"https://scholar.google.com/scholar?cluster=11012021022689991240&hl=en&as_sdt=0,10",2,2021
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation,38,neurips,75,23,2023-06-16 16:08:46.505000,https://github.com/st-tech/zr-obp,549,Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation,"https://scholar.google.com/scholar?cluster=10707722556009377278&hl=en&as_sdt=0,36",88,2021
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI,88,neurips,9,2,2023-06-16 16:08:46.706000,https://github.com/facebookresearch/habitat-matterport3d-dataset,91,Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai,"https://scholar.google.com/scholar?cluster=16347568328896129172&hl=en&as_sdt=0,5",8,2021
A realistic approach to generate masked faces applied on two novel masked face recognition data sets,11,neurips,3,3,2023-06-16 16:08:46.906000,https://github.com/securifai/masked_faces,28,A realistic approach to generate masked faces applied on two novel masked face recognition data sets,"https://scholar.google.com/scholar?cluster=6898524933140941644&hl=en&as_sdt=0,14",3,2021
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark ,15,neurips,3,3,2023-06-16 16:08:47.107000,https://github.com/mlii0117/FFA-IR,32,Ffa-ir: Towards an explainable and reliable medical report generation benchmark,"https://scholar.google.com/scholar?cluster=6645019312139456748&hl=en&as_sdt=0,39",1,2021
What Would Jiminy Cricket Do? Towards Agents That Behave Morally,14,neurips,3,0,2023-06-16 16:08:47.309000,https://github.com/hendrycks/jiminy-cricket,19,What would jiminy cricket do? Towards agents that behave morally,"https://scholar.google.com/scholar?cluster=14711980494808596715&hl=en&as_sdt=0,5",2,2021
Programming Puzzles,16,neurips,88,20,2023-06-16 16:08:47.509000,https://github.com/microsoft/PythonProgrammingPuzzles,880,Programming puzzles,"https://scholar.google.com/scholar?cluster=5425926029419561217&hl=en&as_sdt=0,23",16,2021
An Extensible Benchmark Suite for Learning to Simulate Physical Systems,7,neurips,2,0,2023-06-16 16:08:47.708000,https://github.com/karlotness/nn-benchmark,17,An extensible benchmark suite for learning to simulate physical systems,"https://scholar.google.com/scholar?cluster=3662433208653304264&hl=en&as_sdt=0,5",7,2021
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting,101,neurips,55,15,2023-06-16 16:08:47.920000,https://github.com/argoverse/av2-api,216,Argoverse 2: Next generation datasets for self-driving perception and forecasting,"https://scholar.google.com/scholar?cluster=650026435189304623&hl=en&as_sdt=0,5",10,2021
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development,113,neurips,145,29,2023-06-16 16:08:48.121000,https://github.com/mims-harvard/TDC,823,Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development,"https://scholar.google.com/scholar?cluster=263016632375932982&hl=en&as_sdt=0,14",22,2021
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation,75,neurips,39,26,2023-06-16 16:08:48.321000,https://github.com/Junjue-Wang/LoveDA,226,LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=7895763680437166641&hl=en&as_sdt=0,5",4,2021
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge,19,neurips,3,1,2023-06-16 16:08:48.521000,https://github.com/yasumasaonoe/creak,16,CREAK: A dataset for commonsense reasoning over entity knowledge,"https://scholar.google.com/scholar?cluster=16825406718835983392&hl=en&as_sdt=0,4",3,2021
A Large-Scale Database for Graph Representation Learning,29,neurips,11,2,2023-06-16 16:08:48.721000,https://github.com/safreita1/malnet-graph,35,A large-scale database for graph representation learning,"https://scholar.google.com/scholar?cluster=10177352581940453815&hl=en&as_sdt=0,11",2,2021
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling,32,neurips,1,1,2023-06-16 16:08:48.921000,https://github.com/HLTCHKUST/BiToD,21,Bitod: A bilingual multi-domain dataset for task-oriented dialogue modeling,"https://scholar.google.com/scholar?cluster=3554059482240566542&hl=en&as_sdt=0,5",5,2021
HumBugDB: A Large-scale Acoustic Mosquito Dataset,16,neurips,9,0,2023-06-16 16:08:49.122000,https://github.com/humbug-mosquito/humbugdb,32,HumBugDB: a large-scale acoustic mosquito dataset,"https://scholar.google.com/scholar?cluster=16288671162507786903&hl=en&as_sdt=0,10",6,2021
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data,32,neurips,52,13,2023-06-16 16:08:49.322000,https://github.com/apple/ARKitScenes,476,ARKitScenes--A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data,"https://scholar.google.com/scholar?cluster=16950635420621153680&hl=en&as_sdt=0,10",25,2021
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information,68,neurips,18,5,2023-06-16 16:08:49.523000,https://github.com/Raldir/FEVEROUS,54,Feverous: Fact extraction and verification over unstructured and structured information,"https://scholar.google.com/scholar?cluster=5675725561486450622&hl=en&as_sdt=0,47",2,2021
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning,22,neurips,15,4,2023-06-16 16:08:49.723000,https://github.com/thudm/grb,77,Graph robustness benchmark: Benchmarking the adversarial robustness of graph machine learning,"https://scholar.google.com/scholar?cluster=740832455944731540&hl=en&as_sdt=0,18",8,2021
CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review,55,neurips,95,9,2023-06-16 16:08:49.924000,https://github.com/TheAtticusProject/cuad,302,Cuad: An expert-annotated nlp dataset for legal contract review,"https://scholar.google.com/scholar?cluster=9100258365947035090&hl=en&as_sdt=0,5",13,2021
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation,165,neurips,68,20,2023-06-16 16:08:50.124000,https://github.com/threedworld-mit/tdw,388,Threedworld: A platform for interactive multi-modal physical simulation,"https://scholar.google.com/scholar?cluster=7060550992548001632&hl=en&as_sdt=0,5",21,2021
Personalized Benchmarking with the Ludwig Benchmarking Toolkit,11,neurips,1046,279,2023-06-16 16:08:50.324000,https://github.com/ludwig-ai/ludwig,8974,Personalized benchmarking with the ludwig benchmarking toolkit,"https://scholar.google.com/scholar?cluster=604774687945155345&hl=en&as_sdt=0,39",186,2021
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs,10,neurips,4,1,2023-06-16 16:08:50.524000,https://github.com/hkust-knowcomp/efo-1-qa-benchmark,17,Benchmarking the combinatorial generalizability of complex query answering on knowledge graphs,"https://scholar.google.com/scholar?cluster=14710134969550409295&hl=en&as_sdt=0,34",2,2021
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions,18,neurips,7,0,2023-06-16 16:08:50.724000,https://github.com/neuroethology/TREBA,66,The multi-agent behavior dataset: Mouse dyadic social interactions,"https://scholar.google.com/scholar?cluster=17767650578818476506&hl=en&as_sdt=0,11",3,2021
DABS: a Domain-Agnostic Benchmark for Self-Supervised Learning,22,neurips,11,0,2023-06-16 16:08:50.925000,https://github.com/alextamkin/dabs,92,DABS: A domain-agnostic benchmark for self-supervised learning,"https://scholar.google.com/scholar?cluster=6831578764269382202&hl=en&as_sdt=0,33",3,2021
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning,21,neurips,13,0,2023-06-16 16:08:51.125000,https://github.com/dido1998/CausalMBRL,37,Systematic evaluation of causal discovery in visual model based reinforcement learning,"https://scholar.google.com/scholar?cluster=10762852414986189275&hl=en&as_sdt=0,33",5,2021
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning,23,neurips,17,9,2023-06-16 16:08:51.325000,https://github.com/sustainlab-group/sustainbench,76,Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning,"https://scholar.google.com/scholar?cluster=11548079407766263618&hl=en&as_sdt=0,33",5,2021
STEP: Segmenting and Tracking Every Pixel,41,neurips,156,30,2023-06-16 16:08:51.526000,https://github.com/google-research/deeplab2,906,Step: Segmenting and tracking every pixel,"https://scholar.google.com/scholar?cluster=3403854428676887512&hl=en&as_sdt=0,5",23,2021
KLUE: Korean Language Understanding Evaluation,133,neurips,57,16,2023-06-16 16:08:51.727000,https://github.com/KLUE-benchmark/KLUE,505,Klue: Korean language understanding evaluation,"https://scholar.google.com/scholar?cluster=12921581347443932322&hl=en&as_sdt=0,33",19,2021
ImageNet-21K Pretraining for the Masses,239,neurips,65,13,2023-06-16 16:08:51.927000,https://github.com/Alibaba-MIIL/ImageNet21K,629,Imagenet-21k pretraining for the masses,"https://scholar.google.com/scholar?cluster=15637978761893120373&hl=en&as_sdt=0,33",10,2021
Benchmarking Multimodal AutoML for Tabular Data with Text Fields,16,neurips,6,1,2023-06-16 16:08:52.140000,https://github.com/sxjscience/automl_multimodal_benchmark,47,Benchmarking multimodal automl for tabular data with text fields,"https://scholar.google.com/scholar?cluster=15129006949053475475&hl=en&as_sdt=0,50",7,2021
EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction,17,neurips,6,1,2023-06-16 16:08:52.340000,https://github.com/ardkastrati/eegeyenet,26,EEGEyeNet: a simultaneous electroencephalography and eye-tracking dataset and benchmark for eye movement prediction,"https://scholar.google.com/scholar?cluster=18415629137722917831&hl=en&as_sdt=0,33",3,2021
RobustBench: a standardized adversarial robustness benchmark,316,neurips,78,2,2023-06-16 16:08:52.540000,https://github.com/RobustBench/robustbench,476,Robustbench: a standardized adversarial robustness benchmark,"https://scholar.google.com/scholar?cluster=2257115641228924434&hl=en&as_sdt=0,14",9,2021
EventNarrative: A Large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation,8,neurips,0,0,2023-06-16 16:08:52.740000,https://github.com/acolas1/EventNarrative,4,EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation,"https://scholar.google.com/scholar?cluster=9691193925909218204&hl=en&as_sdt=0,7",1,2021
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents,11,neurips,19,1,2023-06-16 16:08:52.951000,https://github.com/deepmind/dm_alchemy,191,Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents,"https://scholar.google.com/scholar?cluster=16056252909542082648&hl=en&as_sdt=0,33",15,2021
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks,60,neurips,180,2,2023-06-16 16:08:53.156000,https://github.com/IBM/Project_CodeNet,1361,CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks,"https://scholar.google.com/scholar?cluster=9700363462544607592&hl=en&as_sdt=0,43",53,2021
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation,62,neurips,5,3,2023-06-16 16:08:53.365000,https://github.com/VALUE-Leaderboard/StarterCode,80,Value: A multi-task benchmark for video-and-language understanding evaluation,"https://scholar.google.com/scholar?cluster=3360639722012536549&hl=en&as_sdt=0,48",4,2021
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks,67,neurips,49,6,2023-06-16 16:08:53.574000,https://github.com/yandex-research/shifts,207,Shifts: A dataset of real distributional shift across multiple large-scale tasks,"https://scholar.google.com/scholar?cluster=6919306211316072115&hl=en&as_sdt=0,33",14,2021
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms,50,neurips,54,17,2023-06-16 16:08:53.775000,https://github.com/indyfree/CARLA,239,Carla: a python library to benchmark algorithmic recourse and counterfactual explanation algorithms,"https://scholar.google.com/scholar?cluster=319623159508225394&hl=en&as_sdt=0,14",6,2021
Measuring Mathematical Problem Solving With the MATH Dataset,115,neurips,40,2,2023-06-16 16:08:53.975000,https://github.com/hendrycks/math,382,Measuring mathematical problem solving with the math dataset,"https://scholar.google.com/scholar?cluster=15840802134856527968&hl=en&as_sdt=0,33",10,2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning,25,neurips,12,1,2023-06-16 16:08:54.175000,https://github.com/abacusai/xai-bench,38,Synthetic benchmarks for scientific research in explainable machine learning,"https://scholar.google.com/scholar?cluster=16562504409000765600&hl=en&as_sdt=0,7",7,2021
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation,305,neurips,313,23,2023-06-16 16:08:54.376000,https://github.com/microsoft/CodeXGLUE,1143,Codexglue: A machine learning benchmark dataset for code understanding and generation,"https://scholar.google.com/scholar?cluster=3348257757676709546&hl=en&as_sdt=0,34",34,2021
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark,23,neurips,116,23,2023-06-16 16:08:54.577000,https://github.com/modeltc/mqbench,586,MQBench: Towards reproducible and deployable model quantization benchmark,"https://scholar.google.com/scholar?cluster=3991463510006314628&hl=en&as_sdt=0,33",15,2021
Measuring Coding Challenge Competence With APPS,101,neurips,38,5,2023-06-16 16:08:54.778000,https://github.com/hendrycks/apps,274,Measuring coding challenge competence with apps,"https://scholar.google.com/scholar?cluster=17541608988106931861&hl=en&as_sdt=0,5",12,2021
ATOM3D: Tasks on Molecules in Three Dimensions,62,neurips,32,18,2023-06-16 16:08:54.981000,https://github.com/drorlab/atom3d,249,Atom3d: Tasks on molecules in three dimensions,"https://scholar.google.com/scholar?cluster=8766868616148993451&hl=en&as_sdt=0,5",14,2021
WaveFake: A Data Set to Facilitate Audio Deepfake Detection,32,neurips,6,0,2023-06-16 16:08:55.188000,https://github.com/rub-syssec/wavefake,42,Wavefake: A data set to facilitate audio deepfake detection,"https://scholar.google.com/scholar?cluster=6599528507595040003&hl=en&as_sdt=0,39",6,2021
RAFT: A Real-World Few-Shot Text Classification Benchmark,23,neurips,10,1,2023-06-16 16:08:55.389000,https://github.com/oughtinc/raft-baselines,11,RAFT: A real-world few-shot text classification benchmark,"https://scholar.google.com/scholar?cluster=14991051401140095655&hl=en&as_sdt=0,14",2,2021
Physion: Evaluating Physical Prediction from Vision in Humans and Machines,25,neurips,2,12,2023-06-16 16:08:55.589000,https://github.com/cogtoolslab/physics-benchmarking-neurips2021,44,Physion: Evaluating physical prediction from vision in humans and machines,"https://scholar.google.com/scholar?cluster=8733318111076645893&hl=en&as_sdt=0,5",9,2021
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning,17,neurips,13,0,2023-06-16 16:08:55.790000,https://github.com/lupantech/iconqa,31,Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning,"https://scholar.google.com/scholar?cluster=6611908787102909279&hl=en&as_sdt=0,5",3,2021
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation,42,neurips,4,0,2023-06-16 16:08:55.992000,https://github.com/segmentmeifyoucan/road-anomaly-benchmark,20,Segmentmeifyoucan: A benchmark for anomaly segmentation,"https://scholar.google.com/scholar?cluster=402806083575370360&hl=en&as_sdt=0,21",0,2021
B-Pref: Benchmarking Preference-Based Reinforcement Learning,29,neurips,17,6,2023-06-16 16:08:56.192000,https://github.com/rll-research/b-pref,76,B-pref: Benchmarking preference-based reinforcement learning,"https://scholar.google.com/scholar?cluster=13266882268362659539&hl=en&as_sdt=0,33",0,2021
NaturalProofs: Mathematical Theorem Proving in Natural Language,22,neurips,6,0,2023-06-16 16:08:56.392000,https://github.com/wellecks/naturalproofs,90,Naturalproofs: Mathematical theorem proving in natural language,"https://scholar.google.com/scholar?cluster=955828414616536580&hl=en&as_sdt=0,32",7,2021
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs,165,neurips,397,17,2023-06-16 16:08:56.592000,https://github.com/snap-stanford/ogb,1685,Ogb-lsc: A large-scale challenge for machine learning on graphs,"https://scholar.google.com/scholar?cluster=15358624115412194871&hl=en&as_sdt=0,33",42,2021
An Information Retrieval Approach to Building Datasets for Hate Speech Detection,12,neurips,1,0,2023-06-16 16:08:56.793000,https://github.com/mdmustafizurrahman/An-Information-Retrieval-Approach-to-Building-Datasets-for-Hate-Speech-Detection,6,An information retrieval approach to building datasets for hate speech detection,"https://scholar.google.com/scholar?cluster=8624990227295438686&hl=en&as_sdt=0,33",3,2021
"RedCaps: Web-curated image-text data created by the people, for the people",54,neurips,7,0,2023-06-16 16:08:56.993000,https://github.com/redcaps-dataset/redcaps-downloader,34,"Redcaps: Web-curated image-text data created by the people, for the people","https://scholar.google.com/scholar?cluster=16709143259160494609&hl=en&as_sdt=0,33",1,2021
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation,35,neurips,2,0,2023-06-16 16:08:57.193000,https://github.com/karazijal/clevrtex-generation,30,Clevrtex: A texture-rich benchmark for unsupervised multi-object segmentation,"https://scholar.google.com/scholar?cluster=13383231498167855057&hl=en&as_sdt=0,37",2,2021
A Channel Coding Benchmark for Meta-Learning,6,neurips,1,0,2023-06-16 16:08:57.393000,https://github.com/ruihuili/MetaCC,8,A channel coding benchmark for meta-learning,"https://scholar.google.com/scholar?cluster=1943764158077040305&hl=en&as_sdt=0,18",5,2021
Chaos as an interpretable benchmark for forecasting and data-driven modelling,31,neurips,26,0,2023-06-16 16:08:57.594000,https://github.com/williamgilpin/dysts,204,Chaos as an interpretable benchmark for forecasting and data-driven modelling,"https://scholar.google.com/scholar?cluster=10113442544337188110&hl=en&as_sdt=0,33",7,2021
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML ,15,neurips,3,1,2023-06-16 16:08:57.794000,https://github.com/releaunifreiburg/HPO-B,17,HPO-B: A large-scale reproducible benchmark for black-box HPO based on OpenML,"https://scholar.google.com/scholar?cluster=7650782388880150578&hl=en&as_sdt=0,39",2,2021
Monash Time Series Forecasting Archive,42,neurips,34,0,2023-06-16 16:08:57.994000,https://github.com/rakshitha123/TSForecasting,103,Monash time series forecasting archive,"https://scholar.google.com/scholar?cluster=2787747679550330203&hl=en&as_sdt=0,31",6,2021
Which priors matter? Benchmarking models for learning latent dynamics,16,neurips,6,2,2023-06-16 16:08:58.195000,https://github.com/deepmind/dm_hamiltonian_dynamics_suite,28,Which priors matter? Benchmarking models for learning latent dynamics,"https://scholar.google.com/scholar?cluster=377030899492556244&hl=en&as_sdt=0,1",5,2021
Benchmarks for Corruption Invariant Person Re-identification,11,neurips,17,2,2023-06-16 16:08:58.395000,https://github.com/MinghuiChen43/CIL-ReID,78,Benchmarks for corruption invariant person re-identification,"https://scholar.google.com/scholar?cluster=13448668385156906300&hl=en&as_sdt=0,10",4,2021
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models,4,neurips,4,0,2023-06-16 16:08:58.595000,https://github.com/RolnickLab/climart,32,ClimART: A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models,"https://scholar.google.com/scholar?cluster=15949022047670845408&hl=en&as_sdt=0,41",2,2021
Variance-Aware Machine Translation Test Sets,1,neurips,0,0,2023-06-16 16:08:58.796000,https://github.com/nlp2ct/variance-aware-mt-test-sets,6,Variance-aware machine translation test sets,"https://scholar.google.com/scholar?cluster=101231479911651461&hl=en&as_sdt=0,10",2,2021
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research,44,neurips,44,2,2023-06-16 16:08:58.996000,https://github.com/facebookresearch/minihack,383,Minihack the planet: A sandbox for open-ended reinforcement learning research,"https://scholar.google.com/scholar?cluster=6630578925704373127&hl=en&as_sdt=0,5",11,2021
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search,8,neurips,2,1,2023-06-16 22:56:56.096000,https://github.com/thumnlab/nas-bench-graph,12,NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search,"https://scholar.google.com/scholar?cluster=974156453210928124&hl=en&as_sdt=0,10",7,2022
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement,3,neurips,0,0,2023-06-16 22:56:56.310000,https://github.com/trevorcampbell/quasi-newton-coresets-experiments,1,Fast Bayesian coresets via subsampling and quasi-Newton refinement,"https://scholar.google.com/scholar?cluster=12514193164456670939&hl=en&as_sdt=0,31",1,2022
What You See is What You Classify: Black Box Attributions,1,neurips,3,0,2023-06-16 22:56:56.520000,https://github.com/stevenstalder/nn-explainer,22,What You See is What You Classify: Black Box Attributions,"https://scholar.google.com/scholar?cluster=7817582227897435675&hl=en&as_sdt=0,5",1,2022
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning,20,neurips,6,3,2023-06-16 22:56:56.731000,https://github.com/dongzelian/ssf,105,Scaling & shifting your features: A new baseline for efficient model tuning,"https://scholar.google.com/scholar?cluster=15457903862760581709&hl=en&as_sdt=0,33",2,2022
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models,30,neurips,19,1,2023-06-16 22:56:56.941000,https://github.com/antoyang/FrozenBiLM,98,Zero-shot video question answering via frozen bidirectional language models,"https://scholar.google.com/scholar?cluster=14506268695911835029&hl=en&as_sdt=0,44",4,2022
Using natural language and program abstractions to instill human inductive biases in machines,5,neurips,3,0,2023-06-16 22:56:57.151000,https://github.com/sreejank/language_and_programs,4,Using natural language and program abstractions to instill human inductive biases in machines,"https://scholar.google.com/scholar?cluster=18321817709222277184&hl=en&as_sdt=0,44",1,2022
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources,0,neurips,1,1,2023-06-16 22:56:57.362000,https://github.com/hci-unihd/branchedot,3,Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources,"https://scholar.google.com/scholar?cluster=2014031354721865805&hl=en&as_sdt=0,21",1,2022
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis,0,neurips,1,0,2023-06-16 22:56:57.573000,https://github.com/niopeng/CHIMLE,3,CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis,"https://scholar.google.com/scholar?cluster=6104344160615943312&hl=en&as_sdt=0,5",2,2022
Diffusion Visual Counterfactual Explanations,6,neurips,5,1,2023-06-16 22:56:57.784000,https://github.com/valentyn1boreiko/dvces,22,Diffusion visual counterfactual explanations,"https://scholar.google.com/scholar?cluster=10867197549616618589&hl=en&as_sdt=0,5",2,2022
Recurrent Video Restoration Transformer with Guided Deformable Attention,17,neurips,17,15,2023-06-16 22:56:57.996000,https://github.com/jingyunliang/rvrt,216,Recurrent video restoration transformer with guided deformable attention,"https://scholar.google.com/scholar?cluster=11993953591906088344&hl=en&as_sdt=0,1",22,2022
On-Demand Sampling: Learning Optimally from Multiple Distributions,5,neurips,1,0,2023-06-16 22:56:58.207000,https://github.com/ericzhao28/multidistributionlearning,7,On-demand sampling: Learning optimally from multiple distributions,"https://scholar.google.com/scholar?cluster=89881707711489723&hl=en&as_sdt=0,5",3,2022
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays,9,neurips,1,0,2023-06-16 22:56:58.417000,https://github.com/konstmish/asynchronous_sgd,4,Asynchronous sgd beats minibatch sgd under arbitrary delays,"https://scholar.google.com/scholar?cluster=2013363266003001191&hl=en&as_sdt=0,5",1,2022
Coresets for Relational Data and The Applications,0,neurips,0,0,2023-06-16 22:56:58.627000,https://github.com/cjx-zar/coresets-for-relational-data-and-the-applications,1,Coresets for Relational Data and The Applications,"https://scholar.google.com/scholar?cluster=9554541870090821318&hl=en&as_sdt=0,5",1,2022
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding,33,neurips,9,1,2023-06-16 22:56:58.838000,https://github.com/yumeng5/supergen,47,Generating training data with language models: Towards zero-shot language understanding,"https://scholar.google.com/scholar?cluster=14481752723663721801&hl=en&as_sdt=0,5",2,2022
Robust Binary Models by Pruning Randomly-initialized Networks,1,neurips,1,0,2023-06-16 22:56:59.049000,https://github.com/IVRL/RobustBinarySubNet,2,Robust Binary Models by Pruning Randomly-initialized Networks,"https://scholar.google.com/scholar?cluster=4369217517871260894&hl=en&as_sdt=0,22",3,2022
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning,2,neurips,0,0,2023-06-16 22:56:59.260000,https://github.com/lviano/identifiability_irl,1,Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning,"https://scholar.google.com/scholar?cluster=14730598469172065139&hl=en&as_sdt=0,5",1,2022
Efficient Knowledge Distillation from Model Checkpoints,6,neurips,0,2,2023-06-16 22:56:59.471000,https://github.com/leaplabthu/checkpointkd,17,Efficient Knowledge Distillation from Model Checkpoints,"https://scholar.google.com/scholar?cluster=2353993256352314616&hl=en&as_sdt=0,10",2,2022
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs,9,neurips,239,19,2023-06-16 22:56:59.682000,https://github.com/divelab/DIG,1503,ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs,"https://scholar.google.com/scholar?cluster=1138590591357875306&hl=en&as_sdt=0,5",33,2022
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret,1,neurips,0,0,2023-06-16 22:56:59.893000,https://github.com/jiaweihhuang/tiered-rl-experiments,1,Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret,"https://scholar.google.com/scholar?cluster=7975992698003675864&hl=en&as_sdt=0,5",1,2022
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling,2,neurips,0,0,2023-06-16 22:57:00.103000,https://github.com/gabrielvc/br_snis,0,BR-SNIS: Bias Reduced Self-Normalized Importance Sampling,"https://scholar.google.com/scholar?cluster=18224130644100416616&hl=en&as_sdt=0,33",2,2022
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks,2,neurips,0,0,2023-06-16 22:57:00.315000,https://github.com/wmz9/early_stage_convergence_neurips2022,0,Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks,"https://scholar.google.com/scholar?cluster=6474562401144643315&hl=en&as_sdt=0,36",1,2022
On Divergence Measures for Bayesian Pseudocoresets,2,neurips,0,0,2023-06-16 22:57:00.525000,https://github.com/balhaekim/bpc-divergences,6,On Divergence Measures for Bayesian Pseudocoresets,"https://scholar.google.com/scholar?cluster=2002320216778529184&hl=en&as_sdt=0,33",1,2022
Unsupervised Learning of Equivariant Structure from Sequences,1,neurips,0,0,2023-06-16 22:57:00.736000,https://github.com/takerum/meta_sequential_prediction,13,Unsupervised Learning of Equivariant Structure from Sequences,"https://scholar.google.com/scholar?cluster=304500116743207302&hl=en&as_sdt=0,33",3,2022
DC-BENCH: Dataset Condensation Benchmark,13,neurips,15,4,2023-06-16 22:57:00.948000,https://github.com/justincui03/dc_benchmark,60,DC-BENCH: Dataset Condensation Benchmark,"https://scholar.google.com/scholar?cluster=16210328737996830947&hl=en&as_sdt=0,31",3,2022
Mask Matching Transformer for Few-Shot Segmentation,2,neurips,2,2,2023-06-16 22:57:01.159000,https://github.com/picsart-ai-research/mask-matching-transformer,9,Mask matching transformer for few-shot segmentation,"https://scholar.google.com/scholar?cluster=10843608391275474221&hl=en&as_sdt=0,14",2,2022
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error,1,neurips,0,0,2023-06-16 22:57:01.369000,https://github.com/yuqin-yang/sem-me-ur,3,Causal Discovery in Linear Latent Variable Models Subject to Measurement Error,"https://scholar.google.com/scholar?cluster=2946367080464939107&hl=en&as_sdt=0,5",1,2022
Sparsity in Continuous-Depth Neural Networks,1,neurips,0,0,2023-06-16 22:57:01.579000,https://github.com/theislab/pathreg,6,Sparsity in Continuous-Depth Neural Networks,"https://scholar.google.com/scholar?cluster=17433656016983930477&hl=en&as_sdt=0,5",2,2022
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM,1,neurips,0,0,2023-06-16 22:57:01.790000,https://github.com/point0bar1/hat-ebm,6,Learning Probabilistic Models from Generator Latent Spaces with Hat EBM,"https://scholar.google.com/scholar?cluster=9884499776664824848&hl=en&as_sdt=0,5",1,2022
Learning Best Combination for Efficient N:M Sparsity,8,neurips,1,1,2023-06-16 22:57:02.001000,https://github.com/zyxxmu/lbc,12,Learning Best Combination for Efficient N: M Sparsity,"https://scholar.google.com/scholar?cluster=16372091815388983729&hl=en&as_sdt=0,47",1,2022
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera,10,neurips,56,2,2023-06-16 22:57:02.212000,https://github.com/USTC3DV/NDR-code,473,Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera,"https://scholar.google.com/scholar?cluster=13429723672791415144&hl=en&as_sdt=0,44",14,2022
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations,6,neurips,4,0,2023-06-16 22:57:02.423000,https://github.com/ramanshsharma2806/dt-pinn,7,Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations,"https://scholar.google.com/scholar?cluster=8326898373618608697&hl=en&as_sdt=0,28",4,2022
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning,7,neurips,2,0,2023-06-16 22:57:02.634000,https://github.com/archanabura/dope-doublyoptimisticpessimisticexploration,1,DOPE: Doubly optimistic and pessimistic exploration for safe reinforcement learning,"https://scholar.google.com/scholar?cluster=15050715295292728061&hl=en&as_sdt=0,5",1,2022
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate,1,neurips,0,0,2023-06-16 22:57:02.845000,https://github.com/kexinjinnn/equitopo,7,Communication-Efficient Topologies for Decentralized Learning with Consensus Rate,"https://scholar.google.com/scholar?cluster=16384367809793868682&hl=en&as_sdt=0,33",1,2022
Dataset Distillation via Factorization,20,neurips,6,3,2023-06-16 22:57:03.055000,https://github.com/huage001/datasetfactorization,47,Dataset distillation via factorization,"https://scholar.google.com/scholar?cluster=1635742164576449623&hl=en&as_sdt=0,5",1,2022
A Large Scale Search Dataset for Unbiased Learning to Rank,8,neurips,8,8,2023-06-16 22:57:03.266000,https://github.com/chuxiaokai/baidu_ultr_dataset,51,A large scale search dataset for unbiased learning to rank,"https://scholar.google.com/scholar?cluster=16787793600985661869&hl=en&as_sdt=0,33",5,2022
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation,58,neurips,66,20,2023-06-16 22:57:03.476000,https://github.com/visual-attention-network/segnext,630,Segnext: Rethinking convolutional attention design for semantic segmentation,"https://scholar.google.com/scholar?cluster=761718241536208511&hl=en&as_sdt=0,47",6,2022
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning,5,neurips,1,0,2023-06-16 22:57:03.687000,https://github.com/cornell-relaxml/hyperdimensional-computing,5,Understanding hyperdimensional computing for parallel single-pass learning,"https://scholar.google.com/scholar?cluster=2441954374351827630&hl=en&as_sdt=0,5",0,2022
Pre-trained Adversarial Perturbations,1,neurips,1,0,2023-06-16 22:57:03.898000,https://github.com/banyuanhao/pap,15,Pre-trained Adversarial Perturbations,"https://scholar.google.com/scholar?cluster=1036412260609158515&hl=en&as_sdt=0,5",1,2022
An Empirical Study on Disentanglement of Negative-free Contrastive Learning,1,neurips,0,0,2023-06-16 22:57:04.108000,https://github.com/noahcao/disentanglement_lib_med,6,An Empirical Study on Disentanglement of Negative-free Contrastive Learning,"https://scholar.google.com/scholar?cluster=8166223620648232228&hl=en&as_sdt=0,10",0,2022
MABSplit: Faster Forest Training Using Multi-Armed Bandits,1,neurips,0,79,2023-06-16 22:57:04.320000,https://github.com/thrungroup/fastforest,4,MABSplit: Faster Forest Training Using Multi-Armed Bandits,"https://scholar.google.com/scholar?cluster=16839682885410953737&hl=en&as_sdt=0,22",0,2022
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints,3,neurips,0,0,2023-06-16 22:57:04.531000,https://github.com/gallego-posada/constrained_sparsity,6,Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints,"https://scholar.google.com/scholar?cluster=3017868657771533183&hl=en&as_sdt=0,5",2,2022
Okapi: Generalising Better by Making Statistical Matches Match,0,neurips,0,1,2023-06-16 22:57:04.743000,https://github.com/wearepal/okapi,5,Okapi: Generalising Better by Making Statistical Matches Match,"https://scholar.google.com/scholar?cluster=14348083558003086680&hl=en&as_sdt=0,5",1,2022
Revisiting Heterophily For Graph Neural Networks,24,neurips,5,0,2023-06-16 22:57:04.953000,https://github.com/SitaoLuan/ACM-GNN,27,Revisiting heterophily for graph neural networks,"https://scholar.google.com/scholar?cluster=10728534830275344250&hl=en&as_sdt=0,5",5,2022
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation,7,neurips,297,60,2023-06-16 22:57:05.164000,https://github.com/microsoft/muzic,3299,Museformer: Transformer with Fine-and Coarse-Grained Attention for Music Generation,"https://scholar.google.com/scholar?cluster=9919738130893761480&hl=en&as_sdt=0,14",63,2022
Emergent Communication: Generalization and Overfitting in Lewis Games,4,neurips,0,0,2023-06-16 22:57:05.375000,https://github.com/mathieurita/population,4,Emergent Communication: Generalization and Overfitting in Lewis Games,"https://scholar.google.com/scholar?cluster=9098136952282832762&hl=en&as_sdt=0,5",2,2022
Efficient and Effective Augmentation Strategy for Adversarial Training,6,neurips,1,0,2023-06-16 22:57:05.585000,https://github.com/val-iisc/dajat,13,Efficient and effective augmentation strategy for adversarial training,"https://scholar.google.com/scholar?cluster=14581218917168092627&hl=en&as_sdt=0,5",14,2022
Adaptive Data Debiasing through Bounded Exploration,1,neurips,0,0,2023-06-16 22:57:05.797000,https://github.com/yifankevin/adaptive_data_debiasing,0,Adaptive Data Debiasing through Bounded Exploration,"https://scholar.google.com/scholar?cluster=6378226570310143908&hl=en&as_sdt=0,40",1,2022
When does return-conditioned supervised learning work for offline reinforcement learning?,13,neurips,0,0,2023-06-16 22:57:06.008000,https://github.com/davidbrandfonbrener/rcsl-paper,6,When does return-conditioned supervised learning work for offline reinforcement learning?,"https://scholar.google.com/scholar?cluster=13396358502953618671&hl=en&as_sdt=0,33",1,2022
PDEBench: An Extensive Benchmark for Scientific Machine Learning,21,neurips,44,6,2023-06-16 22:57:06.220000,https://github.com/pdebench/pdebench,402,PDEBench: An extensive benchmark for scientific machine learning,"https://scholar.google.com/scholar?cluster=15542719739478133736&hl=en&as_sdt=0,47",15,2022
Learning Robust Dynamics through Variational Sparse Gating,0,neurips,1,1,2023-06-16 22:57:06.431000,https://github.com/arnavkj1995/vsg,19,Learning Robust Dynamics through Variational Sparse Gating,"https://scholar.google.com/scholar?cluster=5582932369755688869&hl=en&as_sdt=0,36",2,2022
Where to Pay Attention in Sparse Training for Feature Selection?,4,neurips,0,1,2023-06-16 22:57:06.641000,https://github.com/ghadasokar/wast,4,Where to Pay Attention in Sparse Training for Feature Selection?,"https://scholar.google.com/scholar?cluster=1186481368031859899&hl=en&as_sdt=0,44",1,2022
General Cutting Planes for Bound-Propagation-Based Neural Network Verification,14,neurips,27,7,2023-06-16 22:57:06.853000,https://github.com/huanzhang12/alpha-beta-CROWN,148,General cutting planes for bound-propagation-based neural network verification,"https://scholar.google.com/scholar?cluster=16952567700251161551&hl=en&as_sdt=0,44",8,2022
Mildly Conservative Q-Learning for Offline Reinforcement Learning,15,neurips,3,0,2023-06-16 22:57:07.064000,https://github.com/dmksjfl/mcq,32,Mildly conservative Q-learning for offline reinforcement learning,"https://scholar.google.com/scholar?cluster=11648694472509786601&hl=en&as_sdt=0,36",4,2022
Functional Ensemble Distillation,0,neurips,0,0,2023-06-16 22:57:07.275000,https://github.com/cobypenso/functional_ensemble_distillation,3,Functional Ensemble Distillation,"https://scholar.google.com/scholar?cluster=7557864995422109600&hl=en&as_sdt=0,5",1,2022
Lethal Dose Conjecture on Data Poisoning,3,neurips,0,0,2023-06-16 22:57:07.486000,https://github.com/wangwenxiao/FiniteAggregation,5,Lethal dose conjecture on data poisoning,"https://scholar.google.com/scholar?cluster=10656232532262319468&hl=en&as_sdt=0,5",1,2022
TempEL: Linking Dynamically Evolving and Newly Emerging Entities,3,neurips,3,0,2023-06-16 22:57:07.697000,https://github.com/klimzaporojets/tempel,3,TempEL: Linking dynamically evolving and newly emerging entities,"https://scholar.google.com/scholar?cluster=3241654383736118484&hl=en&as_sdt=0,5",1,2022
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative,2,neurips,2,0,2023-06-16 22:57:07.908000,https://github.com/weitianxin/HyperGCL,28,Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative,"https://scholar.google.com/scholar?cluster=8987357747154997241&hl=en&as_sdt=0,5",2,2022
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization,6,neurips,3,1,2023-06-16 22:57:08.119000,https://github.com/alstn12088/sym-nco,13,Sym-nco: Leveraging symmetricity for neural combinatorial optimization,"https://scholar.google.com/scholar?cluster=8234123365488999500&hl=en&as_sdt=0,33",1,2022
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning,93,neurips,39,9,2023-06-16 22:57:08.330000,https://github.com/r-three/t-few,298,Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning,"https://scholar.google.com/scholar?cluster=242306292951569763&hl=en&as_sdt=0,1",6,2022
DeepInteraction: 3D Object Detection via Modality Interaction,17,neurips,10,13,2023-06-16 22:57:08.540000,https://github.com/fudan-zvg/deepinteraction,162,Deepinteraction: 3d object detection via modality interaction,"https://scholar.google.com/scholar?cluster=2369292758377733249&hl=en&as_sdt=0,15",19,2022
Deep Differentiable Logic Gate Networks,2,neurips,20,0,2023-06-16 22:57:08.752000,https://github.com/felix-petersen/difflogic,241,Deep Differentiable Logic Gate Networks,"https://scholar.google.com/scholar?cluster=12936836443171799268&hl=en&as_sdt=0,41",12,2022
Maximizing and Satisficing in Multi-armed Bandits with Graph Information,0,neurips,0,0,2023-06-16 22:57:08.962000,https://github.com/parththaker/Bandits-GRUB,0,Maximizing and Satisficing in Multi-armed Bandits with Graph Information,"https://scholar.google.com/scholar?cluster=5836306663005448433&hl=en&as_sdt=0,5",4,2022
GOOD: A Graph Out-of-Distribution Benchmark,14,neurips,15,1,2023-06-16 22:57:09.173000,https://github.com/divelab/good,130,Good: A graph out-of-distribution benchmark,"https://scholar.google.com/scholar?cluster=5688487541372761713&hl=en&as_sdt=0,5",4,2022
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits,1,neurips,0,0,2023-06-16 22:57:09.384000,https://github.com/jajajang/sparse,0,PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits,"https://scholar.google.com/scholar?cluster=15122938357126562545&hl=en&as_sdt=0,5",1,2022
What You See is What You Get: Principled Deep Learning via Distributional Generalization,1,neurips,1,0,2023-06-16 22:57:09.594000,https://github.com/yangarbiter/dp-dg,6,What You See is What You Get: Principled Deep Learning via Distributional Generalization,"https://scholar.google.com/scholar?cluster=2822362220882233132&hl=en&as_sdt=0,24",3,2022
GAPX: Generalized Autoregressive Paraphrase-Identification X,0,neurips,0,0,2023-06-16 22:57:09.806000,https://github.com/yifeizhou02/generalized_paraphrase_identification,2,GAPX: Generalized Autoregressive Paraphrase-Identification X,"https://scholar.google.com/scholar?cluster=17804560355779547348&hl=en&as_sdt=0,26",1,2022
Scalable Infomin Learning,1,neurips,1,0,2023-06-16 22:57:10.016000,https://github.com/cyz-ai/infomin,8,Scalable Infomin Learning,"https://scholar.google.com/scholar?cluster=10543006398190520114&hl=en&as_sdt=0,5",1,2022
Learning to Accelerate Partial Differential Equations via Latent Global Evolution,6,neurips,4,0,2023-06-16 22:57:10.227000,https://github.com/snap-stanford/le_pde,11,Learning to accelerate partial differential equations via latent global evolution,"https://scholar.google.com/scholar?cluster=11413037155228818629&hl=en&as_sdt=0,43",41,2022
"Not too little, not too much: a theoretical analysis of graph (over)smoothing",13,neurips,0,0,2023-06-16 22:57:10.438000,https://github.com/nkeriven/graphsmoothing,2,"Not too little, not too much: a theoretical analysis of graph (over) smoothing","https://scholar.google.com/scholar?cluster=2063487353980385484&hl=en&as_sdt=0,10",1,2022
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers,1,neurips,0,0,2023-06-16 22:57:10.650000,https://github.com/nerdslab/EIT,6,Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers,"https://scholar.google.com/scholar?cluster=7588522259705770791&hl=en&as_sdt=0,33",1,2022
Riemannian Score-Based Generative Modelling,41,neurips,12,2,2023-06-16 22:57:10.861000,https://github.com/oxcsml/riemannian-score-sde,56,Riemannian score-based generative modeling,"https://scholar.google.com/scholar?cluster=11808970878216966405&hl=en&as_sdt=0,5",7,2022
Open-Ended Reinforcement Learning with Neural Reward Functions,2,neurips,0,0,2023-06-16 22:57:11.074000,https://github.com/amujika/open-ended-reinforcement-learning-with-neural-reward-functions,8,Open-ended reinforcement learning with neural reward functions,"https://scholar.google.com/scholar?cluster=12071061069808672843&hl=en&as_sdt=0,5",1,2022
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks,7,neurips,2,3,2023-06-16 22:57:11.285000,https://github.com/casia-iva-lab/obj2seq,72,Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks,"https://scholar.google.com/scholar?cluster=9616302849095650848&hl=en&as_sdt=0,5",3,2022
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering,42,neurips,47,2,2023-06-16 22:57:11.495000,https://github.com/lupantech/ScienceQA,337,Learn to explain: Multimodal reasoning via thought chains for science question answering,"https://scholar.google.com/scholar?cluster=15090414004847508782&hl=en&as_sdt=0,44",7,2022
Planning for Sample Efficient Imitation Learning,1,neurips,2,0,2023-06-16 22:57:11.706000,https://github.com/zhaohengyin/EfficientImitate,23,Planning for Sample Efficient Imitation Learning,"https://scholar.google.com/scholar?cluster=5323017540550695246&hl=en&as_sdt=0,5",1,2022
Towards Safe Reinforcement Learning with a Safety Editor Policy,7,neurips,0,1,2023-06-16 22:57:11.917000,https://github.com/hnyu/seditor,8,Towards safe reinforcement learning with a safety editor policy,"https://scholar.google.com/scholar?cluster=5028356496095011487&hl=en&as_sdt=0,21",1,2022
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty,0,neurips,7,0,2023-06-16 22:57:12.128000,https://github.com/sungnyun/understanding-cdfsl,18,Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty,"https://scholar.google.com/scholar?cluster=5252362604705009687&hl=en&as_sdt=0,47",2,2022
Sustainable Online Reinforcement Learning for Auto-bidding,0,neurips,3,0,2023-06-16 22:57:12.339000,https://github.com/nobodymx/sorl-for-auto-bidding,13,Sustainable Online Reinforcement Learning for Auto-bidding,"https://scholar.google.com/scholar?cluster=6790569068711156469&hl=en&as_sdt=0,11",1,2022
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs,11,neurips,15,3,2023-06-16 22:57:12.550000,https://github.com/fundamentalvision/Uni-Perceiver,195,Uni-perceiver-moe: Learning sparse generalist models with conditional moes,"https://scholar.google.com/scholar?cluster=8405812116415915225&hl=en&as_sdt=0,5",10,2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning,2,neurips,3,0,2023-06-16 22:57:12.761000,https://github.com/thudzj/ella,13,Accelerated Linearized Laplace Approximation for Bayesian Deep Learning,"https://scholar.google.com/scholar?cluster=8567091747078651114&hl=en&as_sdt=0,5",1,2022
"Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures",3,neurips,1,0,2023-06-16 22:57:12.973000,https://github.com/aryol/booleanpvr,9,"Learning to reason with neural networks: Generalization, unseen data and boolean measures","https://scholar.google.com/scholar?cluster=5899767711713652917&hl=en&as_sdt=0,5",2,2022
Training and Inference on Any-Order Autoregressive Models the Right Way,1,neurips,2,0,2023-06-16 22:57:13.183000,https://github.com/andyshih12/mac,7,Training and Inference on Any-Order Autoregressive Models the Right Way,"https://scholar.google.com/scholar?cluster=17556958914030914345&hl=en&as_sdt=0,5",2,2022
Lazy and Fast Greedy MAP Inference for Determinantal Point Process,0,neurips,0,0,2023-06-16 22:57:13.400000,https://github.com/Alnusjaponica/DPP-MAP-Inference,1,Lazy and Fast Greedy MAP Inference for Determinantal Point Process,"https://scholar.google.com/scholar?cluster=6823574159955750227&hl=en&as_sdt=0,5",2,2022
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant,2,neurips,5,0,2023-06-16 22:57:13.610000,https://github.com/Jin-Ying/GTA-Seg,22,Semi-supervised semantic segmentation via gentle teaching assistant,"https://scholar.google.com/scholar?cluster=4347716352468380052&hl=en&as_sdt=0,5",1,2022
"What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods",22,neurips,0,0,2023-06-16 22:57:13.824000,https://github.com/serre-lab/meta-predictor,3,"What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods","https://scholar.google.com/scholar?cluster=5412890546069619633&hl=en&as_sdt=0,5",16,2022
TransTab: Learning Transferable Tabular Transformers Across Tables,15,neurips,13,4,2023-06-16 22:57:14.084000,https://github.com/ryanwangzf/transtab,93,Transtab: Learning transferable tabular transformers across tables,"https://scholar.google.com/scholar?cluster=5025075385855240360&hl=en&as_sdt=0,5",6,2022
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop,3,neurips,0,0,2023-06-16 22:57:14.296000,https://github.com/zwx8981/PerceptualAttack_BIQA,7,Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop,"https://scholar.google.com/scholar?cluster=8403042660344902079&hl=en&as_sdt=0,33",1,2022
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks,5,neurips,2,0,2023-06-16 22:57:14.507000,https://github.com/chingyaoc/tmd,32,Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks,"https://scholar.google.com/scholar?cluster=15681270444210282135&hl=en&as_sdt=0,5",2,2022
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum,5,neurips,3,0,2023-06-16 22:57:14.718000,https://github.com/liun-online/spco,21,Revisiting graph contrastive learning from the perspective of graph spectrum,"https://scholar.google.com/scholar?cluster=9580149588228619113&hl=en&as_sdt=0,5",2,2022
(De-)Randomized Smoothing for Decision Stump Ensembles,0,neurips,0,0,2023-06-16 22:57:14.929000,https://github.com/eth-sri/drs,2,(De-) Randomized Smoothing for Decision Stump Ensembles,"https://scholar.google.com/scholar?cluster=9534504421648606260&hl=en&as_sdt=0,5",5,2022
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation ,7,neurips,4,0,2023-06-16 22:57:15.172000,https://github.com/Jxu-Thu/DITTO,19,Learning to break the loop: Analyzing and mitigating repetitions for neural text generation,"https://scholar.google.com/scholar?cluster=305884743851229055&hl=en&as_sdt=0,10",1,2022
Integral Probability Metrics PAC-Bayes Bounds,4,neurips,0,0,2023-06-16 22:57:15.399000,https://github.com/ron-amit/pac_bayes_reg,0,Integral Probability Metrics PAC-Bayes Bounds,"https://scholar.google.com/scholar?cluster=17771180228755273754&hl=en&as_sdt=0,11",0,2022
Self-explaining deep models with logic rule reasoning,3,neurips,5,3,2023-06-16 22:57:15.610000,https://github.com/archon159/selor,34,Self-explaining deep models with logic rule reasoning,"https://scholar.google.com/scholar?cluster=17380550052737130818&hl=en&as_sdt=0,11",2,2022
Contrastive Neural Ratio Estimation,3,neurips,0,0,2023-06-16 22:57:15.820000,https://github.com/bkmi/cnre,1,Contrastive neural ratio estimation,"https://scholar.google.com/scholar?cluster=10243773059505759044&hl=en&as_sdt=0,5",1,2022
EgoTaskQA: Understanding Human Tasks in Egocentric Videos,5,neurips,0,1,2023-06-16 22:57:16.030000,https://github.com/Buzz-Beater/EgoTaskQA,17,Egotaskqa: Understanding human tasks in egocentric videos,"https://scholar.google.com/scholar?cluster=2618582324466290943&hl=en&as_sdt=0,5",1,2022
C-Mixup: Improving Generalization in Regression,7,neurips,0,1,2023-06-16 22:57:16.241000,https://github.com/huaxiuyao/c-mixup,45,C-mixup: Improving generalization in regression,"https://scholar.google.com/scholar?cluster=15175213809542606261&hl=en&as_sdt=0,33",3,2022
Generalised Mutual Information for Discriminative Clustering,2,neurips,0,0,2023-06-16 22:57:16.453000,https://github.com/oshillou/gemini,2,Generalised Mutual Information for Discriminative Clustering,"https://scholar.google.com/scholar?cluster=17126945082306251507&hl=en&as_sdt=0,33",2,2022
Pseudo-Riemannian Graph Convolutional Networks,5,neurips,0,0,2023-06-16 22:57:16.663000,https://github.com/xiongbo010/qgcn,4,Pseudo-Riemannian Graph Convolutional Networks,"https://scholar.google.com/scholar?cluster=8375225836111812142&hl=en&as_sdt=0,5",0,2022
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion,3,neurips,0,0,2023-06-16 22:57:16.874000,https://github.com/naver/croco,23,CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion,"https://scholar.google.com/scholar?cluster=16202141712210963294&hl=en&as_sdt=0,5",5,2022
Sound and Complete Verification of Polynomial Networks,2,neurips,0,0,2023-06-16 22:57:17.085000,https://github.com/megaelius/PNVerification,2,Sound and Complete Verification of Polynomial Networks,"https://scholar.google.com/scholar?cluster=4570751371641493882&hl=en&as_sdt=0,14",2,2022
CalFAT: Calibrated Federated Adversarial Training with Label Skewness,4,neurips,1,1,2023-06-16 22:57:17.295000,https://github.com/cc233/calfat,0,CalFAT: Calibrated federated adversarial training with label skewness,"https://scholar.google.com/scholar?cluster=16082019978611352733&hl=en&as_sdt=0,13",0,2022
Rethinking Generalization in Few-Shot Classification,7,neurips,3,3,2023-06-16 22:57:17.506000,https://github.com/mrkshllr/FewTURE,32,Rethinking generalization in few-shot classification,"https://scholar.google.com/scholar?cluster=2312996917630319931&hl=en&as_sdt=0,5",4,2022
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing,2,neurips,0,0,2023-06-16 22:57:17.717000,https://github.com/sunshine-ye/nips22-st,6,Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing,"https://scholar.google.com/scholar?cluster=11081706588643944252&hl=en&as_sdt=0,5",2,2022
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations,42,neurips,8,2,2023-06-16 22:57:17.927000,https://github.com/ML-GSAI/EGSDE,111,Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations,"https://scholar.google.com/scholar?cluster=8785482238856182484&hl=en&as_sdt=0,33",3,2022
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems,4,neurips,1,0,2023-06-16 22:57:18.138000,https://github.com/m3rg-iitd/benchmarking_graph,3,Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems,"https://scholar.google.com/scholar?cluster=12287057853788727578&hl=en&as_sdt=0,41",0,2022
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning,3,neurips,1,0,2023-06-16 22:57:18.368000,https://github.com/MrHuff/GWI,5,Generalized variational inference in function spaces: Gaussian measures meet Bayesian deep learning,"https://scholar.google.com/scholar?cluster=15952581512655430688&hl=en&as_sdt=0,20",1,2022
Communicating Natural Programs to Humans and Machines,18,neurips,6,0,2023-06-16 22:57:18.579000,https://github.com/samacqua/LARC,49,Communicating natural programs to humans and machines,"https://scholar.google.com/scholar?cluster=13373939616457240114&hl=en&as_sdt=0,39",4,2022
SCAMPS: Synthetics for Camera Measurement of Physiological Signals,10,neurips,2,4,2023-06-16 22:57:18.790000,https://github.com/danmcduff/scampsdataset,34,Scamps: Synthetics for camera measurement of physiological signals,"https://scholar.google.com/scholar?cluster=15226072589725201524&hl=en&as_sdt=0,23",3,2022
"A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases",5,neurips,53,45,2023-06-16 22:57:19.002000,https://github.com/google/learned_optimization,649,"A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases","https://scholar.google.com/scholar?cluster=10651202979674165812&hl=en&as_sdt=0,5",11,2022
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions,0,neurips,0,0,2023-06-16 22:57:19.213000,https://github.com/haanvid/kmis,1,Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions,"https://scholar.google.com/scholar?cluster=11353779178954280803&hl=en&as_sdt=0,44",1,2022
Flare7K: A Phenomenological Nighttime Flare Removal Dataset,7,neurips,6,1,2023-06-16 22:57:19.424000,https://github.com/ykdai/Flare7K,63,Flare7K: A Phenomenological Nighttime Flare Removal Dataset,"https://scholar.google.com/scholar?cluster=4666672639396573877&hl=en&as_sdt=0,22",6,2022
USB: A Unified Semi-supervised Learning Benchmark for Classification,5,neurips,116,24,2023-06-16 22:57:19.635000,https://github.com/microsoft/semi-supervised-learning,804,Usb: A unified semi-supervised learning benchmark for classification,"https://scholar.google.com/scholar?cluster=10960877857326492306&hl=en&as_sdt=0,5",14,2022
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world,5,neurips,20,20,2023-06-16 22:57:19.846000,https://github.com/facebookresearch/nocturne,202,Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world,"https://scholar.google.com/scholar?cluster=10789605761114029551&hl=en&as_sdt=0,5",11,2022
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency,23,neurips,55,7,2023-06-16 22:57:20.057000,https://github.com/mims-harvard/tfc-pretraining,235,Self-supervised contrastive pre-training for time series via time-frequency consistency,"https://scholar.google.com/scholar?cluster=18283822055997916844&hl=en&as_sdt=0,5",5,2022
Uncalibrated Models Can Improve Human-AI Collaboration,7,neurips,1,0,2023-06-16 22:57:20.268000,https://github.com/kailas-v/human-ai-interactions,7,Uncalibrated models can improve human-ai collaboration,"https://scholar.google.com/scholar?cluster=12469546917170199830&hl=en&as_sdt=0,33",1,2022
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces,8,neurips,0,0,2023-06-16 22:57:20.478000,https://github.com/CSML-IIT-UCL/kooplearn,7,Learning dynamical systems via koopman operator regression in reproducing kernel hilbert spaces,"https://scholar.google.com/scholar?cluster=12157593222906117840&hl=en&as_sdt=0,10",4,2022
A Policy-Guided Imitation Approach for Offline Reinforcement Learning,7,neurips,2,0,2023-06-16 22:57:20.689000,https://github.com/ryanxhr/por,44,A policy-guided imitation approach for offline reinforcement learning,"https://scholar.google.com/scholar?cluster=17364397345225831453&hl=en&as_sdt=0,47",3,2022
On the Convergence Theory for Hessian-Free Bilevel Algorithms,3,neurips,2,0,2023-06-16 22:57:20.899000,https://github.com/sowmaster/esjacobians,6,On the convergence theory for hessian-free bilevel algorithms,"https://scholar.google.com/scholar?cluster=15140633553551921538&hl=en&as_sdt=0,47",1,2022
Spartan: Differentiable Sparsity via Regularized Transportation,0,neurips,0,0,2023-06-16 22:57:21.111000,https://github.com/facebookresearch/spartan,19,Spartan: Differentiable Sparsity via Regularized Transportation,"https://scholar.google.com/scholar?cluster=15812271986166410699&hl=en&as_sdt=0,46",3,2022
Focal Modulation Networks,39,neurips,51,10,2023-06-16 22:57:21.322000,https://github.com/microsoft/FocalNet,552,Focal modulation networks,"https://scholar.google.com/scholar?cluster=12867511582517934835&hl=en&as_sdt=0,10",16,2022
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces,5,neurips,2,0,2023-06-16 22:57:21.533000,https://github.com/leoqli/hsurf-net,25,HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces,"https://scholar.google.com/scholar?cluster=8622760401117810211&hl=en&as_sdt=0,5",4,2022
Robust Streaming PCA,1,neurips,0,0,2023-06-16 22:57:21.743000,https://github.com/MinchanJeong/Robust-Streaming-PCA,1,Robust Streaming PCA,"https://scholar.google.com/scholar?cluster=9313897813400392144&hl=en&as_sdt=0,10",1,2022
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression,10,neurips,0,0,2023-06-16 22:57:21.954000,https://github.com/haoyuzhao123/soteriafl,4,SoteriaFL: A unified framework for private federated learning with communication compression,"https://scholar.google.com/scholar?cluster=6684992000278225554&hl=en&as_sdt=0,5",2,2022
Your Transformer May Not be as Powerful as You Expect,12,neurips,1,1,2023-06-16 22:57:22.165000,https://github.com/lsj2408/urpe,21,Your transformer may not be as powerful as you expect,"https://scholar.google.com/scholar?cluster=13623285884170722320&hl=en&as_sdt=0,5",2,2022
Diffusion-LM Improves Controllable Text Generation,139,neurips,104,45,2023-06-16 22:57:22.377000,https://github.com/xiangli1999/diffusion-lm,836,Diffusion-lm improves controllable text generation,"https://scholar.google.com/scholar?cluster=17910853149942433121&hl=en&as_sdt=0,36",18,2022
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure,7,neurips,1,1,2023-06-16 22:57:22.587000,https://github.com/paulnovello/hsic-attribution-method,10,Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure,"https://scholar.google.com/scholar?cluster=7791180788979429607&hl=en&as_sdt=0,21",1,2022
Energy-Based Contrastive Learning of Visual Representations,1,neurips,0,1,2023-06-16 22:57:22.798000,https://github.com/1202kbs/ebclr,5,Energy-Based Contrastive Learning of Visual Representations,"https://scholar.google.com/scholar?cluster=14002446974731282321&hl=en&as_sdt=0,10",2,2022
On the Generalizability and Predictability of Recommender Systems,0,neurips,1,5,2023-06-16 22:57:23.009000,https://github.com/naszilla/reczilla,20,On the Generalizability and Predictability of Recommender Systems,"https://scholar.google.com/scholar?cluster=17151097798328031409&hl=en&as_sdt=0,22",5,2022
Divert More Attention to Vision-Language Tracking,3,neurips,71,25,2023-06-16 22:57:23.221000,https://github.com/JudasDie/SOTS,417,Divert More Attention to Vision-Language Tracking,"https://scholar.google.com/scholar?cluster=6209180784126725956&hl=en&as_sdt=0,1",11,2022
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning,15,neurips,8,2,2023-06-16 22:57:23.432000,https://github.com/ist-daslab/obc,38,Optimal Brain Compression: A framework for accurate post-training quantization and pruning,"https://scholar.google.com/scholar?cluster=2227477302772250547&hl=en&as_sdt=0,5",4,2022
Association Graph Learning for Multi-Task Classification with Category Shifts,2,neurips,1,1,2023-06-16 22:57:23.642000,https://github.com/autumn9999/mtc-with-category-shifts,6,Association graph learning for multi-task classification with category shifts,"https://scholar.google.com/scholar?cluster=8917197566031875925&hl=en&as_sdt=0,44",1,2022
A Unified Model for Multi-class Anomaly Detection,8,neurips,12,0,2023-06-16 22:57:23.853000,https://github.com/zhiyuanyou/uniad,136,A Unified Model for Multi-class Anomaly Detection,"https://scholar.google.com/scholar?cluster=11558725855987199082&hl=en&as_sdt=0,15",1,2022
Learning with little mixing,8,neurips,7321,1026,2023-06-16 22:57:24.064000,https://github.com/google-research/google-research,29788,Learning with little mixing,"https://scholar.google.com/scholar?cluster=55245308812869418&hl=en&as_sdt=0,45",727,2022
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming,2,neurips,1,0,2023-06-16 22:57:24.275000,https://github.com/elemisi/vael,14,VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming,"https://scholar.google.com/scholar?cluster=10135207146367765358&hl=en&as_sdt=0,39",3,2022
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks,9,neurips,0,0,2023-06-16 22:57:24.486000,https://github.com/leirunlin/evennet,7,Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks,"https://scholar.google.com/scholar?cluster=15300270171268425828&hl=en&as_sdt=0,5",1,2022
Differentiable Analog Quantum Computing for Optimization and Control,4,neurips,2,0,2023-06-16 22:57:24.697000,https://github.com/yilingqiao/diffquantum,16,Differentiable Analog Quantum Computing for Optimization and Control,"https://scholar.google.com/scholar?cluster=2405301331103163699&hl=en&as_sdt=0,5",2,2022
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images,6,neurips,1,0,2023-06-16 22:57:24.908000,https://github.com/vlar-group/unsupobjseg,26,Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images,"https://scholar.google.com/scholar?cluster=1761568637963446015&hl=en&as_sdt=0,47",1,2022
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation,7,neurips,1,3,2023-06-16 22:57:25.119000,https://github.com/usr922/fst,29,Learning from Future: A Novel Self-Training Framework for Semantic Segmentation,"https://scholar.google.com/scholar?cluster=6027127191801048854&hl=en&as_sdt=0,43",2,2022
How Powerful are K-hop Message Passing Graph Neural Networks,15,neurips,3,0,2023-06-16 22:57:25.331000,https://github.com/JiaruiFeng/KP-GNN,48,How powerful are k-hop message passing graph neural networks,"https://scholar.google.com/scholar?cluster=3067212826478566297&hl=en&as_sdt=0,47",2,2022
Exploitability Minimization in Games and Beyond,4,neurips,0,0,2023-06-16 22:57:25.543000,https://github.com/denizalp/exploit-min,0,Exploitability Minimization in Games and Beyond,"https://scholar.google.com/scholar?cluster=4856157037704483082&hl=en&as_sdt=0,44",1,2022
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps,23,neurips,10,8,2023-06-16 22:57:25.753000,https://github.com/mediabrain-sjtu/where2comm,75,Where2comm: Communication-efficient collaborative perception via spatial confidence maps,"https://scholar.google.com/scholar?cluster=15169095000176396543&hl=en&as_sdt=0,41",2,2022
Your Out-of-Distribution Detection Method is Not Robust!,5,neurips,1,0,2023-06-16 22:57:25.964000,https://github.com/rohban-lab/atd,9,Your Out-of-Distribution Detection Method is Not Robust!,"https://scholar.google.com/scholar?cluster=1414819434166798732&hl=en&as_sdt=0,5",2,2022
NaturalProver: Grounded Mathematical Proof Generation with Language Models,4,neurips,1,0,2023-06-16 22:57:26.175000,https://github.com/wellecks/naturalprover,23,Naturalprover: Grounded mathematical proof generation with language models,"https://scholar.google.com/scholar?cluster=7878492470641044970&hl=en&as_sdt=0,41",2,2022
One for All: Simultaneous Metric and Preference Learning over Multiple Users,1,neurips,1,0,2023-06-16 22:57:26.386000,https://github.com/gregcanal/multiuser-metric-preference,0,One for all: Simultaneous metric and preference learning over multiple users,"https://scholar.google.com/scholar?cluster=4938147600895831412&hl=en&as_sdt=0,25",1,2022
SegViT: Semantic Segmentation with Plain Vision Transformers,13,neurips,5,4,2023-06-16 22:57:26.597000,https://github.com/zbwxp/SegVit,67,Segvit: Semantic segmentation with plain vision transformers,"https://scholar.google.com/scholar?cluster=4636047207088039334&hl=en&as_sdt=0,21",1,2022
Unsupervised Learning From Incomplete Measurements for Inverse Problems,3,neurips,2,1,2023-06-16 22:57:26.808000,https://github.com/edongdongchen/moi,7,Unsupervised Learning From Incomplete Measurements for Inverse Problems,"https://scholar.google.com/scholar?cluster=14843076631440223178&hl=en&as_sdt=0,36",1,2022
Redeeming intrinsic rewards via constrained optimization,2,neurips,6,2,2023-06-16 22:57:27.018000,https://github.com/improbable-ai/eipo,59,Redeeming intrinsic rewards via constrained optimization,"https://scholar.google.com/scholar?cluster=1760121311943802855&hl=en&as_sdt=0,5",5,2022
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks,9,neurips,14,5,2023-06-16 22:57:27.230000,https://github.com/thunlp/openbackdoor,94,A unified evaluation of textual backdoor learning: Frameworks and benchmarks,"https://scholar.google.com/scholar?cluster=12638294460038796289&hl=en&as_sdt=0,5",8,2022
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models,2,neurips,0,0,2023-06-16 22:57:27.441000,https://github.com/ErdunGAO/MissDAG,2,MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models,"https://scholar.google.com/scholar?cluster=8771512698541826516&hl=en&as_sdt=0,25",1,2022
A Theoretical Study on Solving Continual Learning,6,neurips,1,0,2023-06-16 22:57:27.652000,https://github.com/k-gyuhak/wptp,8,A Theoretical Study on Solving Continual Learning,"https://scholar.google.com/scholar?cluster=11651266848032744688&hl=en&as_sdt=0,5",1,2022
Misspecified Phase Retrieval with Generative Priors,0,neurips,0,0,2023-06-16 22:57:27.862000,https://github.com/jiulongliu/MPRG,0,Misspecified Phase Retrieval with Generative Priors,"https://scholar.google.com/scholar?cluster=1648135207641613717&hl=en&as_sdt=0,5",2,2022
Data-Efficient Augmentation for Training Neural Networks,1,neurips,2,0,2023-06-16 22:57:28.074000,https://github.com/tianyu139/data-efficient-augmentation,2,Data-Efficient Augmentation for Training Neural Networks,"https://scholar.google.com/scholar?cluster=16120463592327015292&hl=en&as_sdt=0,33",2,2022
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning,9,neurips,2,6,2023-06-16 22:57:28.284000,https://github.com/zyezhang/dac,29,Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning,"https://scholar.google.com/scholar?cluster=14836332941736923065&hl=en&as_sdt=0,33",6,2022
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning,17,neurips,34,17,2023-06-16 22:57:28.496000,https://github.com/pku-marl/dexteroushands,319,Towards human-level bimanual dexterous manipulation with reinforcement learning,"https://scholar.google.com/scholar?cluster=3451546095013207545&hl=en&as_sdt=0,26",13,2022
Local-Global MCMC kernels: the best of both worlds,3,neurips,2,0,2023-06-16 22:57:28.706000,https://github.com/svsamsonov/ex2mcmc_new,2,Local-Global MCMC kernels: the best of both worlds,"https://scholar.google.com/scholar?cluster=6444779825968376973&hl=en&as_sdt=0,14",3,2022
The computational and learning benefits of Daleian neural networks,0,neurips,0,0,2023-06-16 22:57:28.917000,https://github.com/adamhaber/daleian_networks,0,The computational and learning benefits of Daleian neural networks,"https://scholar.google.com/scholar?cluster=7045665223313726154&hl=en&as_sdt=0,15",1,2022
Efficient and Modular Implicit Differentiation,99,neurips,55,80,2023-06-16 22:57:29.127000,https://github.com/google/jaxopt,713,Efficient and modular implicit differentiation,"https://scholar.google.com/scholar?cluster=17447288700726145942&hl=en&as_sdt=0,23",19,2022
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations,17,neurips,1,0,2023-06-16 22:57:29.339000,https://github.com/AI4LIFE-GROUP/lfa,1,Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations,"https://scholar.google.com/scholar?cluster=14882559489186994501&hl=en&as_sdt=0,5",2,2022
Accelerating Certified Robustness Training via Knowledge Transfer,0,neurips,0,0,2023-06-16 22:57:29.550000,https://github.com/ethos-lab/crt-neurips22,1,Accelerating Certified Robustness Training via Knowledge Transfer,"https://scholar.google.com/scholar?cluster=16137440255270375978&hl=en&as_sdt=0,5",2,2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings,13,neurips,14,23,2023-06-16 22:57:29.761000,https://github.com/owkin/flamby,158,FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings,"https://scholar.google.com/scholar?cluster=7376958489532657973&hl=en&as_sdt=0,32",8,2022
Blackbox Attacks via Surrogate Ensemble Search,2,neurips,3,0,2023-06-16 22:57:29.972000,https://github.com/csiplab/bases,5,Blackbox attacks via surrogate ensemble search,"https://scholar.google.com/scholar?cluster=3551879013092176593&hl=en&as_sdt=0,6",2,2022
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking,10,neurips,8,0,2023-06-16 22:57:30.182000,https://github.com/vita-group/large_scale_gcn_benchmarking,42,A comprehensive study on large-scale graph training: Benchmarking and rethinking,"https://scholar.google.com/scholar?cluster=1620706562706665630&hl=en&as_sdt=0,31",10,2022
Scale-invariant Learning by Physics Inversion,0,neurips,0,0,2023-06-16 22:57:30.394000,https://github.com/tum-pbs/sip,9,Scale-invariant Learning by Physics Inversion,"https://scholar.google.com/scholar?cluster=11653236116859810051&hl=en&as_sdt=0,11",2,2022
Sample Constrained Treatment Effect Estimation,1,neurips,0,0,2023-06-16 22:57:30.604000,https://github.com/raddanki/sample-constrained-treatment-effect-estimation,1,Sample Constrained Treatment Effect Estimation,"https://scholar.google.com/scholar?cluster=8394950395338055772&hl=en&as_sdt=0,14",2,2022
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks,10,neurips,0,0,2023-06-16 22:57:30.816000,https://github.com/xlhex/cater_neurips,3,CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks,"https://scholar.google.com/scholar?cluster=14890378325788554569&hl=en&as_sdt=0,34",2,2022
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs,2,neurips,1,0,2023-06-16 22:57:31.027000,https://github.com/seijimaekawa/empirical-study-of-gnns,2,Beyond real-world benchmark datasets: An empirical study of node classification with GNNs,"https://scholar.google.com/scholar?cluster=6075046742984586862&hl=en&as_sdt=0,10",1,2022
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps,158,neurips,90,12,2023-06-16 22:57:31.237000,https://github.com/luchengthu/dpm-solver,1022,Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,"https://scholar.google.com/scholar?cluster=2427327523938680723&hl=en&as_sdt=0,44",19,2022
Active Exploration for Inverse Reinforcement Learning,2,neurips,1,0,2023-06-16 22:57:31.449000,https://github.com/lasgroup/aceirl,4,Active Exploration for Inverse Reinforcement Learning,"https://scholar.google.com/scholar?cluster=2422293204605820403&hl=en&as_sdt=0,5",2,2022
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences,8,neurips,3,0,2023-06-16 22:57:31.661000,https://github.com/wangsiwei2010/neurips22-fmvacc,12,Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences,"https://scholar.google.com/scholar?cluster=6953275277959692680&hl=en&as_sdt=0,33",1,2022
Geodesic Graph Neural Network for Efficient Graph Representation Learning,4,neurips,2,0,2023-06-16 22:57:31.871000,https://github.com/woodcutter1998/gdgnn,13,Geodesic Graph Neural Network for Efficient Graph Representation Learning,"https://scholar.google.com/scholar?cluster=15655553108751060031&hl=en&as_sdt=0,51",1,2022
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams,12,neurips,177,12,2023-06-16 22:57:32.083000,https://github.com/google-research/federated,555,Improved differential privacy for sgd via optimal private linear operators on adaptive streams,"https://scholar.google.com/scholar?cluster=7562865688859267077&hl=en&as_sdt=0,5",26,2022
On Privacy and Personalization in Cross-Silo Federated Learning,8,neurips,1,0,2023-06-16 22:57:32.294000,https://github.com/kenziyuliu/private-cross-silo-fl,21,On privacy and personalization in cross-silo federated learning,"https://scholar.google.com/scholar?cluster=5435954743553051960&hl=en&as_sdt=0,47",2,2022
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning,4,neurips,10,0,2023-06-16 22:57:32.506000,https://github.com/hsvgbkhgbv/shapley-q-learning,27,Shaq: Incorporating shapley value theory into multi-agent q-learning,"https://scholar.google.com/scholar?cluster=5920175691861441269&hl=en&as_sdt=0,5",2,2022
Trajectory balance: Improved credit assignment in GFlowNets,18,neurips,67,8,2023-06-16 22:57:32.717000,https://github.com/gfnorg/gflownet,457,Trajectory balance: Improved credit assignment in gflownets,"https://scholar.google.com/scholar?cluster=6680117776194765384&hl=en&as_sdt=0,5",10,2022
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning,4,neurips,2,0,2023-06-16 22:57:32.928000,https://github.com/starrskyy/fedgda-gt,2,A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning,"https://scholar.google.com/scholar?cluster=7833139237183266538&hl=en&as_sdt=0,23",1,2022
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline,21,neurips,0,0,2023-06-16 22:57:33.138000,https://github.com/OpenPerceptionX/TCP,1,Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline,"https://scholar.google.com/scholar?cluster=1817675006219450608&hl=en&as_sdt=0,5",2,2022
Falsification before Extrapolation in Causal Effect Estimation,1,neurips,0,0,2023-06-16 22:57:33.349000,https://github.com/clinicalml/rct-obs-extrapolation,1,Falsification before Extrapolation in Causal Effect Estimation,"https://scholar.google.com/scholar?cluster=958040257149340285&hl=en&as_sdt=0,5",8,2022
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG,5,neurips,2,0,2023-06-16 22:57:33.561000,https://github.com/rkobler/TSMNet,22,SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG,"https://scholar.google.com/scholar?cluster=18096469291943406428&hl=en&as_sdt=0,10",1,2022
Semantic uncertainty intervals for disentangled latent spaces,6,neurips,0,0,2023-06-16 22:57:33.771000,https://github.com/swamiviv/CLASP,2,Semantic uncertainty intervals for disentangled latent spaces,"https://scholar.google.com/scholar?cluster=15336613379158293365&hl=en&as_sdt=0,5",1,2022
Meta-Learning with Self-Improving Momentum Target,1,neurips,1,0,2023-06-16 22:57:33.982000,https://github.com/jihoontack/SiMT,23,Meta-Learning with Self-Improving Momentum Target,"https://scholar.google.com/scholar?cluster=8856141874430455067&hl=en&as_sdt=0,36",2,2022
On the Robustness of Graph Neural Diffusion to Topology Perturbations,6,neurips,1,0,2023-06-16 22:57:34.192000,https://github.com/zknus/robustness-of-graph-neural-diffusion,10,On the robustness of graph neural diffusion to topology perturbations,"https://scholar.google.com/scholar?cluster=12358515421385829046&hl=en&as_sdt=0,11",2,2022
Few-shot Relational Reasoning via Connection Subgraph Pretraining,12,neurips,5,4,2023-06-16 22:57:34.404000,https://github.com/snap-stanford/csr,20,Few-shot Relational Reasoning via Connection Subgraph Pretraining,"https://scholar.google.com/scholar?cluster=7808961295486020115&hl=en&as_sdt=0,5",4,2022
Equivariant Networks for Zero-Shot Coordination,3,neurips,0,0,2023-06-16 22:57:34.616000,https://github.com/gfppoy/equivariant-zsc,1,Equivariant networks for zero-shot coordination,"https://scholar.google.com/scholar?cluster=8378470160963031417&hl=en&as_sdt=0,5",1,2022
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability,0,neurips,0,1,2023-06-16 22:57:34.828000,https://github.com/wyjung0625/qcpo,2,Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability,"https://scholar.google.com/scholar?cluster=2759019976865790748&hl=en&as_sdt=0,44",1,2022
Procedural Image Programs for Representation Learning,1,neurips,2,0,2023-06-16 22:57:35.039000,https://github.com/mbaradad/shaders21k,19,Procedural Image Programs for Representation Learning,"https://scholar.google.com/scholar?cluster=9270170976993930491&hl=en&as_sdt=0,5",1,2022
Motion Transformer with Global Intention Localization and Local Movement Refinement,15,neurips,46,4,2023-06-16 22:57:35.250000,https://github.com/sshaoshuai/mtr,349,Motion transformer with global intention localization and local movement refinement,"https://scholar.google.com/scholar?cluster=17050187484850062043&hl=en&as_sdt=0,18",28,2022
Conformal Frequency Estimation with Sketched Data,1,neurips,1,0,2023-06-16 22:57:35.462000,https://github.com/msesia/conformalized-sketching,3,Conformal Frequency Estimation with Sketched Data,"https://scholar.google.com/scholar?cluster=9560083140059478955&hl=en&as_sdt=0,5",1,2022
Revisiting Active Sets for Gaussian Process Decoders,1,neurips,1,0,2023-06-16 22:57:35.672000,https://github.com/pmorenoz/SASGP,4,Revisiting active sets for Gaussian process decoders,"https://scholar.google.com/scholar?cluster=2795726720266164112&hl=en&as_sdt=0,5",1,2022
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness,1,neurips,1,0,2023-06-16 22:57:35.883000,https://github.com/mccree177/amp,26,AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness,"https://scholar.google.com/scholar?cluster=4092261735524740694&hl=en&as_sdt=0,5",1,2022
CyCLIP: Cyclic Contrastive Language-Image Pretraining,33,neurips,6,1,2023-06-16 22:57:36.095000,https://github.com/goel-shashank/CyCLIP,84,Cyclip: Cyclic contrastive language-image pretraining,"https://scholar.google.com/scholar?cluster=7059915234869339584&hl=en&as_sdt=0,14",5,2022
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet,15,neurips,1,0,2023-06-16 22:57:36.306000,https://github.com/google-research/imagenet-mistakes,15,When does dough become a bagel? analyzing the remaining mistakes on imagenet,"https://scholar.google.com/scholar?cluster=8522271283148753556&hl=en&as_sdt=0,5",3,2022
Non-deep Networks,36,neurips,42,7,2023-06-16 22:57:36.517000,https://github.com/imankgoyal/NonDeepNetworks,577,Non-deep networks,"https://scholar.google.com/scholar?cluster=16588786431597949156&hl=en&as_sdt=0,41",47,2022
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation,3,neurips,0,0,2023-06-16 22:57:36.728000,https://github.com/kristian-georgiev/privacy-induces-robustness,1,Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation,"https://scholar.google.com/scholar?cluster=14209092131686935951&hl=en&as_sdt=0,5",1,2022
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel,1,neurips,0,0,2023-06-16 22:57:36.939000,https://github.com/changyong-oh/law2order,1,Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel,"https://scholar.google.com/scholar?cluster=14203375121421572867&hl=en&as_sdt=0,10",1,2022
Positively Weighted Kernel Quadrature via Subsampling,10,neurips,0,0,2023-06-16 22:57:37.149000,https://github.com/satoshi-hayakawa/kernel-quadrature,4,Positively weighted kernel quadrature via subsampling,"https://scholar.google.com/scholar?cluster=16160100637122636412&hl=en&as_sdt=0,39",1,2022
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics,1,neurips,4,0,2023-06-16 22:57:37.376000,https://github.com/tum-pbs/dmcf,31,Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics,"https://scholar.google.com/scholar?cluster=5915590166499828539&hl=en&as_sdt=0,31",3,2022
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty,0,neurips,0,0,2023-06-16 22:57:37.587000,https://github.com/luningsun/splinelearningequation,3,Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty,"https://scholar.google.com/scholar?cluster=7412491486510109194&hl=en&as_sdt=0,10",3,2022
FR: Folded Rationalization with a Unified Encoder,3,neurips,0,0,2023-06-16 22:57:37.797000,https://github.com/jugechengzi/fr,9,FR: Folded Rationalization with a Unified Encoder,"https://scholar.google.com/scholar?cluster=17701298430512519187&hl=en&as_sdt=0,10",2,2022
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification,0,neurips,0,0,2023-06-16 22:57:38.008000,https://github.com/akimotolab/m2td3,1,Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification,"https://scholar.google.com/scholar?cluster=2762930312674513633&hl=en&as_sdt=0,3",0,2022
When Does Group Invariant Learning Survive Spurious Correlations?,4,neurips,0,0,2023-06-16 22:57:38.219000,https://github.com/beastlyprime/group-invariant-learning,3,When Does Group Invariant Learning Survive Spurious Correlations?,"https://scholar.google.com/scholar?cluster=16534284812687563601&hl=en&as_sdt=0,10",1,2022
SNAKE: Shape-aware Neural 3D Keypoint Field,0,neurips,5,0,2023-06-16 22:57:38.432000,https://github.com/zhongcl-thu/snake,199,SNAKE: Shape-aware Neural 3D Keypoint Field,"https://scholar.google.com/scholar?cluster=16201409541555687414&hl=en&as_sdt=0,5",5,2022
Minimax Optimal Online Imitation Learning via Replay Estimation,2,neurips,1,0,2023-06-16 22:57:38.643000,https://github.com/gkswamy98/replay_est,2,Minimax optimal online imitation learning via replay estimation,"https://scholar.google.com/scholar?cluster=17967164041276198597&hl=en&as_sdt=0,10",3,2022
Multi-layer State Evolution Under Random Convolutional Design,0,neurips,1,0,2023-06-16 22:57:38.853000,https://github.com/mdnls/conv-ml-amp,0,Multi-layer State Evolution Under Random Convolutional Design,"https://scholar.google.com/scholar?cluster=10470374566280377653&hl=en&as_sdt=0,33",1,2022
GULP: a prediction-based metric between representations,1,neurips,0,0,2023-06-16 22:57:39.064000,https://github.com/sgstepaniants/gulp,5,GULP: a prediction-based metric between representations,"https://scholar.google.com/scholar?cluster=17478835353985668968&hl=en&as_sdt=0,5",3,2022
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks,0,neurips,0,1,2023-06-16 22:57:39.275000,https://github.com/shariqiqbal2810/alma,14,ALMA: Hierarchical Learning for Composite Multi-Agent Tasks,"https://scholar.google.com/scholar?cluster=3111894008525567959&hl=en&as_sdt=0,36",1,2022
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions,4,neurips,0,0,2023-06-16 22:57:39.486000,https://github.com/kjason/cpwl2relunetwork,0,Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions,"https://scholar.google.com/scholar?cluster=6114292183557641648&hl=en&as_sdt=0,5",1,2022
Assaying Out-Of-Distribution Generalization in Transfer Learning,19,neurips,0,0,2023-06-16 22:57:39.697000,https://github.com/amazon-research/assaying-ood,10,Assaying out-of-distribution generalization in transfer learning,"https://scholar.google.com/scholar?cluster=2028336304446280911&hl=en&as_sdt=0,34",6,2022
Learning Interface Conditions in Domain Decomposition Solvers,3,neurips,0,0,2023-06-16 22:57:39.907000,https://github.com/compdyn/learning-oras,2,Learning interface conditions in domain decomposition solvers,"https://scholar.google.com/scholar?cluster=7720297619688650714&hl=en&as_sdt=0,5",2,2022
Hamiltonian Latent Operators for content and motion disentanglement in image sequences,0,neurips,0,0,2023-06-16 22:57:40.118000,https://github.com/mdasifkhan/halo,1,Hamiltonian Latent Operators for content and motion disentanglement in image sequences,"https://scholar.google.com/scholar?cluster=3449357233115494687&hl=en&as_sdt=0,14",2,2022
"Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited",14,neurips,4,0,2023-06-16 22:57:40.329000,https://github.com/ivam-he/chebnetii,16,"Convolutional neural networks on graphs with chebyshev approximation, revisited","https://scholar.google.com/scholar?cluster=8441578707111569242&hl=en&as_sdt=0,33",3,2022
A Kernelised Stein Statistic for Assessing Implicit Generative Models,1,neurips,0,0,2023-06-16 22:57:40.541000,https://github.com/wenkaixl/npksd,2,A kernelised Stein statistic for assessing implicit generative models,"https://scholar.google.com/scholar?cluster=18442369245856609106&hl=en&as_sdt=0,39",1,2022
Fine-Grained Semantically Aligned Vision-Language Pre-Training,12,neurips,1,5,2023-06-16 22:57:40.752000,https://github.com/yyjmjc/loupe,34,Fine-grained semantically aligned vision-language pre-training,"https://scholar.google.com/scholar?cluster=238317474783907025&hl=en&as_sdt=0,5",8,2022
Outlier-Robust Sparse Estimation via Non-Convex Optimization,11,neurips,0,0,2023-06-16 22:57:40.963000,https://github.com/guptashvm/sparse-gd,0,Outlier-robust sparse estimation via non-convex optimization,"https://scholar.google.com/scholar?cluster=8059244212591008232&hl=en&as_sdt=0,39",1,2022
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning,0,neurips,0,0,2023-06-16 22:57:41.174000,https://github.com/liuyejia/gps_cl,0,Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning,"https://scholar.google.com/scholar?cluster=6762628467691411042&hl=en&as_sdt=0,34",1,2022
Contact-aware Human Motion Forecasting,2,neurips,0,0,2023-06-16 22:57:41.385000,https://github.com/wei-mao-2019/contawaremotionpred,18,Contact-aware human motion forecasting,"https://scholar.google.com/scholar?cluster=4638557404830348541&hl=en&as_sdt=0,5",2,2022
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer,6,neurips,1520,270,2023-06-16 22:57:41.596000,https://github.com/PaddlePaddle/PaddleSeg,7245,RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer,"https://scholar.google.com/scholar?cluster=9262270613134229&hl=en&as_sdt=0,23",84,2022
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks,3,neurips,1,0,2023-06-16 22:57:41.807000,https://github.com/ylhz/natural-color-fool,19,Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks,"https://scholar.google.com/scholar?cluster=1908653488262515792&hl=en&as_sdt=0,5",1,2022
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again,0,neurips,0,1,2023-06-16 22:57:42.018000,https://github.com/vita-group/gradientgcn,7,Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again,"https://scholar.google.com/scholar?cluster=11879351906859238595&hl=en&as_sdt=0,5",10,2022
Egocentric Video-Language Pretraining,7,neurips,16,3,2023-06-16 22:57:42.229000,https://github.com/showlab/egovlp,153,Egocentric video-language pretraining,"https://scholar.google.com/scholar?cluster=13386829043972751350&hl=en&as_sdt=0,5",5,2022
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations,0,neurips,0,0,2023-06-16 22:57:42.440000,https://github.com/289371298/ceip,0,CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations,"https://scholar.google.com/scholar?cluster=12367064193622872891&hl=en&as_sdt=0,37",0,2022
LAMP: Extracting Text from Gradients with Language Model Priors,6,neurips,5,0,2023-06-16 22:57:42.651000,https://github.com/eth-sri/lamp,14,Lamp: Extracting text from gradients with language model priors,"https://scholar.google.com/scholar?cluster=6444993593639997976&hl=en&as_sdt=0,11",6,2022
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms,7,neurips,0,0,2023-06-16 22:57:42.863000,https://github.com/abhishekpanigrahi1996/Adaptive-SDE,0,On the SDEs and scaling rules for adaptive gradient algorithms,"https://scholar.google.com/scholar?cluster=81871230063577322&hl=en&as_sdt=0,5",1,2022
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement,3,neurips,378,170,2023-06-16 22:57:43.074000,https://github.com/facebookresearch/habitat-lab,1109,VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement,"https://scholar.google.com/scholar?cluster=6680559903388090895&hl=en&as_sdt=0,50",43,2022
Evaluating Graph Generative Models with Contrastively Learned Features,3,neurips,0,0,2023-06-16 22:57:43.284000,https://github.com/hamed1375/self-supervised-models-for-ggm-evaluation,3,Evaluating Graph Generative Models with Contrastively Learned Features,"https://scholar.google.com/scholar?cluster=11402654840281713194&hl=en&as_sdt=0,23",1,2022
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning,8,neurips,0,0,2023-06-16 22:57:43.495000,https://github.com/taoqi98/fairvfl,4,Fairvfl: A fair vertical federated learning framework with contrastive adversarial learning,"https://scholar.google.com/scholar?cluster=8028849683969991301&hl=en&as_sdt=0,5",1,2022
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering,7,neurips,1,2,2023-06-16 22:57:43.707000,https://github.com/anzhang314/bc-loss,19,Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering,"https://scholar.google.com/scholar?cluster=17056519215023278484&hl=en&as_sdt=0,7",2,2022
A Consistent and Differentiable Lp Canonical Calibration Error Estimator,10,neurips,0,0,2023-06-16 22:57:43.918000,https://github.com/tpopordanoska/ece-kde,5,A consistent and differentiable lp canonical calibration error estimator,"https://scholar.google.com/scholar?cluster=1430371157106751705&hl=en&as_sdt=0,33",1,2022
Transform Once: Efficient Operator Learning in Frequency Domain,4,neurips,1,1,2023-06-16 22:57:44.128000,https://github.com/diffeqml/kairos,12,Transform once: Efficient operator learning in frequency domain,"https://scholar.google.com/scholar?cluster=5960111959260104318&hl=en&as_sdt=0,44",3,2022
A Solver-free Framework for Scalable Learning in Neural ILP Architectures,0,neurips,1,0,2023-06-16 22:57:44.340000,https://github.com/dair-iitd/ilploss,8,A Solver-Free Framework for Scalable Learning in Neural ILP Architectures,"https://scholar.google.com/scholar?cluster=10416996433754364895&hl=en&as_sdt=0,44",4,2022
High-dimensional Additive Gaussian Processes under Monotonicity Constraints,2,neurips,0,0,2023-06-16 22:57:44.551000,https://github.com/anfelopera/lineqGPR,5,High-dimensional additive Gaussian processes under monotonicity constraints,"https://scholar.google.com/scholar?cluster=806848619663910077&hl=en&as_sdt=0,6",4,2022
Spherical Channels for Modeling Atomic Interactions,8,neurips,163,18,2023-06-16 22:57:44.763000,https://github.com/Open-Catalyst-Project/ocp,411,Spherical channels for modeling atomic interactions,"https://scholar.google.com/scholar?cluster=11935092226375810491&hl=en&as_sdt=0,47",24,2022
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning,6,neurips,1,0,2023-06-16 22:57:44.976000,https://github.com/hbzju/solar,21,SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning,"https://scholar.google.com/scholar?cluster=10356040081332575576&hl=en&as_sdt=0,41",1,2022
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels,0,neurips,0,0,2023-06-16 22:57:45.190000,https://github.com/mkc1000/btgp,3,Log-Linear-Time Gaussian Processes Using Binary Tree Kernels,"https://scholar.google.com/scholar?cluster=7844571481684303154&hl=en&as_sdt=0,10",1,2022
Recovering Private Text in Federated Learning of Language Models,6,neurips,6,3,2023-06-16 22:57:45.450000,https://github.com/princeton-sysml/film,37,Recovering private text in federated learning of language models,"https://scholar.google.com/scholar?cluster=12587257399289185667&hl=en&as_sdt=0,5",4,2022
 Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation,7,neurips,1,1,2023-06-16 22:57:45.662000,https://github.com/ictnlp/nmla-nat,18,Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation,"https://scholar.google.com/scholar?cluster=12848987996954988542&hl=en&as_sdt=0,37",2,2022
 Learning Deep Input-Output Stable Dynamics,1,neurips,0,0,2023-06-16 22:57:45.873000,https://github.com/clinfo/deepiostability,4,Learning Deep Input-Output Stable Dynamics,"https://scholar.google.com/scholar?cluster=12258814525562910843&hl=en&as_sdt=0,10",3,2022
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems,1,neurips,5,0,2023-06-16 22:57:46.084000,https://github.com/ericyangyu/pocar,5,Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems,"https://scholar.google.com/scholar?cluster=14223207610228521971&hl=en&as_sdt=0,44",1,2022
Gradient Descent: The Ultimate Optimizer,17,neurips,20,1,2023-06-16 22:57:46.295000,https://github.com/kach/gradient-descent-the-ultimate-optimizer,328,Gradient descent: The ultimate optimizer,"https://scholar.google.com/scholar?cluster=5346772952705282375&hl=en&as_sdt=0,44",4,2022
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization,8,neurips,8,0,2023-06-16 22:57:46.507000,https://github.com/kevinsbello/dagma,33,DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization,"https://scholar.google.com/scholar?cluster=8930082693367383470&hl=en&as_sdt=0,44",3,2022
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization,27,neurips,2,2,2023-06-16 22:57:46.718000,https://github.com/salesforce/ensemble-of-averages,23,Ensemble of averages: Improving model selection and boosting performance in domain generalization,"https://scholar.google.com/scholar?cluster=15173888902899249726&hl=en&as_sdt=0,14",4,2022
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward,1,neurips,4,2,2023-06-16 22:57:46.929000,https://github.com/stanfordvl/alignment,15,ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward,"https://scholar.google.com/scholar?cluster=8301128745364008098&hl=en&as_sdt=0,33",13,2022
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption,3,neurips,1,0,2023-06-16 22:57:47.140000,https://github.com/graphpku/open-world-kg,15,Rethinking Knowledge Graph Evaluation Under the Open-World Assumption,"https://scholar.google.com/scholar?cluster=12035243594832230326&hl=en&as_sdt=0,5",1,2022
Neural Basis Models for Interpretability,10,neurips,11,2,2023-06-16 22:57:47.351000,https://github.com/facebookresearch/nbm-spam,67,Neural basis models for interpretability,"https://scholar.google.com/scholar?cluster=7073329211572606092&hl=en&as_sdt=0,43",7,2022
RecursiveMix: Mixed Learning with History,9,neurips,0,0,2023-06-16 22:57:47.562000,https://github.com/implus/RecursiveMix-pytorch,20,Recursivemix: Mixed learning with history,"https://scholar.google.com/scholar?cluster=6486900347398545273&hl=en&as_sdt=0,5",2,2022
Truly Deterministic Policy Optimization,0,neurips,1,0,2023-06-16 22:57:47.774000,https://github.com/ehsansaleh/code_tdpo,6,Truly Deterministic Policy Optimization,"https://scholar.google.com/scholar?cluster=11328055735791293135&hl=en&as_sdt=0,5",1,2022
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners,21,neurips,1,1,2023-06-16 22:57:47.986000,https://github.com/mikewangwzhl/vidil,86,Language models with image descriptors are strong few-shot video-language learners,"https://scholar.google.com/scholar?cluster=15080693781137869549&hl=en&as_sdt=0,5",5,2022
3DB: A Framework for Debugging Computer Vision Models,33,neurips,4,3,2023-06-16 22:57:48.199000,https://github.com/3db/3db,119,3db: A framework for debugging computer vision models,"https://scholar.google.com/scholar?cluster=8728632579792166672&hl=en&as_sdt=0,5",2,2022
Formulating Robustness Against Unforeseen Attacks,0,neurips,1,0,2023-06-16 22:57:48.414000,https://github.com/inspire-group/variation-regularization,5,Formulating Robustness Against Unforeseen Attacks,"https://scholar.google.com/scholar?cluster=5421072397038680742&hl=en&as_sdt=0,40",2,2022
Single Model Uncertainty Estimation via Stochastic Data Centering,5,neurips,0,0,2023-06-16 22:57:48.625000,https://github.com/llnl/deltauq,7,Single model uncertainty estimation via stochastic data centering,"https://scholar.google.com/scholar?cluster=2306475952584377994&hl=en&as_sdt=0,39",7,2022
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries,0,neurips,0,1,2023-06-16 22:57:48.837000,https://github.com/atomwiseinc/cslvae,9,An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries,"https://scholar.google.com/scholar?cluster=11892068807664304889&hl=en&as_sdt=0,5",5,2022
Learning to Discover and Detect Objects,0,neurips,6,3,2023-06-16 22:57:49.049000,https://github.com/vlfom/rncdl,103,Learning to Discover and Detect Objects,"https://scholar.google.com/scholar?cluster=11909305933195951417&hl=en&as_sdt=0,10",6,2022
Simulation-guided Beam Search for Neural Combinatorial Optimization,3,neurips,3,0,2023-06-16 22:57:49.260000,https://github.com/yd-kwon/sgbs,14,Simulation-guided beam search for neural combinatorial optimization,"https://scholar.google.com/scholar?cluster=8865912688547118342&hl=en&as_sdt=0,5",2,2022
VICRegL: Self-Supervised Learning of Local Visual Features,22,neurips,23,4,2023-06-16 22:57:49.471000,https://github.com/facebookresearch/vicregl,207,Vicregl: Self-supervised learning of local visual features,"https://scholar.google.com/scholar?cluster=11133634648290997125&hl=en&as_sdt=0,5",3,2022
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC,3,neurips,0,0,2023-06-16 22:57:49.682000,https://github.com/akuzina/defend_vae_mcmc,8,Alleviating adversarial attacks on variational autoencoders with mcmc,"https://scholar.google.com/scholar?cluster=8237174979788219482&hl=en&as_sdt=0,33",1,2022
Human-AI Shared Control via Policy Dissection,1,neurips,18,2,2023-06-16 22:57:49.894000,https://github.com/mehooz/vision4leg,147,Human-AI Shared Control via Policy Dissection,"https://scholar.google.com/scholar?cluster=17744727893155269891&hl=en&as_sdt=0,32",3,2022
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model,16,neurips,0,1,2023-06-16 22:57:50.105000,https://github.com/FreddieRao/ShapeCrafter,14,Shapecrafter: A recursive text-conditioned 3d shape generation model,"https://scholar.google.com/scholar?cluster=1052962092907886930&hl=en&as_sdt=0,21",4,2022
GraB: Finding Provably Better Data Permutations than Random Reshuffling,6,neurips,1,0,2023-06-16 22:57:50.332000,https://github.com/eugenelyc/grab,2,GraB: Finding Provably Better Data Permutations than Random Reshuffling,"https://scholar.google.com/scholar?cluster=3880285491961366198&hl=en&as_sdt=0,44",2,2022
Neural Stochastic Control,6,neurips,0,0,2023-06-16 22:57:50.543000,https://github.com/jingddong-zhang/neural-stochastic-control,1,Neural Stochastic Control,"https://scholar.google.com/scholar?cluster=14553634387997941759&hl=en&as_sdt=0,5",1,2022
Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales,0,neurips,0,0,2023-06-16 22:57:50.770000,https://github.com/tliu1997/ti-svm,3,Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales,"https://scholar.google.com/scholar?cluster=10710745064843707287&hl=en&as_sdt=0,10",1,2022
Equivariant Graph Hierarchy-Based Neural Networks,4,neurips,2,0,2023-06-16 22:57:50.981000,https://github.com/hanjq17/eghn,13,Equivariant graph hierarchy-based neural networks,"https://scholar.google.com/scholar?cluster=18252825735214401175&hl=en&as_sdt=0,5",3,2022
Learning interacting dynamical systems with latent Gaussian process ODEs,0,neurips,0,0,2023-06-16 22:57:51.193000,https://github.com/boschresearch/igpode,3,Learning interacting dynamical systems with latent Gaussian process ODEs,"https://scholar.google.com/scholar?cluster=12254489423226434147&hl=en&as_sdt=0,37",5,2022
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics,4,neurips,0,0,2023-06-16 22:57:51.417000,https://github.com/olivesgatech/olives_dataset,2,Olives dataset: Ophthalmic labels for investigating visual eye semantics,"https://scholar.google.com/scholar?cluster=15665408901365199710&hl=en&as_sdt=0,5",5,2022
Off-Policy Evaluation for Action-Dependent Non-stationary Environments,1,neurips,0,0,2023-06-16 22:57:51.627000,https://github.com/yashchandak/activens,0,Off-policy evaluation for action-dependent non-stationary environments,"https://scholar.google.com/scholar?cluster=10431719067625816055&hl=en&as_sdt=0,25",2,2022
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning,12,neurips,126,26,2023-06-16 22:57:51.839000,https://github.com/alibaba/federatedscope,956,pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning,"https://scholar.google.com/scholar?cluster=18376990207026660571&hl=en&as_sdt=0,48",14,2022
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition,18,neurips,17,2,2023-06-16 22:57:52.051000,https://github.com/kssteven418/squeezeformer,191,Squeezeformer: An efficient transformer for automatic speech recognition,"https://scholar.google.com/scholar?cluster=8988041508983958224&hl=en&as_sdt=0,45",14,2022
Deep Generalized Schrödinger Bridge,8,neurips,1,0,2023-06-16 22:57:52.262000,https://github.com/ghliu/deepgsb,36,"Deep Generalized Schr\"" odinger Bridge","https://scholar.google.com/scholar?cluster=6936079050426001825&hl=en&as_sdt=0,7",2,2022
Learning sparse features can lead to overfitting in neural networks,3,neurips,0,0,2023-06-16 22:57:52.475000,https://github.com/pcsl-epfl/regressionsphere,3,Learning sparse features can lead to overfitting in neural networks,"https://scholar.google.com/scholar?cluster=8395151871691062338&hl=en&as_sdt=0,14",2,2022
Learning Distinct and Representative Modes for Image Captioning,3,neurips,0,2,2023-06-16 22:57:52.687000,https://github.com/bladewaltz1/modecap,20,Learning Distinct and Representative Modes for Image Captioning,"https://scholar.google.com/scholar?cluster=10888606721940900950&hl=en&as_sdt=0,5",2,2022
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics,32,neurips,14,2,2023-06-16 22:57:52.898000,https://github.com/qkaren/cold_decoding,75,Cold decoding: Energy-based constrained text generation with langevin dynamics,"https://scholar.google.com/scholar?cluster=12031688945546236055&hl=en&as_sdt=0,33",5,2022
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution,8,neurips,0,0,2023-06-16 22:57:53.108000,https://github.com/xyvivian/robod,3,Hyperparameter sensitivity in deep outlier detection: Analysis and a scalable hyper-ensemble solution,"https://scholar.google.com/scholar?cluster=14214777377381746715&hl=en&as_sdt=0,41",1,2022
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions,1,neurips,1,0,2023-06-16 22:57:53.320000,https://github.com/aria-systems-group/neuralnetcontrolbarrier,4,Safety guarantees for neural network dynamic systems via stochastic barrier functions,"https://scholar.google.com/scholar?cluster=18263541328322655403&hl=en&as_sdt=0,33",1,2022
On Margins and Generalisation for Voting Classifiers,4,neurips,0,0,2023-06-16 22:57:53.531000,https://github.com/vzantedeschi/dirichlet-margin-bound,0,On margins and generalisation for voting classifiers,"https://scholar.google.com/scholar?cluster=12765469893892514877&hl=en&as_sdt=0,5",1,2022
Rethinking the Reverse-engineering of Trojan Triggers,1,neurips,2,0,2023-06-16 22:57:53.742000,https://github.com/ru-system-software-and-security/featurere,12,Rethinking the Reverse-engineering of Trojan Triggers,"https://scholar.google.com/scholar?cluster=17539542989635625416&hl=en&as_sdt=0,5",1,2022
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling,0,neurips,8,4,2023-06-16 22:57:53.953000,https://github.com/neuralchen/RainNet,31,RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling,"https://scholar.google.com/scholar?cluster=2526557995454698490&hl=en&as_sdt=0,5",1,2022
Dataset Distillation using Neural Feature Regression,24,neurips,7,1,2023-06-16 22:57:54.176000,https://github.com/yongchao97/FRePo,28,Dataset distillation using neural feature regression,"https://scholar.google.com/scholar?cluster=15355176449784124932&hl=en&as_sdt=0,33",3,2022
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time,7,neurips,2,0,2023-06-16 22:57:54.403000,https://github.com/snap-stanford/zeroc,19,Zeroc: A neuro-symbolic model for zero-shot concept recognition and acquisition at inference time,"https://scholar.google.com/scholar?cluster=3612242931318475489&hl=en&as_sdt=0,22",44,2022
Risk-Driven Design of Perception Systems,0,neurips,1,0,2023-06-16 22:57:54.614000,https://github.com/sisl/riskdrivenperception,3,Risk-Driven Design of Perception Systems,"https://scholar.google.com/scholar?cluster=3006168152104696613&hl=en&as_sdt=0,5",4,2022
A Simple Approach to Automated Spectral Clustering,2,neurips,0,1,2023-06-16 22:57:54.825000,https://github.com/jicongfan/automated-spectral-clustering,2,A simple approach to automated spectral clustering,"https://scholar.google.com/scholar?cluster=2848547418778533477&hl=en&as_sdt=0,36",1,2022
Joint Entropy Search for Multi-Objective Bayesian Optimization,6,neurips,1,0,2023-06-16 22:57:55.036000,https://github.com/benmltu/jes,12,Joint entropy search for multi-objective bayesian optimization,"https://scholar.google.com/scholar?cluster=15207167627489331903&hl=en&as_sdt=0,19",1,2022
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation,1,neurips,0,1,2023-06-16 22:57:55.248000,https://github.com/jpgard/subgroup-robustness-grows-on-trees,1,Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation,"https://scholar.google.com/scholar?cluster=1553255834314203495&hl=en&as_sdt=0,47",1,2022
LION: Latent Point Diffusion Models for 3D Shape Generation,62,neurips,32,12,2023-06-16 22:57:55.460000,https://github.com/nv-tlabs/LION,548,LION: Latent point diffusion models for 3D shape generation,"https://scholar.google.com/scholar?cluster=11609382506929684644&hl=en&as_sdt=0,11",44,2022
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples,1,neurips,1,0,2023-06-16 22:57:55.672000,https://github.com/quwenjie/multiguard,2,MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples,"https://scholar.google.com/scholar?cluster=16148192613023633792&hl=en&as_sdt=0,22",1,2022
On Measuring Excess Capacity in Neural Networks,4,neurips,0,0,2023-06-16 22:57:55.883000,https://github.com/rkwitt/excess_capacity,0,On measuring excess capacity in neural networks,"https://scholar.google.com/scholar?cluster=8286514853614308295&hl=en&as_sdt=0,23",2,2022
Parameter-Efficient Masking Networks,1,neurips,0,0,2023-06-16 22:57:56.094000,https://github.com/yueb17/pemn,14,Parameter-Efficient Masking Networks,"https://scholar.google.com/scholar?cluster=3375567812720133580&hl=en&as_sdt=0,5",2,2022
End-to-end Symbolic Regression with Transformers,32,neurips,6,4,2023-06-16 22:57:56.305000,https://github.com/facebookresearch/symbolicregression,39,End-to-end symbolic regression with transformers,"https://scholar.google.com/scholar?cluster=13569402473810241669&hl=en&as_sdt=0,44",4,2022
EcoFormer: Energy-Saving Attention with Linear Complexity,8,neurips,1,1,2023-06-16 22:57:56.516000,https://github.com/ziplab/ecoformer,60,Ecoformer: Energy-saving attention with linear complexity,"https://scholar.google.com/scholar?cluster=12196003903025483137&hl=en&as_sdt=0,48",5,2022
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time,13,neurips,7,6,2023-06-16 22:57:56.727000,https://github.com/huaxiuyao/wild-time,49,Wild-time: A benchmark of in-the-wild distribution shift over time,"https://scholar.google.com/scholar?cluster=12470744137018985399&hl=en&as_sdt=0,44",3,2022
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions,57,neurips,35,2,2023-06-16 22:57:56.939000,https://github.com/raoyongming/hornet,277,Hornet: Efficient high-order spatial interactions with recursive gated convolutions,"https://scholar.google.com/scholar?cluster=12938213222665733645&hl=en&as_sdt=0,44",4,2022
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification,3,neurips,0,0,2023-06-16 22:57:57.150000,https://github.com/tsuchhiii/fixed-budget-bai,1,Minimax optimal algorithms for fixed-budget best arm identification,"https://scholar.google.com/scholar?cluster=2208314113749435910&hl=en&as_sdt=0,34",1,2022
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching,0,neurips,2,0,2023-06-16 22:57:57.360000,https://github.com/sentient07/deformationbasis,9,Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching,"https://scholar.google.com/scholar?cluster=6711357441182968462&hl=en&as_sdt=0,10",3,2022
Automatic Differentiation of Programs with Discrete Randomness,4,neurips,8,10,2023-06-16 22:57:57.571000,https://github.com/gaurav-arya/stochasticad.jl,144,Automatic differentiation of programs with discrete randomness,"https://scholar.google.com/scholar?cluster=4520468158435418424&hl=en&as_sdt=0,5",4,2022
NS3: Neuro-symbolic Semantic Code Search,4,neurips,0,0,2023-06-16 22:57:57.782000,https://github.com/shushanarakelyan/modular_code_search,3,NS3: Neuro-symbolic semantic code search,"https://scholar.google.com/scholar?cluster=12732470567380886921&hl=en&as_sdt=0,5",1,2022
Revisiting Sparse Convolutional Model for Visual Recognition,2,neurips,6,2,2023-06-16 22:57:57.994000,https://github.com/delay-xili/sdnet,111,Revisiting sparse convolutional model for visual recognition,"https://scholar.google.com/scholar?cluster=7681982241768438501&hl=en&as_sdt=0,7",9,2022
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning,28,neurips,24,4,2023-06-16 22:57:58.206000,https://github.com/sclbd/backdoorbench,162,Backdoorbench: A comprehensive benchmark of backdoor learning,"https://scholar.google.com/scholar?cluster=13477998480458836443&hl=en&as_sdt=0,5",3,2022
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering,12,neurips,1,0,2023-06-16 22:57:58.423000,https://github.com/yzleroy/revive,19,Revive: Regional visual representation matters in knowledge-based visual question answering,"https://scholar.google.com/scholar?cluster=15826539500910476875&hl=en&as_sdt=0,33",8,2022
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation,3,neurips,0,0,2023-06-16 22:57:58.634000,https://github.com/peidehuang/gradient,5,Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation,"https://scholar.google.com/scholar?cluster=13844074007413994501&hl=en&as_sdt=0,4",3,2022
Symbolic Distillation for Learned TCP Congestion Control,0,neurips,0,1,2023-06-16 22:57:58.846000,https://github.com/vita-group/symbolicpcc,6,Symbolic Distillation for Learned TCP Congestion Control,"https://scholar.google.com/scholar?cluster=13401562754080828114&hl=en&as_sdt=0,5",9,2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees,1,neurips,1,0,2023-06-16 22:57:59.056000,https://github.com/d-tiapkin/optimistic-psrl-experiments,0,Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees,"https://scholar.google.com/scholar?cluster=12169857257765503180&hl=en&as_sdt=0,6",1,2022
Can Push-forward Generative Models Fit Multimodal Distributions?,6,neurips,0,0,2023-06-16 22:57:59.278000,https://github.com/antoinesalmona/push-forward-generative-models,1,Can Push-forward Generative Models Fit Multimodal Distributions?,"https://scholar.google.com/scholar?cluster=15185434637554418912&hl=en&as_sdt=0,5",1,2022
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination,19,neurips,4,1,2023-06-16 22:57:59.493000,https://github.com/zyzisastudyreallyhardguy/graph-group-discrimination,43,Rethinking and scaling up graph contrastive learning: An extremely efficient approach with group discrimination,"https://scholar.google.com/scholar?cluster=13490371651179732416&hl=en&as_sdt=0,5",2,2022
Diverse Weight Averaging for Out-of-Distribution Generalization,21,neurips,5,0,2023-06-16 22:57:59.704000,https://github.com/alexrame/diwa,16,Diverse weight averaging for out-of-distribution generalization,"https://scholar.google.com/scholar?cluster=5971058245242972538&hl=en&as_sdt=0,14",2,2022
Posterior and Computational Uncertainty in Gaussian Processes,2,neurips,0,0,2023-06-16 22:57:59.916000,https://github.com/jonathanwenger/itergp,24,Posterior and Computational Uncertainty in Gaussian Processes,"https://scholar.google.com/scholar?cluster=10582501668199508293&hl=en&as_sdt=0,10",2,2022
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning,0,neurips,1,0,2023-06-16 22:58:00.127000,https://github.com/setarehc/deep_rl_regions,1,Understanding the Evolution of Linear Regions in Deep Reinforcement Learning,"https://scholar.google.com/scholar?cluster=11513659328041109175&hl=en&as_sdt=0,34",1,2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis,7,neurips,0,0,2023-06-16 22:58:00.338000,https://github.com/rflperry/sparse_shift,6,Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis,"https://scholar.google.com/scholar?cluster=13871332689539937340&hl=en&as_sdt=0,23",2,2022
Towards Practical Control of Singular Values of Convolutional Layers,2,neurips,1,0,2023-06-16 22:58:00.549000,https://github.com/whiteteadragon/practical_svd_conv,4,Towards practical control of singular values of convolutional layers,"https://scholar.google.com/scholar?cluster=1506769972607423712&hl=en&as_sdt=0,14",2,2022
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds,12,neurips,9,3,2023-06-16 22:58:00.760000,https://github.com/xiaoaoran/polarmix,41,PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds,"https://scholar.google.com/scholar?cluster=2359394852358979496&hl=en&as_sdt=0,5",6,2022
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees,1,neurips,1,3,2023-06-16 22:58:00.972000,https://github.com/jjbrophy47/ibug,20,Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees,"https://scholar.google.com/scholar?cluster=4132673829192129135&hl=en&as_sdt=0,33",5,2022
AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos,8,neurips,24,5,2023-06-16 22:58:01.183000,https://github.com/tencentarc/animesr,230,AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos,"https://scholar.google.com/scholar?cluster=3700240933025394368&hl=en&as_sdt=0,19",17,2022
Fairness Transferability Subject to Bounded Distribution Shift,13,neurips,0,0,2023-06-16 22:58:01.416000,https://github.com/ucsc-real/fairness_transferability,2,Fairness transferability subject to bounded distribution shift,"https://scholar.google.com/scholar?cluster=15393835531531070174&hl=en&as_sdt=0,44",0,2022
Improving Self-Supervised Learning by Characterizing Idealized Representations,11,neurips,3,1,2023-06-16 22:58:01.627000,https://github.com/yanndubs/invariant-self-supervised-learning,34,Improving self-supervised learning by characterizing idealized representations,"https://scholar.google.com/scholar?cluster=6601803486555515746&hl=en&as_sdt=0,1",1,2022
On the difficulty of learning chaotic dynamics with RNNs,4,neurips,0,0,2023-06-16 22:58:01.839000,https://github.com/durstewitzlab/chaosrnn,3,On the difficulty of learning chaotic dynamics with RNNs,"https://scholar.google.com/scholar?cluster=1853395383421685801&hl=en&as_sdt=0,39",1,2022
SKFlow: Learning Optical Flow with Super Kernels,10,neurips,1,0,2023-06-16 22:58:02.050000,https://github.com/littlespray/SKFlow,29,SKFlow: Learning Optical Flow with Super Kernels,"https://scholar.google.com/scholar?cluster=5401118479575242953&hl=en&as_sdt=0,33",2,2022
End-to-end Stochastic Optimization with Energy-based Model,0,neurips,0,0,2023-06-16 22:58:02.261000,https://github.com/Lingkai-Kong/SO-EBM,8,End-to-End Stochastic Optimization with Energy-Based Model,"https://scholar.google.com/scholar?cluster=7358543026013028300&hl=en&as_sdt=0,44",2,2022
Wasserstein $K$-means for clustering probability distributions,6,neurips,0,0,2023-06-16 22:58:02.474000,https://github.com/yubo02/wasserstein-k-means-for-clustering-probability-distributions,6,Wasserstein -means for clustering probability distributions,"https://scholar.googleusercontent.com/scholar?q=cache:S92oB8p7PW0J:scholar.google.com/+Wasserstein+%24K%24-means+for+clustering+probability+distributions&hl=en&as_sdt=0,5",1,2022
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields,44,neurips,50,22,2023-06-16 22:58:02.685000,https://github.com/ACEsuit/mace,161,MACE: Higher order equivariant message passing neural networks for fast and accurate force fields,"https://scholar.google.com/scholar?cluster=14632576704960076515&hl=en&as_sdt=0,33",17,2022
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems,0,neurips,20,13,2023-06-16 22:58:02.896000,https://github.com/yuangh-x/2022-nips-tenrec,125,Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems,"https://scholar.google.com/scholar?cluster=17000003816321898501&hl=en&as_sdt=0,50",2,2022
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning,2,neurips,1,2,2023-06-16 22:58:03.107000,https://github.com/dsshim0125/s2p,2,S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning,"https://scholar.google.com/scholar?cluster=10891821671072337532&hl=en&as_sdt=0,6",1,2022
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold,9,neurips,0,0,2023-06-16 22:58:03.318000,https://github.com/cjyaras/normalized-neural-collapse,1,Neural collapse with normalized features: A geometric analysis over the riemannian manifold,"https://scholar.google.com/scholar?cluster=184799387067688052&hl=en&as_sdt=0,5",1,2022
Conformalized Fairness via Quantile Regression,1,neurips,1,0,2023-06-16 22:58:03.529000,https://github.com/lei-ding07/conformal_quantile_fairness,4,Conformalized Fairness via Quantile Regression,"https://scholar.google.com/scholar?cluster=13625755204473996808&hl=en&as_sdt=0,23",1,2022
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces,1,neurips,0,0,2023-06-16 22:58:03.740000,https://github.com/leoiv/baxus,6,Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces,"https://scholar.google.com/scholar?cluster=16209973749760389058&hl=en&as_sdt=0,5",1,2022
Evolution of Neural Tangent Kernels under Benign and Adversarial Training,5,neurips,0,0,2023-06-16 22:58:03.951000,https://github.com/yolky/adversarial_ntk_evolution,3,Evolution of neural tangent kernels under benign and adversarial training,"https://scholar.google.com/scholar?cluster=10465513161130337378&hl=en&as_sdt=0,34",1,2022
Zero-Sum Stochastic Stackelberg Games,1,neurips,1,0,2023-06-16 22:58:04.162000,https://github.com/sadie-zhao/zero-sum-stochastic-stackelberg-games-neurips,9,Zero-Sum Stochastic Stackelberg Games,"https://scholar.google.com/scholar?cluster=16214046013626830778&hl=en&as_sdt=0,5",2,2022
Evaluating Out-of-Distribution Performance on Document Image Classifiers,0,neurips,0,0,2023-06-16 22:58:04.402000,https://github.com/gxlarson/rvl-cdip-ood,3,Evaluating Out-of-Distribution Performance on Document Image Classifiers,"https://scholar.google.com/scholar?cluster=6783517671736097904&hl=en&as_sdt=0,39",1,2022
Spatial Mixture-of-Experts,1,neurips,0,0,2023-06-16 22:58:04.613000,https://github.com/spcl/smoe,6,Spatial Mixture-of-Experts,"https://scholar.google.com/scholar?cluster=14828485216186842836&hl=en&as_sdt=0,44",7,2022
Hilbert Distillation for Cross-Dimensionality Networks,0,neurips,0,0,2023-06-16 22:58:04.824000,https://github.com/EagleMIT/Hilbert-Distillation,2,Hilbert Distillation for Cross-Dimensionality Networks,"https://scholar.google.com/scholar?cluster=12406023256328088160&hl=en&as_sdt=0,14",1,2022
LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks,13,neurips,6,4,2023-06-16 22:58:05.035000,https://github.com/uw-madison-lee-lab/languageinterfacedfinetuning,74,Lift: Language-interfaced fine-tuning for non-language machine learning tasks,"https://scholar.google.com/scholar?cluster=15884163467852791519&hl=en&as_sdt=0,44",5,2022
Template based Graph Neural Network with Optimal Transport Distances,2,neurips,0,2,2023-06-16 22:58:05.247000,https://github.com/cedricvincentcuaz/TFGW,1,Template based graph neural network with optimal transport distances,"https://scholar.google.com/scholar?cluster=10849560620712329134&hl=en&as_sdt=0,5",2,2022
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations,2,neurips,1,0,2023-06-16 22:58:05.458000,https://github.com/dem123456789/gal-gradient-assisted-learning-for-decentralized-multi-organization-collaborations,5,GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations,"https://scholar.google.com/scholar?cluster=14405130347379679970&hl=en&as_sdt=0,5",2,2022
Direct Advantage Estimation,3,neurips,1,0,2023-06-16 22:58:05.670000,https://github.com/hrpan/dae,4,Direct advantage estimation,"https://scholar.google.com/scholar?cluster=723226367131333982&hl=en&as_sdt=0,19",3,2022
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning,5,neurips,58,8,2023-06-16 22:58:05.880000,https://github.com/tencent-ailab/hok_env,438,Honor of kings arena: an environment for generalization in competitive reinforcement learning,"https://scholar.google.com/scholar?cluster=547818193126660523&hl=en&as_sdt=0,5",12,2022
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets,1,neurips,3,0,2023-06-16 22:58:06.091000,https://github.com/google-research/tabnas,6,TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets,"https://scholar.google.com/scholar?cluster=8517070308098238947&hl=en&as_sdt=0,5",4,2022
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification,0,neurips,0,0,2023-06-16 22:58:06.304000,https://github.com/roxie62/embed-and-emulate,5,Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification,"https://scholar.google.com/scholar?cluster=8584482491663793021&hl=en&as_sdt=0,43",1,2022
Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech,3,neurips,9,1,2023-06-16 22:58:06.516000,https://github.com/zain-jiang/dict-tts,120,Dict-tts: Learning to pronounce with prior dictionary knowledge for text-to-speech,"https://scholar.google.com/scholar?cluster=18386504940057315518&hl=en&as_sdt=0,5",7,2022
Task-Agnostic Graph Explanations,9,neurips,239,19,2023-06-16 22:58:06.726000,https://github.com/divelab/DIG,1503,Task-agnostic graph explanations,"https://scholar.google.com/scholar?cluster=17298628046382170776&hl=en&as_sdt=0,5",33,2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis,4,neurips,0,0,2023-06-16 22:58:06.938000,https://github.com/rpatrik96/ima-vae,19,Embrace the Gap: VAEs Perform Independent Mechanism Analysis,"https://scholar.google.com/scholar?cluster=2566376193853302421&hl=en&as_sdt=0,14",3,2022
Improved Feature Distillation via Projector Ensemble,4,neurips,3,0,2023-06-16 22:58:07.150000,https://github.com/chenyd7/pefd,18,Improved Feature Distillation via Projector Ensemble,"https://scholar.google.com/scholar?cluster=7163318270535099201&hl=en&as_sdt=0,45",1,2022
Introspective Learning : A Two-Stage approach for Inference in Neural Networks,6,neurips,1,0,2023-06-16 22:58:07.362000,https://github.com/olivesgatech/introspective-learning,4,Introspective learning: A two-stage approach for inference in neural networks,"https://scholar.google.com/scholar?cluster=16860968089703315753&hl=en&as_sdt=0,5",5,2022
Bayesian Active Learning with Fully Bayesian Gaussian Processes,5,neurips,1,0,2023-06-16 22:58:07.574000,https://github.com/CoRiis/active-learning-fbgp,3,Bayesian active learning with fully Bayesian Gaussian processes,"https://scholar.google.com/scholar?cluster=7248161076733979181&hl=en&as_sdt=0,39",1,2022
In Defense of the Unitary Scalarization for Deep Multi-Task Learning,25,neurips,1,0,2023-06-16 22:58:07.786000,https://github.com/yobibyte/unitary-scalarization-dmtl,12,In defense of the unitary scalarization for deep multi-task learning,"https://scholar.google.com/scholar?cluster=11217111018249719675&hl=en&as_sdt=0,5",2,2022
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction,0,neurips,1,0,2023-06-16 22:58:07.998000,https://github.com/uoft-ecosystem/tempo,13,Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction,"https://scholar.google.com/scholar?cluster=4376824659528049474&hl=en&as_sdt=0,14",1,2022
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning,0,neurips,1,0,2023-06-16 22:58:08.224000,https://github.com/taoyang225/ad-drop,17,AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning,"https://scholar.google.com/scholar?cluster=15838069514720957159&hl=en&as_sdt=0,5",1,2022
Reinforced Genetic Algorithm for Structure-based Drug Design,7,neurips,5,1,2023-06-16 22:58:08.438000,https://github.com/futianfan/reinforced-genetic-algorithm,43,Reinforced genetic algorithm for structure-based drug design,"https://scholar.google.com/scholar?cluster=7318600000502726060&hl=en&as_sdt=0,5",1,2022
A Variational Edge Partition Model for Supervised Graph Representation Learning,0,neurips,0,0,2023-06-16 22:58:08.649000,https://github.com/yh-utmsb/vepm,3,A Variational Edge Partition Model for Supervised Graph Representation Learning,"https://scholar.google.com/scholar?cluster=13490644048075543667&hl=en&as_sdt=0,5",2,2022
Learning Optimal Flows for Non-Equilibrium Importance Sampling,1,neurips,0,0,2023-06-16 22:58:08.861000,https://github.com/yucaoyc/neis,2,Learning optimal flows for non-equilibrium importance sampling,"https://scholar.google.com/scholar?cluster=4036926059814968086&hl=en&as_sdt=0,5",1,2022
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks,7,neurips,3,0,2023-06-16 22:58:09.073000,https://github.com/rtu715/nas-bench-360,41,NAS-bench-360: Benchmarking neural architecture search on diverse tasks,"https://scholar.google.com/scholar?cluster=16735333097872491854&hl=en&as_sdt=0,5",4,2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations,6,neurips,1,0,2023-06-16 22:58:09.285000,https://github.com/tychovdo/lila,15,Invariance learning in deep neural networks with differentiable Laplace approximations,"https://scholar.google.com/scholar?cluster=1429247926698502662&hl=en&as_sdt=0,14",1,2022
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query,1,neurips,6,0,2023-06-16 22:58:09.497000,https://github.com/buptxyb666/querypose,18,QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query,"https://scholar.google.com/scholar?cluster=15275535778333612126&hl=en&as_sdt=0,10",6,2022
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL,3,neurips,0,0,2023-06-16 22:58:09.714000,https://github.com/flowersteam/eager,8,Eager: Asking and answering questions for automatic reward shaping in language-guided rl,"https://scholar.google.com/scholar?cluster=15918390192447267703&hl=en&as_sdt=0,5",2,2022
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters,3,neurips,1,0,2023-06-16 22:58:09.926000,https://github.com/ellisalicante/openfilter,4,OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters,"https://scholar.google.com/scholar?cluster=5342281461625207848&hl=en&as_sdt=0,10",1,2022
Improving Policy Learning via Language Dynamics Distillation,2,neurips,0,0,2023-06-16 22:58:10.138000,https://github.com/vzhong/language-dynamics-distillation,7,Improving Policy Learning via Language Dynamics Distillation,"https://scholar.google.com/scholar?cluster=6541543257718525054&hl=en&as_sdt=0,33",1,2022
The Neural Testbed: Evaluating Joint Predictions,6,neurips,13,2,2023-06-16 22:58:10.350000,https://github.com/deepmind/neural_testbed,181,The neural testbed: Evaluating joint predictions,"https://scholar.google.com/scholar?cluster=9820249592356438993&hl=en&as_sdt=0,34",12,2022
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization,0,neurips,0,1,2023-06-16 22:58:10.562000,https://github.com/rehg-lab/dope_selfsup,9,Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization,"https://scholar.google.com/scholar?cluster=15982502156616388651&hl=en&as_sdt=0,37",2,2022
Teacher Forcing Recovers Reward Functions for Text Generation,4,neurips,1,1,2023-06-16 22:58:10.773000,https://github.com/manga-uofa/lmreward,16,Teacher Forcing Recovers Reward Functions for Text Generation,"https://scholar.google.com/scholar?cluster=8015164160931191027&hl=en&as_sdt=0,26",3,2022
Masked Autoencoding for Scalable and Generalizable Decision Making,6,neurips,1,0,2023-06-16 22:58:10.986000,https://github.com/fangchenliu/maskdp_public,24,Masked Autoencoding for Scalable and Generalizable Decision Making,"https://scholar.google.com/scholar?cluster=5876325032505210747&hl=en&as_sdt=0,33",1,2022
Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning,0,neurips,1,1,2023-06-16 22:58:11.197000,https://github.com/jf-hu/dre-marl,3,Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning,"https://scholar.google.com/scholar?cluster=4608771757173948810&hl=en&as_sdt=0,44",1,2022
ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler,2,neurips,5,1,2023-06-16 22:58:11.420000,https://github.com/neurasearch/neurips-2022-submission-3358,18,ELASTIC: numerical reasoning with adaptive symbolic compiler,"https://scholar.google.com/scholar?cluster=6782406897046377184&hl=en&as_sdt=0,47",1,2022
Training Spiking Neural Networks with Local Tandem Learning,6,neurips,0,0,2023-06-16 22:58:11.632000,https://github.com/aries231/local_tandem_learning_rule,4,Training Spiking Neural Networks with Local Tandem Learning,"https://scholar.google.com/scholar?cluster=213134529644040528&hl=en&as_sdt=0,33",1,2022
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting,17,neurips,4,1,2023-06-16 22:58:11.844000,https://github.com/tianzhou2011/FiLM,59,Film: Frequency improved legendre memory model for long-term time series forecasting,"https://scholar.google.com/scholar?cluster=13865604697725904904&hl=en&as_sdt=0,14",1,2022
Scalable Neural Video Representations with Learnable Positional Features,10,neurips,3,0,2023-06-16 22:58:12.056000,https://github.com/subin-kim-cv/NVP,54,Scalable neural video representations with learnable positional features,"https://scholar.google.com/scholar?cluster=418170278424044647&hl=en&as_sdt=0,5",5,2022
Data Augmentation MCMC for Bayesian Inference from Privatized Data,7,neurips,0,0,2023-06-16 22:58:12.269000,https://github.com/nianqiaoju/dataaugmentation-mcmc-differentialprivacy,1,Data augmentation MCMC for bayesian inference from privatized data,"https://scholar.google.com/scholar?cluster=15062825802466844692&hl=en&as_sdt=0,45",3,2022
Verification and search algorithms for causal DAGs,3,neurips,0,0,2023-06-16 22:58:12.481000,https://github.com/cxjdavin/verification-and-search-algorithms-for-causal-dags,1,Verification and search algorithms for causal DAGs,"https://scholar.google.com/scholar?cluster=5973326212150461189&hl=en&as_sdt=0,49",1,2022
Learning Equivariant Segmentation with Instance-Unique Querying,12,neurips,0,4,2023-06-16 22:58:12.693000,https://github.com/jamesliang819/instance_unique_querying,20,Learning Equivariant Segmentation with Instance-Unique Querying,"https://scholar.google.com/scholar?cluster=258748143190805226&hl=en&as_sdt=0,5",2,2022
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training,10,neurips,2,1,2023-06-16 22:58:12.904000,https://github.com/pdejorge/n-fgsm,18,Make some noise: Reliable and efficient single-step adversarial training,"https://scholar.google.com/scholar?cluster=4411689672191629194&hl=en&as_sdt=0,31",1,2022
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal,6,neurips,1,4,2023-06-16 22:58:13.116000,https://github.com/shiyuchengtju/par,6,Decision-based black-box attack against vision transformers via patch-wise adversarial removal,"https://scholar.google.com/scholar?cluster=10811327262491458953&hl=en&as_sdt=0,5",1,2022
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations,1,neurips,0,0,2023-06-16 22:58:13.327000,https://github.com/Laborieux-Axel/holomorphic_eqprop,6,Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations,"https://scholar.google.com/scholar?cluster=2660045405208851732&hl=en&as_sdt=0,47",1,2022
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning,27,neurips,5,3,2023-06-16 22:58:13.545000,https://github.com/ylsung/ladder-side-tuning,150,Lst: Ladder side-tuning for parameter and memory efficient transfer learning,"https://scholar.google.com/scholar?cluster=5847102735661395022&hl=en&as_sdt=0,5",2,2022
Amortized Inference for Causal Structure Learning,5,neurips,4,0,2023-06-16 22:58:13.756000,https://github.com/larslorch/avici,30,Amortized inference for causal structure learning,"https://scholar.google.com/scholar?cluster=12367761673759456964&hl=en&as_sdt=0,33",1,2022
Selective compression learning of latent representations for variable-rate image compression,1,neurips,0,0,2023-06-16 22:58:13.968000,https://github.com/jooyoungleeetri/scr,17,Selective compression learning of latent representations for variable-rate image compression,"https://scholar.google.com/scholar?cluster=13909155481844367559&hl=en&as_sdt=0,11",1,2022
Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple Granularities,13,neurips,0,0,2023-06-16 22:58:14.181000,https://github.com/multilexsum/dataset,15,Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities,"https://scholar.google.com/scholar?cluster=5062559571179489712&hl=en&as_sdt=0,33",0,2022
Local Bayesian optimization via maximizing probability of descent,1,neurips,0,0,2023-06-16 22:58:14.425000,https://github.com/kayween/local-bo-mpd,6,Local Bayesian optimization via maximizing probability of descent,"https://scholar.google.com/scholar?cluster=3485637555120117353&hl=en&as_sdt=0,44",3,2022
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection,14,neurips,2,1,2023-06-16 22:58:14.636000,https://github.com/thuyimingli/untargeted_backdoor_watermark,36,Untargeted backdoor watermark: Towards harmless and stealthy dataset copyright protection,"https://scholar.google.com/scholar?cluster=741958679609205316&hl=en&as_sdt=0,18",4,2022
Learning Symmetric Rules with SATNet,1,neurips,0,0,2023-06-16 22:58:14.849000,https://github.com/Lim-Sangho/SymSATNet,0,Learning Symmetric Rules with SATNet,"https://scholar.google.com/scholar?cluster=13706901058852825985&hl=en&as_sdt=0,5",2,2022
Langevin Autoencoders for Learning Deep Latent Variable Models,0,neurips,1,0,2023-06-16 22:58:15.061000,https://github.com/ishohei220/lae,5,Langevin Autoencoders for Learning Deep Latent Variable Models,"https://scholar.google.com/scholar?cluster=4451193403290607763&hl=en&as_sdt=0,5",1,2022
Fault-Aware Neural Code Rankers,12,neurips,5,1,2023-06-16 22:58:15.272000,https://github.com/microsoft/coderanker,22,Fault-aware neural code rankers,"https://scholar.google.com/scholar?cluster=11520887599770538288&hl=en&as_sdt=0,5",4,2022
Streaming Radiance Fields for 3D Video Synthesis,10,neurips,5,3,2023-06-16 22:58:15.484000,https://github.com/algohunt/streamrf,96,Streaming radiance fields for 3d video synthesis,"https://scholar.google.com/scholar?cluster=1594613451261987052&hl=en&as_sdt=0,14",8,2022
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence,2,neurips,1,2,2023-06-16 22:58:15.696000,https://github.com/KU-CVLAB/NeMF,70,Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence,"https://scholar.google.com/scholar?cluster=1968290052561441459&hl=en&as_sdt=0,5",7,2022
Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis,3,neurips,2,1,2023-06-16 22:58:15.909000,https://github.com/mengweiren/longitudinal-representation-learning,14,Local spatiotemporal representation learning for longitudinally-consistent neuroimage analysis,"https://scholar.google.com/scholar?cluster=8437472979024832790&hl=en&as_sdt=0,31",2,2022
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning,2,neurips,2,0,2023-06-16 22:58:16.122000,https://github.com/tliu1997/arnpg-morl,5,Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning,"https://scholar.google.com/scholar?cluster=15219127852751471694&hl=en&as_sdt=0,23",1,2022
TAP-Vid: A Benchmark for Tracking Any Point in a Video,4,neurips,19,1,2023-06-16 22:58:16.336000,https://github.com/deepmind/tapnet,212,TAP-Vid: A Benchmark for Tracking Any Point in a Video,"https://scholar.google.com/scholar?cluster=17092201381170534981&hl=en&as_sdt=0,33",17,2022
A Classification of $G$-invariant Shallow Neural Networks,5,neurips,0,0,2023-06-16 22:58:16.558000,https://github.com/dagrawa2/gsnn_classification_code,0,A Classification of -invariant Shallow Neural Networks,"https://scholar.google.com/scholar?cluster=11077075131989361762&hl=en&as_sdt=0,33",1,2022
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources,1,neurips,0,1,2023-06-16 22:58:16.810000,https://github.com/bariscanbozkurt/biologically-plausible-detmaxnns-for-blind-source-separation,0,Biologically-plausible determinant maximization neural networks for blind separation of correlated sources,"https://scholar.google.com/scholar?cluster=1796611740779169279&hl=en&as_sdt=0,5",1,2022
What Makes Graph Neural Networks Miscalibrated?,3,neurips,0,0,2023-06-16 22:58:17.023000,https://github.com/hans66hsu/gats,12,What Makes Graph Neural Networks Miscalibrated?,"https://scholar.google.com/scholar?cluster=18376762019790948001&hl=en&as_sdt=0,5",2,2022
Stochastic Adaptive Activation Function,0,neurips,0,0,2023-06-16 22:58:17.241000,https://github.com/kyungsu-lee-ksl/ash,4,Stochastic Adaptive Activation Function,"https://scholar.google.com/scholar?cluster=12146555690401173811&hl=en&as_sdt=0,5",2,2022
Video compression dataset and benchmark of learning-based video-quality metrics,4,neurips,0,0,2023-06-16 22:58:17.453000,https://github.com/msu-video-group/msu_vqm_compression_benchmark,16,Video compression dataset and benchmark of learning-based video-quality metrics,"https://scholar.google.com/scholar?cluster=11117086154139094350&hl=en&as_sdt=0,47",3,2022
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection,1,neurips,2,0,2023-06-16 22:58:17.664000,https://github.com/cvmi-lab/fs3d,36,Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection,"https://scholar.google.com/scholar?cluster=15115934605186565266&hl=en&as_sdt=0,43",5,2022
Efficient Dataset Distillation using Random Feature Approximation,11,neurips,1,1,2023-06-16 22:58:17.876000,https://github.com/yolky/rfad,23,Efficient dataset distillation using random feature approximation,"https://scholar.google.com/scholar?cluster=12794285551052496052&hl=en&as_sdt=0,5",3,2022
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?,9,neurips,1,0,2023-06-16 22:58:18.087000,https://github.com/justkolesov/wasserstein1benchmark,17,Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?,"https://scholar.google.com/scholar?cluster=168357485459111534&hl=en&as_sdt=0,18",2,2022
PALBERT: Teaching ALBERT to Ponder,0,neurips,0,0,2023-06-16 22:58:18.299000,https://github.com/tinkoff-ai/palbert,34,PALBERT: Teaching ALBERT to Ponder,"https://scholar.google.com/scholar?cluster=13888821126915681625&hl=en&as_sdt=0,14",3,2022
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes,2,neurips,0,0,2023-06-16 22:58:18.512000,https://github.com/tipt0p/three_regimes_on_the_sphere,3,Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes,"https://scholar.google.com/scholar?cluster=6680465751161236858&hl=en&as_sdt=0,31",1,2022
Exploring the Whole Rashomon Set of Sparse Decision Trees,12,neurips,5,1,2023-06-16 22:58:18.724000,https://github.com/ubc-systopia/treeFarms,19,Exploring the whole rashomon set of sparse decision trees,"https://scholar.google.com/scholar?cluster=8197518784888953073&hl=en&as_sdt=0,34",1,2022
Graph Self-supervised Learning with Accurate Discrepancy Learning,6,neurips,2,1,2023-06-16 22:58:18.936000,https://github.com/dongkikim95/d-sla,12,Graph self-supervised learning with accurate discrepancy learning,"https://scholar.google.com/scholar?cluster=6899266835558351745&hl=en&as_sdt=0,11",1,2022
Multi-Scale Adaptive Network for Single Image Denoising,5,neurips,1,0,2023-06-16 22:58:19.153000,https://github.com/xlearning-scu/2022-neurips-msanet,2,Multi-Scale Adaptive Network for Single Image Denoising,"https://scholar.google.com/scholar?cluster=12092498430345383404&hl=en&as_sdt=0,39",2,2022
Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy,2,neurips,0,0,2023-06-16 22:58:19.364000,https://github.com/ttesileanu/bio-pcn,5,Constrained predictive coding as a biologically plausible model of the cortical hierarchy,"https://scholar.google.com/scholar?cluster=11118175748957488346&hl=en&as_sdt=0,5",1,2022
Near-Optimal Collaborative Learning in Bandits,5,neurips,0,0,2023-06-16 22:58:19.577000,https://github.com/clreda/near-optimal-federated,1,Near-optimal collaborative learning in bandits,"https://scholar.google.com/scholar?cluster=11872427930011371643&hl=en&as_sdt=0,11",1,2022
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers,8,neurips,3,0,2023-06-16 22:58:19.788000,https://github.com/mlvlab/tokenmixup,39,Tokenmixup: Efficient attention-guided token-level data augmentation for transformers,"https://scholar.google.com/scholar?cluster=3326108237146565481&hl=en&as_sdt=0,25",5,2022
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models,24,neurips,12,2,2023-06-16 22:58:20,https://github.com/azshue/TPT,64,Test-time prompt tuning for zero-shot generalization in vision-language models,"https://scholar.google.com/scholar?cluster=213109028691722316&hl=en&as_sdt=0,1",3,2022
SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders,22,neurips,3,2,2023-06-16 22:58:20.214000,https://github.com/ucasligang/semmae,16,Semmae: Semantic-guided masking for learning masked autoencoders,"https://scholar.google.com/scholar?cluster=16607040036096933653&hl=en&as_sdt=0,23",1,2022
BiT: Robustly Binarized Multi-distilled Transformer,13,neurips,9,5,2023-06-16 22:58:20.437000,https://github.com/facebookresearch/bit,67,Bit: Robustly binarized multi-distilled transformer,"https://scholar.google.com/scholar?cluster=1714008465250842352&hl=en&as_sdt=0,5",12,2022
Knowledge-Aware Bayesian Deep Topic Model,6,neurips,2,1,2023-06-16 22:58:20.647000,https://github.com/wds2014/topickg,3,Knowledge-aware Bayesian deep topic model,"https://scholar.google.com/scholar?cluster=2627842395179821875&hl=en&as_sdt=0,44",1,2022
SelecMix: Debiased Learning by Contradicting-pair Sampling,1,neurips,1,0,2023-06-16 22:58:20.862000,https://github.com/bluemoon010/selecmix,7,SelecMix: Debiased Learning by Contradicting-pair Sampling,"https://scholar.google.com/scholar?cluster=2915792353103786474&hl=en&as_sdt=0,5",2,2022
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting,19,neurips,9,3,2023-06-16 22:58:21.073000,https://github.com/wangzy22/P2P,99,P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting,"https://scholar.google.com/scholar?cluster=16387925596110304701&hl=en&as_sdt=0,44",8,2022
Variational inference via Wasserstein gradient flows,18,neurips,0,0,2023-06-16 22:58:21.288000,https://github.com/marc-h-lambert/w-vi,4,Variational inference via Wasserstein gradient flows,"https://scholar.google.com/scholar?cluster=6278239632923753494&hl=en&as_sdt=0,5",1,2022
projUNN: efficient method for training deep networks with unitary matrices,5,neurips,4,2,2023-06-16 22:58:21.500000,https://github.com/facebookresearch/projunn,20,projUNN: efficient method for training deep networks with unitary matrices,"https://scholar.google.com/scholar?cluster=1850320121010807682&hl=en&as_sdt=0,5",49,2022
Multi-dataset Training of Transformers for Robust Action Recognition,2,neurips,0,0,2023-06-16 22:58:21.711000,https://github.com/junweiliang/multitrain,9,Multi-dataset Training of Transformers for Robust Action Recognition,"https://scholar.google.com/scholar?cluster=18278928779930263666&hl=en&as_sdt=0,31",5,2022
"Recipe for a General, Powerful, Scalable Graph Transformer",62,neurips,63,5,2023-06-16 22:58:21.922000,https://github.com/rampasek/GraphGPS,390,"Recipe for a general, powerful, scalable graph transformer","https://scholar.google.com/scholar?cluster=6992910764828744943&hl=en&as_sdt=0,33",11,2022
Rare Gems: Finding Lottery Tickets at Initialization,10,neurips,2,9,2023-06-16 22:58:22.134000,https://github.com/ksreenivasan/pruning_is_enough,8,Rare gems: Finding lottery tickets at initialization,"https://scholar.google.com/scholar?cluster=18354752168208884490&hl=en&as_sdt=0,14",4,2022
Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model,4,neurips,0,0,2023-06-16 22:58:22.345000,https://github.com/mapleox/matching_predictions,1,Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model,"https://scholar.google.com/scholar?cluster=10540192598939165742&hl=en&as_sdt=0,14",1,2022
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials ,4,neurips,0,0,2023-06-16 22:58:22.556000,https://github.com/eshnich/escape_ntk,0,Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials,"https://scholar.google.com/scholar?cluster=9098044485141039309&hl=en&as_sdt=0,36",1,2022
Pure Transformers are Powerful Graph Learners,20,neurips,35,8,2023-06-16 22:58:22.766000,https://github.com/jw9730/tokengt,226,Pure transformers are powerful graph learners,"https://scholar.google.com/scholar?cluster=1854387804616571098&hl=en&as_sdt=0,5",10,2022
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation,0,neurips,2,0,2023-06-16 22:58:22.978000,https://github.com/jeremiemelo/neurolight,23,NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation,"https://scholar.google.com/scholar?cluster=8881238430961631710&hl=en&as_sdt=0,5",5,2022
Learning the Structure of Large Networked Systems Obeying Conservation Laws,1,neurips,0,0,2023-06-16 22:58:23.190000,https://github.com/anirudhrayas/slnscl,0,Learning the Structure of Large Networked Systems Obeying Conservation Laws,"https://scholar.google.com/scholar?cluster=5489652265848095626&hl=en&as_sdt=0,5",1,2022
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets,2,neurips,3,0,2023-06-16 22:58:23.409000,https://github.com/arieseirack/dhvt,41,Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets,"https://scholar.google.com/scholar?cluster=10766475797615971517&hl=en&as_sdt=0,14",3,2022
Private Set Generation with Discriminative Information,11,neurips,0,2,2023-06-16 22:58:23.619000,https://github.com/dingfanchen/private-set,13,Private set generation with discriminative information,"https://scholar.google.com/scholar?cluster=1058785882009175393&hl=en&as_sdt=0,44",1,2022
Provable Defense against Backdoor Policies in Reinforcement Learning,0,neurips,0,0,2023-06-16 22:58:23.830000,https://github.com/skbharti/provable-defense-in-rl,4,Provable Defense against Backdoor Policies in Reinforcement Learning,"https://scholar.google.com/scholar?cluster=15582632130939406311&hl=en&as_sdt=0,5",1,2022
Diffusion Models as Plug-and-Play Priors,32,neurips,10,3,2023-06-16 22:58:24.042000,https://github.com/alexgraikos/diffusion_priors,134,Diffusion models as plug-and-play priors,"https://scholar.google.com/scholar?cluster=1664893972448348110&hl=en&as_sdt=0,47",3,2022
VaiPhy: a Variational Inference Based Algorithm for Phylogeny,2,neurips,0,0,2023-06-16 22:58:24.253000,https://github.com/lagergren-lab/vaiphy,1,VaiPhy: a Variational Inference Based Algorithm for Phylogeny,"https://scholar.google.com/scholar?cluster=8569696227907853831&hl=en&as_sdt=0,5",1,2022
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal,9,neurips,2,0,2023-06-16 22:58:24.465000,https://github.com/yaqianzhang/repeatedaugmentedrehearsal,6,A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal,"https://scholar.google.com/scholar?cluster=9507643277060053536&hl=en&as_sdt=0,48",2,2022
Compressible-composable NeRF via Rank-residual Decomposition,23,neurips,10,4,2023-06-16 22:58:24.675000,https://github.com/ashawkey/ccnerf,116,Compressible-composable nerf via rank-residual decomposition,"https://scholar.google.com/scholar?cluster=15357102335001383949&hl=en&as_sdt=0,5",11,2022
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties,1,neurips,0,0,2023-06-16 22:58:24.886000,https://github.com/shuix007/eip4nnpotentials,1,Injecting domain knowledge from empirical interatomic potentials to neural networks for predicting material properties,"https://scholar.google.com/scholar?cluster=1090911456582952021&hl=en&as_sdt=0,10",3,2022
Learning Modular Simulations for Homogeneous Systems,0,neurips,1,0,2023-06-16 22:58:25.097000,https://github.com/microsoft/mpnode.jl,29,Learning Modular Simulations for Homogeneous Systems,"https://scholar.google.com/scholar?cluster=16943302604921582247&hl=en&as_sdt=0,48",4,2022
Semi-Discrete Normalizing Flows through Differentiable Tessellation,2,neurips,1,0,2023-06-16 22:58:25.308000,https://github.com/facebookresearch/semi-discrete-flow,20,Semi-Discrete Normalizing Flows through Differentiable Tessellation,"https://scholar.google.com/scholar?cluster=2894615893347018628&hl=en&as_sdt=0,39",3,2022
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks,5,neurips,0,1,2023-06-16 22:58:25.519000,https://github.com/sizhe-chen/aaa,13,Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks,"https://scholar.google.com/scholar?cluster=1904818914099445692&hl=en&as_sdt=0,21",1,2022
Sequence-to-Set Generative Models,0,neurips,2,0,2023-06-16 22:58:25.731000,https://github.com/longtaotang/setlearning,1,Sequence-to-Set Generative Models,"https://scholar.google.com/scholar?cluster=11832911442532697900&hl=en&as_sdt=0,5",1,2022
Near-Optimal Multi-Agent Learning for Safe Coverage Control,1,neurips,1,0,2023-06-16 22:58:25.943000,https://github.com/manish-pra/safemac,7,Near-Optimal Multi-Agent Learning for Safe Coverage Control,"https://scholar.google.com/scholar?cluster=9831092712630856956&hl=en&as_sdt=0,33",2,2022
Beyond spectral gap: the role of the topology in decentralized learning,6,neurips,0,0,2023-06-16 22:58:26.155000,https://github.com/epfml/topology-in-decentralized-learning,6,Beyond spectral gap: The role of the topology in decentralized learning,"https://scholar.google.com/scholar?cluster=1362974330315569640&hl=en&as_sdt=0,44",3,2022
Periodic Graph Transformers for Crystal Material Property Prediction,11,neurips,3,1,2023-06-16 22:58:26.366000,https://github.com/YKQ98/Matformer,47,Periodic Graph Transformers for Crystal Material Property Prediction,"https://scholar.google.com/scholar?cluster=9619404030822952789&hl=en&as_sdt=0,38",5,2022
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation,6,neurips,5,2,2023-06-16 22:58:26.579000,https://github.com/xiaoachen98/DDB,52,Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation,"https://scholar.google.com/scholar?cluster=12908675739985569858&hl=en&as_sdt=0,5",3,2022
DreamShard: Generalizable Embedding Table Placement for Recommender Systems,9,neurips,1,0,2023-06-16 22:58:26.790000,https://github.com/daochenzha/dreamshard,26,Dreamshard: Generalizable embedding table placement for recommender systems,"https://scholar.google.com/scholar?cluster=5762579680936509835&hl=en&as_sdt=0,5",3,2022
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE,2,neurips,2,0,2023-06-16 22:58:27.001000,https://github.com/smlc-nysbc/target-vae,13,Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE,"https://scholar.google.com/scholar?cluster=4643268267251719909&hl=en&as_sdt=0,33",3,2022
PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points,2,neurips,1,2,2023-06-16 22:58:27.213000,https://github.com/mcg-nju/pointtad,31,Pointtad: Multi-label temporal action detection with learnable query points,"https://scholar.google.com/scholar?cluster=4239613475999349516&hl=en&as_sdt=0,33",3,2022
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions,0,neurips,0,0,2023-06-16 22:58:27.424000,https://github.com/Stalence/NeuralExt,4,Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions,"https://scholar.google.com/scholar?cluster=11142300575635398098&hl=en&as_sdt=0,5",1,2022
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification,4,neurips,10,1,2023-06-16 22:58:27.635000,https://github.com/miccaiif/weno,34,Bi-directional weakly supervised knowledge distillation for whole slide image classification,"https://scholar.google.com/scholar?cluster=8347896172205638655&hl=en&as_sdt=0,36",3,2022
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient,4,neurips,181,91,2023-06-16 22:58:27.846000,https://github.com/open-mmlab/mmrazor,1088,PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient,"https://scholar.google.com/scholar?cluster=15197137746726757661&hl=en&as_sdt=0,10",19,2022
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis,17,neurips,153,14,2023-06-16 22:58:28.057000,https://github.com/microsoft/nuwa,2707,Nuwa-infinity: Autoregressive over autoregressive generation for infinite visual synthesis,"https://scholar.google.com/scholar?cluster=13240374514444074345&hl=en&as_sdt=0,7",143,2022
Stability Analysis and Generalization Bounds of Adversarial Training,3,neurips,0,0,2023-06-16 22:58:28.268000,https://github.com/JiancongXiao/Stability-of-Adversarial-Training,2,Stability analysis and generalization bounds of adversarial training,"https://scholar.google.com/scholar?cluster=4247121934226238783&hl=en&as_sdt=0,33",1,2022
STaR: Bootstrapping Reasoning With Reasoning,85,neurips,6,0,2023-06-16 22:58:28.484000,https://github.com/ezelikman/STaR,20,Star: Bootstrapping reasoning with reasoning,"https://scholar.google.com/scholar?cluster=6588800596180274414&hl=en&as_sdt=0,14",1,2022
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation,2,neurips,1,0,2023-06-16 22:58:28.695000,https://github.com/stilwell-git/adaptation-with-noisy-oracle,3,Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation,"https://scholar.google.com/scholar?cluster=12503746065360790746&hl=en&as_sdt=0,5",2,2022
Weakly Supervised Representation Learning with Sparse Perturbations,11,neurips,0,0,2023-06-16 22:58:28.906000,https://github.com/ahujak/wsrl,0,Weakly supervised representation learning with sparse perturbations,"https://scholar.google.com/scholar?cluster=5928274395682008683&hl=en&as_sdt=0,41",1,2022
Watermarking for Out-of-distribution Detection,4,neurips,2,0,2023-06-16 22:58:29.117000,https://github.com/qizhouwang/watermarking,10,Watermarking for Out-of-distribution Detection,"https://scholar.google.com/scholar?cluster=14042029283291490588&hl=en&as_sdt=0,33",1,2022
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records,2,neurips,9,0,2023-06-16 22:58:29.329000,https://github.com/glee4810/EHRSQL,36,EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records,"https://scholar.google.com/scholar?cluster=8956258088205666681&hl=en&as_sdt=0,23",3,2022
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability,2,neurips,1,0,2023-06-16 22:58:29.540000,https://github.com/LevinRoman/parameter-space-saliency,21,Where do Models go wrong? Parameter-space saliency maps for explainability,"https://scholar.google.com/scholar?cluster=6375709581845585510&hl=en&as_sdt=0,5",2,2022
Using Embeddings for Causal Estimation of Peer Influence in Social Networks,2,neurips,3,0,2023-06-16 22:58:29.751000,https://github.com/irinacristali/peer-contagion-on-networks,6,Using embeddings for causal estimation of peer influence in social networks,"https://scholar.google.com/scholar?cluster=10956063829097823219&hl=en&as_sdt=0,15",1,2022
Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?,5,neurips,1,0,2023-06-16 22:58:29.962000,https://github.com/dendorferpatrick/quovadis,19,Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?,"https://scholar.google.com/scholar?cluster=17768927827009981298&hl=en&as_sdt=0,14",3,2022
Wasserstein Iterative Networks for Barycenter Estimation,11,neurips,0,1,2023-06-16 22:58:30.174000,https://github.com/iamalexkorotin/wassersteiniterativenetworks,3,Wasserstein iterative networks for barycenter estimation,"https://scholar.google.com/scholar?cluster=6505548225666677645&hl=en&as_sdt=0,33",2,2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations,14,neurips,21,4,2023-06-16 22:58:30.402000,https://github.com/ai4life-group/openxai,158,Openxai: Towards a transparent evaluation of model explanations,"https://scholar.google.com/scholar?cluster=1602716306137073411&hl=en&as_sdt=0,15",6,2022
The Hessian Screening Rule,1,neurips,0,0,2023-06-16 22:58:30.614000,https://github.com/jolars/HessianScreening,2,The hessian screening rule,"https://scholar.google.com/scholar?cluster=4519092645139921267&hl=en&as_sdt=0,5",3,2022
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging,6,neurips,0,0,2023-06-16 22:58:30.825000,https://github.com/totilas/muffliato,0,Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging,"https://scholar.google.com/scholar?cluster=1367771846266948746&hl=en&as_sdt=0,5",1,2022
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment,5,neurips,0,0,2023-06-16 22:58:31.036000,https://github.com/causalml/boundsonfractionnegativelyaffected,1,What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment,"https://scholar.google.com/scholar?cluster=15108195108201398305&hl=en&as_sdt=0,33",0,2022
Training Subset Selection for Weak Supervision,7,neurips,1,0,2023-06-16 22:58:31.247000,https://github.com/hunterlang/weaksup-subset-selection,11,Training Subset Selection for Weak Supervision,"https://scholar.google.com/scholar?cluster=8350401146899292084&hl=en&as_sdt=0,33",1,2022
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation,7,neurips,0,1,2023-06-16 22:58:31.460000,https://github.com/tyroneli/esol_wsss,13,Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation,"https://scholar.google.com/scholar?cluster=7949251840753978462&hl=en&as_sdt=0,14",4,2022
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning,23,neurips,5,0,2023-06-16 22:58:31.671000,https://github.com/marc-rigter/rambo,13,Rambo-rl: Robust adversarial model-based offline reinforcement learning,"https://scholar.google.com/scholar?cluster=10956894200939947900&hl=en&as_sdt=0,5",3,2022
Improved techniques for deterministic l2 robustness,2,neurips,0,0,2023-06-16 22:58:31.882000,https://github.com/singlasahil14/improved_l2_robustness,2,Improved techniques for deterministic l2 robustness,"https://scholar.google.com/scholar?cluster=7826478224730238594&hl=en&as_sdt=0,5",1,2022
Normalizing Flows for Knockoff-free Controlled Feature Selection,1,neurips,2,1,2023-06-16 22:58:32.093000,https://github.com/dereklhansen/flowselect,6,Normalizing flows for knockoff-free controlled feature selection,"https://scholar.google.com/scholar?cluster=1427873937634321585&hl=en&as_sdt=0,5",1,2022
Efficient Architecture Search for Diverse Tasks,5,neurips,3,0,2023-06-16 22:58:32.305000,https://github.com/sjunhongshen/dash,20,Efficient architecture search for diverse tasks,"https://scholar.google.com/scholar?cluster=6159039417231853231&hl=en&as_sdt=0,39",1,2022
Inherently Explainable Reinforcement Learning in Natural Language,4,neurips,0,0,2023-06-16 22:58:32.516000,https://github.com/xiangyu-peng/hex-rl,5,Inherently explainable reinforcement learning in natural language,"https://scholar.google.com/scholar?cluster=14816477869397516232&hl=en&as_sdt=0,10",1,2022
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting,7,neurips,21,0,2023-06-16 22:58:32.727000,https://github.com/naver/gdc,108,On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting,"https://scholar.google.com/scholar?cluster=852205239586657946&hl=en&as_sdt=0,51",10,2022
Ask4Help: Learning to Leverage an Expert for Embodied Tasks,2,neurips,0,0,2023-06-16 22:58:32.939000,https://github.com/allenai/ask4help,17,Ask4help: Learning to leverage an expert for embodied tasks,"https://scholar.google.com/scholar?cluster=893074409326064845&hl=en&as_sdt=0,33",3,2022
Active Bayesian Causal Inference,7,neurips,2,0,2023-06-16 22:58:33.150000,https://github.com/chritoth/active-bayesian-causal-inference,21,Active Bayesian Causal Inference,"https://scholar.google.com/scholar?cluster=14185975867772832007&hl=en&as_sdt=0,5",2,2022
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training,3,neurips,58,10,2023-06-16 22:58:33.361000,https://github.com/microsoft/ContextualSP,310,Logigan: Learning logical reasoning via adversarial pre-training,"https://scholar.google.com/scholar?cluster=16806536241461518439&hl=en&as_sdt=0,5",15,2022
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness,88,neurips,308,111,2023-06-16 22:58:33.573000,https://github.com/hazyresearch/flash-attention,3654,Flashattention: Fast and memory-efficient exact attention with io-awareness,"https://scholar.google.com/scholar?cluster=4436654227589737701&hl=en&as_sdt=0,5",67,2022
Self-Supervised Visual Representation Learning with Semantic Grouping,14,neurips,6,4,2023-06-16 22:58:33.784000,https://github.com/CVMI-Lab/SlotCon,76,Self-supervised visual representation learning with semantic grouping,"https://scholar.google.com/scholar?cluster=11920603760559197380&hl=en&as_sdt=0,5",3,2022
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds,8,neurips,2,0,2023-06-16 22:58:33.995000,https://github.com/junshengzhou/cap-udf,37,Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds,"https://scholar.google.com/scholar?cluster=3947486102565885083&hl=en&as_sdt=0,47",3,2022
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem,26,neurips,26,4,2023-06-16 22:58:34.209000,https://github.com/pku-marl/multi-agent-transformer,147,Multi-agent reinforcement learning is a sequence modeling problem,"https://scholar.google.com/scholar?cluster=14170076594522259195&hl=en&as_sdt=0,39",7,2022
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination,6,neurips,1,0,2023-06-16 22:58:34.420000,https://github.com/ma921/basq,11,Fast Bayesian inference with batch Bayesian quadrature via kernel recombination,"https://scholar.google.com/scholar?cluster=9942624906464459479&hl=en&as_sdt=0,14",1,2022
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks,0,neurips,0,0,2023-06-16 22:58:34.632000,https://github.com/mlohaus/disparatetreatment,0,Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks,"https://scholar.google.com/scholar?cluster=8811649943714147381&hl=en&as_sdt=0,5",1,2022
Fast Instrument Learning with Faster Rates,1,neurips,0,0,2023-06-16 22:58:34.843000,https://github.com/meta-inf/fil,0,Fast Instrument Learning with Faster Rates,"https://scholar.google.com/scholar?cluster=6761597304576361829&hl=en&as_sdt=0,31",1,2022
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition,62,neurips,13,12,2023-06-16 22:58:35.055000,https://github.com/ShoufaChen/AdaptFormer,194,Adaptformer: Adapting vision transformers for scalable visual recognition,"https://scholar.google.com/scholar?cluster=17752815312316743733&hl=en&as_sdt=0,47",6,2022
Symmetry Teleportation for Accelerated Optimization,2,neurips,3,0,2023-06-16 22:58:35.266000,https://github.com/rose-stl-lab/symmetry-teleportation,6,Symmetry Teleportation for Accelerated Optimization,"https://scholar.google.com/scholar?cluster=1373110452926814805&hl=en&as_sdt=0,5",2,2022
Wasserstein Logistic Regression with Mixed Features,1,neurips,0,0,2023-06-16 22:58:35.477000,https://github.com/selvi-aras/wassersteinlr,3,Wasserstein logistic regression with mixed features,"https://scholar.google.com/scholar?cluster=7859002643668729721&hl=en&as_sdt=0,6",3,2022
Trajectory Inference via Mean-field Langevin in Path Space,5,neurips,0,0,2023-06-16 22:58:35.689000,https://github.com/zsteve/mfl,1,Trajectory inference via mean-field Langevin in path space,"https://scholar.google.com/scholar?cluster=14010724729856799724&hl=en&as_sdt=0,33",1,2022
SwinTrack: A Simple and Strong Baseline for Transformer Tracking,79,neurips,37,25,2023-06-16 22:58:35.902000,https://github.com/litinglin/swintrack,213,Swintrack: A simple and strong baseline for transformer tracking,"https://scholar.google.com/scholar?cluster=6278077695056066484&hl=en&as_sdt=0,44",5,2022
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation,3,neurips,0,2,2023-06-16 22:58:36.115000,https://github.com/joonho-jang/uadal,8,Unknown-aware domain adversarial learning for open-set domain adaptation,"https://scholar.google.com/scholar?cluster=17997080445903067240&hl=en&as_sdt=0,33",2,2022
Poisson Flow Generative Models,17,neurips,60,3,2023-06-16 22:58:36.326000,https://github.com/newbeeer/poisson_flow,747,Poisson flow generative models,"https://scholar.google.com/scholar?cluster=14573129279323287718&hl=en&as_sdt=0,5",15,2022
Invertible Monotone Operators for Normalizing Flows,0,neurips,0,0,2023-06-16 22:58:36.538000,https://github.com/mlvlab/monotoneflows,7,Invertible Monotone Operators for Normalizing Flows,"https://scholar.google.com/scholar?cluster=9497056797525394758&hl=en&as_sdt=0,5",3,2022
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets,3,neurips,2,0,2023-06-16 22:58:36.748000,https://github.com/clinicalml/parametric-robustness-evaluation,4,Evaluating robustness to dataset shift via parametric robustness sets,"https://scholar.google.com/scholar?cluster=13183637754887103370&hl=en&as_sdt=0,44",8,2022
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers,78,neurips,68,21,2023-06-16 22:58:36.959000,https://github.com/thudm/cogview2,862,Cogview2: Faster and better text-to-image generation via hierarchical transformers,"https://scholar.google.com/scholar?cluster=13690046467918196748&hl=en&as_sdt=0,24",36,2022
Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method,1,neurips,0,0,2023-06-16 22:58:37.171000,https://github.com/zichuliu/submission,3,Recursive Reasoning in Minimax Games: A Level Gradient Play Method,"https://scholar.google.com/scholar?cluster=9230671350422718821&hl=en&as_sdt=0,5",1,2022
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning,4,neurips,2,0,2023-06-16 22:58:37.383000,https://github.com/tajwarfahim/proactive_interventions,6,When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning,"https://scholar.google.com/scholar?cluster=552685687177516453&hl=en&as_sdt=0,33",4,2022
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering,6,neurips,2,0,2023-06-16 22:58:37.594000,https://github.com/kepsail/SHGP,17,Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering,"https://scholar.google.com/scholar?cluster=11543677254444809912&hl=en&as_sdt=0,47",1,2022
coVariance Neural Networks,3,neurips,0,0,2023-06-16 22:58:37.809000,https://github.com/pennbindlab/vnn,2,coVariance Neural Networks,"https://scholar.google.com/scholar?cluster=5746884455895587002&hl=en&as_sdt=0,48",0,2022
Two-Stream Network for Sign Language Recognition and Translation,8,neurips,9,10,2023-06-16 22:58:38.020000,https://github.com/FangyunWei/SLRT,89,Two-Stream Network for Sign Language Recognition and Translation,"https://scholar.google.com/scholar?cluster=18038872806670059767&hl=en&as_sdt=0,5",3,2022
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives,3,neurips,1,1,2023-06-16 22:58:38.232000,https://github.com/zfying/visfis,4,VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives,"https://scholar.google.com/scholar?cluster=11221935189799088705&hl=en&as_sdt=0,34",1,2022
Batch size-invariance for policy optimization,6,neurips,14,0,2023-06-16 22:58:38.443000,https://github.com/openai/ppo-ewma,42,Batch size-invariance for policy optimization,"https://scholar.google.com/scholar?cluster=2296025407370141358&hl=en&as_sdt=0,5",2,2022
Variational Model Perturbation for Source-Free Domain Adaptation,4,neurips,1,0,2023-06-16 22:58:38.654000,https://github.com/mmjing/variational_model_perturbation,4,Variational model perturbation for source-free domain adaptation,"https://scholar.google.com/scholar?cluster=11797225835673378824&hl=en&as_sdt=0,18",1,2022
A Unified Framework for Alternating Offline Model Training and Policy Learning,3,neurips,1,0,2023-06-16 22:58:38.865000,https://github.com/shentao-yang/ampl_neurips2022,7,A Unified Framework for Alternating Offline Model Training and Policy Learning,"https://scholar.google.com/scholar?cluster=1237354038205563544&hl=en&as_sdt=0,49",1,2022
Peer Prediction for Learning Agents,2,neurips,0,0,2023-06-16 22:58:39.076000,https://github.com/fengtony686/peer-prediction-convergence,2,Peer Prediction for Learning Agents,"https://scholar.google.com/scholar?cluster=6943061375108468617&hl=en&as_sdt=0,5",2,2022
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution,8,neurips,7,5,2023-06-16 22:58:39.288000,https://github.com/sunny2109/mobilesr-ntire2022,56,ShuffleMixer: An Efficient ConvNet for Image Super-Resolution,"https://scholar.google.com/scholar?cluster=15307398465334207013&hl=en&as_sdt=0,5",4,2022
Locating and Editing Factual Associations in GPT,77,neurips,52,10,2023-06-16 22:58:39.500000,https://github.com/kmeng01/rome,237,Locating and editing factual associations in GPT,"https://scholar.google.com/scholar?cluster=6676170860106418721&hl=en&as_sdt=0,45",6,2022
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models,11,neurips,2,0,2023-06-16 22:58:39.712000,https://github.com/wimh966/outlier_suppression,28,Outlier suppression: Pushing the limit of low-bit transformer language models,"https://scholar.google.com/scholar?cluster=10349903029841353318&hl=en&as_sdt=0,10",1,2022
DataMUX: Data Multiplexing for Neural Networks,4,neurips,8,0,2023-06-16 22:58:39.923000,https://github.com/princeton-nlp/datamux,53,Datamux: Data multiplexing for neural networks,"https://scholar.google.com/scholar?cluster=3955638905484690082&hl=en&as_sdt=0,33",7,2022
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators,2,neurips,0,0,2023-06-16 22:58:40.134000,https://github.com/helena-yuhan-liu/modprop,3,Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators,"https://scholar.google.com/scholar?cluster=2884524613792294582&hl=en&as_sdt=0,41",1,2022
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering,8,neurips,2,0,2023-06-16 22:58:40.346000,https://github.com/gorilla-lab-scut/ttac,32,Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering,"https://scholar.google.com/scholar?cluster=15662895642331219475&hl=en&as_sdt=0,36",1,2022
Active Labeling: Streaming Stochastic Gradients,1,neurips,0,0,2023-06-16 22:58:40.557000,https://github.com/viviencabannes/active-labeling,1,Active Labeling: Streaming Stochastic Gradients,"https://scholar.google.com/scholar?cluster=15951285451586696904&hl=en&as_sdt=0,44",2,2022
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation,3,neurips,2,0,2023-06-16 22:58:40.769000,https://github.com/air-discover/toist,117,TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation,"https://scholar.google.com/scholar?cluster=12198126632106334540&hl=en&as_sdt=0,44",5,2022
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning,50,neurips,5,1,2023-06-16 22:58:40.980000,https://github.com/weixin-liang/modality-gap,46,Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning,"https://scholar.google.com/scholar?cluster=9899703375781547991&hl=en&as_sdt=0,5",3,2022
Sequence Model Imitation Learning with Unobserved Contexts,3,neurips,0,0,2023-06-16 22:58:41.198000,https://github.com/gkswamy98/sequence_model_il,3,Sequence model imitation learning with unobserved contexts,"https://scholar.google.com/scholar?cluster=2920440114291350523&hl=en&as_sdt=0,5",2,2022
Merging Models with Fisher-Weighted Averaging,35,neurips,2,0,2023-06-16 22:58:41.417000,https://github.com/mmatena/model_merging,28,Merging models with fisher-weighted averaging,"https://scholar.google.com/scholar?cluster=6334185910733231827&hl=en&as_sdt=0,38",1,2022
FasterRisk: Fast and Accurate Interpretable Risk Scores,2,neurips,1,0,2023-06-16 22:58:41.628000,https://github.com/jiachangliu/fasterrisk,17,FasterRisk: Fast and Accurate Interpretable Risk Scores,"https://scholar.google.com/scholar?cluster=16531707730202339054&hl=en&as_sdt=0,33",4,2022
Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution,12,neurips,1,0,2023-06-16 22:58:41.840000,https://github.com/ut-austin-data-science-group/csw,4,Revisiting sliced Wasserstein on images: From vectorization to convolution,"https://scholar.google.com/scholar?cluster=16632120304055085115&hl=en&as_sdt=0,5",0,2022
A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning,3,neurips,1,0,2023-06-16 22:58:42.052000,https://github.com/ml-postech/rown,18,A rotated hyperbolic wrapped normal distribution for hierarchical representation learning,"https://scholar.google.com/scholar?cluster=12794077703223787887&hl=en&as_sdt=0,5",7,2022
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training,4,neurips,4,1,2023-06-16 22:58:42.263000,https://github.com/dem123456789/semifl-semi-supervised-federated-learning-for-unlabeled-clients-with-alternate-training,13,SemiFL: Semi-supervised federated learning for unlabeled clients with alternate training,"https://scholar.google.com/scholar?cluster=15626144916318485438&hl=en&as_sdt=0,47",3,2022
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection,2,neurips,1,0,2023-06-16 22:58:42.474000,https://github.com/kingjamessong/rankfeat,14,RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection,"https://scholar.google.com/scholar?cluster=15686388667832765832&hl=en&as_sdt=0,5",1,2022
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model,3,neurips,2,1,2023-06-16 22:58:42.686000,https://github.com/srishtigautam/protovae,12,Protovae: A trustworthy self-explainable prototypical variational model,"https://scholar.google.com/scholar?cluster=16989445926776575392&hl=en&as_sdt=0,47",1,2022
"If Influence Functions are the Answer, Then What is the Question?",14,neurips,0,0,2023-06-16 22:58:42.897000,https://github.com/pomonam/jax-influence,7,"If Influence Functions are the Answer, Then What is the Question?","https://scholar.google.com/scholar?cluster=17591064813348027664&hl=en&as_sdt=0,23",1,2022
Hierarchical classification at multiple operating points,1,neurips,1,0,2023-06-16 22:58:43.107000,https://github.com/jvlmdr/hiercls,11,Hierarchical classification at multiple operating points,"https://scholar.google.com/scholar?cluster=6696040702671773446&hl=en&as_sdt=0,14",2,2022
CARD: Classification and Regression Diffusion Models,10,neurips,16,1,2023-06-16 22:58:43.319000,https://github.com/xzwhan/card,108,CARD: Classification and regression diffusion models,"https://scholar.google.com/scholar?cluster=13161498921981862309&hl=en&as_sdt=0,5",5,2022
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?,2,neurips,0,0,2023-06-16 22:58:43.531000,https://github.com/Tsili42/adv-ntk,0,What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?,"https://scholar.google.com/scholar?cluster=765440786974281242&hl=en&as_sdt=0,33",1,2022
MoCoDA: Model-based Counterfactual Data Augmentation,3,neurips,1,0,2023-06-16 22:58:43.742000,https://github.com/spitis/mocoda,8,MoCoDA: Model-based Counterfactual Data Augmentation,"https://scholar.google.com/scholar?cluster=7948314758864851403&hl=en&as_sdt=0,34",1,2022
"On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification",5,neurips,1,0,2023-06-16 22:58:43.954000,https://github.com/activatedgeek/bayesian-classification,18,"On uncertainty, tempering, and data augmentation in bayesian classification","https://scholar.google.com/scholar?cluster=5049318542021404538&hl=en&as_sdt=0,33",2,2022
"Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters",18,neurips,7321,1026,2023-06-16 22:58:44.165000,https://github.com/google-research/google-research,29788,"Why so pessimistic? estimating uncertainties for offline rl through ensembles, and why their independence matters","https://scholar.google.com/scholar?cluster=6972415736332431556&hl=en&as_sdt=0,44",727,2022
Advancing Model Pruning via Bi-level Optimization,7,neurips,34,1,2023-06-16 22:58:44.377000,https://github.com/optml-group/bip,130,Advancing Model Pruning via Bi-level Optimization,"https://scholar.google.com/scholar?cluster=13543295038180870418&hl=en&as_sdt=0,43",24,2022
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge,61,neurips,93,24,2023-06-16 22:58:44.588000,https://github.com/MineDojo/MineDojo,1310,Minedojo: Building open-ended embodied agents with internet-scale knowledge,"https://scholar.google.com/scholar?cluster=231281729668967714&hl=en&as_sdt=0,11",27,2022
Truncated Matrix Power Iteration for Differentiable DAG Learning,1,neurips,1,0,2023-06-16 22:58:44.800000,https://github.com/zzhang1987/truncated-matrix-power-iteration-for-differentiable-dag-learning,1,Truncated Matrix Power Iteration for Differentiable DAG Learning,"https://scholar.google.com/scholar?cluster=9166467047019565651&hl=en&as_sdt=0,5",2,2022
Learning Debiased Classifier with Biased Committee,8,neurips,0,0,2023-06-16 22:58:45.011000,https://github.com/nayeong-v-kim/lwbc,12,Learning debiased classifier with biased committee,"https://scholar.google.com/scholar?cluster=2775898324803541021&hl=en&as_sdt=0,44",1,2022
Unifying Voxel-based Representation with Transformer for 3D Object Detection,53,neurips,12,8,2023-06-16 22:58:45.223000,https://github.com/dvlab-research/uvtr,187,Unifying voxel-based representation with transformer for 3d object detection,"https://scholar.google.com/scholar?cluster=2319515305755204659&hl=en&as_sdt=0,5",6,2022
On Scrambling Phenomena for Randomly Initialized Recurrent Networks,0,neurips,1,0,2023-06-16 22:58:45.435000,https://github.com/steliostavroulakis/chaos_rnns,2,On Scrambling Phenomena for Randomly Initialized Recurrent Networks,"https://scholar.google.com/scholar?cluster=7078078811342818102&hl=en&as_sdt=0,43",1,2022
Learning to Branch with Tree MDPs,14,neurips,9,2,2023-06-16 22:58:45.645000,https://github.com/lascavana/rl2branch,7,Learning to branch with tree mdps,"https://scholar.google.com/scholar?cluster=5953866441971807828&hl=en&as_sdt=0,47",1,2022
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs,48,neurips,9,0,2023-06-16 22:58:45.856000,https://github.com/twitter-research/neural-sheaf-diffusion,43,Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns,"https://scholar.google.com/scholar?cluster=14875672783767429079&hl=en&as_sdt=0,50",5,2022
How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios,1,neurips,0,1,2023-06-16 22:58:46.068000,https://github.com/hendrycks/emodiversity,7,How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios,"https://scholar.google.com/scholar?cluster=7719508504871552377&hl=en&as_sdt=0,33",4,2022
On Elimination Strategies for Bandit Fixed-Confidence Identification,0,neurips,0,0,2023-06-16 22:58:46.279000,https://github.com/andreatirinzoni/bandit-elimination,2,On Elimination Strategies for Bandit Fixed-Confidence Identification,"https://scholar.google.com/scholar?cluster=7723207511483790063&hl=en&as_sdt=0,5",1,2022
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture,7,neurips,3,0,2023-06-16 22:58:46.492000,https://github.com/mo666666/when-adversarial-training-meets-vision-transformers,13,When adversarial training meets vision transformers: Recipes from training to architecture,"https://scholar.google.com/scholar?cluster=4979980809128856359&hl=en&as_sdt=0,31",2,2022
Private Estimation with Public Data,48,neurips,0,0,2023-06-16 22:58:46.704000,https://github.com/alexbie98/1pub-priv-mean-est,0,Sharing social network data: differentially private estimation of exponential family random-graph models,"https://scholar.google.com/scholar?cluster=15510004526104950140&hl=en&as_sdt=0,5",1,2022
Most Activation Functions Can Win the Lottery Without Excessive Depth,2,neurips,0,0,2023-06-16 22:58:46.915000,https://github.com/relationalml/lt-existence,2,Most activation functions can win the lottery without excessive depth,"https://scholar.google.com/scholar?cluster=2762350726974066343&hl=en&as_sdt=0,47",0,2022
Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition,1,neurips,4,0,2023-06-16 22:58:47.127000,https://github.com/kdhht2334/elim_fer,24,Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition,"https://scholar.google.com/scholar?cluster=9348912629792592227&hl=en&as_sdt=0,32",1,2022
SHINE: SubHypergraph Inductive Neural nEtwork,1,neurips,2,0,2023-06-16 22:58:47.339000,https://github.com/luoyuanlab/shine,9,SHINE: SubHypergraph Inductive Neural nEtwork,"https://scholar.google.com/scholar?cluster=5043594054485770914&hl=en&as_sdt=0,44",2,2022
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics,7,neurips,0,0,2023-06-16 22:58:47.553000,https://github.com/antoninschrab/agginc-paper,3,Efficient Aggregated Kernel Tests using Incomplete -statistics,"https://scholar.google.com/scholar?cluster=14498936236963978885&hl=en&as_sdt=0,5",1,2022
Influencing Long-Term Behavior in Multiagent Reinforcement Learning,7,neurips,5,0,2023-06-16 22:58:47.766000,https://github.com/dkkim93/further,16,Influencing long-term behavior in multiagent reinforcement learning,"https://scholar.google.com/scholar?cluster=12230303792245064491&hl=en&as_sdt=0,33",1,2022
Quantized Training of Gradient Boosting Decision Trees,0,neurips,1,0,2023-06-16 22:58:47.978000,https://github.com/quantized-gbdt/quantized-gbdt,9,Quantized Training of Gradient Boosting Decision Trees,"https://scholar.google.com/scholar?cluster=4058197876307352226&hl=en&as_sdt=0,10",2,2022
Data Distributional Properties Drive Emergent In-Context Learning in Transformers,37,neurips,10,3,2023-06-16 22:58:48.192000,https://github.com/deepmind/emergent_in_context_learning,53,Data distributional properties drive emergent in-context learning in transformers,"https://scholar.google.com/scholar?cluster=16209854431595052414&hl=en&as_sdt=0,33",3,2022
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks,5,neurips,1,1,2023-06-16 22:58:48.417000,https://github.com/mansheej/lth_diet,8,Lottery tickets on a data diet: Finding initializations with sparse trainable networks,"https://scholar.google.com/scholar?cluster=17203687298264030475&hl=en&as_sdt=0,5",3,2022
Memory safe computations with XLA compiler,1,neurips,2,5,2023-06-16 22:58:48.630000,https://github.com/awav/tensorflow,1,Memory safe computations with XLA compiler,"https://scholar.google.com/scholar?cluster=18390099303465948139&hl=en&as_sdt=0,47",1,2022
Towards Theoretically Inspired Neural Initialization Optimization,0,neurips,1,0,2023-06-16 22:58:48.841000,https://github.com/HarborYuan/GradCosine,8,Towards Theoretically Inspired Neural Initialization Optimization,"https://scholar.google.com/scholar?cluster=6350876524339921816&hl=en&as_sdt=0,14",2,2022
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies,0,neurips,3,4,2023-06-16 22:58:49.052000,https://github.com/lisiyao21/animerun,69,AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies,"https://scholar.google.com/scholar?cluster=2206932835628309531&hl=en&as_sdt=0,5",11,2022
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization,6,neurips,0,0,2023-06-16 22:58:49.265000,https://github.com/yuri-k111/neurips2022_code,0,Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization,"https://scholar.google.com/scholar?cluster=14537579459449046280&hl=en&as_sdt=0,5",1,2022
Efficient learning of nonlinear prediction models with time-series privileged information,2,neurips,1,0,2023-06-16 22:58:49.477000,https://github.com/healthy-ai/glupts,0,Efficient learning of nonlinear prediction models with time-series privileged information,"https://scholar.google.com/scholar?cluster=18191800989177614120&hl=en&as_sdt=0,5",0,2022
Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training,0,neurips,1,0,2023-06-16 22:58:49.693000,https://github.com/snap-research/spfde,8,Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training,"https://scholar.google.com/scholar?cluster=8941325294447745327&hl=en&as_sdt=0,33",4,2022
"Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity",1,neurips,0,0,2023-06-16 22:58:49.904000,https://github.com/andrew-cullen/doublebubble,1,"Double bubble, toil and trouble: enhancing certified robustness through transitivity","https://scholar.google.com/scholar?cluster=15829183381578160837&hl=en&as_sdt=0,44",2,2022
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch,35,neurips,4,2,2023-06-16 22:58:50.117000,https://github.com/hsouri/Sleeper-Agent,45,Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch,"https://scholar.google.com/scholar?cluster=9248176712796866973&hl=en&as_sdt=0,1",3,2022
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models,2,neurips,2,0,2023-06-16 22:58:50.328000,https://github.com/llyx97/sparse-and-robust-plm,20,A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models,"https://scholar.google.com/scholar?cluster=12965321937141963299&hl=en&as_sdt=0,39",1,2022
Pareto Set Learning for Expensive Multi-Objective Optimization,6,neurips,5,1,2023-06-16 22:58:50.539000,https://github.com/xi-l/psl-mobo,6,Pareto Set Learning for Expensive Multi-Objective Optimization,"https://scholar.google.com/scholar?cluster=16507134535796504804&hl=en&as_sdt=0,32",3,2022
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem,0,neurips,0,0,2023-06-16 22:58:50.750000,https://github.com/raunakkmr/non-monotonic-resource-utilization-in-the-bandits-with-knapsacks-problem-code,3,Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem,"https://scholar.google.com/scholar?cluster=12804888557627073813&hl=en&as_sdt=0,5",1,2022
Efficient identification of informative features in simulation-based inference,2,neurips,0,0,2023-06-16 22:58:50.962000,https://github.com/berenslab/fslm_repo,0,Efficient identification of informative features in simulation-based inference,"https://scholar.google.com/scholar?cluster=9408830879778530143&hl=en&as_sdt=0,5",2,2022
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift,18,neurips,0,0,2023-06-16 22:58:51.173000,https://github.com/kebaek/agreement-on-the-line,1,Agreement-on-the-line: Predicting the performance of neural networks under distribution shift,"https://scholar.google.com/scholar?cluster=16040179081922789785&hl=en&as_sdt=0,15",1,2022
Large-Scale Differentiable Causal Discovery of Factor Graphs,7,neurips,2,1,2023-06-16 22:58:51.385000,https://github.com/genentech/dcdfg,15,Large-scale differentiable causal discovery of factor graphs,"https://scholar.google.com/scholar?cluster=336010023327316095&hl=en&as_sdt=0,29",2,2022
Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss,1,neurips,0,0,2023-06-16 22:58:51.597000,https://github.com/IBM/JLPlusPlus,5,Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss,"https://scholar.google.com/scholar?cluster=5393693491306876887&hl=en&as_sdt=0,5",3,2022
Few-shot Image Generation via Adaptation-Aware Kernel Modulation,5,neurips,1,0,2023-06-16 22:58:51.808000,https://github.com/yunqing-me/AdAM,10,Few-shot image generation via adaptation-aware kernel modulation,"https://scholar.google.com/scholar?cluster=4742360547792769040&hl=en&as_sdt=0,5",2,2022
Learning to Follow Instructions in Text-Based Games,5,neurips,0,0,2023-06-16 22:58:52.019000,https://github.com/mathieutuli/ltl-gata,4,Learning to follow instructions in text-based games,"https://scholar.google.com/scholar?cluster=2065963607262919529&hl=en&as_sdt=0,44",2,2022
Improving Variational Autoencoders with Density Gap-based Regularization,1,neurips,0,0,2023-06-16 22:58:52.230000,https://github.com/zhangjf-nlp/dg-vaes,3,Improving Variational Autoencoders with Density Gap-based Regularization,"https://scholar.google.com/scholar?cluster=5008460593978673315&hl=en&as_sdt=0,26",1,2022
RISE: Robust Individualized Decision Learning with Sensitive Variables,5,neurips,1,0,2023-06-16 22:58:52.442000,https://github.com/ellenxtan/rise,6,Rise: Robust individualized decision learning with sensitive variables,"https://scholar.google.com/scholar?cluster=14552433169165007620&hl=en&as_sdt=0,39",3,2022
Beyond neural scaling laws: beating power law scaling via data pruning,67,neurips,2,0,2023-06-16 22:58:52.653000,https://github.com/rgeirhos/dataset-pruning-metrics,19,Beyond neural scaling laws: beating power law scaling via data pruning,"https://scholar.google.com/scholar?cluster=14309238955014761855&hl=en&as_sdt=0,33",1,2022
Maximum Class Separation as Inductive Bias in One Matrix,6,neurips,2,1,2023-06-16 22:58:52.866000,https://github.com/tkasarla/max-separation-as-inductive-bias,23,Maximum class separation as inductive bias in one matrix,"https://scholar.google.com/scholar?cluster=15315241654161942906&hl=en&as_sdt=0,48",5,2022
Redundant representations help generalization in wide neural networks,1,neurips,0,0,2023-06-16 22:58:53.077000,https://github.com/diegodoimo/redundant_representation,1,Redundant representations help generalization in wide neural networks,"https://scholar.google.com/scholar?cluster=11398110079007886002&hl=en&as_sdt=0,10",1,2022
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning,3,neurips,0,0,2023-06-16 22:58:53.289000,https://github.com/romainchor/datascience,0,Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning,"https://scholar.google.com/scholar?cluster=16572219026525753208&hl=en&as_sdt=0,5",2,2022
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes,0,neurips,0,0,2023-06-16 22:58:53.501000,https://github.com/BatsResearch/mazzetto-neurips22-code,2,Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes,"https://scholar.google.com/scholar?cluster=8233985458905085430&hl=en&as_sdt=0,34",3,2022
Graph Neural Networks with Adaptive Readouts,5,neurips,1,0,2023-06-16 22:58:53.713000,https://github.com/davidbuterez/gnn-neural-readouts,16,Graph Neural Networks with Adaptive Readouts,"https://scholar.google.com/scholar?cluster=16233387568833455709&hl=en&as_sdt=0,22",2,2022
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games,5,neurips,3,1,2023-06-16 22:58:53.924000,https://github.com/shichangzh/gstarx,8,GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games,"https://scholar.google.com/scholar?cluster=7993639036305387244&hl=en&as_sdt=0,5",2,2022
Low-Rank Modular Reinforcement Learning via Muscle Synergy,2,neurips,1,1,2023-06-16 22:58:54.136000,https://github.com/drdh/synergy-rl,4,Low-Rank Modular Reinforcement Learning via Muscle Synergy,"https://scholar.google.com/scholar?cluster=15949324168109968004&hl=en&as_sdt=0,5",1,2022
Faster Deep Reinforcement Learning with Slower Online Network,0,neurips,1,0,2023-06-16 22:58:54.348000,https://github.com/amazon-research/fast-rl-with-slow-updates,15,Faster deep reinforcement learning with slower online network,"https://scholar.google.com/scholar?cluster=8991673976969240285&hl=en&as_sdt=0,5",1,2022
Green Hierarchical Vision Transformer for Masked Image Modeling,24,neurips,5,1,2023-06-16 22:58:54.559000,https://github.com/layneh/greenmim,146,Green hierarchical vision transformer for masked image modeling,"https://scholar.google.com/scholar?cluster=5575721172969217810&hl=en&as_sdt=0,5",3,2022
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation,7,neurips,2,0,2023-06-16 22:58:54.771000,https://github.com/montefiore-ai/balanced-nre,11,Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation,"https://scholar.google.com/scholar?cluster=2070151199404142004&hl=en&as_sdt=0,5",4,2022
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs,19,neurips,0,0,2023-06-16 22:58:54.982000,https://github.com/eboursier/gfdynamics,4,Gradient flow dynamics of shallow relu networks for square loss and orthogonal inputs,"https://scholar.google.com/scholar?cluster=7952131240669274846&hl=en&as_sdt=0,5",2,2022
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited,0,neurips,0,0,2023-06-16 22:58:55.193000,https://github.com/nlskrg/node_centric_walk_kernels,0,Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited,"https://scholar.google.com/scholar?cluster=3035963861391187619&hl=en&as_sdt=0,33",1,2022
Multi-agent Dynamic Algorithm Configuration,9,neurips,7,0,2023-06-16 22:58:55.423000,https://github.com/lamda-bbo/madac,18,Multi-agent Dynamic Algorithm Configuration,"https://scholar.google.com/scholar?cluster=18124893361074952166&hl=en&as_sdt=0,5",1,2022
TaSIL: Taylor Series Imitation Learning,9,neurips,1,0,2023-06-16 22:58:55.634000,https://github.com/unstable-zeros/tasil,3,Tasil: Taylor series imitation learning,"https://scholar.google.com/scholar?cluster=5196638265754138969&hl=en&as_sdt=0,33",1,2022
Continuous MDP Homomorphisms and Homomorphic Policy Gradient,2,neurips,0,0,2023-06-16 22:58:55.846000,https://github.com/sahandrez/homomorphic_policy_gradient,12,Continuous MDP Homomorphisms and Homomorphic Policy Gradient,"https://scholar.google.com/scholar?cluster=765221308115729349&hl=en&as_sdt=0,33",3,2022
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance,5,neurips,2,0,2023-06-16 22:58:56.058000,https://github.com/uw-madison-lee-lab/score-wasserstein,12,Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance,"https://scholar.google.com/scholar?cluster=2627264767154274760&hl=en&as_sdt=0,14",2,2022
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs,11,neurips,1,1,2023-06-16 22:58:56.287000,https://github.com/yangzez/ood-link-prediction-generalization-mpnn,1,Ood link prediction generalization capabilities of message-passing gnns in larger test graphs,"https://scholar.google.com/scholar?cluster=14377211411789123424&hl=en&as_sdt=0,36",1,2022
Algorithms with Prediction Portfolios,1,neurips,0,0,2023-06-16 22:58:56.499000,https://github.com/tlavastida/predictionportfolios,1,Algorithms with Prediction Portfolios,"https://scholar.google.com/scholar?cluster=15626362245695114867&hl=en&as_sdt=0,5",2,2022
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs,3,neurips,3,0,2023-06-16 22:58:56.711000,https://github.com/csuastt/HardConstraint,4,A unified Hard-constraint framework for solving geometrically complex PDEs,"https://scholar.google.com/scholar?cluster=12354346662201419102&hl=en&as_sdt=0,5",2,2022
Optimal and Adaptive Monteiro-Svaiter Acceleration,11,neurips,0,0,2023-06-16 22:58:56.922000,https://github.com/danielle-hausler/ms-optimal,1,Optimal and adaptive monteiro-svaiter acceleration,"https://scholar.google.com/scholar?cluster=6181840744509618668&hl=en&as_sdt=0,5",1,2022
SparCL: Sparse Continual Learning on the Edge,8,neurips,2,0,2023-06-16 22:58:57.134000,https://github.com/neu-spiral/SparCL,14,Sparcl: Sparse continual learning on the edge,"https://scholar.google.com/scholar?cluster=7160494277089589433&hl=en&as_sdt=0,5",5,2022
Adaptively Exploiting d-Separators with Causal Bandits,3,neurips,0,0,2023-06-16 22:58:57.345000,https://github.com/blairbilodeau/adaptive-causal-bandits,5,Adaptively exploiting d-separators with causal bandits,"https://scholar.google.com/scholar?cluster=10113006239041370847&hl=en&as_sdt=0,23",1,2022
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP,37,neurips,12,1,2023-06-16 22:58:57.558000,https://github.com/ml-jku/cloob,143,Cloob: Modern hopfield networks with infoloob outperform clip,"https://scholar.google.com/scholar?cluster=3714890763443837424&hl=en&as_sdt=0,33",9,2022
Language Conditioned Spatial Relation Reasoning for 3D Object Grounding,3,neurips,3,1,2023-06-16 22:58:57.770000,https://github.com/cshizhe/vil3dref,31,Language Conditioned Spatial Relation Reasoning for 3D Object Grounding,"https://scholar.google.com/scholar?cluster=14666951856631208351&hl=en&as_sdt=0,14",2,2022
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome,1,neurips,0,1,2023-06-16 22:58:57.980000,https://github.com/cunningham-lab/augcoda,1,Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome,"https://scholar.google.com/scholar?cluster=14450220872219871310&hl=en&as_sdt=0,14",3,2022
Wavelet Feature Maps Compression for Image-to-Image CNNs,2,neurips,4,0,2023-06-16 22:58:58.192000,https://github.com/BGUCompSci/WaveletCompressedConvolution,27,Wavelet Feature Maps Compression for Image-to-Image CNNs,"https://scholar.google.com/scholar?cluster=14881442533144434153&hl=en&as_sdt=0,5",3,2022
Model-Based Imitation Learning for Urban Driving,10,neurips,17,5,2023-06-16 22:58:58.449000,https://github.com/wayveai/mile,183,Model-based imitation learning for urban driving,"https://scholar.google.com/scholar?cluster=4528068241168957372&hl=en&as_sdt=0,22",4,2022
Online Training Through Time for Spiking Neural Networks,5,neurips,3,1,2023-06-16 22:58:58.661000,https://github.com/pkuxmq/ottt-snn,19,Online Training Through Time for Spiking Neural Networks,"https://scholar.google.com/scholar?cluster=4277557500374843996&hl=en&as_sdt=0,5",1,2022
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration,1,neurips,0,0,2023-06-16 22:58:58.872000,https://github.com/anttwo/scone,17,SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration,"https://scholar.google.com/scholar?cluster=4160345323864835005&hl=en&as_sdt=0,1",2,2022
WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents,14,neurips,16,1,2023-06-16 22:58:59.084000,https://github.com/princeton-nlp/WebShop,106,Webshop: Towards scalable real-world web interaction with grounded language agents,"https://scholar.google.com/scholar?cluster=11660577557490092707&hl=en&as_sdt=0,5",9,2022
Boosting Out-of-distribution Detection with Typical Features,5,neurips,34,2,2023-06-16 22:58:59.296000,https://github.com/alibaba/easyrobust,236,Boosting Out-of-distribution Detection with Typical Features,"https://scholar.google.com/scholar?cluster=8201302688725034478&hl=en&as_sdt=0,26",8,2022
Invariant and Transportable Representations for Anti-Causal Domain Shifts,5,neurips,0,0,2023-06-16 22:58:59.507000,https://github.com/ybjiaang/actir,10,Invariant and Transportable Representations for Anti-Causal Domain Shifts,"https://scholar.google.com/scholar?cluster=6490723146131513979&hl=en&as_sdt=0,5",1,2022
Bayesian inference via sparse Hamiltonian flows,4,neurips,0,0,2023-06-16 22:58:59.722000,https://github.com/naitongchen/sparse-hamiltonian-flows,0,Bayesian inference via sparse Hamiltonian flows,"https://scholar.google.com/scholar?cluster=11938722905840215074&hl=en&as_sdt=0,5",1,2022
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling,0,neurips,0,0,2023-06-16 22:58:59.934000,https://github.com/poppinace/sapa,25,SAPA: Similarity-Aware Point Affiliation for Feature Upsampling,"https://scholar.google.com/scholar?cluster=14123536763818309865&hl=en&as_sdt=0,5",2,2022
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing,1,neurips,1,0,2023-06-16 22:59:00.146000,https://github.com/gatech-eic/s3-router,12,Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing,"https://scholar.google.com/scholar?cluster=15574684827691207630&hl=en&as_sdt=0,5",3,2022
Diversity vs. Recognizability: Human-like generalization in one-shot generative models,1,neurips,0,0,2023-06-16 22:59:00.357000,https://github.com/serre-lab/diversity_vs_recognizability,4,Diversity vs. Recognizability: Human-like generalization in one-shot generative models,"https://scholar.google.com/scholar?cluster=14721950743942422651&hl=en&as_sdt=0,5",16,2022
Laplacian Autoencoders for Learning Stochastic Representations,3,neurips,7,0,2023-06-16 22:59:00.568000,https://github.com/frederikwarburg/laplaceae,26,Laplacian autoencoders for learning stochastic representations,"https://scholar.google.com/scholar?cluster=11700677382101407411&hl=en&as_sdt=0,44",3,2022
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid,3,neurips,2,2,2023-06-16 22:59:00.779000,https://github.com/kikimormay/fsl-tcbr,8,Alleviating the sample selection bias in few-shot learning by removing projection to the centroid,"https://scholar.google.com/scholar?cluster=13443086589553855773&hl=en&as_sdt=0,5",3,2022
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning,2,neurips,2,0,2023-06-16 22:59:00.991000,https://github.com/xtra-computing/fedsim,14,A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning,"https://scholar.google.com/scholar?cluster=14897829456700189277&hl=en&as_sdt=0,22",2,2022
Cooperative Distribution Alignment via JSD Upper Bound,0,neurips,0,0,2023-06-16 22:59:01.203000,https://github.com/inouye-lab/alignment-upper-bound,4,Cooperative Distribution Alignment via JSD Upper Bound,"https://scholar.google.com/scholar?cluster=10366168387134029153&hl=en&as_sdt=0,5",0,2022
Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts,1,neurips,0,0,2023-06-16 22:59:01.432000,https://github.com/neerajwagh/evaluating-eeg-representations,12,Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts,"https://scholar.google.com/scholar?cluster=6355936438645874712&hl=en&as_sdt=0,14",2,2022
Hierarchical Graph Transformer with Adaptive Node Sampling,10,neurips,4,0,2023-06-16 22:59:01.644000,https://github.com/zaixizhang/ans-gt,28,Hierarchical Graph Transformer with Adaptive Node Sampling,"https://scholar.google.com/scholar?cluster=3439990593504526316&hl=en&as_sdt=0,33",3,2022
Learning Options via Compression,1,neurips,3,1,2023-06-16 22:59:01.855000,https://github.com/yidingjiang/love,16,Learning Options via Compression,"https://scholar.google.com/scholar?cluster=662325377379730259&hl=en&as_sdt=0,5",2,2022
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data,7,neurips,7,2,2023-06-16 22:59:02.067000,https://github.com/athms/learning-from-brains,32,Self-supervised learning of brain dynamics from broad neuroimaging data,"https://scholar.google.com/scholar?cluster=16840620641875869687&hl=en&as_sdt=0,47",3,2022
Characterization of Excess Risk for Locally Strongly Convex Population Risk,1,neurips,2047,105,2023-06-16 22:59:02.278000,https://github.com/kuangliu/pytorch-cifar,5349,Characterization of Excess Risk for Locally Strongly Convex Population Risk,"https://scholar.google.com/scholar?cluster=17104238636879599315&hl=en&as_sdt=0,5",81,2022
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models,3,neurips,1,0,2023-06-16 22:59:02.491000,https://github.com/zhoulijia/moreau-envelope,0,A non-asymptotic moreau envelope theory for high-dimensional generalized linear models,"https://scholar.google.com/scholar?cluster=2093430636599193484&hl=en&as_sdt=0,47",1,2022
Towards Efficient 3D Object Detection with Knowledge Distillation,11,neurips,10,2,2023-06-16 22:59:02.702000,https://github.com/cvmi-lab/sparsekd,83,Towards efficient 3d object detection with knowledge distillation,"https://scholar.google.com/scholar?cluster=4669452180689530857&hl=en&as_sdt=0,44",4,2022
CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning,34,neurips,47,29,2023-06-16 22:59:02.913000,https://github.com/salesforce/coderl,376,Coderl: Mastering code generation through pretrained models and deep reinforcement learning,"https://scholar.google.com/scholar?cluster=16132461608551265231&hl=en&as_sdt=0,5",18,2022
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization,28,neurips,21,1,2023-06-16 22:59:03.124000,https://github.com/wenhao-gao/mol_opt,103,Sample efficiency matters: a benchmark for practical molecular optimization,"https://scholar.google.com/scholar?cluster=5930505572386998572&hl=en&as_sdt=0,47",7,2022
MGNNI: Multiscale Graph Neural Networks with Implicit Layers,3,neurips,0,0,2023-06-16 22:59:03.336000,https://github.com/liu-jc/mgnni,6,MGNNI: Multiscale Graph Neural Networks with Implicit Layers,"https://scholar.google.com/scholar?cluster=16464433978431539899&hl=en&as_sdt=0,5",2,2022
UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs,1,neurips,1,0,2023-06-16 22:59:03.548000,https://github.com/ronmckay/uqgan,7,UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs,"https://scholar.google.com/scholar?cluster=13580912183352857731&hl=en&as_sdt=0,33",1,2022
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively,4,neurips,0,1,2023-06-16 22:59:03.759000,https://github.com/zhanghaojie077/dps,8,Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively,"https://scholar.google.com/scholar?cluster=204679375623303358&hl=en&as_sdt=0,5",2,2022
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP,13,neurips,0,0,2023-06-16 22:59:03.971000,https://github.com/mlfoundations/clip_quality_not_quantity,14,Quality not quantity: On the interaction between dataset design and robustness of clip,"https://scholar.google.com/scholar?cluster=1636514590207209786&hl=en&as_sdt=0,31",4,2022
PaCo: Parameter-Compositional Multi-task Reinforcement Learning,2,neurips,1,0,2023-06-16 22:59:04.182000,https://github.com/ttotmoon/paco-mtrl,11,PaCo: Parameter-Compositional Multi-Task Reinforcement Learning,"https://scholar.google.com/scholar?cluster=9186813213951917156&hl=en&as_sdt=0,5",5,2022
A Contrastive Framework for Neural Text Generation,31,neurips,36,7,2023-06-16 22:59:04.394000,https://github.com/yxuansu/simctg,407,A contrastive framework for neural text generation,"https://scholar.google.com/scholar?cluster=6130101757033194122&hl=en&as_sdt=0,33",9,2022
Exploring the Latent Space of Autoencoders with Interventional Assays,0,neurips,1,0,2023-06-16 22:59:04.606000,https://github.com/felixludos/latent-responses,4,Exploring the Latent Space of Autoencoders with Interventional Assays,"https://scholar.google.com/scholar?cluster=11218726005566161204&hl=en&as_sdt=0,5",2,2022
Fair Wrapping for Black-box Predictions,0,neurips,0,0,2023-06-16 22:59:04.818000,https://github.com/alexandersoen/alpha-tree-fair-wrappers,0,Fair Wrapping for Black-box Predictions,"https://scholar.google.com/scholar?cluster=17408270699563829594&hl=en&as_sdt=0,47",2,2022
Meta-Learning Dynamics Forecasting Using Task Inference,12,neurips,3,0,2023-06-16 22:59:05.030000,https://github.com/rose-stl-lab/dynamic-adaptation-network,19,Meta-learning dynamics forecasting using task inference,"https://scholar.google.com/scholar?cluster=1635699152041148916&hl=en&as_sdt=0,33",3,2022
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement,7,neurips,0,0,2023-06-16 22:59:05.260000,https://github.com/palm-ml/smile,7,One positive label is sufficient: Single-positive multi-label learning with label enhancement,"https://scholar.google.com/scholar?cluster=17678484826346617889&hl=en&as_sdt=0,5",1,2022
"This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish",3,neurips,1,2,2023-06-16 22:59:05.476000,https://github.com/clarin-pl/lepiszcze,10,"This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish","https://scholar.google.com/scholar?cluster=4248229270344393819&hl=en&as_sdt=0,33",5,2022
Insights into Pre-training via Simpler Synthetic Tasks,6,neurips,3,3,2023-06-16 22:59:05.687000,https://github.com/felixzli/synthetic_pretraining,35,Insights into pre-training via simpler synthetic tasks,"https://scholar.google.com/scholar?cluster=16551759409379033165&hl=en&as_sdt=0,1",3,2022
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities,12,neurips,0,0,2023-06-16 22:59:05.899000,https://github.com/eduardgorbunov/potentials_and_last_iter_convergence_for_vips,1,Last-iterate convergence of optimistic gradient method for monotone variational inequalities,"https://scholar.google.com/scholar?cluster=15310707348220215972&hl=en&as_sdt=0,5",3,2022
3DILG: Irregular Latent Grids for 3D Generative Modeling,12,neurips,3,7,2023-06-16 22:59:06.110000,https://github.com/1zb/3DILG,73,3DILG: Irregular latent grids for 3d generative modeling,"https://scholar.google.com/scholar?cluster=9112340556841265802&hl=en&as_sdt=0,10",6,2022
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets,2,neurips,4,1,2023-06-16 22:59:06.322000,https://github.com/ylab-open/mets-cov,29,METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets,"https://scholar.google.com/scholar?cluster=14166404945235521589&hl=en&as_sdt=0,33",1,2022
Continual Learning In Environments With Polynomial Mixing Times,3,neurips,0,0,2023-06-16 22:59:06.534000,https://github.com/sharathraparthy/polynomial-mixing-times,1,Continual learning in environments with polynomial mixing times,"https://scholar.google.com/scholar?cluster=148193105914487593&hl=en&as_sdt=0,20",2,2022
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts,6,neurips,1,1,2023-06-16 22:59:06.745000,https://github.com/spcl/ens10,12,ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast,"https://scholar.google.com/scholar?cluster=1680500847408838511&hl=en&as_sdt=0,14",7,2022
Unsupervised Cross-Task Generalization via Retrieval Augmentation,16,neurips,1,1,2023-06-16 22:59:06.961000,https://github.com/INK-USC/ReCross,20,Unsupervised cross-task generalization via retrieval augmentation,"https://scholar.google.com/scholar?cluster=17714217089004895750&hl=en&as_sdt=0,19",2,2022
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach,0,neurips,0,0,2023-06-16 22:59:07.172000,https://github.com/kai-wen-yang/lpa3,5,Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach,"https://scholar.google.com/scholar?cluster=13625284013490795521&hl=en&as_sdt=0,44",1,2022
Coordinate Linear Variance Reduction for Generalized Linear Programming,5,neurips,0,0,2023-06-16 22:59:07.384000,https://github.com/ericlincc/efficient-glp,0,Coordinate linear variance reduction for generalized linear programming,"https://scholar.google.com/scholar?cluster=4608782560643046588&hl=en&as_sdt=0,23",1,2022
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models,3,neurips,15,2,2023-06-16 22:59:07.598000,https://github.com/ckczzj/pdae,184,Unsupervised representation learning from pre-trained diffusion probabilistic models,"https://scholar.google.com/scholar?cluster=10369587863928600247&hl=en&as_sdt=0,5",11,2022
To update or not to update? Neurons at equilibrium in deep models,1,neurips,2,0,2023-06-16 22:59:07.810000,https://github.com/eidoslab/neq,1,To update or not to update? Neurons at equilibrium in deep models,"https://scholar.google.com/scholar?cluster=16721968109836533918&hl=en&as_sdt=0,10",2,2022
Large Language Models are Zero-Shot Reasoners,361,neurips,38,3,2023-06-16 22:59:08.029000,https://github.com/kojima-takeshi188/zero_shot_cot,218,Large language models are zero-shot reasoners,"https://scholar.google.com/scholar?cluster=3629340874362196998&hl=en&as_sdt=0,5",2,2022
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation,5,neurips,3,0,2023-06-16 22:59:08.252000,https://github.com/prs-eth/film-ensemble,20,Film-ensemble: Probabilistic deep learning via feature-wise linear modulation,"https://scholar.google.com/scholar?cluster=13764162934319607563&hl=en&as_sdt=0,31",5,2022
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts,6,neurips,1,0,2023-06-16 22:59:08.464000,https://github.com/n3il666/meta-dmoe,18,Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts,"https://scholar.google.com/scholar?cluster=18362067030660551332&hl=en&as_sdt=0,11",2,2022
Revisiting Neural Scaling Laws in Language and Vision,13,neurips,7321,1026,2023-06-16 22:59:08.675000,https://github.com/google-research/google-research,29788,Revisiting neural scaling laws in language and vision,"https://scholar.google.com/scholar?cluster=13068882041594031695&hl=en&as_sdt=0,5",727,2022
Long Range Graph Benchmark,22,neurips,10,4,2023-06-16 22:59:08.887000,https://github.com/vijaydwivedi75/lrgb,91,Long range graph benchmark,"https://scholar.google.com/scholar?cluster=15245934587823122580&hl=en&as_sdt=0,48",2,2022
Active Learning Through a Covering Lens,7,neurips,4,0,2023-06-16 22:59:09.098000,https://github.com/avihu111/typiclust,44,Active learning through a covering lens,"https://scholar.google.com/scholar?cluster=6727917146532281789&hl=en&as_sdt=0,44",4,2022
Training Uncertainty-Aware Classifiers with Conformalized Deep Learning,7,neurips,3,0,2023-06-16 22:59:09.310000,https://github.com/bat-sheva/conformal-learning,12,Training Uncertainty-Aware Classifiers with Conformalized Deep Learning,"https://scholar.google.com/scholar?cluster=9463717142610823747&hl=en&as_sdt=0,9",1,2022
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine,10,neurips,72,44,2023-06-16 22:59:09.523000,https://github.com/sail-sg/envpool,858,Envpool: A highly parallel reinforcement learning environment execution engine,"https://scholar.google.com/scholar?cluster=16477244974274952547&hl=en&as_sdt=0,33",20,2022
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models,4,neurips,5,0,2023-06-16 22:59:09.736000,https://github.com/chenwu98/generative-visual-prompt,108,Generative visual prompt: Unifying distributional control of pre-trained generative models,"https://scholar.google.com/scholar?cluster=818769065864571776&hl=en&as_sdt=0,37",1,2022
FNeVR: Neural Volume Rendering for Face Animation,5,neurips,2,2,2023-06-16 22:59:09.947000,https://github.com/zengbohan0217/FNeVR,24,FNeVR: Neural Volume Rendering for Face Animation,"https://scholar.google.com/scholar?cluster=15199852463833222528&hl=en&as_sdt=0,5",2,2022
Domain Adaptation under Open Set Label Shift,8,neurips,2,1,2023-06-16 22:59:10.158000,https://github.com/acmi-lab/open-set-label-shift,22,Domain adaptation under open set label shift,"https://scholar.google.com/scholar?cluster=16553393786888596205&hl=en&as_sdt=0,5",2,2022
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning,5,neurips,0,1,2023-06-16 22:59:10.371000,https://github.com/umd-huang-lab/wocar-rl,10,Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning,"https://scholar.google.com/scholar?cluster=12094552498707389158&hl=en&as_sdt=0,48",3,2022
Stochastic Multiple Target Sampling Gradient Descent,4,neurips,0,0,2023-06-16 22:59:10.583000,https://github.com/VietHoang1512/MT-SGD,10,Stochastic Multiple Target Sampling Gradient Descent,"https://scholar.google.com/scholar?cluster=10047163033454446473&hl=en&as_sdt=0,43",1,2022
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment,2,neurips,0,1,2023-06-16 22:59:10.794000,https://github.com/chr26195/caseq,17,Towards out-of-distribution sequential event prediction: A causal treatment,"https://scholar.google.com/scholar?cluster=17121151690728293112&hl=en&as_sdt=0,44",1,2022
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?,2,neurips,0,0,2023-06-16 22:59:11.006000,https://github.com/yimengmin/geometricscatteringmaximalclique,3,Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem?,"https://scholar.google.com/scholar?cluster=7138348032927670715&hl=en&as_sdt=0,5",2,2022
Physically-Based Face Rendering for NIR-VIS Face Recognition,1,neurips,4432,910,2023-06-16 22:59:11.218000,https://github.com/deepinsight/insightface,16032,Physically-Based Face Rendering for NIR-VIS Face Recognition,"https://scholar.google.com/scholar?cluster=6409917825922546177&hl=en&as_sdt=0,43",479,2022
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data,3,neurips,2,0,2023-06-16 22:59:11.439000,https://github.com/val-iisc/saddle-longtail,11,Escaping saddle points for effective generalization on class-imbalanced data,"https://scholar.google.com/scholar?cluster=12550749956843640624&hl=en&as_sdt=0,5",13,2022
A2: Efficient Automated Attacker for Boosting Adversarial Training,4,neurips,1,0,2023-06-16 22:59:11.650000,https://github.com/alipay/A2-efficient-automated-attacker-for-boosting-adversarial-training,4,A2: Efficient automated attacker for boosting adversarial training,"https://scholar.google.com/scholar?cluster=13326470772747253603&hl=en&as_sdt=0,5",2,2022
"Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising",23,neurips,20,12,2023-06-16 22:59:11.862000,https://github.com/NVlabs/nvdiffrecmc,249,"Shape, light & material decomposition from images using monte carlo rendering and denoising","https://scholar.google.com/scholar?cluster=16786831417304918950&hl=en&as_sdt=0,5",9,2022
Reconstructing Training Data From Trained Neural Networks,21,neurips,13,0,2023-06-16 22:59:12.074000,https://github.com/nivha/dataset_reconstruction,33,Reconstructing training data from trained neural networks,"https://scholar.google.com/scholar?cluster=4430126406980448960&hl=en&as_sdt=0,43",5,2022
Behavior Transformers: Cloning $k$ modes with one stone,28,neurips,10,1,2023-06-16 22:59:12.285000,https://github.com/notmahi/bet,59,Behavior Transformers: Cloning modes with one stone,"https://scholar.google.com/scholar?cluster=6874272481284678006&hl=en&as_sdt=0,5",7,2022
"Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement",6,neurips,158,4,2023-06-16 22:59:12.497000,https://github.com/paddlepaddle/paddlespatial,278,"Generative time series forecasting with diffusion, denoise, and disentanglement","https://scholar.google.com/scholar?cluster=10694050975663316103&hl=en&as_sdt=0,5",10,2022
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples,14,neurips,4,0,2023-06-16 22:59:12.708000,https://github.com/pralab/IndicatorsOfAttackFailure,16,Indicators of attack failure: Debugging and improving optimization of adversarial examples,"https://scholar.google.com/scholar?cluster=6397860680603996993&hl=en&as_sdt=0,40",4,2022
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules,3,neurips,0,0,2023-06-16 22:59:12.920000,https://github.com/helena-yuhan-liu/biolhessrnn,2,Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules,"https://scholar.google.com/scholar?cluster=6396873608730348265&hl=en&as_sdt=0,11",1,2022
VITA: Video Instance Segmentation via Object Token Association,21,neurips,11,4,2023-06-16 22:59:13.131000,https://github.com/sukjunhwang/vita,79,Vita: Video instance segmentation via object token association,"https://scholar.google.com/scholar?cluster=14992032927196950732&hl=en&as_sdt=0,47",6,2022
Truncated proposals for scalable and hassle-free simulation-based inference,7,neurips,2,0,2023-06-16 22:59:13.343000,https://github.com/mackelab/tsnpe_neurips,2,Truncated proposals for scalable and hassle-free simulation-based inference,"https://scholar.google.com/scholar?cluster=16561248332012832367&hl=en&as_sdt=0,23",2,2022
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies,95,neurips,84,17,2023-06-16 22:59:13.556000,https://github.com/guochengqian/pointnext,534,Pointnext: Revisiting pointnet++ with improved training and scaling strategies,"https://scholar.google.com/scholar?cluster=14072888861532659606&hl=en&as_sdt=0,19",12,2022
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions,3,neurips,0,0,2023-06-16 22:59:13.767000,https://github.com/yellowshippo/penn-neurips2022,19,Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions,"https://scholar.google.com/scholar?cluster=17090530239776984300&hl=en&as_sdt=0,5",2,2022
Mismatched No More: Joint Model-Policy Optimization for Model-Based RL,11,neurips,1,0,2023-06-16 22:59:13.979000,https://github.com/ben-eysenbach/mnm,18,Mismatched no more: Joint model-policy optimization for model-based rl,"https://scholar.google.com/scholar?cluster=5999896080884397819&hl=en&as_sdt=0,23",2,2022
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks,13,neurips,0,0,2023-06-16 22:59:14.191000,https://github.com/rodsveiga/phdiag_sgd,3,Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks,"https://scholar.google.com/scholar?cluster=5970904952393293482&hl=en&as_sdt=0,10",2,2022
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency,3,neurips,2,0,2023-06-16 22:59:14.403000,https://github.com/virajprabhu/pacmac,18,Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency,"https://scholar.google.com/scholar?cluster=13259793333316816742&hl=en&as_sdt=0,22",3,2022
Sample-Then-Optimize Batch Neural Thompson Sampling,2,neurips,0,0,2023-06-16 22:59:14.614000,https://github.com/daizhongxiang/sto-bnts,5,Sample-then-optimize batch neural thompson sampling,"https://scholar.google.com/scholar?cluster=866282396542393930&hl=en&as_sdt=0,3",1,2022
Efficient and Stable Fully Dynamic Facility Location,1,neurips,7321,1026,2023-06-16 22:59:14.825000,https://github.com/google-research/google-research,29788,Efficient and Stable Fully Dynamic Facility Location,"https://scholar.google.com/scholar?cluster=12708856198271717764&hl=en&as_sdt=0,5",727,2022
Sharpness-Aware Training for Free,29,neurips,1,0,2023-06-16 22:59:15.036000,https://github.com/angusdujw/saf,9,Sharpness-aware training for free,"https://scholar.google.com/scholar?cluster=5747357425500146304&hl=en&as_sdt=0,5",2,2022
Inception Transformer,111,neurips,16,7,2023-06-16 22:59:15.248000,https://github.com/sail-sg/iformer,192,Inception transformer,"https://scholar.google.com/scholar?cluster=610621467807251926&hl=en&as_sdt=0,44",16,2022
Mesoscopic modeling of hidden spiking neurons,2,neurips,1,0,2023-06-16 22:59:15.470000,https://github.com/epfl-lcn/neulvm,0,Mesoscopic modeling of hidden spiking neurons,"https://scholar.google.com/scholar?cluster=7842440954111495341&hl=en&as_sdt=0,5",0,2022
SageMix: Saliency-Guided Mixup for Point Clouds,6,neurips,2,2,2023-06-16 22:59:15.681000,https://github.com/mlvlab/SageMix,19,Sagemix: Saliency-guided mixup for point clouds,"https://scholar.google.com/scholar?cluster=1906739869004818181&hl=en&as_sdt=0,14",5,2022
Denoising Diffusion Restoration Models,136,neurips,38,15,2023-06-16 22:59:15.892000,https://github.com/bahjat-kawar/ddrm,375,Denoising diffusion restoration models,"https://scholar.google.com/scholar?cluster=9684379988322593312&hl=en&as_sdt=0,3",6,2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks,0,neurips,3,0,2023-06-16 22:59:16.104000,https://github.com/RoyalSkye/ATCL,12,Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks,"https://scholar.google.com/scholar?cluster=7990357189849554296&hl=en&as_sdt=0,5",2,2022
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis,10,neurips,133,24,2023-06-16 22:59:16.315000,https://github.com/microsoft/NeuralSpeech,1007,Binauralgrad: A two-stage conditional diffusion probabilistic model for binaural audio synthesis,"https://scholar.google.com/scholar?cluster=3061602532633994428&hl=en&as_sdt=0,36",30,2022
"ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild",0,neurips,2,1,2023-06-16 22:59:16.528000,https://github.com/tudelft-spc-lab/conflab,0,"ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild","https://scholar.google.com/scholar?cluster=10626615625989793283&hl=en&as_sdt=0,43",2,2022
Predictive Querying for Autoregressive Neural Sequence Models,2,neurips,2,0,2023-06-16 22:59:16.739000,https://github.com/ajboyd2/prob_seq_queries,0,Predictive querying for autoregressive neural sequence models,"https://scholar.google.com/scholar?cluster=9455015108688236225&hl=en&as_sdt=0,26",1,2022
Learning State-Aware Visual Representations from Audible Interactions,5,neurips,2,5,2023-06-16 22:59:16.951000,https://github.com/HimangiM/RepLAI,8,Learning state-aware visual representations from audible interactions,"https://scholar.google.com/scholar?cluster=10557769016177465822&hl=en&as_sdt=0,15",1,2022
DISCO: Adversarial Defense with Local Implicit Functions,4,neurips,1,2,2023-06-16 22:59:17.162000,https://github.com/chihhuiho/disco,5,DISCO: Adversarial Defense with Local Implicit Functions,"https://scholar.google.com/scholar?cluster=14390816602060782578&hl=en&as_sdt=0,43",1,2022
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing,14,neurips,2,0,2023-06-16 22:59:17.374000,https://github.com/yangrui2015/rorl,9,Rorl: Robust offline reinforcement learning via conservative smoothing,"https://scholar.google.com/scholar?cluster=12160465194138286098&hl=en&as_sdt=0,5",2,2022
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces,3,neurips,0,0,2023-06-16 22:59:17.586000,https://github.com/ha0ransun/lbp_scale,6,Optimal scaling for locally balanced proposals in discrete spaces,"https://scholar.google.com/scholar?cluster=9220497344062023085&hl=en&as_sdt=0,31",1,2022
Zero-Shot 3D Drug Design by Sketching and Generating,2,neurips,8,3,2023-06-16 22:59:17.799000,https://github.com/longlongman/DESERT,17,Zero-Shot 3D Drug Design by Sketching and Generating,"https://scholar.google.com/scholar?cluster=17297896301377574979&hl=en&as_sdt=0,33",2,2022
Optimal Comparator Adaptive Online Learning with Switching Cost,0,neurips,0,0,2023-06-16 22:59:18.010000,https://github.com/zhiyuzz/neurips2022-adaptive-switching,0,Optimal Comparator Adaptive Online Learning with Switching Cost,"https://scholar.google.com/scholar?cluster=14092705801881163803&hl=en&as_sdt=0,5",1,2022
Neur2SP: Neural Two-Stage Stochastic Programming,7,neurips,3,0,2023-06-16 22:59:18.226000,https://github.com/khalil-research/neur2sp,14,Neur2sp: Neural two-stage stochastic programming,"https://scholar.google.com/scholar?cluster=297850610846238239&hl=en&as_sdt=0,20",2,2022
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization,1,neurips,0,0,2023-06-16 22:59:18.455000,https://github.com/puetpaper/PUExtraTrees,9,Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization,"https://scholar.google.com/scholar?cluster=749148422214785418&hl=en&as_sdt=0,26",1,2022
A Fast Post-Training Pruning Framework for Transformers,11,neurips,13,6,2023-06-16 22:59:18.667000,https://github.com/WoosukKwon/retraining-free-pruning,98,A Fast Post-Training Pruning Framework for Transformers,"https://scholar.google.com/scholar?cluster=8295752471626103240&hl=en&as_sdt=0,33",5,2022
"Interventions, Where and How? Experimental Design for Causal Models at Scale",9,neurips,4,0,2023-06-16 22:59:18.881000,https://github.com/yannadani/cbed,15,"Interventions, where and how? experimental design for causal models at scale","https://scholar.google.com/scholar?cluster=2079194149700665764&hl=en&as_sdt=0,5",1,2022
Single-phase deep learning in cortico-cortical networks,8,neurips,0,0,2023-06-16 22:59:19.092000,https://github.com/neuralml/burstccn,6,Single-phase deep learning in cortico-cortical networks,"https://scholar.google.com/scholar?cluster=17225201709003399719&hl=en&as_sdt=0,5",1,2022
Domain Generalization by Learning and Removing Domain-specific Features,1,neurips,0,0,2023-06-16 22:59:19.310000,https://github.com/yulearningg/LRDG,8,Domain Generalization by Learning and Removing Domain-specific Features,"https://scholar.google.com/scholar?cluster=5494103796376605602&hl=en&as_sdt=0,10",1,2022
Torsional Diffusion for Molecular Conformer Generation,64,neurips,31,6,2023-06-16 22:59:19.524000,https://github.com/gcorso/torsional-diffusion,164,Torsional diffusion for molecular conformer generation,"https://scholar.google.com/scholar?cluster=1524640103154353919&hl=en&as_sdt=0,5",3,2022
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators,3,neurips,0,0,2023-06-16 22:59:19.735000,https://github.com/wenkaixl/agrasst,0,AgraSSt: Approximate graph Stein statistics for interpretable assessment of implicit graph generators,"https://scholar.google.com/scholar?cluster=8628149654729531365&hl=en&as_sdt=0,39",1,2022
On the Limitations of Stochastic Pre-processing Defenses,5,neurips,1,0,2023-06-16 22:59:19.947000,https://github.com/wi-pi/stochastic-preprocessing-defenses,0,On the Limitations of Stochastic Pre-processing Defenses,"https://scholar.google.com/scholar?cluster=7806519586437026308&hl=en&as_sdt=0,33",1,2022
Proximal Point Imitation Learning,3,neurips,1,0,2023-06-16 22:59:20.159000,https://github.com/lviano/p2il,3,Proximal Point Imitation Learning,"https://scholar.google.com/scholar?cluster=17949003719943015717&hl=en&as_sdt=0,5",1,2022
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation,1,neurips,1,0,2023-06-16 22:59:20.371000,https://github.com/zkzhang98/microseg,8,Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation,"https://scholar.google.com/scholar?cluster=10459431178117202282&hl=en&as_sdt=0,3",1,2022
Smoothed Embeddings for Certified Few-Shot Learning,0,neurips,0,0,2023-06-16 22:59:20.582000,https://github.com/koava36/certrob-fewshot,2,Smoothed Embeddings for Certified Few-Shot Learning,"https://scholar.google.com/scholar?cluster=5547919878197628339&hl=en&as_sdt=0,31",0,2022
Group Meritocratic Fairness in Linear Contextual Bandits,0,neurips,0,0,2023-06-16 22:59:20.793000,https://github.com/csml-iit-ucl/gmfbandits,1,Group Meritocratic Fairness in Linear Contextual Bandits,"https://scholar.google.com/scholar?cluster=9571107907427385262&hl=en&as_sdt=0,5",4,2022
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm,4,neurips,1,0,2023-06-16 22:59:21.005000,https://github.com/akjayant/mbppol,12,Model-based safe deep reinforcement learning via a constrained proximal policy optimization algorithm,"https://scholar.google.com/scholar?cluster=7177631673389924386&hl=en&as_sdt=0,5",2,2022
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects,1,neurips,0,0,2023-06-16 22:59:21.221000,https://github.com/vothanhvinh/causalrff,0,An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects,"https://scholar.google.com/scholar?cluster=17335848302268873481&hl=en&as_sdt=0,25",2,2022
Towards Improving Faithfulness in Abstractive Summarization,4,neurips,0,0,2023-06-16 22:59:21.452000,https://github.com/iriscxy/fes,9,Towards Improving Faithfulness in Abstractive Summarization,"https://scholar.google.com/scholar?cluster=9202173853245340528&hl=en&as_sdt=0,5",2,2022
ZIN: When and How to Learn Invariance Without Environment Partition?,7,neurips,2,1,2023-06-16 22:59:21.663000,https://github.com/linyongver/zin_official,12,ZIN: When and How to Learn Invariance Without Environment Partition?,"https://scholar.google.com/scholar?cluster=16781280623432832625&hl=en&as_sdt=0,5",5,2022
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation,4,neurips,2,0,2023-06-16 22:59:21.875000,https://github.com/jlko/active-surrogate-estimators,5,Active surrogate estimators: An active learning approach to label-efficient model evaluation,"https://scholar.google.com/scholar?cluster=12181705407954202218&hl=en&as_sdt=0,36",1,2022
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions,1,neurips,2,2,2023-06-16 22:59:22.087000,https://github.com/lchen001/hapi,16,HAPI: A large-scale longitudinal dataset of commercial ML API predictions,"https://scholar.google.com/scholar?cluster=5762229029469931969&hl=en&as_sdt=0,5",2,2022
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos,69,neurips,105,6,2023-06-16 22:59:22.299000,https://github.com/openai/Video-Pre-Training,944,Video pretraining (vpt): Learning to act by watching unlabeled online videos,"https://scholar.google.com/scholar?cluster=17704984102832894583&hl=en&as_sdt=0,43",27,2022
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization,1,neurips,21,2,2023-06-16 22:59:22.512000,https://github.com/uw-exp/globem,110,GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization,"https://scholar.google.com/scholar?cluster=8900774154166669565&hl=en&as_sdt=0,15",11,2022
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost,0,neurips,0,0,2023-06-16 22:59:22.724000,https://github.com/sc782/sbm-transformer,10,Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost,"https://scholar.google.com/scholar?cluster=8950920198279158483&hl=en&as_sdt=0,41",1,2022
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning,17,neurips,10,3,2023-06-16 22:59:22.935000,https://github.com/polixir/NeoRL,76,NeoRL: A near real-world benchmark for offline reinforcement learning,"https://scholar.google.com/scholar?cluster=4124559435421105174&hl=en&as_sdt=0,34",4,2022
Counterfactual Temporal Point Processes,7,neurips,3,0,2023-06-16 22:59:23.146000,https://github.com/networks-learning/counterfactual-ttp,11,Counterfactual temporal point processes,"https://scholar.google.com/scholar?cluster=5471926667923328181&hl=en&as_sdt=0,14",3,2022
Dungeons and Data: A Large-Scale NetHack Dataset,1,neurips,102,16,2023-06-16 22:59:23.358000,https://github.com/facebookresearch/nle,871,Dungeons and Data: A Large-Scale NetHack Dataset,"https://scholar.google.com/scholar?cluster=10376659435054658161&hl=en&as_sdt=0,5",29,2022
GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions,7,neurips,10,0,2023-06-16 22:59:23.569000,https://github.com/princeton-computational-imaging/gensdf,87,GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions,"https://scholar.google.com/scholar?cluster=11531522694580627214&hl=en&as_sdt=0,41",7,2022
Forecasting Human Trajectory from Scene History,3,neurips,3,3,2023-06-16 22:59:23.780000,https://github.com/makaruinah/shenet,11,Forecasting human trajectory from scene history,"https://scholar.google.com/scholar?cluster=5059609174660170314&hl=en&as_sdt=0,47",1,2022
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure,7,neurips,11,0,2023-06-16 22:59:23.992000,https://github.com/googlebaba/disc,26,Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure,"https://scholar.google.com/scholar?cluster=8726960753760538986&hl=en&as_sdt=0,5",3,2022
Asymptotics of $\ell_2$ Regularized Network Embeddings,0,neurips,0,0,2023-06-16 22:59:24.204000,https://github.com/aday651/embed-reg,0,Asymptotics of Regularized Network Embeddings,"https://scholar.google.com/scholar?cluster=10375708066059724613&hl=en&as_sdt=0,5",1,2022
On Embeddings for Numerical Features in Tabular Deep Learning,19,neurips,18,1,2023-06-16 22:59:24.416000,https://github.com/Yura52/tabular-dl-num-embeddings,170,On embeddings for numerical features in tabular deep learning,"https://scholar.google.com/scholar?cluster=2553810460800723920&hl=en&as_sdt=0,1",4,2022
Visual Prompting via Image Inpainting,29,neurips,12,4,2023-06-16 22:59:24.628000,https://github.com/amirbar/visual_prompting,197,Visual prompting via image inpainting,"https://scholar.google.com/scholar?cluster=15899337886963537746&hl=en&as_sdt=0,5",12,2022
OpenAUC: Towards AUC-Oriented Open-Set Recognition,1,neurips,0,0,2023-06-16 22:59:24.839000,https://github.com/wang22ti/openauc,5,OpenAUC: Towards AUC-Oriented Open-Set Recognition,"https://scholar.google.com/scholar?cluster=17140867226806315612&hl=en&as_sdt=0,5",2,2022
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT,0,neurips,0,0,2023-06-16 22:59:25.051000,https://github.com/cakcora/PersistentHomologyWithCoralPrunit,3,Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT,"https://scholar.google.com/scholar?cluster=7224655115635333850&hl=en&as_sdt=0,10",2,2022
GAUDI: A Neural Architect for Immersive 3D Scene Generation,28,neurips,24,0,2023-06-16 22:59:25.264000,https://github.com/apple/ml-gaudi,577,Gaudi: A neural architect for immersive 3d scene generation,"https://scholar.google.com/scholar?cluster=14944404431434808615&hl=en&as_sdt=0,13",36,2022
Mask-based Latent Reconstruction for Reinforcement Learning,5,neurips,2,0,2023-06-16 22:59:25.476000,https://github.com/microsoft/Mask-based-Latent-Reconstruction,21,Mask-based latent reconstruction for reinforcement learning,"https://scholar.google.com/scholar?cluster=11030675521552103190&hl=en&as_sdt=0,5",5,2022
Product Ranking for Revenue Maximization with Multiple Purchases,1,neurips,0,0,2023-06-16 22:59:25.688000,https://github.com/windxrz/mpb-ucb,3,Product Ranking for Revenue Maximization with Multiple Purchases,"https://scholar.google.com/scholar?cluster=5497221065518652797&hl=en&as_sdt=0,33",1,2022
One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations,7,neurips,0,1,2023-06-16 22:59:25.899000,https://github.com/kumapowerliu/ffclip,36,One model to edit them all: Free-form text-driven image manipulation with semantic modulations,"https://scholar.google.com/scholar?cluster=9106501574546184017&hl=en&as_sdt=0,47",6,2022
LieGG: Studying Learned Lie Group Generators,5,neurips,0,0,2023-06-16 22:59:26.110000,https://github.com/amoskalev/liegg,3,LieGG: Studying Learned Lie Group Generators,"https://scholar.google.com/scholar?cluster=6458900076329173639&hl=en&as_sdt=0,5",1,2022
FourierNets enable the design of highly non-local optical encoders for computational imaging,2,neurips,2,0,2023-06-16 22:59:26.323000,https://github.com/turagalab/snapshotscope,3,FourierNets enable the design of highly non-local optical encoders for computational imaging,"https://scholar.google.com/scholar?cluster=17235458650551264923&hl=en&as_sdt=0,47",3,2022
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks,1,neurips,2,0,2023-06-16 22:59:26.534000,https://github.com/dchiji-ntt/meta-ticket,4,Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks,"https://scholar.google.com/scholar?cluster=355485473057301987&hl=en&as_sdt=0,5",1,2022
LAION-5B: An open large-scale dataset for training next generation image-text models,310,neurips,548,89,2023-06-16 22:59:26.746000,https://github.com/mlfoundations/open_clip,5243,Laion-5b: An open large-scale dataset for training next generation image-text models,"https://scholar.google.com/scholar?cluster=8018158103125985189&hl=en&as_sdt=0,36",59,2022
Constants of motion network,2,neurips,0,0,2023-06-16 22:59:26.957000,https://github.com/machine-discovery/comet,2,Constants of motion network,"https://scholar.google.com/scholar?cluster=10578402621842665146&hl=en&as_sdt=0,33",3,2022
Online Deep Equilibrium Learning for Regularization by Denoising,6,neurips,8,2,2023-06-16 22:59:27.168000,https://github.com/phernst/pytorch_radon,22,Online deep equilibrium learning for regularization by denoising,"https://scholar.google.com/scholar?cluster=12374699513175757258&hl=en&as_sdt=0,6",2,2022
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting,12,neurips,41,2,2023-06-16 22:59:27.380000,https://github.com/amazon-science/earth-forecasting-transformer,212,Earthformer: Exploring space-time transformers for earth system forecasting,"https://scholar.google.com/scholar?cluster=6165560125598001271&hl=en&as_sdt=0,5",11,2022
"Benchopt: Reproducible, efficient and collaborative optimization benchmarks",6,neurips,35,85,2023-06-16 22:59:27.591000,https://github.com/benchopt/benchopt,158,"Benchopt: Reproducible, efficient and collaborative optimization benchmarks","https://scholar.google.com/scholar?cluster=3504541958783431314&hl=en&as_sdt=0,33",6,2022
SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems,1,neurips,1,0,2023-06-16 22:59:27.803000,https://github.com/sb-ai-lab/sketchboost-paper,9,SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems,"https://scholar.google.com/scholar?cluster=12204750564848511287&hl=en&as_sdt=0,5",2,2022
Decentralized Training of Foundation Models in Heterogeneous Environments,10,neurips,10,3,2023-06-16 22:59:28.016000,https://github.com/DS3Lab/DT-FM,59,Decentralized training of foundation models in heterogeneous environments,"https://scholar.google.com/scholar?cluster=13763983237898796416&hl=en&as_sdt=0,3",1,2022
Cross Aggregation Transformer for Image Restoration,11,neurips,5,2,2023-06-16 22:59:28.230000,https://github.com/zhengchen1999/cat,77,Cross Aggregation Transformer for Image Restoration,"https://scholar.google.com/scholar?cluster=17495936545828523011&hl=en&as_sdt=0,43",3,2022
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems,5,neurips,3,0,2023-06-16 22:59:28.452000,https://github.com/dimesteam/dimes,24,DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems,"https://scholar.google.com/scholar?cluster=7607751078671404883&hl=en&as_sdt=0,47",3,2022
NSNet: A General Neural Probabilistic Framework for Satisfiability Problems,0,neurips,3,0,2023-06-16 22:59:28.664000,https://github.com/zhaoyu-li/nsnet,13,NSNet: A General Neural Probabilistic Framework for Satisfiability Problems,"https://scholar.google.com/scholar?cluster=1383198639431989116&hl=en&as_sdt=0,5",1,2022
Brain Network Transformer,16,neurips,8,1,2023-06-16 22:59:28.876000,https://github.com/wayfear/brainnetworktransformer,34,Brain network transformer,"https://scholar.google.com/scholar?cluster=10818376030441199053&hl=en&as_sdt=0,31",2,2022
Improved Utility Analysis of Private CountSketch,4,neurips,0,0,2023-06-16 22:59:29.087000,https://github.com/rasmus-pagh/private-countsketch,3,Improved Utility Analysis of Private CountSketch,"https://scholar.google.com/scholar?cluster=9045975206203918002&hl=en&as_sdt=0,14",1,2022
Improving Diffusion Models for Inverse Problems using Manifold Constraints,53,neurips,15,1,2023-06-16 22:59:29.299000,https://github.com/hj-harry/mcg_diffusion,121,Improving diffusion models for inverse problems using manifold constraints,"https://scholar.google.com/scholar?cluster=18097862330271049483&hl=en&as_sdt=0,11",5,2022
Deep Model Reassembly,31,neurips,7,2,2023-06-16 22:59:29.511000,https://github.com/adamdad/dery,176,Deep model reassembly,"https://scholar.google.com/scholar?cluster=17041268371866200453&hl=en&as_sdt=0,43",2,2022
BigBio: A Framework for Data-Centric Biomedical Natural Language Processing,11,neurips,100,187,2023-06-16 22:59:29.723000,https://github.com/bigscience-workshop/biomedical,335,Bigbio: a framework for data-centric biomedical natural language processing,"https://scholar.google.com/scholar?cluster=16248185859280855738&hl=en&as_sdt=0,33",27,2022
Gradient Estimation with Discrete Stein Operators,6,neurips,0,0,2023-06-16 22:59:29.934000,https://github.com/thjashin/rodeo,15,Gradient estimation with discrete Stein operators,"https://scholar.google.com/scholar?cluster=17367160563592360698&hl=en&as_sdt=0,5",3,2022
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems,2,neurips,0,0,2023-06-16 22:59:30.145000,https://github.com/changwoo-lee/rapidmtm,3,Rapidly mixing multiple-try Metropolis algorithms for model selection problems,"https://scholar.google.com/scholar?cluster=8288484673444745873&hl=en&as_sdt=0,38",1,2022
Online Agnostic Multiclass Boosting,0,neurips,0,0,2023-06-16 22:59:30.356000,https://github.com/vinodkraman/onlineagnosticmulticlassboosting,0,Online Agnostic Multiclass Boosting,"https://scholar.google.com/scholar?cluster=17530449480068506498&hl=en&as_sdt=0,31",2,2022
A contrastive rule for meta-learning,14,neurips,0,1,2023-06-16 22:59:30.569000,https://github.com/smonsays/contrastive-meta-learning,9,A contrastive rule for meta-learning,"https://scholar.google.com/scholar?cluster=1536313672687965148&hl=en&as_sdt=0,33",1,2022
Distinguishing Learning Rules with Brain Machine Interfaces,2,neurips,0,0,2023-06-16 22:59:30.780000,https://github.com/jacobfulano/learning-rules-with-bmi,0,Distinguishing learning rules with brain machine interfaces,"https://scholar.google.com/scholar?cluster=11051656974979640667&hl=en&as_sdt=0,5",2,2022
Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex,1,neurips,0,0,2023-06-16 22:59:30.991000,https://github.com/jzf2101/alphatology,3,Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex,"https://scholar.google.com/scholar?cluster=8603391882567020641&hl=en&as_sdt=0,5",1,2022
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization,7,neurips,0,3,2023-06-16 22:59:31.203000,https://github.com/heimingx/semi_seg_proto,22,Semi-supervised semantic segmentation with prototype-based consistency regularization,"https://scholar.google.com/scholar?cluster=2500907054917724227&hl=en&as_sdt=0,5",2,2022
Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms,8,neurips,4,1,2023-06-16 22:59:31.414000,https://github.com/smplbody/hmr-benchmarks,106,Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms,"https://scholar.google.com/scholar?cluster=4376621748772936242&hl=en&as_sdt=0,24",8,2022
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning,10,neurips,0,0,2023-06-16 22:59:31.626000,https://github.com/andreichertkov/ttopt,13,TTOpt: A maximum volume quantized tensor train-based optimization and its application to reinforcement learning,"https://scholar.google.com/scholar?cluster=6175341780524530089&hl=en&as_sdt=0,3",2,2022
A Mixture Of Surprises for Unsupervised Reinforcement Learning,1,neurips,0,1,2023-06-16 22:59:31.838000,https://github.com/leaplabthu/moss,13,A Mixture of Surprises for Unsupervised Reinforcement Learning,"https://scholar.google.com/scholar?cluster=9731296982002152035&hl=en&as_sdt=0,39",2,2022
PeRFception: Perception using Radiance Fields,2,neurips,15,9,2023-06-16 22:59:32.049000,https://github.com/POSTECH-CVLab/PeRFception,301,PeRFception: Perception using Radiance Fields,"https://scholar.google.com/scholar?cluster=13895322647029601648&hl=en&as_sdt=0,5",14,2022
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems,0,neurips,1,0,2023-06-16 22:59:32.261000,https://github.com/ThyrixYang/gdfm_nips22,8,Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems,"https://scholar.google.com/scholar?cluster=10242536886571160185&hl=en&as_sdt=0,10",2,2022
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks,2,neurips,0,0,2023-06-16 22:59:32.472000,https://github.com/mingruiliu-ml-lab/communication-efficient-local-gradient-clipping,0,A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks,"https://scholar.google.com/scholar?cluster=5333604100052232790&hl=en&as_sdt=0,3",0,2022
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models,5,neurips,1,0,2023-06-16 22:59:32.684000,https://github.com/kamildeja/analysing_ddgm,5,On analyzing generative and denoising capabilities of diffusion-based deep generative models,"https://scholar.google.com/scholar?cluster=995225694240773141&hl=en&as_sdt=0,10",1,2022
DiSC: Differential Spectral Clustering of Features,0,neurips,1,0,2023-06-16 22:59:32.896000,https://github.com/Mishne-Lab/DiSC,2,DiSC: Differential Spectral Clustering of Features,"https://scholar.google.com/scholar?cluster=7617996408610291337&hl=en&as_sdt=0,33",2,2022
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes,24,neurips,60,6,2023-06-16 22:59:33.108000,https://github.com/google-research/big_vision,890,Uvim: A unified modeling approach for vision with learned guiding codes,"https://scholar.google.com/scholar?cluster=13016594180316687621&hl=en&as_sdt=0,5",23,2022
Proximal Learning With Opponent-Learning Awareness,1,neurips,3,4,2023-06-16 22:59:33.320000,https://github.com/silent-zebra/pola,4,Proximal Learning With Opponent-Learning Awareness,"https://scholar.google.com/scholar?cluster=6796004730417376000&hl=en&as_sdt=0,5",5,2022
Coresets for Wasserstein Distributionally Robust Optimization Problems,0,neurips,0,0,2023-06-16 22:59:33.532000,https://github.com/h305142/wdro_coreset,2,Coresets for Wasserstein Distributionally Robust Optimization Problems,"https://scholar.google.com/scholar?cluster=15328832581114921682&hl=en&as_sdt=0,5",2,2022
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning,5,neurips,2,8,2023-06-16 22:59:33.744000,https://github.com/linziyi96/st-adapter,20,St-adapter: Parameter-efficient image-to-video transfer learning,"https://scholar.google.com/scholar?cluster=16710270545076573950&hl=en&as_sdt=0,23",6,2022
Can Adversarial Training Be Manipulated By Non-Robust Features?,1,neurips,0,0,2023-06-16 22:59:33.955000,https://github.com/tlmichael/hypocritical-perturbation,2,Can Adversarial Training Be Manipulated By Non-Robust Features?,"https://scholar.google.com/scholar?cluster=7120256042443644794&hl=en&as_sdt=0,3",1,2022
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning,7,neurips,1,0,2023-06-16 22:59:34.166000,https://github.com/gilgameshd/grader,13,Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning,"https://scholar.google.com/scholar?cluster=3294634165353463281&hl=en&as_sdt=0,10",2,2022
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models,6,neurips,0,0,2023-06-16 22:59:34.378000,https://github.com/winogavil/winogavil-experiments,1,WinoGAViL: Gamified association benchmark to challenge vision-and-language models,"https://scholar.google.com/scholar?cluster=2502557314883549286&hl=en&as_sdt=0,26",1,2022
Elucidating the Design Space of Diffusion-Based Generative Models,182,neurips,52,4,2023-06-16 22:59:34.589000,https://github.com/nvlabs/edm,611,Elucidating the design space of diffusion-based generative models,"https://scholar.google.com/scholar?cluster=5258718823597512255&hl=en&as_sdt=0,5",28,2022
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent,2,neurips,0,0,2023-06-16 22:59:34.802000,https://github.com/shoelim/mpgd,1,Chaotic regularization and heavy-tailed limits for deterministic gradient descent,"https://scholar.google.com/scholar?cluster=15394418026673969383&hl=en&as_sdt=0,48",2,2022
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments,0,neurips,2,1,2023-06-16 22:59:35.014000,https://github.com/smpl-env/smpl,12,SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments,"https://scholar.google.com/scholar?cluster=11656776523650390398&hl=en&as_sdt=0,33",3,2022
The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models,8,neurips,3103,884,2023-06-16 22:59:35.235000,https://github.com/microsoft/DeepSpeed,25950,The stability-efficiency dilemma: Investigating sequence length warmup for training GPT models,"https://scholar.google.com/scholar?cluster=2863317000596137587&hl=en&as_sdt=0,44",290,2022
Generalization Gap in Amortized Inference,5,neurips,0,0,2023-06-16 22:59:35.455000,https://github.com/zmtomorrow/generalizationgapinamortizedinference,1,Generalization gap in amortized inference,"https://scholar.google.com/scholar?cluster=8684926098848417995&hl=en&as_sdt=0,1",1,2022
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation,0,neurips,0,0,2023-06-16 22:59:35.667000,https://github.com/rehg-lab/pulseimpute,14,PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation,"https://scholar.google.com/scholar?cluster=16974721368633186321&hl=en&as_sdt=0,5",4,2022
What are the best Systems? New Perspectives on NLP Benchmarking,7,neurips,3,3,2023-06-16 22:59:35.879000,https://github.com/pierrecolombo/rankingnlpsystems,12,What are the best systems? new perspectives on nlp benchmarking,"https://scholar.google.com/scholar?cluster=6399800265216949784&hl=en&as_sdt=0,33",1,2022
Learning from Label Proportions by Learning with Label Noise,4,neurips,0,0,2023-06-16 22:59:36.091000,https://github.com/z-jianxin/llpfc,3,Learning from label proportions by learning with label noise,"https://scholar.google.com/scholar?cluster=5147088171143783724&hl=en&as_sdt=0,5",1,2022
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution,5,neurips,0,0,2023-06-16 22:59:36.302000,https://github.com/aorvieto/decsps,4,Dynamics of sgd with stochastic polyak stepsizes: Truly adaptive variants and convergence to exact solution,"https://scholar.google.com/scholar?cluster=1202208377216276410&hl=en&as_sdt=0,25",1,2022
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs,15,neurips,92,3,2023-06-16 22:59:36.515000,https://github.com/pygod-team/pygod,906,Bond: Benchmarking unsupervised outlier node detection on static attributed graphs,"https://scholar.google.com/scholar?cluster=4649486946947801284&hl=en&as_sdt=0,10",11,2022
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training,45,neurips,17,5,2023-06-16 22:59:36.727000,https://github.com/zrrskywalker/point-m2ae,130,Point-M2AE: multi-scale masked autoencoders for hierarchical point cloud pre-training,"https://scholar.google.com/scholar?cluster=8230127879015912569&hl=en&as_sdt=0,21",11,2022
Exploring Example Influence in Continual Learning,8,neurips,0,0,2023-06-16 22:59:36.938000,https://github.com/sssunqing/example_influence_cl,14,Exploring Example Influence in Continual Learning,"https://scholar.google.com/scholar?cluster=4168097285182390934&hl=en&as_sdt=0,5",1,2022
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap,0,neurips,1,0,2023-06-16 22:59:37.150000,https://github.com/lucpoisson/subspaceclustering,1,Subspace clustering in high-dimensions: Phase transitions\& Statistical-to-Computational gap,"https://scholar.googleusercontent.com/scholar?q=cache:4HwC6_qwHhoJ:scholar.google.com/+Subspace+clustering+in+high-dimensions:+Phase+transitions+%26+Statistical-to-Computational+gap&hl=en&as_sdt=0,3",1,2022
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders,4,neurips,4,0,2023-06-16 22:59:37.362000,https://github.com/zhangq327/u-mae,32,How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders,"https://scholar.google.com/scholar?cluster=12421230382199683849&hl=en&as_sdt=0,34",3,2022
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors,1,neurips,0,0,2023-06-16 22:59:37.575000,https://github.com/novaglow646/nips22-mat-and-ldat-for-ood,6,Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors,"https://scholar.google.com/scholar?cluster=847890003773472313&hl=en&as_sdt=0,44",1,2022
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers,34,neurips,3103,884,2023-06-16 22:59:37.787000,https://github.com/microsoft/DeepSpeed,25950,Zeroquant: Efficient and affordable post-training quantization for large-scale transformers,"https://scholar.google.com/scholar?cluster=14601198018737164595&hl=en&as_sdt=0,33",290,2022
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping,0,neurips,0,0,2023-06-16 22:59:37.998000,https://github.com/rrags/ProtoX_NeurIPS,2,ProtoX: Explaining a Reinforcement Learning Agent via Prototyping,"https://scholar.google.com/scholar?cluster=15061235494194718383&hl=en&as_sdt=0,46",1,2022
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation,12,neurips,3,0,2023-06-16 22:59:38.210000,https://github.com/taesikgong/note,25,Note: Robust continual test-time adaptation against temporal correlation,"https://scholar.google.com/scholar?cluster=342119686943612993&hl=en&as_sdt=0,5",1,2022
Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation,6,neurips,1,2,2023-06-16 22:59:38.422000,https://github.com/zoilsen/clom,6,Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation,"https://scholar.google.com/scholar?cluster=13623703872858769843&hl=en&as_sdt=0,3",1,2022
Forecasting Future World Events With Neural Networks,7,neurips,50,1,2023-06-16 22:59:38.635000,https://github.com/andyzoujm/autocast,157,Forecasting Future World Events with Neural Networks,"https://scholar.google.com/scholar?cluster=17792483394679760594&hl=en&as_sdt=0,5",7,2022
Autoregressive Perturbations for Data Poisoning,9,neurips,3,0,2023-06-16 22:59:38.847000,https://github.com/psandovalsegura/autoregressive-poisoning,10,Autoregressive perturbations for data poisoning,"https://scholar.google.com/scholar?cluster=17109390722215919135&hl=en&as_sdt=0,47",3,2022
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine,1,neurips,1,0,2023-06-16 22:59:39.059000,https://github.com/bilkent-cyborg/escada,1,Escada: Efficient safety and context aware dose allocation for precision medicine,"https://scholar.google.com/scholar?cluster=12799291179330941239&hl=en&as_sdt=0,3",1,2022
Improved Algorithms for Neural Active Learning,2,neurips,0,0,2023-06-16 22:59:39.270000,https://github.com/matouk98/i-neural,0,Improved Algorithms for Neural Active Learning,"https://scholar.google.com/scholar?cluster=14846687732402339872&hl=en&as_sdt=0,33",1,2022
CUP: Critic-Guided Policy Reuse,1,neurips,1,0,2023-06-16 22:59:39.482000,https://github.com/nagisazj/cup,4,CUP: Critic-Guided Policy Reuse,"https://scholar.google.com/scholar?cluster=11253594017005639050&hl=en&as_sdt=0,39",1,2022
QUARK: Controllable Text Generation with Reinforced Unlearning,25,neurips,9,1,2023-06-16 22:59:39.693000,https://github.com/gximinglu/quark,50,Quark: Controllable text generation with reinforced unlearning,"https://scholar.google.com/scholar?cluster=15982538186848433892&hl=en&as_sdt=0,31",3,2022
Parameter-free Dynamic Graph Embedding for Link Prediction,2,neurips,1,1,2023-06-16 22:59:39.904000,https://github.com/fudancisl/freegem,9,Parameter-free Dynamic Graph Embedding for Link Prediction,"https://scholar.google.com/scholar?cluster=737985874382634688&hl=en&as_sdt=0,44",1,2022
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning,3,neurips,0,0,2023-06-16 22:59:40.115000,https://github.com/jaearly/mil-for-non-markovian-reward-modelling,3,Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning,"https://scholar.google.com/scholar?cluster=9372994966597211189&hl=en&as_sdt=0,47",1,2022
Explaining Preferences with Shapley Values,1,neurips,0,0,2023-06-16 22:59:40.327000,https://github.com/mrhuff/pref-shap,3,Explaining Preferences with Shapley Values,"https://scholar.google.com/scholar?cluster=13809288685377851579&hl=en&as_sdt=0,5",2,2022
Fair Bayes-Optimal Classifiers Under Predictive Parity,3,neurips,0,0,2023-06-16 22:59:40.538000,https://github.com/xianlizeng/fairbayes-dpp,1,Fair Bayes-Optimal Classifiers Under Predictive Parity,"https://scholar.google.com/scholar?cluster=1276001185503240257&hl=en&as_sdt=0,5",1,2022
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors,8,neurips,11,1,2023-06-16 22:59:40.750000,https://github.com/hsouri/bayesiantransferlearning,98,Pre-train your loss: Easy bayesian transfer learning with informative priors,"https://scholar.google.com/scholar?cluster=16170264225104963616&hl=en&as_sdt=0,34",5,2022
Training language models to follow instructions with human feedback,1152,neurips,123,3,2023-06-16 22:59:40.962000,https://github.com/openai/following-instructions-human-feedback,994,Training language models to follow instructions with human feedback,"https://scholar.google.com/scholar?cluster=12979976309017799162&hl=en&as_sdt=0,10",114,2022
Non-rigid Point Cloud Registration with Neural Deformation Pyramid,6,neurips,9,3,2023-06-16 22:59:41.174000,https://github.com/rabbityl/deformationpyramid,97,Non-rigid Point Cloud Registration with Neural Deformation Pyramid,"https://scholar.google.com/scholar?cluster=6583649970645189814&hl=en&as_sdt=0,14",9,2022
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders,2,neurips,0,0,2023-06-16 22:59:41.386000,https://github.com/olivierjeunen/disentangling-neurips-2022,2,Disentangling causal effects from sets of interventions in the presence of unobserved confounders,"https://scholar.google.com/scholar?cluster=11308179641811912058&hl=en&as_sdt=0,11",2,2022
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights,3,neurips,2,0,2023-06-16 22:59:41.599000,https://github.com/hsg-aiml/neurips_2022-generative_hyper_representations,6,Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights,"https://scholar.google.com/scholar?cluster=5719530018346300751&hl=en&as_sdt=0,5",4,2022
Flexible Diffusion Modeling of Long Videos,40,neurips,5,0,2023-06-16 22:59:41.812000,https://github.com/plai-group/flexible-video-diffusion-modeling,64,Flexible diffusion modeling of long videos,"https://scholar.google.com/scholar?cluster=14027817982126481605&hl=en&as_sdt=0,5",5,2022
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers,2,neurips,0,0,2023-06-16 22:59:42.023000,https://github.com/jw4hv/geo-sic,3,Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers,"https://scholar.google.com/scholar?cluster=17159159244649320976&hl=en&as_sdt=0,47",1,2022
Segmenting Moving Objects via an Object-Centric Layered Representation,9,neurips,0,0,2023-06-16 22:59:42.240000,https://github.com/Jyxarthur/OCLR_model,13,Segmenting moving objects via an object-centric layered representation,"https://scholar.google.com/scholar?cluster=725725608410804919&hl=en&as_sdt=0,44",1,2022
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies,4,neurips,94,29,2023-06-16 22:59:42.461000,https://github.com/automl/NASLib,403,NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies,"https://scholar.google.com/scholar?cluster=12406911975162843820&hl=en&as_sdt=0,10",14,2022
Controllable Text Generation with Neurally-Decomposed Oracle,3,neurips,2,0,2023-06-16 22:59:42.672000,https://github.com/mtsomethree/constrdecoding,11,Controllable Text Generation with Neurally-Decomposed Oracle,"https://scholar.google.com/scholar?cluster=9870671818275677250&hl=en&as_sdt=0,31",4,2022
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments,1,neurips,0,0,2023-06-16 22:59:42.883000,https://github.com/caselles/neurips22-demonstrations-pedagogy-pragmatism,0,Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments,"https://scholar.google.com/scholar?cluster=13569490094707234684&hl=en&as_sdt=0,47",1,2022
"Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances",8,neurips,0,0,2023-06-16 22:59:43.096000,https://github.com/sbnietert/sliced-wp,0,"Statistical, robustness, and computational guarantees for sliced wasserstein distances","https://scholar.google.com/scholar?cluster=13763656485291132199&hl=en&as_sdt=0,5",1,2022
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs,3,neurips,1,0,2023-06-16 22:59:43.308000,https://github.com/talshaharabany/what-is-where-by-looking,14,What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs,"https://scholar.google.com/scholar?cluster=887087732905998506&hl=en&as_sdt=0,11",1,2022
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning,4,neurips,0,0,2023-06-16 22:59:43.521000,https://github.com/siqixu/deepmed,2,DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning,"https://scholar.google.com/scholar?cluster=2967140859437535127&hl=en&as_sdt=0,5",1,2022
A Continuous Time Framework for Discrete Denoising Models,10,neurips,4,1,2023-06-16 22:59:43.732000,https://github.com/andrew-cr/tauldr,20,A continuous time framework for discrete denoising models,"https://scholar.google.com/scholar?cluster=12065158919379277046&hl=en&as_sdt=0,5",1,2022
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization,6,neurips,0,0,2023-06-16 22:59:43.945000,https://github.com/qingguo666/FLO,9,Tight mutual information estimation with contrastive fenchel-legendre optimization,"https://scholar.google.com/scholar?cluster=11580465232288190410&hl=en&as_sdt=0,5",2,2022
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability,0,neurips,1,0,2023-06-16 22:59:44.157000,https://github.com/kid-7391/soprc,4,Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability,"https://scholar.google.com/scholar?cluster=2443951446559121514&hl=en&as_sdt=0,5",1,2022
TransBoost: Improving the Best ImageNet Performance using Deep Transduction,0,neurips,1,0,2023-06-16 22:59:44.369000,https://github.com/omerb01/transboost,6,TransBoost: Improving the Best ImageNet Performance using Deep Transduction,"https://scholar.google.com/scholar?cluster=7854158254206635581&hl=en&as_sdt=0,5",1,2022
Sparse Probabilistic Circuits via Pruning and Growing,4,neurips,0,0,2023-06-16 22:59:44.581000,https://github.com/ucla-starai/sparsepc,10,Sparse probabilistic circuits via pruning and growing,"https://scholar.google.com/scholar?cluster=11141675136195823156&hl=en&as_sdt=0,3",2,2022
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment,10,neurips,3,0,2023-06-16 22:59:44.793000,https://github.com/feradauto/moralcot,29,When to make exceptions: Exploring language models as accounts of human moral judgment,"https://scholar.google.com/scholar?cluster=15747656978235543700&hl=en&as_sdt=0,47",1,2022
Exponential Family Model-Based Reinforcement Learning via Score Matching,1,neurips,0,0,2023-06-16 22:59:45.005000,https://github.com/anmolkabra/score-matching-rl,2,Exponential family model-based reinforcement learning via score matching,"https://scholar.google.com/scholar?cluster=13487904936270229304&hl=en&as_sdt=0,5",3,2022
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization,3,neurips,3,0,2023-06-16 22:59:45.216000,https://github.com/lamda-bbo/mcts-vs,14,Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization,"https://scholar.google.com/scholar?cluster=11812344942865377060&hl=en&as_sdt=0,33",2,2022
Assistive Teaching of Motor Control Tasks to Humans,3,neurips,0,0,2023-06-16 22:59:45.429000,https://github.com/stanford-iliad/teaching,2,Assistive Teaching of Motor Control Tasks to Humans,"https://scholar.google.com/scholar?cluster=4146414116857689554&hl=en&as_sdt=0,5",5,2022
Adversarial Reprogramming Revisited,2,neurips,1,0,2023-06-16 22:59:45.640000,https://github.com/englert-m/adversarial_reprogramming,2,Adversarial Reprogramming Revisited,"https://scholar.google.com/scholar?cluster=5745042332144042845&hl=en&as_sdt=0,18",1,2022
When Does Differentially Private Learning Not Suffer in High Dimensions?,11,neurips,17,4,2023-06-16 22:59:45.853000,https://github.com/lxuechen/private-transformers,100,When Does Differentially Private Learning Not Suffer in High Dimensions?,"https://scholar.google.com/scholar?cluster=12738886860685825235&hl=en&as_sdt=0,5",5,2022
Masked Autoencoders that Listen,45,neurips,24,11,2023-06-16 22:59:46.064000,https://github.com/facebookresearch/audiomae,344,Masked autoencoders that listen,"https://scholar.google.com/scholar?cluster=13233494379811120690&hl=en&as_sdt=0,33",39,2022
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning,7,neurips,44,27,2023-06-16 22:59:46.276000,https://github.com/decile-team/cords,272,Automata: Gradient based data subset selection for compute-efficient hyper-parameter tuning,"https://scholar.google.com/scholar?cluster=8803292945419400795&hl=en&as_sdt=0,5",10,2022
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs,1,neurips,0,0,2023-06-16 22:59:46.489000,https://github.com/khalednakhleh/deeptop,0,DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs,"https://scholar.google.com/scholar?cluster=13570932694368744960&hl=en&as_sdt=0,11",1,2022
Is a Modular Architecture Enough?,11,neurips,3,0,2023-06-16 22:59:46.700000,https://github.com/sarthmit/mod_arch,31,Is a Modular Architecture Enough?,"https://scholar.google.com/scholar?cluster=5707197899340562621&hl=en&as_sdt=0,5",2,2022
Exploration via Planning for Information about the Optimal Trajectory,1,neurips,1,0,2023-06-16 22:59:46.912000,https://github.com/fusion-ml/trajectory-information-rl,14,Exploration via planning for information about the optimal trajectory,"https://scholar.google.com/scholar?cluster=14433349520441278180&hl=en&as_sdt=0,39",3,2022
Subquadratic Kronecker Regression with Applications to Tensor Decomposition,6,neurips,1,0,2023-06-16 22:59:47.124000,https://github.com/fahrbach/subquadratic-kronecker-regression,0,Subquadratic kronecker regression with applications to tensor decomposition,"https://scholar.google.com/scholar?cluster=16694254702569927793&hl=en&as_sdt=0,33",2,2022
Robust Anytime Learning of Markov Decision Processes,7,neurips,1,0,2023-06-16 22:59:47.336000,https://github.com/lava-lab/luiaard,2,Robust anytime learning of Markov decision processes,"https://scholar.google.com/scholar?cluster=13918485196268093813&hl=en&as_sdt=0,39",2,2022
Discovering Design Concepts for CAD Sketches,2,neurips,1,3,2023-06-16 22:59:47.561000,https://github.com/yyuezhi/sketchconcept,4,Discovering Design Concepts for CAD Sketches,"https://scholar.google.com/scholar?cluster=13612243371244513176&hl=en&as_sdt=0,18",1,2022
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game,0,neurips,1,0,2023-06-16 22:59:47.778000,https://github.com/zhiyuanyaoj/marllb,2,Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game,"https://scholar.google.com/scholar?cluster=3976860628797287838&hl=en&as_sdt=0,14",2,2022
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching,3,neurips,0,0,2023-06-16 22:59:47.991000,https://github.com/pvnieo/ncp,2,NCP: Neural correspondence prior for effective unsupervised shape matching,"https://scholar.google.com/scholar?cluster=3458823752936331324&hl=en&as_sdt=0,47",1,2022
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models,5,neurips,7,1,2023-06-16 22:59:48.206000,https://github.com/lmxyy/sige,212,Efficient spatially sparse inference for conditional gans and diffusion models,"https://scholar.google.com/scholar?cluster=949267028420813363&hl=en&as_sdt=0,5",5,2022
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis,0,neurips,1,0,2023-06-16 22:59:48.420000,https://github.com/dritsa-konstantina/greparl,2,A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis,"https://scholar.google.com/scholar?cluster=18337461361366657304&hl=en&as_sdt=0,5",2,2022
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress,11,neurips,11,2,2023-06-16 22:59:48.633000,https://github.com/google-research/reincarnating_rl,81,Reincarnating reinforcement learning: Reusing prior computation to accelerate progress,"https://scholar.google.com/scholar?cluster=2191734016134843580&hl=en&as_sdt=0,25",7,2022
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression,3,neurips,0,0,2023-06-16 22:59:48.858000,https://github.com/liangzu/irls-neurips2022,0,Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression,"https://scholar.google.com/scholar?cluster=145441446786155398&hl=en&as_sdt=0,5",2,2022
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization,2,neurips,1,0,2023-06-16 22:59:49.071000,https://github.com/manga-uofa/nacc,4,A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization,"https://scholar.google.com/scholar?cluster=3534208302188234048&hl=en&as_sdt=0,5",1,2022
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees,8,neurips,0,0,2023-06-16 22:59:49.283000,https://github.com/ruikunzhou/unknown_neural_lyapunov,2,Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees,"https://scholar.google.com/scholar?cluster=7399734234325202121&hl=en&as_sdt=0,33",1,2022
A Lower Bound of Hash Codes' Performance,0,neurips,0,1,2023-06-16 22:59:49.499000,https://github.com/vl-group/lbhash,4,A Lower Bound of Hash Codes' Performance,"https://scholar.google.com/scholar?cluster=1910707024863961077&hl=en&as_sdt=0,5",1,2022
Self-Supervised Image Restoration with Blurry and Noisy Pairs,1,neurips,2,0,2023-06-16 22:59:49.722000,https://github.com/cszhilu1998/selfir,33,Self-Supervised Image Restoration with Blurry and Noisy Pairs,"https://scholar.google.com/scholar?cluster=12118320256260943816&hl=en&as_sdt=0,10",1,2022
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding,1,neurips,0,0,2023-06-16 22:59:49.943000,https://github.com/jy0205/stcat,25,Embracing consistency: A one-stage approach for spatio-temporal video grounding,"https://scholar.google.com/scholar?cluster=2054637694993057366&hl=en&as_sdt=0,31",2,2022
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset,12,neurips,6,1,2023-06-16 22:59:50.159000,https://github.com/breakend/pileoflaw,63,Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset,"https://scholar.google.com/scholar?cluster=16242802812264116024&hl=en&as_sdt=0,33",3,2022
Patching open-vocabulary models by interpolating weights,29,neurips,5,0,2023-06-16 22:59:50.384000,https://github.com/mlfoundations/patching,66,Patching open-vocabulary models by interpolating weights,"https://scholar.google.com/scholar?cluster=12287111402475287292&hl=en&as_sdt=0,10",6,2022
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching,3,neurips,3,0,2023-06-16 22:59:50.597000,https://github.com/craigleili/attentivefmaps,5,Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching,"https://scholar.google.com/scholar?cluster=11801194413397973375&hl=en&as_sdt=0,5",4,2022
Practical Adversarial Multivalid Conformal Prediction,13,neurips,5,0,2023-06-16 22:59:50.810000,https://github.com/progbelarus/multivalidprediction,12,Practical adversarial multivalid conformal prediction,"https://scholar.google.com/scholar?cluster=6409760077625712140&hl=en&as_sdt=0,33",2,2022
Test-Time Training with Masked Autoencoders,25,neurips,1,0,2023-06-16 22:59:51.021000,https://github.com/yossigandelsman/test_time_training_mae,48,Test-time training with masked autoencoders,"https://scholar.google.com/scholar?cluster=2544097260576053446&hl=en&as_sdt=0,5",3,2022
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems,5,neurips,4,1,2023-06-16 22:59:51.246000,https://github.com/thorben-frank/mlff,30,So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems,"https://scholar.google.com/scholar?cluster=16550039961851369955&hl=en&as_sdt=0,5",3,2022
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks,5,neurips,5,0,2023-06-16 22:59:51.480000,https://github.com/macderru/hyperdomainnet,76,Hyperdomainnet: Universal domain adaptation for generative adversarial networks,"https://scholar.google.com/scholar?cluster=14001675056345163311&hl=en&as_sdt=0,10",3,2022
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks,13,neurips,3,1,2023-06-16 22:59:51.693000,https://github.com/glamor-usc/climb,39,Climb: A continual learning benchmark for vision-and-language tasks,"https://scholar.google.com/scholar?cluster=2434194050994506336&hl=en&as_sdt=0,5",5,2022
Bidirectional Learning for Offline Infinite-width Model-based Optimization,9,neurips,0,0,2023-06-16 22:59:51.904000,https://github.com/ggchen1997/bdi,7,Bidirectional learning for offline infinite-width model-based optimization,"https://scholar.google.com/scholar?cluster=13019462606638546457&hl=en&as_sdt=0,29",2,2022
Unified Optimal Transport Framework for Universal Domain Adaptation,3,neurips,3,0,2023-06-16 22:59:52.116000,https://github.com/changwxx/uniot-for-unida,29,Unified optimal transport framework for universal domain adaptation,"https://scholar.google.com/scholar?cluster=16909534816090473474&hl=en&as_sdt=0,41",4,2022
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering,0,neurips,1,0,2023-06-16 22:59:52.327000,https://github.com/haoyuzhao123/coreset-vfl-codes,3,Coresets for Vertical Federated Learning: Regularized Linear Regression and -Means Clustering,"https://scholar.google.com/scholar?cluster=6637629427663807&hl=en&as_sdt=0,5",1,2022
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment,0,neurips,0,0,2023-06-16 22:59:52.539000,https://github.com/movinghoon/mira,8,Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment,"https://scholar.google.com/scholar?cluster=15425564159709405182&hl=en&as_sdt=0,32",1,2022
Mind Reader: Reconstructing complex images from brain activities,10,neurips,3,3,2023-06-16 22:59:52.751000,https://github.com/sklin93/mind-reader,38,Mind Reader: Reconstructing complex images from brain activities,"https://scholar.google.com/scholar?cluster=206404245897193541&hl=en&as_sdt=0,5",4,2022
An Investigation into Whitening Loss for Self-supervised Learning,4,neurips,2,1,2023-06-16 22:59:52.963000,https://github.com/winci-ai/cw-rgp,12,An investigation into whitening loss for self-supervised learning,"https://scholar.google.com/scholar?cluster=8085947162457980477&hl=en&as_sdt=0,10",1,2022
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks,3,neurips,2,0,2023-06-16 22:59:53.175000,https://github.com/chr26195/gkd,16,Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks,"https://scholar.google.com/scholar?cluster=3988720192874696300&hl=en&as_sdt=0,33",2,2022
A Benchmark for Compositional Visual Reasoning,4,neurips,2,0,2023-06-16 22:59:53.387000,https://github.com/aimzer/cvr,12,A benchmark for compositional visual reasoning,"https://scholar.google.com/scholar?cluster=11272228500855667208&hl=en&as_sdt=0,14",1,2022
Myriad: a real-world testbed to bridge trajectory optimization and deep learning,0,neurips,3,0,2023-06-16 22:59:53.598000,https://github.com/nikihowe/myriad,45,Myriad: a real-world testbed to bridge trajectory optimization and deep learning,"https://scholar.google.com/scholar?cluster=6826074521392801836&hl=en&as_sdt=0,14",2,2022
Batch Bayesian optimisation via density-ratio estimation with guarantees,1,neurips,0,0,2023-06-16 22:59:53.810000,https://github.com/rafaol/batch-bore-with-guarantees,1,Batch Bayesian optimisation via density-ratio estimation with guarantees,"https://scholar.google.com/scholar?cluster=17612558782197429855&hl=en&as_sdt=0,47",1,2022
Amplifying Membership Exposure via Data Poisoning,3,neurips,0,0,2023-06-16 22:59:54.022000,https://github.com/yfchen1994/poisoning_membership,9,Amplifying Membership Exposure via Data Poisoning,"https://scholar.google.com/scholar?cluster=13772127157500094294&hl=en&as_sdt=0,31",1,2022
BayesPCN: A Continually Learnable Predictive Coding Associative Memory,1,neurips,0,0,2023-06-16 22:59:54.234000,https://github.com/plai-group/bayes-pcn,4,BayesPCN: A Continually Learnable Predictive Coding Associative Memory,"https://scholar.google.com/scholar?cluster=6318188315590566524&hl=en&as_sdt=0,5",3,2022
Semantic Probabilistic Layers for Neuro-Symbolic Learning,11,neurips,1,3,2023-06-16 22:59:54.460000,https://github.com/KareemYousrii/SPL,14,Semantic probabilistic layers for neuro-symbolic learning,"https://scholar.google.com/scholar?cluster=790768995509318385&hl=en&as_sdt=0,33",5,2022
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds,10,neurips,6,2,2023-06-16 22:59:54.672000,https://github.com/haiyang-w/cagroup3d,67,Cagroup3d: Class-aware grouping for 3d object detection on point clouds,"https://scholar.google.com/scholar?cluster=10922971019763222861&hl=en&as_sdt=0,5",7,2022
Characterizing Datapoints via Second-Split Forgetting,5,neurips,0,0,2023-06-16 22:59:54.884000,https://github.com/pratyushmaini/ssft,8,Characterizing datapoints via second-split forgetting,"https://scholar.google.com/scholar?cluster=15661926582422861854&hl=en&as_sdt=0,5",1,2022
GENIE: Higher-Order Denoising Diffusion Solvers,17,neurips,2,0,2023-06-16 22:59:55.096000,https://github.com/nv-tlabs/GENIE,75,GENIE: Higher-order denoising diffusion solvers,"https://scholar.google.com/scholar?cluster=7162863738522405281&hl=en&as_sdt=0,41",27,2022
Tsetlin Machine for Solving Contextual Bandit Problems,2,neurips,0,0,2023-06-16 22:59:55.308000,https://github.com/raihan-seraj/tsetlin-machine-for-solving-contextual-bandit-problems,0,Tsetlin Machine for Solving Contextual Bandit Problems,"https://scholar.google.com/scholar?cluster=3151730412209496386&hl=en&as_sdt=0,3",2,2022
Matryoshka Representation Learning,3,neurips,6,0,2023-06-16 22:59:55.520000,https://github.com/raivnlab/mrl,55,Matryoshka Representation Learning,"https://scholar.google.com/scholar?cluster=15922805360081593111&hl=en&as_sdt=0,5",3,2022
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations,11,neurips,3,0,2023-06-16 22:59:55.731000,https://github.com/jpthu17/emcl,34,Expectation-maximization contrastive learning for compact video-and-language representations,"https://scholar.google.com/scholar?cluster=11969840580847474339&hl=en&as_sdt=0,33",3,2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks,1,neurips,0,0,2023-06-16 22:59:55.944000,https://github.com/runame/laplace-refinement,7,Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks,"https://scholar.google.com/scholar?cluster=9536243879108520698&hl=en&as_sdt=0,44",1,2022
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders,2,neurips,2,0,2023-06-16 22:59:56.155000,https://github.com/kiarashza/graphvae-mm,3,Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders,"https://scholar.google.com/scholar?cluster=13109008245041775500&hl=en&as_sdt=0,5",1,2022
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning,22,neurips,2,0,2023-06-16 22:59:56.368000,https://github.com/xiye17/textualexplincontext,7,The unreliability of explanations in few-shot prompting for textual reasoning,"https://scholar.google.com/scholar?cluster=10734606259015724525&hl=en&as_sdt=0,36",1,2022
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward,1,neurips,0,0,2023-06-16 22:59:56.581000,https://github.com/kakaobrain/leco,3,LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward,"https://scholar.google.com/scholar?cluster=2977860683890519748&hl=en&as_sdt=0,18",4,2022
Generalised Implicit Neural Representations,5,neurips,4,0,2023-06-16 22:59:56.793000,https://github.com/danielegrattarola/ginr,57,Generalised Implicit Neural Representations,"https://scholar.google.com/scholar?cluster=8630199693995819513&hl=en&as_sdt=0,47",1,2022
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks,2,neurips,0,0,2023-06-16 22:59:57.006000,https://github.com/ennisthemennis/sparse-combo-net,3,RNNs of RNNs: Recursive construction of stable assemblies of recurrent neural networks,"https://scholar.google.com/scholar?cluster=6568419832870731540&hl=en&as_sdt=0,10",1,2022
Efficient Non-Parametric Optimizer Search for Diverse Tasks,1,neurips,0,0,2023-06-16 22:59:57.217000,https://github.com/ruocwang/efficient-optimizer-search,5,Efficient Non-Parametric Optimizer Search for Diverse Tasks,"https://scholar.google.com/scholar?cluster=1101981355374817614&hl=en&as_sdt=0,14",2,2022
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes,42,neurips,12,0,2023-06-16 22:59:57.430000,https://github.com/dtsip/in-context-learning,87,What can transformers learn in-context? a case study of simple function classes,"https://scholar.google.com/scholar?cluster=11860366070256877583&hl=en&as_sdt=0,44",3,2022
Towards Robust Blind Face Restoration with Codebook Lookup Transformer,19,neurips,1839,124,2023-06-16 22:59:57.643000,https://github.com/sczhou/codeformer,8639,Towards robust blind face restoration with codebook lookup transformer,"https://scholar.google.com/scholar?cluster=7620815108092344146&hl=en&as_sdt=0,29",236,2022
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems,0,neurips,0,0,2023-06-16 22:59:57.854000,https://github.com/aair-lab/grapl,4,Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems,"https://scholar.google.com/scholar?cluster=4265468888207305535&hl=en&as_sdt=0,5",3,2022
Information-Theoretic Safe Exploration with Gaussian Processes,0,neurips,0,0,2023-06-16 22:59:58.066000,https://github.com/boschresearch/information-theoretic-safe-exploration,0,Information-Theoretic Safe Exploration with Gaussian Processes,"https://scholar.google.com/scholar?cluster=14061812239298858431&hl=en&as_sdt=0,5",3,2022
Instance-based Learning for Knowledge Base Completion,1,neurips,3,1,2023-06-16 22:59:58.277000,https://github.com/chenxran/instancebasedlearning,8,Instance-based Learning for Knowledge Base Completion,"https://scholar.google.com/scholar?cluster=14765487766577879365&hl=en&as_sdt=0,43",2,2022
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds,8,neurips,6,2,2023-06-16 22:59:58.490000,https://github.com/vlar-group/ogc,87,OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds,"https://scholar.google.com/scholar?cluster=15508509084461848604&hl=en&as_sdt=0,33",5,2022
Look More but Care Less in Video Recognition,2,neurips,1,0,2023-06-16 22:59:58.702000,https://github.com/bespontaneous/afnet-pytorch,17,Look More but Care Less in Video Recognition,"https://scholar.google.com/scholar?cluster=9829246812468140188&hl=en&as_sdt=0,5",2,2022
BLOX: Macro Neural Architecture Search Benchmark and Algorithms,2,neurips,2,0,2023-06-16 22:59:58.914000,https://github.com/samsunglabs/blox,16,BLOX: Macro neural architecture search benchmark and algorithms,"https://scholar.google.com/scholar?cluster=14998161186597977202&hl=en&as_sdt=0,5",5,2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels,4,neurips,2,0,2023-06-16 22:59:59.127000,https://github.com/yaodongyu/tct,2,TCT: Convexifying federated learning using bootstrapped neural tangent kernels,"https://scholar.google.com/scholar?cluster=17046807913297835630&hl=en&as_sdt=0,6",6,2022
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction,2,neurips,2,1,2023-06-16 22:59:59.338000,https://github.com/xuehansheng/neurhap,3,Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction,"https://scholar.google.com/scholar?cluster=4924728742271479697&hl=en&as_sdt=0,5",2,2022
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition,14,neurips,4,5,2023-06-16 22:59:59.551000,https://github.com/Gorilla-Lab-SCUT/tango,117,Tango: Text-driven photorealistic and robust 3d stylization via lighting decomposition,"https://scholar.google.com/scholar?cluster=5164034802871142304&hl=en&as_sdt=0,11",4,2022
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach,15,neurips,5,0,2023-06-16 22:59:59.764000,https://github.com/mi-peng/sparse-sharpness-aware-minimization,21,Make sharpness-aware minimization stronger: A sparsified perturbation approach,"https://scholar.google.com/scholar?cluster=18129366560164232465&hl=en&as_sdt=0,43",3,2022
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space,6,neurips,0,0,2023-06-16 22:59:59.976000,https://github.com/elicassion/3dtrl,16,Learning viewpoint-agnostic visual representations by recovering tokens in 3D space,"https://scholar.google.com/scholar?cluster=9274676018097824562&hl=en&as_sdt=0,5",6,2022
Certifying Some Distributional Fairness with Subpopulation Decomposition,3,neurips,0,0,2023-06-16 23:00:00.188000,https://github.com/ai-secure/certified-fairness,3,Certifying some distributional fairness with subpopulation decomposition,"https://scholar.google.com/scholar?cluster=4221362036776726241&hl=en&as_sdt=0,5",3,2022
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning,4,neurips,0,0,2023-06-16 23:00:00.404000,https://github.com/Benjamin-eecs/Theoretical-GMRL,3,A theoretical understanding of gradient bias in meta-reinforcement learning,"https://scholar.google.com/scholar?cluster=9240869542719622997&hl=en&as_sdt=0,5",2,2022
MAtt: A Manifold Attention Network for EEG Decoding,3,neurips,7,1,2023-06-16 23:00:00.619000,https://github.com/cecnl/matt,21,MAtt: A Manifold Attention Network for EEG Decoding,"https://scholar.google.com/scholar?cluster=9527737114617546773&hl=en&as_sdt=0,33",1,2022
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators,0,neurips,0,0,2023-06-16 23:00:00.832000,https://github.com/abhrac/relational-proxies,6,Relational Proxies: Emergent Relationships as Fine-Grained Discriminators,"https://scholar.google.com/scholar?cluster=1413072596102938227&hl=en&as_sdt=0,44",1,2022
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search,2,neurips,1,0,2023-06-16 23:00:01.045000,https://github.com/ninhpham/falconnlsf,4,Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search,"https://scholar.google.com/scholar?cluster=9694551963166215273&hl=en&as_sdt=0,5",1,2022
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation,6,neurips,1,0,2023-06-16 23:00:01.309000,https://github.com/jieyibi/amdkd,19,Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation,"https://scholar.google.com/scholar?cluster=6693564818674377475&hl=en&as_sdt=0,7",1,2022
The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound,0,neurips,0,0,2023-06-16 23:00:01.541000,https://github.com/vaidehi8913/burer-monteiro,3,The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound,"https://scholar.google.com/scholar?cluster=693050207219401404&hl=en&as_sdt=0,5",2,2022
MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training,20,neurips,21,6,2023-06-16 23:00:01.754000,https://github.com/nvlabs/minvis,242,Minvis: A minimal video instance segmentation framework without video-based training,"https://scholar.google.com/scholar?cluster=9646541593785601186&hl=en&as_sdt=0,5",6,2022
Supported Policy Optimization for Offline Reinforcement Learning,8,neurips,0,0,2023-06-16 23:00:01.967000,https://github.com/thuml/SPOT,10,Supported policy optimization for offline reinforcement learning,"https://scholar.google.com/scholar?cluster=6270305527768915360&hl=en&as_sdt=0,32",5,2022
DDXPlus: A New Dataset For Automatic Medical Diagnosis,6,neurips,0,1,2023-06-16 23:00:02.179000,https://github.com/bruzwen/ddxplus,12,Ddxplus: A new dataset for automatic medical diagnosis,"https://scholar.google.com/scholar?cluster=3028229938614227838&hl=en&as_sdt=0,5",1,2022
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise,8,neurips,0,0,2023-06-16 23:00:02.393000,https://github.com/busycalibrating/clipped-stochastic-methods,0,Clipped stochastic methods for variational inequalities with heavy-tailed noise,"https://scholar.google.com/scholar?cluster=3888795941605104858&hl=en&as_sdt=0,29",1,2022
A Unified Sequence Interface for Vision Tasks,33,neurips,55,21,2023-06-16 23:00:02.606000,https://github.com/google-research/pix2seq,650,A unified sequence interface for vision tasks,"https://scholar.google.com/scholar?cluster=14680303082655356082&hl=en&as_sdt=0,5",17,2022
"Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning",1,neurips,0,0,2023-06-16 23:00:02.818000,https://github.com/project-pragma/expected-frequency-matrices-neurips-2022,0,"Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning","https://scholar.google.com/scholar?cluster=11960031454206755033&hl=en&as_sdt=0,18",0,2022
GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models,11,neurips,11,7,2023-06-16 23:00:03.032000,https://github.com/leonnnop/gmmseg,124,GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models,"https://scholar.google.com/scholar?cluster=10640577107442772357&hl=en&as_sdt=0,3",11,2022
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries,26,neurips,3,0,2023-06-16 23:00:03.255000,https://github.com/beabevi/sun,32,Understanding and extending subgraph gnns by rethinking their symmetries,"https://scholar.google.com/scholar?cluster=14966370671903147583&hl=en&as_sdt=0,33",3,2022
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning,2,neurips,15,0,2023-06-16 23:00:03.501000,https://github.com/kaist-dmlab/mqnet,22,Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning,"https://scholar.google.com/scholar?cluster=12545815856367613650&hl=en&as_sdt=0,38",2,2022
Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention,2,neurips,1,0,2023-06-16 23:00:03.714000,https://github.com/monstersecond/dasbe,3,Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention,"https://scholar.google.com/scholar?cluster=6935604356068693641&hl=en&as_sdt=0,47",1,2022
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation,1,neurips,0,0,2023-06-16 23:00:03.927000,https://github.com/graph-com/co_proxydesign,12,Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation,"https://scholar.google.com/scholar?cluster=16393681527735462837&hl=en&as_sdt=0,29",0,2022
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization,5,neurips,0,1,2023-06-16 23:00:04.139000,https://github.com/activatedgeek/tight-pac-bayes,10,PAC-bayes compression bounds so tight that they can explain generalization,"https://scholar.google.com/scholar?cluster=7786972960841977431&hl=en&as_sdt=0,5",6,2022
BagFlip: A Certified Defense Against Data Poisoning,2,neurips,0,0,2023-06-16 23:00:04.362000,https://github.com/foreverzyh/defend_framework,2,BagFlip: A Certified Defense against Data Poisoning,"https://scholar.google.com/scholar?cluster=12286341512846726817&hl=en&as_sdt=0,47",2,2022
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization,3,neurips,2,1,2023-06-16 23:00:04.574000,https://github.com/rddy/mimi,21,First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization,"https://scholar.google.com/scholar?cluster=8778426534420691089&hl=en&as_sdt=0,10",1,2022
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding,2,neurips,5,1,2023-06-16 23:00:04.787000,https://github.com/novicestone/hyperminer,11,Hyperminer: Topic taxonomy mining with hyperbolic embedding,"https://scholar.google.com/scholar?cluster=9819074122178900305&hl=en&as_sdt=0,5",1,2022
Visual Concepts Tokenization,2,neurips,0,1,2023-06-16 23:00:05,https://github.com/thomasmry/vct,15,Visual Concepts Tokenization,"https://scholar.google.com/scholar?cluster=8458085041466969555&hl=en&as_sdt=0,19",2,2022
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression,11,neurips,1,0,2023-06-16 23:00:05.213000,https://github.com/liboyue/beer,4,BEER: Fast Rate for Decentralized Nonconvex Optimization with Communication Compression,"https://scholar.google.com/scholar?cluster=7137425453983349577&hl=en&as_sdt=0,33",1,2022
Autoregressive Search Engines: Generating Substrings as Document Identifiers,32,neurips,21,4,2023-06-16 23:00:05.429000,https://github.com/facebookresearch/seal,242,Autoregressive search engines: Generating substrings as document identifiers,"https://scholar.google.com/scholar?cluster=8414649729617248348&hl=en&as_sdt=0,44",6,2022
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space,14,neurips,5,1,2023-06-16 23:00:05.642000,https://github.com/constrainedsampler/polytopesamplermatlab,10,Sampling with riemannian hamiltonian monte carlo in a constrained space,"https://scholar.google.com/scholar?cluster=18009004710620196060&hl=en&as_sdt=0,48",5,2022
Fair Ranking with Noisy Protected Attributes,1,neurips,0,0,2023-06-16 23:00:05.855000,https://github.com/anaymehrotra/fairrankingwithnoisyattributes,2,Fair Ranking with Noisy Protected Attributes,"https://scholar.google.com/scholar?cluster=7053102236966869756&hl=en&as_sdt=0,10",1,2022
Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations,1,neurips,0,0,2023-06-16 23:00:06.067000,https://github.com/cdslabamotong/social_inverse,1,Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations,"https://scholar.google.com/scholar?cluster=10879672753670618782&hl=en&as_sdt=0,5",1,2022
Modeling Human Exploration Through Resource-Rational Reinforcement Learning,6,neurips,0,0,2023-06-16 23:00:06.323000,https://github.com/marcelbinz/resource-rational-reinforcement-learning,3,Modeling Human Exploration Through Resource-Rational Reinforcement Learning,"https://scholar.google.com/scholar?cluster=16794822202235026210&hl=en&as_sdt=0,5",1,2022
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data,0,neurips,1,3,2023-06-16 23:00:06.581000,https://github.com/ailsaf/diggan,5,DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data,"https://scholar.google.com/scholar?cluster=1540818415084096338&hl=en&as_sdt=0,44",2,2022
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images,86,neurips,293,31,2023-06-16 23:00:06.793000,https://github.com/nv-tlabs/GET3D,3574,Get3d: A generative model of high quality 3d textured shapes learned from images,"https://scholar.google.com/scholar?cluster=16330894889594665221&hl=en&as_sdt=0,31",145,2022
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks,10,neurips,0,0,2023-06-16 23:00:07.006000,https://github.com/DavideBuffelli/SizeShiftReg,9,Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks,"https://scholar.google.com/scholar?cluster=17580849325875477854&hl=en&as_sdt=0,33",2,2022
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning,6,neurips,2,0,2023-06-16 23:00:07.221000,https://github.com/aimagelab/lider,8,On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning,"https://scholar.google.com/scholar?cluster=17593160937952833613&hl=en&as_sdt=0,14",3,2022
MAgNet: Mesh Agnostic Neural PDE Solver,5,neurips,2,1,2023-06-16 23:00:07.435000,https://github.com/jaggbow/magnet,25,MAgnet: Mesh agnostic neural PDE solver,"https://scholar.google.com/scholar?cluster=4350112799912824064&hl=en&as_sdt=0,5",1,2022
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks,1,neurips,3,2,2023-06-16 23:00:07.648000,https://github.com/ds4dm/learn2comparenodes,13,Learning to Compare Nodes in Branch and Bound with Graph Neural Networks,"https://scholar.google.com/scholar?cluster=2705976177527772812&hl=en&as_sdt=0,5",4,2022
ATD: Augmenting CP Tensor Decomposition by Self Supervision,2,neurips,2,0,2023-06-16 23:00:07.862000,https://github.com/ycq091044/atd,6,ATD: Augmenting CP Tensor Decomposition by Self Supervision,"https://scholar.google.com/scholar?cluster=606172582822954692&hl=en&as_sdt=0,11",2,2022
Towards Learning Universal Hyperparameter Optimizers with Transformers,8,neurips,6,4,2023-06-16 23:00:08.077000,https://github.com/google-research/optformer,62,Towards learning universal hyperparameter optimizers with transformers,"https://scholar.google.com/scholar?cluster=16320840565400406534&hl=en&as_sdt=0,3",4,2022
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations,5,neurips,3,1,2023-06-16 23:00:08.289000,https://github.com/Graph-Machine-Learning-Group/spin,20,Learning to reconstruct missing data from spatiotemporal graphs with sparse observations,"https://scholar.google.com/scholar?cluster=8334658282792961058&hl=en&as_sdt=0,5",5,2022
Peripheral Vision Transformer,9,neurips,1,2,2023-06-16 23:00:08.509000,https://github.com/juhongm999/pervit,30,Peripheral vision transformer,"https://scholar.google.com/scholar?cluster=13097315276803844133&hl=en&as_sdt=0,5",2,2022
ADBench: Anomaly Detection Benchmark,60,neurips,101,9,2023-06-16 23:00:08.721000,https://github.com/minqi824/adbench,579,Adbench: Anomaly detection benchmark,"https://scholar.google.com/scholar?cluster=4407607921916219597&hl=en&as_sdt=0,21",12,2022
GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks,6,neurips,7,1,2023-06-16 23:00:08.933000,https://github.com/ikarosy/gated-lif,26,GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks,"https://scholar.google.com/scholar?cluster=2076403375085197634&hl=en&as_sdt=0,47",2,2022
Robust Reinforcement Learning using Offline Data,16,neurips,1,0,2023-06-16 23:00:09.146000,https://github.com/zaiyan-x/RFQI,12,Robust reinforcement learning using offline data,"https://scholar.google.com/scholar?cluster=1874625503427910762&hl=en&as_sdt=0,23",2,2022
Provably expressive temporal graph networks,6,neurips,2,1,2023-06-16 23:00:09.360000,https://github.com/aaltopml/pint,11,Provably expressive temporal graph networks,"https://scholar.google.com/scholar?cluster=16593401358862246597&hl=en&as_sdt=0,5",8,2022
First is Better Than Last for Language Data Influence,1,neurips,0,0,2023-06-16 23:00:09.573000,https://github.com/chihkuanyeh/TracIn-WE,3,First is Better Than Last for Language Data Influence,"https://scholar.google.com/scholar?cluster=13589739763293612430&hl=en&as_sdt=0,33",2,2022
Deep Combinatorial Aggregation,1,neurips,1,0,2023-06-16 23:00:09.785000,https://github.com/tum-vision/dca,4,Deep Combinatorial Aggregation,"https://scholar.google.com/scholar?cluster=11229599811485616676&hl=en&as_sdt=0,3",12,2022
Model-based Lifelong Reinforcement Learning with Bayesian Exploration,0,neurips,1,0,2023-06-16 23:00:09.997000,https://github.com/minusadd/vblrl,6,Model-based Lifelong Reinforcement Learning with Bayesian Exploration,"https://scholar.google.com/scholar?cluster=1429823804057000001&hl=en&as_sdt=0,43",1,2022
Debiased Self-Training for Semi-Supervised Learning,7,neurips,3,1,2023-06-16 23:00:10.209000,https://github.com/thuml/debiased-self-training,37,Debiased Self-Training for Semi-Supervised Learning,"https://scholar.google.com/scholar?cluster=1562024070888687879&hl=en&as_sdt=0,47",4,2022
Weak-shot Semantic Segmentation via Dual Similarity Transfer,0,neurips,1,1,2023-06-16 23:00:10.422000,https://github.com/bcmi/simformer-weak-shot-semantic-segmentation,39,Weak-shot Semantic Segmentation via Dual Similarity Transfer,"https://scholar.google.com/scholar?cluster=1110171901581878158&hl=en&as_sdt=0,4",8,2022
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction,3,neurips,3,6,2023-06-16 23:00:10.634000,https://github.com/lbox-kr/lbox-open,70,A multi-task benchmark for korean legal language understanding and judgement prediction,"https://scholar.google.com/scholar?cluster=6229647396436406608&hl=en&as_sdt=0,33",5,2022
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization,5,neurips,1,1,2023-06-16 23:00:10.846000,https://github.com/umd-huang-lab/transfer-fairness,1,Transferring fairness under distribution shifts via fair consistency regularization,"https://scholar.google.com/scholar?cluster=2045183985933877126&hl=en&as_sdt=0,3",2,2022
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection,27,neurips,51,14,2023-06-16 23:00:11.058000,https://github.com/jingkang50/openood,459,OpenOOD: Benchmarking generalized out-of-distribution detection,"https://scholar.google.com/scholar?cluster=1091474511097006762&hl=en&as_sdt=0,14",5,2022
KSD Aggregated Goodness-of-fit Test,6,neurips,0,0,2023-06-16 23:00:11.274000,https://github.com/antoninschrab/ksdagg-paper,6,KSD aggregated goodness-of-fit test,"https://scholar.google.com/scholar?cluster=495383308571140426&hl=en&as_sdt=0,34",2,2022
Efficient Risk-Averse Reinforcement Learning,6,neurips,2,0,2023-06-16 23:00:11.486000,https://github.com/ido90/CeSoR,10,Efficient risk-averse reinforcement learning,"https://scholar.google.com/scholar?cluster=13537611132511952539&hl=en&as_sdt=0,23",1,2022
Benefits of Additive Noise in Composing Classes with Bounded Capacity,2,neurips,0,0,2023-06-16 23:00:11.698000,https://github.com/fathollahpour/composition_noise,0,Benefits of Additive Noise in Composing Classes with Bounded Capacity,"https://scholar.google.com/scholar?cluster=7148521168238224557&hl=en&as_sdt=0,5",2,2022
Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning,1,neurips,0,1,2023-06-16 23:00:11.910000,https://github.com/huangzy225/3d-gcl,4,Towards hard-pose virtual try-on via 3d-aware global correspondence learning,"https://scholar.google.com/scholar?cluster=18342403964707797536&hl=en&as_sdt=0,34",1,2022
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection,5,neurips,2,0,2023-06-16 23:00:12.122000,https://github.com/bit-ml/anoshift,32,AnoShift: A distribution shift benchmark for unsupervised anomaly detection,"https://scholar.google.com/scholar?cluster=17123244700110721819&hl=en&as_sdt=0,14",8,2022
Towards Better Evaluation for Dynamic Link Prediction,5,neurips,5,1,2023-06-16 23:00:12.333000,https://github.com/fpour/dgb,37,Towards Better Evaluation for Dynamic Link Prediction,"https://scholar.google.com/scholar?cluster=2464517726378679836&hl=en&as_sdt=0,5",1,2022
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone ,27,neurips,7,6,2023-06-16 23:00:12.545000,https://github.com/microsoft/fiber,86,Coarse-to-fine vision-language pre-training with fusion in the backbone,"https://scholar.google.com/scholar?cluster=7539527092820284785&hl=en&as_sdt=0,5",8,2022
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning,13,neurips,68,11,2023-06-16 23:00:12.757000,https://github.com/facebookresearch/drqv2,269,Vrl3: A data-driven framework for visual deep reinforcement learning,"https://scholar.google.com/scholar?cluster=18285263434804961573&hl=en&as_sdt=0,5",9,2022
"New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound",0,neurips,1,0,2023-06-16 23:00:12.969000,https://github.com/agup/soundness_saliency,1,"New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound","https://scholar.google.com/scholar?cluster=10451995006190134842&hl=en&as_sdt=0,47",1,2022
Finding and Listing Front-door Adjustment Sets,2,neurips,0,0,2023-06-16 23:00:13.182000,https://github.com/causalailab/frontdooradjustmentsets,2,Finding and Listing Front-door Adjustment Sets,"https://scholar.google.com/scholar?cluster=5503246239780227329&hl=en&as_sdt=0,5",0,2022
MorphTE: Injecting Morphology in Tensorized Embeddings,2,neurips,3,0,2023-06-16 23:00:13.394000,https://github.com/bigganbing/Fairseq_MorphTE,13,MorphTE: Injecting Morphology in Tensorized Embeddings,"https://scholar.google.com/scholar?cluster=11621395100232513695&hl=en&as_sdt=0,31",1,2022
Block-Recurrent Transformers,21,neurips,18,1,2023-06-16 23:00:13.607000,https://github.com/google-research/meliad,158,Block-recurrent transformers,"https://scholar.google.com/scholar?cluster=15684096473797838415&hl=en&as_sdt=0,5",7,2022
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling,20,neurips,29,15,2023-06-16 23:00:13.819000,https://github.com/Pointcept/Pointcept,346,Point transformer v2: Grouped vector attention and partition-based pooling,"https://scholar.google.com/scholar?cluster=2723001857482086032&hl=en&as_sdt=0,39",10,2022
Neural Approximation of Graph Topological Features,3,neurips,1,1,2023-06-16 23:00:14.031000,https://github.com/pkuyzy/TLC-GNN,8,Neural Approximation of Graph Topological Features,"https://scholar.google.com/scholar?cluster=13389512890302778008&hl=en&as_sdt=0,10",2,2022
MOVE: Unsupervised Movable Object Segmentation and Detection,2,neurips,2,2,2023-06-16 23:00:14.244000,https://github.com/adambielski/move-seg,17,MOVE: Unsupervised Movable Object Segmentation and Detection,"https://scholar.google.com/scholar?cluster=8173455362624893467&hl=en&as_sdt=0,31",2,2022
FreGAN: Exploiting Frequency Components for Training GANs under Limited Data,3,neurips,1,0,2023-06-16 23:00:14.455000,https://github.com/kobeshegu/fregan_neurips2022,38,FreGAN: Exploiting Frequency Components for Training GANs under Limited Data,"https://scholar.google.com/scholar?cluster=1815918906998343465&hl=en&as_sdt=0,5",1,2022
Collaborative Decision Making Using Action Suggestions,1,neurips,0,0,2023-06-16 23:00:14.668000,https://github.com/sisl/action_suggestions,7,Collaborative Decision Making Using Action Suggestions,"https://scholar.google.com/scholar?cluster=10468143367870116616&hl=en&as_sdt=0,5",2,2022
Universally Expressive Communication in Multi-Agent Reinforcement Learning,2,neurips,0,0,2023-06-16 23:00:14.880000,https://github.com/mmorris44/expressive-gdns,1,Universally Expressive Communication in Multi-Agent Reinforcement Learning,"https://scholar.google.com/scholar?cluster=8093380292358835878&hl=en&as_sdt=0,5",1,2022
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization,3,neurips,0,0,2023-06-16 23:00:15.093000,https://github.com/bpauld/pfw,0,Fast stochastic composite minimization and an accelerated frank-wolfe algorithm under parallelization,"https://scholar.google.com/scholar?cluster=14357658369907461850&hl=en&as_sdt=0,34",1,2022
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning,8,neurips,10,7,2023-06-16 23:00:15.305000,https://github.com/fuying-wang/mgca,52,Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning,"https://scholar.google.com/scholar?cluster=16722403537302150812&hl=en&as_sdt=0,5",2,2022
"Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation",2,neurips,1,2,2023-06-16 23:00:15.517000,https://github.com/feedzai/bank-account-fraud,31,"Turning the tables: Biased, imbalanced, dynamic tabular datasets for ml evaluation","https://scholar.google.com/scholar?cluster=5567088687742955249&hl=en&as_sdt=0,44",4,2022
Optimizing Relevance Maps of Vision Transformers Improves Robustness,11,neurips,9,1,2023-06-16 23:00:15.729000,https://github.com/hila-chefer/robustvit,117,Optimizing relevance maps of vision transformers improves robustness,"https://scholar.google.com/scholar?cluster=4540065452590589915&hl=en&as_sdt=0,5",3,2022
"Deep Ensembles Work, But Are They Necessary?",17,neurips,1,0,2023-06-16 23:00:15.940000,https://github.com/cellistigs/interp_ensembles,5,"Deep ensembles work, but are they necessary?","https://scholar.google.com/scholar?cluster=17084457719894473759&hl=en&as_sdt=0,34",1,2022
VICE: Variational Interpretable Concept Embeddings,5,neurips,3,0,2023-06-16 23:00:16.153000,https://github.com/lukasmut/vice,9,VICE: Variational Interpretable Concept Embeddings,"https://scholar.google.com/scholar?cluster=12914224895734200193&hl=en&as_sdt=0,44",6,2022
Knowledge Distillation from A Stronger Teacher,20,neurips,8,3,2023-06-16 23:00:16.365000,https://github.com/hunto/dist_kd,83,Knowledge distillation from a stronger teacher,"https://scholar.google.com/scholar?cluster=9782451594224614440&hl=en&as_sdt=0,6",2,2022
Optimal Transport of Classifiers to Fairness,1,neurips,1,0,2023-06-16 23:00:16.577000,https://github.com/aida-ugent/OTF,1,Optimal Transport of Classifiers to Fairness,"https://scholar.google.com/scholar?cluster=16219423422077161743&hl=en&as_sdt=0,5",4,2022
Rethinking the compositionality of point clouds through regularization in the hyperbolic space,6,neurips,2,2,2023-06-16 23:00:16.789000,https://github.com/diegovalsesia/hycore,15,Rethinking the compositionality of point clouds through regularization in the hyperbolic space,"https://scholar.google.com/scholar?cluster=5390155762200714510&hl=en&as_sdt=0,22",3,2022
ReCo: Retrieve and Co-segment for Zero-shot Transfer,18,neurips,5,5,2023-06-16 23:00:17.001000,https://github.com/NoelShin/reco,52,Reco: Retrieve and co-segment for zero-shot transfer,"https://scholar.google.com/scholar?cluster=2541893392537318474&hl=en&as_sdt=0,33",1,2022
Monocular Dynamic View Synthesis: A Reality Check,20,neurips,7,1,2023-06-16 23:00:17.214000,https://github.com/kair-bair/dycheck,130,Monocular dynamic view synthesis: A reality check,"https://scholar.google.com/scholar?cluster=4051245421926210617&hl=en&as_sdt=0,3",10,2022
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection,30,neurips,17,3,2023-06-16 23:00:17.426000,https://github.com/hanoonaR/object-centric-ovd,250,Bridging the gap between object and image-level representations for open-vocabulary detection,"https://scholar.google.com/scholar?cluster=2701303524777814353&hl=en&as_sdt=0,5",5,2022
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids,48,neurips,6,0,2023-06-16 23:00:17.638000,https://github.com/autonomousvision/voxgraf,112,Voxgraf: Fast 3d-aware image synthesis with sparse voxel grids,"https://scholar.google.com/scholar?cluster=14022665138113076252&hl=en&as_sdt=0,5",17,2022
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective,9,neurips,0,0,2023-06-16 23:00:17.851000,https://github.com/graph-com/bayesian_inference_based_gnn,3,Understanding non-linearity in graph neural networks from the bayesian-inference perspective,"https://scholar.google.com/scholar?cluster=15550644623606214670&hl=en&as_sdt=0,5",0,2022
Explainable Reinforcement Learning via Model Transforms,1,neurips,0,1,2023-06-16 23:00:18.063000,https://github.com/sarah-keren/rlpe,1,Explainable Reinforcement Learning via Model Transforms,"https://scholar.google.com/scholar?cluster=12642694616127148920&hl=en&as_sdt=0,33",1,2022
Self-Supervised Learning via Maximum Entropy Coding,5,neurips,0,2,2023-06-16 23:00:18.278000,https://github.com/xinliu20/mec,33,Self-supervised learning via maximum entropy coding,"https://scholar.google.com/scholar?cluster=4670554254496466202&hl=en&as_sdt=0,14",12,2022
"A Practical, Progressively-Expressive GNN",6,neurips,1,0,2023-06-16 23:00:18.492000,https://github.com/lingxiaoshawn/kcsetgnn,8,"A practical, progressively-expressive GNN","https://scholar.google.com/scholar?cluster=1801555861503089995&hl=en&as_sdt=0,5",1,2022
On Learning Fairness and Accuracy on Multiple Subgroups,8,neurips,1,0,2023-06-16 23:00:18.704000,https://github.com/xugezheng/fams,2,On learning fairness and accuracy on multiple subgroups,"https://scholar.google.com/scholar?cluster=4933287508687209050&hl=en&as_sdt=0,47",3,2022
Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment,2,neurips,5,12,2023-06-16 23:00:18.917000,https://github.com/mediatechnologycenter/aestheval,56,Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment,"https://scholar.google.com/scholar?cluster=15317983619506277329&hl=en&as_sdt=0,14",4,2022
Black-box coreset variational inference,1,neurips,4,0,2023-06-16 23:00:19.130000,https://github.com/facebookresearch/blackbox-coresets-vi,7,Black-box Coreset Variational Inference,"https://scholar.google.com/scholar?cluster=16155121271564700916&hl=en&as_sdt=0,44",8,2022
Distilling Representations from GAN Generator via Squeeze and Span,0,neurips,0,1,2023-06-16 23:00:19.341000,https://github.com/yangyu12/squeeze-and-span,7,Distilling Representations from GAN Generator via Squeeze and Span,"https://scholar.google.com/scholar?cluster=806447244804186364&hl=en&as_sdt=0,5",4,2022
A Quantitative Geometric Approach to Neural-Network Smoothness,2,neurips,0,0,2023-06-16 23:00:19.553000,https://github.com/z1w/geolip,0,A Quantitative Geometric Approach to Neural Network Smoothness,"https://scholar.google.com/scholar?cluster=6789257021629578865&hl=en&as_sdt=0,48",1,2022
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning,2,neurips,4,1,2023-06-16 23:00:19.765000,https://github.com/zhanglijun95/AutoMTL,47,Automtl: A programming framework for automating efficient multi-task learning,"https://scholar.google.com/scholar?cluster=7878464515755964059&hl=en&as_sdt=0,31",2,2022
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare,3,neurips,0,0,2023-06-16 23:00:19.977000,https://github.com/mld3/offlinerl_factoredactions,4,Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare,"https://scholar.google.com/scholar?cluster=6410167541170420183&hl=en&as_sdt=0,5",3,2022
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy,9,neurips,2,0,2023-06-16 23:00:20.190000,https://github.com/anguyen8/visual-correspondence-xai,37,Visual correspondence-based explanations improve AI robustness and human-AI team accuracy,"https://scholar.google.com/scholar?cluster=9102002858022546042&hl=en&as_sdt=0,44",3,2022
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice,2,neurips,0,0,2023-06-16 23:00:20.402000,https://github.com/marcus-nordstrom/optimal-solutions-to-accuracy-and-dice,1,On image segmentation with noisy labels: Characterization and volume properties of the optimal solutions to accuracy and dice,"https://scholar.google.com/scholar?cluster=12620304652080944393&hl=en&as_sdt=0,37",1,2022
Fairness Reprogramming,6,neurips,3,1,2023-06-16 23:00:20.614000,https://github.com/ucsb-nlp-chang/fairness-reprogramming,9,Fairness reprogramming,"https://scholar.google.com/scholar?cluster=10104950810882497858&hl=en&as_sdt=0,4",3,2022
WeightedSHAP: analyzing and improving Shapley based feature attributions,5,neurips,15,0,2023-06-16 23:00:20.827000,https://github.com/ykwon0407/weightedshap,146,WeightedSHAP: analyzing and improving Shapley based feature attributions,"https://scholar.google.com/scholar?cluster=15930531007434220976&hl=en&as_sdt=0,47",2,2022
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks,13,neurips,6,1,2023-06-16 23:00:21.040000,https://github.com/princetonvisualai/rememberthepast-datasetdistillation,27,Remember the past: Distilling datasets into addressable memories for neural networks,"https://scholar.google.com/scholar?cluster=10137780628795331558&hl=en&as_sdt=0,5",8,2022
PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories,4,neurips,1,2,2023-06-16 23:00:21.258000,https://github.com/yuchenrao/PatchComplete,31,Patchcomplete: Learning multi-resolution patch priors for 3d shape completion on unseen categories,"https://scholar.google.com/scholar?cluster=3702047949220397378&hl=en&as_sdt=0,1",2,2022
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer,15,neurips,7,11,2023-06-16 23:00:21.521000,https://github.com/yanjingli0202/q-vit,36,Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer,"https://scholar.google.com/scholar?cluster=3955595566743652517&hl=en&as_sdt=0,21",4,2022
Local Latent Space Bayesian Optimization over Structured Inputs,17,neurips,3,0,2023-06-16 23:00:21.733000,https://github.com/nataliemaus/lolbo,17,Local latent space bayesian optimization over structured inputs,"https://scholar.google.com/scholar?cluster=11212834051035239107&hl=en&as_sdt=0,5",3,2022
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification,4,neurips,0,1,2023-06-16 23:00:21.946000,https://github.com/stephanieschoch/cs-shapley,6,CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification,"https://scholar.google.com/scholar?cluster=1153833541425284379&hl=en&as_sdt=0,5",1,2022
Factuality Enhanced Language Models for Open-Ended Text Generation,14,neurips,0,0,2023-06-16 23:00:22.158000,https://github.com/nayeon7lee/factualityprompt,32,Factuality enhanced language models for open-ended text generation,"https://scholar.google.com/scholar?cluster=1383756650317492432&hl=en&as_sdt=0,5",3,2022
On the Representation Collapse of Sparse Mixture of Experts,13,neurips,1868,365,2023-06-16 23:00:22.371000,https://github.com/microsoft/unilm,12785,On the representation collapse of sparse mixture of experts,"https://scholar.google.com/scholar?cluster=3896458754067259677&hl=en&as_sdt=0,33",260,2022
Towards Understanding Grokking: An Effective Theory of Representation Learning,14,neurips,1,0,2023-06-16 23:00:22.583000,https://github.com/ejmichaud/grokking-squared,5,Towards understanding grokking: An effective theory of representation learning,"https://scholar.google.com/scholar?cluster=13179441772130531947&hl=en&as_sdt=0,10",3,2022
Towards Practical Few-shot Query Sets: Transductive Minimum Description Length Inference,0,neurips,0,0,2023-06-16 23:00:22.796000,https://github.com/segolenemartin/paddle,3,Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference,"https://scholar.google.com/scholar?cluster=18347644409364092901&hl=en&as_sdt=0,5",1,2022
Learning Manifold Dimensions with Conditional Variational Autoencoders,2,neurips,0,0,2023-06-16 23:00:23.008000,https://github.com/zhengyjzoe/manifold-dimensions-cvae,3,Learning Manifold Dimensions with Conditional Variational Autoencoders,"https://scholar.google.com/scholar?cluster=10642265351504424278&hl=en&as_sdt=0,5",1,2022
Optimal-er Auctions through Attention,8,neurips,0,0,2023-06-16 23:00:23.220000,https://github.com/dimonenka/optimaler,6,Optimal-er auctions through attention,"https://scholar.google.com/scholar?cluster=16264768269300742707&hl=en&as_sdt=0,21",1,2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients,2,neurips,0,0,2023-06-16 23:00:23.432000,https://github.com/red-portal/klpqvi.jl,0,Markov chain score ascent: A unifying framework of variational inference with Markovian gradients,"https://scholar.google.com/scholar?cluster=9999896485416486947&hl=en&as_sdt=0,5",2,2022
Rethinking Value Function Learning for Generalization in Reinforcement Learning,0,neurips,1,0,2023-06-16 23:00:23.644000,https://github.com/snu-mllab/dcpg,9,Rethinking Value Function Learning for Generalization in Reinforcement Learning,"https://scholar.google.com/scholar?cluster=17768972917538912915&hl=en&as_sdt=0,5",4,2022
Improving Certified Robustness via Statistical Learning with Logical Reasoning,2,neurips,0,1,2023-06-16 23:00:23.857000,https://github.com/sensing-reasoning/sensing-reasoning-pipeline,3,Improving certified robustness via statistical learning with logical reasoning,"https://scholar.google.com/scholar?cluster=12962831424296042350&hl=en&as_sdt=0,46",1,2022
Understanding Robust Learning through the Lens of Representation Similarities,2,neurips,0,0,2023-06-16 23:00:24.069000,https://github.com/uchicago-sandlab/robust_representation_similarity,1,Understanding robust learning through the lens of representation similarities,"https://scholar.google.com/scholar?cluster=13729841239622676756&hl=en&as_sdt=0,47",0,2022
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization,3,neurips,0,0,2023-06-16 23:00:24.346000,https://github.com/weijiazhang24/causalmil,6,Multi-instance causal representation learning for instance label prediction and out-of-distribution generalization,"https://scholar.google.com/scholar?cluster=5803800343677787178&hl=en&as_sdt=0,5",1,2022
PerfectDou: Dominating DouDizhu with Perfect Information Distillation,11,neurips,21,0,2023-06-16 23:00:24.606000,https://github.com/netease-games-ai-lab-guangzhou/perfectdou,89,Perfectdou: Dominating doudizhu with perfect information distillation,"https://scholar.google.com/scholar?cluster=10276583276169438358&hl=en&as_sdt=0,34",5,2022
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech,5,neurips,2,3,2023-06-16 23:00:24.818000,https://github.com/chorowski-lab/hcpc,16,Variable-rate hierarchical CPC leads to acoustic unit discovery in speech,"https://scholar.google.com/scholar?cluster=15342183140020352170&hl=en&as_sdt=0,5",3,2022
Learning Neural Set Functions Under the Optimal Subset Oracle,0,neurips,0,0,2023-06-16 23:00:25.039000,https://github.com/SubsetSelection/EquiVSet,16,Learning Neural Set Functions Under the Optimal Subset Oracle,"https://scholar.google.com/scholar?cluster=14074525399634060470&hl=en&as_sdt=0,5",1,2022
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models,5,neurips,1,0,2023-06-16 23:00:25.252000,https://github.com/naver-ai/mid.metric,23,Mutual Information Divergence: A Unified Metric for Multimodal Generative Models,"https://scholar.google.com/scholar?cluster=7729417185496732731&hl=en&as_sdt=0,33",2,2022
Delving into Out-of-Distribution Detection with Vision-Language Representations,13,neurips,3,2,2023-06-16 23:00:25.484000,https://github.com/deeplearning-wisc/mcm,27,Delving into Out-of-Distribution Detection with Vision-Language Representations,"https://scholar.google.com/scholar?cluster=5820179747828691857&hl=en&as_sdt=0,47",4,2022
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve,0,neurips,1,0,2023-06-16 23:00:25.696000,https://github.com/giannisdaras/multilingual_robustness,10,Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve,"https://scholar.google.com/scholar?cluster=1557526934945118330&hl=en&as_sdt=0,47",2,2022
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding,7,neurips,8,1,2023-06-16 23:00:25.907000,https://github.com/deepgraphlearning/peer_benchmark,51,Peer: a comprehensive and multi-task benchmark for protein sequence understanding,"https://scholar.google.com/scholar?cluster=14330854305087707376&hl=en&as_sdt=0,5",4,2022
Deep Counterfactual Estimation with Categorical Background Variables,1,neurips,1,1,2023-06-16 23:00:26.120000,https://github.com/edebrouwer/cfqp,7,Deep Counterfactual Estimation with Categorical Background Variables,"https://scholar.google.com/scholar?cluster=16244902668087959747&hl=en&as_sdt=0,33",2,2022
Self-Supervised Learning with an Information Maximization Criterion,6,neurips,4,2,2023-06-16 23:00:26.332000,https://github.com/serdarozsoy/corinfomax-ssl,16,Self-supervised learning with an information maximization criterion,"https://scholar.google.com/scholar?cluster=3815127622526777729&hl=en&as_sdt=0,47",3,2022
TwiBot-22: Towards Graph-Based Twitter Bot Detection,12,neurips,25,8,2023-06-16 23:00:26.544000,https://github.com/luoundergradxjtu/twibot-22,90,TwiBot-22: Towards graph-based Twitter bot detection,"https://scholar.google.com/scholar?cluster=6456058773715528503&hl=en&as_sdt=0,5",5,2022
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF,4,neurips,0,0,2023-06-16 23:00:26.756000,https://github.com/jayneelparekh/l2i-code,3,Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF,"https://scholar.google.com/scholar?cluster=12104450137353790860&hl=en&as_sdt=0,5",2,2022
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression,2,neurips,2,0,2023-06-16 23:00:26.978000,https://github.com/xk-huang/OrdinalCLIP,18,OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression,"https://scholar.google.com/scholar?cluster=3053611634838674005&hl=en&as_sdt=0,33",2,2022
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control,1,neurips,15,0,2023-06-16 23:00:27.190000,https://github.com/microsoft/MoCapAct,92,MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control,"https://scholar.google.com/scholar?cluster=11298263061250398476&hl=en&as_sdt=0,5",9,2022
On the Effectiveness of Persistent Homology,5,neurips,0,1,2023-06-16 23:00:27.402000,https://github.com/renata-turkes/turkevs2022on,4,On the effectiveness of persistent homology,"https://scholar.google.com/scholar?cluster=17747599099493045319&hl=en&as_sdt=0,5",1,2022
Flowification: Everything is a normalizing flow,3,neurips,1,0,2023-06-16 23:00:27.615000,https://github.com/balintmate/flowification,3,Flowification: Everything is a normalizing flow,"https://scholar.google.com/scholar?cluster=10643002561590578659&hl=en&as_sdt=0,32",1,2022
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective,10,neurips,2,0,2023-06-16 23:00:27.826000,https://github.com/naver-ai/hmix-gmix,16,A unified analysis of mixed sample data augmentation: A loss function perspective,"https://scholar.google.com/scholar?cluster=14554827738828101158&hl=en&as_sdt=0,3",6,2022
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings,3,neurips,2,0,2023-06-16 23:00:28.038000,https://github.com/yiren-jian/NonLing-CSE,18,Non-linguistic supervision for contrastive learning of sentence embeddings,"https://scholar.google.com/scholar?cluster=5735790098682052651&hl=en&as_sdt=0,5",2,2022
4D Unsupervised Object Discovery,4,neurips,1,3,2023-06-16 23:00:28.250000,https://github.com/robertwyq/lsmol,46,4d unsupervised object discovery,"https://scholar.google.com/scholar?cluster=15078826490225309292&hl=en&as_sdt=0,10",3,2022
Deep invariant networks with differentiable augmentation layers,1,neurips,0,0,2023-06-16 23:00:28.462000,https://github.com/cedricrommel/augnet,14,Deep invariant networks with differentiable augmentation layers,"https://scholar.google.com/scholar?cluster=6037019697272911487&hl=en&as_sdt=0,33",1,2022
Reinforcement Learning with a Terminator,2,neurips,0,0,2023-06-16 23:00:28.674000,https://github.com/guytenn/terminator,3,Reinforcement Learning with a Terminator,"https://scholar.google.com/scholar?cluster=7563547842459702948&hl=en&as_sdt=0,33",1,2022
A Multilabel Classification Framework for Approximate Nearest Neighbor Search,0,neurips,0,0,2023-06-16 23:00:28.886000,https://github.com/vioshyvo/a-multilabel-classification-framework,1,A Multilabel Classification Framework for Approximate Nearest Neighbor Search,"https://scholar.google.com/scholar?cluster=2936492944429726858&hl=en&as_sdt=0,10",1,2022
Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks,1,neurips,1,0,2023-06-16 23:00:29.098000,https://github.com/raymondyeh07/learnable_polyphase_sampling,8,Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks,"https://scholar.google.com/scholar?cluster=12661870794117490476&hl=en&as_sdt=0,5",4,2022
Deep Generative Model for Periodic Graphs,13,neurips,1,3,2023-06-16 23:00:29.311000,https://github.com/shi-yu-wang/pgd-vae,5,Deep generative model for periodic graphs,"https://scholar.google.com/scholar?cluster=12918861137062671900&hl=en&as_sdt=0,43",2,2022
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models,7,neurips,1128,230,2023-06-16 23:00:29.523000,https://github.com/NVIDIA/Megatron-LM,5442,Exploring the limits of domain-adaptive training for detoxifying large-scale language models,"https://scholar.google.com/scholar?cluster=13821301846979103824&hl=en&as_sdt=0,5",114,2022
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo,7,neurips,0,0,2023-06-16 23:00:29.735000,https://github.com/ipeis/HH-VAEM,9,Missing data imputation and acquisition with deep hierarchical models and hamiltonian monte carlo,"https://scholar.google.com/scholar?cluster=8364326333884223136&hl=en&as_sdt=0,5",1,2022
DNA: Proximal Policy Optimization with a Dual Network Architecture,0,neurips,3,1,2023-06-16 23:00:29.946000,https://github.com/maitchison/PPO,10,DNA: Proximal Policy Optimization with a Dual Network Architecture,"https://scholar.google.com/scholar?cluster=14725366901420334322&hl=en&as_sdt=0,39",2,2022
Masked Autoencoders As Spatiotemporal Learners,135,neurips,18,7,2023-06-16 23:00:30.159000,https://github.com/facebookresearch/mae_st,167,Masked autoencoders as spatiotemporal learners,"https://scholar.google.com/scholar?cluster=5215096183189163093&hl=en&as_sdt=0,48",6,2022
On the Parameterization and Initialization of Diagonal State Space Models,19,neurips,161,22,2023-06-16 23:00:30.372000,https://github.com/hazyresearch/state-spaces,1217,On the parameterization and initialization of diagonal state space models,"https://scholar.google.com/scholar?cluster=7664274811979401457&hl=en&as_sdt=0,43",42,2022
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork,2,neurips,0,1,2023-06-16 23:00:30.584000,https://github.com/vita-group/trap-and-replace-backdoor-defense,8,Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork,"https://scholar.google.com/scholar?cluster=9232182512273650158&hl=en&as_sdt=0,33",10,2022
Cluster and Aggregate: Face Recognition with Large Probe Set,3,neurips,2,3,2023-06-16 23:00:30.796000,https://github.com/mk-minchul/caface,23,Cluster and aggregate: Face recognition with large probe set,"https://scholar.google.com/scholar?cluster=1137447088637227795&hl=en&as_sdt=0,33",7,2022
GLIPv2: Unifying Localization and Vision-Language Understanding ,57,neurips,125,52,2023-06-16 23:00:31.009000,https://github.com/microsoft/GLIP,1330,Glipv2: Unifying localization and vision-language understanding,"https://scholar.google.com/scholar?cluster=4160517527641475312&hl=en&as_sdt=0,5",44,2022
Rethinking Alignment in Video Super-Resolution Transformers,9,neurips,3,3,2023-06-16 23:00:31.221000,https://github.com/xpixelgroup/rethinkvsralignment,60,Rethinking alignment in video super-resolution transformers,"https://scholar.google.com/scholar?cluster=13813872909195716054&hl=en&as_sdt=0,39",2,2022
Learning to Scaffold: Optimizing Model Explanations for Teaching,6,neurips,4,0,2023-06-16 23:00:31.433000,https://github.com/coderpat/learning-scaffold,18,Learning to scaffold: Optimizing model explanations for teaching,"https://scholar.google.com/scholar?cluster=6201332313543501646&hl=en&as_sdt=0,19",3,2022
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints,3,neurips,5,0,2023-06-16 23:00:31.645000,https://github.com/xingzhehe/AutoLink-Self-supervised-Learning-of-Human-Skeletons-and-Object-Outlines-by-Linking-Keypoints,26,Autolink: Self-supervised learning of human skeletons and object outlines by linking keypoints,"https://scholar.google.com/scholar?cluster=290662636948878015&hl=en&as_sdt=0,5",2,2022
Giving Feedback on Interactive Student Programs with Meta-Exploration,2,neurips,1,0,2023-06-16 23:00:31.857000,https://github.com/ezliu/dreamgrader,7,Giving Feedback on Interactive Student Programs with Meta-Exploration,"https://scholar.google.com/scholar?cluster=7333217017498365852&hl=en&as_sdt=0,33",1,2022
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network,0,neurips,1,0,2023-06-16 23:00:32.069000,https://github.com/stat-ml/nuq,5,Nonparametric Uncertainty Quantification for Single Deterministic Neural Network,"https://scholar.google.com/scholar?cluster=5318025374154758978&hl=en&as_sdt=0,36",7,2022
Decoupling Features in Hierarchical Propagation for Video Object Segmentation,12,neurips,6,0,2023-06-16 23:00:32.287000,https://github.com/z-x-yang/AOT,91,Decoupling Features in Hierarchical Propagation for Video Object Segmentation,"https://scholar.google.com/scholar?cluster=9093499936003644917&hl=en&as_sdt=0,47",13,2022
Chain of Thought Imitation with Procedure Cloning,5,neurips,7321,1026,2023-06-16 23:00:32.499000,https://github.com/google-research/google-research,29788,Chain of thought imitation with procedure cloning,"https://scholar.google.com/scholar?cluster=11561247381511573929&hl=en&as_sdt=0,5",727,2022
"ResT V2: Simpler, Faster and Stronger",1,neurips,27,10,2023-06-16 23:00:32.711000,https://github.com/wofmanaf/ResT,233,"Rest v2: simpler, faster and stronger","https://scholar.google.com/scholar?cluster=7008614846201767249&hl=en&as_sdt=0,10",6,2022
Learning Partial Equivariances From Data,11,neurips,0,0,2023-06-16 23:00:32.923000,https://github.com/merlresearch/partial_gcnn,7,Learning partial equivariances from data,"https://scholar.google.com/scholar?cluster=13426434973387392229&hl=en&as_sdt=0,5",0,2022
A Simple Decentralized Cross-Entropy Method,0,neurips,0,0,2023-06-16 23:00:33.135000,https://github.com/vincentzhang/decentcem,2,A Simple Decentralized Cross-Entropy Method,"https://scholar.google.com/scholar?cluster=11544076991942656328&hl=en&as_sdt=0,5",2,2022
MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification,0,neurips,1,0,2023-06-16 23:00:33.347000,https://github.com/hciilab/msds,28,MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification,"https://scholar.google.com/scholar?cluster=16618815475951417675&hl=en&as_sdt=0,19",2,2022
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks,1,neurips,2,1,2023-06-16 23:00:33.559000,https://github.com/guanjiyang/sac,9,Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks,"https://scholar.google.com/scholar?cluster=1273042545223201349&hl=en&as_sdt=0,5",1,2022
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning,4,neurips,1,0,2023-06-16 23:00:33.780000,https://github.com/t6-thu/H2O,42,When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning,"https://scholar.google.com/scholar?cluster=17890075669123951660&hl=en&as_sdt=0,25",2,2022
Data-Efficient Structured Pruning via Submodular Optimization,2,neurips,2,0,2023-06-16 23:00:33.993000,https://github.com/marwash25/subpruning,5,Data-efficient structured pruning via submodular optimization,"https://scholar.google.com/scholar?cluster=16143049953682779562&hl=en&as_sdt=0,33",1,2022
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification,4,neurips,1,0,2023-06-16 23:00:34.205000,https://github.com/mpatacchiola/contextual-squeeze-and-excitation,21,Contextual squeeze-and-excitation for efficient few-shot image classification,"https://scholar.google.com/scholar?cluster=12106343171515681246&hl=en&as_sdt=0,5",3,2022
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation,42,neurips,4,1,2023-06-16 23:00:34.418000,https://github.com/jiyuanfeng/amos2022,16,Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation,"https://scholar.google.com/scholar?cluster=10453212939134874202&hl=en&as_sdt=0,5",3,2022
Scalable Interpretability via Polynomials,8,neurips,11,2,2023-06-16 23:00:34.630000,https://github.com/facebookresearch/nbm-spam,67,Scalable Interpretability via Polynomials,"https://scholar.google.com/scholar?cluster=11992772218251377209&hl=en&as_sdt=0,33",7,2022
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes,21,neurips,10,2,2023-06-16 23:00:34.843000,https://github.com/showlab/devrf,158,Devrf: Fast deformable voxel radiance fields for dynamic scenes,"https://scholar.google.com/scholar?cluster=11949927249170979085&hl=en&as_sdt=0,23",9,2022
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints,12,neurips,2,2,2023-06-16 23:00:35.054000,https://github.com/heathcliff-saku/viewfool_,17,ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints,"https://scholar.google.com/scholar?cluster=4486454263174539234&hl=en&as_sdt=0,33",1,2022
Latency-aware Spatial-wise Dynamic Networks,2,neurips,1,0,2023-06-16 23:00:35.272000,https://github.com/leaplabthu/lasnet,9,Latency-aware Spatial-wise Dynamic Networks,"https://scholar.google.com/scholar?cluster=7885868681172675457&hl=en&as_sdt=0,21",2,2022
Towards Versatile Embodied Navigation,1,neurips,1,0,2023-06-16 23:00:35.493000,https://github.com/hanqingwangai/vxn,14,Towards versatile embodied navigation,"https://scholar.google.com/scholar?cluster=1358245884279440150&hl=en&as_sdt=0,10",3,2022
Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes,0,neurips,1,0,2023-06-16 23:00:35.706000,https://github.com/griver/memup,0,Explain My Surprise: Learning Efficient Long-Term Memory by Predicting Uncertain Outcomes,"https://scholar.google.com/scholar?cluster=14873450018728548996&hl=en&as_sdt=0,5",1,2022
Transformers from an Optimization Perspective,8,neurips,0,0,2023-06-16 23:00:35.917000,https://github.com/fftyyy/transformers-from-optimization,2,Transformers from an optimization perspective,"https://scholar.google.com/scholar?cluster=3271621775430662676&hl=en&as_sdt=0,23",1,2022
Amortized Projection Optimization for Sliced Wasserstein Generative Models,13,neurips,0,0,2023-06-16 23:00:36.129000,https://github.com/ut-austin-data-science-group/amortizedsw,7,Amortized projection optimization for sliced Wasserstein generative models,"https://scholar.google.com/scholar?cluster=4767006857593439261&hl=en&as_sdt=0,33",0,2022
DART: Articulated Hand Model with Diverse Accessories and Rich Textures,5,neurips,7,2,2023-06-16 23:00:36.342000,https://github.com/DART2022/DART,97,DART: Articulated Hand Model with Diverse Accessories and Rich Textures,"https://scholar.google.com/scholar?cluster=7571309201531991447&hl=en&as_sdt=0,5",3,2022
BadPrompt: Backdoor Attacks on Continuous Prompts,5,neurips,1,2,2023-06-16 23:00:36.555000,https://github.com/paperspapers/badprompt,17,Badprompt: Backdoor attacks on continuous prompts,"https://scholar.google.com/scholar?cluster=12437827439430094599&hl=en&as_sdt=0,7",1,2022
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding,3,neurips,2,0,2023-06-16 23:00:36.766000,https://github.com/eslambakr/LAR-Look-Around-and-Refer,17,Look around and refer: 2d synthetic semantics knowledge distillation for 3d visual grounding,"https://scholar.google.com/scholar?cluster=4825555452150751793&hl=en&as_sdt=0,33",2,2022
DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body,1,neurips,0,0,2023-06-16 23:00:36.978000,https://github.com/amathislab/dmap,13,DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body,"https://scholar.google.com/scholar?cluster=17998464088526482192&hl=en&as_sdt=0,5",1,2022
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks,5,neurips,1,1,2023-06-16 23:00:37.190000,https://github.com/1170300521/DiFa,37,Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks,"https://scholar.google.com/scholar?cluster=15242817500819796035&hl=en&as_sdt=0,47",3,2022
Deep Bidirectional Language-Knowledge Graph Pretraining,28,neurips,29,1,2023-06-16 23:00:37.402000,https://github.com/michiyasunaga/dragon,201,Deep bidirectional language-knowledge graph pretraining,"https://scholar.google.com/scholar?cluster=3831570526448132220&hl=en&as_sdt=0,47",5,2022
A Theoretical Framework for Inference Learning,3,neurips,0,0,2023-06-16 23:00:37.613000,https://github.com/nalonso2/iltheory,1,A theoretical framework for inference learning,"https://scholar.google.com/scholar?cluster=2593807461259318440&hl=en&as_sdt=0,11",1,2022
Cross-modal Learning for Image-Guided Point Cloud Shape Completion,1,neurips,4,3,2023-06-16 23:00:37.825000,https://github.com/diegovalsesia/xmfnet,21,Cross-modal Learning for Image-Guided Point Cloud Shape Completion,"https://scholar.google.com/scholar?cluster=8948872736066673993&hl=en&as_sdt=0,33",4,2022
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning,2,neurips,1,1,2023-06-16 23:00:38.037000,https://github.com/uoe-agents/robust_onpolicy_data_collection,3,Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning,"https://scholar.google.com/scholar?cluster=1440594365083807013&hl=en&as_sdt=0,47",2,2022
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection,6,neurips,3,0,2023-06-16 23:00:38.249000,https://github.com/jacobyuan7/rlip,49,RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection,"https://scholar.google.com/scholar?cluster=15237439848602268466&hl=en&as_sdt=0,10",4,2022
The Implicit Delta Method,123,neurips,0,0,2023-06-16 23:00:38.461000,https://github.com/jamesmcinerney/implicit-delta,1,A delta method for implicitly defined random variables,"https://scholar.google.com/scholar?cluster=4313312882856489116&hl=en&as_sdt=0,5",1,2022
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning,11,neurips,5,0,2023-06-16 23:00:38.674000,https://github.com/syp2ysy/SVF,58,Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning,"https://scholar.google.com/scholar?cluster=12823222114383862400&hl=en&as_sdt=0,34",3,2022
On the relationship between variational inference and auto-associative memory,0,neurips,0,0,2023-06-16 23:00:38.887000,https://github.com/sino7/predictive_coding_associative_memories,3,On the Relationship Between Variational Inference and Auto-Associative Memory,"https://scholar.google.com/scholar?cluster=2785842017536639&hl=en&as_sdt=0,10",1,2022
A Closer Look at Weakly-Supervised Audio-Visual Source Localization,12,neurips,3,3,2023-06-16 23:00:39.098000,https://github.com/stonemo/slavc,10,A Closer Look at Weakly-Supervised Audio-Visual Source Localization,"https://scholar.google.com/scholar?cluster=13873896709239769203&hl=en&as_sdt=0,16",3,2022
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion,1,neurips,0,0,2023-06-16 23:00:39.310000,https://github.com/hong-ming/scaledsgd,0,Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion,"https://scholar.google.com/scholar?cluster=505692052743334879&hl=en&as_sdt=0,33",2,2022
Lifting Weak Supervision To Structured Prediction,0,neurips,0,0,2023-06-16 23:00:39.522000,https://github.com/sprocketlab/ws-structured-prediction,2,Lifting Weak Supervision To Structured Prediction,"https://scholar.google.com/scholar?cluster=17266476389711347506&hl=en&as_sdt=0,31",4,2022
A Lagrangian Duality Approach to Active Learning,6,neurips,0,0,2023-06-16 23:00:39.735000,https://github.com/juanelenter/ally,2,A lagrangian duality approach to active learning,"https://scholar.google.com/scholar?cluster=11681313256965630916&hl=en&as_sdt=0,5",2,2022
Understanding the Failure of Batch Normalization for Transformers in NLP,0,neurips,0,0,2023-06-16 23:00:39.947000,https://github.com/wjxts/regularizedbn,13,Understanding the Failure of Batch Normalization for Transformers in NLP,"https://scholar.google.com/scholar?cluster=6560684434761979086&hl=en&as_sdt=0,5",2,2022
Exploration via Elliptical Episodic Bonuses,6,neurips,9,0,2023-06-16 23:00:40.159000,https://github.com/facebookresearch/e3b,66,Exploration via Elliptical Episodic Bonuses,"https://scholar.google.com/scholar?cluster=2613239820780112903&hl=en&as_sdt=0,14",8,2022
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup,4,neurips,0,0,2023-06-16 23:00:40.371000,https://github.com/tencentailabhealthcare/umix,10,Umix: Improving importance weighting for subpopulation shift via uncertainty-aware mixup,"https://scholar.google.com/scholar?cluster=9446890541395197883&hl=en&as_sdt=0,33",2,2022
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging,25,neurips,70,0,2023-06-16 23:00:40.583000,https://github.com/caiyuanhao1998/MST,386,Degradation-aware unfolding half-shuffle transformer for spectral compressive imaging,"https://scholar.google.com/scholar?cluster=7746611837210116803&hl=en&as_sdt=0,5",7,2022
FLAIR: Federated Learning Annotated Image Repository,1,neurips,11,0,2023-06-16 23:00:40.795000,https://github.com/apple/ml-flair,62,FLAIR: Federated Learning Annotated Image Repository,"https://scholar.google.com/scholar?cluster=3690272585566553585&hl=en&as_sdt=0,15",8,2022
Detecting Abrupt Changes in Sequential Pairwise Comparison Data,1,neurips,0,1,2023-06-16 23:00:41.007000,https://github.com/mountlee/cpd_bt,0,Detecting Abrupt Changes in Sequential Pairwise Comparison Data,"https://scholar.google.com/scholar?cluster=6701386184904567179&hl=en&as_sdt=0,32",1,2022
Rethinking Resolution in the Context of Efficient Video Recognition,3,neurips,1,0,2023-06-16 23:00:41.220000,https://github.com/cvmi-lab/reskd,28,Rethinking resolution in the context of efficient video recognition,"https://scholar.google.com/scholar?cluster=9701240362700437697&hl=en&as_sdt=0,33",4,2022
Deep Equilibrium Approaches to Diffusion Models,2,neurips,3,0,2023-06-16 23:00:41.432000,https://github.com/locuslab/deq-ddim,50,Deep equilibrium approaches to diffusion models,"https://scholar.google.com/scholar?cluster=14854015404116338033&hl=en&as_sdt=0,5",2,2022
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?,23,neurips,3,2,2023-06-16 23:00:41.644000,https://github.com/NeuralCollapseApplications/ImbalancedLearning,29,Do we really need a learnable classifier at the end of deep neural network?,"https://scholar.google.com/scholar?cluster=13915965631648718729&hl=en&as_sdt=0,3",1,2022
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation,9,neurips,1,3,2023-06-16 23:00:41.855000,https://github.com/liuyuanwei98/ipmt,15,Intermediate prototype mining transformer for few-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=9369835073666589032&hl=en&as_sdt=0,10",2,2022
Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning,8,neurips,13,0,2023-06-16 23:00:42.067000,https://github.com/microsoft/xpretrain,290,Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning,"https://scholar.google.com/scholar?cluster=14516544053429726965&hl=en&as_sdt=0,21",13,2022
Neural Conservation Laws: A Divergence-Free Perspective,8,neurips,1,0,2023-06-16 23:00:42.278000,https://github.com/facebookresearch/neural-conservation-law,29,Neural conservation laws: A divergence-free perspective,"https://scholar.google.com/scholar?cluster=11358706941570605831&hl=en&as_sdt=0,5",3,2022
Model Zoos: A Dataset of Diverse Populations of Neural Network Models,4,neurips,0,0,2023-06-16 23:00:42.507000,https://github.com/ModelZoos/ModelZooDataset,21,Model Zoos: A Dataset of Diverse Populations of Neural Network Models,"https://scholar.google.com/scholar?cluster=11134475911805065050&hl=en&as_sdt=0,33",3,2022
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation,6,neurips,3,0,2023-06-16 23:00:42.719000,https://github.com/peihaochen/ws-mgmap,13,Weakly-supervised multi-granularity map learning for vision-and-language navigation,"https://scholar.google.com/scholar?cluster=10538814385598827849&hl=en&as_sdt=0,5",1,2022
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination,2,neurips,0,0,2023-06-16 23:00:42.931000,https://github.com/dmksjfl/CABI,2,Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination,"https://scholar.google.com/scholar?cluster=360756721662557774&hl=en&as_sdt=0,47",2,2022
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy,8,neurips,2,0,2023-06-16 23:00:43.143000,https://github.com/woodyx218/private_vision,4,Scalable and efficient training of large convolutional neural networks with differential privacy,"https://scholar.google.com/scholar?cluster=2508850479410885483&hl=en&as_sdt=0,29",2,2022
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks,2,neurips,0,0,2023-06-16 23:00:43.356000,https://github.com/hongjoon0805/halo,5,Descent steps of a relation-aware energy produce heterogeneous graph neural networks,"https://scholar.google.com/scholar?cluster=18379331258021041231&hl=en&as_sdt=0,33",1,2022
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning,27,neurips,12,0,2023-06-16 23:00:43.570000,https://github.com/Lee-Gihun/FedNTD,37,Preservation of the Global Knowledge by Not-True Distillation in Federated Learning,"https://scholar.google.com/scholar?cluster=17418553757029920054&hl=en&as_sdt=0,51",2,2022
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning,0,neurips,0,0,2023-06-16 23:00:43.783000,https://github.com/lionellee9089/metamask,6,MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning,"https://scholar.google.com/scholar?cluster=14621291428401560306&hl=en&as_sdt=0,33",1,2022
On Feature Learning in the Presence of Spurious Correlations,15,neurips,2,0,2023-06-16 23:00:43.995000,https://github.com/izmailovpavel/spurious_feature_learning,27,On feature learning in the presence of spurious correlations,"https://scholar.google.com/scholar?cluster=8309037915604326672&hl=en&as_sdt=0,5",3,2022
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection,4,neurips,3,1,2023-06-16 23:00:44.209000,https://github.com/stevewongv/sparse2dense,57,Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection,"https://scholar.google.com/scholar?cluster=13399141401949023952&hl=en&as_sdt=0,5",5,2022
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation,75,neurips,118,58,2023-06-16 23:00:44.421000,https://github.com/vitae-transformer/vitpose,765,Vitpose: Simple vision transformer baselines for human pose estimation,"https://scholar.google.com/scholar?cluster=9439766841533136382&hl=en&as_sdt=0,5",19,2022
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules,4,neurips,2,0,2023-06-16 23:00:44.640000,https://github.com/idsia/neuraldiffeq-fwp,13,Neural differential equations for learning to program neural nets through continuous learning rules,"https://scholar.google.com/scholar?cluster=8895930076370351035&hl=en&as_sdt=0,31",4,2022
MEMO: Test Time Robustness via Adaptation and Augmentation,63,neurips,4,1,2023-06-16 23:00:44.854000,https://github.com/zhangmarvin/memo,34,Memo: Test time robustness via adaptation and augmentation,"https://scholar.google.com/scholar?cluster=1448618539109048791&hl=en&as_sdt=0,11",2,2022
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm,0,neurips,0,0,2023-06-16 23:00:45.065000,https://github.com/shaocr/pauci,4,Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm,"https://scholar.google.com/scholar?cluster=11237720995546922276&hl=en&as_sdt=0,10",2,2022
Error Correction Code Transformer,7,neurips,6,0,2023-06-16 23:00:45.281000,https://github.com/yonilc/ecct,13,Error correction code transformer,"https://scholar.google.com/scholar?cluster=903759423999065870&hl=en&as_sdt=0,33",2,2022
Capturing Graphs with Hypo-Elliptic Diffusions,0,neurips,0,0,2023-06-16 23:00:45.499000,https://github.com/tgcsaba/graph2tens,2,Capturing Graphs with Hypo-Elliptic Diffusions,"https://scholar.google.com/scholar?cluster=15681689406304217341&hl=en&as_sdt=0,10",1,2022
SIXO: Smoothing Inference with Twisted Objectives,0,neurips,0,0,2023-06-16 23:00:45.712000,https://github.com/lindermanlab/sixo,3,SIXO: Smoothing Inference with Twisted Objectives,"https://scholar.google.com/scholar?cluster=12038259047812745507&hl=en&as_sdt=0,34",3,2022
Exploring evolution-aware & -free protein language models as protein function predictors,2,neurips,8,3,2023-06-16 16:57:06.093000,https://github.com/elttaes/revisiting-plms,40,On pre-trained language models for antibody,"https://scholar.google.com/scholar?cluster=3644203748348349044&hl=en&as_sdt=0,33",2,2022
Breaking Bad: A Dataset for Geometric Fracture and Reassembly,4,neurips,8,0,2023-06-16 23:00:45.923000,https://github.com/wuziyi616/multi_part_assembly,39,Breaking Bad: A Dataset for Geometric Fracture and Reassembly,"https://scholar.google.com/scholar?cluster=14499530288450300317&hl=en&as_sdt=0,5",2,2022
Geoclidean: Few-Shot Generalization in Euclidean Geometry,2,neurips,0,0,2023-06-16 23:00:46.136000,https://github.com/joyhsu0504/geoclidean_framework,6,Geoclidean: Few-shot generalization in euclidean geometry,"https://scholar.google.com/scholar?cluster=15302234923717650723&hl=en&as_sdt=0,5",2,2022
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport,1,neurips,0,0,2023-06-16 23:00:46.349000,https://github.com/boschresearch/bosot,0,Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport,"https://scholar.google.com/scholar?cluster=5846774864854783055&hl=en&as_sdt=0,4",3,2022
Robust Models are less Over-Confident,1,neurips,1,0,2023-06-16 23:00:46.561000,https://github.com/gejulia/robustness_confidences_evaluation,17,Robust Models are less Over-Confident,"https://scholar.google.com/scholar?cluster=11840327885361702172&hl=en&as_sdt=0,33",3,2022
ComMU: Dataset for Combinatorial Music Generation,0,neurips,24,0,2023-06-16 23:00:46.773000,https://github.com/POZAlabs/ComMU-code,118,ComMU: Dataset for Combinatorial Music Generation,"https://scholar.google.com/scholar?cluster=17767260003172440235&hl=en&as_sdt=0,5",6,2022
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,83,eccv,91,0,2023-06-16 23:55:11.795000,https://github.com/XiaohangZhan/cdp,444,Consensus-driven propagation in massive unlabeled data for face recognition,"https://scholar.google.com/scholar?cluster=3229094614013455360&hl=en&as_sdt=0,43",22,2018
Deep Cross-Modal Projection Learning for Image-Text Matching,280,eccv,20,12,2023-06-16 23:55:12.010000,https://github.com/YingZhangDUT/Cross-Modal-Projection-Learning,88,Deep cross-modal projection learning for image-text matching,"https://scholar.google.com/scholar?cluster=17800185495041580477&hl=en&as_sdt=0,5",2,2018
Multi-Class Model Fitting by Energy Minimization and Mode-Seeking,41,eccv,2,0,2023-06-16 23:55:12.222000,https://github.com/danini/multi-x,14,Multi-class model fitting by energy minimization and mode-seeking,"https://scholar.google.com/scholar?cluster=9207771737491685637&hl=en&as_sdt=0,5",3,2018
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network,261,eccv,95,25,2023-06-16 23:55:12.434000,https://github.com/XinJCheng/CSPN,482,Depth estimation via affinity learned with convolutional spatial propagation network,"https://scholar.google.com/scholar?cluster=15331233772685404808&hl=en&as_sdt=0,18",20,2018
Fast Light Field Reconstruction With Deep Coarse-To-Fine Modeling of Spatial-Angular Clues,121,eccv,9,2,2023-06-16 23:55:12.645000,https://github.com/angularsr/LightFieldAngularSR,16,Fast light field reconstruction with deep coarse-to-fine modeling of spatial-angular clues,"https://scholar.google.com/scholar?cluster=2431722100986178790&hl=en&as_sdt=0,5",2,2018
Deep Expander Networks: Efficient Deep Networks from Graph Theory,68,eccv,12,0,2023-06-16 23:55:12.856000,https://github.com/DrImpossible/Deep-Expander-Networks,43,Deep expander networks: Efficient deep networks from graph theory,"https://scholar.google.com/scholar?cluster=14046312150868626891&hl=en&as_sdt=0,5",4,2018
Attend and Rectify: a gated attention mechanism for fine-grained recovery,45,eccv,10,2,2023-06-16 23:55:13.067000,https://github.com/prlz77/attend-and-rectify,52,Attend and rectify: a gated attention mechanism for fine-grained recovery,"https://scholar.google.com/scholar?cluster=13738598675286043975&hl=en&as_sdt=0,34",4,2018
PyramidBox: A Context-assisted Single Shot Face Detector,369,eccv,2965,869,2023-06-16 23:55:13.279000,https://github.com/PaddlePaddle/models,6794,Pyramidbox: A context-assisted single shot face detector,"https://scholar.google.com/scholar?cluster=15584112941596045225&hl=en&as_sdt=0,10",275,2018
Learning to Blend Photos,9,eccv,1,3,2023-06-16 23:55:13.490000,https://github.com/hfslyc/LearnToBlend,51,Learning to blend photos,"https://scholar.google.com/scholar?cluster=3769330447198783594&hl=en&as_sdt=0,5",11,2018
Parallel Feature Pyramid Network for Object Detection,265,eccv,0,1,2023-06-16 23:55:13.702000,https://github.com/chosj95/PFPNet.pytorch,7,Parallel feature pyramid network for object detection,"https://scholar.google.com/scholar?cluster=3087633766276723421&hl=en&as_sdt=0,44",4,2018
AMC: AutoML for Model Compression and Acceleration on Mobile Devices,1290,eccv,101,18,2023-06-16 23:55:13.914000,https://github.com/mit-han-lab/amc,389,Amc: Automl for model compression and acceleration on mobile devices,"https://scholar.google.com/scholar?cluster=2282234460810497997&hl=en&as_sdt=0,5",17,2018
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes,4,eccv,0,0,2023-06-16 23:55:14.126000,https://github.com/SSAW14/Image_Generation_with_Latent_Code,5,Diverse conditional image generation by stochastic regression with latent drop-out codes,"https://scholar.google.com/scholar?cluster=12901521834315921854&hl=en&as_sdt=0,39",3,2018
PS-FCN: A Flexible Learning Framework for Photometric Stereo,125,eccv,31,0,2023-06-16 23:55:14.336000,https://github.com/guanyingc/PS-FCN,81,PS-FCN: A flexible learning framework for photometric stereo,"https://scholar.google.com/scholar?cluster=2638846704814041836&hl=en&as_sdt=0,36",6,2018
Instance-level Human Parsing via Part Grouping Network,294,eccv,101,38,2023-06-16 23:55:14.547000,https://github.com/Engineering-Course/CIHP_PGN,381,Instance-level human parsing via part grouping network,"https://scholar.google.com/scholar?cluster=9349119763164764615&hl=en&as_sdt=0,41",17,2018
Constrained Optimization Based Low-Rank Approximation of Deep Neural Networks,62,eccv,1,0,2023-06-16 23:55:14.759000,https://github.com/chongli-uw/cobla,2,Constrained optimization based low-rank approximation of deep neural networks,"https://scholar.google.com/scholar?cluster=16361848153367526892&hl=en&as_sdt=0,14",2,2018
CTAP: Complementary Temporal Action Proposal Generation,183,eccv,11,6,2023-06-16 23:55:14.969000,https://github.com/jiyanggao/CTAP,42,Ctap: Complementary temporal action proposal generation,"https://scholar.google.com/scholar?cluster=6089124584810492910&hl=en&as_sdt=0,3",5,2018
Dist-GAN: An Improved GAN using Distance Constraints,92,eccv,21,5,2023-06-16 23:55:15.180000,https://github.com/tntrung/gan,66,Dist-gan: An improved gan using distance constraints,"https://scholar.google.com/scholar?cluster=14413655732899864103&hl=en&as_sdt=0,10",5,2018
Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline,235,eccv,559,84,2023-06-16 23:55:15.391000,https://github.com/detectRecog/CCPD,1964,Towards end-to-end license plate detection and recognition: A large dataset and baseline,"https://scholar.google.com/scholar?cluster=14804408137924957473&hl=en&as_sdt=0,5",63,2018
Cross-Modal and Hierarchical Modeling of Video and Text,115,eccv,6,2,2023-06-16 23:55:15.648000,https://github.com/Sha-Lab/CMHSE,17,Cross-modal and hierarchical modeling of video and text,"https://scholar.google.com/scholar?cluster=4253130459603491568&hl=en&as_sdt=0,5",5,2018
StarMap for Category-Agnostic Keypoint and Viewpoint Estimation,70,eccv,18,5,2023-06-16 23:55:15.858000,https://github.com/xingyizhou/StarMap,101,Starmap for category-agnostic keypoint and viewpoint estimation,"https://scholar.google.com/scholar?cluster=182684951534940435&hl=en&as_sdt=0,5",10,2018
Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization,176,eccv,0,1,2023-06-16 23:55:16.069000,https://github.com/danieljakubovitz/Jacobian_Regularization,3,Improving dnn robustness to adversarial attacks using jacobian regularization,"https://scholar.google.com/scholar?cluster=15145149459046831045&hl=en&as_sdt=0,5",1,2018
Folded Recurrent Neural Networks for Future Video Prediction,103,eccv,12,2,2023-06-16 23:55:16.280000,https://github.com/moliusimon/frnn,39,Folded recurrent neural networks for future video prediction,"https://scholar.google.com/scholar?cluster=14311378408238305215&hl=en&as_sdt=0,5",2,2018
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation,200,eccv,120,41,2023-06-16 23:55:16.491000,https://github.com/peteanderson80/Matterport3DSimulator,378,Look before you leap: Bridging model-free and model-based reinforcement learning for planned-ahead vision-and-language navigation,"https://scholar.google.com/scholar?cluster=4362703551818063501&hl=en&as_sdt=0,28",19,2018
Acquisition of Localization Confidence for Accurate Object Detection,841,eccv,151,20,2023-06-16 23:55:16.703000,https://github.com/vacancy/PreciseRoIPooling,761,Acquisition of localization confidence for accurate object detection,"https://scholar.google.com/scholar?cluster=14154791864857863721&hl=en&as_sdt=0,31",24,2018
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network,691,eccv,940,164,2023-06-16 23:55:16.914000,https://github.com/YadiraF/PRNet,4799,Joint 3d face reconstruction and dense alignment with position map regression network,"https://scholar.google.com/scholar?cluster=2161439188175601394&hl=en&as_sdt=0,26",190,2018
Multimodal Unsupervised Image-to-image Translation,2289,eccv,484,61,2023-06-16 23:55:17.125000,https://github.com/nvlabs/MUNIT,2565,Multimodal unsupervised image-to-image translation,"https://scholar.google.com/scholar?cluster=13317525907573308290&hl=en&as_sdt=0,6",76,2018
Diverse feature visualizations reveal invariances in early layers of deep neural networks,25,eccv,0,0,2023-06-16 23:55:17.336000,https://github.com/sacadena/diverse_feature_vis,5,Diverse feature visualizations reveal invariances in early layers of deep neural networks,"https://scholar.google.com/scholar?cluster=5475890318105532537&hl=en&as_sdt=0,5",5,2018
Learning Dynamic Memory Networks for Object Tracking,285,eccv,12,4,2023-06-16 23:55:17.547000,https://github.com/skyoung/MemTrack,81,Learning dynamic memory networks for object tracking,"https://scholar.google.com/scholar?cluster=5536802084548288754&hl=en&as_sdt=0,36",9,2018
Statistically-motivated Second-order Pooling,51,eccv,2,1,2023-06-16 23:55:17.758000,https://github.com/kcyu2014/smsop,32,Statistically-motivated second-order pooling,"https://scholar.google.com/scholar?cluster=12417165649470483444&hl=en&as_sdt=0,39",5,2018
Improving Generalization via Scalable Neighborhood Component Analysis,125,eccv,0,0,2023-06-16 23:55:17.969000,https://github.com/zhirongw/snca.pytorch,8,Improving generalization via scalable neighborhood component analysis,"https://scholar.google.com/scholar?cluster=1930762472534444949&hl=en&as_sdt=0,5",2,2018
Distractor-aware Siamese Networks for Visual Object Tracking,1214,eccv,360,31,2023-06-16 23:55:18.181000,https://github.com/foolwood/DaSiamRPN,1228,Distractor-aware siamese networks for visual object tracking,"https://scholar.google.com/scholar?cluster=5659298886572595606&hl=en&as_sdt=0,46",56,2018
Escaping from Collapsing Modes in a Constrained Space,16,eccv,1,0,2023-06-16 23:55:18.392000,https://github.com/chang810249/BEGAN-CS,12,Escaping from collapsing modes in a constrained space,"https://scholar.google.com/scholar?cluster=8912424288441556924&hl=en&as_sdt=0,10",2,2018
Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation,53,eccv,9,6,2023-06-16 23:55:18.615000,https://github.com/godisboy/DRPAN,51,Discriminative region proposal adversarial networks for high-quality image-to-image translation,"https://scholar.google.com/scholar?cluster=3209031446584734295&hl=en&as_sdt=0,5",6,2018
Learning Blind Video Temporal Consistency,252,eccv,62,14,2023-06-16 23:55:18.826000,https://github.com/phoenix104104/fast_blind_video_consistency,367,Learning blind video temporal consistency,"https://scholar.google.com/scholar?cluster=7698730627265999813&hl=en&as_sdt=0,50",9,2018
Graph Distillation for Action Detection with Privileged Modalities,100,eccv,18,3,2023-06-16 23:55:19.037000,https://github.com/google/graph_distillation,64,Graph distillation for action detection with privileged modalities,"https://scholar.google.com/scholar?cluster=11357384495865637099&hl=en&as_sdt=0,5",6,2018
Efficient Uncertainty Estimation for Semantic Segmentation in Videos,99,eccv,8,2,2023-06-16 23:55:19.249000,https://github.com/andyhahaha/Efficient-Uncertainty-Video-Segmentation,22,Efficient uncertainty estimation for semantic segmentation in videos,"https://scholar.google.com/scholar?cluster=12752926199091689307&hl=en&as_sdt=0,5",4,2018
Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal,131,eccv,12,5,2023-06-16 23:55:19.460000,https://github.com/yangj1e/bdn-refremv,46,Seeing deeply and bidirectionally: A deep learning approach for single image reflection removal,"https://scholar.google.com/scholar?cluster=10372612231120248014&hl=en&as_sdt=0,5",7,2018
Learning SO(3) Equivariant Representations with Spherical CNNs,424,eccv,48,6,2023-06-16 23:55:19.671000,https://github.com/daniilidis-group/spherical-cnn,275,Learning so (3) equivariant representations with spherical cnns,"https://scholar.google.com/scholar?cluster=13360112200606529500&hl=en&as_sdt=0,5",14,2018
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks,162,eccv,41,8,2023-06-16 23:55:19.882000,https://github.com/lyndonzheng/Synthetic2Realistic,177,T2net: Synthetic-to-realistic translation for solving single-image depth estimation tasks,"https://scholar.google.com/scholar?cluster=2045753887928749430&hl=en&as_sdt=0,5",6,2018
Partial Adversarial Domain Adaptation,384,eccv,41,1,2023-06-16 23:55:20.094000,https://github.com/thuml/PADA,90,Partial adversarial domain adaptation,"https://scholar.google.com/scholar?cluster=5435435641375957692&hl=en&as_sdt=0,33",11,2018
Diverse Image-to-Image Translation via Disentangled Representations,1135,eccv,153,30,2023-06-16 23:55:20.305000,https://github.com/HsinYingLee/DRIT,810,Diverse image-to-image translation via disentangled representations,"https://scholar.google.com/scholar?cluster=2272463241175511122&hl=en&as_sdt=0,20",15,2018
BOP: Benchmark for 6D Object Pose Estimation,334,eccv,112,7,2023-06-16 23:55:20.515000,https://github.com/thodan/bop_toolkit,287,Bop: Benchmark for 6d object pose estimation,"https://scholar.google.com/scholar?cluster=7913199704113527&hl=en&as_sdt=0,5",11,2018
Generative Domain-Migration Hashing for Sketch-to-Image Retrieval,85,eccv,6,3,2023-06-16 23:55:20.726000,https://github.com/YCJGG/GDH,21,Generative domain-migration hashing for sketch-to-image retrieval,"https://scholar.google.com/scholar?cluster=11613774012144257188&hl=en&as_sdt=0,5",2,2018
FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans,122,eccv,49,15,2023-06-16 23:55:20.936000,https://github.com/art-programmer/FloorNet,189,Floornet: A unified framework for floorplan reconstruction from 3d scans,"https://scholar.google.com/scholar?cluster=6050985589125390059&hl=en&as_sdt=0,5",12,2018
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency,442,eccv,33,4,2023-06-16 23:55:21.147000,https://github.com/vt-vl-lab/DF-Net,209,Df-net: Unsupervised joint learning of depth and flow using cross-task consistency,"https://scholar.google.com/scholar?cluster=14124367292083542005&hl=en&as_sdt=0,6",9,2018
Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition,278,eccv,23,7,2023-06-16 23:55:21.358000,https://github.com/ChaojianYu/Hierarchical-Bilinear-Pooling,102,Hierarchical bilinear pooling for fine-grained visual recognition,"https://scholar.google.com/scholar?cluster=4033149798374032404&hl=en&as_sdt=0,5",1,2018
Face De-Spoofing: Anti-Spoofing via Noise Modeling,265,eccv,42,15,2023-06-16 23:55:21.569000,https://github.com/yaojieliu/ECCV2018-FaceDeSpoofing,143,Face de-spoofing: Anti-spoofing via noise modeling,"https://scholar.google.com/scholar?cluster=6923482401871998322&hl=en&as_sdt=0,23",8,2018
Efficient Relative Attribute Learning using Graph Neural Networks,30,eccv,4,1,2023-06-16 23:55:21.780000,https://github.com/zihangm/RAL_GNN,20,Efficient relative attribute learning using graph neural networks,"https://scholar.google.com/scholar?cluster=11106726889748018924&hl=en&as_sdt=0,5",0,2018
Localization Recall Precision (LRP): A New Performance Metric for Object Detection,115,eccv,13,0,2023-06-16 23:55:21.992000,https://github.com/cancam/LRP,63,Localization recall precision (LRP): A new performance metric for object detection,"https://scholar.google.com/scholar?cluster=18184659199813329105&hl=en&as_sdt=0,5",8,2018
Image Super-Resolution Using Very Deep Residual Channel Attention Networks,3523,eccv,319,76,2023-06-16 23:55:22.204000,https://github.com/yulunzhang/RCAN,1245,Image super-resolution using very deep residual channel attention networks,"https://scholar.google.com/scholar?cluster=3748973811121591896&hl=en&as_sdt=0,10",21,2018
Extending Layered Models to 3D Motion,32,eccv,0,0,2023-06-16 23:55:22.414000,https://github.com/donglao/layers3Dmotion,2,Extending layered models to 3d motion,"https://scholar.google.com/scholar?cluster=3699102061773173844&hl=en&as_sdt=0,25",3,2018
License Plate Detection and Recognition in Unconstrained Scenarios,268,eccv,595,112,2023-06-16 23:55:22.641000,https://github.com/sergiomsilva/alpr-unconstrained,1623,License plate detection and recognition in unconstrained scenarios,"https://scholar.google.com/scholar?cluster=12853420144391201059&hl=en&as_sdt=0,5",87,2018
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking,85,eccv,10,8,2023-06-16 23:55:22.851000,https://github.com/ogroth/shapestacks,41,Shapestacks: Learning vision-based physical intuition for generalised object stacking,"https://scholar.google.com/scholar?cluster=11796899814392889836&hl=en&as_sdt=0,5",5,2018
"SRDA: Generating Instance Segmentation Annotation via Scanning, Reasoning and Domain Adaptation",19,eccv,1,0,2023-06-16 23:55:23.064000,https://github.com/DirtyHarryLYL/SRDA-ECCV2018,7,"Srda: Generating instance segmentation annotation via scanning, reasoning and domain adaptation","https://scholar.google.com/scholar?cluster=14701934472659497490&hl=en&as_sdt=0,5",2,2018
On the Solvability of Viewing Graphs,10,eccv,0,0,2023-06-16 23:55:23.278000,https://github.com/mtrager/viewing-graphs,1,On the solvability of viewing graphs,"https://scholar.google.com/scholar?cluster=11272659149049993124&hl=en&as_sdt=0,5",2,2018
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers,390,eccv,33,6,2023-06-16 23:55:23.490000,https://github.com/KaiqiZhang/admm-pruning,95,A systematic dnn weight pruning framework using alternating direction method of multipliers,"https://scholar.google.com/scholar?cluster=17353545770360369624&hl=en&as_sdt=0,5",8,2018
Single Shot Scene Text Retrieval,42,eccv,30,1,2023-06-16 23:55:23.702000,https://github.com/lluisgomez/single-shot-str,66,Single shot scene text retrieval,"https://scholar.google.com/scholar?cluster=18342223451780815542&hl=en&as_sdt=0,5",10,2018
Deep Shape Matching,76,eccv,47,5,2023-06-16 23:55:23.922000,https://github.com/janesjanes/sketchy,157,Deep shape matching,"https://scholar.google.com/scholar?cluster=8039392131029835381&hl=en&as_sdt=0,33",6,2018
Learning to Navigate for Fine-grained Classification,462,eccv,119,38,2023-06-16 23:55:24.134000,https://github.com/yangze0930/NTS-Net,435,Learning to navigate for fine-grained classification,"https://scholar.google.com/scholar?cluster=14152137546438136393&hl=en&as_sdt=0,5",11,2018
Improving Shape Deformation in Unsupervised Image-to-Image Translation,80,eccv,22,17,2023-06-16 23:55:24.347000,https://github.com/brownvc/ganimorph,119,Improving shape deformation in unsupervised image-to-image translation,"https://scholar.google.com/scholar?cluster=8740306489765068872&hl=en&as_sdt=0,5",17,2018
LSQ++: Lower running time and higher recall in multi-codebook quantization,31,eccv,4,27,2023-06-16 23:55:24.565000,https://github.com/una-dinosauria/Rayuela.jl,57,LSQ++: Lower running time and higher recall in multi-codebook quantization,"https://scholar.google.com/scholar?cluster=2638321853527220522&hl=en&as_sdt=0,11",5,2018
Depth-aware CNN for RGB-D Segmentation,240,eccv,83,34,2023-06-16 23:55:24.802000,https://github.com/laughtervv/DepthAwareCNN,292,Depth-aware cnn for rgb-d segmentation,"https://scholar.google.com/scholar?cluster=13093843379314761716&hl=en&as_sdt=0,23",14,2018
Weakly- and Semi-Supervised Panoptic Segmentation,177,eccv,24,0,2023-06-16 23:55:25.039000,https://github.com/qizhuli/Weakly-Supervised-Panoptic-Segmentation,159,Weakly-and semi-supervised panoptic segmentation,"https://scholar.google.com/scholar?cluster=7210150945066091860&hl=en&as_sdt=0,41",12,2018
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation,79,eccv,20,3,2023-06-16 23:55:25.251000,https://github.com/NVlabs/learningrigidity,143,Learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation,"https://scholar.google.com/scholar?cluster=827835377637646447&hl=en&as_sdt=0,10",17,2018
Textual Explanations for Self-Driving Vehicles,214,eccv,14,9,2023-06-16 23:55:25.463000,https://github.com/JinkyuKimUCB/explainable-deep-driving,51,Textual explanations for self-driving vehicles,"https://scholar.google.com/scholar?cluster=3588149335447094159&hl=en&as_sdt=0,5",4,2018
Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features,79,eccv,30,19,2023-06-16 23:55:25.674000,https://github.com/yangxuntu/vrd,90,Shuffle-then-assemble: Learning object-agnostic visual relationship features,"https://scholar.google.com/scholar?cluster=11457586717303926687&hl=en&as_sdt=0,5",4,2018
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors,67,eccv,21,2,2023-06-16 23:55:25.885000,https://github.com/dbaranchuk/ivf-hnsw,163,Revisiting the inverted indices for billion-scale approximate nearest neighbors,"https://scholar.google.com/scholar?cluster=17143188662932206713&hl=en&as_sdt=0,33",6,2018
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection,441,eccv,27,7,2023-06-16 23:55:26.096000,https://github.com/shenjianbing/PDB-ConvLSTM,113,Pyramid dilated deeper convlstm for video salient object detection,"https://scholar.google.com/scholar?cluster=4923326738440851048&hl=en&as_sdt=0,32",9,2018
Beyond local reasoning for stereo confidence estimation with deep learning,59,eccv,5,1,2023-06-16 23:55:26.308000,https://github.com/fabiotosi92/LGC-Tensorflow,10,Beyond local reasoning for stereo confidence estimation with deep learning,"https://scholar.google.com/scholar?cluster=15944809692028106974&hl=en&as_sdt=0,23",3,2018
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights,487,eccv,23,2,2023-06-16 23:55:26.518000,https://github.com/arunmallya/piggyback,172,Piggyback: Adapting a single network to multiple tasks by learning to mask weights,"https://scholar.google.com/scholar?cluster=14326835779502008295&hl=en&as_sdt=0,5",4,2018
PSANet: Point-wise Spatial Attention Network for Scene Parsing,918,eccv,37,1,2023-06-16 23:55:26.730000,https://github.com/hszhao/PSANet,216,Psanet: Point-wise spatial attention network for scene parsing,"https://scholar.google.com/scholar?cluster=5612902286487550404&hl=en&as_sdt=0,5",12,2018
SkipNet: Learning Dynamic Routing in Convolutional Networks,561,eccv,47,7,2023-06-16 23:55:26.941000,https://github.com/ucbdrive/skipnet,223,Skipnet: Learning dynamic routing in convolutional networks,"https://scholar.google.com/scholar?cluster=16831193156348049998&hl=en&as_sdt=0,18",14,2018
The Contextual Loss for Image Transformation with Non-Aligned Data,333,eccv,78,13,2023-06-16 23:55:27.151000,https://github.com/roimehrez/contextualLoss,472,The contextual loss for image transformation with non-aligned data,"https://scholar.google.com/scholar?cluster=4201014753742149913&hl=en&as_sdt=0,19",19,2018
Fully-Convolutional Point Networks for Large-Scale Point Clouds,180,eccv,23,3,2023-06-16 23:55:27.362000,https://github.com/drethage/fully-convolutional-point-network,86,Fully-convolutional point networks for large-scale point clouds,"https://scholar.google.com/scholar?cluster=15796798606510706475&hl=en&as_sdt=0,30",13,2018
Integral Human Pose Regression,725,eccv,75,10,2023-06-16 23:55:27.573000,https://github.com/JimmySuen/integral-human-pose,456,Integral human pose regression,"https://scholar.google.com/scholar?cluster=13367121804975374310&hl=en&as_sdt=0,5",25,2018
A Dataset and Architecture for Visual Reasoning with a Working Memory,51,eccv,13,0,2023-06-16 23:55:27.784000,https://github.com/google/cog,41,A dataset and architecture for visual reasoning with a working memory,"https://scholar.google.com/scholar?cluster=7092743520972213867&hl=en&as_sdt=0,3",7,2018
Affinity Derivation and Graph Merge for Instance Segmentation,103,eccv,8,3,2023-06-16 23:55:27.995000,https://github.com/xck36/GMIS,39,Affinity derivation and graph merge for instance segmentation,"https://scholar.google.com/scholar?cluster=10478540241382688376&hl=en&as_sdt=0,11",4,2018
Modality Distillation with Multiple Stream Networks for Action Recognition,154,eccv,3,3,2023-06-16 23:55:28.207000,https://github.com/ncgarcia/modality-distillation,21,Modality distillation with multiple stream networks for action recognition,"https://scholar.google.com/scholar?cluster=11766651177768406473&hl=en&as_sdt=0,11",4,2018
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency,33,eccv,7,3,2023-06-16 23:55:28.418000,https://github.com/xingyizhou/3DKeypoints-DA,82,Unsupervised domain adaptation for 3d keypoint estimation via view consistency,"https://scholar.google.com/scholar?cluster=10330474330891828727&hl=en&as_sdt=0,14",11,2018
Group Normalization,3074,eccv,20,1,2023-06-16 23:55:28.643000,https://github.com/ppwwyyxx/GroupNorm-reproduce,113,Group normalization,"https://scholar.google.com/scholar?cluster=14814179610283147593&hl=en&as_sdt=0,5",6,2018
Conditional Image-Text Embedding Networks,103,eccv,91,1,2023-06-16 23:55:28.855000,https://github.com/BryanPlummer/cite,38,Conditional image-text embedding networks,"https://scholar.google.com/scholar?cluster=16144402408937710486&hl=en&as_sdt=0,44",2,2018
Object Level Visual Reasoning in Videos,161,eccv,20,5,2023-06-16 23:55:29.066000,https://github.com/fabienbaradel/object_level_visual_reasoning,172,Object level visual reasoning in videos,"https://scholar.google.com/scholar?cluster=17632579279713545301&hl=en&as_sdt=0,5",15,2018
Deep Clustering for Unsupervised Learning of Visual Features,2326,eccv,310,8,2023-06-16 23:55:29.277000,https://github.com/facebookresearch/deepcluster,1549,Deep clustering for unsupervised learning of visual features,"https://scholar.google.com/scholar?cluster=9776210521429980111&hl=en&as_sdt=0,10",33,2018
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models,364,eccv,21,1,2023-06-16 23:55:29.488000,https://github.com/huanzhang12/Adversarial_Survey,98,Is Robustness the Cost of Accuracy?--A Comprehensive Study on the Robustness of 18 Deep Image Classification Models,"https://scholar.google.com/scholar?cluster=380810929013428531&hl=en&as_sdt=0,5",8,2018
Single Image Water Hazard Detection using FCN with Reflection Attention Units,30,eccv,16,12,2023-06-16 23:55:29.700000,https://github.com/Cow911/SingleImageWaterHazardDetectionWithRAU,50,Single image water hazard detection using fcn with reflection attention units,"https://scholar.google.com/scholar?cluster=7504719303538721682&hl=en&as_sdt=0,44",4,2018
Predicting Gaze in Egocentric Video by Learning Task-dependent Attention Transition,103,eccv,18,1,2023-06-16 23:55:29.911000,https://github.com/hyf015/egocentric-gaze-prediction,55,Predicting gaze in egocentric video by learning task-dependent attention transition,"https://scholar.google.com/scholar?cluster=1151610918319195215&hl=en&as_sdt=0,24",4,2018
Joint Learning of Intrinsic Images and Semantic Segmentation,43,eccv,1,1,2023-06-16 23:55:30.123000,https://github.com/Morpheus3000/intrinseg,12,Joint learning of intrinsic images and semantic segmentation,"https://scholar.google.com/scholar?cluster=76148957542870676&hl=en&as_sdt=0,11",2,2018
Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection,183,eccv,26,13,2023-06-16 23:55:30.335000,https://github.com/zijundeng/BDRAR,112,Bidirectional feature pyramid network with recurrent attention residual modules for shadow detection,"https://scholar.google.com/scholar?cluster=9596477260782472154&hl=en&as_sdt=0,5",7,2018
Deep Regression Tracking with Shrinkage Loss,238,eccv,14,2,2023-06-16 23:55:30.546000,https://github.com/chaoma99/DSLT,58,Deep regression tracking with shrinkage loss,"https://scholar.google.com/scholar?cluster=13852835873107246854&hl=en&as_sdt=0,14",9,2018
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes,563,eccv,96,42,2023-06-16 23:55:30.758000,https://github.com/MhLiao/MaskTextSpotter,416,Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes,"https://scholar.google.com/scholar?cluster=3537153192230704531&hl=en&as_sdt=0,5",17,2018
Deterministic Consensus Maximization with Biconvex Programming,32,eccv,4,2,2023-06-16 23:55:30.969000,https://github.com/ZhipengCai/Demo---Deterministic-consensus-maximization-with-biconvex-programming,11,Deterministic consensus maximization with biconvex programming,"https://scholar.google.com/scholar?cluster=4292317912687886232&hl=en&as_sdt=0,5",4,2018
Quantized Densely Connected U-Nets for Efficient Landmark Localization,150,eccv,39,11,2023-06-16 23:55:31.180000,https://github.com/zhiqiangdon/CU-Net,222,Quantized densely connected u-nets for efficient landmark localization,"https://scholar.google.com/scholar?cluster=8723635568504904410&hl=en&as_sdt=0,38",13,2018
Repeatability Is Not Enough: Learning Affine Regions via Discriminability,181,eccv,46,3,2023-06-16 23:55:31.391000,https://github.com/ducha-aiki/affnet,241,Repeatability is not enough: Learning affine regions via discriminability,"https://scholar.google.com/scholar?cluster=14226910740419570959&hl=en&as_sdt=0,5",17,2018
ADVIO: An Authentic Dataset for Visual-Inertial Odometry,76,eccv,38,10,2023-06-16 23:55:31.601000,https://github.com/AaltoVision/ADVIO,209,ADVIO: An authentic dataset for visual-inertial odometry,"https://scholar.google.com/scholar?cluster=14618513620593447739&hl=en&as_sdt=0,42",9,2018
Attributes as Operators: Factorizing Unseen Attribute-Object Compositions,123,eccv,16,2,2023-06-16 23:55:31.813000,https://github.com/Tushar-N/attributes-as-operators,63,Attributes as operators: factorizing unseen attribute-object compositions,"https://scholar.google.com/scholar?cluster=11627198158637727139&hl=en&as_sdt=0,11",4,2018
Generating 3D Faces using Convolutional Mesh Autoencoders,485,eccv,98,20,2023-06-16 23:55:32.024000,https://github.com/anuragranj/coma,424,Generating 3D faces using convolutional mesh autoencoders,"https://scholar.google.com/scholar?cluster=7357268074603147679&hl=en&as_sdt=0,5",34,2018
Data-Driven Sparse Structure Selection for Deep Neural Networks,524,eccv,5,0,2023-06-16 23:55:32.235000,https://github.com/huangzehao/sparse-structure-selection,39,Data-driven sparse structure selection for deep neural networks,"https://scholar.google.com/scholar?cluster=17407171928050222407&hl=en&as_sdt=0,14",3,2018
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction,53,eccv,4,1,2023-06-16 23:55:32.447000,https://github.com/PSCLab-ASU/LAPRAN-PyTorch,8,Lapran: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction,"https://scholar.google.com/scholar?cluster=7232900847478305958&hl=en&as_sdt=0,5",2,2018
Learning Warped Guidance for Blind Face Restoration,100,eccv,11,1,2023-06-16 23:55:32.659000,https://github.com/csxmli2016/GFRNet,95,Learning warped guidance for blind face restoration,"https://scholar.google.com/scholar?cluster=2135603410227164832&hl=en&as_sdt=0,5",8,2018
Shift-Net: Image Inpainting via Deep Feature Rearrangement,420,eccv,80,30,2023-06-16 23:55:32.869000,https://github.com/Zhaoyi-Yan/Shift-Net_pytorch,355,Shift-net: Image inpainting via deep feature rearrangement,"https://scholar.google.com/scholar?cluster=12909199709571911075&hl=en&as_sdt=0,5",13,2018
Estimating the Success of Unsupervised Image to Image Translation,11,eccv,3,0,2023-06-16 23:55:33.081000,https://github.com/sagiebenaim/gan_bound,18,Estimating the success of unsupervised image to image translation,"https://scholar.google.com/scholar?cluster=12228389234712563972&hl=en&as_sdt=0,1",3,2018
Sparsely Aggregated Convolutional Networks,64,eccv,26,4,2023-06-16 23:55:33.292000,https://github.com/Lyken17/SparseNet,127,Sparsely aggregated convolutional networks,"https://scholar.google.com/scholar?cluster=18018235047234605783&hl=en&as_sdt=0,22",7,2018
Toward Characteristic-Preserving Image-based Virtual Try-On Network,278,eccv,172,18,2023-06-16 23:55:33.503000,https://github.com/sergeywong/cp-vton,421,Toward characteristic-preserving image-based virtual try-on network,"https://scholar.google.com/scholar?cluster=8505438031219592659&hl=en&as_sdt=0,6",16,2018
A Closed-form Solution to Photorealistic Image Stylization,387,eccv,1210,56,2023-06-16 23:55:33.714000,https://github.com/NVIDIA/FastPhotoStyle,11014,A closed-form solution to photorealistic image stylization,"https://scholar.google.com/scholar?cluster=3998372881584054724&hl=en&as_sdt=0,5",277,2018
Bi-Real Net: Enhancing the Performance of 1-bit CNNs with Improved Representational Capability and Advanced Training Algorithm,480,eccv,38,17,2023-06-16 23:55:33.926000,https://github.com/liuzechun/Bi-Real-net,161,Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm,"https://scholar.google.com/scholar?cluster=11618306684748410423&hl=en&as_sdt=0,14",8,2018
Conditional Prior Networks for Optical Flow,36,eccv,1,3,2023-06-16 23:55:34.138000,https://github.com/YanchaoYang/Conditional-Prior-Networks,20,Conditional prior networks for optical flow,"https://scholar.google.com/scholar?cluster=13612783089952575161&hl=en&as_sdt=0,5",2,2018
Stacked Cross Attention for Image-Text Matching,930,eccv,105,19,2023-06-16 23:55:34.349000,https://github.com/kuanghuei/SCAN,467,Stacked cross attention for image-text matching,"https://scholar.google.com/scholar?cluster=16231211588572724532&hl=en&as_sdt=0,33",10,2018
The Devil of Face Recognition is in the Noise,198,eccv,67,13,2023-06-16 23:55:34.560000,https://github.com/fwang91/IMDb-Face,422,The devil of face recognition is in the noise,"https://scholar.google.com/scholar?cluster=4982043129055880162&hl=en&as_sdt=0,5",34,2018
Person Search in Videos with One Portrait Through Visual and Temporal Links,61,eccv,29,1,2023-06-16 23:55:34.771000,https://github.com/hqqasw/person-search-PPCC,157,Person search in videos with one portrait through visual and temporal links,"https://scholar.google.com/scholar?cluster=3082328045180079825&hl=en&as_sdt=0,14",10,2018
Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,69,eccv,34,0,2023-06-16 23:55:34.982000,https://github.com/barisgecer/facegan,194,Semi-supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model,"https://scholar.google.com/scholar?cluster=17824311887123970500&hl=en&as_sdt=0,5",10,2018
Deep Model-Based 6D Pose Refinement in RGB,150,eccv,11,3,2023-06-16 23:55:35.194000,https://github.com/fabi92/eccv18-rgb_pose_refinement,49,Deep model-based 6d pose refinement in rgb,"https://scholar.google.com/scholar?cluster=15165666525406932772&hl=en&as_sdt=0,47",12,2018
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation,626,eccv,58,8,2023-06-16 23:55:35.404000,https://github.com/wzmsltw/BSN-boundary-sensitive-network.pytorch,241,Bsn: Boundary sensitive network for temporal action proposal generation,"https://scholar.google.com/scholar?cluster=2284253027481047573&hl=en&as_sdt=0,5",7,2018
Visual Reasoning with Multi-hop Feature Modulation,22,eccv,0,1,2023-06-16 23:55:35.615000,https://github.com/GuessWhatGame/referit,4,Visual reasoning with multi-hop feature modulation,"https://scholar.google.com/scholar?cluster=17349904381942744760&hl=en&as_sdt=0,10",4,2018
Multiresolution Tree Networks for 3D Point Cloud Processing,237,eccv,7,0,2023-06-16 23:55:35.826000,https://github.com/matheusgadelha/MRTNet,42,Multiresolution tree networks for 3d point cloud processing,"https://scholar.google.com/scholar?cluster=11110567683598189426&hl=en&as_sdt=0,11",2,2018
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs,68,eccv,15,2,2023-06-16 23:55:36.036000,https://github.com/neycyanshi/DDRNet,138,Ddrnet: Depth map denoising and refinement for consumer depth cameras using cascaded cnns,"https://scholar.google.com/scholar?cluster=7378092134524924747&hl=en&as_sdt=0,44",11,2018
Video Re-localization,67,eccv,17,4,2023-06-16 23:55:36.248000,https://github.com/fengyang0317/video_reloc,78,Video re-localization,"https://scholar.google.com/scholar?cluster=10920264094728832257&hl=en&as_sdt=0,11",6,2018
Self-produced Guidance for Weakly-supervised Object Localization,227,eccv,25,11,2023-06-16 23:55:36.459000,https://github.com/xiaomengyc/SPG,148,Self-produced guidance for weakly-supervised object localization,"https://scholar.google.com/scholar?cluster=2512369611028449806&hl=en&as_sdt=0,5",9,2018
Multi-modal Cycle-consistent Generalized Zero-Shot Learning,369,eccv,21,2,2023-06-16 23:55:36.679000,https://github.com/rfelixmg/frwgan-eccv18,55,Multi-modal cycle-consistent generalized zero-shot learning,"https://scholar.google.com/scholar?cluster=61764259088935736&hl=en&as_sdt=0,5",8,2018
Generalizing A Person Retrieval Model Hetero- and Homogeneously,463,eccv,31,4,2023-06-16 23:55:36.893000,https://github.com/zhunzhong07/HHL,129,Generalizing a person retrieval model hetero-and homogeneously,"https://scholar.google.com/scholar?cluster=15527730131190693987&hl=en&as_sdt=0,43",6,2018
Fighting Fake News: Image Splice Detection via Learned Self-Consistency,355,eccv,43,9,2023-06-16 23:55:37.105000,https://github.com/minyoungg/selfconsistency,177,Fighting fake news: Image splice detection via learned self-consistency,"https://scholar.google.com/scholar?cluster=16441872986118182453&hl=en&as_sdt=0,33",11,2018
Receptive Field Block Net for Accurate and Fast Object Detection,1215,eccv,360,57,2023-06-16 23:55:37.316000,https://github.com/ruinmessi/RFBNet,1393,Receptive field block net for accurate and fast object detection,"https://scholar.google.com/scholar?cluster=17707966079671814827&hl=en&as_sdt=0,33",47,2018
Weakly-supervised Video Summarization using Variational Encoder-Decoder and Web Prior,70,eccv,0,2,2023-06-16 23:55:37.528000,https://github.com/cssjcai/vesd,10,Weakly-supervised video summarization using variational encoder-decoder and web prior,"https://scholar.google.com/scholar?cluster=9256080473361800934&hl=en&as_sdt=0,5",9,2018
Deep High Dynamic Range Imaging with Large Foreground Motions,209,eccv,38,2,2023-06-16 23:55:37.739000,https://github.com/elliottwu/DeepHDR,172,Deep high dynamic range imaging with large foreground motions,"https://scholar.google.com/scholar?cluster=17997707085476439964&hl=en&as_sdt=0,47",10,2018
Hierarchical Relational Networks for Group Activity Recognition and Retrieval,112,eccv,9,1,2023-06-16 23:55:37.951000,https://github.com/mostafa-saad/hierarchical-relational-network,44,Hierarchical relational networks for group activity recognition and retrieval,"https://scholar.google.com/scholar?cluster=2996130825866411769&hl=en&as_sdt=0,14",6,2018
Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization,88,eccv,0,0,2023-06-16 23:55:38.162000,https://github.com/HumamAlwassel/action-search,15,Action search: Spotting actions in videos and its application to temporal action localization,"https://scholar.google.com/scholar?cluster=2315724837598657489&hl=en&as_sdt=0,34",2,2018
Simple Baselines for Human Pose Estimation and Tracking,1584,eccv,7,0,2023-06-16 23:55:38.373000,https://github.com/leoxiaobin/pose.pytorch,74,Simple baselines for human pose estimation and tracking,"https://scholar.google.com/scholar?cluster=16372249111646534211&hl=en&as_sdt=0,5",20,2018
Clustering Convolutional Kernels to Compress Deep Neural Networks,79,eccv,3,0,2023-06-16 23:55:38.585000,https://github.com/sanghyun-son/clustering-kernels,30,Clustering convolutional kernels to compress deep neural networks,"https://scholar.google.com/scholar?cluster=12419171226701641771&hl=en&as_sdt=0,44",2,2018
CornerNet: Detecting Objects as Paired Keypoints,3315,eccv,477,133,2023-06-16 23:55:38.796000,https://github.com/princeton-vl/CornerNet,2334,Cornernet: Detecting objects as paired keypoints,"https://scholar.google.com/scholar?cluster=5999650257677576183&hl=en&as_sdt=0,5",62,2018
Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance,42,eccv,10,1,2023-06-16 23:55:39.007000,https://github.com/ramprs/neuron-importance-zsl,57,Choose your neuron: Incorporating domain knowledge through neuron-importance,"https://scholar.google.com/scholar?cluster=11966532585080079409&hl=en&as_sdt=0,47",5,2018
Visual Question Generation for Class Acquisition of Unknown Objects,16,eccv,4,0,2023-06-16 23:55:39.219000,https://github.com/mil-tokyo/vqg-unknown,10,Visual question generation for class acquisition of unknown objects,"https://scholar.google.com/scholar?cluster=15532793729573101805&hl=en&as_sdt=0,21",16,2018
Pairwise Confusion for Fine-Grained Visual Classification,193,eccv,39,3,2023-06-16 23:55:39.431000,https://github.com/abhimanyudubey/confusion,196,Pairwise confusion for fine-grained visual classification,"https://scholar.google.com/scholar?cluster=4225334889143437735&hl=en&as_sdt=0,31",7,2018
Deep Recursive HDRI: Inverse Tone Mapping using Generative Adversarial Networks,103,eccv,1,2,2023-06-16 23:55:39.651000,https://github.com/Siyeong-Lee/Deep_Recursive_HDRI,17,Deep recursive hdri: Inverse tone mapping using generative adversarial networks,"https://scholar.google.com/scholar?cluster=13565930299432705431&hl=en&as_sdt=0,33",2,2018
Implicit 3D Orientation Learning for 6D Object Detection from RGB Images,543,eccv,91,2,2023-06-16 23:55:39.863000,https://github.com/DLR-RM/AugmentedAutoencoder,316,Implicit 3d orientation learning for 6d object detection from rgb images,"https://scholar.google.com/scholar?cluster=7729841025223671845&hl=en&as_sdt=0,48",15,2018
Compressing the Input for CNNs with the First-Order Scattering Transform,31,eccv,2,1,2023-06-16 23:55:40.074000,https://github.com/edouardoyallon/pyscatlight,9,Compressing the input for cnns with the first-order scattering transform,"https://scholar.google.com/scholar?cluster=16128832336651542063&hl=en&as_sdt=0,47",5,2018
Unified Perceptual Parsing for Scene Understanding,964,eccv,69,13,2023-06-16 23:55:40.286000,https://github.com/CSAILVision/unifiedparsing,371,Unified perceptual parsing for scene understanding,"https://scholar.google.com/scholar?cluster=7624463121009759752&hl=en&as_sdt=0,5",31,2018
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,571,eccv,117,14,2023-06-16 23:55:40.497000,https://github.com/XingangPan/IBN-Net,761,Two at once: Enhancing learning and generalization capacities via ibn-net,"https://scholar.google.com/scholar?cluster=12108818558057639369&hl=en&as_sdt=0,5",20,2018
Learning Type-Aware Embeddings for Fashion Compatibility,219,eccv,42,16,2023-06-16 23:55:40.707000,https://github.com/mvasil/fashion-compatibility,134,Learning type-aware embeddings for fashion compatibility,"https://scholar.google.com/scholar?cluster=4893895307388417428&hl=en&as_sdt=0,15",8,2018
Image Inpainting for Irregular Holes Using Partial Convolutions,1775,eccv,211,26,2023-06-16 23:55:40.919000,https://github.com/NVIDIA/partialconv,1138,Image inpainting for irregular holes using partial convolutions,"https://scholar.google.com/scholar?cluster=13570032890679637624&hl=en&as_sdt=0,33",35,2018
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,10656,eccv,46279,1207,2023-06-16 23:55:41.132000,https://github.com/tensorflow/models,75921,Encoder-decoder with atrous separable convolution for semantic image segmentation,"https://scholar.google.com/scholar?cluster=5370440400011509498&hl=en&as_sdt=0,43",2774,2018
W-TALC: Weakly-supervised Temporal Activity Localization and Classification,299,eccv,26,12,2023-06-16 23:55:41.343000,https://github.com/sujoyp/wtalc-pytorch,126,W-talc: Weakly-supervised temporal activity localization and classification,"https://scholar.google.com/scholar?cluster=15163808419344516354&hl=en&as_sdt=0,33",5,2018
"Asynchronous, Photometric Feature Tracking using Events and Frames",128,eccv,31,3,2023-06-16 23:55:41.554000,https://github.com/uzh-rpg/rpg_eklt,110,"Asynchronous, photometric feature tracking using events and frames","https://scholar.google.com/scholar?cluster=4703945787385512&hl=en&as_sdt=0,5",14,2018
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation,289,eccv,38,14,2023-06-16 23:55:41.766000,https://github.com/yikang-li/FactorizableNet,211,Factorizable net: an efficient subgraph-based framework for scene graph generation,"https://scholar.google.com/scholar?cluster=8979043837696997695&hl=en&as_sdt=0,14",8,2018
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks,651,eccv,66,5,2023-06-16 23:55:41.978000,https://github.com/Microsoft/LQ-Nets,226,Lq-nets: Learned quantization for highly accurate and compact deep neural networks,"https://scholar.google.com/scholar?cluster=3426009727719019497&hl=en&as_sdt=0,5",17,2018
Deep Randomized Ensembles for Metric Learning,110,eccv,14,2,2023-06-16 23:55:42.189000,https://github.com/littleredxh/DREML,67,Deep randomized ensembles for metric learning,"https://scholar.google.com/scholar?cluster=3555520024083710651&hl=en&as_sdt=0,43",7,2018
ECO: Efficient Convolutional Network for Online Video Understanding,504,eccv,96,5,2023-06-16 23:55:42.401000,https://github.com/mzolfaghari/ECO-efficient-video-understanding,428,Eco: Efficient convolutional network for online video understanding,"https://scholar.google.com/scholar?cluster=10960896672902242638&hl=en&as_sdt=0,5",40,2018
Stereo relative pose from line and point feature triplets,14,eccv,0,0,2023-06-16 23:55:42.613000,https://github.com/alexandervakhitov/sego-paper-code,2,Stereo relative pose from line and point feature triplets,"https://scholar.google.com/scholar?cluster=10920537995535353213&hl=en&as_sdt=0,46",1,2018
Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks,119,eccv,23,3,2023-06-16 23:55:42.824000,https://github.com/recasens/Saliency-Sampler,152,Learning to zoom: a saliency-based sampling layer for neural networks,"https://scholar.google.com/scholar?cluster=1561037491831749107&hl=en&as_sdt=0,9",4,2018
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence,736,eccv,41,6,2023-06-16 23:55:43.036000,https://github.com/facebookresearch/agem,188,Riemannian walk for incremental learning: Understanding forgetting and intransigence,"https://scholar.google.com/scholar?cluster=4026195560787576416&hl=en&as_sdt=0,41",11,2018
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation,710,eccv,112,8,2023-06-16 23:55:43.247000,https://github.com/sacmehta/ESPNet,519,Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation,"https://scholar.google.com/scholar?cluster=11517977191335408434&hl=en&as_sdt=0,5",13,2018
ICNet for Real-Time Semantic Segmentation on High-Resolution Images,1301,eccv,199,27,2023-06-16 23:55:43.458000,https://github.com/hszhao/ICNet,591,Icnet for real-time semantic segmentation on high-resolution images,"https://scholar.google.com/scholar?cluster=4797791240153082941&hl=en&as_sdt=0,31",48,2018
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting,238,eccv,36,0,2023-06-16 23:55:43.668000,https://github.com/VideoObjectSearch/ALFNet,84,Learning efficient single-stage pedestrian detectors by asymptotic localization fitting,"https://scholar.google.com/scholar?cluster=11222490521494349722&hl=en&as_sdt=0,3",2,2018
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration,280,eccv,46,1,2023-06-16 23:55:43.879000,https://github.com/yewzijian/3DFeatNet,213,3dfeat-net: Weakly supervised local 3d features for point cloud registration,"https://scholar.google.com/scholar?cluster=1523813137015825736&hl=en&as_sdt=0,44",19,2018
Learning Human-Object Interactions by Graph Parsing Neural Networks,494,eccv,54,16,2023-06-16 23:55:44.090000,https://github.com/SiyuanQi/gpnn,223,Learning human-object interactions by graph parsing neural networks,"https://scholar.google.com/scholar?cluster=10430461003414485356&hl=en&as_sdt=0,5",13,2018
Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification,59,eccv,26,2,2023-06-16 23:55:44.301000,https://github.com/TIBHannover/GeoEstimation,85,Geolocation estimation of photos using a hierarchical model and scene classification,"https://scholar.google.com/scholar?cluster=17539665549069158810&hl=en&as_sdt=0,1",8,2018
Multi-scale Residual Network for Image Super-Resolution,641,eccv,58,2,2023-06-16 23:55:44.512000,https://github.com/MIVRC/MSRN-PyTorch,283,Multi-scale residual network for image super-resolution,"https://scholar.google.com/scholar?cluster=1889935112564144187&hl=en&as_sdt=0,47",11,2018
A Modulation Module for Multi-task Learning with Applications in Image Retrieval,105,eccv,8,1,2023-06-16 23:55:44.723000,https://github.com/Zhaoxiangyun/Multi-Task-Modulation-Module,29,A modulation module for multi-task learning with applications in image retrieval,"https://scholar.google.com/scholar?cluster=4124839274668983312&hl=en&as_sdt=0,5",8,2018
Triplet Loss in Siamese Network for Object Tracking,544,eccv,17,4,2023-06-16 23:55:44.935000,https://github.com/shenjianbing/TripletTracking,73,Triplet loss in siamese network for object tracking,"https://scholar.google.com/scholar?cluster=14601093234970980253&hl=en&as_sdt=0,5",11,2018
ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes,184,eccv,43,7,2023-06-16 23:55:45.146000,https://github.com/Prinsphield/ELEGANT,263,Elegant: Exchanging latent encodings with gan for transferring multiple face attributes,"https://scholar.google.com/scholar?cluster=17637222949126620423&hl=en&as_sdt=0,10",10,2018
Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation,78,eccv,28,4,2023-06-16 23:55:45.357000,https://github.com/awesome-davian/Text2Colors,145,Coloring with words: Guiding image colorization through text-based palette generation,"https://scholar.google.com/scholar?cluster=508233184644091396&hl=en&as_sdt=0,34",2,2018
Variational Wasserstein Clustering,44,eccv,3,0,2023-06-16 23:55:45.568000,https://github.com/icemiliang/vot,6,Variational wasserstein clustering,"https://scholar.google.com/scholar?cluster=1321628383156996431&hl=en&as_sdt=0,47",4,2018
Diagnosing Error in Temporal Action Detectors,84,eccv,19,0,2023-06-16 23:55:45.779000,https://github.com/HumamAlwassel/DETAD,59,Diagnosing error in temporal action detectors,"https://scholar.google.com/scholar?cluster=5337171087786136227&hl=en&as_sdt=0,44",6,2018
Efficient Semantic Scene Completion Network with Spatial Group Convolution,70,eccv,7,1,2023-06-16 23:55:45.990000,https://github.com/zjhthu/SGC-Release,42,Efficient semantic scene completion network with spatial group convolution,"https://scholar.google.com/scholar?cluster=13896431120284365470&hl=en&as_sdt=0,36",2,2018
Macro-Micro Adversarial Network for Human Parsing,141,eccv,35,7,2023-06-16 23:55:46.201000,https://github.com/RoyalVane/MMAN,208,Macro-micro adversarial network for human parsing,"https://scholar.google.com/scholar?cluster=14930739577336981642&hl=en&as_sdt=0,11",12,2018
SketchyScene: Richly-Annotated Scene Sketches,53,eccv,22,1,2023-06-16 23:55:46.413000,https://github.com/SketchyScene/SketchyScene,93,Sketchyscene: Richly-annotated scene sketches,"https://scholar.google.com/scholar?cluster=8715577846603708273&hl=en&as_sdt=0,5",7,2018
Contour Knowledge Transfer for Salient Object Detection,288,eccv,7,4,2023-06-16 23:55:46.625000,https://github.com/lixin666/C2SNet,21,Contour knowledge transfer for salient object detection,"https://scholar.google.com/scholar?cluster=13596246652369619853&hl=en&as_sdt=0,23",2,2018
GANimation: Anatomically-aware Facial Animation from a Single Image,563,eccv,405,43,2023-06-16 23:55:46.836000,https://github.com/albertpumarola/GANimation,1875,Ganimation: Anatomically-aware facial animation from a single image,"https://scholar.google.com/scholar?cluster=15397536897136412791&hl=en&as_sdt=0,39",76,2018
Interpretable Basis Decomposition for Visual Explanation,251,eccv,12,3,2023-06-16 23:55:47.048000,https://github.com/CSAILVision/IBD,48,Interpretable basis decomposition for visual explanation,"https://scholar.google.com/scholar?cluster=17221817189442156654&hl=en&as_sdt=0,31",6,2018
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification,89,eccv,6,0,2023-06-16 23:55:47.261000,https://github.com/sergeyprokudin/deep_direct_stat,24,Deep directional statistics: Pose estimation with uncertainty quantification,"https://scholar.google.com/scholar?cluster=9539003849820712184&hl=en&as_sdt=0,5",4,2018
Decouple Learning for Parameterized Image Operators,56,eccv,10,1,2023-06-16 23:55:47.472000,https://github.com/fqnchina/DecoupleLearning,61,Decouple learning for parameterized image operators,"https://scholar.google.com/scholar?cluster=10946920911350547430&hl=en&as_sdt=0,5",4,2018
Pose-Normalized Image Generation for Person Re-identification,450,eccv,39,0,2023-06-16 23:55:47.684000,https://github.com/naiq/PN_GAN,136,Pose-normalized image generation for person re-identification,"https://scholar.google.com/scholar?cluster=15077486839262294602&hl=en&as_sdt=0,10",10,2018
ReenactGAN: Learning to Reenact Faces via Boundary Transfer,179,eccv,41,11,2023-06-16 23:55:47.895000,https://github.com/wywu/ReenactGAN,183,Reenactgan: Learning to reenact faces via boundary transfer,"https://scholar.google.com/scholar?cluster=14279350909850753234&hl=en&as_sdt=0,5",12,2018
DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares,45,eccv,22,1,2023-06-17 00:17:45.481000,https://github.com/sitzikbs/DeepFit,121,DeepFit: 3D surface fitting via neural network weighted least squares,"https://scholar.google.com/scholar?cluster=10456104335954104816&hl=en&as_sdt=0,3",7,2020
NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search,120,eccv,36,16,2023-06-17 00:17:45.692000,https://github.com/mikelzc1990/nsganetv2,123,Nsganetv2: Evolutionary multi-objective surrogate-assisted neural architecture search,"https://scholar.google.com/scholar?cluster=14365548612592408605&hl=en&as_sdt=0,48",3,2020
AiR: Attention with Reasoning Capability,20,eccv,5,0,2023-06-17 00:17:45.905000,https://github.com/szzexpoi/AiR,42,Air: Attention with reasoning capability,"https://scholar.google.com/scholar?cluster=17537398935505742003&hl=en&as_sdt=0,10",1,2020
Self6D: Self-Supervised Monocular 6D Object Pose Estimation,94,eccv,16,1,2023-06-17 00:17:46.132000,https://github.com/THU-DA-6D-Pose-Group/Self6D-Diff-Renderer,98,Self6d: Self-supervised monocular 6d object pose estimation,"https://scholar.google.com/scholar?cluster=6319219398403719659&hl=en&as_sdt=0,41",6,2020
Invertible Image Rescaling,140,eccv,86,27,2023-06-17 00:17:46.344000,https://github.com/pkuxmq/Invertible-Image-Rescaling,575,Invertible image rescaling,"https://scholar.google.com/scholar?cluster=8259694236280829797&hl=en&as_sdt=0,5",17,2020
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation,99,eccv,8,6,2023-06-17 00:17:46.587000,https://github.com/YingdaXia/SynthCP,56,Synthesize then compare: Detecting failures and anomalies for semantic segmentation,"https://scholar.google.com/scholar?cluster=13185305449171460350&hl=en&as_sdt=0,5",6,2020
VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment,97,eccv,81,39,2023-06-17 00:17:46.810000,https://github.com/microsoft/voxelpose-pytorch,396,Voxelpose: Towards multi-camera 3d human pose estimation in wild environment,"https://scholar.google.com/scholar?cluster=6897562467519549173&hl=en&as_sdt=0,5",24,2020
End-to-End Object Detection with Transformers,6498,eccv,2035,223,2023-06-17 00:17:47.021000,https://github.com/facebookresearch/detr,11190,End-to-end object detection with transformers,"https://scholar.google.com/scholar?cluster=1672665553767281734&hl=en&as_sdt=0,5",146,2020
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation,70,eccv,46,17,2023-06-17 00:17:47.232000,https://github.com/detectRecog/PointTrack,256,Segment as points for efficient online multi-object tracking and segmentation,"https://scholar.google.com/scholar?cluster=8044121534115806381&hl=en&as_sdt=0,44",17,2020
Conditional Convolutions for Instance Segmentation,421,eccv,635,291,2023-06-17 00:17:47.443000,https://github.com/aim-uofa/AdelaiDet,3151,Conditional convolutions for instance segmentation,"https://scholar.google.com/scholar?cluster=12541147144211358106&hl=en&as_sdt=0,5",86,2020
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution,68,eccv,33,2,2023-06-17 00:17:47.655000,https://github.com/taoyang1122/MutualNet,153,Mutualnet: Adaptive convnet via mutual learning from network width and resolution,"https://scholar.google.com/scholar?cluster=13502072080557203486&hl=en&as_sdt=0,22",8,2020
Rewriting a Deep Generative Model,86,eccv,74,7,2023-06-17 00:17:47.867000,https://github.com/davidbau/rewriting,534,Rewriting a deep generative model,"https://scholar.google.com/scholar?cluster=8376346966151872875&hl=en&as_sdt=0,15",20,2020
Long-term Human Motion Prediction with Scene Context,134,eccv,17,7,2023-06-17 00:17:48.078000,https://github.com/ZheC/GTA-IM-Dataset,226,Long-term human motion prediction with scene context,"https://scholar.google.com/scholar?cluster=5087686040073754886&hl=en&as_sdt=0,44",13,2020
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis,3251,eccv,1137,98,2023-06-17 00:17:48.289000,https://github.com/bmild/nerf,7744,Nerf: Representing scenes as neural radiance fields for view synthesis,"https://scholar.google.com/scholar?cluster=14862000915171929687&hl=en&as_sdt=0,5",137,2020
ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes,101,eccv,13,4,2023-06-17 00:17:48.500000,https://github.com/referit3d/referit3d,76,Referit3d: Neural listeners for fine-grained 3d object identification in real-world scenes,"https://scholar.google.com/scholar?cluster=16275930031096682017&hl=en&as_sdt=0,47",3,2020
MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images,71,eccv,14,1,2023-06-17 00:17:48.711000,https://github.com/brownvc/matryodshka,81,MatryODShka: Real-time 6DoF video view synthesis using multi-sphere images,"https://scholar.google.com/scholar?cluster=10989159617796509767&hl=en&as_sdt=0,33",18,2020
Learning and Aggregating Deep Local Descriptors for Instance-level Recognition,73,eccv,9,1,2023-06-17 00:17:48.921000,https://github.com/gtolias/how,80,Learning and aggregating deep local descriptors for instance-level recognition,"https://scholar.google.com/scholar?cluster=194520734846708730&hl=en&as_sdt=0,5",9,2020
A Consistently Fast and Globally Optimal Solution to the Perspective-n-Point Problem,44,eccv,17,1,2023-06-17 00:17:49.133000,https://github.com/terzakig/sqpnp,110,A consistently fast and globally optimal solution to the perspective-n-point problem,"https://scholar.google.com/scholar?cluster=13709100810172483109&hl=en&as_sdt=0,33",10,2020
Learn to Recover Visible Color for Video Surveillance in a Day,6,eccv,2,3,2023-06-17 00:17:49.344000,https://github.com/huster-wgm/VSIAD,11,Learn to recover visible color for video surveillance in a day,"https://scholar.google.com/scholar?cluster=6478617608883726542&hl=en&as_sdt=0,5",2,2020
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation,67,eccv,5,4,2023-06-17 00:17:49.556000,https://github.com/zdaxie/SpatiallyAdaptiveInference-Detection,59,Spatially adaptive inference with stochastic feature sampling and interpolation,"https://scholar.google.com/scholar?cluster=4445767428870928092&hl=en&as_sdt=0,5",4,2020
BorderDet: Border Feature for Dense Object Detection,117,eccv,66,6,2023-06-17 00:17:49.768000,https://github.com/Megvii-BaseDetection/BorderDet,432,Borderdet: Border feature for dense object detection,"https://scholar.google.com/scholar?cluster=1318874163147907809&hl=en&as_sdt=0,5",19,2020
Regularization with Latent Space Virtual Adversarial Training,9,eccv,0,0,2023-06-17 00:17:49.979000,https://github.com/geosada/LVAT,14,Regularization with latent space virtual adversarial training,"https://scholar.google.com/scholar?cluster=14570002296313298357&hl=en&as_sdt=0,5",1,2020
Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot learning,79,eccv,1,1,2023-06-17 00:17:50.190000,https://github.com/jaekyeom/mabas,15,Model-agnostic boundary-adversarial sampling for test-time generalization in few-shot learning,"https://scholar.google.com/scholar?cluster=6721525993252660695&hl=en&as_sdt=0,5",1,2020
Targeted Attack for Deep Hashing based Retrieval,58,eccv,5,0,2023-06-17 00:17:50.403000,https://github.com/jiawangbai/DHTA-master,26,Targeted attack for deep hashing based retrieval,"https://scholar.google.com/scholar?cluster=10440819536070326203&hl=en&as_sdt=0,33",3,2020
Gradient Centralization: A New Optimization Technique for Deep Neural Networks,137,eccv,79,6,2023-06-17 00:17:50.614000,https://github.com/Yonghongwei/Gradient-Centralization,525,Gradient centralization: A new optimization technique for deep neural networks,"https://scholar.google.com/scholar?cluster=16809113917737467953&hl=en&as_sdt=0,33",17,2020
Content-Aware Unsupervised Deep Homography Estimation,108,eccv,55,28,2023-06-17 00:17:50.827000,https://github.com/JirongZhang/DeepHomography,276,Content-aware unsupervised deep homography estimation,"https://scholar.google.com/scholar?cluster=3888444801202062317&hl=en&as_sdt=0,49",25,2020
Multi-View Optimization of Local Feature Geometry,25,eccv,25,3,2023-06-17 00:17:51.039000,https://github.com/mihaidusmanu/local-feature-refinement,206,Multi-view optimization of local feature geometry,"https://scholar.google.com/scholar?cluster=17323119786165831654&hl=en&as_sdt=0,44",12,2020
Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video,69,eccv,10,1,2023-06-17 00:17:51.252000,https://github.com/2020aptx4869lm/Forecasting-Human-Object-Interaction-in-FPV,23,Forecasting human-object interaction: joint prediction of motor attention and actions in first person video,"https://scholar.google.com/scholar?cluster=227263367744579299&hl=en&as_sdt=0,5",1,2020
Learning Stereo from Single Images,41,eccv,50,0,2023-06-17 00:17:51.464000,https://github.com/nianticlabs/stereo-from-mono,350,Learning stereo from single images,"https://scholar.google.com/scholar?cluster=11349409566203411981&hl=en&as_sdt=0,33",33,2020
Suppress and Balance: A Simple Gated Network for Salient Object Detection,293,eccv,15,2,2023-06-17 00:17:51.675000,https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency,146,Suppress and balance: A simple gated network for salient object detection,"https://scholar.google.com/scholar?cluster=9212536462000789632&hl=en&as_sdt=0,21",11,2020
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning,21,eccv,7,1,2023-06-17 00:17:51.887000,https://github.com/wang-chen/interestingness,38,Visual memorability for robotic interestingness via unsupervised online learning,"https://scholar.google.com/scholar?cluster=36662884533233602&hl=en&as_sdt=0,33",4,2020
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification,178,eccv,17,5,2023-06-17 00:17:52.099000,https://github.com/NVlabs/DG-Net-PP,79,Joint disentangling and adaptation for cross-domain person re-identification,"https://scholar.google.com/scholar?cluster=4098355156118946376&hl=en&as_sdt=0,5",8,2020
Multitask Learning Strengthens Adversarial Robustness,65,eccv,3,1,2023-06-17 00:17:52.319000,https://github.com/columbia/MTRobust,17,Multitask learning strengthens adversarial robustness,"https://scholar.google.com/scholar?cluster=1210185653037489148&hl=en&as_sdt=0,44",10,2020
Motion Capture from Internet Videos,40,eccv,10,4,2023-06-17 00:17:52.546000,https://github.com/zju3dv/iMoCap,100,Motion capture from internet videos,"https://scholar.google.com/scholar?cluster=15847497864374083611&hl=en&as_sdt=0,33",25,2020
Appearance-Preserving 3D Convolution for Video-based Person Re-identification,91,eccv,24,0,2023-06-17 00:17:52.758000,https://github.com/guxinqian/AP3D,91,Appearance-preserving 3d convolution for video-based person re-identification,"https://scholar.google.com/scholar?cluster=13337642614867304745&hl=en&as_sdt=0,33",3,2020
Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization,38,eccv,15,0,2023-06-17 00:17:52.970000,https://github.com/dylan-campbell/bpnpnet,56,Solving the blind perspective-n-point problem end-to-end with robust differentiable geometric optimization,"https://scholar.google.com/scholar?cluster=12394133310408441935&hl=en&as_sdt=0,5",4,2020
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation,218,eccv,63,4,2023-06-17 00:17:53.183000,https://github.com/XingangPan/deep-generative-prior,447,Exploiting deep generative prior for versatile image restoration and manipulation,"https://scholar.google.com/scholar?cluster=14389153301568786365&hl=en&as_sdt=0,5",13,2020
Deep Spatial-angular Regularization for Compressive Light Field Reconstruction over Coded Apertures,18,eccv,1,0,2023-06-17 00:17:53.394000,https://github.com/angmt2008/LFCA,6,Deep spatial-angular regularization for compressive light field reconstruction over coded apertures,"https://scholar.google.com/scholar?cluster=1290728802438016137&hl=en&as_sdt=0,5",1,2020
Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling,89,eccv,21,9,2023-06-17 00:17:53.610000,https://github.com/nxsEdson/CVD-Physiological-Measurement,83,Video-based remote physiological measurement via cross-verified feature disentangling,"https://scholar.google.com/scholar?cluster=10432194192196602071&hl=en&as_sdt=0,5",11,2020
Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network,70,eccv,3,3,2023-06-17 00:17:53.825000,https://github.com/tsaishien-chen/SPAN,22,Orientation-aware vehicle re-identification with semantics-guided part attention network,"https://scholar.google.com/scholar?cluster=17106221795006748010&hl=en&as_sdt=0,21",2,2020
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation,229,eccv,15,5,2023-06-17 00:17:54.037000,https://github.com/GuoleiSun/MCIS_wsss,151,Mining cross-image semantics for weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=1803466270479184475&hl=en&as_sdt=0,5",17,2020
CoReNet: Coherent 3D Scene Reconstruction from a Single RGB Image,40,eccv,10,2,2023-06-17 00:17:54.248000,https://github.com/google-research/corenet,87,Corenet: Coherent 3d scene reconstruction from a single rgb image,"https://scholar.google.com/scholar?cluster=385603289457372690&hl=en&as_sdt=0,22",9,2020
RAFT: Recurrent All-Pairs Field Transforms for Optical Flow,1128,eccv,545,59,2023-06-17 00:17:54.460000,https://github.com/princeton-vl/RAFT,2497,Raft: Recurrent all-pairs field transforms for optical flow,"https://scholar.google.com/scholar?cluster=4922601694276634409&hl=en&as_sdt=0,47",39,2020
Domain-invariant Stereo Matching Networks,112,eccv,29,14,2023-06-17 00:17:54.674000,https://github.com/feihuzhang/DSMNet,211,Domain-invariant stereo matching networks,"https://scholar.google.com/scholar?cluster=5852498635419711510&hl=en&as_sdt=0,33",13,2020
DeepHandMesh: A Weakly-supervised Deep Encoder-Decoder Framework for High-fidelity Hand Mesh Modeling,49,eccv,13,4,2023-06-17 00:17:54.886000,https://github.com/facebookresearch/DeepHandMesh,88,Deephandmesh: A weakly-supervised deep encoder-decoder framework for high-fidelity hand mesh modeling,"https://scholar.google.com/scholar?cluster=10255907958365810717&hl=en&as_sdt=0,36",9,2020
GDumb: A Simple Approach that Questions Our Progress in Continual Learning,309,eccv,8,1,2023-06-17 00:17:55.098000,https://github.com/drimpossible/GDumb,87,Gdumb: A simple approach that questions our progress in continual learning,"https://scholar.google.com/scholar?cluster=4950901170042935517&hl=en&as_sdt=0,5",1,2020
Learning Lane Graph Representations for Motion Forecasting,268,eccv,122,17,2023-06-17 00:17:55.314000,https://github.com/uber-research/LaneGCN,403,Learning lane graph representations for motion forecasting,"https://scholar.google.com/scholar?cluster=68615350220075469&hl=en&as_sdt=0,34",10,2020
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters,26,eccv,4,6,2023-06-17 00:17:55.529000,https://github.com/hyliang96/CSGCNN,24,Training interpretable convolutional neural networks by differentiating class-specific filters,"https://scholar.google.com/scholar?cluster=4019505950868378842&hl=en&as_sdt=0,5",3,2020
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning,137,eccv,67,19,2023-06-17 00:17:55.740000,https://github.com/anonymous47823493/EagleEye,293,Eagleeye: Fast sub-net evaluation for efficient neural network pruning,"https://scholar.google.com/scholar?cluster=10271877345232340901&hl=en&as_sdt=0,21",12,2020
Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation,10,eccv,4,0,2023-06-17 00:17:55.952000,https://github.com/mrakotosaon/intrinsic_interpolations,14,Intrinsic point cloud interpolation via dual latent space navigation,"https://scholar.google.com/scholar?cluster=17530580146417788501&hl=en&as_sdt=0,5",3,2020
"“Look Ma, no landmarks!” – Unsupervised, Model-based Dense Face Alignment",16,eccv,1,2,2023-06-17 00:17:56.164000,https://github.com/kzmttr/UMDFA,8,"“Look Ma, no landmarks!”–Unsupervised, model-based dense face alignment","https://scholar.google.com/scholar?cluster=18052363214420622280&hl=en&as_sdt=0,5",2,2020
Online Invariance Selection for Local Feature Descriptors,52,eccv,19,2,2023-06-17 00:17:56.379000,https://github.com/rpautrat/LISRD,237,Online invariance selection for local feature descriptors,"https://scholar.google.com/scholar?cluster=16324041681453692222&hl=en&as_sdt=0,33",9,2020
Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations,210,eccv,52,28,2023-06-17 00:17:56.590000,https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE,355,Rethinking image inpainting via a mutual encoder-decoder with feature equalizations,"https://scholar.google.com/scholar?cluster=18129183330952721488&hl=en&as_sdt=0,33",16,2020
It is not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction,250,eccv,73,18,2023-06-17 00:17:56.801000,https://github.com/HarshayuGirase/PECNet,263,It is not the journey but the destination: Endpoint conditioned trajectory prediction,"https://scholar.google.com/scholar?cluster=14869553753645055346&hl=en&as_sdt=0,47",11,2020
Learning What to Learn for Video Object Segmentation,111,eccv,578,56,2023-06-17 00:17:57.013000,https://github.com/visionml/pytracking,2795,Learning what to learn for video object segmentation,"https://scholar.google.com/scholar?cluster=11592531918456989368&hl=en&as_sdt=0,5",90,2020
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing,72,eccv,13,3,2023-06-17 00:17:57.225000,https://github.com/garvita-tiwari/sizer,81,Sizer: A dataset and model for parsing 3d clothing and learning size sensitive 3d clothing,"https://scholar.google.com/scholar?cluster=7651647498190000321&hl=en&as_sdt=0,3",6,2020
Learning to Localize Actions from Moments,12,eccv,2,1,2023-06-17 00:17:57.436000,https://github.com/FuchenUSTC/AherNet,16,Learning to localize actions from moments,"https://scholar.google.com/scholar?cluster=2292806304006132873&hl=en&as_sdt=0,33",2,2020
TSIT: A Simple and Versatile Framework for Image-to-Image Translation,73,eccv,32,4,2023-06-17 00:17:57.648000,https://github.com/EndlessSora/TSIT,262,Tsit: A simple and versatile framework for image-to-image translation,"https://scholar.google.com/scholar?cluster=6228459104800637542&hl=en&as_sdt=0,22",22,2020
A Unified Framework of Surrogate Loss by Refactoring and Interpolation,12,eccv,0,0,2023-06-17 00:17:57.859000,https://github.com/princeton-vl/uniloss,8,A unified framework of surrogate loss by refactoring and interpolation,"https://scholar.google.com/scholar?cluster=7491574393262633303&hl=en&as_sdt=0,10",6,2020
Memory-augmented Dense Predictive Coding for Video Representation Learning,208,eccv,23,1,2023-06-17 00:17:58.071000,https://github.com/TengdaHan/MemDPC,163,Memory-augmented dense predictive coding for video representation learning,"https://scholar.google.com/scholar?cluster=13182085823541601706&hl=en&as_sdt=0,5",6,2020
PointMixup: Augmentation for Point Clouds,101,eccv,9,8,2023-06-17 00:17:58.284000,https://github.com/yunlu-chen/PointMixup,88,Pointmixup: Augmentation for point clouds,"https://scholar.google.com/scholar?cluster=16304425416174390270&hl=en&as_sdt=0,44",8,2020
Identity-Guided Human Semantic Parsing for Person Re-Identification,186,eccv,16,1,2023-06-17 00:17:58.503000,https://github.com/CASIA-IVA-Lab/ISP-reID,82,Identity-guided human semantic parsing for person re-identification,"https://scholar.google.com/scholar?cluster=8107604897256612639&hl=en&as_sdt=0,33",4,2020
Learning Gradient Fields for Shape Generation,140,eccv,33,2,2023-06-17 00:17:58.715000,https://github.com/RuojinCai/ShapeGF,173,Learning gradient fields for shape generation,"https://scholar.google.com/scholar?cluster=7047622093404369098&hl=en&as_sdt=0,5",7,2020
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder,60,eccv,431,47,2023-06-17 00:17:58.926000,https://github.com/nvlabs/imaginaire,3776,Coco-funit: Few-shot unsupervised image translation with a content conditioned style encoder,"https://scholar.google.com/scholar?cluster=1749322342302697628&hl=en&as_sdt=0,5",111,2020
"Corner Proposal Network for Anchor-free, Two-stage Object Detection",83,eccv,24,13,2023-06-17 00:17:59.137000,https://github.com/Duankaiwen/CPNDet,190,"Corner proposal network for anchor-free, two-stage object detection","https://scholar.google.com/scholar?cluster=13568718297544497544&hl=en&as_sdt=0,5",19,2020
Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing,92,eccv,19,3,2023-06-17 00:17:59.348000,https://github.com/YapengTian/AVVP-ECCV20,62,Unified multisensory perception: Weakly-supervised audio-visual video parsing,"https://scholar.google.com/scholar?cluster=1790969886249366260&hl=en&as_sdt=0,21",6,2020
Learning Delicate Local Representations for Multi-Person Pose Estimation,118,eccv,79,22,2023-06-17 00:17:59.559000,https://github.com/caiyuanhao1998/RSN,454,Learning delicate local representations for multi-person pose estimation,"https://scholar.google.com/scholar?cluster=17901896041453960317&hl=en&as_sdt=0,5",15,2020
Convolutional Occupancy Networks,537,eccv,101,8,2023-06-17 00:17:59.770000,https://github.com/autonomousvision/convolutional_occupancy_networks,707,Convolutional occupancy networks,"https://scholar.google.com/scholar?cluster=12790840199166322014&hl=en&as_sdt=0,31",24,2020
Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry,35,eccv,5,5,2023-06-17 00:17:59.981000,https://github.com/HeCraneChen/3D-Crowd-Pose-Estimation-Based-on-MVG,43,Multi-person 3d pose estimation in crowded scenes based on multi-view geometry,"https://scholar.google.com/scholar?cluster=8080096260443565054&hl=en&as_sdt=0,31",13,2020
TIDE: A General Toolbox for Identifying Object Detection Errors,99,eccv,108,31,2023-06-17 00:18:00.193000,https://github.com/dbolya/tide,642,Tide: A general toolbox for identifying object detection errors,"https://scholar.google.com/scholar?cluster=3544704127592613171&hl=en&as_sdt=0,31",16,2020
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding,331,eccv,34,19,2023-06-17 00:18:00.404000,https://github.com/facebookresearch/PointContrast,300,Pointcontrast: Unsupervised pre-training for 3d point cloud understanding,"https://scholar.google.com/scholar?cluster=15181171144501826129&hl=en&as_sdt=0,44",13,2020
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving,219,eccv,82,48,2023-06-17 00:18:00.617000,https://github.com/Banconxuan/RTM3D,423,Rtm3d: Real-time monocular 3d detection from object keypoints for autonomous driving,"https://scholar.google.com/scholar?cluster=732104654465203720&hl=en&as_sdt=0,33",46,2020
Video Object Segmentation with Episodic Graph Memory Networks,207,eccv,8,9,2023-06-17 00:18:00.828000,https://github.com/carrierlxk/GraphMemVOS,92,Video object segmentation with episodic graph memory networks,"https://scholar.google.com/scholar?cluster=2295523921146535547&hl=en&as_sdt=0,44",13,2020
Contrastive Learning for Weakly Supervised Phrase Grounding,88,eccv,16,6,2023-06-17 00:18:01.043000,https://github.com/BigRedT/info-ground,68,Contrastive learning for weakly supervised phrase grounding,"https://scholar.google.com/scholar?cluster=12213524588688919788&hl=en&as_sdt=0,5",4,2020
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images,111,eccv,16,12,2023-06-17 00:18:01.254000,https://github.com/linjx-ustc1106/TuiGAN-PyTorch,184,Tuigan: Learning versatile image-to-image translation with two unpaired images,"https://scholar.google.com/scholar?cluster=15312911022874818284&hl=en&as_sdt=0,33",6,2020
Semi-Siamese Training for Shallow Face Learning,25,eccv,9,3,2023-06-17 00:18:01.466000,https://github.com/dituu/Semi-Siamese-Training,38,Semi-siamese training for shallow face learning,"https://scholar.google.com/scholar?cluster=10441538273877830731&hl=en&as_sdt=0,1",1,2020
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework,58,eccv,24,6,2023-06-17 00:18:01.677000,https://github.com/TAMU-VITA/GAN-Slimming,106,Gan slimming: All-in-one gan compression by a unified optimization framework,"https://scholar.google.com/scholar?cluster=3933920459494244581&hl=en&as_sdt=0,10",14,2020
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation,518,eccv,156,30,2023-06-17 00:18:01.889000,https://github.com/google-research/deeplab2,906,Axial-deeplab: Stand-alone axial-attention for panoptic segmentation,"https://scholar.google.com/scholar?cluster=1046882767001529262&hl=en&as_sdt=0,49",23,2020
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking,243,eccv,47,24,2023-06-17 00:18:02.101000,https://github.com/pjl1995/CTracker,245,Chained-tracker: Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking,"https://scholar.google.com/scholar?cluster=14989052962975314118&hl=en&as_sdt=0,44",15,2020
Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets,142,eccv,42,6,2023-06-17 00:18:02.312000,https://github.com/wutong16/DistributionBalancedLoss,327,Distribution-balanced loss for multi-label classification in long-tailed datasets,"https://scholar.google.com/scholar?cluster=13527314869380191777&hl=en&as_sdt=0,11",9,2020
Surface Normal Estimation of Tilted Images via Spatial Rectifier,24,eccv,5,0,2023-06-17 00:18:02.523000,https://github.com/MARSLab-UMN/TiltedImageSurfaceNormal,26,Surface Normal Estimation of Tilted Images via Spatial Rectifier,"https://scholar.google.com/scholar?cluster=16013808941221840450&hl=en&as_sdt=0,10",6,2020
Multimodal Shape Completion via Conditional Generative Adversarial Networks,52,eccv,10,2,2023-06-17 00:18:02.734000,https://github.com/ChrisWu1997/Multimodal-Shape-Completion,87,Multimodal shape completion via conditional generative adversarial networks,"https://scholar.google.com/scholar?cluster=5251804413160075616&hl=en&as_sdt=0,5",4,2020
Generative Sparse Detection Networks for 3D Single-shot Object Detection,57,eccv,5,1,2023-06-17 00:18:02.946000,https://github.com/jgwak/GSDN,33,Generative sparse detection networks for 3d single-shot object detection,"https://scholar.google.com/scholar?cluster=6320925240225729320&hl=en&as_sdt=0,43",7,2020
Unpaired Learning of Deep Image Denoising,74,eccv,11,7,2023-06-17 00:18:03.156000,https://github.com/XHWXD/DBSN,123,Unpaired learning of deep image denoising,"https://scholar.google.com/scholar?cluster=5229685355061248122&hl=en&as_sdt=0,5",5,2020
Self-supervising Fine-grained Region Similarities for Large-scale Image Localization,83,eccv,44,20,2023-06-17 00:18:03.367000,https://github.com/yxgeee/SFRS,235,Self-supervising fine-grained region similarities for large-scale image localization,"https://scholar.google.com/scholar?cluster=1058701415174194881&hl=en&as_sdt=0,5",9,2020
Side-Aware Boundary Localization for More Precise Object Detection,98,eccv,8712,845,2023-06-17 00:18:03.580000,https://github.com/open-mmlab/mmdetection,24607,Side-aware boundary localization for more precise object detection,"https://scholar.google.com/scholar?cluster=5772535238998777627&hl=en&as_sdt=0,49",372,2020
SF-Net: Single-Frame Supervision for Temporal Action Localization,102,eccv,9,3,2023-06-17 00:18:03.791000,https://github.com/Flowerfan/SF-Net,69,Sf-net: Single-frame supervision for temporal action localization,"https://scholar.google.com/scholar?cluster=3928205527032749715&hl=en&as_sdt=0,10",3,2020
Negative Margin Matters: Understanding Margin in Few-shot Classification,231,eccv,17,1,2023-06-17 00:18:04.002000,https://github.com/bl0/negative-margin.few-shot,139,Negative margin matters: Understanding margin in few-shot classification,"https://scholar.google.com/scholar?cluster=8028630105979977197&hl=en&as_sdt=0,5",10,2020
Tracking Objects as Points,715,eccv,514,191,2023-06-17 00:18:04.215000,https://github.com/xingyizhou/CenterTrack,2231,Tracking objects as points,"https://scholar.google.com/scholar?cluster=1564404986299438483&hl=en&as_sdt=0,10",53,2020
MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning,138,eccv,100,17,2023-06-17 00:18:04.426000,https://github.com/SimonVandenhende/Multi-Task-Learning-PyTorch,628,Mti-net: Multi-scale task interaction networks for multi-task learning,"https://scholar.google.com/scholar?cluster=6510109055568028495&hl=en&as_sdt=0,33",16,2020
GRAB: A Dataset of Whole-Body Human Grasping of Objects,153,eccv,20,3,2023-06-17 00:18:04.637000,https://github.com/otaheri/GRAB,173,GRAB: A dataset of whole-body human grasping of objects,"https://scholar.google.com/scholar?cluster=2290117580249334673&hl=en&as_sdt=0,5",12,2020
Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images,16,eccv,8,1,2023-06-17 00:18:04.847000,https://github.com/kirumang/NOL,30,Neural object learning for 6d pose estimation using a few cluttered images,"https://scholar.google.com/scholar?cluster=6679762187196417030&hl=en&as_sdt=0,5",4,2020
Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking,90,eccv,9,5,2023-06-17 00:18:05.059000,https://github.com/yhw-yhw/D2HC-RMVSNet,108,Dense hybrid recurrent multi-view stereo net with dynamic consistency checking,"https://scholar.google.com/scholar?cluster=11593766892811419595&hl=en&as_sdt=0,15",10,2020
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization,64,eccv,17,0,2023-06-17 00:18:05.270000,https://github.com/JuanDuGit/DH3D,144,Dh3d: Deep hierarchical 3d descriptors for robust large-scale 6dof relocalization,"https://scholar.google.com/scholar?cluster=16560386436692517722&hl=en&as_sdt=0,47",7,2020
Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation,65,eccv,3,0,2023-06-17 00:18:05.497000,https://github.com/YBZh/Label-Propagation-with-Augmented-Anchors,20,Label propagation with augmented anchors: A simple semi-supervised learning baseline for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=14734391019450578035&hl=en&as_sdt=0,36",4,2020
Are Labels Necessary for Neural Architecture Search?,73,eccv,15,3,2023-06-17 00:18:05.709000,https://github.com/facebookresearch/unnas,92,Are labels necessary for neural architecture search?,"https://scholar.google.com/scholar?cluster=7756621635676011996&hl=en&as_sdt=0,41",11,2020
View-Invariant Probabilistic Embedding for Human Pose,62,eccv,7322,1026,2023-06-17 00:18:05.920000,https://github.com/google-research/google-research,29788,View-invariant probabilistic embedding for human pose,"https://scholar.google.com/scholar?cluster=7784472457768654919&hl=en&as_sdt=0,39",727,2020
Contact and Human Dynamics from Monocular Video,56,eccv,38,11,2023-06-17 00:18:06.131000,https://github.com/davrempe/contact-human-dynamics,226,Contact and human dynamics from monocular video,"https://scholar.google.com/scholar?cluster=5354853544269994035&hl=en&as_sdt=0,5",7,2020
PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation,90,eccv,20,3,2023-06-17 00:18:06.343000,https://github.com/DylanWusee/PointPWC,126,Pointpwc-net: Cost volume on point clouds for (self-) supervised scene flow estimation,"https://scholar.google.com/scholar?cluster=9242854520678800803&hl=en&as_sdt=0,5",4,2020
Few-Shot Scene-Adaptive Anomaly Detection,94,eccv,20,10,2023-06-17 00:18:06.555000,https://github.com/yiweilu3/Few-shot-Scene-adaptive-Anomaly-Detection,64,Few-shot scene-adaptive anomaly detection,"https://scholar.google.com/scholar?cluster=7604484229597483619&hl=en&as_sdt=0,33",8,2020
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments,142,eccv,40,13,2023-06-17 00:18:06.766000,https://github.com/clobotics/piou,184,Piou loss: Towards accurate oriented object detection in complex environments,"https://scholar.google.com/scholar?cluster=12353159982052869953&hl=en&as_sdt=0,32",6,2020
Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification,168,eccv,5,2,2023-06-17 00:18:06.977000,https://github.com/xiangly55/LFME,29,Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification,"https://scholar.google.com/scholar?cluster=10072224309423455263&hl=en&as_sdt=0,10",0,2020
DTVNet: Dynamic Time-lapse Video Generation via Single Still Image,18,eccv,6,4,2023-06-17 00:18:07.188000,https://github.com/zhangzjn/DTVNet,57,Dtvnet: Dynamic time-lapse video generation via single still image,"https://scholar.google.com/scholar?cluster=8543456608729588740&hl=en&as_sdt=0,48",6,2020
Collaborative Video Object Segmentation by Foreground-Background Integration,175,eccv,43,0,2023-06-17 00:18:07.400000,https://github.com/z-x-yang/CFBI,316,Collaborative video object segmentation by foreground-background integration,"https://scholar.google.com/scholar?cluster=7748785869028179006&hl=en&as_sdt=0,5",20,2020
ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation,133,eccv,29,9,2023-06-17 00:18:07.621000,https://github.com/xucong-zhang/ETH-XGaze,148,Eth-xgaze: A large scale dataset for gaze estimation under extreme head pose and gaze variation,"https://scholar.google.com/scholar?cluster=6143408045800102066&hl=en&as_sdt=0,5",4,2020
Occupancy Anticipation for Efficient Exploration and Navigation,108,eccv,27,6,2023-06-17 00:18:07.832000,https://github.com/facebookresearch/OccupancyAnticipation,69,Occupancy anticipation for efficient exploration and navigation,"https://scholar.google.com/scholar?cluster=713069138791923725&hl=en&as_sdt=0,33",6,2020
Unified Image and Video Saliency Modeling,104,eccv,33,1,2023-06-17 00:18:08.044000,https://github.com/rdroste/unisal,109,Unified image and video saliency modeling,"https://scholar.google.com/scholar?cluster=2324985920032308354&hl=en&as_sdt=0,34",7,2020
A Generalization of Otsu’s Method and Minimum Error Thresholding,17,eccv,12,1,2023-06-17 00:18:08.255000,https://github.com/jonbarron/hist_thresh,93,A generalization of Otsu's method and minimum error thresholding,"https://scholar.google.com/scholar?cluster=17503498248549422833&hl=en&as_sdt=0,15",7,2020
Big Transfer (BiT): General Visual Representation Learning,856,eccv,174,42,2023-06-17 00:18:08.467000,https://github.com/google-research/big_transfer,1433,Big transfer (bit): General visual representation learning,"https://scholar.google.com/scholar?cluster=17091430629023814864&hl=en&as_sdt=0,14",43,2020
Learning Visual Context by Comparison,757,eccv,5,4,2023-06-17 00:18:08.678000,https://github.com/mk-minchul/attend-and-compare,24,Cortical analysis of visual context,"https://scholar.google.com/scholar?cluster=8319428113008699987&hl=en&as_sdt=0,49",4,2020
Large Scale Holistic Video Understanding,71,eccv,8,1,2023-06-17 00:18:08.890000,https://github.com/holistic-video-understanding/HVU-Dataset,67,Large scale holistic video understanding,"https://scholar.google.com/scholar?cluster=11783124008070444099&hl=en&as_sdt=0,5",14,2020
Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings,17,eccv,8,0,2023-06-17 00:18:09.101000,https://github.com/nianticlabs/image-box-overlap,48,Predicting visual overlap of images through interpretable non-metric box embeddings,"https://scholar.google.com/scholar?cluster=17417215056753881033&hl=en&as_sdt=0,22",10,2020
Connecting Vision and Language with Localized Narratives,130,eccv,13,1,2023-06-17 00:18:09.313000,https://github.com/google/localized-narratives,69,Connecting vision and language with localized narratives,"https://scholar.google.com/scholar?cluster=3498887869112609391&hl=en&as_sdt=0,5",11,2020
SRFlow: Learning the Super-Resolution Space with Normalizing Flow,224,eccv,113,38,2023-06-17 00:18:09.524000,https://github.com/andreas128/SRFlow,780,Srflow: Learning the super-resolution space with normalizing flow,"https://scholar.google.com/scholar?cluster=10332710296842413172&hl=en&as_sdt=0,26",69,2020
DeepGMR: Learning Latent Gaussian Mixture Models for Registration,139,eccv,14,5,2023-06-17 00:18:09.735000,https://github.com/wentaoyuan/deepgmr,114,Deepgmr: Learning latent gaussian mixture models for registration,"https://scholar.google.com/scholar?cluster=82174238332873034&hl=en&as_sdt=0,44",8,2020
Invertible Neural BRDF for Object Inverse Rendering,15,eccv,2,0,2023-06-17 00:18:09.947000,https://github.com/chenzhekl/iBRDF,16,Invertible neural BRDF for object inverse rendering,"https://scholar.google.com/scholar?cluster=15326337480239403123&hl=en&as_sdt=0,5",4,2020
Practical Deep Raw Image Denoising on Mobile Devices,62,eccv,31,16,2023-06-17 00:18:10.158000,https://github.com/megvii-research/PMRID,172,Practical deep raw image denoising on mobile devices,"https://scholar.google.com/scholar?cluster=14817397554395527733&hl=en&as_sdt=0,33",9,2020
SoundSpaces: Audio-Visual Navigation in 3D Environments,165,eccv,50,35,2023-06-17 00:18:10.369000,https://github.com/facebookresearch/sound-spaces,264,Soundspaces: Audio-visual navigation in 3d environments,"https://scholar.google.com/scholar?cluster=12346381146066687230&hl=en&as_sdt=0,39",14,2020
JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image,36,eccv,9,4,2023-06-17 00:18:10.582000,https://github.com/fanglinpu/JGR-P2O,36,Jgr-p2o: Joint graph reasoning based pixel-to-offset prediction network for 3d hand pose estimation from a single depth image,"https://scholar.google.com/scholar?cluster=9945614563627279865&hl=en&as_sdt=0,11",2,2020
Dynamic Group Convolution for Accelerating Convolutional Neural Networks,30,eccv,19,6,2023-06-17 00:18:10.794000,https://github.com/zhuogege1943/dgc,123,Dynamic group convolution for accelerating convolutional neural networks,"https://scholar.google.com/scholar?cluster=15250993372370753171&hl=en&as_sdt=0,5",4,2020
Photon-Efficient 3D Imaging with A Non-Local Neural Network,35,eccv,7,7,2023-06-17 00:18:11.006000,https://github.com/JiayongO-O/PENonLocal,22,Photon-efficient 3d imaging with a non-local neural network,"https://scholar.google.com/scholar?cluster=16002398866201588716&hl=en&as_sdt=0,5",2,2020
Improving Vision-and-Language Navigation with Image-Text Pairs from the Web,148,eccv,9,3,2023-06-17 00:18:11.217000,https://github.com/arjunmajum/vln-bert,47,Improving vision-and-language navigation with image-text pairs from the web,"https://scholar.google.com/scholar?cluster=5223130819859992392&hl=en&as_sdt=0,32",4,2020
CoTeRe-Net: Discovering Collaborative Ternary Relations in Videos,3,eccv,2,1,2023-06-17 00:18:11.429000,https://github.com/zhenglab/cotere-net,3,CoTeRe-Net: Discovering collaborative ternary relations in videos,"https://scholar.google.com/scholar?cluster=10175573041674181180&hl=en&as_sdt=0,14",1,2020
Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search,14,eccv,7,2,2023-06-17 00:18:11.641000,https://github.com/ambekarsameer96/GLSS,31,Unsupervised domain adaptation for semantic segmentation of NIR images through generative latent search,"https://scholar.google.com/scholar?cluster=18397040174251573091&hl=en&as_sdt=0,5",3,2020
PROFIT: A Novel Training Method for sub-4-bit MobileNet Models,70,eccv,8,5,2023-06-17 00:18:11.855000,https://github.com/EunhyeokPark/PROFIT,40,Profit: A novel training method for sub-4-bit mobilenet models,"https://scholar.google.com/scholar?cluster=7864152585647915312&hl=en&as_sdt=0,33",4,2020
Visual Relation Grounding in Videos,29,eccv,7,4,2023-06-17 00:18:12.066000,https://github.com/doc-doc/vRGV,56,Visual relation grounding in videos,"https://scholar.google.com/scholar?cluster=17262208065957569842&hl=en&as_sdt=0,47",3,2020
Controlling Style and Semantics in Weakly-Supervised Image Generation,33,eccv,21,1,2023-06-17 00:18:12.284000,https://github.com/dariopavllo/style-semantics,141,Controlling style and semantics in weakly-supervised image generation,"https://scholar.google.com/scholar?cluster=16760433186567388134&hl=en&as_sdt=0,44",10,2020
SODA: Story Oriented Dense Video Captioning Evaluation Framework,15,eccv,3,2,2023-06-17 00:18:12.503000,https://github.com/fujiso/SODA,7,SODA: Story oriented dense video captioning evaluation framework,"https://scholar.google.com/scholar?cluster=1063726150354296484&hl=en&as_sdt=0,36",3,2020
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses,98,eccv,18,2,2023-06-17 00:18:12.715000,https://github.com/jeromerony/dml_cross_entropy,156,A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses,"https://scholar.google.com/scholar?cluster=15432822648565804462&hl=en&as_sdt=0,10",6,2020
The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement,94,eccv,22,2,2023-06-17 00:18:12.926000,https://github.com/wpeebles/hessian_penalty,200,The hessian penalty: A weak prior for unsupervised disentanglement,"https://scholar.google.com/scholar?cluster=2114387420606237840&hl=en&as_sdt=0,5",12,2020
STAR: Sparse Trained Articulated Human Body Regressor,167,eccv,88,7,2023-06-17 00:18:13.139000,https://github.com/ahmedosman/STAR,556,Star: Sparse trained articulated human body regressor,"https://scholar.google.com/scholar?cluster=766398854031447932&hl=en&as_sdt=0,37",20,2020
Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians,38,eccv,10,6,2023-06-17 00:18:13.350000,https://github.com/X-BrainLab/PI-ReID,43,Do not disturb me: Person re-identification under the interference of other pedestrians,"https://scholar.google.com/scholar?cluster=10061558687540529622&hl=en&as_sdt=0,5",2,2020
Highly Efficient Salient Object Detection with 100K Parameters,136,eccv,45,1,2023-06-17 00:18:13.562000,https://github.com/ShangHua-Gao/SOD100K,230,Highly efficient salient object detection with 100k parameters,"https://scholar.google.com/scholar?cluster=10561938600149288701&hl=en&as_sdt=0,5",13,2020
Simulating Content Consistent Vehicle Datasets with Attribute Descent,140,eccv,19,4,2023-06-17 00:18:13.773000,https://github.com/yorkeyao/VehicleX,145,Simulating content consistent vehicle datasets with attribute descent,"https://scholar.google.com/scholar?cluster=1008066265294345684&hl=en&as_sdt=0,5",6,2020
Multiview Detection with Feature Perspective Transformation,49,eccv,26,1,2023-06-17 00:18:13.984000,https://github.com/hou-yz/MVDet,133,Multiview detection with feature perspective transformation,"https://scholar.google.com/scholar?cluster=12083775198861358360&hl=en&as_sdt=0,7",5,2020
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search,40,eccv,12,1,2023-06-17 00:18:14.195000,https://github.com/Yuantian013/E2GAN,36,Off-policy reinforcement learning for efficient and effective gan architecture search,"https://scholar.google.com/scholar?cluster=1035067928547475214&hl=en&as_sdt=0,33",5,2020
The Group Loss for Deep Metric Learning,56,eccv,14,3,2023-06-17 00:18:14.407000,https://github.com/dvl-tum/group_loss,44,The group loss for deep metric learning,"https://scholar.google.com/scholar?cluster=13659281019313761829&hl=en&as_sdt=0,47",4,2020
Learning Object Depth from Camera Motion and Video Object Segmentation,3,eccv,10,0,2023-06-17 00:18:14.619000,https://github.com/griffbr/ODMS,69,Learning object depth from camera motion and video object segmentation,"https://scholar.google.com/scholar?cluster=6889613697176178772&hl=en&as_sdt=0,21",5,2020
OnlineAugment: Online Data Augmentation with Less Domain Knowledge,33,eccv,5,2,2023-06-17 00:18:14.830000,https://github.com/zhiqiangdon/online-augment,37,OnlineAugment: Online data augmentation with less domain knowledge,"https://scholar.google.com/scholar?cluster=5067479430831174615&hl=en&as_sdt=0,5",2,2020
Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction,18,eccv,4,5,2023-06-17 00:18:15.041000,https://github.com/yi-ming-qian/interplane,38,Learning pairwise inter-plane relations for piecewise planar reconstruction,"https://scholar.google.com/scholar?cluster=590926112783724445&hl=en&as_sdt=0,5",7,2020
Intra-class Feature Variation Distillation for Semantic Segmentation,73,eccv,10,7,2023-06-17 00:18:15.252000,https://github.com/YukangWang/IFVD,62,Intra-class feature variation distillation for semantic segmentation,"https://scholar.google.com/scholar?cluster=15909434654296625267&hl=en&as_sdt=0,5",4,2020
Colorization of Depth Map via Disentanglement,4,eccv,3,0,2023-06-17 00:18:15.464000,https://github.com/alanlai199/ColorizeDepthNet,3,Colorization of depth map via disentanglement,"https://scholar.google.com/scholar?cluster=5966508110541895307&hl=en&as_sdt=0,8",1,2020
Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes,30,eccv,5,3,2023-06-17 00:18:15.675000,https://github.com/WaldJohannaU/RIO10,21,Beyond controlled environments: 3d camera re-localization in changing indoor scenes,"https://scholar.google.com/scholar?cluster=8394466864426606177&hl=en&as_sdt=0,33",2,2020
Localizing the Common Action Among a Few Videos,15,eccv,2,2,2023-06-17 00:18:15.887000,https://github.com/PengWan-Yang/commonLocalization,16,Localizing the common action among a few videos,"https://scholar.google.com/scholar?cluster=16507642119898920462&hl=en&as_sdt=0,33",2,2020
Traffic Accident Benchmark for Causality Recognition,24,eccv,2,2,2023-06-17 00:18:16.097000,https://github.com/tackgeun/CausalityInTrafficAccident,24,Traffic accident benchmark for causality recognition,"https://scholar.google.com/scholar?cluster=15783573589328009426&hl=en&as_sdt=0,23",2,2020
Multiple Expert Brainstorming for Domain Adaptive Person Re-identification,162,eccv,15,4,2023-06-17 00:18:16.309000,https://github.com/YunpengZhai/MEB-Net,77,Multiple expert brainstorming for domain adaptive person re-identification,"https://scholar.google.com/scholar?cluster=9325205339636219475&hl=en&as_sdt=0,5",4,2020
Towards Unique and Informative Captioning of Images,25,eccv,1,0,2023-06-17 00:18:16.529000,https://github.com/princetonvisualai/SPICE-U,10,Towards unique and informative captioning of images,"https://scholar.google.com/scholar?cluster=13185280862262111547&hl=en&as_sdt=0,33",3,2020
I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image,264,eccv,125,42,2023-06-17 00:18:16.741000,https://github.com/mks0601/I2L-MeshNet_RELEASE,656,I2l-meshnet: Image-to-lixel prediction network for accurate 3d human pose and mesh estimation from a single rgb image,"https://scholar.google.com/scholar?cluster=11169012709456918431&hl=en&as_sdt=0,47",25,2020
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose,225,eccv,60,43,2023-06-17 00:18:16.953000,https://github.com/hongsukchoi/Pose2Mesh_RELEASE,476,Pose2mesh: Graph convolutional network for 3d human pose and mesh recovery from a 2d human pose,"https://scholar.google.com/scholar?cluster=16146709528762580346&hl=en&as_sdt=0,5",15,2020
Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition,3,eccv,0,1,2023-06-17 00:18:17.171000,https://github.com/KeleiHe/DAAN,8,Unsupervised domain attention adaptation network for caricature attribute recognition,"https://scholar.google.com/scholar?cluster=2636273584791928506&hl=en&as_sdt=0,3",1,2020
Curriculum DeepSDF,62,eccv,11,1,2023-06-17 00:18:17.384000,https://github.com/haidongz-usc/Curriculum-DeepSDF,74,Curriculum deepsdf,"https://scholar.google.com/scholar?cluster=15992411980424655075&hl=en&as_sdt=0,5",7,2020
Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance,27,eccv,9,2,2023-06-17 00:18:17.596000,https://github.com/Colin97/Point2Mesh,77,Meshing point clouds with predicted intrinsic-extrinsic ratio guidance,"https://scholar.google.com/scholar?cluster=17672250156896907210&hl=en&as_sdt=0,10",6,2020
Component Divide-and-Conquer for Real-World Image Super-Resolution,106,eccv,19,17,2023-06-17 00:18:17.808000,https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution,156,Component divide-and-conquer for real-world image super-resolution,"https://scholar.google.com/scholar?cluster=6228594614718931211&hl=en&as_sdt=0,10",3,2020
Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier,32,eccv,6,1,2023-06-17 00:18:18.019000,https://github.com/gina9726/Deep-RTC,17,Solving long-tailed recognition with deep realistic taxonomic classifier,"https://scholar.google.com/scholar?cluster=5239208418514304648&hl=en&as_sdt=0,33",1,2020
Faster Person Re-Identification,48,eccv,89,5,2023-06-17 00:18:18.231000,https://github.com/wangguanan/light-reid,467,Faster person re-identification,"https://scholar.google.com/scholar?cluster=16177394885432269514&hl=en&as_sdt=0,47",14,2020
Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction,29,eccv,10,10,2023-06-17 00:18:18.442000,https://github.com/XiangyuZhu-open/Beyond3DMM,102,Beyond 3dmm space: Towards fine-grained 3d face reconstruction,"https://scholar.google.com/scholar?cluster=17906338760505888832&hl=en&as_sdt=0,26",22,2020
Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation,24,eccv,47,55,2023-06-17 00:18:18.655000,https://github.com/fanq15/FewX,316,Commonality-parsing network across shape and appearance for partially supervised instance segmentation,"https://scholar.google.com/scholar?cluster=2402539617532346879&hl=en&as_sdt=0,5",14,2020
Event-based Asynchronous Sparse Convolutional Networks,87,eccv,21,7,2023-06-17 00:18:18.867000,https://github.com/uzh-rpg/rpg_asynet,105,Event-based asynchronous sparse convolutional networks,"https://scholar.google.com/scholar?cluster=758733237410689514&hl=en&as_sdt=0,7",12,2020
AtlantaNet: Inferring the 3D Indoor Layout from a Single 360(∘) Image beyond the Manhattan World Assumption,42,eccv,9,0,2023-06-17 00:18:19.079000,https://github.com/crs4/AtlantaNet,67,AtlantaNet: Inferring the 3D Indoor Layout from a Single Image Beyond the Manhattan World Assumption,"https://scholar.google.com/scholar?cluster=5436865001180080395&hl=en&as_sdt=0,34",15,2020
REMIND Your Neural Network to Prevent Catastrophic Forgetting,180,eccv,23,2,2023-06-17 00:18:19.291000,https://github.com/tyler-hayes/REMIND,73,Remind your neural network to prevent catastrophic forgetting,"https://scholar.google.com/scholar?cluster=12242659965192476328&hl=en&as_sdt=0,5",12,2020
n-Reference Transfer Learning for Saliency Prediction,3,eccv,1,0,2023-06-17 00:18:19.531000,https://github.com/luoyan407/n-reference,10,n-Reference Transfer Learning for Saliency Prediction,"https://scholar.google.com/scholar?cluster=6476777819073493860&hl=en&as_sdt=0,14",1,2020
Bottom-Up Temporal Action Localization with Mutual Regularization,132,eccv,7,5,2023-06-17 00:18:19.745000,https://github.com/PeisenZhao/Bottom-Up-TAL-with-MR,43,Bottom-up temporal action localization with mutual regularization,"https://scholar.google.com/scholar?cluster=10726191543392549862&hl=en&as_sdt=0,34",3,2020
On Modulating the Gradient for Meta-Learning,43,eccv,1,0,2023-06-17 00:18:19.957000,https://github.com/chrysts/generative_preconditioner,10,On modulating the gradient for meta-learning,"https://scholar.google.com/scholar?cluster=15551882239669772127&hl=en&as_sdt=0,5",2,2020
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning,52,eccv,11,0,2023-06-17 00:18:20.169000,https://github.com/Confusezius/ECCV2020_DiVA_MultiFeature_DML,33,Diva: Diverse visual feature aggregation for deep metric learning,"https://scholar.google.com/scholar?cluster=10654101508102369352&hl=en&as_sdt=0,5",3,2020
DHP: Differentiable Meta Pruning via HyperNetworks,88,eccv,8,2,2023-06-17 00:18:20.380000,https://github.com/ofsoundof/dhp,54,Dhp: Differentiable meta pruning via hypernetworks,"https://scholar.google.com/scholar?cluster=8040242482873444313&hl=en&as_sdt=0,5",8,2020
Deep Transferring Quantization,9,eccv,3,3,2023-06-17 00:18:20.592000,https://github.com/xiezheng-cs/DTQ,16,Deep transferring quantization,"https://scholar.google.com/scholar?cluster=4949281948519150882&hl=en&as_sdt=0,5",1,2020
Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation,72,eccv,14,3,2023-06-17 00:18:20.803000,https://github.com/ChrisAllenMing/LtC-MSDA,62,Learning to combine: Knowledge aggregation for multi-source domain adaptation,"https://scholar.google.com/scholar?cluster=10311437176755219330&hl=en&as_sdt=0,6",5,2020
Prototype Mixture Models for Few-shot Semantic Segmentation,217,eccv,26,13,2023-06-17 00:18:21.015000,https://github.com/Yang-Bob/PMMs,158,Prototype mixture models for few-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=1752297880140271762&hl=en&as_sdt=0,5",10,2020
Talking-head Generation with Rhythmic Head Motion,98,eccv,27,14,2023-06-17 00:18:21.259000,https://github.com/lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion,183,Talking-head generation with rhythmic head motion,"https://scholar.google.com/scholar?cluster=17698096516436433943&hl=en&as_sdt=0,5",12,2020
VPN: Learning Video-Pose Embedding for Activities of Daily Living,71,eccv,7,3,2023-06-17 00:18:21.505000,https://github.com/srijandas07/VPN,32,Vpn: Learning video-pose embedding for activities of daily living,"https://scholar.google.com/scholar?cluster=11383453681577787475&hl=en&as_sdt=0,11",5,2020
Part-aware Prototype Network for Few-shot Semantic Segmentation,196,eccv,17,4,2023-06-17 00:18:21.716000,https://github.com/Xiangyi1996/PPNet-PyTorch,113,Part-aware prototype network for few-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=16634938413302258623&hl=en&as_sdt=0,11",10,2020
Whole-Body Human Pose Estimation in the Wild,130,eccv,71,6,2023-06-17 00:18:21.928000,https://github.com/jin-s13/COCO-WholeBody,626,Whole-body human pose estimation in the wild,"https://scholar.google.com/scholar?cluster=3476113466825776687&hl=en&as_sdt=0,44",24,2020
Relative Pose Estimation of Calibrated Cameras with Known SE(3) Invariants,5,eccv,7,0,2023-06-17 00:18:22.139000,https://github.com/prclibo/relative_pose,28,Relative Pose Estimation of Calibrated Cameras with Known Invariants,"https://scholar.google.com/scholar?cluster=15442781977472727349&hl=en&as_sdt=0,5",2,2020
Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing,4,eccv,3,4,2023-06-17 00:18:22.350000,https://github.com/rkteddy/RK-CCSNet,11,Sequential convolution and runge-kutta residual architecture for image compressed sensing,"https://scholar.google.com/scholar?cluster=293112628386870123&hl=en&as_sdt=0,33",2,2020
Deep Hough Transform for Semantic Line Detection,104,eccv,65,8,2023-06-17 00:18:22.562000,https://github.com/Hanqer/deep-hough-transform,277,Deep hough transform for semantic line detection,"https://scholar.google.com/scholar?cluster=365795054103351102&hl=en&as_sdt=0,10",13,2020
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning,33,eccv,3,5,2023-06-17 00:18:22.774000,https://github.com/xuxy09/RSC-Net,45,3d human shape and pose from a single low-resolution image with self-supervised learning,"https://scholar.google.com/scholar?cluster=11400661144126393693&hl=en&as_sdt=0,5",2,2020
Learning to Balance Specificity and Invariance for In and Out of Domain Generalization,122,eccv,3,2,2023-06-17 00:18:22.985000,https://github.com/prithv1/DMG,50,Learning to balance specificity and invariance for in and out of domain generalization,"https://scholar.google.com/scholar?cluster=2971128396165767360&hl=en&as_sdt=0,30",2,2020
DLow: Diversifying Latent Flows for Diverse Human Motion Prediction,136,eccv,16,1,2023-06-17 00:18:23.197000,https://github.com/Khrylx/DLow,93,Dlow: Diversifying latent flows for diverse human motion prediction,"https://scholar.google.com/scholar?cluster=16681509370624324705&hl=en&as_sdt=0,14",5,2020
GRNet: Gridding Residual Network for Dense Point Cloud Completion,198,eccv,55,2,2023-06-17 00:18:23.409000,https://github.com/hzxie/GRNet,249,Grnet: Gridding residual network for dense point cloud completion,"https://scholar.google.com/scholar?cluster=11624547905159494632&hl=en&as_sdt=0,43",4,2020
Blind Face Restoration via Deep Multi-scale Component Dictionaries,102,eccv,221,36,2023-06-17 00:18:23.622000,https://github.com/csxmli2016/DFDNet,851,Blind face restoration via deep multi-scale component dictionaries,"https://scholar.google.com/scholar?cluster=4053134039615204121&hl=en&as_sdt=0,10",37,2020
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods,4,eccv,2,0,2023-06-17 00:18:23.833000,https://github.com/matbambbang/sspnet,14,Robust neural networks inspired by strong stability preserving Runge-Kutta methods,"https://scholar.google.com/scholar?cluster=11614410574122465773&hl=en&as_sdt=0,11",1,2020
Inequality-Constrained and Robust 3D Face Model Fitting,11,eccv,4,1,2023-06-17 00:18:24.046000,https://github.com/sariyanidi/3DI,9,Inequality-constrained and robust 3d face model fitting,"https://scholar.google.com/scholar?cluster=10349705549775155101&hl=en&as_sdt=0,5",4,2020
Learnable Cost Volume Using the Cayley Representation,11,eccv,0,0,2023-06-17 00:18:24.258000,https://github.com/Prinsphield/LCV,2,Learnable cost volume using the cayley representation,"https://scholar.google.com/scholar?cluster=12178650096757131507&hl=en&as_sdt=0,5",2,2020
HALO: Hardware-Aware Learning to Optimize,22,eccv,0,0,2023-06-17 00:18:24.469000,https://github.com/RICE-EIC/HALO,7,HALO: Hardware-aware learning to optimize,"https://scholar.google.com/scholar?cluster=15679986134089724572&hl=en&as_sdt=0,5",3,2020
Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling,144,eccv,54,2,2023-06-17 00:18:24.682000,https://github.com/bertjiazheng/Structured3D,420,Structured3d: A large photo-realistic dataset for structured 3d modeling,"https://scholar.google.com/scholar?cluster=10565232241444480575&hl=en&as_sdt=0,5",16,2020
Knowledge Distillation Meets Self-Supervision,202,eccv,49,10,2023-06-17 00:18:24.893000,https://github.com/xuguodong03/SSKD,212,Knowledge distillation meets self-supervision,"https://scholar.google.com/scholar?cluster=1628469133386608876&hl=en&as_sdt=0,11",8,2020
Occlusion-Aware Depth Estimation with Adaptive Normal Constraints,41,eccv,5,1,2023-06-17 00:18:25.105000,https://github.com/xxlong0/CNMNet,46,Occlusion-aware depth estimation with adaptive normal constraints,"https://scholar.google.com/scholar?cluster=5257398877657773735&hl=en&as_sdt=0,7",4,2020
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation,153,eccv,46279,1207,2023-06-17 00:18:25.316000,https://github.com/tensorflow/models,75921,Naive-student: Leveraging semi-supervised learning in video sequences for urban scene segmentation,"https://scholar.google.com/scholar?cluster=12595970701818011045&hl=en&as_sdt=0,5",2774,2020
Spatially Aware Multimodal Transformers for TextVQA,64,eccv,12,6,2023-06-17 00:18:25.540000,https://github.com/yashkant/sam-textvqa,57,Spatially aware multimodal transformers for textvqa,"https://scholar.google.com/scholar?cluster=15544961945586431301&hl=en&as_sdt=0,47",3,2020
Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector,113,eccv,20,12,2023-06-17 00:18:25.751000,https://github.com/chengchunhsu/EveryPixelMatters,152,Every pixel matters: Center-aware feature alignment for domain adaptive object detector,"https://scholar.google.com/scholar?cluster=12865822687242866746&hl=en&as_sdt=0,44",8,2020
Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation,72,eccv,11,7,2023-06-17 00:18:25.963000,https://github.com/yhw-yhw/PVAMVSNet,73,Pyramid multi-view stereo net with self-adaptive view aggregation,"https://scholar.google.com/scholar?cluster=5324599381865835017&hl=en&as_sdt=0,5",12,2020
Unpaired Image-to-Image Translation using Adversarial Consistency Loss,78,eccv,32,5,2023-06-17 00:18:26.173000,https://github.com/hyperplane-lab/ACL-GAN,133,Unpaired image-to-image translation using adversarial consistency loss,"https://scholar.google.com/scholar?cluster=4792912034124725763&hl=en&as_sdt=0,19",7,2020
Monocular Expressive Body Regression through Body-Driven Attention,150,eccv,90,38,2023-06-17 00:18:26.385000,https://github.com/vchoutas/expose,525,Monocular expressive body regression through body-driven attention,"https://scholar.google.com/scholar?cluster=13234841314490938022&hl=en&as_sdt=0,5",20,2020
Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation,141,eccv,32,4,2023-06-17 00:18:26.597000,https://github.com/zsyOAOA/DANet,160,Dual adversarial network: Toward real-world noise removal and noise generation,"https://scholar.google.com/scholar?cluster=5519869533522154004&hl=en&as_sdt=0,5",2,2020
Linguistic Structure Guided Context Modeling for Referring Image Segmentation,70,eccv,5,1,2023-06-17 00:18:26.808000,https://github.com/spyflying/LSCM-Refseg,9,Linguistic structure guided context modeling for referring image segmentation,"https://scholar.google.com/scholar?cluster=1017126320110663053&hl=en&as_sdt=0,47",5,2020
Robust Re-Identification by Multiple Views Knowledge Distillation,53,eccv,15,4,2023-06-17 00:18:27.019000,https://github.com/aimagelab/VKD,68,Robust re-identification by multiple views knowledge distillation,"https://scholar.google.com/scholar?cluster=5847782043574863283&hl=en&as_sdt=0,22",9,2020
Defocus Deblurring Using Dual-Pixel Data,88,eccv,18,1,2023-06-17 00:18:27.233000,https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel,138,Defocus deblurring using dual-pixel data,"https://scholar.google.com/scholar?cluster=5025107258941686870&hl=en&as_sdt=0,33",7,2020
Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networks,23,eccv,3,3,2023-06-17 00:18:27.444000,https://github.com/gilshm/zap,0,Thanks for nothing: Predicting zero-valued activations with lightweight convolutional neural networks,"https://scholar.google.com/scholar?cluster=6597464735432470260&hl=en&as_sdt=0,34",3,2020
Layered Neighborhood Expansion for Incremental Multiple Graph Matching,5,eccv,0,0,2023-06-17 00:18:27.656000,https://github.com/fffffarmer/LNE_IMGM,3,Layered neighborhood expansion for incremental multiple graph matching,"https://scholar.google.com/scholar?cluster=13489924232243333530&hl=en&as_sdt=0,5",1,2020
SCAN: Learning to Classify Images without Labels,357,eccv,251,15,2023-06-17 00:18:27.868000,https://github.com/wvangansbeke/Unsupervised-Classification,1200,Scan: Learning to classify images without labels,"https://scholar.google.com/scholar?cluster=12970908709596385976&hl=en&as_sdt=0,6",53,2020
Graph convolutional networks for learning with few clean and many noisy labels,16,eccv,7,1,2023-06-17 00:18:28.080000,https://github.com/google-research/noisy-fewshot-learning,23,Graph convolutional networks for learning with few clean and many noisy labels,"https://scholar.google.com/scholar?cluster=3928299844756290038&hl=en&as_sdt=0,5",5,2020
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions,33,eccv,3,1,2023-06-17 00:18:28.295000,https://github.com/matheusgadelha/PointCloudLearningACD,33,Label-efficient learning on point clouds using approximate convex decompositions,"https://scholar.google.com/scholar?cluster=6679535776776128148&hl=en&as_sdt=0,5",7,2020
"Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation",96,eccv,8,5,2023-06-17 00:18:28.520000,https://github.com/FangyunWei/PointSetAnchor,81,"Point-set anchors for object detection, instance segmentation and pose estimation","https://scholar.google.com/scholar?cluster=8225197904370189459&hl=en&as_sdt=0,14",5,2020
Scene Text Image Super-resolution in the wild,73,eccv,68,33,2023-06-17 00:18:28.732000,https://github.com/JasonBoy1/TextZoom,358,Scene text image super-resolution in the wild,"https://scholar.google.com/scholar?cluster=12716484500306157033&hl=en&as_sdt=0,23",17,2020
Learning Disentangled Representations with Latent Variation Predictability,22,eccv,2,0,2023-06-17 00:18:28.943000,https://github.com/zhuxinqimac/stylegan2vp,12,Learning disentangled representations with latent variation predictability,"https://scholar.google.com/scholar?cluster=17070159104288451181&hl=en&as_sdt=0,18",3,2020
Deep Space-Time Video Upsampling Networks,13,eccv,2,4,2023-06-17 00:18:29.155000,https://github.com/JaeYeonKang/STVUN-Pytorch,46,Deep space-time video upsampling networks,"https://scholar.google.com/scholar?cluster=727307812790935313&hl=en&as_sdt=0,33",9,2020
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification,72,eccv,11,7,2023-06-17 00:18:29.381000,https://github.com/dvornikita/SUR,39,Selecting relevant features from a multi-domain representation for few-shot classification,"https://scholar.google.com/scholar?cluster=5482290086183738345&hl=en&as_sdt=0,5",4,2020
Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions,19,eccv,4,2,2023-06-17 00:18:29.593000,https://github.com/xh-liu/Open-Edit,56,Open-edit: Open-domain image manipulation with open-vocabulary instructions,"https://scholar.google.com/scholar?cluster=329234440978181878&hl=en&as_sdt=0,26",5,2020
Towards Real-Time Multi-Object Tracking,637,eccv,544,126,2023-06-17 00:18:29.804000,https://github.com/Zhongdao/Towards-Realtime-MOT,2261,Towards real-time multi-object tracking,"https://scholar.google.com/scholar?cluster=7515568945301804297&hl=en&as_sdt=0,22",58,2020
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation,78,eccv,12,0,2023-06-17 00:18:30.016000,https://github.com/tim-learn/BA3US,36,A balanced and uncertainty-aware approach for partial domain adaptation,"https://scholar.google.com/scholar?cluster=5974367685957311964&hl=en&as_sdt=0,47",2,2020
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos,153,eccv,26,6,2023-06-17 00:18:30.228000,https://github.com/sabarim/STEm-Seg,145,Stem-seg: Spatio-temporal embeddings for instance segmentation in videos,"https://scholar.google.com/scholar?cluster=16201378227810610737&hl=en&as_sdt=0,29",12,2020
Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop,67,eccv,8,2,2023-06-17 00:18:30.440000,https://github.com/benjiebob/WLDO,34,Who left the dogs out? 3d animal reconstruction with expectation maximization in the loop,"https://scholar.google.com/scholar?cluster=3196992100130211506&hl=en&as_sdt=0,22",4,2020
Diverse and Admissible Trajectory Prediction through Multimodal Context Understanding,62,eccv,17,11,2023-06-17 00:18:30.652000,https://github.com/kami93/CMU-DATF,65,Diverse and admissible trajectory forecasting through multimodal context understanding,"https://scholar.google.com/scholar?cluster=4331898533787437974&hl=en&as_sdt=0,43",7,2020
CONFIG: Controllable Neural Face Image Generation,63,eccv,14,3,2023-06-17 00:18:30.865000,https://github.com/microsoft/ConfigNet,102,Config: Controllable neural face image generation,"https://scholar.google.com/scholar?cluster=1580264651872331301&hl=en&as_sdt=0,5",15,2020
Single View Metrology in the Wild,29,eccv,10,5,2023-06-17 00:18:31.077000,https://github.com/Jerrypiglet/ScaleNet,47,Single view metrology in the wild,"https://scholar.google.com/scholar?cluster=5020261105544637693&hl=en&as_sdt=0,16",14,2020
Funnel Activation for Visual Recognition,66,eccv,21,6,2023-06-17 00:18:31.334000,https://github.com/megvii-model/FunnelAct,171,Funnel activation for visual recognition,"https://scholar.google.com/scholar?cluster=7314887533256724657&hl=en&as_sdt=0,36",9,2020
GIQA: Generated Image Quality Assessment,44,eccv,29,5,2023-06-17 00:18:31.587000,https://github.com/cientgu/GIQA,189,Giqa: Generated image quality assessment,"https://scholar.google.com/scholar?cluster=17000009364134963010&hl=en&as_sdt=0,18",4,2020
Adversarial Continual Learning,133,eccv,29,25,2023-06-17 00:18:31.800000,https://github.com/facebookresearch/Adversarial-Continual-Learning,251,Adversarial continual learning,"https://scholar.google.com/scholar?cluster=17591338544420610570&hl=en&as_sdt=0,5",12,2020
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting,85,eccv,31,0,2023-06-17 00:18:32.012000,https://github.com/shengcailiao/QAConv,182,Interpretable and generalizable person re-identification with query-adaptive convolution and temporal lifting,"https://scholar.google.com/scholar?cluster=10014299988538351791&hl=en&as_sdt=0,5",5,2020
DPDist: Comparing Point Clouds Using Deep Point Cloud Distance,21,eccv,7,0,2023-06-17 00:18:32.223000,https://github.com/dahliau/DPDist,57,DPDist: Comparing point clouds using deep point cloud distance,"https://scholar.google.com/scholar?cluster=3522483961311630044&hl=en&as_sdt=0,10",4,2020
Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation,39,eccv,8,11,2023-06-17 00:18:32.435000,https://github.com/FilippoAleotti/Reversing,46,Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation,"https://scholar.google.com/scholar?cluster=13122380710342216141&hl=en&as_sdt=0,48",3,2020
Why do These Match? Explaining the Behavior of Image Similarity Models,17,eccv,2,0,2023-06-17 00:18:32.650000,https://github.com/VisionLearningGroup/SANE,2,Why do these match? explaining the behavior of image similarity models,"https://scholar.google.com/scholar?cluster=3515395364581743131&hl=en&as_sdt=0,5",4,2020
CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing,10,eccv,5,0,2023-06-17 00:18:32.862000,https://github.com/neuralchen/CooGAN,22,Coogan: A memory-efficient framework for high-resolution facial attribute editing,"https://scholar.google.com/scholar?cluster=9885225435421677771&hl=en&as_sdt=0,5",7,2020
Progressive Transformers for End-to-End Sign Language Production,71,eccv,35,36,2023-06-17 00:18:33.074000,https://github.com/BenSaunders27/ProgressiveTransformersSLP,65,Progressive transformers for end-to-end sign language production,"https://scholar.google.com/scholar?cluster=636627572859576137&hl=en&as_sdt=0,34",8,2020
Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting,123,eccv,121,50,2023-06-17 00:18:33.306000,https://github.com/MhLiao/MaskTextSpotterV3,542,Mask textspotter v3: Segmentation proposal network for robust scene text spotting,"https://scholar.google.com/scholar?cluster=18438889963270998568&hl=en&as_sdt=0,5",17,2020
Making Affine Correspondences Work in Camera Geometry Computation,24,eccv,7,2,2023-06-17 00:18:33.534000,https://github.com/danini/affine-correspondences-for-camera-geometry,41,Making affine correspondences work in camera geometry computation,"https://scholar.google.com/scholar?cluster=3031034213537360804&hl=en&as_sdt=0,5",8,2020
Contrastive Multiview Coding,1794,eccv,178,25,2023-06-17 00:18:33.752000,https://github.com/HobbitLong/CMC,1218,Contrastive multiview coding,"https://scholar.google.com/scholar?cluster=17466907634599741918&hl=en&as_sdt=0,4",29,2020
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses,50,eccv,9,0,2023-06-17 00:18:33.970000,https://github.com/LiYingwei/Regional-Homogeneity,41,Regional homogeneity: Towards learning transferable universal adversarial perturbations against defenses,"https://scholar.google.com/scholar?cluster=979755717962216129&hl=en&as_sdt=0,33",6,2020
Generative Low-bitwidth Data Free Quantization,78,eccv,14,4,2023-06-17 00:18:34.182000,https://github.com/xushoukai/GDFQ,43,Generative low-bitwidth data free quantization,"https://scholar.google.com/scholar?cluster=8045897304906704345&hl=en&as_sdt=0,39",3,2020
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild,79,eccv,22,1,2023-06-17 00:18:34.394000,https://github.com/facebookresearch/phosa,164,Perceiving 3d human-object spatial arrangements from a single image in the wild,"https://scholar.google.com/scholar?cluster=8349260818122508144&hl=en&as_sdt=0,5",12,2020
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations,105,eccv,86,3,2023-06-17 00:18:34.607000,https://github.com/Davidzhangyuanhan/CelebA-Spoof,474,Celeba-spoof: Large-scale face anti-spoofing dataset with rich annotations,"https://scholar.google.com/scholar?cluster=6942608929062184257&hl=en&as_sdt=0,43",18,2020
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation,15,eccv,3,1,2023-06-17 00:18:34.818000,https://github.com/naivete5656/WSCTBFP,11,Weakly-supervised cell tracking via backward-and-forward propagation,"https://scholar.google.com/scholar?cluster=7803162838538359376&hl=en&as_sdt=0,4",1,2020
Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization,120,eccv,13,2,2023-06-17 00:18:35.029000,https://github.com/automan000/Camera-based-Person-ReID,101,Rethinking the distribution gap of person re-identification with camera-based batch normalization,"https://scholar.google.com/scholar?cluster=17828412959744881211&hl=en&as_sdt=0,5",7,2020
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds,81,eccv,4,4,2023-06-17 00:18:35.243000,https://github.com/ajhamdi/AdvPC,38,Advpc: Transferable adversarial perturbations on 3d point clouds,"https://scholar.google.com/scholar?cluster=18118562316404948079&hl=en&as_sdt=0,5",5,2020
Edge-aware Graph Representation Learning and Reasoning for Face Parsing,37,eccv,21,11,2023-06-17 00:18:35.455000,https://github.com/tegusi/EAGRNet,111,Edge-aware graph representation learning and reasoning for face parsing,"https://scholar.google.com/scholar?cluster=8344059590585496769&hl=en&as_sdt=0,14",4,2020
BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network,214,eccv,14,4,2023-06-17 00:18:35.667000,https://github.com/zyjwuyan/BBS-Net,68,BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network,"https://scholar.google.com/scholar?cluster=1530878989828983821&hl=en&as_sdt=0,11",3,2020
G-LBM:Generative Low-dimensional Background Model Estimation from Video Sequences,12,eccv,1,6,2023-06-17 00:18:35.878000,https://github.com/brezaei/G-LBM,3,G-lbm: Generative low-dimensional background model estimation from video sequences,"https://scholar.google.com/scholar?cluster=6090370780802957068&hl=en&as_sdt=0,44",1,2020
H3DNet: 3D Object Detection Using Hybrid Geometric Primitives,124,eccv,24,1,2023-06-17 00:18:36.090000,https://github.com/zaiweizhang/H3DNet,204,H3dnet: 3d object detection using hybrid geometric primitives,"https://scholar.google.com/scholar?cluster=12288324259415652609&hl=en&as_sdt=0,29",11,2020
Cascade Graph Neural Networks for RGB-D Salient Object Detection,95,eccv,7,2,2023-06-17 00:18:36.314000,https://github.com/LA30/Cas-Gnn,39,Cascade graph neural networks for RGB-D salient object detection,"https://scholar.google.com/scholar?cluster=16210451130224946759&hl=en&as_sdt=0,47",9,2020
FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret,25,eccv,1,1,2023-06-17 00:18:36.548000,https://github.com/lokhande-vishnu/FairALM,2,Fairalm: Augmented lagrangian method for training fair models with little regret,"https://scholar.google.com/scholar?cluster=1243937665557972851&hl=en&as_sdt=0,5",3,2020
ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language,74,eccv,10,7,2023-06-17 00:18:36.760000,https://github.com/Jarr0d/ViTAA,33,ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language,"https://scholar.google.com/scholar?cluster=448801737607472813&hl=en&as_sdt=0,5",2,2020
Renovating Parsing R-CNN for Accurate Multiple Human Parsing,43,eccv,9,7,2023-06-17 00:18:36.972000,https://github.com/soeaver/RP-R-CNN,88,Renovating parsing R-CNN for accurate multiple human parsing,"https://scholar.google.com/scholar?cluster=2379828379277703866&hl=en&as_sdt=0,5",2,2020
SegFix: Model-Agnostic Boundary Refinement for Segmentation,139,eccv,139,26,2023-06-17 00:18:37.184000,https://github.com/openseg-group/openseg.pytorch,1123,Segfix: Model-agnostic boundary refinement for segmentation,"https://scholar.google.com/scholar?cluster=206335766777621127&hl=en&as_sdt=0,5",41,2020
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction,270,eccv,67,7,2023-06-17 00:18:37.395000,https://github.com/Majiker/STAR,264,Spatio-temporal graph transformer networks for pedestrian trajectory prediction,"https://scholar.google.com/scholar?cluster=4185484464605610926&hl=en&as_sdt=0,33",10,2020
Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars,104,eccv,48,18,2023-06-17 00:18:37.607000,https://github.com/saic-violet/bilayer-model,228,Fast bi-layer neural synthesis of one-shot realistic head avatars,"https://scholar.google.com/scholar?cluster=16681083832920140131&hl=en&as_sdt=0,5",15,2020
Neural Geometric Parser for Single Image Camera Calibration,11,eccv,0,1,2023-06-17 00:18:37.819000,https://github.com/jwlee-vcl/gpnet,8,Neural geometric parser for single image camera calibration,"https://scholar.google.com/scholar?cluster=12175272525072704333&hl=en&as_sdt=0,5",2,2020
Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision,25,eccv,24,2,2023-06-17 00:18:38.031000,https://github.com/csyxwei/FFWM,115,Learning flow-based feature warping for face frontalization with illumination inconsistent supervision,"https://scholar.google.com/scholar?cluster=14146867028905351167&hl=en&as_sdt=0,33",4,2020
Learning Architectures for Binary Networks,36,eccv,12,1,2023-06-17 00:18:38.242000,https://github.com/gistvision/bnas,25,Learning architectures for binary networks,"https://scholar.google.com/scholar?cluster=11475122672492647220&hl=en&as_sdt=0,5",2,2020
Semantic View Synthesis,32,eccv,4,1,2023-06-17 00:18:38.454000,https://github.com/hhsinping/svs,27,Semantic view synthesis,"https://scholar.google.com/scholar?cluster=680387232525671779&hl=en&as_sdt=0,5",2,2020
Relative Pose from Deep Learned Depth and a Single Affine Correspondence,10,eccv,1,0,2023-06-17 00:18:38.666000,https://github.com/eivan/one-ac-pose,6,Relative pose from deep learned depth and a single affine correspondence,"https://scholar.google.com/scholar?cluster=11374175095805301874&hl=en&as_sdt=0,39",5,2020
Video Super-Resolution with Recurrent Structure-Detail Network,144,eccv,16,8,2023-06-17 00:18:38.877000,https://github.com/junpan19/RSDN,84,Video super-resolution with recurrent structure-detail network,"https://scholar.google.com/scholar?cluster=449375873375642979&hl=en&as_sdt=0,44",17,2020
Shape Adaptor: A Learnable Resizing Module,4,eccv,8,0,2023-06-17 00:18:39.089000,https://github.com/lorenmt/shape-adaptor,72,Shape adaptor: A learnable resizing module,"https://scholar.google.com/scholar?cluster=13587895934651869796&hl=en&as_sdt=0,5",5,2020
DRG: Dual Relation Graph for Human-Object Interaction Detection,135,eccv,21,1,2023-06-17 00:18:39.300000,https://github.com/vt-vl-lab/DRG,61,Drg: Dual relation graph for human-object interaction detection,"https://scholar.google.com/scholar?cluster=8964532937622863790&hl=en&as_sdt=0,5",7,2020
Flow-edge Guided Video Completion,103,eccv,259,12,2023-06-17 00:18:39.523000,https://github.com/vt-vl-lab/FGVC,1503,Flow-edge guided video completion,"https://scholar.google.com/scholar?cluster=4208934232095562555&hl=en&as_sdt=0,5",69,2020
Towards End-to-end Video-based Eye-Tracking,45,eccv,16,11,2023-06-17 00:18:39.735000,https://github.com/swook/EVE,90,Towards end-to-end video-based eye-tracking,"https://scholar.google.com/scholar?cluster=17516287532228690599&hl=en&as_sdt=0,5",5,2020
Generating Handwriting via Decoupled Style Descriptors,14,eccv,13,2,2023-06-17 00:18:39.946000,https://github.com/brownvc/decoupled-style-descriptors,40,Generating handwriting via decoupled style descriptors,"https://scholar.google.com/scholar?cluster=15966601535651946598&hl=en&as_sdt=0,5",9,2020
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards,51,eccv,11,7,2023-06-17 00:18:40.158000,https://github.com/xuewyang/Fashion_Captioning,66,Fashion captioning: Towards generating accurate descriptions with semantic rewards,"https://scholar.google.com/scholar?cluster=16295173810633273841&hl=en&as_sdt=0,33",5,2020
Anti-Bandit Neural Architecture Search for Model Defense,31,eccv,3,1,2023-06-17 00:18:40.369000,https://github.com/bczhangbczhang/ABanditNAS,9,Anti-bandit neural architecture search for model defense,"https://scholar.google.com/scholar?cluster=17374376448644514078&hl=en&as_sdt=0,10",3,2020
Non-Local Spatial Propagation Network for Depth Completion,200,eccv,52,1,2023-06-17 00:18:40.589000,https://github.com/zzangjinsun/NLSPN_ECCV20,261,Non-local spatial propagation network for depth completion,"https://scholar.google.com/scholar?cluster=3163564214350783940&hl=en&as_sdt=0,21",10,2020
DanbooRegion: An Illustration Region Dataset,13,eccv,37,1,2023-06-17 00:18:40.801000,https://github.com/lllyasviel/DanbooRegion,346,Danbooregion: An illustration region dataset,"https://scholar.google.com/scholar?cluster=3677649040778361989&hl=en&as_sdt=0,38",16,2020
Event Enhanced High-Quality Image Recovery,60,eccv,12,1,2023-06-17 00:18:41.013000,https://github.com/ShinyWang33/eSL-Net,51,Event enhanced high-quality image recovery,"https://scholar.google.com/scholar?cluster=11671716190477867378&hl=en&as_sdt=0,23",3,2020
PackDet: Packed Long-Head Object Detector,0,eccv,0,0,2023-06-17 00:18:41.225000,https://github.com/kding1225/PackDet,3,PackDet: Packed Long-Head Object Detector,"https://scholar.google.com/scholar?cluster=18227002066610083626&hl=en&as_sdt=0,5",1,2020
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS,81,eccv,27,13,2023-06-17 00:18:41.436000,https://github.com/walkerning/aw_nas,224,A generic graph-based neural architecture encoding scheme for predictor-based nas,"https://scholar.google.com/scholar?cluster=8446710884110461906&hl=en&as_sdt=0,5",20,2020
Sketching Image Gist: Human-Mimetic Hierarchical Scene Graph Generation,38,eccv,2,1,2023-06-17 00:18:41.653000,https://github.com/Kenneth-Wong/het-eccv20,13,Sketching image gist: Human-mimetic hierarchical scene graph generation,"https://scholar.google.com/scholar?cluster=4587639982110827528&hl=en&as_sdt=0,6",2,2020
SimAug: Learning Robust Representations from Simulation for Trajectory Prediction,45,eccv,61,1,2023-06-17 00:18:41.864000,https://github.com/JunweiLiang/Multiverse,229,SimAug: Learning Robust Representations from Simulation for Trajectory Prediction,"https://scholar.google.com/scholar?cluster=11097924326076170401&hl=en&as_sdt=0,5",10,2020
Rethinking Pseudo-LiDAR Representation,131,eccv,16,0,2023-06-17 00:18:42.076000,https://github.com/xinzhuma/patchnet,156,Rethinking pseudo-lidar representation,"https://scholar.google.com/scholar?cluster=18102454286026050156&hl=en&as_sdt=0,5",6,2020
API-Net: Robust Generative Classifier via a Single Discriminator,8,eccv,0,0,2023-06-17 00:18:42.287000,https://github.com/dongxinshuai/API-Net,1,Api-net: Robust generative classifier via a single discriminator,"https://scholar.google.com/scholar?cluster=14305902327179062854&hl=en&as_sdt=0,5",2,2020
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning,138,eccv,28,4,2023-06-17 00:18:42.525000,https://github.com/ZHKKKe/PixelSSL,263,Guided collaborative training for pixel-wise semi-supervised learning,"https://scholar.google.com/scholar?cluster=10147529659500916974&hl=en&as_sdt=0,5",12,2020
Weakly Supervised 3D Object Detection from Lidar Point Cloud,70,eccv,9,4,2023-06-17 00:18:42.736000,https://github.com/hlesmqh/WS3D,116,Weakly supervised 3d object detection from lidar point cloud,"https://scholar.google.com/scholar?cluster=5361806344197632515&hl=en&as_sdt=0,37",18,2020
Adaptive Offline Quintuplet Loss for Image-Text Matching,50,eccv,2,4,2023-06-17 00:18:42.947000,https://github.com/sunnychencool/AOQ,34,Adaptive offline quintuplet loss for image-text matching,"https://scholar.google.com/scholar?cluster=2484421407887153307&hl=en&as_sdt=0,5",2,2020
Deep Vectorization of Technical Drawings,39,eccv,11,5,2023-06-17 00:18:43.163000,https://github.com/Vahe1994/Deep-Vectorization-of-Technical-Drawings,69,Deep vectorization of technical drawings,"https://scholar.google.com/scholar?cluster=4990718098147920866&hl=en&as_sdt=0,5",10,2020
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans,16,eccv,10,3,2023-06-17 00:18:43.375000,https://github.com/alexeybokhovkin/CAD-Deform,83,Cad-deform: Deformable fitting of cad models to 3d scans,"https://scholar.google.com/scholar?cluster=3767652087548502155&hl=en&as_sdt=0,33",11,2020
Conditional Sequential Modulation for Efficient Global Image Retouching,70,eccv,15,4,2023-06-17 00:18:43.587000,https://github.com/hejingwenhejingwen/CSRNet,107,Conditional sequential modulation for efficient global image retouching,"https://scholar.google.com/scholar?cluster=16014007106784424947&hl=en&as_sdt=0,39",4,2020
Segmenting Transparent Objects in the Wild,61,eccv,20,3,2023-06-17 00:18:43.798000,https://github.com/xieenze/Segment_Transparent_Objects,84,Segmenting transparent objects in the wild,"https://scholar.google.com/scholar?cluster=4309840741010423202&hl=en&as_sdt=0,5",6,2020
Length-Controllable Image Captioning,42,eccv,10,5,2023-06-17 00:18:44.011000,https://github.com/bearcatt/LaBERT,59,Length-controllable image captioning,"https://scholar.google.com/scholar?cluster=12373298806873230610&hl=en&as_sdt=0,10",7,2020
Defocus Blur Detection via Depth Distillation,21,eccv,12,1,2023-06-17 00:18:44.222000,https://github.com/vinthony/depth-distillation,56,Defocus blur detection via depth distillation,"https://scholar.google.com/scholar?cluster=1232292395800359360&hl=en&as_sdt=0,7",8,2020
SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation,158,eccv,53,38,2023-06-17 00:18:44.435000,https://github.com/JialeCao001/SipMask,327,Sipmask: Spatial information preservation for fast image and video instance segmentation,"https://scholar.google.com/scholar?cluster=5547164646432784489&hl=en&as_sdt=0,10",11,2020
Deep Image Clustering with Category-Style Representation,28,eccv,2,0,2023-06-17 00:18:44.646000,https://github.com/sKamiJ/DCCS,16,Deep image clustering with category-style representation,"https://scholar.google.com/scholar?cluster=17599997442473062177&hl=en&as_sdt=0,33",1,2020
BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation,133,eccv,6,3,2023-06-17 00:18:44.858000,https://github.com/JunHeum/BMBC,78,Bmbc: Bilateral motion estimation with bilateral cost volume for video interpolation,"https://scholar.google.com/scholar?cluster=4910160861472176676&hl=en&as_sdt=0,11",5,2020
"Hard negative examples are hard, but useful",82,eccv,7,1,2023-06-17 00:18:45.070000,https://github.com/littleredxh/HardNegative,42,"Hard negative examples are hard, but useful","https://scholar.google.com/scholar?cluster=16144312343366136340&hl=en&as_sdt=0,43",6,2020
ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions,232,eccv,41,6,2023-06-17 00:18:45.282000,https://github.com/liuzechun/ReActNet,224,Reactnet: Towards precise binary neural network with generalized activation functions,"https://scholar.google.com/scholar?cluster=5123006683473933503&hl=en&as_sdt=0,5",10,2020
"Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D",286,eccv,149,31,2023-06-17 00:18:45.493000,https://github.com/nv-tlabs/lift-splat-shoot,665,"Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d","https://scholar.google.com/scholar?cluster=8252938939623518132&hl=en&as_sdt=0,47",18,2020
Symbiotic Adversarial Learning for Attribute-based Person Search,20,eccv,1,2,2023-06-17 00:18:45.704000,https://github.com/ycao5602/SAL,20,Symbiotic adversarial learning for attribute-based person search,"https://scholar.google.com/scholar?cluster=13349363750467880535&hl=en&as_sdt=0,14",3,2020
Rethinking Few-shot Image Classification: A Good Embedding is All You Need?,659,eccv,65,28,2023-06-17 00:18:45.916000,https://github.com/WangYueFt/rfs,345,Rethinking few-shot image classification: a good embedding is all you need?,"https://scholar.google.com/scholar?cluster=1948820087348790590&hl=en&as_sdt=0,5",9,2020
Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization,78,eccv,8,1,2023-06-17 00:18:46.128000,https://github.com/kylemin/A2CL-PT,45,Adversarial background-aware loss for weakly-supervised temporal activity localization,"https://scholar.google.com/scholar?cluster=8877195915258561283&hl=en&as_sdt=0,44",4,2020
READ: Reciprocal Attention Discriminator for Image-to-Video Re-Identification,7,eccv,1,0,2023-06-17 00:18:46.340000,https://github.com/minostauros/READ,7,Read: Reciprocal attention discriminator for image-to-video re-identification,"https://scholar.google.com/scholar?cluster=11485239296163331870&hl=en&as_sdt=0,47",1,2020
Improving One-stage Visual Grounding by Recursive Sub-query Construction,116,eccv,14,0,2023-06-17 00:18:46.551000,https://github.com/zyang-ur/ReSC,73,Improving one-stage visual grounding by recursive sub-query construction,"https://scholar.google.com/scholar?cluster=16368472932648996603&hl=en&as_sdt=0,47",1,2020
Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video,40,eccv,9,0,2023-06-17 00:18:46.763000,https://github.com/IceClear/MW-GAN,41,Multi-level wavelet-based generative adversarial network for perceptual quality enhancement of compressed video,"https://scholar.google.com/scholar?cluster=14110232485051774982&hl=en&as_sdt=0,5",5,2020
History Repeats Itself: Human Motion Prediction via Motion Attention,155,eccv,12,9,2023-06-17 00:18:46.976000,https://github.com/wei-mao-2019/HisRepItself,83,History repeats itself: Human motion prediction via motion attention,"https://scholar.google.com/scholar?cluster=17893257785328286475&hl=en&as_sdt=0,5",2,2020
SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach,100,eccv,3,3,2023-06-17 00:18:47.189000,https://github.com/ailingzengzzz/Split-and-Recombine-Net,29,Srnet: Improving generalization in 3d human pose estimation with a split-and-recombine approach,"https://scholar.google.com/scholar?cluster=7559170808930152374&hl=en&as_sdt=0,5",5,2020
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation,189,eccv,25,4,2023-06-17 00:18:47.400000,https://github.com/JDAI-CV/FADA,132,Classes matter: A fine-grained adversarial approach to cross-domain semantic segmentation,"https://scholar.google.com/scholar?cluster=11392839451919176813&hl=en&as_sdt=0,36",10,2020
Boundary-preserving Mask R-CNN,159,eccv,37,13,2023-06-17 00:18:47.612000,https://github.com/hustvl/BMaskR-CNN,178,Boundary-preserving mask r-cnn,"https://scholar.google.com/scholar?cluster=13123672982809022248&hl=en&as_sdt=0,39",12,2020
Adversarial Ranking Attack and Defense,30,eccv,5,0,2023-06-17 00:18:47.823000,https://github.com/cdluminate/advrank,24,Adversarial ranking attack and defense,"https://scholar.google.com/scholar?cluster=10560396602707526737&hl=en&as_sdt=0,5",4,2020
Graph-Based Social Relation Reasoning,21,eccv,3,0,2023-06-17 00:18:48.034000,https://github.com/Li-Wanhua/GR2N,15,Graph-based social relation reasoning,"https://scholar.google.com/scholar?cluster=9073286133211904572&hl=en&as_sdt=0,47",2,2020
EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection,223,eccv,36,19,2023-06-17 00:18:48.246000,https://github.com/happinesslz/EPNet,198,Epnet: Enhancing point features with image semantics for 3d object detection,"https://scholar.google.com/scholar?cluster=14048124561243162043&hl=en&as_sdt=0,33",7,2020
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency,89,eccv,46,7,2023-06-17 00:18:48.458000,https://github.com/jiaxiangshang/MGCNet,321,Self-supervised monocular 3d face reconstruction by occlusion-aware multi-view geometry consistency,"https://scholar.google.com/scholar?cluster=9717045561700719579&hl=en&as_sdt=0,5",24,2020
Asynchronous Interaction Aggregation for Action Detection,84,eccv,75,36,2023-06-17 00:18:48.670000,https://github.com/MVIG-SJTU/AlphAction,359,Asynchronous interaction aggregation for action detection,"https://scholar.google.com/scholar?cluster=11054690416072815640&hl=en&as_sdt=0,5",18,2020
TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search,38,eccv,11,3,2023-06-17 00:18:48.881000,https://github.com/AberHu/TF-NAS,70,Tf-nas: Rethinking three search freedoms of latency-constrained differentiable neural architecture search,"https://scholar.google.com/scholar?cluster=3389506832990377817&hl=en&as_sdt=0,5",2,2020
Associative3D: Volumetric Reconstruction from Sparse Views,16,eccv,3,0,2023-06-17 00:18:49.094000,https://github.com/JasonQSY/Associative3D,30,Associative3d: Volumetric reconstruction from sparse views,"https://scholar.google.com/scholar?cluster=689763050695234428&hl=en&as_sdt=0,5",5,2020
URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark,56,eccv,1,1,2023-06-17 00:18:49.314000,https://github.com/skynbe/Refer-Youtube-VOS,20,Urvos: Unified referring video object segmentation network with a large-scale benchmark,"https://scholar.google.com/scholar?cluster=17317940783091587297&hl=en&as_sdt=0,10",2,2020
Generalizing Person Re-Identification by Camera-Aware Invariance Learning and Cross-Domain Mixup,80,eccv,6,3,2023-06-17 00:18:49.531000,https://github.com/LuckyDC/generalizing-reid,40,Generalizing person re-identification by camera-aware invariance learning and cross-domain mixup,"https://scholar.google.com/scholar?cluster=956483140212203757&hl=en&as_sdt=0,5",2,2020
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training,292,eccv,23,3,2023-06-17 00:18:49.743000,https://github.com/hkzhang95/DynamicRCNN,172,Dynamic R-CNN: Towards high quality object detection via dynamic training,"https://scholar.google.com/scholar?cluster=17049037070556303909&hl=en&as_sdt=0,5",4,2020
Knowledge Transfer via Dense Cross-Layer Mutual-Distillation,40,eccv,4,1,2023-06-17 00:18:49.954000,https://github.com/sundw2014/DCM,28,Knowledge transfer via dense cross-layer mutual-distillation,"https://scholar.google.com/scholar?cluster=11144776012902077086&hl=en&as_sdt=0,34",1,2020
Matching Guided Distillation,36,eccv,13,0,2023-06-17 00:18:50.166000,https://github.com/KaiyuYue/mgd,60,Matching guided distillation,"https://scholar.google.com/scholar?cluster=14578720690057730740&hl=en&as_sdt=0,15",5,2020
Learning to Compose Hypercolumns for Visual Correspondence,53,eccv,4,0,2023-06-17 00:18:50.378000,https://github.com/juhongm999/dhpf,38,Learning to compose hypercolumns for visual correspondence,"https://scholar.google.com/scholar?cluster=8312023944017551748&hl=en&as_sdt=0,33",4,2020
Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction,21,eccv,29,3,2023-06-17 00:18:50.590000,https://github.com/zlthinker/STBA,187,Stochastic bundle adjustment for efficient and scalable 3d reconstruction,"https://scholar.google.com/scholar?cluster=16314934327483505968&hl=en&as_sdt=0,33",10,2020
Progressive Point Cloud Deconvolution Generation Network,49,eccv,6,2,2023-06-17 00:18:50.802000,https://github.com/fpthink/PDGN,28,Progressive point cloud deconvolution generation network,"https://scholar.google.com/scholar?cluster=11437143308560473463&hl=en&as_sdt=0,47",3,2020
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search,246,eccv,34,11,2023-06-17 00:18:51.014000,https://github.com/xiaomi-automl/fairdarts,164,Fair darts: Eliminating unfair advantages in differentiable architecture search,"https://scholar.google.com/scholar?cluster=7452897056156362460&hl=en&as_sdt=0,5",9,2020
GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision,32,eccv,22,3,2023-06-17 00:18:51.225000,https://github.com/lkeab/gsnet,114,Gsnet: Joint vehicle pose and shape reconstruction with geometrical and scene-aware supervision,"https://scholar.google.com/scholar?cluster=1705665853422337551&hl=en&as_sdt=0,5",11,2020
Resolution Switchable Networks for Runtime Efficient Image Recognition,22,eccv,8,2,2023-06-17 00:18:51.437000,https://github.com/yikaiw/RS-Nets,39,Resolution switchable networks for runtime efficient image recognition,"https://scholar.google.com/scholar?cluster=4474861744596857649&hl=en&as_sdt=0,14",5,2020
Learning to Detect Open Classes for Universal Domain Adaptation,82,eccv,7,1,2023-06-17 00:18:51.650000,https://github.com/thuml/Calibrated-Multiple-Uncertainties,26,Learning to detect open classes for universal domain adaptation,"https://scholar.google.com/scholar?cluster=4130342387096871913&hl=en&as_sdt=0,5",4,2020
Visual Compositional Learning for Human-Object Interaction Detection,107,eccv,4,2,2023-06-17 00:18:51.862000,https://github.com/zhihou7/VCL,29,Visual compositional learning for human-object interaction detection,"https://scholar.google.com/scholar?cluster=13200594757543064854&hl=en&as_sdt=0,21",5,2020
Rethinking Class Activation Mapping for Weakly Supervised Object Localization,92,eccv,1,3,2023-06-17 00:18:52.075000,https://github.com/won-bae/rethinkingCAM,19,Rethinking class activation mapping for weakly supervised object localization,"https://scholar.google.com/scholar?cluster=7179328230490861261&hl=en&as_sdt=0,5",3,2020
OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features,39,eccv,38,1,2023-06-17 00:18:52.287000,https://github.com/aosokin/os2d,164,Os2d: One-stage one-shot object detection by matching anchor features,"https://scholar.google.com/scholar?cluster=13501926092263077120&hl=en&as_sdt=0,14",9,2020
Omni-sourced Webly-supervised Learning for Video Recognition,72,eccv,354,55,2023-06-17 00:18:52.498000,https://github.com/open-mmlab/mmaction,1826,Omni-sourced webly-supervised learning for video recognition,"https://scholar.google.com/scholar?cluster=15620219412368393882&hl=en&as_sdt=0,5",42,2020
Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation,119,eccv,0,3,2023-06-17 00:18:52.708000,https://github.com/jxhuang0508/CrCDA,8,Contextual-relation consistent domain adaptation for semantic segmentation,"https://scholar.google.com/scholar?cluster=3562256144425666059&hl=en&as_sdt=0,6",2,2020
Estimating People Flows to Better Count Them in Crowded Scenes,37,eccv,15,1,2023-06-17 00:18:52.920000,https://github.com/weizheliu/People-Flows,50,Estimating people flows to better count them in crowded scenes,"https://scholar.google.com/scholar?cluster=2591521453586595276&hl=en&as_sdt=0,5",1,2020
WeightNet: Revisiting the Design Space of Weight Networks,66,eccv,24,1,2023-06-17 00:18:53.132000,https://github.com/megvii-model/WeightNet,169,Weightnet: Revisiting the design space of weight networks,"https://scholar.google.com/scholar?cluster=16855598594631544066&hl=en&as_sdt=0,5",6,2020
Learning Where to Focus for Efficient Video Object Detection,49,eccv,7,0,2023-06-17 00:18:53.348000,https://github.com/jiangzhengkai/LSTS,84,Learning where to focus for efficient video object detection,"https://scholar.google.com/scholar?cluster=1208462202645362859&hl=en&as_sdt=0,5",8,2020
Actions as Moving Points,78,eccv,38,13,2023-06-17 00:18:53.561000,https://github.com/MCG-NJU/MOC-Detector,250,Actions as moving points,"https://scholar.google.com/scholar?cluster=15056768592491474343&hl=en&as_sdt=0,10",10,2020
Contextual Diversity for Active Learning,71,eccv,2,2,2023-06-17 00:18:53.772000,https://github.com/sharat29ag/CDAL,35,Contextual diversity for active learning,"https://scholar.google.com/scholar?cluster=6510353152850152457&hl=en&as_sdt=0,5",1,2020
Temporal Aggregate Representations for Long-Range Video Understanding,77,eccv,3,2,2023-06-17 00:18:53.983000,https://github.com/dibschat/tempAgg,11,Temporal aggregate representations for long-range video understanding,"https://scholar.google.com/scholar?cluster=8073792548832069076&hl=en&as_sdt=0,25",1,2020
Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition,53,eccv,5,4,2023-06-17 00:18:54.195000,https://github.com/zheniu/stochastic-cslr,31,Stochastic fine-grained labeling of multi-state sign glosses for continuous sign language recognition,"https://scholar.google.com/scholar?cluster=2178144737272949490&hl=en&as_sdt=0,21",2,2020
General 3D Room Layout from a Single View by Render-and-Compare,15,eccv,8,1,2023-06-17 00:18:54.406000,https://github.com/vevenom/RoomLayout3D_RandC,39,General 3d room layout from a single view by render-and-compare,"https://scholar.google.com/scholar?cluster=3186828296910797192&hl=en&as_sdt=0,5",3,2020
Yet Another Intermediate-Level Attack,29,eccv,0,0,2023-06-17 00:18:54.619000,https://github.com/qizhangli/ila-plus-plus,8,Yet another intermediate-level attack,"https://scholar.google.com/scholar?cluster=5759130165300312234&hl=en&as_sdt=0,14",2,2020
Early Exit Or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images,24,eccv,6,0,2023-06-17 00:18:54.830000,https://github.com/RyanXingQL/RBQE,66,Early exit or not: Resource-efficient blind quality enhancement for compressed images,"https://scholar.google.com/scholar?cluster=6965414435637296889&hl=en&as_sdt=0,5",3,2020
Infrastructure-based Multi-Camera Calibration using Radial Projections,13,eccv,52,1,2023-06-17 00:18:55.042000,https://github.com/youkely/InfrasCal,192,Infrastructure-based multi-camera calibration using radial projections,"https://scholar.google.com/scholar?cluster=5774733199336963348&hl=en&as_sdt=0,10",10,2020
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning,114,eccv,4,2,2023-06-17 00:18:55.253000,https://github.com/yaoyao-liu/E3BM,48,An ensemble of epoch-wise empirical bayes for few-shot learning,"https://scholar.google.com/scholar?cluster=3656158935031424598&hl=en&as_sdt=0,33",10,2020
On the Effectiveness of Image Rotation for Open Set Domain Adaptation,81,eccv,8,3,2023-06-17 00:18:55.465000,https://github.com/silvia1993/ROS,35,On the effectiveness of image rotation for open set domain adaptation,"https://scholar.google.com/scholar?cluster=6328626470823546716&hl=en&as_sdt=0,5",3,2020
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection,208,eccv,18,21,2023-06-17 00:18:55.676000,https://github.com/jiaxi-wu/MPSR,132,Multi-scale positive sample refinement for few-shot object detection,"https://scholar.google.com/scholar?cluster=2779990252202731803&hl=en&as_sdt=0,44",7,2020
Learning Joint Spatial-Temporal Transformations for Video Inpainting,203,eccv,71,8,2023-06-17 00:18:55.889000,https://github.com/researchmm/STTN,371,Learning joint spatial-temporal transformations for video inpainting,"https://scholar.google.com/scholar?cluster=3794150204274042070&hl=en&as_sdt=0,47",20,2020
Neural Voice Puppetry: Audio-driven Facial Reenactment,215,eccv,26,0,2023-06-17 00:18:56.101000,https://github.com/miu200521358/NeuralVoicePuppetryMMD,56,Neural voice puppetry: Audio-driven facial reenactment,"https://scholar.google.com/scholar?cluster=16317069924068686856&hl=en&as_sdt=0,34",3,2020
Suppressing Mislabeled Data via Grouping and Self-Attention,18,eccv,5,1,2023-06-17 00:18:56.314000,https://github.com/kaiwang960112/AFM,19,Suppressing mislabeled data via grouping and self-attention,"https://scholar.google.com/scholar?cluster=1682395356539181441&hl=en&as_sdt=0,33",1,2020
Character-Preserving Coherent Story Visualization,18,eccv,4,11,2023-06-17 00:18:56.540000,https://github.com/yunzhusong/ECCV2020_CPCSV,4,Character-preserving coherent story visualization,"https://scholar.google.com/scholar?cluster=17902203540270868524&hl=en&as_sdt=0,14",2,2020
GINet: Graph Interaction Network for Scene Parsing,34,eccv,1520,270,2023-06-17 00:18:56.751000,https://github.com/PaddlePaddle/PaddleSeg,7245,GINet: Graph interaction network for scene parsing,"https://scholar.google.com/scholar?cluster=8294990700533742729&hl=en&as_sdt=0,11",84,2020
Spatiotemporal Attacks for Embodied Agents,39,eccv,3,7,2023-06-17 00:18:56.964000,https://github.com/liuaishan/SpatiotemporalAttack,9,Spatiotemporal attacks for embodied agents,"https://scholar.google.com/scholar?cluster=6506390349923478405&hl=en&as_sdt=0,5",1,2020
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification,201,eccv,14,3,2023-06-17 00:18:57.178000,https://github.com/mangye16/DDAG,65,Dynamic dual-attentive aggregation learning for visible-infrared person re-identification,"https://scholar.google.com/scholar?cluster=15806552395554554128&hl=en&as_sdt=0,4",2,2020
A Closest Point Proposal for MCMC-based Probabilistic Surface Registration,14,eccv,5,1,2023-06-17 00:18:57.392000,https://github.com/unibas-gravis/icp-proposal,29,A closest point proposal for MCMC-based probabilistic surface registration,"https://scholar.google.com/scholar?cluster=16422523338962640624&hl=en&as_sdt=0,10",8,2020
Interactive Video Object Segmentation Using Global and Local Transfer Modules,19,eccv,2,0,2023-06-17 00:18:57.606000,https://github.com/yuk6heo/IVOS-ATNet,30,Interactive video object segmentation using global and local transfer modules,"https://scholar.google.com/scholar?cluster=6821924426780280222&hl=en&as_sdt=0,5",3,2020
End-to-end Interpretable Learning of Non-blind Image Deblurring,32,eccv,10,6,2023-06-17 00:18:57.819000,https://github.com/teboli/CPCR,24,End-to-end interpretable learning of non-blind image deblurring,"https://scholar.google.com/scholar?cluster=2680629908673287729&hl=en&as_sdt=0,36",2,2020
Rethinking Image Deraining via Rain Streaks and Vapors,42,eccv,1,3,2023-06-17 00:18:58.031000,https://github.com/yluestc/derain,14,Rethinking image deraining via rain streaks and vapors,"https://scholar.google.com/scholar?cluster=5336161740827308030&hl=en&as_sdt=0,33",4,2020
Improving Semantic Segmentation via Decoupled Body and Edge Supervision,171,eccv,34,7,2023-06-17 00:18:58.243000,https://github.com/lxtGH/DecoupleSegNets,345,Improving semantic segmentation via decoupled body and edge supervision,"https://scholar.google.com/scholar?cluster=17990846409743098772&hl=en&as_sdt=0,5",12,2020
Self-supervised Video Representation Learning by Pace Prediction,209,eccv,12,4,2023-06-17 00:18:58.456000,https://github.com/laura-wang/video-pace,97,Self-supervised video representation learning by pace prediction,"https://scholar.google.com/scholar?cluster=11755603088951717604&hl=en&as_sdt=0,44",14,2020
Key Frame Proposal Network for Efficient Pose Estimation in Videos,20,eccv,6,5,2023-06-17 00:18:58.669000,https://github.com/Yuexiaoxi10/Key-Frame-Proposal-Network-for-Efficient-Pose-Estimation-in-Videos,53,Key frame proposal network for efficient pose estimation in videos,"https://scholar.google.com/scholar?cluster=5780538835693144589&hl=en&as_sdt=0,44",6,2020
Cross-Modal Weighting Network for RGB-D Salient Object Detection,137,eccv,4,2,2023-06-17 00:18:58.881000,https://github.com/MathLee/CMWNet,27,Cross-modal weighting network for RGB-D salient object detection,"https://scholar.google.com/scholar?cluster=13461283797024351194&hl=en&as_sdt=0,21",4,2020
Open-set Adversarial Defense,22,eccv,2,0,2023-06-17 00:18:59.094000,https://github.com/rshaojimmy/ECCV2020-OSAD,16,Open-set adversarial defense,"https://scholar.google.com/scholar?cluster=5739438982546963750&hl=en&as_sdt=0,5",2,2020
Deep Image Compression using Decoder Side Information,15,eccv,11,4,2023-06-17 00:18:59.326000,https://github.com/ayziksha/DSIN,45,Deep image compression using decoder side information,"https://scholar.google.com/scholar?cluster=5418906621119582530&hl=en&as_sdt=0,34",4,2020
A Generic Visualization Approach for Convolutional Neural Networks,7,eccv,0,0,2023-06-17 00:18:59.555000,https://github.com/ahmdtaha/l2_caf_pytorch,2,A generic visualization approach for convolutional neural networks,"https://scholar.google.com/scholar?cluster=9356973463750501833&hl=en&as_sdt=0,5",2,2020
Multi-Loss Rebalancing Algorithm for Monocular Depth Estimation,40,eccv,5,4,2023-06-17 00:18:59.769000,https://github.com/jaehanlee-mcl/multi-loss-rebalancing-depth,22,Multi-loss rebalancing algorithm for monocular depth estimation,"https://scholar.google.com/scholar?cluster=7800563958572063267&hl=en&as_sdt=0,39",1,2020
"3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View",29,eccv,9,2,2023-06-17 00:18:59.982000,https://github.com/marcbadger/avian-mesh,47,"3D bird reconstruction: a dataset, model, and shape recovery from a single view","https://scholar.google.com/scholar?cluster=9773970848112763198&hl=en&as_sdt=0,31",7,2020
Joint Optimization for Multi-Person Shape Models from Markerless 3D-Scans,2,eccv,0,0,2023-06-17 00:19:00.197000,https://github.com/Intelligent-Systems-Research-Group/JOMS,1,Joint optimization for multi-person shape models from markerless 3d-scans,"https://scholar.google.com/scholar?cluster=12606764570932551321&hl=en&as_sdt=0,37",1,2020
Accurate RGB-D Salient Object Detection via Collaborative Learning,137,eccv,0,0,2023-06-17 00:19:00.409000,https://github.com/OIPLab-DUT/CoNet,4,Accurate RGB-D salient object detection via collaborative learning,"https://scholar.google.com/scholar?cluster=466665308156454914&hl=en&as_sdt=0,5",1,2020
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection,64,eccv,4,4,2023-06-17 00:19:00.621000,https://github.com/GanlongZhao/CST_DA_detection,20,Collaborative training between region proposal localization and classification for domain adaptive object detection,"https://scholar.google.com/scholar?cluster=6973714808761672235&hl=en&as_sdt=0,47",2,2020
Modeling Artistic Workflows for Image Generation and Editing,19,eccv,8,2,2023-06-17 00:19:00.833000,https://github.com/hytseng0509/ArtEditing,89,Modeling artistic workflows for image generation and editing,"https://scholar.google.com/scholar?cluster=7486664813148391458&hl=en&as_sdt=0,44",12,2020
A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks,44,eccv,3,2,2023-06-17 00:19:01.045000,https://github.com/stnoah1/mcb,33,A large-scale annotated mechanical components benchmark for classification and retrieval tasks with deep neural networks,"https://scholar.google.com/scholar?cluster=10372829528749478123&hl=en&as_sdt=0,43",1,2020
Style Transfer for Co-Speech Gesture Animation: A Multi-Speaker Conditional-Mixture Approach,53,eccv,7,4,2023-06-17 00:19:01.258000,https://github.com/chahuja/mix-stage,24,Style transfer for co-speech gesture animation: A multi-speaker conditional-mixture approach,"https://scholar.google.com/scholar?cluster=6277916960012129598&hl=en&as_sdt=0,15",2,2020
Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline,90,eccv,18,5,2023-06-17 00:19:01.473000,https://github.com/vmurahari3/visdial-bert,91,Large-scale pretraining for visual dialog: A simple state-of-the-art baseline,"https://scholar.google.com/scholar?cluster=2652951031582281171&hl=en&as_sdt=0,5",7,2020
On Disentangling Spoof Trace for Generic Face Anti-Spoofing,99,eccv,35,17,2023-06-17 00:19:01.685000,https://github.com/yaojieliu/ECCV20-STDN,140,On disentangling spoof trace for generic face anti-spoofing,"https://scholar.google.com/scholar?cluster=13718839637326721225&hl=en&as_sdt=0,21",11,2020
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search,39,eccv,20,0,2023-06-17 00:19:01.899000,https://github.com/YunChunChen/NAS-DIP-pytorch,120,Nas-dip: Learning deep image prior with neural architecture search,"https://scholar.google.com/scholar?cluster=14360739967987694057&hl=en&as_sdt=0,5",9,2020
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes,8,eccv,0,1,2023-06-17 00:19:02.112000,https://github.com/ChenhongyiYang/SG-NMS,11,Learning to separate: Detecting heavily-occluded objects in urban scenes,"https://scholar.google.com/scholar?cluster=11678187234453170400&hl=en&as_sdt=0,44",1,2020
Knowledge-Based Video Question Answering with Unsupervised Scene Descriptions,34,eccv,4,1,2023-06-17 00:19:02.324000,https://github.com/noagarcia/ROLL-VideoQA,20,Knowledge-based video question answering with unsupervised scene descriptions,"https://scholar.google.com/scholar?cluster=4210077672555312031&hl=en&as_sdt=0,5",3,2020
HDNet: Human Depth Estimation for Multi-Person Camera-Space Localization,32,eccv,3,6,2023-06-17 00:19:02.609000,https://github.com/jiahaoLjh/HumanDepth,28,Hdnet: Human depth estimation for multi-person camera-space localization,"https://scholar.google.com/scholar?cluster=10715256745450524626&hl=en&as_sdt=0,23",6,2020
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data,417,eccv,152,8,2023-06-17 00:19:02.823000,https://github.com/StanfordASL/Trajectron-plus-plus,504,Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,"https://scholar.google.com/scholar?cluster=17437805472118459497&hl=en&as_sdt=0,6",23,2020
Context-Gated Convolution,31,eccv,8,2,2023-06-17 00:19:03.036000,https://github.com/XudongLinthu/context-gated-convolution,52,Context-gated convolution,"https://scholar.google.com/scholar?cluster=7480677484970516009&hl=en&as_sdt=0,5",4,2020
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems,90,eccv,29,41,2023-06-17 00:19:03.249000,https://github.com/CalayZhou/MBNet,83,Improving multispectral pedestrian detection by addressing modality imbalance problems,"https://scholar.google.com/scholar?cluster=16938297042857904132&hl=en&as_sdt=0,33",2,2020
Robust Tracking against Adversarial Attacks,33,eccv,6,0,2023-06-17 00:19:03.463000,https://github.com/joshuajss/RTAA,24,Robust tracking against adversarial attacks,"https://scholar.google.com/scholar?cluster=649250452177818272&hl=en&as_sdt=0,33",3,2020
Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement,52,eccv,8,1,2023-06-17 00:19:03.680000,https://github.com/NIEQiang001/unsupervised-human-pose,43,Unsupervised 3d human pose representation with viewpoint and pose disentanglement,"https://scholar.google.com/scholar?cluster=17894504217135638951&hl=en&as_sdt=0,15",8,2020
"Towards Fast, Accurate and Stable 3D Dense Face Alignment",263,eccv,465,88,2023-06-17 00:19:03.892000,https://github.com/cleardusk/3DDFA_V2,2549,"Towards fast, accurate and stable 3d dense face alignment","https://scholar.google.com/scholar?cluster=4527555153823382927&hl=en&as_sdt=0,48",67,2020
Iterative Feature Transformation for Fast and Versatile Universal Style Transfer,14,eccv,2,1,2023-06-17 00:19:04.105000,https://github.com/chiutaiyin/Iterative-feature-transformation-for-style-transfer,4,Iterative feature transformation for fast and versatile universal style transfer,"https://scholar.google.com/scholar?cluster=10160402618495000648&hl=en&as_sdt=0,10",0,2020
Inter-Image Communication for Weakly Supervised Localization,74,eccv,4,3,2023-06-17 00:19:04.328000,https://github.com/xiaomengyc/I2C,28,Inter-image communication for weakly supervised localization,"https://scholar.google.com/scholar?cluster=10166725510802700554&hl=en&as_sdt=0,33",3,2020
UFO²: A Unified Framework towards Omni-supervised Object Detection,40,eccv,45,11,2023-06-17 00:19:04.548000,https://github.com/NVlabs/wetectron,348,UFO: A Unified Framework Towards Omni-supervised Object Detection,"https://scholar.google.com/scholar?cluster=2851012986526988677&hl=en&as_sdt=0,5",31,2020
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering,35,eccv,2,7,2023-06-17 00:19:04.761000,https://github.com/zaynmi/seada-vqa,20,Semantic equivalent adversarial data augmentation for visual question answering,"https://scholar.google.com/scholar?cluster=17023539325112614662&hl=en&as_sdt=0,5",1,2020
Unsupervised Multi-View CNN for Salient View Selection of 3D Objects and Scenes,1,eccv,0,0,2023-06-17 00:19:04.973000,https://github.com/rsong/UMVCNN,1,Unsupervised multi-view cnn for salient view selection of 3d objects and scenes,"https://scholar.google.com/scholar?cluster=2764183113241505414&hl=en&as_sdt=0,31",1,2020
Representation Sharing for Fast Object Detector Search and Beyond,9,eccv,7,2,2023-06-17 00:19:05.186000,https://github.com/MalongTech/research-fad,26,Representation sharing for fast object detector search and beyond,"https://scholar.google.com/scholar?cluster=10341654826007288793&hl=en&as_sdt=0,5",4,2020
RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition,60,eccv,9,5,2023-06-17 00:19:05.397000,https://github.com/StanfordVL/RubiksNet,90,Rubiksnet: Learnable 3d-shift for efficient video action recognition,"https://scholar.google.com/scholar?cluster=7287945314698043527&hl=en&as_sdt=0,5",6,2020
Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing,35,eccv,40,14,2023-06-17 00:19:05.609000,https://github.com/charlesCXK/RGBD_Semantic_Segmentation_PyTorch,222,Malleable 2.5 d convolution: Learning receptive fields along the depth-axis for rgb-d scene parsing,"https://scholar.google.com/scholar?cluster=11253706908375155337&hl=en&as_sdt=0,5",4,2020
Feature-metric Loss for Self-supervised Learning of Depth and Egomotion,152,eccv,28,1,2023-06-17 00:19:05.821000,https://github.com/sconlyshootery/FeatDepth,228,Feature-metric loss for self-supervised learning of depth and egomotion,"https://scholar.google.com/scholar?cluster=10633872730785437280&hl=en&as_sdt=0,5",7,2020
Adversarial Semantic Data Augmentation for Human Pose Estimation,52,eccv,6,7,2023-06-17 00:19:06.034000,https://github.com/Binyr/ASDA,40,Adversarial semantic data augmentation for human pose estimation,"https://scholar.google.com/scholar?cluster=10168117646368706914&hl=en&as_sdt=0,5",12,2020
Free View Synthesis,203,eccv,42,4,2023-06-17 00:19:06.245000,https://github.com/intel-isl/FreeViewSynthesis,261,Free view synthesis,"https://scholar.google.com/scholar?cluster=12851228195475145432&hl=en&as_sdt=0,11",22,2020
Spiral Generative Network for Image Extrapolation,20,eccv,2,4,2023-06-17 00:19:06.457000,https://github.com/zhenglab/spiralnet,13,Spiral generative network for image extrapolation,"https://scholar.google.com/scholar?cluster=17519049291772461252&hl=en&as_sdt=0,47",3,2020
Few-shot Compositional Font Generation with Dual Memory,45,eccv,33,2,2023-06-17 00:19:06.669000,https://github.com/clovaai/dmfont,119,Few-shot compositional font generation with dual memory,"https://scholar.google.com/scholar?cluster=18279316731684412054&hl=en&as_sdt=0,10",7,2020
Handcrafted Outlier Detection Revisited,36,eccv,41,4,2023-06-17 00:19:06.881000,https://github.com/cavalli1234/AdaLAM,274,Handcrafted outlier detection revisited,"https://scholar.google.com/scholar?cluster=9246231653604114804&hl=en&as_sdt=0,5",9,2020
The Average Mixing Kernel Signature,8,eccv,0,0,2023-06-17 00:19:07.093000,https://github.com/lcosmo/amks-descriptor,5,The average mixing kernel signature,"https://scholar.google.com/scholar?cluster=8382074612041864326&hl=en&as_sdt=0,5",5,2020
BCNet: Learning Body and Cloth Shape from A Single Image,90,eccv,16,8,2023-06-17 00:19:07.305000,https://github.com/jby1993/BCNet,90,Bcnet: Learning body and cloth shape from a single image,"https://scholar.google.com/scholar?cluster=10624435503248718332&hl=en&as_sdt=0,33",7,2020
Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration,30,eccv,7,3,2023-06-17 00:19:07.531000,https://github.com/hejingwenhejingwen/CResMD,92,Interactive multi-dimension modulation with dynamic controllable residual learning for image restoration,"https://scholar.google.com/scholar?cluster=7345386656492672682&hl=en&as_sdt=0,5",5,2020
Polysemy Deciphering Network for Human-Object Interaction Detection,47,eccv,2,0,2023-06-17 00:19:07.742000,https://github.com/MuchHair/PD-Net,18,Polysemy deciphering network for human-object interaction detection,"https://scholar.google.com/scholar?cluster=14500470924143720074&hl=en&as_sdt=0,10",4,2020
PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning,324,eccv,53,6,2023-06-17 00:19:07.955000,https://github.com/arthurdouillard/incremental_learning.pytorch,324,Podnet: Pooled outputs distillation for small-tasks incremental learning,"https://scholar.google.com/scholar?cluster=7958959693731950230&hl=en&as_sdt=0,5",14,2020
Learning Graph-Convolutional Representations for Point Cloud Denoising,43,eccv,6,6,2023-06-17 00:19:08.167000,https://github.com/diegovalsesia/GPDNet,29,Learning graph-convolutional representations for point cloud denoising,"https://scholar.google.com/scholar?cluster=8172733278643108058&hl=en&as_sdt=0,33",2,2020
Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching,7,eccv,2,0,2023-06-17 00:19:08.378000,https://github.com/dongkwonjin/Semantic-Line-DRM,32,Semantic line detection using mirror attention and comparative ranking and matching,"https://scholar.google.com/scholar?cluster=11538926710696025851&hl=en&as_sdt=0,33",7,2020
LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation,58,eccv,18,1,2023-06-17 00:19:08.590000,https://github.com/twhui/LiteFlowNet3,213,Liteflownet3: Resolving correspondence ambiguity for more accurate optical flow estimation,"https://scholar.google.com/scholar?cluster=15678055772600239326&hl=en&as_sdt=0,5",15,2020
Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters,25,eccv,3,0,2023-06-17 00:19:08.802000,https://github.com/vpronina/DeepWienerRestoration,10,Microscopy image restoration with deep wiener-kolmogorov filters,"https://scholar.google.com/scholar?cluster=12805576976461121338&hl=en&as_sdt=0,5",1,2020
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds,97,eccv,10,0,2023-06-17 00:19:09.015000,https://github.com/hzykent/JSENet,102,Jsenet: Joint semantic segmentation and edge detection network for 3d point clouds,"https://scholar.google.com/scholar?cluster=13232109569898944500&hl=en&as_sdt=0,44",7,2020
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior,28,eccv,2,4,2023-06-17 00:19:09.227000,https://github.com/xiaofanustc/ME-Sampler,11,Motion-excited sampler: Video adversarial attack with sparked prior,"https://scholar.google.com/scholar?cluster=7197516693747807433&hl=en&as_sdt=0,47",3,2020
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100,1,eccv,0,0,2023-06-17 00:19:09.439000,https://github.com/ishantshanu/ML-Minnorm,1,An Inference Algorithm for Multi-label MRF-MAP Problems with Clique Size 100,"https://scholar.google.com/scholar?cluster=15254223690658200700&hl=en&as_sdt=0,21",2,2020
Dual Refinement Underwater Object Detection Network,37,eccv,0,2,2023-06-17 00:19:09.651000,https://github.com/Peterchen111/FERNet,16,Dual refinement underwater object detection network,"https://scholar.google.com/scholar?cluster=3220721688142718280&hl=en&as_sdt=0,32",4,2020
Multiple Sound Sources Localization from Coarse to Fine,85,eccv,15,7,2023-06-17 00:19:09.863000,https://github.com/shvdiwnkozbw/Multi-Source-Sound-Localization,62,Multiple sound sources localization from coarse to fine,"https://scholar.google.com/scholar?cluster=11111004644812659216&hl=en&as_sdt=0,25",4,2020
Energy-Based Models for Deep Probabilistic Regression,50,eccv,18,6,2023-06-17 00:19:10.075000,https://github.com/fregu856/ebms_regression,85,Energy-based models for deep probabilistic regression,"https://scholar.google.com/scholar?cluster=414963039998112333&hl=en&as_sdt=0,14",4,2020
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images,86,eccv,14,4,2023-06-17 00:19:10.289000,https://github.com/ClancyZhou/P_Net_Anomaly_Detection,54,Encoding structure-texture relation with p-net for anomaly detection in retinal images,"https://scholar.google.com/scholar?cluster=5668631453871706518&hl=en&as_sdt=0,5",3,2020
CLNet: A Compact Latent Network for Fast Adjusting Siamese Trackers,69,eccv,5,2,2023-06-17 00:19:10.502000,https://github.com/xingpingdong/CLNet-tracking,31,CLNet: A compact latent network for fast adjusting Siamese trackers,"https://scholar.google.com/scholar?cluster=17959306132449137102&hl=en&as_sdt=0,5",3,2020
Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model,11,eccv,1,0,2023-06-17 00:19:10.715000,https://github.com/MinglangQiao/MVVA-Database,7,Learning to predict salient faces: A novel visual-audio saliency model,"https://scholar.google.com/scholar?cluster=10407966517330902821&hl=en&as_sdt=0,20",2,2020
NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image,29,eccv,13,6,2023-06-17 00:19:10.929000,https://github.com/LizhenWangT/NormalGAN,66,Normalgan: Learning detailed 3d human from a single rgb-d image,"https://scholar.google.com/scholar?cluster=17372069612448752096&hl=en&as_sdt=0,5",5,2020
On the Usage of the Trifocal Tensor in Motion Segmentation,5,eccv,0,0,2023-06-17 00:19:11.141000,https://github.com/federica-arrigoni/ECCV_20,0,On the usage of the trifocal tensor in motion segmentation,"https://scholar.google.com/scholar?cluster=6076373986302334740&hl=en&as_sdt=0,47",2,2020
Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance,208,eccv,23,16,2023-06-17 00:19:11.353000,https://github.com/ifnspaml/SGDepth,187,Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance,"https://scholar.google.com/scholar?cluster=8598900070520286121&hl=en&as_sdt=0,5",9,2020
Hierarchical Visual-Textual Graph for Temporal Activity Localization via Language,46,eccv,2,1,2023-06-17 00:19:11.565000,https://github.com/forwchen/HVTG,17,Hierarchical visual-textual graph for temporal activity localization via language,"https://scholar.google.com/scholar?cluster=4992836478532047682&hl=en&as_sdt=0,5",4,2020
NODIS: Neural Ordinary Differential Scene Understanding,17,eccv,3,0,2023-06-17 00:19:11.777000,https://github.com/yrcong/NODIS,9,Nodis: Neural ordinary differential scene understanding,"https://scholar.google.com/scholar?cluster=10668378863380867275&hl=en&as_sdt=0,38",3,2020
Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference,43,eccv,4,0,2023-06-17 00:19:11.989000,https://github.com/menelaoskanakis/RCM,29,Reparameterizing convolutions for incremental multi-task learning without task interference,"https://scholar.google.com/scholar?cluster=11050373307584128908&hl=en&as_sdt=0,50",7,2020
Unifying Deep Local and Global Features for Image Search,230,eccv,46279,1207,2023-06-17 00:19:12.201000,https://github.com/tensorflow/models,75921,Unifying deep local and global features for image search,"https://scholar.google.com/scholar?cluster=4844482508106875299&hl=en&as_sdt=0,31",2774,2020
DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition,40,eccv,5,3,2023-06-17 00:19:12.413000,https://github.com/MengzSun/DDGCN,20,Ddgcn: A dynamic directed graph convolutional network for action recognition,"https://scholar.google.com/scholar?cluster=10891789842765257479&hl=en&as_sdt=0,39",1,2020
Learning latent representations across multiple data domains using Lifelong VAEGAN,43,eccv,1,1,2023-06-17 00:19:12.625000,https://github.com/dtuzi123/LifelongVAEGAN,7,Learning latent representations across multiple data domains using lifelong VAEGAN,"https://scholar.google.com/scholar?cluster=8524866969127110319&hl=en&as_sdt=0,6",1,2020
DVI: Depth Guided Video Inpainting for Autonomous Driving,27,eccv,8,2,2023-06-17 00:19:12.837000,https://github.com/sibozhang/Depth-Guided-Inpainting,54,Dvi: Depth guided video inpainting for autonomous driving,"https://scholar.google.com/scholar?cluster=13578799766225685537&hl=en&as_sdt=0,39",2,2020
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points,31,eccv,19,4,2023-06-17 00:19:13.048000,https://github.com/magicleap/DELTAS,96,Deltas: Depth estimation by learning triangulation and densification of sparse points,"https://scholar.google.com/scholar?cluster=2573086286039164925&hl=en&as_sdt=0,44",11,2020
Backpropagated Gradient Representations for Anomaly Detection,59,eccv,6,2,2023-06-17 00:19:13.260000,https://github.com/olivesgatech/gradcon-anomaly,29,Backpropagated gradient representations for anomaly detection,"https://scholar.google.com/scholar?cluster=10657672650004009459&hl=en&as_sdt=0,14",4,2020
Dense RepPoints: Representing Visual Objects with Dense Point Sets,44,eccv,15,8,2023-06-17 00:19:13.471000,https://github.com/justimyhxu/Dense-RepPoints,144,Dense reppoints: Representing visual objects with dense point sets,"https://scholar.google.com/scholar?cluster=9322617383465008408&hl=en&as_sdt=0,47",10,2020
Adaptive Video Highlight Detection by Learning from User History,20,eccv,6,0,2023-06-17 00:19:13.683000,https://github.com/mrochan/adaptive-highlight,20,Adaptive video highlight detection by learning from user history,"https://scholar.google.com/scholar?cluster=1437416319953428090&hl=en&as_sdt=0,31",3,2020
SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization,86,eccv,2,6,2023-06-17 00:19:13.895000,https://github.com/ZhiHanZ/IRIS0-SPAN,26,SPAN: Spatial pyramid attention network for image manipulation localization,"https://scholar.google.com/scholar?cluster=9061603191086183717&hl=en&as_sdt=0,5",3,2020
Identity-Aware Multi-Sentence Video Description,13,eccv,5,1,2023-06-17 00:19:14.108000,https://github.com/jamespark3922/lsmdc-fillin,11,Identity-aware multi-sentence video description,"https://scholar.google.com/scholar?cluster=10690664751687301834&hl=en&as_sdt=0,40",2,2020
Mining Inter-Video Proposal Relations for Video Object Detection,63,eccv,6,5,2023-06-17 00:19:14.354000,https://github.com/youthHan/HVRNet,43,Mining inter-video proposal relations for video object detection,"https://scholar.google.com/scholar?cluster=1174142847294902832&hl=en&as_sdt=0,5",6,2020
Minimum Class Confusion for Versatile Domain Adaptation,181,eccv,9,1,2023-06-17 00:19:14.566000,https://github.com/thuml/Versatile-Domain-Adaptation,33,Minimum class confusion for versatile domain adaptation,"https://scholar.google.com/scholar?cluster=10435611166714181688&hl=en&as_sdt=0,10",5,2020
Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation,101,eccv,27,15,2023-06-17 00:19:14.777000,https://github.com/mentian/object-deformnet,90,Shape prior deformation for categorical 6d object pose and size estimation,"https://scholar.google.com/scholar?cluster=11609666056541462591&hl=en&as_sdt=0,5",5,2020
"Image-based table recognition: data, model, and evaluation",122,eccv,76,18,2023-06-17 00:19:14.990000,https://github.com/ibm-aur-nlp/PubTabNet,311,"Image-based table recognition: data, model, and evaluation","https://scholar.google.com/scholar?cluster=1470638859426309286&hl=en&as_sdt=0,5",26,2020
Group Activity Prediction with Sequential Relational Anticipation Model,17,eccv,4,1,2023-06-17 00:19:15.202000,https://github.com/junwenchen/GAP_SRAM,5,Group activity prediction with sequential relational anticipation model,"https://scholar.google.com/scholar?cluster=17951203888493140676&hl=en&as_sdt=0,18",4,2020
PiP: Planning-informed Trajectory Prediction for Autonomous Driving,72,eccv,39,4,2023-06-17 00:19:15.414000,https://github.com/Haoran-SONG/PiP-Planning-informed-Prediction,118,Pip: Planning-informed trajectory prediction for autonomous driving,"https://scholar.google.com/scholar?cluster=17120645766965351579&hl=en&as_sdt=0,11",9,2020
PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer,32,eccv,26,4,2023-06-17 00:19:15.625000,https://github.com/d-li14/PSConv,173,Psconv: Squeezing feature pyramid into one compact poly-scale convolutional layer,"https://scholar.google.com/scholar?cluster=11530394236688752217&hl=en&as_sdt=0,1",5,2020
Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition,102,eccv,15,10,2023-06-17 00:19:15.837000,https://github.com/Yejin0111/ADD-GCN,99,Attention-driven dynamic graph convolutional network for multi-label image recognition,"https://scholar.google.com/scholar?cluster=8340322428020937763&hl=en&as_sdt=0,37",2,2020
Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection,46,eccv,67,11,2023-06-17 00:19:16.049000,https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection,254,Gen-lanenet: A generalized and scalable approach for 3d lane detection,"https://scholar.google.com/scholar?cluster=14353297151814587863&hl=en&as_sdt=0,15",8,2020
MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation,94,eccv,18,10,2023-06-17 00:19:16.261000,https://github.com/uniBruce/Mead,145,Mead: A large-scale audio-visual dataset for emotional talking-face generation,"https://scholar.google.com/scholar?cluster=13979801806685660367&hl=en&as_sdt=0,4",9,2020
Detecting Human-Object Interactions with Action Co-occurrence Priors,89,eccv,3,3,2023-06-17 00:19:16.473000,https://github.com/Dong-JinKim/ActionCooccurrencePriors,31,Detecting human-object interactions with action co-occurrence priors,"https://scholar.google.com/scholar?cluster=15097499293777107863&hl=en&as_sdt=0,33",7,2020
Ocean: Object-aware Anchor-free Tracking,413,eccv,96,23,2023-06-17 00:19:16.684000,https://github.com/researchmm/TracKit,591,Ocean: Object-aware anchor-free tracking,"https://scholar.google.com/scholar?cluster=10517065804384853053&hl=en&as_sdt=0,44",23,2020
Pillar-based Object Detection for Autonomous Driving,153,eccv,29,4,2023-06-17 00:19:16.896000,https://github.com/WangYueFt/pillar-od,129,Pillar-based object detection for autonomous driving,"https://scholar.google.com/scholar?cluster=10623128829331842758&hl=en&as_sdt=0,5",9,2020
Sparse Adversarial Attack via Perturbation Factorization,61,eccv,6,3,2023-06-17 00:19:17.107000,https://github.com/wubaoyuan/Sparse-Adversarial-Attack,22,Sparse adversarial attack via perturbation factorization,"https://scholar.google.com/scholar?cluster=4143006191799187632&hl=en&as_sdt=0,41",2,2020
3D Scene Reconstruction from a Single Viewport,21,eccv,26,0,2023-06-17 00:19:17.322000,https://github.com/DLR-RM/SingleViewReconstruction,256,3d scene reconstruction from a single viewport,"https://scholar.google.com/scholar?cluster=16026825988876484104&hl=en&as_sdt=0,5",14,2020
Leveraging Acoustic Images for Effective Self-Supervised Audio Representation Learning,7,eccv,3,0,2023-06-17 00:19:17.540000,https://github.com/IIT-PAVIS/acoustic-images-self-supervision,9,Leveraging acoustic images for effective self-supervised audio representation learning,"https://scholar.google.com/scholar?cluster=8843512448486468080&hl=en&as_sdt=0,11",4,2020
Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration,32,eccv,17,0,2023-06-17 00:19:17.752000,https://github.com/bruno-31/groupsc,55,Fully trainable and interpretable non-local sparse models for image restoration,"https://scholar.google.com/scholar?cluster=3423565215321641101&hl=en&as_sdt=0,31",6,2020
Active Visual Information Gathering for Vision-Language Navigation,44,eccv,7,1,2023-06-17 00:19:17.964000,https://github.com/HanqingWangAI/Active_VLN,39,Active visual information gathering for vision-language navigation,"https://scholar.google.com/scholar?cluster=8052885211122651190&hl=en&as_sdt=0,47",6,2020
Deep Hough-Transform Line Priors,49,eccv,26,0,2023-06-17 00:19:18.176000,https://github.com/yanconglin/Deep-Hough-Transform-Line-Priors,147,Deep hough-transform line priors,"https://scholar.google.com/scholar?cluster=12003688217579198486&hl=en&as_sdt=0,5",8,2020
Inclusive GAN: Improving Data and Minority Coverage in Generative Models,43,eccv,2,6,2023-06-17 00:19:18.388000,https://github.com/ningyu1991/InclusiveGAN,26,Inclusive gan: Improving data and minority coverage in generative models,"https://scholar.google.com/scholar?cluster=4978614561818263716&hl=en&as_sdt=0,11",4,2020
"SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects",55,eccv,5,4,2023-06-17 00:19:18.599000,https://github.com/vglsd/OpenSESAME,60,"Sesame: Semantic editing of scenes by adding, manipulating or erasing objects","https://scholar.google.com/scholar?cluster=2274519534011711574&hl=en&as_sdt=0,5",3,2020
Geometric Estimation via Robust Subspace Recovery,5,eccv,4,1,2023-06-17 00:19:18.811000,https://github.com/AoxiangFan/EifficientDeterministicSearch,17,Geometric estimation via robust subspace recovery,"https://scholar.google.com/scholar?cluster=8413359248750655470&hl=en&as_sdt=0,15",2,2020
Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification,155,eccv,30,0,2023-06-17 00:19:19.023000,https://github.com/akshitac8/tfvaegan,122,Latent embedding feedback and discriminative features for zero-shot classification,"https://scholar.google.com/scholar?cluster=4680747810672318587&hl=en&as_sdt=0,5",5,2020
Human Correspondence Consensus for 3D Object Semantic Understanding,3,eccv,0,0,2023-06-17 00:19:19.235000,https://github.com/yokinglou/CorresPondenceNet,4,Human correspondence consensus for 3d object semantic understanding,"https://scholar.google.com/scholar?cluster=2975836306490192966&hl=en&as_sdt=0,47",2,2020
Learning Memory Augmented Cascading Network for Compressed Sensing of Images,27,eccv,0,3,2023-06-17 00:19:19.446000,https://github.com/DFLyan/MAC-Net,1,Learning memory augmented cascading network for compressed sensing of images,"https://scholar.google.com/scholar?cluster=8500245635977660528&hl=en&as_sdt=0,5",2,2020
Least squares surface reconstruction on arbitrary domains,11,eccv,1,0,2023-06-17 00:19:19.660000,https://github.com/waps101/LSQSurfaceReconstruction,14,Least squares surface reconstruction on arbitrary domains,"https://scholar.google.com/scholar?cluster=3621133046468693272&hl=en&as_sdt=0,14",5,2020
Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery,38,eccv,9,4,2023-06-17 00:19:19.870000,https://github.com/mrkieumy/task-conditioned,25,Task-conditioned domain adaptation for pedestrian detection in thermal imagery,"https://scholar.google.com/scholar?cluster=16944714894806834150&hl=en&as_sdt=0,33",2,2020
"Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting",45,eccv,1,0,2023-06-17 00:19:20.083000,https://github.com/278287847/DEM,7,"Improving the transferability of adversarial examples with resized-diverse-inputs, diversity-ensemble and region fitting","https://scholar.google.com/scholar?cluster=344016441438004647&hl=en&as_sdt=0,5",1,2020
DADA: Differentiable Automatic Data Augmentation,81,eccv,29,24,2023-06-17 00:19:20.296000,https://github.com/VDIGPKU/DADA,183,Dada: Differentiable automatic data augmentation,"https://scholar.google.com/scholar?cluster=16395754498022932115&hl=en&as_sdt=0,10",6,2020
A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection,153,eccv,5,1,2023-06-17 00:19:20.508000,https://github.com/Xiaoqi-Zhao-DLUT/DANet-RGBD-Saliency,63,A single stream network for robust and real-time RGB-D salient object detection,"https://scholar.google.com/scholar?cluster=3975747780397189790&hl=en&as_sdt=0,33",7,2020
FHDe²Net: Full High Definition Demoireing Network,22,eccv,1,2,2023-06-17 00:19:20.722000,https://github.com/PKU-IMRE/FHDe2Net,9,FHDe2Net: Full High Definition Demoireing Network,"https://scholar.google.com/scholar?cluster=483050893726309262&hl=en&as_sdt=0,5",1,2020
Learning Structural Similarity of User Interface Layouts using Graph Networks,15,eccv,9,2,2023-06-17 00:19:20.935000,https://github.com/dips4717/gcn-cnn,31,Learning structural similarity of user interface layouts using graph networks,"https://scholar.google.com/scholar?cluster=3222113775540211762&hl=en&as_sdt=0,5",3,2020
Principal Feature Visualisation in Convolutional Neural Networks,4,eccv,5,0,2023-06-17 00:19:21.148000,https://github.com/SINTEF/PFV,11,Principal Feature Visualisation in Convolutional Neural Networks,"https://scholar.google.com/scholar?cluster=16229385557152617305&hl=en&as_sdt=0,5",2,2020
Monocular Real-Time Volumetric Performance Capture,64,eccv,87,12,2023-06-17 00:19:21.359000,https://github.com/Project-Splinter/MonoPort,524,Monocular real-time volumetric performance capture,"https://scholar.google.com/scholar?cluster=9890425650052236496&hl=en&as_sdt=0,44",26,2020
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational Autoencoders,19,eccv,3,2,2023-06-17 00:19:21.571000,https://github.com/SUTDBrainLab/MGP-VAE,33,Disentangling multiple features in video sequences using gaussian processes in variational autoencoders,"https://scholar.google.com/scholar?cluster=16184091877881145332&hl=en&as_sdt=0,33",8,2020
Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents,11,eccv,1,0,2023-06-17 00:19:21.783000,https://github.com/L-YeZhu/Video-Description-via-Dialog-Agents-ECCV2020,4,Describing unseen videos via multi-modal cooperative dialog agents,"https://scholar.google.com/scholar?cluster=8076917916440704977&hl=en&as_sdt=0,36",2,2020
End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention,99,eccv,15,5,2023-06-17 00:19:21.996000,https://github.com/mengziyi64/TSA-Net,51,End-to-end low cost compressive spectral imaging with spatial-spectral self-attention,"https://scholar.google.com/scholar?cluster=17178330015368510885&hl=en&as_sdt=0,5",2,2020
Know Your Surroundings: Exploiting Scene Information for Object Tracking,221,eccv,578,56,2023-06-17 00:19:22.208000,https://github.com/visionml/pytracking,2795,Know your surroundings: Exploiting scene information for object tracking,"https://scholar.google.com/scholar?cluster=14973327530459618585&hl=en&as_sdt=0,5",90,2020
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases,98,eccv,33,3,2023-06-17 00:19:22.420000,https://github.com/wangren09/TrojanNetDetector,150,Practical detection of trojan neural networks: Data-limited and data-free cases,"https://scholar.google.com/scholar?cluster=586474924613404005&hl=en&as_sdt=0,44",6,2020
DeepLandscape: Adversarial Modeling of Landscape Videos,19,eccv,21,2,2023-06-17 00:19:22.633000,https://github.com/saic-mdal/deep-landscape,114,Deeplandscape: Adversarial modeling of landscape videos,"https://scholar.google.com/scholar?cluster=14645416467086611327&hl=en&as_sdt=0,5",15,2020
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images,59,eccv,20,9,2023-06-17 00:19:22.845000,https://github.com/omni-us/research-GANwriting,57,GANwriting: content-conditioned generation of styled handwritten word images,"https://scholar.google.com/scholar?cluster=5162011778227363297&hl=en&as_sdt=0,5",10,2020
A Closer Look at Local Aggregation Operators in Point Cloud Analysis,130,eccv,33,20,2023-06-17 00:19:23.056000,https://github.com/zeliu98/CloserLook3D,241,A closer look at local aggregation operators in point cloud analysis,"https://scholar.google.com/scholar?cluster=1109845975513218522&hl=en&as_sdt=0,5",10,2020
Towards Recognizing Unseen Categories in Unseen Domains,64,eccv,5,4,2023-06-17 00:19:23.269000,https://github.com/mancinimassimiliano/CuMix,58,Towards recognizing unseen categories in unseen domains,"https://scholar.google.com/scholar?cluster=18089422724152684148&hl=en&as_sdt=0,5",6,2020
Square Attack: a query-efficient black-box adversarial attack via random search,583,eccv,23,0,2023-06-17 00:19:23.482000,https://github.com/max-andr/square-attack,119,Square attack: a query-efficient black-box adversarial attack via random search,"https://scholar.google.com/scholar?cluster=7351628162658796317&hl=en&as_sdt=0,5",4,2020
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation,35,eccv,1,0,2023-06-17 00:19:23.695000,https://github.com/SSAW14/segmentation_membership_inference,8,Segmentations-leak: Membership inference attacks and defenses in semantic image segmentation,"https://scholar.google.com/scholar?cluster=16292623475727784070&hl=en&as_sdt=0,11",2,2020
Bridging Knowledge Graphs to Generate Scene Graphs,151,eccv,15,18,2023-06-17 00:19:23.906000,https://github.com/alirezazareian/gbnet,56,Bridging knowledge graphs to generate scene graphs,"https://scholar.google.com/scholar?cluster=13893220962602314344&hl=en&as_sdt=0,5",2,2020
MPCC: Matching Priors and Conditionals for Clustering,3,eccv,0,3,2023-06-17 00:19:24.119000,https://github.com/jumpynitro/MPCC,3,MPCC: Matching Priors and Conditionals for Clustering,"https://scholar.google.com/scholar?cluster=17260625805829530978&hl=en&as_sdt=0,22",4,2020
PointAR: Efficient Lighting Estimation for Mobile Augmented Reality,24,eccv,4,0,2023-06-17 00:19:24.343000,https://github.com/cake-lab/PointAR,18,PointAR: Efficient Lighting Estimation for Mobile Augmented Reality,"https://scholar.google.com/scholar?cluster=5884474712443257298&hl=en&as_sdt=0,33",2,2020
Discrete Point Flow Networks for Efficient Point Cloud Generation,51,eccv,5,4,2023-06-17 00:19:24.563000,https://github.com/Regenerator/dpf-nets,37,Discrete point flow networks for efficient point cloud generation,"https://scholar.google.com/scholar?cluster=13562048764910928987&hl=en&as_sdt=0,5",5,2020
PointTriNet: Learned Triangulation of 3D Point Sets,34,eccv,12,5,2023-06-17 00:19:24.776000,https://github.com/nmwsharp/learned-triangulation,96,Pointtrinet: Learned triangulation of 3d point sets,"https://scholar.google.com/scholar?cluster=16548310191585500069&hl=en&as_sdt=0,5",5,2020
"Toward Unsupervised, Multi-Object Discovery in Large-Scale Image Collections",46,eccv,2,0,2023-06-17 00:19:24.988000,https://github.com/huyvvo/rOSD,9,"Toward unsupervised, multi-object discovery in large-scale image collections","https://scholar.google.com/scholar?cluster=12626600374666568078&hl=en&as_sdt=0,14",1,2020
Deep Novel View Synthesis from Colored 3D Point Clouds,11,eccv,1,1,2023-06-17 00:19:25.200000,https://github.com/ZhenboSong/synpts-pytorch,7,Deep novel view synthesis from colored 3D point clouds,"https://scholar.google.com/scholar?cluster=11269887321687858977&hl=en&as_sdt=0,33",1,2020
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching,107,eccv,21,2,2023-06-17 00:19:25.412000,https://github.com/BruceW91/CVSE,168,Consensus-aware visual-semantic embedding for image-text matching,"https://scholar.google.com/scholar?cluster=12353349572836706134&hl=en&as_sdt=0,5",3,2020
Spatial Hierarchy Aware Residual Pyramid Network for Time-of-Flight Depth Denoising,14,eccv,6,1,2023-06-17 00:19:25.625000,https://github.com/ashesknight/tof-mpi-remove,8,Spatial hierarchy aware residual pyramid network for time-of-flight depth denoising,"https://scholar.google.com/scholar?cluster=3164716006623926373&hl=en&as_sdt=0,5",2,2020
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition,16,eccv,9,3,2023-06-17 00:19:25.837000,https://github.com/DTaoo/Multimodal-Aerial-Scene-Recognition,34,Cross-task transfer for geotagged audiovisual aerial scene recognition,"https://scholar.google.com/scholar?cluster=1568884674049360989&hl=en&as_sdt=0,44",3,2020
NeuRoRA: Neural Robust Rotation Averaging,39,eccv,5,0,2023-06-17 00:19:26.049000,https://github.com/pulak09/NeuRoRA,19,Neurora: Neural robust rotation averaging,"https://scholar.google.com/scholar?cluster=16681143085427072261&hl=en&as_sdt=0,6",2,2020
P²Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation,40,eccv,23,3,2023-06-17 00:19:26.261000,https://github.com/svip-lab/Indoor-SfMLearner,136,PNet: Patch-Match and Plane-Regularization for Unsupervised Indoor Depth Estimation,"https://scholar.google.com/scholar?cluster=5702379087216201963&hl=en&as_sdt=0,5",9,2020
Efficient Attention Mechanism for Visual Dialog that can Handle All the Interactions between Multiple Inputs,26,eccv,4,3,2023-06-17 00:19:26.474000,https://github.com/davidnvq/visdial,30,Efficient attention mechanism for visual dialog that can handle all the interactions between multiple inputs,"https://scholar.google.com/scholar?cluster=16559345796879535216&hl=en&as_sdt=0,5",2,2020
Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting,92,eccv,16,13,2023-06-17 00:19:26.686000,https://github.com/xiyang1012/Local-Crowd-Counting,74,Adaptive mixture regression network with local counting map for crowd counting,"https://scholar.google.com/scholar?cluster=9062190127785989869&hl=en&as_sdt=0,4",3,2020
BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging,56,eccv,9,6,2023-06-17 00:19:26.898000,https://github.com/BoChenGroup/BIRNAT,31,BIRNAT: Bidirectional recurrent neural networks with adversarial training for video snapshot compressive imaging,"https://scholar.google.com/scholar?cluster=18271975102489248839&hl=en&as_sdt=0,5",0,2020
Ultra Fast Structure-aware Deep Lane Detection,258,eccv,461,67,2023-06-17 00:19:27.110000,https://github.com/cfzd/Ultra-Fast-Lane-Detection,1507,Ultra fast structure-aware deep lane detection,"https://scholar.google.com/scholar?cluster=4307981733498358842&hl=en&as_sdt=0,5",29,2020
Learning Camera-Aware Noise Models,25,eccv,7,3,2023-06-17 00:19:27.325000,https://github.com/arcchang1236/CA-NoiseGAN,67,Learning camera-aware noise models,"https://scholar.google.com/scholar?cluster=16086994040307661266&hl=en&as_sdt=0,29",7,2020
Towards Precise Completion of Deformable Shapes,5,eccv,0,0,2023-06-17 00:19:27.559000,https://github.com/OshriHalimi/precise_shape_completion,7,Towards precise completion of deformable shapes,"https://scholar.google.com/scholar?cluster=10748412439464550571&hl=en&as_sdt=0,33",4,2020
Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration,90,eccv,3,2,2023-06-17 00:19:27.771000,https://github.com/jiahaowork/idam,33,Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration,"https://scholar.google.com/scholar?cluster=4655299501700749074&hl=en&as_sdt=0,26",2,2020
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization,12,eccv,3,0,2023-06-17 00:19:27.983000,https://github.com/AmirooR/Pairwise-Similarity-knowledge-Transfer-WSOL,6,Pairwise similarity knowledge transfer for weakly supervised object localization,"https://scholar.google.com/scholar?cluster=3851340864201109280&hl=en&as_sdt=0,33",3,2020
Environment-agnostic Multitask Learning for Natural Language Grounded Navigation,50,eccv,12,2,2023-06-17 00:19:28.195000,https://github.com/google-research/valan,70,Environment-agnostic multitask learning for natural language grounded navigation,"https://scholar.google.com/scholar?cluster=4744216243220542177&hl=en&as_sdt=0,47",13,2020
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition,159,eccv,15,5,2023-06-17 00:19:28.408000,https://github.com/kchengiva/DecoupleGCN-DropGraph,89,Decoupling gcn with dropgraph module for skeleton-based action recognition,"https://scholar.google.com/scholar?cluster=11675879757427718212&hl=en&as_sdt=0,44",2,2020
Self-adapting confidence estimation for stereo,6,eccv,2,1,2023-06-17 00:19:28.620000,https://github.com/mattpoggi/self-adapting-confidence,18,Self-adapting confidence estimation for stereo,"https://scholar.google.com/scholar?cluster=6797819544409195929&hl=en&as_sdt=0,44",4,2020
AutoSTR: Efficient Backbone Search for Scene Text Recognition,42,eccv,17,4,2023-06-17 00:19:28.833000,https://github.com/AutoML-4Paradigm/AutoSTR,81,AutoSTR: efficient backbone search for scene text recognition,"https://scholar.google.com/scholar?cluster=2950335387977279839&hl=en&as_sdt=0,11",11,2020
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification,28,eccv,3,0,2023-06-17 00:19:29.045000,https://github.com/dscig/TwoStageUC,22,Mitigating embedding and class assignment mismatch in unsupervised image classification,"https://scholar.google.com/scholar?cluster=12911089765250493769&hl=en&as_sdt=0,31",3,2020
Boundary-Aware Cascade Networks for Temporal Action Segmentation,93,eccv,10,1,2023-06-17 00:19:29.256000,https://github.com/MCG-NJU/BCN,83,Boundary-aware cascade networks for temporal action segmentation,"https://scholar.google.com/scholar?cluster=17606623347168890044&hl=en&as_sdt=0,5",4,2020
Inference Graphs for CNN Interpretation,5,eccv,1,1,2023-06-17 00:19:29.468000,https://github.com/yaelkon/GMM-CNN,3,Inference graphs for CNN interpretation,"https://scholar.google.com/scholar?cluster=9940765063209575060&hl=en&as_sdt=0,14",2,2020
Improving Query Efficiency of Black-box Adversarial Attack,34,eccv,2,4,2023-06-17 00:19:29.680000,https://github.com/Sandy-Zeng/NPAttack,11,Improving query efficiency of black-box adversarial attack,"https://scholar.google.com/scholar?cluster=10845525895682642585&hl=en&as_sdt=0,5",2,2020
Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection,144,eccv,11,0,2023-06-17 00:19:29.891000,https://github.com/lartpang/HDFNet,78,Hierarchical dynamic filtering network for RGB-D salient object detection,"https://scholar.google.com/scholar?cluster=752805221454606502&hl=en&as_sdt=0,6",4,2020
SOLAR: Second-Order Loss and Attention for Image Retrieval,91,eccv,35,8,2023-06-17 00:19:30.104000,https://github.com/tonyngjichun/SOLAR,164,SOLAR: second-order loss and attention for image retrieval,"https://scholar.google.com/scholar?cluster=6184834961024257446&hl=en&as_sdt=0,10",12,2020
Fixing Localization Errors to Improve Image Classification,11,eccv,4,1,2023-06-17 00:19:30.316000,https://github.com/GuoleiSun/HNC_loss,17,Fixing localization errors to improve image classification,"https://scholar.google.com/scholar?cluster=7951230438275608083&hl=en&as_sdt=0,44",2,2020
PatchPerPix for Instance Segmentation,21,eccv,4,0,2023-06-17 00:19:30.548000,https://github.com/Kainmueller-Lab/PatchPerPix,25,Patchperpix for instance segmentation,"https://scholar.google.com/scholar?cluster=11878472458041329664&hl=en&as_sdt=0,5",4,2020
Probabilistic Anchor Assignment with IoU Prediction for Object Detection,250,eccv,27,1,2023-06-17 00:19:30.761000,https://github.com/kkhoot/PAA,235,Probabilistic anchor assignment with iou prediction for object detection,"https://scholar.google.com/scholar?cluster=2210620764419829906&hl=en&as_sdt=0,5",8,2020
Temporal Complementary Learning for Video Person Re-Identification,76,eccv,18,3,2023-06-17 00:19:30.973000,https://github.com/blue-blue272/VideoReID-TCLNet,65,Temporal complementary learning for video person re-identification,"https://scholar.google.com/scholar?cluster=17196102730108668552&hl=en&as_sdt=0,5",5,2020
Context-Aware RCNN: A Baseline for Action Detection in Videos,53,eccv,5,1,2023-06-17 00:19:31.185000,https://github.com/MCG-NJU/CRCNN-Action,51,Context-aware rcnn: A baseline for action detection in videos,"https://scholar.google.com/scholar?cluster=8596574892293241366&hl=en&as_sdt=0,44",7,2020
Full-Time Monocular Road Detection Using Zero-Distribution Prior of Angle of Polarization,16,eccv,2,0,2023-06-17 00:19:31.396000,https://github.com/polwork/LDDRS,12,Full-time monocular road detection using zero-distribution prior of angle of polarization,"https://scholar.google.com/scholar?cluster=16260094581243070192&hl=en&as_sdt=0,41",1,2020
Learning Enriched Features for Real Image Restoration and Enhancement,363,eccv,87,17,2023-06-17 00:19:31.608000,https://github.com/swz30/MIRNet,550,Learning enriched features for real image restoration and enhancement,"https://scholar.google.com/scholar?cluster=5608917971836736227&hl=en&as_sdt=0,47",13,2020
Detail Preserved Point Cloud Completion via Separated Feature Aggregation,90,eccv,6,3,2023-06-17 00:19:31.820000,https://github.com/XLechter/Detail-Preserved-Point-Cloud-Completion-via-SFA,55,Detail preserved point cloud completion via separated feature aggregation,"https://scholar.google.com/scholar?cluster=3597287494806062878&hl=en&as_sdt=0,5",4,2020
LabelEnc: A New Intermediate Supervision Method for Object Detection,14,eccv,3,5,2023-06-17 00:19:32.032000,https://github.com/megvii-model/LabelEnc,69,Labelenc: A new intermediate supervision method for object detection,"https://scholar.google.com/scholar?cluster=298280353894529101&hl=en&as_sdt=0,5",8,2020
Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets,31,eccv,3,1,2023-06-17 00:19:32.244000,https://github.com/cfernandezlab/Category-Specific-Keypoints,19,Unsupervised learning of category-specific symmetric 3d keypoints from point sets,"https://scholar.google.com/scholar?cluster=7200367023586171301&hl=en&as_sdt=0,31",4,2020
Feature Normalized Knowledge Distillation for Image Classification,50,eccv,3,0,2023-06-17 00:19:32.456000,https://github.com/aztc/FNKD,6,Feature normalized knowledge distillation for image classification,"https://scholar.google.com/scholar?cluster=5906934930671459567&hl=en&as_sdt=0,38",3,2020
A Metric Learning Reality Check,386,eccv,42,3,2023-06-17 00:19:32.669000,https://github.com/KevinMusgrave/powerful-benchmarker,416,A metric learning reality check,"https://scholar.google.com/scholar?cluster=10434889476564925352&hl=en&as_sdt=0,11",10,2020
XingGAN for Person Image Generation,125,eccv,34,3,2023-06-17 00:19:32.880000,https://github.com/Ha0Tang/XingGAN,226,Xinggan for person image generation,"https://scholar.google.com/scholar?cluster=13002514267953665372&hl=en&as_sdt=0,44",13,2020
GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering,62,eccv,3,1,2023-06-17 00:19:33.092000,https://github.com/niuchuangnn/GATCluster,18,Gatcluster: Self-supervised gaussian-attention network for image clustering,"https://scholar.google.com/scholar?cluster=13492882753218802467&hl=en&as_sdt=0,10",2,2020
VCNet: A Robust Approach to Blind Image Inpainting,51,eccv,6,6,2023-06-17 00:19:33.304000,https://github.com/shepnerd/blindinpainting_vcnet,47,Vcnet: A robust approach to blind image inpainting,"https://scholar.google.com/scholar?cluster=16048240966576030331&hl=en&as_sdt=0,14",8,2020
Embedding Propagation: Smoother Manifold for Few-Shot Classification,160,eccv,24,7,2023-06-17 00:19:33.516000,https://github.com/ElementAI/embedding-propagation,195,Embedding propagation: Smoother manifold for few-shot classification,"https://scholar.google.com/scholar?cluster=9641071672844005128&hl=en&as_sdt=0,44",11,2020
High-Fidelity Synthesis with Disentangled Representation,65,eccv,9,0,2023-06-17 00:19:33.727000,https://github.com/1Konny/idgan,28,High-fidelity synthesis with disentangled representation,"https://scholar.google.com/scholar?cluster=7229924402240233352&hl=en&as_sdt=0,33",5,2020
Learning Canonical Representations for Scene Graph to Image Generation,66,eccv,2,5,2023-06-17 00:19:33.940000,https://github.com/roeiherz/CanonicalSg2Im,24,Learning canonical representations for scene graph to image generation,"https://scholar.google.com/scholar?cluster=5597763055434982558&hl=en&as_sdt=0,21",4,2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability,56,eccv,4,1,2023-06-17 00:19:34.152000,https://github.com/M4xim4l/InNOutRobustness,13,Adversarial robustness on in-and out-distribution improves explainability,"https://scholar.google.com/scholar?cluster=2575047789396912819&hl=en&as_sdt=0,48",1,2020
Deformable Style Transfer,36,eccv,31,2,2023-06-17 00:19:34.364000,https://github.com/sunniesuhyoung/DST,255,Deformable style transfer,"https://scholar.google.com/scholar?cluster=5293823384444086307&hl=en&as_sdt=0,5",10,2020
Neural Wireframe Renderer: Learning Wireframe to Image Translations,2,eccv,0,1,2023-06-17 00:19:34.576000,https://github.com/YuanXue1993/WireframeRenderer,6,Neural wireframe renderer: Learning wireframe to image translations,"https://scholar.google.com/scholar?cluster=9515018151910735953&hl=en&as_sdt=0,10",2,2020
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax,11,eccv,7,2,2023-06-17 00:19:34.788000,https://github.com/2han9x1a0release/RBF-Softmax,37,RBF-Softmax: Learning deep representative prototypes with radial basis function softmax,"https://scholar.google.com/scholar?cluster=4371716610691344996&hl=en&as_sdt=0,5",2,2020
Instance Adaptive Self-Training for Unsupervised Domain Adaptation,182,eccv,4,0,2023-06-17 00:19:35.002000,https://github.com/bupt-ai-cz/IAST-ECCV2020,85,Instance adaptive self-training for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=932608527693049395&hl=en&as_sdt=0,5",3,2020
HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs,33,eccv,13,0,2023-06-17 00:19:35.215000,https://github.com/sony-si/ai-research,46,Hmq: Hardware friendly mixed precision quantization block for cnns,"https://scholar.google.com/scholar?cluster=8154175130422296943&hl=en&as_sdt=0,6",5,2020
Geometry Constrained Weakly Supervised Object Localization,56,eccv,4,1,2023-06-17 00:19:35.426000,https://github.com/lwzeng/GC-Net,26,Geometry constrained weakly supervised object localization,"https://scholar.google.com/scholar?cluster=9194441646409901384&hl=en&as_sdt=0,5",3,2020
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning,16,eccv,4,1,2023-06-17 00:19:35.638000,https://github.com/cvai-repo/duality-diagram-similarity,9,Duality diagram similarity: a generic framework for initialization selection in task transfer learning,"https://scholar.google.com/scholar?cluster=16585166869587738775&hl=en&as_sdt=0,39",4,2020
Mining self-similarity: Label super-resolution with epitomic representations,10,eccv,3,0,2023-06-17 00:19:35.850000,https://github.com/anthonymlortiz/epitomes_lsr,8,Mining self-similarity: Label super-resolution with epitomic representations,"https://scholar.google.com/scholar?cluster=2664410015517481753&hl=en&as_sdt=0,11",1,2020
Null-sampling for Interpretable and Fair Representations,21,eccv,0,8,2023-06-17 00:19:36.061000,https://github.com/predictive-analytics-lab/nifr,9,Null-sampling for interpretable and fair representations,"https://scholar.google.com/scholar?cluster=8383882563296788153&hl=en&as_sdt=0,5",2,2020
Guiding Monocular Depth Estimation Using Depth-Attention Volume,108,eccv,3,7,2023-06-17 00:19:36.274000,https://github.com/HuynhLam/DAV,35,Guiding monocular depth estimation using depth-attention volume,"https://scholar.google.com/scholar?cluster=14459496716654337844&hl=en&as_sdt=0,5",6,2020
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer,43,eccv,7,2,2023-06-17 00:19:36.486000,https://github.com/mikuhatsune/wsod_transfer,36,Boosting weakly supervised object detection with progressive knowledge transfer,"https://scholar.google.com/scholar?cluster=5930793902797730222&hl=en&as_sdt=0,10",1,2020
BézierSketch: A generative model for scalable vector sketches,34,eccv,7,4,2023-06-17 00:19:36.698000,https://github.com/dasayan05/stroke-ae,26,Béziersketch: A generative model for scalable vector sketches,"https://scholar.google.com/scholar?cluster=7735802383076334528&hl=en&as_sdt=0,5",3,2020
Domain Adaptation Through Task Distillation,12,eccv,4,7,2023-06-17 00:19:36.910000,https://github.com/bradyz/task-distillation,47,Domain adaptation through task distillation,"https://scholar.google.com/scholar?cluster=14208444845509300278&hl=en&as_sdt=0,25",4,2020
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning,68,eccv,7,1,2023-06-17 00:19:37.123000,https://github.com/Chenglin-Yang/PatchAttack,44,Patchattack: A black-box texture-based attack with reinforcement learning,"https://scholar.google.com/scholar?cluster=13442814540472236910&hl=en&as_sdt=0,5",6,2020
"More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning",47,eccv,3,0,2023-06-17 00:19:37.343000,https://github.com/Liuy8/MUC,10,"More classifiers, less forgetting: A generic multi-classifier paradigm for incremental learning","https://scholar.google.com/scholar?cluster=5010168576518209&hl=en&as_sdt=0,5",0,2020
Extending and Analyzing Self-Supervised Learning Across Domains,32,eccv,0,0,2023-06-17 00:19:37.565000,https://github.com/BramSW/Extending_SSRL_Across_Domains,9,Extending and analyzing self-supervised learning across domains,"https://scholar.google.com/scholar?cluster=3709483693030760570&hl=en&as_sdt=0,22",2,2020
LEMMA: A Multi-view Dataset for LEarning Multi-agent Multi-task Activities,25,eccv,0,0,2023-06-17 00:19:37.777000,https://github.com/Buzz-Beater/LEMMA,26,LEMMA: A Multi-view Dataset for LE arning M ulti-agent M ulti-task A ctivities,"https://scholar.google.com/scholar?cluster=11676801942809220755&hl=en&as_sdt=0,33",3,2020
Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From Images,9,eccv,1,1,2023-06-17 00:19:37.989000,https://github.com/matthewpurri/Teaching-Cameras-to-Feel,10,Teaching cameras to feel: Estimating tactile physical properties of surfaces from images,"https://scholar.google.com/scholar?cluster=9220316749417109631&hl=en&as_sdt=0,44",3,2020
Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification,108,eccv,6,2,2023-06-17 00:19:38.201000,https://github.com/djidje/D-MMD,24,Unsupervised domain adaptation in the dissimilarity space for person re-identification,"https://scholar.google.com/scholar?cluster=15028534376881973001&hl=en&as_sdt=0,5",2,2020
Learn distributed GAN with Temporary Discriminators,14,eccv,3,0,2023-06-17 00:19:38.413000,https://github.com/huiqu18/TDGAN-PyTorch,13,Learn distributed gan with temporary discriminators,"https://scholar.google.com/scholar?cluster=2068516580116127522&hl=en&as_sdt=0,47",2,2020
Improving Adversarial Robustness by Enforcing Local and Global Compactness,20,eccv,1,0,2023-06-17 00:19:38.624000,https://github.com/tuananhbui89/Adversarial-Divergence-Reduction,15,Improving adversarial robustness by enforcing local and global compactness,"https://scholar.google.com/scholar?cluster=4168951253624170834&hl=en&as_sdt=0,14",2,2020
Channel selection using Gumbel Softmax,18,eccv,2,1,2023-06-17 00:19:38.837000,https://github.com/irwinherrmann/stochastic-gates,14,Channel selection using gumbel softmax,"https://scholar.google.com/scholar?cluster=5413676769547873619&hl=en&as_sdt=0,5",3,2020
Structure-Aware Generation Network for Recipe Generation from Images,22,eccv,2,0,2023-06-17 00:19:39.048000,https://github.com/hwang1996/SGN,6,Structure-aware generation network for recipe generation from images,"https://scholar.google.com/scholar?cluster=871021465897283825&hl=en&as_sdt=0,14",1,2020
A Simple and Effective Framework for Pairwise Deep Metric Learning,14,eccv,2,0,2023-06-17 00:19:39.260000,https://github.com/qiqi-helloworld/A-Simple-and-Effective-Framework-for-Pairewise-Distance-Metric-Learning,15,A simple and effective framework for pairwise deep metric learning,"https://scholar.google.com/scholar?cluster=13095033614856237207&hl=en&as_sdt=0,33",5,2020
Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner,105,eccv,37,18,2023-06-17 00:19:39.472000,https://github.com/eugenelet/Meta-rPPG,152,Meta-rppg: Remote heart rate estimation using a transductive meta-learner,"https://scholar.google.com/scholar?cluster=7850359782762326007&hl=en&as_sdt=0,8",8,2020
A Recurrent Transformer Network for Novel View Action Synthesis,11,eccv,3,0,2023-06-17 00:19:39.684000,https://github.com/schatzkara/cross-view-video,5,A recurrent transformer network for novel view action synthesis,"https://scholar.google.com/scholar?cluster=15711295404970654559&hl=en&as_sdt=0,22",1,2020
Multi-view Action Recognition using Cross-view Video Prediction,41,eccv,2,1,2023-06-17 00:19:39.896000,https://github.com/svyas23/cross-view-action,10,Multi-view action recognition using cross-view video prediction,"https://scholar.google.com/scholar?cluster=11827356971279525643&hl=en&as_sdt=0,15",2,2020
Attributional Robustness Training using Input-Gradient Spatial Alignment,14,eccv,1,1,2023-06-17 00:19:40.108000,https://github.com/nupurkmr9/Attributional-Robustness,9,Attributional robustness training using input-gradient spatial alignment,"https://scholar.google.com/scholar?cluster=5894581805263046049&hl=en&as_sdt=0,5",2,2020
Learning Data Augmentation Strategies for Object Detection,460,eccv,1790,294,2023-06-17 00:19:40.320000,https://github.com/tensorflow/tpu,5128,Learning data augmentation strategies for object detection,"https://scholar.google.com/scholar?cluster=6812705747376678918&hl=en&as_sdt=0,5",368,2020
A Closer Look at Generalisation in RAVEN,27,eccv,2,1,2023-06-17 00:19:40.548000,https://github.com/SvenShade/Rel-AIR,9,A closer look at generalisation in raven,"https://scholar.google.com/scholar?cluster=13561133634232462860&hl=en&as_sdt=0,47",1,2020
Supervised Edge Attention Network for Accurate Image Instance Segmentation,23,eccv,3,3,2023-06-17 00:19:40.760000,https://github.com/IPIU-detection/SEANet,37,Supervised edge attention network for accurate image instance segmentation,"https://scholar.google.com/scholar?cluster=14091777638969346720&hl=en&as_sdt=0,6",1,2020
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model,0,eccv,2,0,2023-06-17 00:19:40.972000,https://github.com/johnjaniczek/InfraRender,9,Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model,"https://scholar.google.com/scholar?cluster=9730877713610471719&hl=en&as_sdt=0,5",2,2020
Fast Adaptation to Super-Resolution Networks via Meta-Learning,59,eccv,10,6,2023-06-17 00:19:41.185000,https://github.com/parkseobin/MLSR,62,Fast adaptation to super-resolution networks via meta-learning,"https://scholar.google.com/scholar?cluster=2341211698472321158&hl=en&as_sdt=0,5",2,2020
TP-LSD: Tri-Points Based Line Segment Detector,28,eccv,3,0,2023-06-17 00:19:41.398000,https://github.com/MegviiRobot/TP-LSD,15,TP-LSD: tri-points based line segment detector,"https://scholar.google.com/scholar?cluster=6400557832052770197&hl=en&as_sdt=0,5",1,2020
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation,256,eccv,44,20,2023-06-17 00:19:41.611000,https://github.com/chenfengxu714/SqueezeSegV3,204,Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation,"https://scholar.google.com/scholar?cluster=7152843580026291823&hl=en&as_sdt=0,5",14,2020
Toward Fine-grained Facial Expression Manipulation,21,eccv,6,2,2023-06-17 00:19:41.823000,https://github.com/junleen/Expression-manipulator,73,Toward fine-grained facial expression manipulation,"https://scholar.google.com/scholar?cluster=4315747798793734423&hl=en&as_sdt=0,44",3,2020
Table Structure Recognition using Top-Down and Bottom-Up Cues,58,eccv,30,16,2023-06-17 00:19:42.035000,https://github.com/sachinraja13/TabStructNet,120,Table structure recognition using top-down and bottom-up cues,"https://scholar.google.com/scholar?cluster=2722841457437928137&hl=en&as_sdt=0,5",7,2020
Novel View Synthesis on Unpaired Data by Conditional Deformable Variational Auto-Encoder,9,eccv,0,1,2023-06-17 00:19:42.247000,https://github.com/MingyuY/deformable-view-synthesis,8,Novel view synthesis on unpaired data by conditional deformable variational auto-encoder,"https://scholar.google.com/scholar?cluster=15129562547738444484&hl=en&as_sdt=0,33",4,2020
Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments,132,eccv,42,17,2023-06-17 00:19:42.460000,https://github.com/jacobkrantz/VLN-CE,156,Beyond the nav-graph: Vision-and-language navigation in continuous environments,"https://scholar.google.com/scholar?cluster=12356535904920559525&hl=en&as_sdt=0,21",10,2020
Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition,6,eccv,0,0,2023-06-17 00:19:42.672000,https://github.com/gyhandy/Pose-Augmentation,10,Pose augmentation: Class-agnostic object pose transformation for object recognition,"https://scholar.google.com/scholar?cluster=5169657450201203734&hl=en&as_sdt=0,33",2,2020
Improving Knowledge Distillation via Category Structure,9,eccv,4,1,2023-06-17 00:19:42.883000,https://github.com/xeanzheng/CSKD,10,Improving knowledge distillation via category structure,"https://scholar.google.com/scholar?cluster=16347836719030291096&hl=en&as_sdt=0,39",1,2020
Patch-wise Attack for Fooling Deep Neural Network,69,eccv,21,0,2023-06-17 00:19:43.095000,https://github.com/qilong-zhang/Patch-wise-iterative-attack,85,Patch-wise attack for fooling deep neural network,"https://scholar.google.com/scholar?cluster=16471811607657933067&hl=en&as_sdt=0,14",5,2020
Feature Pyramid Transformer,191,eccv,64,13,2023-06-17 00:19:43.307000,https://github.com/ZHANGDONG-NJUST/FPT,387,Feature pyramid transformer,"https://scholar.google.com/scholar?cluster=12312432945168097183&hl=en&as_sdt=0,5",13,2020
Guided Saliency Feature Learning for Person Re-identification in Crowded Scenes,83,eccv,780,13,2023-06-17 00:19:43.519000,https://github.com/JDAI-CV/fast-reid,2986,Guided saliency feature learning for person re-identification in crowded scenes,"https://scholar.google.com/scholar?cluster=2838799385744804703&hl=en&as_sdt=0,15",61,2020
Asymmetric Two-Stream Architecture for Accurate RGB-D Saliency Detection,92,eccv,1,0,2023-06-17 00:19:43.730000,https://github.com/OIPLab-DUT/ATSA,12,Asymmetric two-stream architecture for accurate RGB-D saliency detection,"https://scholar.google.com/scholar?cluster=5345318718218751578&hl=en&as_sdt=0,5",1,2020
Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature Adaptation,21,eccv,0,3,2023-06-17 00:19:43.942000,https://github.com/madhubabuv/NightDepthADFA,14,Unsupervised monocular depth estimation for night-time images using adversarial domain feature adaptation,"https://scholar.google.com/scholar?cluster=9606453005281318583&hl=en&as_sdt=0,5",8,2020
Deep Decomposition Learning for Inverse Imaging Problems,21,eccv,5,1,2023-06-17 00:19:44.154000,https://github.com/edongdongchen/DDN,20,Deep decomposition learning for inverse imaging problems,"https://scholar.google.com/scholar?cluster=17334961326198581936&hl=en&as_sdt=0,5",3,2020
FLOT: Scene Flow on Point Clouds guided by Optimal Transport,94,eccv,14,3,2023-06-17 00:19:44.366000,https://github.com/valeoai/FLOT,82,Flot: Scene flow on point clouds guided by optimal transport,"https://scholar.google.com/scholar?cluster=2677092422885067102&hl=en&as_sdt=0,44",5,2020
Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks,19,eccv,1,3,2023-06-17 00:19:44.578000,https://github.com/baldassarreFe/ws-vrd,16,Explanation-based weakly-supervised learning of visual relations with graph networks,"https://scholar.google.com/scholar?cluster=11480370248214628981&hl=en&as_sdt=0,43",5,2020
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution,335,eccv,104,4,2023-06-17 00:19:44.791000,https://github.com/mit-han-lab/spvnas,507,Searching efficient 3d architectures with sparse point-voxel convolution,"https://scholar.google.com/scholar?cluster=3595179925785709996&hl=en&as_sdt=0,5",26,2020
Online Continual Learning under Extreme Memory Constraints,47,eccv,0,1,2023-06-17 00:19:45.003000,https://github.com/DonkeyShot21/batch-level-distillation,14,Online continual learning under extreme memory constraints,"https://scholar.google.com/scholar?cluster=4749754622818449232&hl=en&as_sdt=0,7",3,2020
Learning to Cluster under Domain Shift,15,eccv,6,0,2023-06-17 00:19:45.215000,https://github.com/willi-menapace/acids-clustering-domain-shift,14,Learning to cluster under domain shift,"https://scholar.google.com/scholar?cluster=3979905123451703276&hl=en&as_sdt=0,33",2,2020
Learning to Learn Parameterized Classification Networks for Scalable Input Images,9,eccv,6,0,2023-06-17 00:19:45.427000,https://github.com/d-li14/SAN,42,Learning to learn parameterized classification networks for scalable input images,"https://scholar.google.com/scholar?cluster=13408591419950977044&hl=en&as_sdt=0,11",3,2020
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction,21,eccv,3,0,2023-06-17 00:19:45.644000,https://github.com/vccimaging/StereoEventPTV,5,Stereo event-based particle tracking velocimetry for 3d fluid flow reconstruction,"https://scholar.google.com/scholar?cluster=3513782980161247262&hl=en&as_sdt=0,36",2,2020
Simplicial Complex based Point Correspondence between Images warped onto Manifolds,0,eccv,2,0,2023-06-17 00:19:45.856000,https://github.com/charusharma1991/PointCorrespondence,3,Simplicial Complex Based Point Correspondence Between Images Warped onto Manifolds,"https://scholar.google.com/scholar?cluster=9937080153951248347&hl=en&as_sdt=0,5",2,2020
Where to Explore Next? ExHistCNN for History-aware Autonomous 3D Exploration,5,eccv,5,0,2023-06-17 00:19:46.068000,https://github.com/IIT-PAVIS/ExHistCNN,6,Where to explore next? ExHistCNN for history-aware autonomous 3D exploration,"https://scholar.google.com/scholar?cluster=5462601627695542953&hl=en&as_sdt=0,3",5,2020
Recurrent Image Annotation With Explicit Inter-Label Dependencies,7,eccv,2,0,2023-06-17 00:19:46.280000,https://github.com/ayushidutta/multi-order-rnn,3,Recurrent image annotation with explicit inter-label dependencies,"https://scholar.google.com/scholar?cluster=2522881965658875611&hl=en&as_sdt=0,44",2,2020
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution,80,eccv,13,2,2023-06-17 00:19:46.502000,https://github.com/danfenghong/ECCV2020_CUCaNet,74,Cross-attention in coupled unmixing nets for unsupervised hyperspectral super-resolution,"https://scholar.google.com/scholar?cluster=12534434538120882272&hl=en&as_sdt=0,23",5,2020
Jointly De-biasing Face Recognition and Demographic Attribute Estimation,89,eccv,6,6,2023-06-17 00:19:46.714000,https://github.com/gongsixue/DebFace,29,Jointly de-biasing face recognition and demographic attribute estimation,"https://scholar.google.com/scholar?cluster=4426968694206713308&hl=en&as_sdt=0,5",1,2020
Regularized Loss for Weakly Supervised Single Class Semantic Segmentation,11,eccv,3,1,2023-06-17 00:19:46.926000,https://github.com/morduspordus/SingleClassRL,16,Regularized loss for weakly supervised single class semantic segmentation,"https://scholar.google.com/scholar?cluster=3634752187915458670&hl=en&as_sdt=0,10",1,2020
Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks,94,eccv,16,4,2023-06-17 00:19:47.138000,https://github.com/chan8972/Spike-FlowNet,75,Spike-flownet: event-based optical flow estimation with energy-efficient hybrid neural networks,"https://scholar.google.com/scholar?cluster=3835815807587343920&hl=en&as_sdt=0,31",4,2020
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations,51,eccv,6,4,2023-06-17 00:19:47.351000,https://github.com/ssharmin/spikingNN-adversarial-attack,11,Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations,"https://scholar.google.com/scholar?cluster=6114430974328967869&hl=en&as_sdt=0,33",1,2020
Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks,53,eccv,37,2,2023-06-17 00:19:47.564000,https://github.com/barisgecer/TBGAN,235,Synthesizing coupled 3d face modalities by trunk-branch generative adversarial networks,"https://scholar.google.com/scholar?cluster=4328029072096013677&hl=en&as_sdt=0,6",22,2020
On Transferability of Histological Tissue Labels in Computational Pathology,7,eccv,2,0,2023-06-17 00:19:47.778000,https://github.com/mahdihosseini/HistoLabelTransfer,6,On transferability of histological tissue labels in computational pathology,"https://scholar.google.com/scholar?cluster=16846387597639543570&hl=en&as_sdt=0,33",1,2020
Laying the Foundations of Deep Long-Term Crowd Flow Prediction,21,eccv,4,0,2023-06-17 00:19:47.991000,https://github.com/SSSohn/LTCF,3,Laying the foundations of deep long-term crowd flow prediction,"https://scholar.google.com/scholar?cluster=5039555755354429934&hl=en&as_sdt=0,14",1,2020
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning,88,eccv,1,2,2023-06-17 00:19:48.203000,https://github.com/airmachine/EM-MIL-WeaklyActionDetection,3,Weakly-supervised action localization with expectation-maximization multi-instance learning,"https://scholar.google.com/scholar?cluster=1132955599066578844&hl=en&as_sdt=0,33",1,2020
Self-supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation,119,eccv,43,12,2023-06-17 00:19:48.415000,https://github.com/cheng-01037/Self-supervised-Fewshot-Medical-Image-Segmentation,278,Self-supervision with superpixels: Training few-shot medical image segmentation without annotation,"https://scholar.google.com/scholar?cluster=11174300114042414673&hl=en&as_sdt=0,23",4,2020
Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery,14,eccv,8,1,2023-06-17 00:19:48.627000,https://github.com/raziehkaviani/rdosr,27,Representative-discriminative learning for open-set land cover classification of satellite imagery,"https://scholar.google.com/scholar?cluster=4804415838454734672&hl=en&as_sdt=0,5",2,2020
Structure-Aware Human-Action Generation,25,eccv,10,1,2023-06-17 00:19:48.839000,https://github.com/PingYu-iris/SA-GCN,36,Structure-aware human-action generation,"https://scholar.google.com/scholar?cluster=3183660845039422264&hl=en&as_sdt=0,5",4,2020
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning,100,eccv,6,0,2023-06-17 00:19:49.051000,https://github.com/Maunil/LsrGAN,15,Leveraging seen and unseen semantic relationships for generative zero-shot learning,"https://scholar.google.com/scholar?cluster=590326590210496504&hl=en&as_sdt=0,33",2,2020
UNITER: UNiversal Image-TExt Representation Learning,1247,eccv,106,45,2023-06-17 00:19:49.263000,https://github.com/ChenRocks/UNITER,727,Uniter: Universal image-text representation learning,"https://scholar.google.com/scholar?cluster=3224460637705754187&hl=en&as_sdt=0,5",17,2020
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks,1220,eccv,244,137,2023-06-17 00:19:49.474000,https://github.com/microsoft/Oscar,977,Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks,"https://scholar.google.com/scholar?cluster=11463347095757497265&hl=en&as_sdt=0,36",25,2020
Improving Face Recognition from Hard Samples via Distribution Distillation Loss,45,eccv,6,1,2023-06-17 00:19:49.686000,https://github.com/HuangYG123/DDL,46,Improving face recognition from hard samples via distribution distillation loss,"https://scholar.google.com/scholar?cluster=7651826713991491344&hl=en&as_sdt=0,43",10,2020
"Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision",146,eccv,11,16,2023-06-17 00:19:49.898000,https://github.com/Roc-Ng/XDVioDet,73,"Not only look, but also listen: Learning multimodal violence detection under weak supervision","https://scholar.google.com/scholar?cluster=10605878118579816072&hl=en&as_sdt=0,5",3,2020
SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection,109,eccv,68,19,2023-06-17 00:19:50.109000,https://github.com/hlwang1124/SNE-RoadSeg,258,Sne-roadseg: Incorporating surface normal information into semantic segmentation for accurate freespace detection,"https://scholar.google.com/scholar?cluster=14160509110411448410&hl=en&as_sdt=0,10",8,2020
PieNet: Personalized Image Enhancement Network,34,eccv,4,6,2023-06-17 00:19:50.321000,https://github.com/hukim1124/PieNet,16,PieNet: Personalized image enhancement network,"https://scholar.google.com/scholar?cluster=6964101604729902042&hl=en&as_sdt=0,11",4,2020
Solving Phase Retrieval with a Learned Reference,17,eccv,2,1,2023-06-17 00:19:50.548000,https://github.com/CSIPlab/learnPR_reference,5,Solving phase retrieval with a learned reference,"https://scholar.google.com/scholar?cluster=14938908551545200917&hl=en&as_sdt=0,5",2,2020
Learning Depth from Focus in the Wild,1,eccv,3,1,2023-06-17 00:57:18.782000,https://github.com/wcy199705/dffinthewild,22,Learning Depth from Focus in the Wild,"https://scholar.google.com/scholar?cluster=2277662841844363477&hl=en&as_sdt=0,18",1,2022
DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection,10,eccv,9,2,2023-06-17 00:57:18.984000,https://github.com/spengliang/did-m3d,60,DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection,"https://scholar.google.com/scholar?cluster=10077939919896091502&hl=en&as_sdt=0,44",2,2022
Lidar Point Cloud Guided Monocular 3D Object Detection,18,eccv,2,7,2023-06-17 00:57:19.185000,https://github.com/spengliang/lpcg,57,Lidar point cloud guided monocular 3d object detection,"https://scholar.google.com/scholar?cluster=6505179244854514534&hl=en&as_sdt=0,5",4,2022
AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction,10,eccv,5,4,2023-06-17 00:57:19.386000,https://github.com/zerchen/AlignSDF,58,AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction,"https://scholar.google.com/scholar?cluster=10747837529898160333&hl=en&as_sdt=0,10",3,2022
A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation,2,eccv,1,0,2023-06-17 00:57:19.586000,https://github.com/elderlab-york-university/onlinefr,11,A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation,"https://scholar.google.com/scholar?cluster=9688991035688790486&hl=en&as_sdt=0,47",3,2022
Share with Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency,11,eccv,13,1,2023-06-17 00:57:19.787000,https://github.com/monniert/unicorn,138,Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency,"https://scholar.google.com/scholar?cluster=905858586646092780&hl=en&as_sdt=0,14",7,2022
AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture,9,eccv,20,0,2023-06-17 00:57:19.988000,https://github.com/lizhe00/avatarcap,160,AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture,"https://scholar.google.com/scholar?cluster=8959785289577170097&hl=en&as_sdt=0,36",16,2022
Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers,20,eccv,10,1,2023-06-17 00:57:20.189000,https://github.com/postech-ami/fastmetro,115,Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers,"https://scholar.google.com/scholar?cluster=730312962073430238&hl=en&as_sdt=0,5",7,2022
Learning Visibility for Robust Dense Human Body Estimation,2,eccv,2,2,2023-06-17 00:57:20.389000,https://github.com/chhankyao/visdb,33,Learning Visibility for Robust Dense Human Body Estimation,"https://scholar.google.com/scholar?cluster=18171136255429603389&hl=en&as_sdt=0,5",5,2022
Towards High-Fidelity Single-View Holistic Reconstruction of Indoor Scenes,4,eccv,5,10,2023-06-17 00:57:20.591000,https://github.com/unclemedm/instpifu,71,Towards high-fidelity single-view holistic reconstruction of indoor scenes,"https://scholar.google.com/scholar?cluster=14506116907832668928&hl=en&as_sdt=0,33",7,2022
SketchSampler: Sketch-Based 3D Reconstruction via View-Dependent Depth Sampling,2,eccv,1,0,2023-06-17 00:57:20.792000,https://github.com/cjeen/sketchsampler,7,SketchSampler: Sketch-Based 3D Reconstruction via View-Dependent Depth Sampling,"https://scholar.google.com/scholar?cluster=18018535923045905073&hl=en&as_sdt=0,31",2,2022
LocalBins: Improving Depth Estimation by Learning Local Distributions,10,eccv,0,3,2023-06-17 00:57:20.993000,https://github.com/shariqfarooq123/localbins,13,LocalBins: Improving Depth Estimation by Learning Local Distributions,"https://scholar.google.com/scholar?cluster=14145433689777855528&hl=en&as_sdt=0,5",3,2022
Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors,5,eccv,0,0,2023-06-17 00:57:21.194000,https://github.com/chenhsing/ssp3d,7,Semi-supervised Single-View 3D Reconstruction via Prototype Shape Priors,"https://scholar.google.com/scholar?cluster=6186898633030921243&hl=en&as_sdt=0,41",1,2022
SC-wLS: Towards Interpretable Feed-Forward Camera Re-localization,5,eccv,1,1,2023-06-17 00:57:21.395000,https://github.com/xinwu98/sc-wls,9,Sc-wls: Towards interpretable feed-forward camera re-localization,"https://scholar.google.com/scholar?cluster=6921042683401788717&hl=en&as_sdt=0,25",2,2022
3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform,7,eccv,0,2,2023-06-17 00:57:21.597000,https://github.com/starrah/dmh-net,22,3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform,"https://scholar.google.com/scholar?cluster=11975581524241113269&hl=en&as_sdt=0,44",2,2022
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation,6,eccv,5,3,2023-06-17 00:57:21.797000,https://github.com/lolrudy/rbp_pose,17,Rbp-pose: Residual bounding box projection for category-level pose estimation,"https://scholar.google.com/scholar?cluster=12126305095965129873&hl=en&as_sdt=0,5",1,2022
Monocular 3D Object Reconstruction with GAN Inversion,5,eccv,4,1,2023-06-17 00:57:21.998000,https://github.com/junzhezhang/mesh-inversion,49,Monocular 3d object reconstruction with gan inversion,"https://scholar.google.com/scholar?cluster=9645145060229732560&hl=en&as_sdt=0,5",4,2022
Map-Free Visual Relocalization: Metric Pose Relative to a Single Image,6,eccv,10,1,2023-06-17 00:57:22.199000,https://github.com/nianticlabs/map-free-reloc,138,Map-Free Visual Relocalization: Metric Pose Relative to a Single Image,"https://scholar.google.com/scholar?cluster=17987996179618612140&hl=en&as_sdt=0,10",11,2022
MHR-Net: Multiple-Hypothesis Reconstruction of Non-rigid Shapes from 2D Views,0,eccv,0,0,2023-06-17 00:57:22.400000,https://github.com/haitianzeng/MHR-Net,2,MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views,"https://scholar.google.com/scholar?cluster=6068809755658094010&hl=en&as_sdt=0,5",1,2022
Depth Map Decomposition for Monocular Depth Estimation,2,eccv,1,1,2023-06-17 00:57:22.601000,https://github.com/jyjunmcl/Depth-Map-Decomposition,7,Depth Map Decomposition for Monocular Depth Estimation,"https://scholar.google.com/scholar?cluster=11990866349503333178&hl=en&as_sdt=0,5",1,2022
Monitored Distillation for Positive Congruent Depth Completion,7,eccv,1,2,2023-06-17 00:57:22.802000,https://github.com/alexklwong/mondi-python,23,Monitored distillation for positive congruent depth completion,"https://scholar.google.com/scholar?cluster=8699066993130708738&hl=en&as_sdt=0,3",3,2022
Perspective Flow Aggregation for Data-Limited 6D Object Pose Estimation,8,eccv,0,1,2023-06-17 00:57:23.002000,https://github.com/cvlab-epfl/perspective-flow-aggregation,20,Perspective flow aggregation for data-limited 6d object pose estimation,"https://scholar.google.com/scholar?cluster=12417557895347708194&hl=en&as_sdt=0,33",5,2022
Self-Calibrating Photometric Stereo by Neural Inverse Rendering,8,eccv,0,0,2023-06-17 00:57:23.203000,https://github.com/junxuan-li/scps-nir,24,Self-calibrating photometric stereo by neural inverse rendering,"https://scholar.google.com/scholar?cluster=10952845318610614207&hl=en&as_sdt=0,5",1,2022
3D Clothed Human Reconstruction in the Wild,12,eccv,11,3,2023-06-17 00:57:23.404000,https://github.com/hygenie1228/clothwild_release,150,3D clothed human reconstruction in the wild,"https://scholar.google.com/scholar?cluster=6884695873536132760&hl=en&as_sdt=0,41",10,2022
3D Siamese Transformer Network for Single Object Tracking on Point Clouds,14,eccv,2,2,2023-06-17 00:57:23.607000,https://github.com/fpthink/stnet,23,3d siamese transformer network for single object tracking on point clouds,"https://scholar.google.com/scholar?cluster=5875426719120511667&hl=en&as_sdt=0,11",3,2022
IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-View Human Reconstruction,6,eccv,1,10,2023-06-17 00:57:23.808000,https://github.com/kcyt/integratedpifu,41,Integratedpifu: Integrated pixel aligned implicit function for single-view human reconstruction,"https://scholar.google.com/scholar?cluster=3324784253364642761&hl=en&as_sdt=0,5",4,2022
Perspective Phase Angle Model for Polarimetric 3D Reconstruction,3,eccv,1,0,2023-06-17 00:57:24.011000,https://github.com/gcchen97/ppa4p3d,10,Perspective Phase Angle Model for Polarimetric 3D Reconstruction,"https://scholar.google.com/scholar?cluster=13045067088047510071&hl=en&as_sdt=0,5",2,2022
DeepShadow: Neural Shape from Shadow,4,eccv,3,1,2023-06-17 00:57:24.211000,https://github.com/asafkar/deep_shadow,8,Deepshadow: Neural shape from shadow,"https://scholar.google.com/scholar?cluster=12388411260509479903&hl=en&as_sdt=0,10",2,2022
Minimal Neural Atlas: Parameterizing Complex Surfaces with Minimal Charts and Distortion,0,eccv,0,0,2023-06-17 00:57:24.413000,https://github.com/low5545/minimal-neural-atlas,19,Minimal Neural Atlas: Parameterizing Complex Surfaces with Minimal Charts and Distortion,"https://scholar.google.com/scholar?cluster=13327805141771536693&hl=en&as_sdt=0,5",2,2022
ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing,3,eccv,2,1,2023-06-17 00:57:24.614000,https://github.com/kimren227/extrudenet,18,ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing,"https://scholar.google.com/scholar?cluster=11236014877503461544&hl=en&as_sdt=0,5",2,2022
CATRE: Iterative Point Clouds Alignment for Category-Level Object Pose Refinement,5,eccv,2,3,2023-06-17 00:57:24.816000,https://github.com/thu-da-6d-pose-group/catre,28,CATRE: Iterative Point Clouds Alignment for Category-Level Object Pose Refinement,"https://scholar.google.com/scholar?cluster=17564435273715634496&hl=en&as_sdt=0,19",3,2022
SUPR: A Sparse Unified Part-Based Human Representation,5,eccv,9,2,2023-06-17 00:57:25.019000,https://github.com/ahmedosman/SUPR,136,SUPR: A Sparse Unified Part-Based Human Representation,"https://scholar.google.com/scholar?cluster=8886858805057775138&hl=en&as_sdt=0,5",12,2022
Masked Autoencoders for Point Cloud Self-Supervised Learning,106,eccv,42,2,2023-06-17 00:57:25.224000,https://github.com/Pang-Yatian/Point-MAE,278,Masked autoencoders for point cloud self-supervised learning,"https://scholar.google.com/scholar?cluster=1930827783125608869&hl=en&as_sdt=0,5",4,2022
Intrinsic Neural Fields: Learning Functions on Manifolds,5,eccv,4,0,2023-06-17 00:57:25.428000,https://github.com/tum-vision/intrinsic-neural-fields,52,Intrinsic neural fields: Learning functions on manifolds,"https://scholar.google.com/scholar?cluster=2662864297016084318&hl=en&as_sdt=0,33",20,2022
Masked Discrimination for Self-Supervised Learning on Point Clouds,47,eccv,4,5,2023-06-17 00:57:25.630000,https://github.com/haotian-liu/maskpoint,71,Masked discrimination for self-supervised learning on point clouds,"https://scholar.google.com/scholar?cluster=12182702384297284737&hl=en&as_sdt=0,5",8,2022
FBNet: Feedback Network for Point Cloud Completion,7,eccv,4,1,2023-06-17 00:57:25.831000,https://github.com/hikvision-research/3dvision,28,Fbnet: Feedback network for point cloud completion,"https://scholar.google.com/scholar?cluster=15376589595507047229&hl=en&as_sdt=0,5",2,2022
Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds,3,eccv,1,2,2023-06-17 00:57:26.036000,https://github.com/ttchengab/MetaSampler,13,Meta-sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds,"https://scholar.google.com/scholar?cluster=9621087449893968006&hl=en&as_sdt=0,30",4,2022
Unsupervised Deep Multi-Shape Matching,5,eccv,1,0,2023-06-17 00:57:26.249000,https://github.com/dongliangcao/Unsupervised-Deep-Multi-Shape-Matching,12,Unsupervised deep multi-shape matching,"https://scholar.google.com/scholar?cluster=2258105310484871583&hl=en&as_sdt=0,5",1,2022
Autoregressive 3D Shape Generation via Canonical Mapping,8,eccv,0,2,2023-06-17 00:57:26.460000,https://github.com/AnjieCheng/CanonicalVAE,23,Autoregressive 3d shape generation via canonical mapping,"https://scholar.google.com/scholar?cluster=10300347603534381674&hl=en&as_sdt=0,31",2,2022
PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees,1,eccv,1,0,2023-06-17 00:57:26.663000,https://github.com/immortalco/pointtree,8,PointTree: Transformation-Robust Point Cloud Encoder with Relaxed KD Trees,"https://scholar.google.com/scholar?cluster=4268756852138321840&hl=en&as_sdt=0,29",1,2022
UNIF: United Neural Implicit Functions for Clothed Human Reconstruction and Animation,3,eccv,3,1,2023-06-17 00:57:26.864000,https://github.com/ShenhanQian/UNIF,66,UNIF: United Neural Implicit Functions for Clothed Human Reconstruction and Animation,"https://scholar.google.com/scholar?cluster=2142114971680111949&hl=en&as_sdt=0,5",7,2022
CLIP-Actor: Text-Driven Recommendation and Stylization for Animating Human Meshes,9,eccv,5,0,2023-06-17 00:57:27.089000,https://github.com/postech-ami/CLIP-Actor,82,Clip-actor: Text-driven recommendation and stylization for animating human meshes,"https://scholar.google.com/scholar?cluster=10210526328486613159&hl=en&as_sdt=0,33",7,2022
PlaneFormers: From Sparse View Planes to 3D Reconstruction,4,eccv,3,0,2023-06-17 00:57:27.289000,https://github.com/samiragarwala/PlaneFormers,73,Planeformers: From sparse view planes to 3d reconstruction,"https://scholar.google.com/scholar?cluster=2688342752520377982&hl=en&as_sdt=0,18",5,2022
Learning Implicit Templates for Point-Based Clothed Human Modeling,13,eccv,3,0,2023-06-17 00:57:27.490000,https://github.com/jsnln/fite,67,Learning implicit templates for point-based clothed human modeling,"https://scholar.google.com/scholar?cluster=17460087153194567520&hl=en&as_sdt=0,5",7,2022
Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks,6,eccv,2,1,2023-06-17 00:57:27.691000,https://github.com/woodwindhu/gsda,5,Exploring the devil in graph spectral domain for 3d point cloud attacks,"https://scholar.google.com/scholar?cluster=9921547500919664228&hl=en&as_sdt=0,21",2,2022
Structure-Aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation,1,eccv,11,0,2023-06-17 00:57:27.932000,https://github.com/gerwang/facial-detail-manipulation,67,Structure-Aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation,"https://scholar.google.com/scholar?cluster=1298202785476030620&hl=en&as_sdt=0,5",4,2022
MoFaNeRF: Morphable Facial Neural Radiance Field,30,eccv,11,3,2023-06-17 00:57:28.140000,https://github.com/zhuhao-nju/mofanerf,183,Mofanerf: Morphable facial neural radiance field,"https://scholar.google.com/scholar?cluster=11013901120864523250&hl=en&as_sdt=0,14",16,2022
Latent Partition Implicit with Surface Codes for 3D Representation,6,eccv,2,1,2023-06-17 00:57:28.341000,https://github.com/chenchao15/lpi,37,Latent partition implicit with surface codes for 3D representation,"https://scholar.google.com/scholar?cluster=7402497927008564242&hl=en&as_sdt=0,5",3,2022
Implicit Field Supervision for Robust Non-rigid Shape Matching,7,eccv,3,2,2023-06-17 00:57:28.542000,https://github.com/Sentient07/IFMatch,18,Implicit field supervision for robust non-rigid shape matching,"https://scholar.google.com/scholar?cluster=3506066397456846278&hl=en&as_sdt=0,5",4,2022
Learning Self-Prior for Mesh Denoising Using Dual Graph Convolutional Networks,1,eccv,2,1,2023-06-17 00:57:28.742000,https://github.com/astaka-pe/Dual-DMP,19,Learning Self-prior for Mesh Denoising Using Dual Graph Convolutional Networks,"https://scholar.google.com/scholar?cluster=9504083230858882624&hl=en&as_sdt=0,5",2,2022
diffConv: Analyzing Irregular Point Clouds with an Irregular View,6,eccv,3,3,2023-06-17 00:57:28.944000,https://github.com/mmmmimic/diffconvnet,21,DiffConv: Analyzing irregular point clouds with an irregular view,"https://scholar.google.com/scholar?cluster=393848593776804929&hl=en&as_sdt=0,5",2,2022
PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows,4,eccv,1,2,2023-06-17 00:57:29.145000,https://github.com/unknownue/pdflow,9,PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows,"https://scholar.google.com/scholar?cluster=15885388352736608557&hl=en&as_sdt=0,5",1,2022
SeedFormer: Patch Seeds Based Point Cloud Completion with Upsample Transformer,15,eccv,5,6,2023-06-17 00:57:29.346000,https://github.com/hrzhou2/seedformer,33,Seedformer: Patch seeds based point cloud completion with upsample transformer,"https://scholar.google.com/scholar?cluster=7575370354960015148&hl=en&as_sdt=0,10",1,2022
DeepMend: Learning Occupancy Functions to Represent Shape for Repair,3,eccv,1,0,2023-06-17 00:57:29.547000,https://github.com/terascale-all-sensing-research-studio/deepmend,1,DeepMend: Learning Occupancy Functions to Represent Shape for Repair,"https://scholar.google.com/scholar?cluster=1042731795533077804&hl=en&as_sdt=0,23",1,2022
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation,1,eccv,2,2,2023-06-17 00:57:29.748000,https://github.com/gitbosun/patchrd,35,PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation,"https://scholar.google.com/scholar?cluster=17474715912278904592&hl=en&as_sdt=0,10",1,2022
3D Shape Sequence of Human Comparison and Classification Using Current and Varifolds,0,eccv,0,0,2023-06-17 00:57:29.948000,https://github.com/cristal-3dsam/humancomparisonvarifolds,2,3D Shape Sequence of Human Comparison and Classification Using Current and Varifolds,"https://scholar.google.com/scholar?cluster=8763719126845192005&hl=en&as_sdt=0,11",2,2022
Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification,6,eccv,0,1,2023-06-17 00:57:30.150000,https://github.com/poetrywanderer/CF-NeRF,19,Conditional-flow nerf: Accurate 3d modelling with reliable uncertainty quantification,"https://scholar.google.com/scholar?cluster=1148213789849009262&hl=en&as_sdt=0,36",1,2022
MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks,17,eccv,5,1,2023-06-17 00:57:30.355000,https://github.com/cvlab-epfl/MeshUDF,83,Meshudf: Fast and differentiable meshing of unsigned distance field networks,"https://scholar.google.com/scholar?cluster=16628800077487592270&hl=en&as_sdt=0,44",6,2022
SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement,4,eccv,1,0,2023-06-17 00:57:30.556000,https://github.com/zhaofanqiu/spe-net,9,SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement,"https://scholar.google.com/scholar?cluster=13702422451721758488&hl=en&as_sdt=0,5",2,2022
Spatiotemporal Self-Attention Modeling with Temporal Patch Shift for Action Recognition,2,eccv,4,1,2023-06-17 00:57:30.756000,https://github.com/martinxm/tps,31,Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition,"https://scholar.google.com/scholar?cluster=36515183332580910&hl=en&as_sdt=0,10",2,2022
Proposal-Free Temporal Action Detection via Global Segmentation Mask Learning,12,eccv,1,0,2023-06-17 00:57:30.957000,https://github.com/sauradip/tags,15,Proposal-free temporal action detection via global segmentation mask learning,"https://scholar.google.com/scholar?cluster=4536094047690638464&hl=en&as_sdt=0,50",2,2022
Semi-Supervised Temporal Action Detection with Proposal-Free Masking,9,eccv,0,3,2023-06-17 00:57:31.158000,https://github.com/sauradip/spot,16,Semi-supervised temporal action detection with proposal-free masking,"https://scholar.google.com/scholar?cluster=15456971806325450236&hl=en&as_sdt=0,5",2,2022
Zero-Shot Temporal Action Detection via Vision-Language Prompting,16,eccv,3,2,2023-06-17 00:57:31.359000,https://github.com/sauradip/stale,59,Zero-shot temporal action detection via vision-language prompting,"https://scholar.google.com/scholar?cluster=527701684105539342&hl=en&as_sdt=0,5",3,2022
CMD: Self-Supervised 3D Action Representation Learning with Cross-Modal Mutual Distillation,6,eccv,2,0,2023-06-17 00:57:31.560000,https://github.com/maoyunyao/cmd,27,CMD: Self-supervised 3D Action Representation Learning with Cross-Modal Mutual Distillation,"https://scholar.google.com/scholar?cluster=11605602306928244319&hl=en&as_sdt=0,5",2,2022
Target-Absent Human Attention,3,eccv,0,0,2023-06-17 00:57:31.760000,https://github.com/cvlab-stonybrook/target-absent-human-attention,11,Characterizing target-absent human attention,"https://scholar.google.com/scholar?cluster=10071954187705138925&hl=en&as_sdt=0,50",4,2022
Rethinking Zero-Shot Action Recognition: Learning from Latent Atomic Actions,2,eccv,0,0,2023-06-17 00:57:31.973000,https://github.com/KevinQian97/JigsawNet,3,Rethinking Zero-shot Action Recognition: Learning from Latent Atomic Actions,"https://scholar.google.com/scholar?cluster=2220139708304842514&hl=en&as_sdt=0,5",2,2022
Mining Cross-Person Cues for Body-Part Interactiveness Learning in HOI Detection,12,eccv,3,2,2023-06-17 00:57:32.173000,https://github.com/enlighten0707/body-part-map-for-interactiveness,29,Mining cross-person cues for body-part interactiveness learning in hoi detection,"https://scholar.google.com/scholar?cluster=10497932793088743320&hl=en&as_sdt=0,5",3,2022
Collaborating Domain-Shared and Target-Specific Feature Clustering for Cross-Domain 3D Action Recognition,1,eccv,1,1,2023-06-17 00:57:32.374000,https://github.com/canbaoburen/CoDT,13,Collaborating Domain-Shared and Target-Specific Feature Clustering for Cross-domain 3D Action Recognition,"https://scholar.google.com/scholar?cluster=10225688823418071762&hl=en&as_sdt=0,11",1,2022
Global-Local Motion Transformer for Unsupervised Skeleton-Based Action Learning,9,eccv,0,1,2023-06-17 00:57:32.578000,https://github.com/boeun-kim/gl-transformer,13,Global-local motion transformer for unsupervised skeleton-based action learning,"https://scholar.google.com/scholar?cluster=17774826768245810824&hl=en&as_sdt=0,5",3,2022
Panoramic Human Activity Recognition,5,eccv,2,4,2023-06-17 00:57:32.779000,https://github.com/ruizehan/par,7,Panoramic Human Activity Recognition,"https://scholar.google.com/scholar?cluster=11423361027178413669&hl=en&as_sdt=0,34",1,2022
Continual 3D Convolutional Neural Networks for Real-Time Processing of Videos,8,eccv,4,1,2023-06-17 00:57:32.979000,https://github.com/LukasHedegaard/co3d,34,Continual 3d convolutional neural networks for real-time processing of videos,"https://scholar.google.com/scholar?cluster=13799294401691207740&hl=en&as_sdt=0,33",4,2022
Geometric Features Informed Multi-Person Human-Object Interaction Recognition in Videos,1,eccv,3,4,2023-06-17 00:57:33.180000,https://github.com/tanqiu98/2g-gcn,16,Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos,"https://scholar.google.com/scholar?cluster=2114065171244515340&hl=en&as_sdt=0,14",2,2022
ActionFormer: Localizing Moments of Actions with Transformers,84,eccv,52,5,2023-06-17 00:57:33.382000,https://github.com/happyharrycn/actionformer_release,277,Actionformer: Localizing moments of actions with transformers,"https://scholar.google.com/scholar?cluster=7487171131395966182&hl=en&as_sdt=0,5",10,2022
SocialVAE: Human Trajectory Prediction Using Timewise Latents,5,eccv,7,2,2023-06-17 00:57:33.583000,https://github.com/xupei0610/socialvae,31,SocialVAE: Human Trajectory Prediction Using Timewise Latents,"https://scholar.google.com/scholar?cluster=4896268270487715536&hl=en&as_sdt=0,34",4,2022
Frequency Domain Model Augmentation for Adversarial Attack,14,eccv,7,6,2023-06-17 00:57:33.785000,https://github.com/yuyang-long/ssa,53,Frequency domain model augmentation for adversarial attack,"https://scholar.google.com/scholar?cluster=12898376323990903198&hl=en&as_sdt=0,43",1,2022
Prior-Guided Adversarial Initialization for Fast Adversarial Training,6,eccv,0,0,2023-06-17 00:57:33.985000,https://github.com/jiaxiaojunqaq/fgsm-pgi,13,Prior-Guided Adversarial Initialization for Fast Adversarial Training,"https://scholar.google.com/scholar?cluster=8105589211471914161&hl=en&as_sdt=0,39",1,2022
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity,5,eccv,0,0,2023-06-17 00:57:34.187000,https://github.com/framartin/lgv-geometric-transferability,10,Lgv: Boosting adversarial example transferability from large geometric vicinity,"https://scholar.google.com/scholar?cluster=14789124225346899125&hl=en&as_sdt=0,34",2,2022
RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN,2,eccv,0,0,2023-06-17 00:57:34.387000,https://github.com/huyvnphan/eccv2022-ribac,6,RIBAC: Towards R obust and I mperceptible B ackdoor A ttack against C ompact DNN,"https://scholar.google.com/scholar?cluster=11495724105477568975&hl=en&as_sdt=0,6",1,2022
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks,9,eccv,2,0,2023-06-17 00:57:34.588000,https://github.com/shawnxyang/c-gsp,8,Boosting transferability of targeted adversarial examples via hierarchical generative networks,"https://scholar.google.com/scholar?cluster=10602808788122937052&hl=en&as_sdt=0,34",1,2022
Adaptive Image Transformations for Transfer-Based Adversarial Attack,3,eccv,0,0,2023-06-17 00:57:34.789000,https://github.com/huitailangyz/AITL,7,Adaptive image transformations for transfer-based adversarial attack,"https://scholar.google.com/scholar?cluster=15908074203089075638&hl=en&as_sdt=0,21",2,2022
Generative Multiplane Images: Making a 2D GAN 3D-Aware,37,eccv,32,0,2023-06-17 00:57:34.989000,https://github.com/apple/ml-gmpi,314,Generative multiplane images: Making a 2d gan 3d-aware,"https://scholar.google.com/scholar?cluster=18092775067843669717&hl=en&as_sdt=0,10",19,2022
Adversarial Contrastive Learning via Asymmetric InfoNCE,5,eccv,2,1,2023-06-17 00:57:35.191000,https://github.com/yqy2001/a-infonce,21,Adversarial Contrastive Learning via Asymmetric InfoNCE,"https://scholar.google.com/scholar?cluster=628233938244691050&hl=en&as_sdt=0,5",1,2022
Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips,7,eccv,0,0,2023-06-17 00:57:35.392000,https://github.com/jiawangbai/hpt,5,Hardly perceptible trojan attack against neural networks with bit flips,"https://scholar.google.com/scholar?cluster=18095628461225888770&hl=en&as_sdt=0,14",1,2022
SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination,0,eccv,2,0,2023-06-17 00:57:35.593000,https://github.com/ai-secure/secretgen,4,SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination,"https://scholar.google.com/scholar?cluster=1331126147127525036&hl=en&as_sdt=0,14",1,2022
Triangle Attack: A Query-Efficient Decision-Based Adversarial Attack,7,eccv,1,0,2023-06-17 00:57:35.794000,https://github.com/xiaosen-wang/TA,12,Triangle Attack: A Query-efficient Decision-based Adversarial Attack,"https://scholar.google.com/scholar?cluster=3041401228170753203&hl=en&as_sdt=0,3",2,2022
Data-Free Backdoor Removal Based on Channel Lipschitzness,17,eccv,2,2,2023-06-17 00:57:35.995000,https://github.com/rkteddy/channel-lipschitzness-based-pruning,15,Data-free backdoor removal based on channel lipschitzness,"https://scholar.google.com/scholar?cluster=33210889481511629&hl=en&as_sdt=0,21",2,2022
Learning Energy-Based Models with Adversarial Training,4,eccv,0,0,2023-06-17 00:57:36.196000,https://github.com/xuwangyin/at-ebms,6,Learning Energy-Based Models with Adversarial Training,"https://scholar.google.com/scholar?cluster=18142164853867043088&hl=en&as_sdt=0,34",1,2022
Scaling Adversarial Training to Large Perturbation Bounds,2,eccv,3,0,2023-06-17 00:57:36.396000,https://github.com/val-iisc/oaat,8,Scaling adversarial training to large perturbation bounds,"https://scholar.google.com/scholar?cluster=14820331195628342236&hl=en&as_sdt=0,5",13,2022
GaitEdge: Beyond Plain End-to-End Gait Recognition for Better Practicality,8,eccv,107,8,2023-06-17 00:57:36.597000,https://github.com/shiqiyu/opengait,438,Gaitedge: Beyond plain end-to-end gait recognition for better practicality,"https://scholar.google.com/scholar?cluster=5245467385820312428&hl=en&as_sdt=0,5",14,2022
PPT: Token-Pruned Pose Transformer for Monocular and Multi-View Human Pose Estimation,7,eccv,1,2,2023-06-17 00:57:36.798000,https://github.com/howiema/ppt,52,PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation,"https://scholar.google.com/scholar?cluster=6476546859896511825&hl=en&as_sdt=0,49",5,2022
AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing,19,eccv,30,9,2023-06-17 00:57:37,https://github.com/eth-siplab/avatarposer,223,Avatarposer: Articulated full-body pose tracking from sparse motion sensing,"https://scholar.google.com/scholar?cluster=16858268099371784430&hl=en&as_sdt=0,11",8,2022
P-STMO: Pre-trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation,24,eccv,7,4,2023-06-17 00:57:37.202000,https://github.com/patrick-swk/p-stmo,98,P-stmo: Pre-trained spatial temporal many-to-one model for 3d human pose estimation,"https://scholar.google.com/scholar?cluster=12642433651443655449&hl=en&as_sdt=0,10",4,2022
Identity-Aware Hand Mesh Estimation and Personalization from RGB Images,4,eccv,1,1,2023-06-17 00:57:37.402000,https://github.com/deyingk/personalizedhandmeshestimation,37,Identity-aware hand mesh estimation and personalization from rgb images,"https://scholar.google.com/scholar?cluster=5413140731462313512&hl=en&as_sdt=0,29",4,2022
SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos,18,eccv,30,7,2023-06-17 00:57:37.604000,https://github.com/cure-lab/SmoothNet,246,Smoothnet: a plug-and-play network for refining human poses in videos,"https://scholar.google.com/scholar?cluster=11316350526053073372&hl=en&as_sdt=0,43",8,2022
PoseTrans: A Simple yet Effective Pose Transformation Augmentation for Human Pose Estimation,4,eccv,5,2,2023-06-17 00:57:37.805000,https://github.com/wtjiang98/PoseTrans,23,PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimation,"https://scholar.google.com/scholar?cluster=12483746133349158288&hl=en&as_sdt=0,33",3,2022
Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement,1,eccv,1,2,2023-06-17 00:57:38.006000,https://github.com/JunukCha/MultiPerson,22,Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement,"https://scholar.google.com/scholar?cluster=6567556122546532858&hl=en&as_sdt=0,5",2,2022
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction,8,eccv,0,3,2023-06-17 00:57:38.207000,https://github.com/mediabrain-sjtu/spgsn,22,Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction,"https://scholar.google.com/scholar?cluster=15843102193534311743&hl=en&as_sdt=0,5",0,2022
Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation,25,eccv,97,25,2023-06-17 00:57:38.407000,https://github.com/wmcnally/kapao,715,Rethinking keypoint representations: Modeling keypoints and poses as objects for multi-person human pose estimation,"https://scholar.google.com/scholar?cluster=16749039856030657986&hl=en&as_sdt=0,44",29,2022
VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data,3,eccv,5,5,2023-06-17 00:57:38.608000,https://github.com/wkom/virtualpose,45,VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data,"https://scholar.google.com/scholar?cluster=71283295073595497&hl=en&as_sdt=0,5",3,2022
Poseur: Direct Human Pose Regression with Transformers,17,eccv,7,7,2023-06-17 00:57:38.808000,https://github.com/aim-uofa/poseur,145,Poseur: Direct human pose regression with transformers,"https://scholar.google.com/scholar?cluster=4496289664508565629&hl=en&as_sdt=0,43",5,2022
SimCC: A Simple Coordinate Classification Perspective for Human Pose Estimation,7,eccv,26,6,2023-06-17 00:57:39.008000,https://github.com/leeyegy/SimDR,251,SimCC: A Simple Coordinate Classification Perspective for Human Pose Estimation,"https://scholar.google.com/scholar?cluster=4470404114148827932&hl=en&as_sdt=0,16",14,2022
A Visual Navigation Perspective for Category-Level Object Pose Estimation,2,eccv,2,0,2023-06-17 00:57:39.210000,https://github.com/wrld/visual_navigation_pose_estimation,15,A Visual Navigation Perspective for Category-Level Object Pose Estimation,"https://scholar.google.com/scholar?cluster=11892425265605942498&hl=en&as_sdt=0,44",1,2022
Faster VoxelPose: Real-Time 3D Human Pose Estimation by Orthographic Projection,4,eccv,14,13,2023-06-17 00:57:39.411000,https://github.com/AlvinYH/Faster-VoxelPose,95,Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection,"https://scholar.google.com/scholar?cluster=7142039322976043909&hl=en&as_sdt=0,43",4,2022
EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices,10,eccv,5,0,2023-06-17 00:57:39.612000,https://github.com/sanweiliti/EgoBody,87,Egobody: Human body shape and motion of interacting people from head-mounted devices,"https://scholar.google.com/scholar?cluster=322231768564187058&hl=en&as_sdt=0,43",5,2022
AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling,2,eccv,7,2,2023-06-17 00:57:39.813000,https://github.com/facebookresearch/AutoAvatar,88,AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling,"https://scholar.google.com/scholar?cluster=6069010176145974454&hl=en&as_sdt=0,10",8,2022
Compositional Human-Scene Interaction Synthesis with Semantic Control,9,eccv,1,2,2023-06-17 00:57:40.014000,https://github.com/zkf1997/coins,68,Compositional human-scene interaction synthesis with semantic control,"https://scholar.google.com/scholar?cluster=12000647280814946947&hl=en&as_sdt=0,5",3,2022
PressureVision: Estimating Hand Pressure from a Single RGB Image,7,eccv,3,1,2023-06-17 00:57:40.216000,https://github.com/facebookresearch/pressurevision,39,PressureVision: estimating hand pressure from a single RGB image,"https://scholar.google.com/scholar?cluster=4229274287212206785&hl=en&as_sdt=0,33",4,2022
PoseScript: 3D Human Poses from Natural Language,3,eccv,6,0,2023-06-17 00:57:40.416000,https://github.com/naver/posescript,34,PoseScript: 3D human poses from natural language,"https://scholar.google.com/scholar?cluster=17752287259042800944&hl=en&as_sdt=0,5",4,2022
DProST: Dynamic Projective Spatial Transformer Network for 6D Pose Estimation,2,eccv,1,0,2023-06-17 00:57:40.616000,https://github.com/parkjaewoo0611/dprost,18,DProST: Dynamic Projective Spatial Transformer Network for 6D Pose Estimation,"https://scholar.google.com/scholar?cluster=1007618959505418426&hl=en&as_sdt=0,44",3,2022
3D Interacting Hand Pose Estimation by Hand De-Occlusion and Removal,8,eccv,3,7,2023-06-17 00:57:40.823000,https://github.com/menghao666/hdr,80,3d interacting hand pose estimation by hand de-occlusion and removal,"https://scholar.google.com/scholar?cluster=13548206922495205714&hl=en&as_sdt=0,21",5,2022
Pose for Everything: Towards Category-Agnostic Pose Estimation,5,eccv,9,9,2023-06-17 00:57:41.024000,https://github.com/luminxu/pose-for-everything,134,Pose for Everything: Towards Category-Agnostic Pose Estimation,"https://scholar.google.com/scholar?cluster=6520324621700578272&hl=en&as_sdt=0,5",6,2022
PoseGPT: Quantization-Based 3D Human Motion Generation and Forecasting,9,eccv,2,5,2023-06-17 00:57:41.225000,https://github.com/naver/posegpt,73,PoseGPT: quantization-based 3D human motion generation and forecasting,"https://scholar.google.com/scholar?cluster=15865183864728633901&hl=en&as_sdt=0,46",8,2022
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation,2,eccv,3,2,2023-06-17 00:57:41.425000,https://github.com/hlz0606/dh-aug-dh-forward-kinematics-model-driven-augmentation-for-3d-human-pose-estimation,10,DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation,"https://scholar.google.com/scholar?cluster=13862822843071497700&hl=en&as_sdt=0,7",2,2022
Semantic-Sparse Colorization Network for Deep Exemplar-Based Colorization,5,eccv,1,2,2023-06-17 00:57:41.626000,https://github.com/bbaaii/SSC-Net,4,Semantic-Sparse Colorization Network for Deep Exemplar-Based Colorization,"https://scholar.google.com/scholar?cluster=11925936575946795440&hl=en&as_sdt=0,5",1,2022
FAST-VQA: Efficient End-to-End Video Quality Assessment with Fragment Sampling,24,eccv,16,1,2023-06-17 00:57:41.827000,https://github.com/timothyhtimothy/fast-vqa,141,Fast-vqa: Efficient end-to-end video quality assessment with fragment sampling,"https://scholar.google.com/scholar?cluster=12307408620730287451&hl=en&as_sdt=0,5",4,2022
LEDNet: Joint Low-Light Enhancement and Deblurring in the Dark,17,eccv,23,4,2023-06-17 00:57:42.027000,https://github.com/sczhou/LEDNet,140,Lednet: Joint low-light enhancement and deblurring in the dark,"https://scholar.google.com/scholar?cluster=13623245822389002080&hl=en&as_sdt=0,33",16,2022
MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects,2,eccv,0,1,2023-06-17 00:57:42.231000,https://github.com/juewenpeng/mpib,23,MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects,"https://scholar.google.com/scholar?cluster=13330152317586129115&hl=en&as_sdt=0,5",2,2022
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset,2,eccv,1,1,2023-06-17 00:57:42.433000,https://github.com/zmzhang1998/Real-RawVSR,34,Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset,"https://scholar.google.com/scholar?cluster=5937904494731616048&hl=en&as_sdt=0,5",2,2022
Seeing Far in the Dark with Patterned Flash,0,eccv,1,0,2023-06-17 00:57:42.634000,https://github.com/zhsun0357/seeing-far-in-the-dark-with-patterned-flash,8,Seeing Far in the Dark with Patterned Flash,"https://scholar.google.com/scholar?cluster=10162340593045720499&hl=en&as_sdt=0,5",1,2022
Simple Baselines for Image Restoration,157,eccv,158,71,2023-06-17 00:57:42.835000,https://github.com/megvii-research/NAFNet,1342,Simple baselines for image restoration,"https://scholar.google.com/scholar?cluster=498268664873674535&hl=en&as_sdt=0,5",15,2022
Improving Image Restoration by Revisiting Global Information Aggregation,28,eccv,7,20,2023-06-17 00:57:43.036000,https://github.com/megvii-research/TLC,166,Improving image restoration by revisiting global information aggregation,"https://scholar.google.com/scholar?cluster=7561375020407203939&hl=en&as_sdt=0,3",8,2022
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration,4,eccv,4,0,2023-06-17 00:57:43.236000,https://github.com/zhaoyuzhi/d2hnet,58,D2hnet: Joint denoising and deblurring with hierarchical network for robust night image restoration,"https://scholar.google.com/scholar?cluster=3617342913437815225&hl=en&as_sdt=0,5",4,2022
DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images,1,eccv,0,0,2023-06-17 00:57:43.436000,https://github.com/ashisht96/DeepPS2,8,DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images,"https://scholar.google.com/scholar?cluster=7668487739080870942&hl=en&as_sdt=0,14",4,2022
Synthesizing Light Field Video from Monocular Video,0,eccv,0,1,2023-06-17 00:57:43.638000,https://github.com/ShrisudhanG/Synthesizing-Light-Field-Video-from-Monocular-Video,3,Synthesizing Light Field Video from Monocular Video,"https://scholar.google.com/scholar?cluster=9388719208065902803&hl=en&as_sdt=0,5",1,2022
Human-Centric Image Cropping with Partition-Aware and Content-Preserving Features,2,eccv,5,0,2023-06-17 00:57:43.839000,https://github.com/bcmi/human-centric-image-cropping,10,Human-Centric Image Cropping with Partition-Aware and Content-Preserving Features,"https://scholar.google.com/scholar?cluster=4878843444912019256&hl=en&as_sdt=0,5",8,2022
DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting,9,eccv,7,2,2023-06-17 00:57:44.040000,https://github.com/JihyongOh/DeMFI,69,DeMFI: deep joint deblurring and multi-frame interpolation with flow-guided attentive correlation and recursive boosting,"https://scholar.google.com/scholar?cluster=18298194846292120570&hl=en&as_sdt=0,5",5,2022
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion,5,eccv,3,0,2023-06-17 00:57:44.241000,https://github.com/zzh-tech/dual-reversed-rs,47,Bringing rolling shutter images alive with dual reversed distortion,"https://scholar.google.com/scholar?cluster=6745803359164856896&hl=en&as_sdt=0,48",1,2022
FILM: Frame Interpolation for Large Motion,31,eccv,209,3,2023-06-17 00:57:44.441000,https://github.com/google-research/frame-interpolation,2122,Film: Frame interpolation for large motion,"https://scholar.google.com/scholar?cluster=8008670965170343058&hl=en&as_sdt=0,10",39,2022
EvAC3D: From Event-Based Apparent Contours to 3D Models via Continuous Visual Hulls,2,eccv,0,0,2023-06-17 00:57:44.642000,https://github.com/daniilidis-group/EvAC3D,0,EvAC3D: From Event-Based Apparent Contours to 3D Models via Continuous Visual Hulls,"https://scholar.google.com/scholar?cluster=12241189082888588279&hl=en&as_sdt=0,22",3,2022
DCCF: Deep Comprehensible Color Filter Learning Framework for High-Resolution Image Harmonization,13,eccv,5,1,2023-06-17 00:57:44.842000,https://github.com/rockeyben/dccf,34,Dccf: Deep comprehensible color filter learning framework for high-resolution image harmonization,"https://scholar.google.com/scholar?cluster=11763509766953763439&hl=en&as_sdt=0,22",4,2022
SelectionConv: Convolutional Neural Networks for Non-Rectilinear Image Data,1,eccv,0,0,2023-06-17 00:57:45.044000,https://github.com/davidmhart/SelectionConv,3,SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data,"https://scholar.google.com/scholar?cluster=7959148085712092506&hl=en&as_sdt=0,44",2,2022
Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization,11,eccv,5,1,2023-06-17 00:57:45.245000,https://github.com/stefanleong/s2crnet,26,Spatial-separated curve rendering network for efficient and high-resolution image harmonization,"https://scholar.google.com/scholar?cluster=17690796992400113153&hl=en&as_sdt=0,5",6,2022
CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution,8,eccv,4,1,2023-06-17 00:57:45.445000,https://github.com/cheeun/cadyq,42,CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution,"https://scholar.google.com/scholar?cluster=17622423999253475066&hl=en&as_sdt=0,5",1,2022
Deep Semantic Statistics Matching (D2SM) Denoising Network,3,eccv,1,2,2023-06-17 00:57:45.647000,https://github.com/MKFMIKU/D2SM,23,Deep Semantic Statistics Matching (D2SM) Denoising Network,"https://scholar.google.com/scholar?cluster=10217193837802599419&hl=en&as_sdt=0,41",1,2022
Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging,1,eccv,3,1,2023-06-17 00:57:45.847000,https://github.com/viengiaan/EDWL,6,Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging,"https://scholar.google.com/scholar?cluster=5411080517450189974&hl=en&as_sdt=0,10",2,2022
Realistic Blur Synthesis for Learning Image Deblurring,8,eccv,6,1,2023-06-17 00:57:46.048000,https://github.com/rimchang/Rsblur,43,Realistic blur synthesis for learning image deblurring,"https://scholar.google.com/scholar?cluster=12876470121987800419&hl=en&as_sdt=0,6",1,2022
Benchmarking Omni-Vision Representation through the Lens of Visual Realms,4,eccv,7,1,2023-06-17 00:57:46.250000,https://github.com/ZhangYuanhan-AI/OmniBenchmark,104,Benchmarking omni-vision representation through the lens of visual realms,"https://scholar.google.com/scholar?cluster=7208385789322575994&hl=en&as_sdt=0,38",6,2022
BEAT: A Large-Scale Semantic and Emotional Multi-modal Dataset for Conversational Gestures Synthesis,12,eccv,127,26,2023-06-17 00:57:46.451000,https://github.com/PantoMatrix/BEAT,596,BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis,"https://scholar.google.com/scholar?cluster=7260513717871606239&hl=en&as_sdt=0,5",46,2022
Neuromorphic Data Augmentation for Training Spiking Neural Networks,25,eccv,2,0,2023-06-17 00:57:46.651000,https://github.com/intelligent-computing-lab-yale/nda_snn,13,Neuromorphic data augmentation for training spiking neural networks,"https://scholar.google.com/scholar?cluster=4978357844166499503&hl=en&as_sdt=0,3",4,2022
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset,16,eccv,21,11,2023-06-17 00:57:46.852000,https://github.com/celebv-hq/celebv-hq,267,CelebV-HQ: A large-scale video facial attributes dataset,"https://scholar.google.com/scholar?cluster=1631605286635488568&hl=en&as_sdt=0,5",17,2022
MovieCuts: A New Dataset and Benchmark for Cut Type Recognition,15,eccv,2,0,2023-06-17 00:57:47.053000,https://github.com/pardoalejo/moviecuts,12,Moviecuts: A new dataset and benchmark for cut type recognition,"https://scholar.google.com/scholar?cluster=16344891051014087091&hl=en&as_sdt=0,14",3,2022
Not Just Streaks: Towards Ground Truth for Single Image Deraining,10,eccv,6,0,2023-06-17 00:57:47.255000,https://github.com/UCLA-VMG/GT-RAIN,16,Not Just Streaks: Towards Ground Truth for Single Image Deraining,"https://scholar.google.com/scholar?cluster=8128851603071658784&hl=en&as_sdt=0,5",0,2022
ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-Verified Image-Caption Associations for MS-COCO,13,eccv,2,1,2023-06-17 00:57:47.455000,https://github.com/naver-ai/eccv-caption,45,Eccv caption: Correcting false negatives by collecting machine-and-human-verified image-caption associations for ms-coco,"https://scholar.google.com/scholar?cluster=11262981520406596194&hl=en&as_sdt=0,33",2,2022
How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?,1,eccv,1,0,2023-06-17 00:57:47.656000,https://github.com/liuyvchi/mie-x,8,How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?,"https://scholar.google.com/scholar?cluster=2920512126325696294&hl=en&as_sdt=0,44",1,2022
REALY: Rethinking the Evaluation of 3D Face Reconstruction,6,eccv,15,4,2023-06-17 00:57:47.858000,https://github.com/czh-98/REALY,164,REALY: Rethinking the Evaluation of 3D Face Reconstruction,"https://scholar.google.com/scholar?cluster=522284677847327970&hl=en&as_sdt=0,1",9,2022
A-OKVQA: A Benchmark for Visual Question Answering Using World Knowledge,29,eccv,4,1,2023-06-17 00:57:48.065000,https://github.com/allenai/aokvqa,27,A-okvqa: A benchmark for visual question answering using world knowledge,"https://scholar.google.com/scholar?cluster=5632700317047117021&hl=en&as_sdt=0,33",4,2022
The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing,4,eccv,1,1,2023-06-17 00:57:48.273000,https://github.com/dawitmureja/ave,19,The anatomy of video editing: A dataset and benchmark suite for ai-assisted video editing,"https://scholar.google.com/scholar?cluster=15734096510585924526&hl=en&as_sdt=0,31",2,2022
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context,14,eccv,3,4,2023-06-17 00:57:48.475000,https://github.com/pinakinathc/fscoco,11,FS-COCO: towards understanding of freehand sketches of common objects in context,"https://scholar.google.com/scholar?cluster=12913917313302878483&hl=en&as_sdt=0,33",2,2022
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset,3,eccv,2,1,2023-06-17 00:57:48.676000,https://github.com/visipedia/ssw60,11,Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset,"https://scholar.google.com/scholar?cluster=11318172812364510822&hl=en&as_sdt=0,47",7,2022
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting,3,eccv,6,0,2023-06-17 00:57:48.878000,https://github.com/visipedia/caltech-fish-counting,21,The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting,"https://scholar.google.com/scholar?cluster=2552219111490923370&hl=en&as_sdt=0,31",5,2022
A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility,8,eccv,3,1,2023-06-17 00:57:49.080000,https://github.com/aburns4/MoTIF,24,A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility,"https://scholar.google.com/scholar?cluster=7400246580397663222&hl=en&as_sdt=0,47",1,2022
BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis,1,eccv,4,1,2023-06-17 00:57:49.293000,https://github.com/dmoltisanti/brace,60,BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis,"https://scholar.google.com/scholar?cluster=9212492746103555729&hl=en&as_sdt=0,3",3,2022
Dress Code: High-Resolution Multi-Category Virtual Try-On,9,eccv,18,5,2023-06-17 00:57:49.494000,https://github.com/aimagelab/dress-code,210,Dress Code: High-Resolution Multi-Category Virtual Try-On,"https://scholar.google.com/scholar?cluster=7454515160743762050&hl=en&as_sdt=0,5",15,2022
A Data-Centric Approach for Improving Ambiguous Labels with Combined Semi-Supervised Classification and Clustering,2,eccv,0,0,2023-06-17 00:57:49.698000,https://github.com/emprime/dc3,2,A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering,"https://scholar.google.com/scholar?cluster=16780375661347906000&hl=en&as_sdt=0,5",0,2022
ClearPose: Large-Scale Transparent Object Dataset and Benchmark,6,eccv,4,0,2023-06-17 00:57:49.900000,https://github.com/opipari/clearpose,20,Clearpose: Large-scale transparent object dataset and benchmark,"https://scholar.google.com/scholar?cluster=4905831487375014650&hl=en&as_sdt=0,44",2,2022
When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics,6,eccv,0,0,2023-06-17 00:57:50.121000,https://github.com/ccc-frankfurt/intrinsic_ordering_nn_training,6,When deep classifiers agree: Analyzing correlations between learning order and image statistics,"https://scholar.google.com/scholar?cluster=11354728863770927806&hl=en&as_sdt=0,22",2,2022
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment,1,eccv,3,0,2023-06-17 00:57:50.322000,https://github.com/kangyeolk/animeceleb,81,AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment,"https://scholar.google.com/scholar?cluster=7350859631265970781&hl=en&as_sdt=0,31",8,2022
MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration,10,eccv,1,0,2023-06-17 00:57:50.524000,https://github.com/mugen-org/MUGEN_baseline,33,Mugen: A playground for video-audio-text multimodal understanding and generation,"https://scholar.google.com/scholar?cluster=3281106057994369191&hl=en&as_sdt=0,5",3,2022
A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing,1,eccv,13,0,2023-06-17 00:57:50.725000,https://github.com/apple/ml-dms-dataset,31,A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing,"https://scholar.google.com/scholar?cluster=3365791015627837272&hl=en&as_sdt=0,1",7,2022
D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights,2,eccv,1,0,2023-06-17 00:57:50.929000,https://github.com/vtp-tl/d2-tpred,9,D2-TPred: Discontinuous Dependency for Trajectory Prediction Under Traffic Lights,"https://scholar.google.com/scholar?cluster=17332870224184443677&hl=en&as_sdt=0,31",1,2022
TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments,1,eccv,2,0,2023-06-17 00:57:51.130000,https://github.com/shubham1810/trove_toolkit,9,TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments,"https://scholar.google.com/scholar?cluster=15126258770510875217&hl=en&as_sdt=0,5",2,2022
Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph,24,eccv,10,0,2023-06-17 00:57:51.330000,https://github.com/nightmare-n/graphrcnn,108,Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph,"https://scholar.google.com/scholar?cluster=15876090926063291773&hl=en&as_sdt=0,33",6,2022
MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection,24,eccv,1118,45,2023-06-17 00:57:51.532000,https://github.com/open-mmlab/OpenPCDet,3652,MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection,"https://scholar.google.com/scholar?cluster=9939036585508006363&hl=en&as_sdt=0,31",73,2022
Long-Tail Detection with Effective Class-Margins,2,eccv,6,1,2023-06-17 00:57:51.732000,https://github.com/janghyuncho/ecm-loss,56,Long-tail detection with effective class-margins,"https://scholar.google.com/scholar?cluster=11001163024932915751&hl=en&as_sdt=0,22",3,2022
PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer towards Video Object Detection,7,eccv,7,4,2023-06-17 00:57:51.933000,https://github.com/hon-wong/ptseformer,16,PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection,"https://scholar.google.com/scholar?cluster=13506042649050803522&hl=en&as_sdt=0,44",2,2022
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers,211,eccv,1,1,2023-06-17 00:57:52.135000,https://github.com/zhiqi-li/BEVFormer,6,Bevformer: Learning bird's-eye-view representation from multi-camera images via spatiotemporal transformers,"https://scholar.google.com/scholar?cluster=5447961710166142858&hl=en&as_sdt=0,10",2,2022
Category-Level 6D Object Pose and Size Estimation Using Self-Supervised Deep Prior Deformation Networks,8,eccv,4,3,2023-06-17 00:57:52.335000,https://github.com/jiehonglin/self-dpdn,33,Category-level 6D object pose and size estimation using self-supervised deep prior deformation networks,"https://scholar.google.com/scholar?cluster=11147746604570298398&hl=en&as_sdt=0,44",3,2022
Dense Teacher: Dense Pseudo-Labels for Semi-Supervised Object Detection,21,eccv,12,12,2023-06-17 00:57:52.536000,https://github.com/megvii-basedetection/denseteacher,100,Dense teacher: Dense pseudo-labels for semi-supervised object detection,"https://scholar.google.com/scholar?cluster=17675636447045263550&hl=en&as_sdt=0,5",4,2022
Point-to-Box Network for Accurate Object Detection via Single Point Supervision,3,eccv,8,2,2023-06-17 00:57:52.738000,https://github.com/ucas-vg/p2bnet,57,Point-to-Box Network for Accurate Object Detection via Single Point Supervision,"https://scholar.google.com/scholar?cluster=13593904962102464183&hl=en&as_sdt=0,33",1,2022
Open-Vocabulary DETR with Conditional Matching,30,eccv,12,14,2023-06-17 00:57:52.939000,https://github.com/yuhangzang/ov-detr,164,Open-vocabulary detr with conditional matching,"https://scholar.google.com/scholar?cluster=9203701250519937347&hl=en&as_sdt=0,5",6,2022
Prediction-Guided Distillation for Dense Object Detection,8,eccv,10,2,2023-06-17 00:57:53.140000,https://github.com/chenhongyiyang/pgd,60,Prediction-guided distillation for dense object detection,"https://scholar.google.com/scholar?cluster=9677628548632218298&hl=en&as_sdt=0,11",1,2022
Multimodal Object Detection via Probabilistic Ensembling,13,eccv,12,9,2023-06-17 00:57:53.341000,https://github.com/Jamie725/RGBT-detection,79,Multimodal object detection via probabilistic ensembling,"https://scholar.google.com/scholar?cluster=2423878409061892272&hl=en&as_sdt=0,5",2,2022
Exploiting Unlabeled Data with Vision and Language Models for Object Detection,17,eccv,5,3,2023-06-17 00:57:53.542000,https://github.com/xiaofeng94/vl-plm,68,Exploiting unlabeled data with vision and language models for object detection,"https://scholar.google.com/scholar?cluster=2792832346656785372&hl=en&as_sdt=0,22",3,2022
INT: Towards Infinite-Frames 3D Detection with an Efficient Framework,6,eccv,0,0,2023-06-17 00:57:53.743000,https://github.com/ADLab-AutoDrive/INT,29,INT: Towards Infinite-Frames 3D Detection with an Efficient Framework,"https://scholar.google.com/scholar?cluster=15766447330986922378&hl=en&as_sdt=0,44",5,2022
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training,9,eccv,0,1,2023-06-17 00:57:53.945000,https://github.com/zhyever/stmono3d,22,Unsupervised domain adaptation for monocular 3d object detection via self-training,"https://scholar.google.com/scholar?cluster=18046147719056980644&hl=en&as_sdt=0,5",10,2022
SuperLine3D: Self-Supervised Line Segmentation and Description for LiDAR Point Cloud,2,eccv,4,5,2023-06-17 00:57:54.145000,https://github.com/zxrzju/superline3d,132,SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud,"https://scholar.google.com/scholar?cluster=1892879897103098418&hl=en&as_sdt=0,36",2,2022
Adversarially-Aware Robust Object Detector,4,eccv,10,7,2023-06-17 00:57:54.345000,https://github.com/7eu7d7/robustdet,41,Adversarially-aware robust object detector,"https://scholar.google.com/scholar?cluster=9399655475680081198&hl=en&as_sdt=0,15",2,2022
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors,4,eccv,0,2,2023-06-17 00:57:54.545000,https://github.com/lutingwang/head,11,Head: Hetero-assists distillation for heterogeneous object detectors,"https://scholar.google.com/scholar?cluster=9132276678803270854&hl=en&as_sdt=0,5",2,2022
You Should Look at All Objects,4,eccv,2,8,2023-06-17 00:57:54.746000,https://github.com/charlespikachu/yslao,71,You Should Look at All Objects,"https://scholar.google.com/scholar?cluster=7436725887660345544&hl=en&as_sdt=0,47",7,2022
Detecting Twenty-Thousand Classes Using Image-Level Supervision,141,eccv,155,49,2023-06-17 00:57:54.948000,https://github.com/facebookresearch/Detic,1466,Detecting twenty-thousand classes using image-level supervision,"https://scholar.google.com/scholar?cluster=2641915666092458000&hl=en&as_sdt=0,5",18,2022
DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation,1,eccv,1,1,2023-06-17 00:57:55.149000,https://github.com/gorilla-lab-scut/dcl-net,15,DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation,"https://scholar.google.com/scholar?cluster=15281342953378875281&hl=en&as_sdt=0,10",2,2022
Monocular 3D Object Detection with Depth from Motion,24,eccv,22,5,2023-06-17 00:57:55.350000,https://github.com/tai-wang/depth-from-motion,264,Monocular 3d object detection with depth from motion,"https://scholar.google.com/scholar?cluster=6486278392250669413&hl=en&as_sdt=0,6",10,2022
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation,4,eccv,1,1,2023-06-17 00:57:55.550000,https://github.com/fylwen/disp-6d,30,Disentangled implicit shape and pose learning for scalable 6D pose estimation,"https://scholar.google.com/scholar?cluster=3070155732865444276&hl=en&as_sdt=0,5",3,2022
Distilling Object Detectors with Global Knowledge,2,eccv,0,2,2023-06-17 00:57:55.752000,https://github.com/hikvision-research/davar-lab-ml,4,Distilling Object Detectors with Global Knowledge,"https://scholar.google.com/scholar?cluster=8385771225908193375&hl=en&as_sdt=0,44",2,2022
Unifying Visual Perception by Dispersible Points Learning,2,eccv,2,0,2023-06-17 00:57:55.952000,https://github.com/sense-x/unihead,45,Unifying Visual Perception by Dispersible Points Learning,"https://scholar.google.com/scholar?cluster=9623652872686078493&hl=en&as_sdt=0,5",1,2022
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection,25,eccv,16,15,2023-06-17 00:57:56.153000,https://github.com/ligang-cs/PseCo,116,Pseco: Pseudo labeling and consistency training for semi-supervised object detection,"https://scholar.google.com/scholar?cluster=10966123175723115004&hl=en&as_sdt=0,43",3,2022
Exploring Resolution and Degradation Clues As Self-Supervised Signal for Low Quality Object Detection,1,eccv,3,2,2023-06-17 00:57:56.354000,https://github.com/cuiziteng/eccv_aeris,41,Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection,"https://scholar.google.com/scholar?cluster=5037952713251039767&hl=en&as_sdt=0,32",2,2022
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features,4,eccv,1,1,2023-06-17 00:57:56.554000,https://github.com/wufeim/6d_pose_eccv22,0,Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features,"https://scholar.google.com/scholar?cluster=4840476718849025600&hl=en&as_sdt=0,5",1,2022
RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection,15,eccv,13,21,2023-06-17 00:57:56.755000,https://github.com/chasel-tsui/mmdet-rfla,159,RFLA: Gaussian receptive field based label assignment for tiny object detection,"https://scholar.google.com/scholar?cluster=16490222600712608071&hl=en&as_sdt=0,3",1,2022
Rethinking IoU-Based Optimization for Single-Stage 3D Object Detection,7,eccv,12,20,2023-06-17 00:57:56.957000,https://github.com/hlsheng1/rdiou,105,Rethinking IoU-based Optimization for Single-stage 3D Object Detection,"https://scholar.google.com/scholar?cluster=6927002374951805343&hl=en&as_sdt=0,5",6,2022
Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection,4,eccv,2,4,2023-06-17 00:57:57.157000,https://github.com/wushuang1998/mfdc,15,Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection,"https://scholar.google.com/scholar?cluster=16573983045210502732&hl=en&as_sdt=0,33",1,2022
PointCLM: A Contrastive Learning-Based Framework for Multi-Instance Point Cloud Registration,0,eccv,0,2,2023-06-17 00:57:57.359000,https://github.com/phdymz/pointclm,12,PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration,"https://scholar.google.com/scholar?cluster=14365361997663714453&hl=en&as_sdt=0,5",2,2022
Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration,7,eccv,7,6,2023-06-17 00:57:57.562000,https://github.com/164140757/scm,24,Weakly supervised object localization via transformer with implicit spatial calibration,"https://scholar.google.com/scholar?cluster=10897332842178251748&hl=en&as_sdt=0,10",1,2022
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection,16,eccv,25,1,2023-06-17 00:57:57.764000,https://github.com/abhi1kumar/deviant,156,Deviant: Depth equivariant network for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=5363090072196311219&hl=en&as_sdt=0,23",4,2022
Label-Guided Auxiliary Training Improves 3D Object Detector,4,eccv,0,0,2023-06-17 00:57:57.965000,https://github.com/fabiencode/lg3d,7,Label-guided auxiliary training improves 3d object detector,"https://scholar.google.com/scholar?cluster=16014327819135887195&hl=en&as_sdt=0,14",3,2022
PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images,18,eccv,6,7,2023-06-17 00:57:58.167000,https://github.com/fcjian/PromptDet,128,Promptdet: Towards open-vocabulary detection using uncurated images,"https://scholar.google.com/scholar?cluster=11282569843876232296&hl=en&as_sdt=0,49",2,2022
Densely Constrained Depth Estimator for Monocular 3D Object Detection,8,eccv,3,1,2023-06-17 00:57:58.369000,https://github.com/bravegroup/dcd,45,Densely Constrained Depth Estimator for Monocular 3D Object Detection,"https://scholar.google.com/scholar?cluster=17925631653392765663&hl=en&as_sdt=0,25",3,2022
DFNet: Enhance Absolute Pose Regression with Direct Feature Matching,10,eccv,6,2,2023-06-17 00:57:58.578000,https://github.com/activevisionlab/dfnet,69,Dfnet: Enhance absolute pose regression with direct feature matching,"https://scholar.google.com/scholar?cluster=18094200173009161158&hl=en&as_sdt=0,32",4,2022
PillarNet: Real-Time and High-Performance Pillar-Based 3D Object Detection,20,eccv,14,8,2023-06-17 00:57:58.794000,https://github.com/agent-sgs/pillarnet,168,Pillarnet: Real-time and high-performance pillar-based 3d object detection,"https://scholar.google.com/scholar?cluster=4019899772206366950&hl=en&as_sdt=0,34",6,2022
Robust Object Detection with Inaccurate Bounding Boxes,3,eccv,2,2,2023-06-17 00:57:58.995000,https://github.com/cxliu0/oa-mil,29,Robust Object Detection with Inaccurate Bounding Boxes,"https://scholar.google.com/scholar?cluster=2089679862624247517&hl=en&as_sdt=0,29",5,2022
Efficient Decoder-Free Object Detection with Transformers,5,eccv,5,6,2023-06-17 00:57:59.197000,https://github.com/Pealing/DFFT,71,Efficient decoder-free object detection with transformers,"https://scholar.google.com/scholar?cluster=5278530239378148719&hl=en&as_sdt=0,6",3,2022
Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection,12,eccv,5,21,2023-06-17 00:57:59.398000,https://github.com/Cc-Hy/CMKD,74,Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection,"https://scholar.google.com/scholar?cluster=2476059482864363015&hl=en&as_sdt=0,5",4,2022
ReAct: Temporal Action Detection with Relational Queries,10,eccv,3,1,2023-06-17 00:57:59.600000,https://github.com/sssste/react,36,React: Temporal action detection with relational queries,"https://scholar.google.com/scholar?cluster=5904831039961991188&hl=en&as_sdt=0,3",0,2022
Towards Accurate Active Camera Localization,1,eccv,7,0,2023-06-17 00:57:59.803000,https://github.com/qhfang/accurateacl,24,Towards Accurate Active Camera Localization,"https://scholar.google.com/scholar?cluster=6184579916001482334&hl=en&as_sdt=0,44",3,2022
Camera Pose Auto-Encoders for Improving Pose Regression,7,eccv,0,4,2023-06-17 00:58:00.005000,https://github.com/yolish/camera-pose-auto-encoders,8,Camera pose auto-encoders for improving pose regression,"https://scholar.google.com/scholar?cluster=1200127166856319634&hl=en&as_sdt=0,5",3,2022
Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization,6,eccv,1,0,2023-06-17 00:58:00.206000,https://github.com/zh460045050/bagcams,11,Bagging regional classification activation maps for weakly supervised object localization,"https://scholar.google.com/scholar?cluster=11753021633093920056&hl=en&as_sdt=0,5",1,2022
UC-OWOD: Unknown-Classified Open World Object Detection,11,eccv,4,4,2023-06-17 00:58:00.407000,https://github.com/johnwuzh/uc-owod,17,UC-OWOD: Unknown-Classified Open World Object Detection,"https://scholar.google.com/scholar?cluster=16063369850373717400&hl=en&as_sdt=0,3",4,2022
3D Object Detection with a Self-Supervised Lidar Scene Flow Backbone,9,eccv,6,7,2023-06-17 00:58:00.607000,https://github.com/emecercelik/ssl-3d-detection,28,3d object detection with a self-supervised lidar scene flow backbone,"https://scholar.google.com/scholar?cluster=3097782278362600887&hl=en&as_sdt=0,14",3,2022
Open Vocabulary Object Detection with Pseudo Bounding-Box Labels,16,eccv,4,4,2023-06-17 00:58:00.819000,https://github.com/salesforce/pb-ovd,40,Open vocabulary object detection with pseudo bounding-box labels,"https://scholar.google.com/scholar?cluster=1851007110321536767&hl=en&as_sdt=0,33",4,2022
Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting,6,eccv,5,11,2023-06-17 00:58:01.074000,https://github.com/aaronwool/rcvpose,19,Vote from the center: 6 dof pose estimation in rgb-d images by radial keypoint voting,"https://scholar.google.com/scholar?cluster=15869715914145770859&hl=en&as_sdt=0,31",1,2022
Long-Tailed Instance Segmentation Using Gumbel Optimized Loss,5,eccv,2,0,2023-06-17 00:58:01.335000,https://github.com/kostas1515/gol,12,Long-tailed Instance Segmentation using Gumbel Optimized Loss,"https://scholar.google.com/scholar?cluster=2756765905530613268&hl=en&as_sdt=0,39",2,2022
DetMatch: Two Teachers Are Better than One for Joint 2D and 3D Semi-Supervised Object Detection,9,eccv,2,0,2023-06-17 00:58:01.548000,https://github.com/divadi/detmatch,30,Detmatch: Two teachers are better than one for joint 2d and 3d semi-supervised object detection,"https://scholar.google.com/scholar?cluster=13044172833366605708&hl=en&as_sdt=0,43",10,2022
ObjectBox: From Centers to Boxes for Anchor-Free Object Detection,13,eccv,15,6,2023-06-17 00:58:01.762000,https://github.com/mohsenzand/objectbox,125,Objectbox: From centers to boxes for anchor-free object detection,"https://scholar.google.com/scholar?cluster=7167680945390018074&hl=en&as_sdt=0,39",7,2022
Is Geometry Enough for Matching in Visual Localization?,6,eccv,6,0,2023-06-17 00:58:01.974000,https://github.com/dvl-tum/gomatch,68,Is Geometry Enough for Matching in Visual Localization?,"https://scholar.google.com/scholar?cluster=7930011242295732308&hl=en&as_sdt=0,14",11,2022
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles,10,eccv,5,5,2023-06-17 00:58:02.188000,https://github.com/gdwang08/jigsaw-vad,26,Video anomaly detection by solving decoupled spatio-temporal jigsaw puzzles,"https://scholar.google.com/scholar?cluster=12003082504440513279&hl=en&as_sdt=0,7",2,2022
Class-Agnostic Object Detection with Multi-modal Transformer,22,eccv,20,3,2023-06-17 00:58:02.403000,https://github.com/mmaaz60/mvits_for_class_agnostic_od,244,Class-agnostic object detection with multi-modal transformer,"https://scholar.google.com/scholar?cluster=6789740800407892437&hl=en&as_sdt=0,47",7,2022
Object Detection As Probabilistic Set Prediction,2,eccv,1,0,2023-06-17 00:58:02.620000,https://github.com/georghess/pmb-nll,7,Object Detection as Probabilistic Set Prediction,"https://scholar.google.com/scholar?cluster=8679090498560042960&hl=en&as_sdt=0,3",1,2022
Weakly-Supervised Temporal Action Detection for Fine-Grained Videos with Hierarchical Atomic Actions,1,eccv,0,3,2023-06-17 00:58:02.833000,https://github.com/lizhi1104/haan,12,Weakly-Supervised Temporal Action Detection for Fine-Grained Videos with Hierarchical Atomic Actions,"https://scholar.google.com/scholar?cluster=17264237154516113642&hl=en&as_sdt=0,36",2,2022
On Label Granularity and Object Localization,4,eccv,2,0,2023-06-17 00:58:03.047000,https://github.com/visipedia/inat_loc,8,On Label Granularity and Object Localization,"https://scholar.google.com/scholar?cluster=2422681559524449972&hl=en&as_sdt=0,48",6,2022
OIMNet++: Prototypical Normalization and Localization-Aware Learning for Person Search,4,eccv,10,0,2023-06-17 00:58:03.259000,https://github.com/cvlab-yonsei/OIMNetPlus,37,OIMNet++: Prototypical Normalization and Localization-Aware Learning for Person Search,"https://scholar.google.com/scholar?cluster=1410585188248909504&hl=en&as_sdt=0,33",4,2022
3D Random Occlusion and Multi-layer Projection for Deep Multi-Camera Pedestrian Localization,3,eccv,0,0,2023-06-17 00:58:03.471000,https://github.com/xjtlu-cvlab/3drom,5,3D Random Occlusion and Multi-layer Projection for Deep Multi-camera Pedestrian Localization,"https://scholar.google.com/scholar?cluster=16600455857951353236&hl=en&as_sdt=0,47",1,2022
Knowledge Condensation Distillation,5,eccv,2,2,2023-06-17 00:58:03.682000,https://github.com/dzy3/kcd,21,Knowledge condensation distillation,"https://scholar.google.com/scholar?cluster=14400509218936192311&hl=en&as_sdt=0,5",1,2022
Masked Generative Distillation,37,eccv,20,2,2023-06-17 00:58:03.893000,https://github.com/yzd-v/MGD,165,Masked generative distillation,"https://scholar.google.com/scholar?cluster=2410146594804744602&hl=en&as_sdt=0,32",1,2022
Fine-Grained Data Distribution Alignment for Post-Training Quantization,8,eccv,0,0,2023-06-17 00:58:04.105000,https://github.com/zysxmu/fdda,11,Fine-grained data distribution alignment for post-training quantization,"https://scholar.google.com/scholar?cluster=1370940591931369082&hl=en&as_sdt=0,41",1,2022
Efficient One Pass Self-Distillation with Zipf's Label Smoothing,4,eccv,3,0,2023-06-17 00:58:04.317000,https://github.com/megvii-research/zipfls,22,Efficient One Pass Self-distillation with Zipf's Label Smoothing,"https://scholar.google.com/scholar?cluster=616033282917040366&hl=en&as_sdt=0,31",3,2022
Prune Your Model before Distill It,5,eccv,3,0,2023-06-17 00:58:04.528000,https://github.com/ososos888/prune-then-distill,32,Prune your model before distill it,"https://scholar.google.com/scholar?cluster=2341562978953359586&hl=en&as_sdt=0,44",2,2022
Patch Similarity Aware Data-Free Quantization for Vision Transformers,9,eccv,11,3,2023-06-17 00:58:04.740000,https://github.com/zkkli/psaq-vit,71,Patch similarity aware data-free quantization for vision transformers,"https://scholar.google.com/scholar?cluster=17711527592354393966&hl=en&as_sdt=0,50",3,2022
Streaming Multiscale Deep Equilibrium Models,0,eccv,0,0,2023-06-17 00:58:04.951000,https://github.com/ufukertenli/streamdeq-code,6,Streaming Multiscale Deep Equilibrium Models,"https://scholar.google.com/scholar?cluster=6815895297598856982&hl=en&as_sdt=0,34",1,2022
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data,6,eccv,2,0,2023-06-17 00:58:05.163000,https://github.com/wangt-cn/eqinv,17,Equivariance and invariance inductive bias for learning from insufficient data,"https://scholar.google.com/scholar?cluster=3438445539546676262&hl=en&as_sdt=0,10",1,2022
Mixed-Precision Neural Network Quantization via Learned Layer-Wise Importance,5,eccv,2,0,2023-06-17 00:58:05.374000,https://github.com/1hunters/LIMPQ,20,Mixed-Precision Neural Network Quantization via Learned Layer-Wise Importance,"https://scholar.google.com/scholar?cluster=12314436994515800017&hl=en&as_sdt=0,34",2,2022
EdgeViTs: Competing Light-Weight CNNs on Mobile Devices with Vision Transformers,43,eccv,4,5,2023-06-17 00:58:05.586000,https://github.com/saic-fi/edgevit,67,Edgevits: Competing light-weight cnns on mobile devices with vision transformers,"https://scholar.google.com/scholar?cluster=14797189077413553145&hl=en&as_sdt=0,5",7,2022
PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators,1,eccv,1,1,2023-06-17 00:58:05.808000,https://github.com/huqinghao/palquant,10,PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators,"https://scholar.google.com/scholar?cluster=17909181500058107497&hl=en&as_sdt=0,44",3,2022
IDa-Det: An Information Discrepancy-Aware Distillation for 1-Bit Detectors,6,eccv,1,3,2023-06-17 00:58:06.019000,https://github.com/stevetsui/ida-det,12,Ida-det: An information discrepancy-aware distillation for 1-bit detectors,"https://scholar.google.com/scholar?cluster=882962750193305921&hl=en&as_sdt=0,33",2,2022
Learning to Weight Samples for Dynamic Early-Exiting Networks,3,eccv,0,0,2023-06-17 00:58:06.231000,https://github.com/leaplabthu/l2w-den,20,Learning to Weight Samples for Dynamic Early-Exiting Networks,"https://scholar.google.com/scholar?cluster=2164454150325810311&hl=en&as_sdt=0,32",2,2022
Adaptive Token Sampling for Efficient Vision Transformers,23,eccv,9,6,2023-06-17 00:58:06.442000,https://github.com/adaptivetokensampling/ATS,58,Adaptive token sampling for efficient vision transformers,"https://scholar.google.com/scholar?cluster=3115530465693214001&hl=en&as_sdt=0,5",3,2022
Weight Fixing Networks,0,eccv,0,0,2023-06-17 00:58:06.653000,https://github.com/subiawaud/weight_fix_networks,2,Weight Fixing Networks,"https://scholar.google.com/scholar?cluster=10314117125960813099&hl=en&as_sdt=0,33",1,2022
Self-Slimmed Vision Transformer,8,eccv,1,0,2023-06-17 00:58:06.872000,https://github.com/sense-x/sit,19,Self-slimmed vision transformer,"https://scholar.google.com/scholar?cluster=11542728323690259520&hl=en&as_sdt=0,3",2,2022
Switchable Online Knowledge Distillation,10,eccv,2,1,2023-06-17 00:58:07.084000,https://github.com/hfutqian/SwitOKD,14,Switchable online knowledge distillation,"https://scholar.google.com/scholar?cluster=6676742329537938355&hl=en&as_sdt=0,5",1,2022
$\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training,4,eccv,0,0,2023-06-17 00:58:07.297000,https://github.com/hmdolatabadi/acs,0,-Robustness and Beyond: Unleashing Efficient Adversarial Training,"https://scholar.google.com/scholar?cluster=2283287874012291495&hl=en&as_sdt=0,5",1,2022
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies,1,eccv,0,0,2023-06-17 00:58:07.509000,https://github.com/sense-gvt/bcdnet,9,Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies,"https://scholar.google.com/scholar?cluster=6373259958026738947&hl=en&as_sdt=0,44",0,2022
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks,0,eccv,4,0,2023-06-17 00:58:07.722000,https://github.com/apple/ml-spin,14,SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks,"https://scholar.google.com/scholar?cluster=2303358432300219665&hl=en&as_sdt=0,33",6,2022
Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning,2,eccv,1,0,2023-06-17 00:58:07.933000,https://github.com/sseung0703/ekg,16,Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning,"https://scholar.google.com/scholar?cluster=6904839666613856209&hl=en&as_sdt=0,3",2,2022
Network Binarization via Contrastive Learning,6,eccv,1,2,2023-06-17 00:58:08.144000,https://github.com/42Shawn/CMIM,12,Network binarization via contrastive learning,"https://scholar.google.com/scholar?cluster=15735655574376328182&hl=en&as_sdt=0,33",2,2022
Lipschitz Continuity Retained Binary Neural Network,6,eccv,0,2,2023-06-17 00:58:08.356000,https://github.com/42shawn/lcr_bnn,3,Lipschitz continuity retained binary neural network,"https://scholar.google.com/scholar?cluster=6812768384057304051&hl=en&as_sdt=0,5",1,2022
Soft Masking for Cost-Constrained Channel Pruning,3,eccv,2,1,2023-06-17 00:58:08.567000,https://github.com/nvlabs/smcp,11,Soft Masking for Cost-Constrained Channel Pruning,"https://scholar.google.com/scholar?cluster=1083006719522141143&hl=en&as_sdt=0,44",5,2022
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning,2,eccv,0,0,2023-06-17 00:58:08.778000,https://github.com/rice-eic/supertickets,15,SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning,"https://scholar.google.com/scholar?cluster=4942576613559407919&hl=en&as_sdt=0,5",1,2022
Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks,6,eccv,0,0,2023-06-17 00:58:08.991000,https://github.com/yfguo91/Real-Spike,6,Real spike: Learning real-valued spikes for spiking neural networks,"https://scholar.google.com/scholar?cluster=14458668834805470627&hl=en&as_sdt=0,5",1,2022
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks,16,eccv,3,1,2023-06-17 00:58:09.203000,https://github.com/intelligent-computing-lab-yale/exploring-lottery-ticket-hypothesis-in-snns,23,Exploring lottery ticket hypothesis in spiking neural networks,"https://scholar.google.com/scholar?cluster=5577399624448590854&hl=en&as_sdt=0,5",2,2022
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search,2,eccv,1,0,2023-06-17 00:58:09.414000,https://github.com/yuezuegu/UBoostNAS,1,U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search,"https://scholar.google.com/scholar?cluster=10686166557327753294&hl=en&as_sdt=0,33",1,2022
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach,0,eccv,1,0,2023-06-17 00:58:09.625000,https://github.com/jsjs0369/MEBQAT,6,Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach,"https://scholar.google.com/scholar?cluster=10533577558725096035&hl=en&as_sdt=0,3",2,2022
Understanding the Dynamics of DNNs Using Graph Modularity,2,eccv,2,0,2023-06-17 00:58:09.836000,https://github.com/yaolu-zjut/dynamic-graphs-construction,12,Understanding the Dynamics of DNNs Using Graph Modularity,"https://scholar.google.com/scholar?cluster=3130723855384229824&hl=en&as_sdt=0,5",1,2022
Latent Discriminant Deterministic Uncertainty,1,eccv,4,2,2023-06-17 00:58:10.048000,https://github.com/ensta-u2is/ldu,31,Latent Discriminant deterministic Uncertainty,"https://scholar.google.com/scholar?cluster=6019200911967615757&hl=en&as_sdt=0,5",3,2022
Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals,7,eccv,2,3,2023-06-17 00:58:10.262000,https://github.com/facebookresearch/visual-counterfactuals,26,Making heads or tails: Towards semantically consistent visual counterfactuals,"https://scholar.google.com/scholar?cluster=15358241880988095277&hl=en&as_sdt=0,31",4,2022
HIVE: Evaluating the Human Interpretability of Visual Explanations,29,eccv,3,0,2023-06-17 00:58:10.474000,https://github.com/princetonvisualai/HIVE,16,Hive: evaluating the human interpretability of visual explanations,"https://scholar.google.com/scholar?cluster=6994210076422193826&hl=en&as_sdt=0,5",6,2022
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks,5,eccv,3,0,2023-06-17 00:58:10.686000,https://github.com/explainableml/bayescap,34,BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks,"https://scholar.google.com/scholar?cluster=17308434351985142486&hl=en&as_sdt=0,33",5,2022
SESS: Saliency Enhancing with Scaling and Sliding,1,eccv,1,0,2023-06-17 00:58:10.905000,https://github.com/neouyghur/sess,6,SESS: Saliency Enhancing with Scaling and Sliding,"https://scholar.google.com/scholar?cluster=8995973640476037455&hl=en&as_sdt=0,39",2,2022
No Token Left Behind: Explainability-Aided Image Classification and Generation,8,eccv,14,1,2023-06-17 00:58:11.145000,https://github.com/apple/ml-no-token-left-behind,129,No token left behind: Explainability-aided image classification and generation,"https://scholar.google.com/scholar?cluster=1190928948401289652&hl=en&as_sdt=0,11",10,2022
Interpretable Image Classification with Differentiable Prototypes Assignment,19,eccv,4,0,2023-06-17 00:58:11.356000,https://github.com/gmum/protopool,15,Interpretable image classification with differentiable prototypes assignment,"https://scholar.google.com/scholar?cluster=16296167149932352355&hl=en&as_sdt=0,5",3,2022
STEEX: Steering Counterfactual Explanations with Semantics,11,eccv,0,0,2023-06-17 00:58:11.568000,https://github.com/valeoai/steex,11,STEEX: steering counterfactual explanations with semantics,"https://scholar.google.com/scholar?cluster=6442354038352446376&hl=en&as_sdt=0,3",4,2022
Cartoon Explanations of Image Classifiers,3,eccv,2,0,2023-06-17 00:58:11.779000,https://github.com/skmda37/CartoonX,5,Cartoon explanations of image classifiers,"https://scholar.google.com/scholar?cluster=6270070436316559917&hl=en&as_sdt=0,5",1,2022
Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain,4,eccv,209,70,2023-06-17 00:58:11.990000,https://github.com/Tencent/TFace,1049,Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain,"https://scholar.google.com/scholar?cluster=6954778808116738247&hl=en&as_sdt=0,5",33,2022
Contrast-Phys: Unsupervised Video-Based Remote Physiological Measurement via Spatiotemporal Contrast,11,eccv,6,0,2023-06-17 00:58:12.203000,https://github.com/zhaodongsun/contrast-phys,26,Contrast-Phys: Unsupervised Video-Based Remote Physiological Measurement via Spatiotemporal Contrast,"https://scholar.google.com/scholar?cluster=5679325064727443124&hl=en&as_sdt=0,5",2,2022
On Mitigating Hard Clusters for Face Clustering,2,eccv,4,1,2023-06-17 00:58:12.415000,https://github.com/echoanran/on-mitigating-hard-clusters,15,On Mitigating Hard Clusters for Face Clustering,"https://scholar.google.com/scholar?cluster=16130959965255916450&hl=en&as_sdt=0,10",2,2022
Label2Label: A Language Modeling Framework for Multi-Attribute Learning,8,eccv,0,0,2023-06-17 00:58:12.627000,https://github.com/li-wanhua/label2label,16,Label2Label: A Language Modeling Framework for Multi-attribute Learning,"https://scholar.google.com/scholar?cluster=15011553915030503496&hl=en&as_sdt=0,33",3,2022
Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition,4,eccv,5,0,2023-06-17 00:58:12.839000,https://github.com/gist-ailab/teaching-where-to-look,40,Teaching where to look: Attention similarity knowledge distillation for low resolution face recognition,"https://scholar.google.com/scholar?cluster=14856315627697119992&hl=en&as_sdt=0,5",3,2022
Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis,20,eccv,30,11,2023-06-17 00:58:13.051000,https://github.com/sstzal/DFRF,243,Learning dynamic facial radiance fields for few-shot talking head synthesis,"https://scholar.google.com/scholar?cluster=16013123018380434224&hl=en&as_sdt=0,29",8,2022
BoundaryFace: A Mining Framework with Noise Label Self-Correction for Face Recognition,1,eccv,1,0,2023-06-17 00:58:13.263000,https://github.com/swjtu-3dvision/boundaryface,16,BoundaryFace: A mining framework with noise label self-correction for Face Recognition,"https://scholar.google.com/scholar?cluster=18205410364397077887&hl=en&as_sdt=0,19",1,2022
Pre-training Strategies and Datasets for Facial Representation Learning,12,eccv,4,1,2023-06-17 00:58:13.474000,https://github.com/1adrianb/unsupervised-face-representation,46,Pre-training strategies and datasets for facial representation learning,"https://scholar.google.com/scholar?cluster=9885962198092429891&hl=en&as_sdt=0,5",3,2022
Multi-Domain Learning for Updating Face Anti-Spoofing Models,6,eccv,6,1,2023-06-17 00:58:13.686000,https://github.com/chelsea234/multi-domain-learning-fas,51,Multi-domain Learning for Updating Face Anti-spoofing Models,"https://scholar.google.com/scholar?cluster=16126198199107928191&hl=en&as_sdt=0,23",5,2022
Towards Metrical Reconstruction of Human Faces,23,eccv,62,0,2023-06-17 00:58:13.897000,https://github.com/Zielon/MICA,384,Towards metrical reconstruction of human faces,"https://scholar.google.com/scholar?cluster=1790459247122342291&hl=en&as_sdt=0,5",7,2022
Discover and Mitigate Unknown Biases with Debiasing Alternate Networks,9,eccv,1,0,2023-06-17 00:58:14.110000,https://github.com/zhihengli-UR/DebiAN,15,Discover and Mitigate Unknown Biases with Debiasing Alternate Networks,"https://scholar.google.com/scholar?cluster=7100367911218047042&hl=en&as_sdt=0,33",2,2022
FairGRAPE: Fairness-Aware GRAdient Pruning mEthod for Face Attribute Classification,9,eccv,1,2,2023-06-17 00:58:14.321000,https://github.com/bernardo1998/fairgrape,8,Fairgrape: Fairness-aware gradient pruning method for face attribute classification,"https://scholar.google.com/scholar?cluster=7797375356062990840&hl=en&as_sdt=0,43",2,2022
Anti-Neuron Watermarking: Protecting Personal Data against Unauthorized Neural Networks,0,eccv,0,0,2023-06-17 00:58:14.533000,https://github.com/zzzucf/anti-neuron-watermarking,5,Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks,"https://scholar.google.com/scholar?cluster=12791067012811994575&hl=en&as_sdt=0,5",1,2022
Privacy-Preserving Action Recognition via Motion Difference Quantization,2,eccv,0,1,2023-06-17 00:58:14.745000,https://github.com/suakaw/bdq_privacyar,6,Privacy-Preserving Action Recognition via Motion Difference Quantization,"https://scholar.google.com/scholar?cluster=10482295319181284449&hl=en&as_sdt=0,33",1,2022
Latent Space Smoothing for Individually Fair Representations,4,eccv,1,0,2023-06-17 00:58:14.958000,https://github.com/eth-sri/lassi,14,Latent space smoothing for individually fair representations,"https://scholar.google.com/scholar?cluster=12962197532517960978&hl=en&as_sdt=0,5",6,2022
Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration,9,eccv,2,0,2023-06-17 00:58:15.169000,https://github.com/tochris/pts-uncertainty,5,Parameterized temperature scaling for boosting the expressive power in post-hoc uncertainty calibration,"https://scholar.google.com/scholar?cluster=1166261285215480874&hl=en&as_sdt=0,11",2,2022
Distilling the Undistillable: Learning from a Nasty Teacher,2,eccv,0,1,2023-06-17 00:58:15.380000,https://github.com/surgan12/nastyattacks,2,Distilling the Undistillable: Learning from a Nasty Teacher,"https://scholar.google.com/scholar?cluster=7326991163599849424&hl=en&as_sdt=0,33",1,2022
Generative Adversarial Network for Future Hand Segmentation from Egocentric Video,4,eccv,0,0,2023-06-17 00:58:15.592000,https://github.com/vjwq/egogan,3,Generative adversarial network for future hand segmentation from egocentric video,"https://scholar.google.com/scholar?cluster=5484506024890510907&hl=en&as_sdt=0,22",1,2022
My View Is the Best View: Procedure Learning from Egocentric Videos,5,eccv,3,0,2023-06-17 00:58:15.804000,https://github.com/Sid2697/EgoProceL-egocentric-procedure-learning,13,My View is the Best View: Procedure Learning from Egocentric Videos,"https://scholar.google.com/scholar?cluster=767508232995055975&hl=en&as_sdt=0,5",1,2022
GIMO: Gaze-Informed Human Motion Prediction in Context,7,eccv,3,2,2023-06-17 00:58:16.016000,https://github.com/y-zheng18/gimo,60,Gimo: Gaze-informed human motion prediction in context,"https://scholar.google.com/scholar?cluster=9657711883563892404&hl=en&as_sdt=0,5",3,2022
Image-Based CLIP-Guided Essence Transfer,19,eccv,23,0,2023-06-17 00:58:16.229000,https://github.com/hila-chefer/targetclip,223,Image-based clip-guided essence transfer,"https://scholar.google.com/scholar?cluster=16130037992547430187&hl=en&as_sdt=0,33",6,2022
Detecting and Recovering Sequential DeepFake Manipulation,3,eccv,8,1,2023-06-17 00:58:16.441000,https://github.com/rshaojimmy/seqdeepfake,94,Detecting and Recovering Sequential DeepFake Manipulation,"https://scholar.google.com/scholar?cluster=16758922200281642852&hl=en&as_sdt=0,14",3,2022
Self-Supervised Sparse Representation for Video Anomaly Detection,9,eccv,7,6,2023-06-17 00:58:16.653000,https://github.com/louisYen/S3R,48,Self-supervised Sparse Representation for Video Anomaly Detection,"https://scholar.google.com/scholar?cluster=14297444957182321222&hl=en&as_sdt=0,5",1,2022
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal,4,eccv,3,1,2023-06-17 00:58:16.877000,https://github.com/thinwayliu/watermark-vaccine,22,Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal,"https://scholar.google.com/scholar?cluster=12688290022858968350&hl=en&as_sdt=0,5",1,2022
Explaining Deepfake Detection by Analysing Image Matching,4,eccv,1,0,2023-06-17 00:58:17.089000,https://github.com/megvii-research/fst-matching,29,Explaining Deepfake Detection by Analysing Image Matching,"https://scholar.google.com/scholar?cluster=11184135980737383589&hl=en&as_sdt=0,5",1,2022
TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations,1,eccv,2,2,2023-06-17 00:58:17.301000,https://github.com/shivangi-aneja/TAFIM,35,TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations,"https://scholar.google.com/scholar?cluster=10194430295026324436&hl=en&as_sdt=0,33",1,2022
RepMix: Representation Mixing for Robust Attribution of Synthesized Images,5,eccv,0,1,2023-06-17 00:58:17.512000,https://github.com/tubui/image_attribution,10,RepMix: Representation Mixing for Robust Attribution of Synthesized Images,"https://scholar.google.com/scholar?cluster=2567453246429238454&hl=en&as_sdt=0,5",1,2022
PASS: Part-Aware Self-Supervised Pre-training for Person Re-identification,5,eccv,7,1,2023-06-17 00:58:17.724000,https://github.com/casia-iva-lab/pass-reid,34,PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification,"https://scholar.google.com/scholar?cluster=4334912752369724562&hl=en&as_sdt=0,16",0,2022
Multi-Query Video Retrieval,13,eccv,2,0,2023-06-17 00:58:17.941000,https://github.com/princetonvisualai/mqvr,20,Multi-query video retrieval,"https://scholar.google.com/scholar?cluster=373211919575033452&hl=en&as_sdt=0,44",5,2022
Hierarchical Average Precision Training for Pertinent Image Retrieval,1,eccv,4,1,2023-06-17 00:58:18.154000,https://github.com/elias-ramzi/happier,12,Hierarchical Average Precision Training for Pertinent Image Retrieval,"https://scholar.google.com/scholar?cluster=7992751292325736220&hl=en&as_sdt=0,5",2,2022
Domain Adaptive Person Search,5,eccv,4,1,2023-06-17 00:58:18.366000,https://github.com/caposerenity/daps,16,Domain Adaptive Person Search,"https://scholar.google.com/scholar?cluster=17332711656821734010&hl=en&as_sdt=0,39",1,2022
TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval,19,eccv,9,1,2023-06-17 00:58:18.578000,https://github.com/yuqi657/ts2_net,51,Ts2-net: Token shift and selection transformer for text-video retrieval,"https://scholar.google.com/scholar?cluster=1773769104865253023&hl=en&as_sdt=0,44",1,2022
Deep Hash Distillation for Image Retrieval,3,eccv,5,2,2023-06-17 00:58:18.790000,https://github.com/youngkyunjang/deep-hash-distillation,29,Deep hash distillation for image retrieval,"https://scholar.google.com/scholar?cluster=10264655525717856077&hl=en&as_sdt=0,33",2,2022
Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification,2,eccv,2,2,2023-06-17 00:58:19.002000,https://github.com/xbq1994/meta,10,Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification,"https://scholar.google.com/scholar?cluster=8699042944012178592&hl=en&as_sdt=0,5",2,2022
RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-Supervised Learning,2,eccv,4,1,2023-06-17 00:58:19.214000,https://github.com/cihsaing/rvsl-robust-vehicle-similarity-learning--eccv22,5,RVSL: Robust vehicle similarity learning in real hazy scenes based on semi-supervised learning,"https://scholar.google.com/scholar?cluster=9327217053746589343&hl=en&as_sdt=0,5",2,2022
Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval,6,eccv,4,4,2023-06-17 00:58:19.426000,https://github.com/ruc-aimc-lab/laff,29,Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval,"https://scholar.google.com/scholar?cluster=17518735408108999983&hl=en&as_sdt=0,28",1,2022
SEMICON: A Learning-to-Hash Solution for Large-Scale Fine-Grained Image Retrieval,2,eccv,0,0,2023-06-17 00:58:19.637000,https://github.com/njust-vipgroup/semicon,2,SEMICON: A Learning-to-Hash Solution for Large-Scale Fine-Grained Image Retrieval,"https://scholar.google.com/scholar?cluster=14218568535389828777&hl=en&as_sdt=0,5",0,2022
Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval,1,eccv,2,0,2023-06-17 00:58:19.849000,https://github.com/dcp15/ual,4,Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval,"https://scholar.google.com/scholar?cluster=10252197058843416825&hl=en&as_sdt=0,36",1,2022
Relighting4D: Neural Relightable Human from Videos,10,eccv,17,1,2023-06-17 00:58:20.061000,https://github.com/frozenburning/relighting4d,213,Relighting4d: Neural relightable human from videos,"https://scholar.google.com/scholar?cluster=17763469044720641433&hl=en&as_sdt=0,31",5,2022
PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation,2,eccv,0,0,2023-06-17 00:58:20.273000,https://github.com/blinghe/pixelfolder,24,PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation,"https://scholar.google.com/scholar?cluster=3679647493154580604&hl=en&as_sdt=0,5",7,2022
Paint2Pix: Interactive Painting Based Progressive Image Synthesis and Editing,3,eccv,12,2,2023-06-17 00:58:20.485000,https://github.com/1jsingh/paint2pix,113,Paint2pix: interactive painting based progressive image synthesis and editing,"https://scholar.google.com/scholar?cluster=8249158505976311908&hl=en&as_sdt=0,5",4,2022
SCAM! Transferring Humans between Images with Semantic Cross Attention Modulation,3,eccv,1,0,2023-06-17 00:58:20.696000,https://github.com/nicolas-dufour/SCAM,46,Scam! transferring humans between images with semantic cross attention modulation,"https://scholar.google.com/scholar?cluster=15976532542691220666&hl=en&as_sdt=0,5",1,2022
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields,6,eccv,7,1,2023-06-17 00:58:20.908000,https://github.com/donydchen/sem2nerf,109,NeRFFaceEditing: Disentangled Face Editing in Neural Radiance Fields,"https://scholar.google.com/scholar?cluster=13488182421709807143&hl=en&as_sdt=0,6",6,2022
WaveGAN: Frequency-Aware GAN for High-Fidelity Few-Shot Image Generation,8,eccv,5,1,2023-06-17 00:58:21.120000,https://github.com/kobeshegu/eccv2022_wavegan,59,WaveGAN: Frequency-Aware GAN for High-Fidelity Few-Shot Image Generation,"https://scholar.google.com/scholar?cluster=3247766894580497169&hl=en&as_sdt=0,5",2,2022
High-Fidelity GAN Inversion with Padding Space,10,eccv,5,5,2023-06-17 00:58:21.331000,https://github.com/ezioby/padinv,83,High-fidelity GAN inversion with padding space,"https://scholar.google.com/scholar?cluster=9284823848742686294&hl=en&as_sdt=0,10",12,2022
Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives,1,eccv,0,1,2023-06-17 00:58:21.543000,https://github.com/megvii-research/Sobolev_INRs,26,Sobolev Training for Implicit Neural Representations with Approximated Image Derivatives,"https://scholar.google.com/scholar?cluster=14044677930376914412&hl=en&as_sdt=0,39",3,2022
Multi-Curve Translator for High-Resolution Photorealistic Image Translation,1,eccv,4,0,2023-06-17 00:58:21.755000,https://github.com/IDKiro/MCT,34,Multi-Curve Translator for High-Resolution Photorealistic Image Translation,"https://scholar.google.com/scholar?cluster=11749069983283105543&hl=en&as_sdt=0,5",1,2022
Cross Attention Based Style Distribution for Controllable Person Image Synthesis,11,eccv,6,10,2023-06-17 00:58:21.967000,https://github.com/xyzhouo/casd,42,Cross attention based style distribution for controllable person image synthesis,"https://scholar.google.com/scholar?cluster=9209475525743779712&hl=en&as_sdt=0,5",1,2022
KeypointNeRF: Generalizing Image-Based Volumetric Avatars Using Relative Spatial Encoding of Keypoints,18,eccv,25,5,2023-06-17 00:58:22.179000,https://github.com/facebookresearch/KeypointNeRF,337,KeypointNeRF: Generalizing image-based volumetric avatars using relative spatial encoding of keypoints,"https://scholar.google.com/scholar?cluster=16360848272016912327&hl=en&as_sdt=0,10",15,2022
ViewFormer: NeRF-Free Neural Rendering from Few Images Using Transformers,17,eccv,14,1,2023-06-17 00:58:22.392000,https://github.com/jkulhanek/viewformer,200,Viewformer: Nerf-free neural rendering from few images using transformers,"https://scholar.google.com/scholar?cluster=5512594723523474841&hl=en&as_sdt=0,10",8,2022
A Perceptual Quality Metric for Video Frame Interpolation,2,eccv,6,2,2023-06-17 00:58:22.606000,https://github.com/hqqxyy/vfips,30,A Perceptual Quality Metric for Video Frame Interpolation,"https://scholar.google.com/scholar?cluster=9562660708549758689&hl=en&as_sdt=0,11",2,2022
Adaptive Feature Interpolation for Low-Shot Image Generation,0,eccv,0,0,2023-06-17 00:58:22.818000,https://github.com/dzld00/adaptive-feature-interpolation-for-low-shot-image-generation,8,Adaptive Feature Interpolation for Low-Shot Image Generation,"https://scholar.google.com/scholar?cluster=14989987349060110294&hl=en&as_sdt=0,33",2,2022
PalGAN: Image Colorization with Palette Generative Adversarial Networks,6,eccv,0,3,2023-06-17 00:58:23.030000,https://github.com/shepnerd/palgan,17,PalGAN: Image Colorization with Palette Generative Adversarial Networks,"https://scholar.google.com/scholar?cluster=3705048691352373757&hl=en&as_sdt=0,31",7,2022
Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis,1,eccv,8,5,2023-06-17 00:58:23.242000,https://github.com/fast-vid2vid/fast-vid2vid,128,Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis,"https://scholar.google.com/scholar?cluster=4910164335098976518&hl=en&as_sdt=0,5",9,2022
Learning Prior Feature and Attention Enhanced Image Inpainting,3,eccv,3,2,2023-06-17 00:58:23.454000,https://github.com/ewrfcas/MAE-FAR,46,Learning Prior Feature and Attention Enhanced Image Inpainting,"https://scholar.google.com/scholar?cluster=729023225466281333&hl=en&as_sdt=0,5",7,2022
3D-Aware Semantic-Guided Generative Model for Human Synthesis,15,eccv,3,2,2023-06-17 00:58:23.665000,https://github.com/zhangqianhui/3DSGAN,32,3D-aware semantic-guided generative model for human synthesis,"https://scholar.google.com/scholar?cluster=6032578839810883949&hl=en&as_sdt=0,5",5,2022
Single Stage Virtual Try-On via Deformable Attention Flows,7,eccv,9,10,2023-06-17 00:58:23.877000,https://github.com/OFA-Sys/DAFlow,88,Single stage virtual try-on via deformable attention flows,"https://scholar.google.com/scholar?cluster=816514931038255814&hl=en&as_sdt=0,3",8,2022
Improving GANs for Long-Tailed Data through Group Spectral Regularization,3,eccv,2,0,2023-06-17 00:58:24.089000,https://github.com/val-iisc/gSRGAN,10,Improving GANs for Long-Tailed Data Through Group Spectral Regularization,"https://scholar.google.com/scholar?cluster=11468125095125646934&hl=en&as_sdt=0,33",12,2022
StyleLight: HDR Panorama Generation for Lighting Estimation and Editing,6,eccv,5,0,2023-06-17 00:58:24.301000,https://github.com/wanggcong/stylelight,87,Stylelight: Hdr panorama generation for lighting estimation and editing,"https://scholar.google.com/scholar?cluster=5315266200307857634&hl=en&as_sdt=0,6",5,2022
Contrastive Monotonic Pixel-Level Modulation,1,eccv,0,0,2023-06-17 00:58:24.513000,https://github.com/lukun199/monopix,5,Contrastive Monotonic Pixel-Level Modulation,"https://scholar.google.com/scholar?cluster=16187508770717297342&hl=en&as_sdt=0,5",1,2022
Learning Cross-Video Neural Representations for High-Quality Frame Interpolation,6,eccv,2,3,2023-06-17 00:58:24.726000,https://github.com/wustl-cig/CURE,10,Learning cross-video neural representations for high-quality frame interpolation,"https://scholar.google.com/scholar?cluster=5727407335413052300&hl=en&as_sdt=0,5",3,2022
Learning Continuous Implicit Representation for Near-Periodic Patterns,2,eccv,4,0,2023-06-17 00:58:24.937000,https://github.com/ArmastusChen/Learning-Continuous-Implicit-Representation-for-Near-Periodic-Patterns,10,Learning Continuous Implicit Representation for Near-Periodic Patterns,"https://scholar.google.com/scholar?cluster=10786354692089181603&hl=en&as_sdt=0,22",3,2022
Few-Shot Image Generation with Mixup-Based Distance Learning,4,eccv,0,0,2023-06-17 00:58:25.160000,https://github.com/reyllama/mixdl,19,Few-Shot Image Generation with Mixup-Based Distance Learning,"https://scholar.google.com/scholar?cluster=11751748346680647468&hl=en&as_sdt=0,5",1,2022
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs,4,eccv,1,0,2023-06-17 00:58:25.372000,https://github.com/iceli1007/fakeclr,18,FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs,"https://scholar.google.com/scholar?cluster=10522416907231616901&hl=en&as_sdt=0,5",0,2022
Discovering Transferable Forensic Features for CNN-Generated Images Detection,6,eccv,1,0,2023-06-17 00:58:25.584000,https://github.com/sutd-visual-computing-group/transferable-forensic-features,11,Discovering Transferable Forensic Features for CNN-generated Images Detection,"https://scholar.google.com/scholar?cluster=16076391671121690755&hl=en&as_sdt=0,5",2,2022
Harmonizer: Learning to Perform White-Box Image and Video Harmonization,11,eccv,18,6,2023-06-17 00:58:25.796000,https://github.com/zhkkke/harmonizer,183,Harmonizer: Learning to perform white-box image and video harmonization,"https://scholar.google.com/scholar?cluster=17664521763664985478&hl=en&as_sdt=0,33",11,2022
Text2LIVE: Text-Driven Layered Image and Video Editing,74,eccv,65,18,2023-06-17 00:58:26.007000,https://github.com/omerbt/Text2LIVE,714,Text2live: Text-driven layered image and video editing,"https://scholar.google.com/scholar?cluster=1643881200367737557&hl=en&as_sdt=0,47",26,2022
StyleGAN-Human: A Data-Centric Odyssey of Human Generation,36,eccv,102,21,2023-06-17 00:58:26.219000,https://github.com/stylegan-human/stylegan-human,864,Stylegan-human: A data-centric odyssey of human generation,"https://scholar.google.com/scholar?cluster=17333199167012013343&hl=en&as_sdt=0,5",36,2022
EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs,3,eccv,2,1,2023-06-17 00:58:26.431000,https://github.com/marsggbo/EAGAN,6,EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs,"https://scholar.google.com/scholar?cluster=7789077417365616404&hl=en&as_sdt=0,33",1,2022
DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation,14,eccv,1,5,2023-06-17 00:58:26.642000,https://github.com/huage001/dynast,42,Dynast: Dynamic sparse transformer for exemplar-guided image generation,"https://scholar.google.com/scholar?cluster=10456276537203160314&hl=en&as_sdt=0,33",1,2022
JoJoGAN: One Shot Face Stylization,27,eccv,200,18,2023-06-17 00:58:26.854000,https://github.com/mchong6/JoJoGAN,1335,Jojogan: One shot face stylization,"https://scholar.google.com/scholar?cluster=3819299370711114953&hl=en&as_sdt=0,39",25,2022
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer,15,eccv,23,3,2023-06-17 00:58:27.065000,https://github.com/JarrentWu1031/CCPL,158,CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer,"https://scholar.google.com/scholar?cluster=4230247138141381244&hl=en&as_sdt=0,8",6,2022
CANF-VC: Conditional Augmented Normalizing Flows for Video Compression,13,eccv,4,2,2023-06-17 00:58:27.277000,https://github.com/nycu-mapl/canf-vc,14,Canf-vc: Conditional augmented normalizing flows for video compression,"https://scholar.google.com/scholar?cluster=9828213896359812187&hl=en&as_sdt=0,21",2,2022
DeltaGAN: Towards Diverse Few-Shot Image Generation with Sample-Specific Delta,16,eccv,3,3,2023-06-17 00:58:27.489000,https://github.com/bcmi/deltagan-few-shot-image-generation,46,Deltagan: Towards diverse few-shot image generation with sample-specific delta,"https://scholar.google.com/scholar?cluster=13654349730598278299&hl=en&as_sdt=0,5",7,2022
The Surprisingly Straightforward Scene Text Removal Method with Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis,0,eccv,4,4,2023-06-17 00:58:27.701000,https://github.com/naver/garnet,35,The Surprisingly Straightforward Scene Text Removal Method with Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis,"https://scholar.google.com/scholar?cluster=3346268277544666601&hl=en&as_sdt=0,47",4,2022
Multiview Regenerative Morphing with Dual Flows,0,eccv,0,0,2023-06-17 00:58:27.914000,https://github.com/jimtsai23/morphflow,5,Multiview Regenerative Morphing with Dual Flows,"https://scholar.google.com/scholar?cluster=178572341855056655&hl=en&as_sdt=0,33",2,2022
Custom Structure Preservation in Face Aging,1,eccv,1,0,2023-06-17 00:58:28.135000,https://github.com/guillermogotre/cusp,15,Custom structure preservation in face aging,"https://scholar.google.com/scholar?cluster=6169634604921471229&hl=en&as_sdt=0,32",1,2022
Spatio-Temporal Deformable Attention Network for Video Deblurring,4,eccv,4,3,2023-06-17 00:58:28.346000,https://github.com/huicongzhang/stdan,36,Spatio-Temporal Deformable Attention Network for Video Deblurring,"https://scholar.google.com/scholar?cluster=3609025100424050379&hl=en&as_sdt=0,5",2,2022
Unbiased Multi-Modality Guidance for Image Inpainting,1,eccv,1,2,2023-06-17 00:58:28.558000,https://github.com/yeates/MMT,24,Unbiased Multi-modality Guidance for Image Inpainting,"https://scholar.google.com/scholar?cluster=10439991380921402413&hl=en&as_sdt=0,33",3,2022
Motion Transformer for Unsupervised Image Animation,1,eccv,2,0,2023-06-17 00:58:28.769000,https://github.com/jialetao/motrans,36,Motion Transformer for Unsupervised Image Animation,"https://scholar.google.com/scholar?cluster=16869199057816863838&hl=en&as_sdt=0,33",3,2022
EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer,5,eccv,17,10,2023-06-17 00:58:28.980000,https://github.com/chenyu-yang-2000/elegant,81,Elegant: Exquisite and locally editable gan for makeup transfer,"https://scholar.google.com/scholar?cluster=6510140835920971269&hl=en&as_sdt=0,5",3,2022
Editing Out-of-Domain GAN Inversion via Differential Activations,5,eccv,3,2,2023-06-17 00:58:29.192000,https://github.com/haoruisong622/editing-out-of-domain,36,Editing Out-of-Domain GAN Inversion via Differential Activations,"https://scholar.google.com/scholar?cluster=18013841559496694332&hl=en&as_sdt=0,33",3,2022
On the Robustness of Quality Measures for GANs,3,eccv,0,0,2023-06-17 00:58:29.404000,https://github.com/motasemalfarra/r-fid-robustness-of-quality-measures-for-gans,6,On the Robustness of Quality Measures for GANs,"https://scholar.google.com/scholar?cluster=11284746856078915311&hl=en&as_sdt=0,5",1,2022
StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN,46,eccv,46,13,2023-06-17 00:58:29.615000,https://github.com/FeiiYin/StyleHEAT,423,StyleHEAT: One-shot high-resolution editable talking face generation via pre-trained StyleGAN,"https://scholar.google.com/scholar?cluster=5864155954786159147&hl=en&as_sdt=0,10",38,2022
Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer,36,eccv,13,3,2023-06-17 00:58:29.826000,https://github.com/songweige/tats,165,Long video generation with time-agnostic vqgan and time-sensitive transformer,"https://scholar.google.com/scholar?cluster=11641368973270167401&hl=en&as_sdt=0,47",10,2022
WISE: Whitebox Image Stylization by Example-Based Learning,0,eccv,0,0,2023-06-17 00:58:30.037000,https://github.com/MaxReimann/WISE-Editing,5,WISE: Whitebox Image Stylization by Example-Based Learning,"https://scholar.google.com/scholar?cluster=11721863994418579715&hl=en&as_sdt=0,5",1,2022
Transformers As Meta-Learners for Implicit Neural Representations,8,eccv,4,3,2023-06-17 00:58:30.249000,https://github.com/yinboc/trans-inr,105,Transformers as Meta-learners for Implicit Neural Representations,"https://scholar.google.com/scholar?cluster=14082790836112531054&hl=en&as_sdt=0,5",4,2022
Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment,4,eccv,23,12,2023-06-17 00:58:30.461000,https://github.com/taeu/style-your-hair,146,Style your hair: Latent optimization for pose-invariant hairstyle transfer via local-style-aware hair alignment,"https://scholar.google.com/scholar?cluster=5314884270219491237&hl=en&as_sdt=0,16",6,2022
High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions,15,eccv,95,37,2023-06-17 00:58:30.672000,https://github.com/sangyun884/hr-viton,479,High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions,"https://scholar.google.com/scholar?cluster=9937789255365356917&hl=en&as_sdt=0,5",13,2022
Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis,8,eccv,13,0,2023-06-17 00:58:30.886000,https://github.com/jgkwak95/surf-gan,105,Injecting 3d perception of controllable nerf-gan into stylegan for editable portrait image synthesis,"https://scholar.google.com/scholar?cluster=1000004118589887288&hl=en&as_sdt=0,10",4,2022
AdaNeRF: Adaptive Sampling for Real-Time Rendering of Neural Radiance Fields,15,eccv,10,4,2023-06-17 00:58:31.103000,https://github.com/thomasneff/AdaNeRF,230,AdaNeRF: Adaptive Sampling for Real-Time Rendering of Neural Radiance Fields,"https://scholar.google.com/scholar?cluster=1612473994682901973&hl=en&as_sdt=0,10",9,2022
Improving the Perceptual Quality of 2D Animation Interpolation,5,eccv,8,0,2023-06-17 00:58:31.315000,https://github.com/shuhongchen/eisai-anime-interpolator,68,Improving the perceptual quality of 2d animation interpolation,"https://scholar.google.com/scholar?cluster=12566095824277126296&hl=en&as_sdt=0,5",9,2022
Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution,3,eccv,7,5,2023-06-17 00:58:31.527000,https://github.com/zhjy2016/splut,42,Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution,"https://scholar.google.com/scholar?cluster=15232388700978391598&hl=en&as_sdt=0,23",5,2022
DoodleFormer: Creative Sketch Drawing with Transformers,7,eccv,4,0,2023-06-17 00:58:31.738000,https://github.com/ankanbhunia/doodleformer,21,Doodleformer: Creative sketch drawing with transformers,"https://scholar.google.com/scholar?cluster=2352232171072001106&hl=en&as_sdt=0,47",3,2022
Implicit Neural Representations for Variable Length Human Motion Generation,11,eccv,1,2,2023-06-17 00:58:31.950000,https://github.com/pacerv/implicitmotion,14,Implicit neural representations for variable length human motion generation,"https://scholar.google.com/scholar?cluster=3880426342699898660&hl=en&as_sdt=0,10",3,2022
Learning Object Placement via Dual-Path Graph Completion,0,eccv,5,1,2023-06-17 00:58:32.161000,https://github.com/bcmi/graconet-object-placement,58,Learning Object Placement via Dual-Path Graph Completion,"https://scholar.google.com/scholar?cluster=13655956839518412245&hl=en&as_sdt=0,31",7,2022
Expanded Adaptive Scaling Normalization for End to End Image Compression,2,eccv,2,2,2023-06-17 00:58:32.373000,https://github.com/ChajinShin/EASN,10,Expanded Adaptive Scaling Normalization for End to End Image Compression,"https://scholar.google.com/scholar?cluster=934780312576435440&hl=en&as_sdt=0,5",1,2022
Generator Knows What Discriminator Should Learn in Unconditional GANs,4,eccv,8,0,2023-06-17 00:58:32.584000,https://github.com/naver-ai/ggdr,102,Generator Knows What Discriminator Should Learn in Unconditional GANs,"https://scholar.google.com/scholar?cluster=2844655096145041282&hl=en&as_sdt=0,5",3,2022
Compositional Visual Generation with Composable Diffusion Models,74,eccv,34,2,2023-06-17 00:58:32.798000,https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch,354,Compositional visual generation with composable diffusion models,"https://scholar.google.com/scholar?cluster=12245459038718110522&hl=en&as_sdt=0,5",13,2022
ManiFest: Manifold Deformation for Few-Shot Image Translation,6,eccv,3,0,2023-06-17 00:58:33.010000,https://github.com/cv-rits/manifest,45,Manifest: Manifold deformation for few-shot image translation,"https://scholar.google.com/scholar?cluster=11893365927357803426&hl=en&as_sdt=0,44",2,2022
Supervised Attribute Information Removal and Reconstruction for Image Manipulation,1,eccv,0,1,2023-06-17 00:58:33.224000,https://github.com/nannanli999/airr,3,Supervised Attribute Information Removal and Reconstruction for Image Manipulation,"https://scholar.google.com/scholar?cluster=791705095185319394&hl=en&as_sdt=0,33",1,2022
Context-Consistent Semantic Image Editing with Style-Preserved Modulation,2,eccv,3,0,2023-06-17 00:58:33.436000,https://github.com/wuyangluo/spmpgan,22,Context-Consistent Semantic Image Editing with Style-Preserved Modulation,"https://scholar.google.com/scholar?cluster=16781287196069917950&hl=en&as_sdt=0,4",3,2022
Eliminating Gradient Conflict in Reference-Based Line-Art Colorization,5,eccv,3,1,2023-06-17 00:58:33.648000,https://github.com/kunkun0w0/sga,29,Eliminating Gradient Conflict in Reference-based Line-Art Colorization,"https://scholar.google.com/scholar?cluster=6270700589308346326&hl=en&as_sdt=0,11",2,2022
Efficient Long-Range Attention Network for Image Super-Resolution,43,eccv,15,16,2023-06-17 00:58:33.860000,https://github.com/xindongzhang/elan,153,Efficient long-range attention network for image super-resolution,"https://scholar.google.com/scholar?cluster=3464402829187061665&hl=en&as_sdt=0,47",9,2022
FlowFormer: A Transformer Architecture for Optical Flow,61,eccv,33,10,2023-06-17 00:58:34.073000,https://github.com/drinkingcoder/FlowFormer-Official,281,Flowformer: A transformer architecture for optical flow,"https://scholar.google.com/scholar?cluster=14023494476653043335&hl=en&as_sdt=0,5",14,2022
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction,31,eccv,70,0,2023-06-17 00:58:34.288000,https://github.com/caiyuanhao1998/MST,386,Coarse-to-fine sparse transformer for hyperspectral image reconstruction,"https://scholar.google.com/scholar?cluster=2900405794489939688&hl=en&as_sdt=0,14",7,2022
Dynamic Dual Trainable Bounds for Ultra-Low Precision Super-Resolution Networks,9,eccv,3,0,2023-06-17 00:58:34.500000,https://github.com/zysxmu/ddtb,19,Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks,"https://scholar.google.com/scholar?cluster=1333565731030185938&hl=en&as_sdt=0,10",1,2022
OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers,11,eccv,15,0,2023-06-17 00:58:34.712000,https://github.com/pjlallen/osformer,70,Osformer: One-stage camouflaged instance segmentation with transformers,"https://scholar.google.com/scholar?cluster=3884436386572116508&hl=en&as_sdt=0,5",7,2022
Highly Accurate Dichotomous Image Segmentation,14,eccv,158,57,2023-06-17 00:58:34.923000,https://github.com/xuebinqin/DIS,1378,Highly accurate dichotomous image segmentation,"https://scholar.google.com/scholar?cluster=14912311473813840654&hl=en&as_sdt=0,15",81,2022
Flow-Guided Transformer for Video Inpainting,9,eccv,20,4,2023-06-17 00:58:35.149000,https://github.com/hitachinsk/fgt,198,Flow-guided transformer for video inpainting,"https://scholar.google.com/scholar?cluster=1751280143087107618&hl=en&as_sdt=0,47",14,2022
Shift-tolerant Perceptual Similarity Metric,2,eccv,0,0,2023-06-17 00:58:35.360000,https://github.com/abhijay9/shifttolerant-lpips,17,Shift-tolerant Perceptual Similarity Metric,"https://scholar.google.com/scholar?cluster=17919204630676311579&hl=en&as_sdt=0,32",1,2022
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution,1,eccv,0,1,2023-06-17 00:58:35.572000,https://github.com/yuehan717/pdasr,16,Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution,"https://scholar.google.com/scholar?cluster=7760707849215243462&hl=en&as_sdt=0,39",3,2022
VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder,18,eccv,33,12,2023-06-17 00:58:35.783000,https://github.com/tencentarc/vqfr,237,Vqfr: Blind face restoration with vector-quantized dictionary and parallel decoder,"https://scholar.google.com/scholar?cluster=17305503687713052200&hl=en&as_sdt=0,25",26,2022
Learning Local Implicit Fourier Representation for Image Warping,2,eccv,3,0,2023-06-17 00:58:35.996000,https://github.com/jaewon-lee-b/ltew,38,Learning Local Implicit Fourier Representation for Image Warping,"https://scholar.google.com/scholar?cluster=1487802789822830782&hl=en&as_sdt=0,5",3,2022
SepLUT: Separable Image-Adaptive Lookup Tables for Real-Time Image Enhancement,6,eccv,3,5,2023-06-17 00:58:36.207000,https://github.com/ImCharlesY/SepLUT,30,SepLUT: Separable Image-Adaptive Lookup Tables for Real-Time Image Enhancement,"https://scholar.google.com/scholar?cluster=13045636902151832924&hl=en&as_sdt=0,33",3,2022
Blind Image Decomposition,8,eccv,7,4,2023-06-17 00:58:36.419000,https://github.com/JunlinHan/BID,86,Blind image decomposition,"https://scholar.google.com/scholar?cluster=13827440915849525842&hl=en&as_sdt=0,26",3,2022
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution,8,eccv,10,10,2023-06-17 00:58:36.630000,https://github.com/researchmm/ftvsr,126,Learning spatiotemporal frequency-transformer for compressed video super-resolution,"https://scholar.google.com/scholar?cluster=3517362769556707866&hl=en&as_sdt=0,33",5,2022
Adaptive Patch Exiting for Scalable Single Image Super-Resolution,4,eccv,1,4,2023-06-17 00:58:36.842000,https://github.com/littlepure2333/ape,43,Adaptive Patch Exiting for Scalable Single Image Super-Resolution,"https://scholar.google.com/scholar?cluster=13105082447976896164&hl=en&as_sdt=0,39",4,2022
Efficient Meta-Tuning for Content-Aware Neural Video Delivery,3,eccv,1,0,2023-06-17 00:58:37.054000,https://github.com/neural-video-delivery/emt-pytorch-eccv2022,6,Efficient Meta-Tuning for Content-Aware Neural Video Delivery,"https://scholar.google.com/scholar?cluster=10130679358783772745&hl=en&as_sdt=0,11",0,2022
Reference-Based Image Super-Resolution with Deformable Attention Transformer,7,eccv,5,10,2023-06-17 00:58:37.266000,https://github.com/caojiezhang/datsr,88,Reference-Based Image Super-Resolution with Deformable Attention Transformer,"https://scholar.google.com/scholar?cluster=16071126281599435175&hl=en&as_sdt=0,33",10,2022
Local Color Distributions Prior for Image Enhancement,9,eccv,9,0,2023-06-17 00:58:37.478000,https://github.com/onpix/LCDPNet,68,Local color distributions prior for image enhancement,"https://scholar.google.com/scholar?cluster=17586761629730586859&hl=en&as_sdt=0,5",3,2022
From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution,5,eccv,6,0,2023-06-17 00:58:37.690000,https://github.com/csxmli2016/redegnet,88,From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution,"https://scholar.google.com/scholar?cluster=13593701505599300377&hl=en&as_sdt=0,5",9,2022
Towards Interpretable Video Super-Resolution via Alternating Optimization,5,eccv,1,5,2023-06-17 00:58:37.903000,https://github.com/caojiezhang/davsr,59,Towards interpretable video super-resolution via alternating optimization,"https://scholar.google.com/scholar?cluster=10238293316792513067&hl=en&as_sdt=0,5",3,2022
Event-Based Fusion for Motion Deblurring with Cross-Modal Attention,17,eccv,10,9,2023-06-17 00:58:38.114000,https://github.com/AHupuJR/EFNet,92,Event-based fusion for motion deblurring with cross-modal attention,"https://scholar.google.com/scholar?cluster=14490474713276822461&hl=en&as_sdt=0,5",4,2022
Uncertainty Inspired Underwater Image Enhancement,9,eccv,4,1,2023-06-17 00:58:38.327000,https://github.com/zhenqifu/puie-net,19,Uncertainty Inspired Underwater Image Enhancement,"https://scholar.google.com/scholar?cluster=10126452681191982181&hl=en&as_sdt=0,33",1,2022
Unfolded Deep Kernel Estimation for Blind Image Super-Resolution,2,eccv,5,2,2023-06-17 00:58:38.539000,https://github.com/natezhenghy/udke,17,Unfolded Deep Kernel Estimation for Blind Image Super-Resolution,"https://scholar.google.com/scholar?cluster=1520667820848489098&hl=en&as_sdt=0,22",3,2022
Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution,7,eccv,8,10,2023-06-17 00:58:38.752000,https://github.com/csjliang/dasr,97,Efficient and degradation-adaptive network for real-world image super-resolution,"https://scholar.google.com/scholar?cluster=3974082769301249568&hl=en&as_sdt=0,48",4,2022
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-Ahead Forward Ones,4,eccv,2,0,2023-06-17 00:58:38.964000,https://github.com/nagejacob/flornn,20,Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-Ahead Forward Ones,"https://scholar.google.com/scholar?cluster=15935770797216345092&hl=en&as_sdt=0,5",3,2022
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations,5,eccv,7,0,2023-06-17 00:58:39.176000,https://github.com/cszhilu1998/selfdzsr,53,Self-supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations,"https://scholar.google.com/scholar?cluster=16385658738161606222&hl=en&as_sdt=0,33",1,2022
Secrets of Event-Based Optical Flow,25,eccv,3,1,2023-06-17 00:58:39.388000,https://github.com/tub-rip/event_based_optical_flow,75,Secrets of event-based optical flow,"https://scholar.google.com/scholar?cluster=13783388448881671161&hl=en&as_sdt=0,25",8,2022
ART-SS: An Adaptive Rejection Technique for Semi-Supervised Restoration for Adverse Weather-Affected Images,1,eccv,0,2,2023-06-17 00:58:39.600000,https://github.com/rajeevyasarla/art-ss,4,Art-ss: An adaptive rejection technique for semi-supervised restoration for adverse weather-affected images,"https://scholar.google.com/scholar?cluster=8634464602596189660&hl=en&as_sdt=0,25",0,2022
Learning Degradation Representations for Image Deblurring,10,eccv,5,0,2023-06-17 00:58:39.811000,https://github.com/dasongli1/learning_degradation,38,Learning Degradation Representations for Image Deblurring,"https://scholar.google.com/scholar?cluster=11119821786788764797&hl=en&as_sdt=0,39",1,2022
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution,4,eccv,0,4,2023-06-17 00:58:40.022000,https://github.com/palmdong/mmsr,43,Learning Mutual Modulation for Self-supervised Cross-Modal Super-Resolution,"https://scholar.google.com/scholar?cluster=298716658447470528&hl=en&as_sdt=0,5",1,2022
Neural Color Operators for Sequential Image Retouching,4,eccv,3,0,2023-06-17 00:58:40.235000,https://github.com/amberwangyili/neurop,39,Neural Color Operators for Sequential Image Retouching,"https://scholar.google.com/scholar?cluster=3823720464495152486&hl=en&as_sdt=0,41",3,2022
Optimizing Image Compression via Joint Learning with Denoising,4,eccv,4,1,2023-06-17 00:58:40.447000,https://github.com/felixcheng97/denoisecompression,29,Optimizing image compression via joint learning with denoising,"https://scholar.google.com/scholar?cluster=13649554563810257260&hl=en&as_sdt=0,5",2,2022
Compiler-Aware Neural Architecture Search for On-Mobile Real-Time Super-Resolution,3,eccv,1,0,2023-06-17 00:58:40.659000,https://github.com/wuyushuwys/compiler-aware-nas-sr,17,Compiler-aware neural architecture search for on-mobile real-time super-resolution,"https://scholar.google.com/scholar?cluster=2243620518317324754&hl=en&as_sdt=0,25",2,2022
Modeling Mask Uncertainty in Hyperspectral Image Reconstruction,3,eccv,2,0,2023-06-17 00:58:40.870000,https://github.com/jiamian-wang/mask_uncertainty_spectral_sci,16,Modeling mask uncertainty in hyperspectral image reconstruction,"https://scholar.google.com/scholar?cluster=6079714254448074761&hl=en&as_sdt=0,36",1,2022
Stripformer: Strip Transformer for Fast Image Deblurring,26,eccv,6,0,2023-06-17 00:58:41.083000,https://github.com/pp00704831/Stripformer,58,Stripformer: Strip transformer for fast image deblurring,"https://scholar.google.com/scholar?cluster=10215844569682190526&hl=en&as_sdt=0,14",1,2022
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution,1,eccv,0,0,2023-06-17 00:58:41.295000,https://github.com/setsunil/dsdnet,2,Learning Discriminative Shrinkage Deep Networks for Image Deconvolution,"https://scholar.google.com/scholar?cluster=13377495633902429445&hl=en&as_sdt=0,5",1,2022
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution,3,eccv,1,3,2023-06-17 00:58:41.507000,https://github.com/jiahong-fu/kxnet,19,KXNet: A model-driven deep neural network for blind super-resolution,"https://scholar.google.com/scholar?cluster=12146394812137134639&hl=en&as_sdt=0,23",1,2022
ARM: Any-Time Super-Resolution Method,7,eccv,6,2,2023-06-17 00:58:41.719000,https://github.com/chenbong/arm-net,66,Arm: Any-time super-resolution method,"https://scholar.google.com/scholar?cluster=9854931424979748889&hl=en&as_sdt=0,23",2,2022
RealFlow: EM-Based Realistic Optical Flow Dataset Generation from Videos,5,eccv,6,9,2023-06-17 00:58:41.930000,https://github.com/megvii-research/realflow,65,RealFlow: EM-Based Realistic Optical Flow Dataset Generation from Videos,"https://scholar.google.com/scholar?cluster=2357872755011993501&hl=en&as_sdt=0,10",5,2022
Ghost-Free High Dynamic Range Imaging with Context-Aware Transformer,9,eccv,8,1,2023-06-17 00:58:42.141000,https://github.com/megvii-research/hdr-transformer,62,Ghost-free High Dynamic Range Imaging with Context-aware Transformer,"https://scholar.google.com/scholar?cluster=11991343694761677136&hl=en&as_sdt=0,23",7,2022
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution,4,eccv,1,1,2023-06-17 00:58:42.353000,https://github.com/megvii-research/d2c-sr,35,D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution,"https://scholar.google.com/scholar?cluster=3563418582975171663&hl=en&as_sdt=0,39",3,2022
GRIT-VLP: Grouped Mini-Batch Sampling for Efficient Vision and Language Pre-training,2,eccv,3,0,2023-06-17 00:58:42.565000,https://github.com/jaeseokbyun/grit-vlp,15,GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training,"https://scholar.google.com/scholar?cluster=15243780204045830710&hl=en&as_sdt=0,6",1,2022
Efficient Video Deblurring Guided by Motion Magnitude,4,eccv,2,0,2023-06-17 00:58:42.777000,https://github.com/sollynoay/mmp-rnn,33,Efficient video deblurring guided by motion magnitude,"https://scholar.google.com/scholar?cluster=18330514522578591072&hl=en&as_sdt=0,39",2,2022
Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and a New Physics-Inspired Transformer Model,9,eccv,3,4,2023-06-17 00:58:42.989000,https://github.com/vita-group/turbnet,17,Single frame atmospheric turbulence mitigation: A benchmark study and a new physics-inspired transformer model,"https://scholar.google.com/scholar?cluster=4037966427869046131&hl=en&as_sdt=0,44",10,2022
Image Super-Resolution with Deep Dictionary,3,eccv,4,0,2023-06-17 00:58:43.201000,https://github.com/shuntama/srdd,21,Image Super-Resolution with Deep Dictionary,"https://scholar.google.com/scholar?cluster=11087658392566964930&hl=en&as_sdt=0,5",1,2022
Transformer with Implicit Edges for Particle-Based Physics Simulation,1,eccv,1,0,2023-06-17 00:58:43.413000,https://github.com/ftbabi/tie_eccv2022,18,Transformer with Implicit Edges for Particle-Based Physics Simulation,"https://scholar.google.com/scholar?cluster=9449642789524236611&hl=en&as_sdt=0,36",1,2022
Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance,8,eccv,3,2,2023-06-17 00:58:43.624000,https://github.com/zzh-tech/animation-from-blur,53,Animation from blur: Multi-modal blur decomposition with motion guidance,"https://scholar.google.com/scholar?cluster=9123494897020961967&hl=en&as_sdt=0,5",4,2022
Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition,4,eccv,1,1,2023-06-17 00:58:43.837000,https://github.com/zouxinyi0625/cdnet,13,Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition,"https://scholar.google.com/scholar?cluster=1956941044982502116&hl=en&as_sdt=0,3",2,2022
Self-Support Few-Shot Semantic Segmentation,17,eccv,7,5,2023-06-17 00:58:44.048000,https://github.com/fanq15/ssp,69,Self-support few-shot semantic segmentation,"https://scholar.google.com/scholar?cluster=9613308018815988294&hl=en&as_sdt=0,3",2,2022
Self-Supervision Can Be a Good Few-Shot Learner,12,eccv,6,0,2023-06-17 00:58:44.261000,https://github.com/bbbdylan/unisiam,23,Self-supervision can be a good few-shot learner,"https://scholar.google.com/scholar?cluster=4096987818886615290&hl=en&as_sdt=0,4",3,2022
tSF: Transformer-Based Semantic Filter for Few-Shot Learning,2,eccv,0,0,2023-06-17 00:58:44.474000,https://github.com/Layjins/FewShotLearning-tSF,3,tSF: Transformer-Based Semantic Filter for Few-Shot Learning,"https://scholar.google.com/scholar?cluster=5698571287241225877&hl=en&as_sdt=0,11",3,2022
Adversarial Feature Augmentation for Cross-Domain Few-Shot Classification,10,eccv,1,2,2023-06-17 00:58:44.685000,https://github.com/youthhoo/afa_for_few_shot_learning,11,Adversarial Feature Augmentation for Cross-domain Few-Shot Classification,"https://scholar.google.com/scholar?cluster=14462214637786993820&hl=en&as_sdt=0,18",2,2022
Constructing Balance from Imbalance for Long-Tailed Image Recognition,7,eccv,0,0,2023-06-17 00:58:44.896000,https://github.com/silicx/dlsa,13,Constructing balance from imbalance for long-tailed image recognition,"https://scholar.google.com/scholar?cluster=12609344790634586279&hl=en&as_sdt=0,11",2,2022
Few-Shot Video Object Detection,5,eccv,47,55,2023-06-17 00:58:45.108000,https://github.com/fanq15/FewX,316,Few-shot video object detection,"https://scholar.google.com/scholar?cluster=7961825092807755752&hl=en&as_sdt=0,10",14,2022
Worst Case Matters for Few-Shot Recognition,1,eccv,0,0,2023-06-17 00:58:45.320000,https://github.com/heekhero/ACSR,7,Worst Case Matters for Few-Shot Recognition,"https://scholar.google.com/scholar?cluster=12823135366704972810&hl=en&as_sdt=0,33",1,2022
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification,3,eccv,1,0,2023-06-17 00:58:45.532000,https://github.com/WilliamYi96/HGR-Net,14,Exploring hierarchical graph representation for large-scale zero-shot image classification,"https://scholar.google.com/scholar?cluster=4800924625104175232&hl=en&as_sdt=0,14",2,2022
Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation,6,eccv,0,2,2023-06-17 00:58:45.743000,https://github.com/shadowxzt/dacm-few-shot.pytorch,8,Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation,"https://scholar.google.com/scholar?cluster=1359227618447849305&hl=en&as_sdt=0,5",1,2022
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation,9,eccv,11,0,2023-06-17 00:58:45.955000,https://github.com/pawn-sxy/dcama,46,Dense cross-query-and-support attention weighted mask aggregation for few-shot segmentation,"https://scholar.google.com/scholar?cluster=17485956023511063051&hl=en&as_sdt=0,14",0,2022
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning,2,eccv,1,0,2023-06-17 00:58:46.182000,https://github.com/xingpingdong/pl-cfe,3,Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning,"https://scholar.google.com/scholar?cluster=9620651412344831514&hl=en&as_sdt=0,44",3,2022
Few-Shot Class-Incremental Learning for 3D Point Cloud Objects,4,eccv,4,3,2023-06-17 00:58:46.395000,https://github.com/townim-faisal/fscil-3d,16,Few-Shot Class-Incremental Learning for 3D Point Cloud Objects,"https://scholar.google.com/scholar?cluster=1772360174390670957&hl=en&as_sdt=0,36",1,2022
Time-rEversed diffusioN tEnsor Transformer: A New TENET of Few-Shot Object Detection,6,eccv,0,1,2023-06-17 00:58:46.606000,https://github.com/zs123-lang/tenet,3,Time-rEversed DiffusioN tEnsor Transformer: A New TENET of Few-Shot Object Detection,"https://scholar.google.com/scholar?cluster=10668839897669475580&hl=en&as_sdt=0,33",2,2022
Self-Promoted Supervision for Few-Shot Transformer,4,eccv,4,4,2023-06-17 00:58:46.818000,https://github.com/dongsky/few-shot-vit,40,Self-Promoted Supervision for Few-Shot Transformer,"https://scholar.google.com/scholar?cluster=10018956318668229427&hl=en&as_sdt=0,5",3,2022
Few-Shot Object Counting and Detection,5,eccv,1,9,2023-06-17 00:58:47.030000,https://github.com/vinairesearch/counting-detr,26,Few-Shot Object Counting and Detection,"https://scholar.google.com/scholar?cluster=11531166092977785304&hl=en&as_sdt=0,24",3,2022
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark,3,eccv,1,1,2023-06-17 00:58:47.243000,https://github.com/amazon-research/few-shot-object-detection-benchmark,8,Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark,"https://scholar.google.com/scholar?cluster=11138126306412509009&hl=en&as_sdt=0,44",9,2022
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations,4,eccv,1,0,2023-06-17 00:58:47.455000,https://github.com/WentaoChen0813/CDCS-FSL,5,Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations,"https://scholar.google.com/scholar?cluster=12324415757438647098&hl=en&as_sdt=0,33",2,2022
Improving Few-Shot Learning through Multi-task Representation Learning Theory,7,eccv,0,0,2023-06-17 00:58:47.666000,https://github.com/cea-list/metamtreg,5,Improving Few-Shot Learning Through Multi-task Representation Learning Theory,"https://scholar.google.com/scholar?cluster=9061027599238574404&hl=en&as_sdt=0,5",2,2022
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation,5,eccv,3,0,2023-06-17 00:58:47.878000,https://github.com/remiMZ/HTS-ECCV22,7,Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation,"https://scholar.google.com/scholar?cluster=7260005370926286580&hl=en&as_sdt=0,44",2,2022
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments,6,eccv,0,1,2023-06-17 00:58:48.091000,https://github.com/vinairesearch/fsvc-ata,13,Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments,"https://scholar.google.com/scholar?cluster=8246881591316575010&hl=en&as_sdt=0,5",4,2022
Temporal and Cross-Modal Attention for Audio-Visual Zero-Shot Learning,3,eccv,0,1,2023-06-17 00:58:48.302000,https://github.com/explainableml/tcaf-gzsl,20,Temporal and cross-modal attention for audio-visual zero-shot learning,"https://scholar.google.com/scholar?cluster=9127263304491981894&hl=en&as_sdt=0,33",5,2022
HM: Hybrid Masking for Few-Shot Segmentation,2,eccv,1,0,2023-06-17 00:58:48.514000,https://github.com/moonsh/hm-hybrid-masking,5,HM: Hybrid Masking for Few-Shot Segmentation,"https://scholar.google.com/scholar?cluster=2746624612924022604&hl=en&as_sdt=0,5",1,2022
Kernel Relative-Prototype Spectral Filtering for Few-Shot Learning,3,eccv,0,0,2023-06-17 00:58:48.726000,https://github.com/zhangtao2022/dsfn,1,Kernel Relative-prototype Spectral Filtering for Few-Shot Learning,"https://scholar.google.com/scholar?cluster=12268793648832428513&hl=en&as_sdt=0,31",1,2022
CLOSE: Curriculum Learning on the Sharing Extent towards Better One-Shot NAS,7,eccv,27,13,2023-06-17 00:58:48.937000,https://github.com/walkerning/aw_nas,224,Close: Curriculum learning on the sharing extent towards better one-shot nas,"https://scholar.google.com/scholar?cluster=18233510394396201076&hl=en&as_sdt=0,34",20,2022
Streamable Neural Fields,6,eccv,2,0,2023-06-17 00:58:49.169000,https://github.com/jwcho5576/streamable_nf,33,Streamable neural fields,"https://scholar.google.com/scholar?cluster=1384360260508089902&hl=en&as_sdt=0,14",2,2022
Gradient-Based Uncertainty for Monocular Depth Estimation,4,eccv,3,1,2023-06-17 00:58:49.381000,https://github.com/jhornauer/grumodepth,28,Gradient-Based Uncertainty for Monocular Depth Estimation,"https://scholar.google.com/scholar?cluster=12746982656582274263&hl=en&as_sdt=0,47",3,2022
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution,3,eccv,3,2,2023-06-17 00:58:49.592000,https://github.com/taehokim20/cprune,9,CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution,"https://scholar.google.com/scholar?cluster=15980442821409200207&hl=en&as_sdt=0,5",2,2022
OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses,4,eccv,1,0,2023-06-17 00:58:49.805000,https://github.com/erobic/occam-nets-v1,5,Occamnets: Mitigating dataset bias by favoring simpler hypotheses,"https://scholar.google.com/scholar?cluster=13162946641295046686&hl=en&as_sdt=0,5",2,2022
Unpaired Image Translation via Vector Symbolic Architectures,5,eccv,4,0,2023-06-17 00:58:50.017000,https://github.com/facebookresearch/vsait,37,Unpaired Image Translation via Vector Symbolic Architectures,"https://scholar.google.com/scholar?cluster=17990095507514740621&hl=en&as_sdt=0,25",5,2022
TinyViT: Fast Pretraining Distillation for Small Vision Transformers,23,eccv,167,24,2023-06-17 00:58:50.228000,https://github.com/microsoft/cream,1078,Tinyvit: Fast pretraining distillation for small vision transformers,"https://scholar.google.com/scholar?cluster=4658683247078177479&hl=en&as_sdt=0,5",25,2022
Equivariant Hypergraph Neural Networks,3,eccv,2,0,2023-06-17 00:58:50.439000,https://github.com/jw9730/ehnn,15,Equivariant Hypergraph Neural Networks,"https://scholar.google.com/scholar?cluster=5938186475562018150&hl=en&as_sdt=0,5",1,2022
ScaleNet: Searching for the Model to Scale,2,eccv,1,0,2023-06-17 00:58:50.651000,https://github.com/luminolx/scalenet,11,ScaleNet: Searching for the Model to Scale,"https://scholar.google.com/scholar?cluster=16572205936902670187&hl=en&as_sdt=0,47",2,2022
Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction,0,eccv,0,2,2023-06-17 00:58:50.863000,https://github.com/vincent-leguen/COMBO,3,Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction,"https://scholar.google.com/scholar?cluster=15266485381591197947&hl=en&as_sdt=0,22",1,2022
ViTAS: Vision Transformer Architecture Search,6,eccv,8,3,2023-06-17 00:58:51.074000,https://github.com/xiusu/ViTAS,46,ViTAS: Vision transformer architecture search,"https://scholar.google.com/scholar?cluster=14119978498301589160&hl=en&as_sdt=0,10",4,2022
Black-Box Few-Shot Knowledge Distillation,2,eccv,1,0,2023-06-17 00:58:51.286000,https://github.com/nphdang/fs-bbt,8,Black-box few-shot knowledge distillation,"https://scholar.google.com/scholar?cluster=5688113168766279249&hl=en&as_sdt=0,50",1,2022
LA3: Efficient Label-Aware AutoAugment,0,eccv,1,0,2023-06-17 00:58:51.499000,https://github.com/simpleple/la3-label-aware-autoaugment,3,LA3: Efficient Label-Aware AutoAugment,"https://scholar.google.com/scholar?cluster=3894389871011653773&hl=en&as_sdt=0,5",1,2022
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps,6,eccv,0,1,2023-06-17 00:58:51.711000,https://github.com/Alii-Ganjj/InterpretationsSteeredPruning,3,Interpretations steered network pruning via amortized inferred saliency maps,"https://scholar.google.com/scholar?cluster=16292895093122182950&hl=en&as_sdt=0,31",2,2022
BA-Net: Bridge Attention for Deep Convolutional Neural Networks,6,eccv,0,3,2023-06-17 00:58:51.922000,https://github.com/zhaoy376/bridge-attention,26,BA-Net: Bridge attention for deep convolutional neural networks,"https://scholar.google.com/scholar?cluster=16233048016302722444&hl=en&as_sdt=0,24",1,2022
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks,1,eccv,0,0,2023-06-17 00:58:52.135000,https://github.com/berndprach/aol,2,Almost-orthogonal layers for efficient general-purpose Lipschitz networks,"https://scholar.google.com/scholar?cluster=11685902648980734119&hl=en&as_sdt=0,5",1,2022
Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration,8,eccv,3,0,2023-06-17 00:58:52.347000,https://github.com/zzzqzhou/ram-dsir,28,Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration,"https://scholar.google.com/scholar?cluster=12473310865572484859&hl=en&as_sdt=0,10",1,2022
Personalizing Federated Medical Image Segmentation via Local Calibration,2,eccv,2,9,2023-06-17 00:58:52.559000,https://github.com/jcwang123/fedlc,32,Personalizing Federated Medical Image Segmentation via Local Calibration,"https://scholar.google.com/scholar?cluster=11468920632271740017&hl=en&as_sdt=0,5",1,2022
Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization,2,eccv,1,1,2023-06-17 00:58:52.772000,https://github.com/kaminyou/urust,26,Ultra-high-resolution unpaired stain transformation via Kernelized Instance Normalization,"https://scholar.google.com/scholar?cluster=16474808232208188455&hl=en&as_sdt=0,14",2,2022
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation,1,eccv,0,1,2023-06-17 00:58:52.984000,https://github.com/wenxuan-1119/med-danet,8,Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation,"https://scholar.google.com/scholar?cluster=3628125222314565557&hl=en&as_sdt=0,50",2,2022
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images,15,eccv,6,5,2023-06-17 00:58:53.195000,https://github.com/compspi/cryoai,38,Cryoai: Amortized inference of poses for ab initio reconstruction of 3d molecular volumes from real cryo-em images,"https://scholar.google.com/scholar?cluster=13315374701548249986&hl=en&as_sdt=0,5",10,2022
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier,9,eccv,3,2,2023-06-17 00:58:53.408000,https://github.com/ytongxie/unimiss-code,27,UniMiSS: Universal Medical Self-supervised Learning via Breaking Dimensionality Barrier,"https://scholar.google.com/scholar?cluster=15914805146298001141&hl=en&as_sdt=0,47",1,2022
DLME: Deep Local-Flatness Manifold Embedding,7,eccv,0,0,2023-06-17 00:58:53.620000,https://github.com/zangzelin/code_ECCV2022_DLME,9,Dlme: Deep local-flatness manifold embedding,"https://scholar.google.com/scholar?cluster=381672874695229650&hl=en&as_sdt=0,34",3,2022
Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching,1,eccv,4,1,2023-06-17 00:58:53.832000,https://github.com/ruc-aimc-lab/superretina,27,Semi-supervised Keypoint Detector and Descriptor for Retinal Image Matching,"https://scholar.google.com/scholar?cluster=13637656622545284175&hl=en&as_sdt=0,11",1,2022
Graph Neural Network for Cell Tracking in Microscopy Videos,8,eccv,6,2,2023-06-17 00:58:54.043000,https://github.com/talbenha/cell-tracker-gnn,42,Graph neural network for cell tracking in microscopy videos,"https://scholar.google.com/scholar?cluster=15512678247201277993&hl=en&as_sdt=0,47",4,2022
CXR Segmentation by AdaIN-Based Domain Adaptation and Knowledge Distillation,0,eccv,1,0,2023-06-17 00:58:54.254000,https://github.com/yjoh12/cxr-segmentation-by-adain-based-domain-adaptation-and-knowledge-distillation,0,CXR Segmentation by AdaIN-Based Domain Adaptation and Knowledge Distillation,"https://scholar.google.com/scholar?cluster=15308907585693777793&hl=en&as_sdt=0,1",1,2022
K-SALSA: K-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment,0,eccv,1,0,2023-06-17 00:58:54.466000,https://github.com/hcholab/k-salsa,0,k-SALSA: k-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment,"https://scholar.google.com/scholar?cluster=18175212415813703092&hl=en&as_sdt=0,33",2,2022
RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-Guided Disease Classification,13,eccv,0,1,2023-06-17 00:58:54.679000,https://github.com/bmi-imaginelab/radiotransformer,4,RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention–Guided Disease Classification,"https://scholar.google.com/scholar?cluster=10312803465014097360&hl=en&as_sdt=0,5",0,2022
Towards Grand Unification of Object Tracking,46,eccv,82,23,2023-06-17 00:58:54.890000,https://github.com/masterbin-iiau/unicorn,896,Towards grand unification of object tracking,"https://scholar.google.com/scholar?cluster=14300935760162828522&hl=en&as_sdt=0,33",20,2022
ByteTrack: Multi-Object Tracking by Associating Every Detection Box,348,eccv,661,236,2023-06-17 00:58:55.102000,https://github.com/ifzhang/ByteTrack,3371,Bytetrack: Multi-object tracking by associating every detection box,"https://scholar.google.com/scholar?cluster=14638466021176544465&hl=en&as_sdt=0,5",39,2022
Particle Video Revisited: Tracking through Occlusions Using Point Trajectories,10,eccv,37,9,2023-06-17 00:58:55.314000,https://github.com/aharley/pips,405,Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories,"https://scholar.google.com/scholar?cluster=7235613830954970670&hl=en&as_sdt=0,33",11,2022
Tracking Objects As Pixel-Wise Distributions,19,eccv,3,13,2023-06-17 00:58:55.526000,https://github.com/dvlab-research/eccv22-p3aformer-tracking-objects-as-pixel-wise-distributions,145,Tracking objects as pixel-wise distributions,"https://scholar.google.com/scholar?cluster=10239175441885037567&hl=en&as_sdt=0,5",6,2022
Hierarchical Latent Structure for Multi-modal Vehicle Trajectory Forecasting,5,eccv,6,0,2023-06-17 00:58:55.738000,https://github.com/d1024choi/hlstrajforecast,25,Hierarchical Latent Structure for Multi-modal Vehicle Trajectory Forecasting,"https://scholar.google.com/scholar?cluster=265805843734417469&hl=en&as_sdt=0,33",3,2022
AiATrack: Attention in Attention for Transformer Visual Tracking,32,eccv,6,1,2023-06-17 00:58:55.949000,https://github.com/Little-Podi/AiATrack,79,Aiatrack: Attention in attention for transformer visual tracking,"https://scholar.google.com/scholar?cluster=6724748843400977919&hl=en&as_sdt=0,44",2,2022
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow,6,eccv,2,0,2023-06-17 00:58:56.170000,https://github.com/cv-stuttgart/pcfa,14,A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow,"https://scholar.google.com/scholar?cluster=16383434306748747261&hl=en&as_sdt=0,5",2,2022
Diverse Human Motion Prediction Guided by Multi-level Spatial-Temporal Anchors,3,eccv,4,0,2023-06-17 00:58:56.382000,https://github.com/sirui-xu/stars,50,Diverse Human Motion Prediction Guided by Multi-level Spatial-Temporal Anchors,"https://scholar.google.com/scholar?cluster=4097166737551548782&hl=en&as_sdt=0,23",4,2022
Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction,4,eccv,4,2,2023-06-17 00:58:56.594000,https://github.com/inhwanbae/gpgraph,32,Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction,"https://scholar.google.com/scholar?cluster=13720968093052724417&hl=en&as_sdt=0,32",4,2022
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework,50,eccv,28,8,2023-06-17 00:58:56.807000,https://github.com/botaoye/ostrack,199,Joint feature learning and relation modeling for tracking: A one-stream framework,"https://scholar.google.com/scholar?cluster=1516895187053369438&hl=en&as_sdt=0,24",4,2022
MotionCLIP: Exposing Human Motion Generation to CLIP Space,58,eccv,21,3,2023-06-17 00:58:57.019000,https://github.com/guytevet/motionclip,254,Motionclip: Exposing human motion generation to clip space,"https://scholar.google.com/scholar?cluster=10636085114698849763&hl=en&as_sdt=0,39",20,2022
Backbone Is All Your Need: A Simplified Architecture for Visual Object Tracking,23,eccv,2,3,2023-06-17 00:58:57.230000,https://github.com/lpxtt/simtrack,29,Backbone is all your need: a simplified architecture for visual object tracking,"https://scholar.google.com/scholar?cluster=7811696988001327455&hl=en&as_sdt=0,44",1,2022
Optical Flow Training under Limited Label Budget via Active Learning,6,eccv,3,0,2023-06-17 00:58:57.445000,https://github.com/duke-vision/optical-flow-active-learning-release,12,Optical flow training under limited label budget via active learning,"https://scholar.google.com/scholar?cluster=16741848411026447304&hl=en&as_sdt=0,36",4,2022
Tackling Background Distraction in Video Object Segmentation,7,eccv,2,0,2023-06-17 00:58:57.662000,https://github.com/suhwan-cho/tbd,30,Tackling background distraction in video object segmentation,"https://scholar.google.com/scholar?cluster=2852504604865860365&hl=en&as_sdt=0,5",2,2022
Social-Implicit: Rethinking Trajectory Prediction Evaluation and the Effectiveness of Implicit Maximum Likelihood Estimation,8,eccv,7,1,2023-06-17 00:58:57.874000,https://github.com/abduallahmohamed/social-implicit,52,Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation,"https://scholar.google.com/scholar?cluster=7434612477820276011&hl=en&as_sdt=0,47",4,2022
TEMOS: Generating Diverse Human Motions from Textual Descriptions,53,eccv,13,7,2023-06-17 00:58:58.085000,https://github.com/Mathux/TEMOS,247,TEMOS: Generating diverse human motions from textual descriptions,"https://scholar.google.com/scholar?cluster=906697653407689869&hl=en&as_sdt=0,5",9,2022
Tracking Every Thing in the Wild,4,eccv,6,4,2023-06-17 00:58:58.298000,https://github.com/SysCV/tet,76,Tracking Every Thing in the Wild,"https://scholar.google.com/scholar?cluster=17643674694055084285&hl=en&as_sdt=0,33",14,2022
Towards Sequence-Level Training for Visual Tracking,2,eccv,2,0,2023-06-17 00:58:58.509000,https://github.com/byminji/SLTtrack,46,Towards Sequence-Level Training for Visual Tracking,"https://scholar.google.com/scholar?cluster=16548636254162117508&hl=en&as_sdt=0,33",2,2022
Robust Visual Tracking by Segmentation,8,eccv,578,56,2023-06-17 00:58:58.722000,https://github.com/visionml/pytracking,2795,Robust visual tracking by segmentation,"https://scholar.google.com/scholar?cluster=16927571156723818733&hl=en&as_sdt=0,11",90,2022
MeshLoc: Mesh-Based Visual Localization,9,eccv,12,0,2023-06-17 00:58:58.934000,https://github.com/tsattler/meshloc_release,157,MeshLoc: Mesh-Based Visual Localization,"https://scholar.google.com/scholar?cluster=1928196166887368454&hl=en&as_sdt=0,5",14,2022
Large-Displacement 3D Object Tracking with Hybrid Non-local Optimization,0,eccv,2,2,2023-06-17 00:58:59.146000,https://github.com/cvbubbles/nonlocal-3dtracking,9,Large-Displacement 3D Object Tracking with Hybrid Non-local Optimization,"https://scholar.google.com/scholar?cluster=11816291111308790806&hl=en&as_sdt=0,5",1,2022
View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums,15,eccv,4,0,2023-06-17 00:58:59.358000,https://github.com/cocoon2wong/Vertical,31,View Vertically: A hierarchical network for trajectory prediction via fourier spectrums,"https://scholar.google.com/scholar?cluster=8328120336151116110&hl=en&as_sdt=0,5",4,2022
SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image,51,eccv,26,4,2023-06-17 00:58:59.570000,https://github.com/VITA-Group/SinNeRF,298,Sinnerf: Training neural radiance fields on complex scenes from a single image,"https://scholar.google.com/scholar?cluster=10013613209913154166&hl=en&as_sdt=0,33",12,2022
Entropy-Driven Sampling and Training Scheme for Conditional Diffusion Generation,1,eccv,5,0,2023-06-17 00:58:59.781000,https://github.com/ZGCTroy/ED-DPM,35,Entropy-Driven Sampling and Training Scheme for Conditional Diffusion Generation,"https://scholar.google.com/scholar?cluster=17257542322181853280&hl=en&as_sdt=0,5",1,2022
Accelerating Score-Based Generative Models with Preconditioned Diffusion Sampling,9,eccv,3,1,2023-06-17 00:58:59.993000,https://github.com/fudan-zvg/pds,47,Accelerating score-based generative models with preconditioned diffusion sampling,"https://scholar.google.com/scholar?cluster=6374985991699368911&hl=en&as_sdt=0,5",6,2022
Learning to Generate Realistic LiDAR Point Clouds,6,eccv,9,5,2023-06-17 00:59:00.205000,https://github.com/vzyrianov/lidargen,79,Learning to Generate Realistic LiDAR Point Clouds,"https://scholar.google.com/scholar?cluster=7015071200961093989&hl=en&as_sdt=0,47",5,2022
RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds,1,eccv,3,0,2023-06-17 00:59:00.416000,https://github.com/hkust-vgd/rfnet-4d,15,RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds,"https://scholar.google.com/scholar?cluster=16154462971381882871&hl=en&as_sdt=0,5",2,2022
Exploring Gradient-Based Multi-directional Controls in GANs,2,eccv,3,0,2023-06-17 00:59:00.628000,https://github.com/zikuncshelly/gradctrl,6,Exploring Gradient-Based Multi-directional Controls in GANs,"https://scholar.google.com/scholar?cluster=6195565596897570824&hl=en&as_sdt=0,47",1,2022
Neural Scene Decoration from a Single Photograph,1,eccv,1,1,2023-06-17 00:59:00.840000,https://github.com/hkust-vgd/neural_scene_decoration,4,Neural Scene Decoration from a Single Photograph,"https://scholar.google.com/scholar?cluster=17327877529397304963&hl=en&as_sdt=0,33",2,2022
Outpainting by Queries,4,eccv,5,1,2023-06-17 00:59:01.053000,https://github.com/kaiseem/queryotr,30,Outpainting by queries,"https://scholar.google.com/scholar?cluster=5922315739372026164&hl=en&as_sdt=0,16",3,2022
Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes,23,eccv,19,2,2023-06-17 00:59:01.265000,https://github.com/samb-t/unleashing-transformers,159,Unleashing transformers: parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes,"https://scholar.google.com/scholar?cluster=7593120029891493996&hl=en&as_sdt=0,47",7,2022
GAN Cocktail: Mixing GANs without Dataset Access,2,eccv,1,0,2023-06-17 00:59:01.477000,https://github.com/omriav/GAN-cocktail,6,GAN Cocktail: mixing GANs without dataset access,"https://scholar.google.com/scholar?cluster=15305393115604923454&hl=en&as_sdt=0,5",1,2022
Subspace Diffusion Generative Models,33,eccv,10,2,2023-06-17 00:59:01.689000,https://github.com/bjing2016/subspace-diffusion,115,Subspace diffusion generative models,"https://scholar.google.com/scholar?cluster=3690537087303403167&hl=en&as_sdt=0,23",4,2022
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning,17,eccv,0,0,2023-06-17 00:59:01.912000,https://github.com/jianzhangcs/r-dfcil,6,R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning,"https://scholar.google.com/scholar?cluster=15505663649422774659&hl=en&as_sdt=0,5",1,2022
Domain Generalization by Mutual-Information Regularization with Pre-trained Models,25,eccv,4,0,2023-06-17 00:59:02.125000,https://github.com/kakaobrain/miro,68,Domain generalization by mutual-information regularization with pre-trained models,"https://scholar.google.com/scholar?cluster=8821874949203772669&hl=en&as_sdt=0,23",3,2022
Neural-Sim: Learning to Generate Training Data with NeRF,5,eccv,5,5,2023-06-17 00:59:02.337000,https://github.com/gyhandy/neural-sim-nerf,133,Neural-Sim: Learning to Generate Training Data with NeRF,"https://scholar.google.com/scholar?cluster=8102904368333644039&hl=en&as_sdt=0,5",7,2022
Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning,2,eccv,0,0,2023-06-17 00:59:02.549000,https://github.com/fanhanwei/bocr,1,Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning,"https://scholar.google.com/scholar?cluster=6772837170787107208&hl=en&as_sdt=0,15",2,2022
Continual Variational Autoencoder Learning via Online Cooperative Memorization,8,eccv,1,0,2023-06-17 00:59:02.762000,https://github.com/dtuzi123/ovae,8,Continual variational autoencoder learning via online cooperative memorization,"https://scholar.google.com/scholar?cluster=1422808793868309749&hl=en&as_sdt=0,5",1,2022
Batch-Efficient EigenDecomposition for Small and Medium Matrices,2,eccv,1,1,2023-06-17 00:59:02.979000,https://github.com/kingjamessong/batched,13,Batch-Efficient EigenDecomposition for Small and Medium Matrices,"https://scholar.google.com/scholar?cluster=444921300368814099&hl=en&as_sdt=0,33",1,2022
A Comparative Study of Graph Matching Algorithms in Computer Vision,1,eccv,0,0,2023-06-17 00:59:03.196000,https://github.com/vislearn/gmbench,4,A comparative study of graph matching algorithms in computer vision,"https://scholar.google.com/scholar?cluster=3115785570681844280&hl=en&as_sdt=0,5",2,2022
Improving Generalization in Federated Learning by Seeking Flat Minima,21,eccv,15,0,2023-06-17 00:59:03.414000,https://github.com/debcaldarola/fedsam,45,Improving generalization in federated learning by seeking flat minima,"https://scholar.google.com/scholar?cluster=17644179753896530288&hl=en&as_sdt=0,5",3,2022
Transfer without Forgetting,7,eccv,1,0,2023-06-17 00:59:03.627000,https://github.com/mbosc/twf,14,Transfer without forgetting,"https://scholar.google.com/scholar?cluster=3940165614619807649&hl=en&as_sdt=0,39",3,2022
Tackling Long-Tailed Category Distribution under Domain Shifts,4,eccv,2,0,2023-06-17 00:59:03.846000,https://github.com/guxiao0822/lt-ds,18,Tackling long-tailed category distribution under domain shifts,"https://scholar.google.com/scholar?cluster=2241964884701464263&hl=en&as_sdt=0,5",1,2022
Improving Vision Transformers by Revisiting High-Frequency Components,18,eccv,1,1,2023-06-17 00:59:04.067000,https://github.com/jiawangbai/HAT,35,Improving vision transformers by revisiting high-frequency components,"https://scholar.google.com/scholar?cluster=13058287836488105152&hl=en&as_sdt=0,43",1,2022
Recurrent Bilinear Optimization for Binary Neural Networks,8,eccv,3,1,2023-06-17 00:59:04.279000,https://github.com/stevetsui/rbonn,14,Recurrent bilinear optimization for binary neural networks,"https://scholar.google.com/scholar?cluster=1062882900912061420&hl=en&as_sdt=0,31",2,2022
Neural Architecture Search for Spiking Neural Networks,36,eccv,4,1,2023-06-17 00:59:04.493000,https://github.com/intelligent-computing-lab-yale/neural-architecture-search-for-spiking-neural-networks,37,Neural architecture search for spiking neural networks,"https://scholar.google.com/scholar?cluster=14056363702522066850&hl=en&as_sdt=0,11",3,2022
DaViT: Dual Attention Vision Transformers,70,eccv,21,10,2023-06-17 00:59:04.705000,https://github.com/dingmyu/davit,232,Davit: Dual attention vision transformers,"https://scholar.google.com/scholar?cluster=18356109755771918503&hl=en&as_sdt=0,33",4,2022
Locality Guidance for Improving Vision Transformers on Tiny Datasets,14,eccv,4,4,2023-06-17 00:59:04.918000,https://github.com/lkhl/tiny-transformers,57,Locality guidance for improving vision transformers on tiny datasets,"https://scholar.google.com/scholar?cluster=1932755719966764406&hl=en&as_sdt=0,39",2,2022
Neighborhood Collective Estimation for Noisy Label Identification and Correction,1,eccv,0,1,2023-06-17 00:59:05.131000,https://github.com/lijichang/lnl-nce,16,Neighborhood Collective Estimation for Noisy Label Identification and Correction,"https://scholar.google.com/scholar?cluster=13078572582755547739&hl=en&as_sdt=0,33",2,2022
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay,21,eccv,0,0,2023-06-17 00:59:05.342000,https://github.com/liuh127/FSCIL-via-Entropy-regularized-DF-Replay,1,Few-shot class-incremental learning via entropy-regularized data-free replay,"https://scholar.google.com/scholar?cluster=15183012276771104173&hl=en&as_sdt=0,5",1,2022
Anti-Retroactive Interference for Lifelong Learning,6,eccv,0,0,2023-06-17 00:59:05.554000,https://github.com/bhrqw/ari,1,Anti-retroactive interference for lifelong learning,"https://scholar.google.com/scholar?cluster=18400262451722000159&hl=en&as_sdt=0,5",1,2022
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning,2,eccv,0,0,2023-06-17 00:59:05.767000,https://github.com/vipailab/vmf_op,3,Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning,"https://scholar.google.com/scholar?cluster=4493883076310837040&hl=en&as_sdt=0,5",2,2022
Learning Hierarchy Aware Features for Reducing Mistake Severity,3,eccv,4,1,2023-06-17 00:59:05.979000,https://github.com/07agarg/haf,9,Learning Hierarchy Aware Features for Reducing Mistake Severity,"https://scholar.google.com/scholar?cluster=971311656327419537&hl=en&as_sdt=0,33",2,2022
Registration Based Few-Shot Anomaly Detection,21,eccv,31,2,2023-06-17 00:59:06.194000,https://github.com/mediabrain-sjtu/regad,200,Registration based few-shot anomaly detection,"https://scholar.google.com/scholar?cluster=16013575763975073067&hl=en&as_sdt=0,5",7,2022
Improving Robustness by Enhancing Weak Subnets,7,eccv,0,0,2023-06-17 00:59:06.406000,https://github.com/guoyongcs/ews,5,Improving robustness by enhancing weak subnets,"https://scholar.google.com/scholar?cluster=8577974336579117564&hl=en&as_sdt=0,31",2,2022
Learning Invariant Visual Representations for Compositional Zero-Shot Learning,7,eccv,0,1,2023-06-17 00:59:06.618000,https://github.com/pris-cv/ivr,9,Learning Invariant Visual Representations for Compositional Zero-Shot Learning,"https://scholar.google.com/scholar?cluster=1973890546224229201&hl=en&as_sdt=0,5",1,2022
Improving Covariance Conditioning of the SVD Meta-Layer by Orthogonality,2,eccv,1,0,2023-06-17 00:59:06.831000,https://github.com/kingjamessong/orthoimprovecond,11,Improving covariance conditioning of the svd meta-layer by orthogonality,"https://scholar.google.com/scholar?cluster=8121098635905344083&hl=en&as_sdt=0,5",1,2022
Out-of-Distribution Detection with Semantic Mismatch under Masking,2,eccv,0,3,2023-06-17 00:59:07.044000,https://github.com/cure-lab/moodcat,10,Out-of-distribution detection with semantic mismatch under masking,"https://scholar.google.com/scholar?cluster=14717717824800977283&hl=en&as_sdt=0,23",3,2022
Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion,2,eccv,0,0,2023-06-17 00:59:07.255000,https://github.com/zhengqigao/learning-from-multiple-annotator-noisy-labels,5,Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion,"https://scholar.google.com/scholar?cluster=11390547638368533077&hl=en&as_sdt=0,21",2,2022
Acknowledging the Unknown for Multi-Label Learning with Single Positive Labels,10,eccv,2,0,2023-06-17 00:59:07.468000,https://github.com/correr-zhou/spml-acktheunknown,33,Acknowledging the unknown for multi-label learning with single positive labels,"https://scholar.google.com/scholar?cluster=17743437837322413809&hl=en&as_sdt=0,33",3,2022
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers,26,eccv,49,4,2023-06-17 00:59:07.680000,https://github.com/Westlake-AI/openmixup,424,Automix: Unveiling the power of mixup for stronger classifiers,"https://scholar.google.com/scholar?cluster=9530153125775586763&hl=en&as_sdt=0,5",15,2022
MaxViT: Multi-axis Vision Transformer,112,eccv,25,5,2023-06-17 00:59:07.892000,https://github.com/google-research/maxvit,348,Maxvit: Multi-axis vision transformer,"https://scholar.google.com/scholar?cluster=6784655767122395745&hl=en&as_sdt=0,5",9,2022
ScalableViT: Rethinking the Context-Oriented Generalization of Vision Transformer,19,eccv,2,2,2023-06-17 00:59:08.105000,https://github.com/yangr116/scalablevit,20,Scalablevit: Rethinking the context-oriented generalization of vision transformer,"https://scholar.google.com/scholar?cluster=15849292167912948189&hl=en&as_sdt=0,5",3,2022
Three Things Everyone Should Know about Vision Transformers,14,eccv,516,12,2023-06-17 00:59:08.318000,https://github.com/facebookresearch/deit,3450,Three things everyone should know about vision transformers,"https://scholar.google.com/scholar?cluster=15397703108844303764&hl=en&as_sdt=0,33",48,2022
DeiT III: Revenge of the ViT,83,eccv,516,12,2023-06-17 00:59:08.531000,https://github.com/facebookresearch/deit,3450,Deit iii: Revenge of the vit,"https://scholar.google.com/scholar?cluster=11150465244321733349&hl=en&as_sdt=0,5",48,2022
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition,14,eccv,10,0,2023-06-17 00:59:08.743000,https://github.com/winycg/self-kd-lib,77,Mixskd: Self-knowledge distillation from mixup for image recognition,"https://scholar.google.com/scholar?cluster=5982918587312837241&hl=en&as_sdt=0,43",1,2022
Discrete-Constrained Regression for Local Counting Models,9,eccv,0,0,2023-06-17 00:59:08.955000,https://github.com/xhp-hust-2018-2011/dcreg,2,Discrete-constrained regression for local counting models,"https://scholar.google.com/scholar?cluster=13535548194896123886&hl=en&as_sdt=0,44",1,2022
Chairs Can Be Stood On: Overcoming Object Bias in Human-Object Interaction Detection,0,eccv,0,1,2023-06-17 00:59:09.186000,https://github.com/daoyuan98/odm,5,Chairs Can Be Stood On: Overcoming Object Bias in Human-Object Interaction Detection,"https://scholar.google.com/scholar?cluster=6938034604533754380&hl=en&as_sdt=0,5",2,2022
A Fast Knowledge Distillation Framework for Visual Recognition,12,eccv,25,1,2023-06-17 00:59:09.398000,https://github.com/szq0214/fkd,141,A fast knowledge distillation framework for visual recognition,"https://scholar.google.com/scholar?cluster=16290481641411763390&hl=en&as_sdt=0,5",8,2022
DICE: Leveraging Sparsification for Out-of-Distribution Detection,25,eccv,6,0,2023-06-17 00:59:09.612000,https://github.com/deeplearning-wisc/dice,30,Dice: Leveraging sparsification for out-of-distribution detection,"https://scholar.google.com/scholar?cluster=878626789575390648&hl=en&as_sdt=0,33",3,2022
Invariant Feature Learning for Generalized Long-Tailed Classification,12,eccv,8,2,2023-06-17 00:59:09.823000,https://github.com/kaihuatang/generalized-long-tailed-benchmarks.pytorch,99,Invariant feature learning for generalized long-tailed classification,"https://scholar.google.com/scholar?cluster=2921674289381974673&hl=en&as_sdt=0,5",2,2022
Sliced Recursive Transformer,12,eccv,10,0,2023-06-17 00:59:10.035000,https://github.com/szq0214/sret,55,Sliced recursive transformer,"https://scholar.google.com/scholar?cluster=6881440757906382227&hl=en&as_sdt=0,11",6,2022
Cross-Domain Ensemble Distillation for Domain Generalization,3,eccv,5,1,2023-06-17 00:59:10.249000,https://github.com/leekyungmoon/XDED,20,Cross-domain Ensemble Distillation for Domain Generalization,"https://scholar.google.com/scholar?cluster=7614016061271891852&hl=en&as_sdt=0,5",4,2022
Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels,2,eccv,2,3,2023-06-17 00:59:10.462000,https://github.com/uitrbn/tscsi_idn,8,Centrality and consistency: two-stage clean samples identification for learning with instance-dependent noisy labels,"https://scholar.google.com/scholar?cluster=17182529425583795058&hl=en&as_sdt=0,5",1,2022
VL-LTR: Learning Class-Wise Visual-Linguistic Representation for Long-Tailed Visual Recognition,15,eccv,10,6,2023-06-17 00:59:10.674000,https://github.com/ChangyaoTian/VL-LTR,51,Vl-ltr: Learning class-wise visual-linguistic representation for long-tailed visual recognition,"https://scholar.google.com/scholar?cluster=3647078390700500997&hl=en&as_sdt=0,32",3,2022
Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-of-Distribution Generalization,3,eccv,0,0,2023-06-17 00:59:10.890000,https://github.com/simpleshinobu/irmcon,16,Class is invariant to context and vice versa: on learning invariance for out-of-distribution generalization,"https://scholar.google.com/scholar?cluster=10029134243700219683&hl=en&as_sdt=0,5",0,2022
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection,2,eccv,1,0,2023-06-17 00:59:11.105000,https://github.com/gaoangw/hscl,7,Hierarchical Semi-supervised Contrastive Learning for Contamination-Resistant Anomaly Detection,"https://scholar.google.com/scholar?cluster=13983761419031899211&hl=en&as_sdt=0,31",1,2022
RealPatch: A Statistical Matching Framework for Model Patching with Real Samples,1,eccv,0,0,2023-06-17 00:59:11.321000,https://github.com/wearepal/realpatch,2,RealPatch: A Statistical Matching Framework for Model Patching with Real Samples,"https://scholar.google.com/scholar?cluster=16389603249076638788&hl=en&as_sdt=0,33",2,2022
Semantic Novelty Detection via Relational Reasoning,0,eccv,0,0,2023-06-17 00:59:11.534000,https://github.com/francescocappio/resend,14,Semantic Novelty Detection via Relational Reasoning,"https://scholar.google.com/scholar?cluster=2885314653619622739&hl=en&as_sdt=0,5",1,2022
Training Vision Transformers with Only 2040 Images,14,eccv,4,2,2023-06-17 00:59:11.745000,https://github.com/CupidJay/Training-Vision-Transformers-with-only-2040-images,43,Training vision transformers with only 2040 images,"https://scholar.google.com/scholar?cluster=15808243844725790365&hl=en&as_sdt=0,31",2,2022
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs,3,eccv,0,0,2023-06-17 00:59:11.957000,https://github.com/shantanuj/tdam_top_down_attention_module,6,TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs,"https://scholar.google.com/scholar?cluster=10902957044896292324&hl=en&as_sdt=0,44",1,2022
Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars,1,eccv,0,2,2023-06-17 00:59:12.190000,https://github.com/hao-chen-njust/psp,1,Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars,"https://scholar.google.com/scholar?cluster=9346886979579970159&hl=en&as_sdt=0,5",1,2022
Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain,1,eccv,0,0,2023-06-17 00:59:12.402000,https://github.com/boschresearch/sourcegen,1,Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain,"https://scholar.google.com/scholar?cluster=11823647734753043783&hl=en&as_sdt=0,5",3,2022
Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning,28,eccv,21,6,2023-06-17 00:59:12.620000,https://github.com/yehli/imagenetmodel,109,Wave-vit: Unifying wavelet and transformers for visual representation learning,"https://scholar.google.com/scholar?cluster=9894263145711509588&hl=en&as_sdt=0,5",5,2022
Tailoring Self-Supervision for Supervised Learning,5,eccv,3,0,2023-06-17 00:59:12.833000,https://github.com/wjun0830/localizable-rotation,19,Tailoring Self-Supervision for Supervised Learning,"https://scholar.google.com/scholar?cluster=7286213705306968536&hl=en&as_sdt=0,33",4,2022
Difficulty-Aware Simulator for Open Set Recognition,5,eccv,2,0,2023-06-17 00:59:13.046000,https://github.com/wjun0830/difficulty-aware-simulator,24,Difficulty-Aware Simulator for Open Set Recognition,"https://scholar.google.com/scholar?cluster=13965399748614059565&hl=en&as_sdt=0,5",1,2022
Few-Shot Class-Incremental Learning from an Open-Set Perspective,14,eccv,3,0,2023-06-17 00:59:13.259000,https://github.com/canpeng123/fscil_alice,19,Few-Shot Class-Incremental Learning from an Open-Set Perspective,"https://scholar.google.com/scholar?cluster=16116173187693664231&hl=en&as_sdt=0,47",2,2022
FOSTER: Feature Boosting and Compression for Class-Incremental Learning,34,eccv,0,2,2023-06-17 00:59:13.471000,https://github.com/G-U-N/ECCV22-FOSTER,31,Foster: Feature boosting and compression for class-incremental learning,"https://scholar.google.com/scholar?cluster=17421080525009780737&hl=en&as_sdt=0,33",3,2022
Visual Knowledge Tracing,0,eccv,1,0,2023-06-17 00:59:13.685000,https://github.com/nkondapa/visualknowledgetracing,13,Visual Knowledge Tracing,"https://scholar.google.com/scholar?cluster=17421247468685964476&hl=en&as_sdt=0,8",1,2022
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism,3,eccv,4,7,2023-06-17 00:59:13.899000,https://github.com/ganperf/sam,19,Improving fine-grained visual recognition in low data regimes via self-boosting attention mechanism,"https://scholar.google.com/scholar?cluster=1093309124842032174&hl=en&as_sdt=0,5",3,2022
VSA: Learning Varied-Size Window Attention in Vision Transformers,25,eccv,6,5,2023-06-17 00:59:14.113000,https://github.com/vitae-transformer/vitae-vsa,132,VSA: learning varied-size window attention in vision transformers,"https://scholar.google.com/scholar?cluster=7134900495559356797&hl=en&as_sdt=0,5",2,2022
DenseHybrid: Hybrid Anomaly Detection for Dense Open-Set Recognition,14,eccv,3,2,2023-06-17 00:59:14.326000,https://github.com/matejgrcic/DenseHybrid,20,Densehybrid: Hybrid anomaly detection for dense open-set recognition,"https://scholar.google.com/scholar?cluster=17117872304717676783&hl=en&as_sdt=0,36",2,2022
Rethinking Confidence Calibration for Failure Prediction,7,eccv,1,0,2023-06-17 00:59:14.542000,https://github.com/impression2805/fmfp,13,Rethinking Confidence Calibration for Failure Prediction,"https://scholar.google.com/scholar?cluster=3192244699956049091&hl=en&as_sdt=0,5",2,2022
Uncertainty-Guided Source-Free Domain Adaptation,14,eccv,3,2,2023-06-17 00:59:14.759000,https://github.com/roysubhankar/uncertainty-sfda,31,Uncertainty-guided source-free domain adaptation,"https://scholar.google.com/scholar?cluster=10598112265751424023&hl=en&as_sdt=0,44",4,2022
Should All Proposals Be Treated Equally in Object Detection?,0,eccv,1,0,2023-06-17 00:59:14.974000,https://github.com/liyunsheng13/dpp,31,Should All Proposals Be Treated Equally in Object Detection?,"https://scholar.google.com/scholar?cluster=12493352248493160142&hl=en&as_sdt=0,24",6,2022
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions,12,eccv,5,1,2023-06-17 00:59:15.192000,https://github.com/amodas/PRIME-augmentations,38,PRIME: A few primitives can boost robustness to common corruptions,"https://scholar.google.com/scholar?cluster=4562095737228687677&hl=en&as_sdt=0,11",3,2022
In Defense of Image Pre-training for Spatiotemporal Recognition,0,eccv,0,1,2023-06-17 00:59:15.422000,https://github.com/ucsc-vlaa/image-pretraining-for-video,17,In Defense of Image Pre-Training for Spatiotemporal Recognition,"https://scholar.google.com/scholar?cluster=18269323448808190712&hl=en&as_sdt=0,33",0,2022
Augmenting Deep Classifiers with Polynomial Neural Networks,5,eccv,0,0,2023-06-17 00:59:15.636000,https://github.com/grigorisg9gr/polynomials-for-augmenting-nns,2,Augmenting deep classifiers with polynomial neural networks,"https://scholar.google.com/scholar?cluster=14218781642284557592&hl=en&as_sdt=0,5",2,2022
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection,3,eccv,1,1,2023-06-17 00:59:15.849000,https://github.com/hyperconnect/fasten,6,Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection,"https://scholar.google.com/scholar?cluster=13055133803881493745&hl=en&as_sdt=0,5",7,2022
Contrastive Deep Supervision,7,eccv,3,1,2023-06-17 00:59:16.064000,https://github.com/archiplab-linfengzhang/contrastive-deep-supervision,50,Contrastive deep supervision,"https://scholar.google.com/scholar?cluster=7265954552843581197&hl=en&as_sdt=0,44",3,2022
Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective,7,eccv,0,0,2023-06-17 00:59:16.281000,https://github.com/dtennant/dt-tradeoff,5,Discriminability-transferability trade-off: an information-theoretic perspective,"https://scholar.google.com/scholar?cluster=4648654949432885317&hl=en&as_sdt=0,33",1,2022
LocVTP: Video-Text Pre-training for Temporal Localization,22,eccv,0,4,2023-06-17 00:59:16.494000,https://github.com/mengcaopku/locvtp,34,Locvtp: Video-text pre-training for temporal localization,"https://scholar.google.com/scholar?cluster=12927720534552603420&hl=en&as_sdt=0,5",2,2022
Learning Ego 3D Representation As Ray Tracing,14,eccv,5,2,2023-06-17 00:59:16.712000,https://github.com/fudan-zvg/ego3rt,92,Learning ego 3d representation as ray tracing,"https://scholar.google.com/scholar?cluster=11031442758029473428&hl=en&as_sdt=0,33",12,2022
Static and Dynamic Concepts for Self-Supervised Video Representation Learning,3,eccv,1,1,2023-06-17 00:59:16.925000,https://github.com/shvdiwnkozbw/Self-supervised-Video-Concept,10,Static and Dynamic Concepts for Self-supervised Video Representation Learning,"https://scholar.google.com/scholar?cluster=2899262297077900123&hl=en&as_sdt=0,5",1,2022
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning,8,eccv,2,1,2023-06-17 00:59:17.138000,https://github.com/yuxiaochen1103/Hi-TRS,18,Hierarchically Self-supervised Transformer for Human Skeleton Representation Learning,"https://scholar.google.com/scholar?cluster=17795536636846688217&hl=en&as_sdt=0,5",1,2022
CoSCL: Cooperation of Small Continual Learners Is Stronger than a Big One,3,eccv,2,0,2023-06-17 00:59:17.350000,https://github.com/lywang3081/coscl,12,CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big One,"https://scholar.google.com/scholar?cluster=10311122253648677302&hl=en&as_sdt=0,41",1,2022
Fast-MoCo: Boost Momentum-Based Contrastive Learning with Combinatorial Patches,3,eccv,0,0,2023-06-17 00:59:17.562000,https://github.com/orashi/fast-moco,9,Fast-MoCo: Boost Momentum-Based Contrastive Learning with Combinatorial Patches,"https://scholar.google.com/scholar?cluster=3809897863505864658&hl=en&as_sdt=0,5",1,2022
LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling,2,eccv,6,0,2023-06-17 00:59:17.774000,https://github.com/BoyanJIANG/LoRD,57,LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling,"https://scholar.google.com/scholar?cluster=18207490584500698956&hl=en&as_sdt=0,10",4,2022
On the Versatile Uses of Partial Distance Correlation in Deep Learning,5,eccv,16,0,2023-06-17 00:59:17.986000,https://github.com/zhenxingjian/partial_distance_correlation,162,On the versatile uses of partial distance correlation in deep learning,"https://scholar.google.com/scholar?cluster=17295760961898440654&hl=en&as_sdt=0,38",4,2022
DAS: Densely-Anchored Sampling for Deep Metric Learning,4,eccv,1,0,2023-06-17 00:59:18.199000,https://github.com/lizhaoliu-Lec/DAS,14,Das: Densely-anchored sampling for deep metric learning,"https://scholar.google.com/scholar?cluster=13410935767802137885&hl=en&as_sdt=0,5",2,2022
Learn from All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition,31,eccv,8,5,2023-06-17 00:59:18.410000,https://github.com/zyh-uaiaaaa/erasing-attention-consistency,48,Learn from all: Erasing attention consistency for noisy label facial expression recognition,"https://scholar.google.com/scholar?cluster=3230431190406827600&hl=en&as_sdt=0,5",2,2022
A Non-Isotropic Probabilistic Take On Proxy-Based Deep Metric Learning,3,eccv,0,0,2023-06-17 00:59:18.623000,https://github.com/explainableml/probabilistic_deep_metric_learning,11,A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning,"https://scholar.google.com/scholar?cluster=18270830460222491727&hl=en&as_sdt=0,5",8,2022
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers,17,eccv,8,6,2023-06-17 00:59:18.841000,https://github.com/sense-x/tokenmix,85,Tokenmix: Rethinking image mixing for data augmentation in vision transformers,"https://scholar.google.com/scholar?cluster=8406088123118572709&hl=en&as_sdt=0,5",6,2022
Sound Localization by Self-Supervised Time Delay Estimation,6,eccv,5,0,2023-06-17 00:59:19.053000,https://github.com/IFICL/stereocrw,10,Sound Localization by Self-Supervised Time Delay Estimation,"https://scholar.google.com/scholar?cluster=13725278977691156575&hl=en&as_sdt=0,5",1,2022
SLIP: Self-Supervision Meets Language-Image Pre-training,163,eccv,61,18,2023-06-17 00:59:19.266000,https://github.com/facebookresearch/slip,672,Slip: Self-supervision meets language-image pre-training,"https://scholar.google.com/scholar?cluster=17384094251372134587&hl=en&as_sdt=0,5",16,2022
A Contrastive Objective for Learning Disentangled Representations,7,eccv,0,0,2023-06-17 00:59:19.478000,https://github.com/jonkahana/dcodr,4,A contrastive objective for learning disentangled representations,"https://scholar.google.com/scholar?cluster=17352506721201259151&hl=en&as_sdt=0,10",1,2022
PT4AL: Using Self-Supervised Pretext Tasks for Active Learning,4,eccv,2,6,2023-06-17 00:59:19.690000,https://github.com/johnsk95/pt4al,45,PT4AL: Using Self-supervised Pretext Tasks for Active Learning,"https://scholar.google.com/scholar?cluster=11229213520949185993&hl=en&as_sdt=0,5",5,2022
DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning,62,eccv,33,4,2023-06-17 00:59:19.902000,https://github.com/google-research/l2p,284,Dualprompt: Complementary prompting for rehearsal-free continual learning,"https://scholar.google.com/scholar?cluster=7069579101447184812&hl=en&as_sdt=0,10",7,2022
Joint Learning of Localized Representations from Medical Images and Reports,20,eccv,3,0,2023-06-17 00:59:20.114000,https://github.com/philip-mueller/lovt,12,Joint learning of localized representations from medical images and reports,"https://scholar.google.com/scholar?cluster=9049923034415496270&hl=en&as_sdt=0,31",1,2022
Identifying Hard Noise in Long-Tailed Sample Distribution,4,eccv,1,4,2023-06-17 00:59:20.327000,https://github.com/yxymessi/h2e-framework,71,Identifying Hard Noise in Long-Tailed Sample Distribution,"https://scholar.google.com/scholar?cluster=5820418443271560279&hl=en&as_sdt=0,5",5,2022
NashAE: Disentangling Representations through Adversarial Covariance Minimization,1,eccv,1,0,2023-06-17 00:59:20.539000,https://github.com/ericyeats/nashae-beamsynthesis,3,NashAE: Disentangling Representations Through Adversarial Covariance Minimization,"https://scholar.google.com/scholar?cluster=11326949042914417761&hl=en&as_sdt=0,33",1,2022
Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training,16,eccv,2,0,2023-06-17 00:59:20.752000,https://github.com/hxyou/msclip,64,Learning visual representation from modality-shared contrastive language-image pre-training,"https://scholar.google.com/scholar?cluster=4379401598499228953&hl=en&as_sdt=0,5",4,2022
Contrasting Quadratic Assignments for Set-Based Representation Learning,3,eccv,0,0,2023-06-17 00:59:20.964000,https://github.com/amoskalev/contrasting_quadratic,8,Contrasting quadratic assignments for set-based representation learning,"https://scholar.google.com/scholar?cluster=6929984875841062884&hl=en&as_sdt=0,33",2,2022
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer,7,eccv,0,4,2023-06-17 00:59:21.190000,https://github.com/ashok-arjun/CSCCT,13,Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer,"https://scholar.google.com/scholar?cluster=18267860036724176528&hl=en&as_sdt=0,39",1,2022
MVDG: A Unified Multi-View Framework for Domain Generalization,4,eccv,0,1,2023-06-17 00:59:21.403000,https://github.com/koncle/mvdg,4,MVDG: A Unified Multi-view Framework for Domain Generalization,"https://scholar.google.com/scholar?cluster=3734859660298356325&hl=en&as_sdt=0,5",1,2022
Panoptic Scene Graph Generation,17,eccv,53,13,2023-06-17 00:59:21.616000,https://github.com/Jingkang50/OpenPSG,305,Panoptic scene graph generation,"https://scholar.google.com/scholar?cluster=4427176906343613222&hl=en&as_sdt=0,5",6,2022
Object-Compositional Neural Implicit Surfaces,22,eccv,5,5,2023-06-17 00:59:21.829000,https://github.com/qianyiwu/objsdf,157,Object-compositional neural implicit surfaces,"https://scholar.google.com/scholar?cluster=9997708934654415598&hl=en&as_sdt=0,32",7,2022
LiDAL: Inter-Frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation,6,eccv,2,0,2023-06-17 00:59:22.042000,https://github.com/hzykent/lidal,25,LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation,"https://scholar.google.com/scholar?cluster=8461922341071305826&hl=en&as_sdt=0,5",3,2022
DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation,2,eccv,4,5,2023-06-17 00:59:22.255000,https://github.com/cvmi-lab/doda,40,DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation,"https://scholar.google.com/scholar?cluster=16242766710045978321&hl=en&as_sdt=0,37",4,2022
TO-Scene: A Large-Scale Dataset for Understanding 3D Tabletop Scenes,5,eccv,5,0,2023-06-17 00:59:22.466000,https://github.com/GAP-LAB-CUHK-SZ/TO-Scene,28,TO-Scene: A Large-Scale Dataset for Understanding 3D Tabletop Scenes,"https://scholar.google.com/scholar?cluster=18062236764908220812&hl=en&as_sdt=0,4",2,2022
Fine-Grained Scene Graph Generation with Data Transfer,14,eccv,6,4,2023-06-17 00:59:22.678000,https://github.com/waxnkw/ietrans-sgg.pytorch,69,Fine-Grained Scene Graph Generation with Data Transfer,"https://scholar.google.com/scholar?cluster=4124673263687372921&hl=en&as_sdt=0,25",1,2022
Towards Hard-Positive Query Mining for DETR-Based Human-Object Interaction Detection,4,eccv,1,0,2023-06-17 00:59:22.890000,https://github.com/muchhair/hqm,25,Towards Hard-Positive Query Mining for DETR-Based Human-Object Interaction Detection,"https://scholar.google.com/scholar?cluster=14859605205968905105&hl=en&as_sdt=0,5",1,2022
PETR: Position Embedding Transformation for Multi-View 3D Object Detection,118,eccv,87,43,2023-06-17 00:59:23.102000,https://github.com/megvii-research/petr,535,Petr: Position embedding transformation for multi-view 3d object detection,"https://scholar.google.com/scholar?cluster=3799744009906269739&hl=en&as_sdt=0,33",13,2022
RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation,8,eccv,4,8,2023-06-17 00:59:23.315000,https://github.com/hmhemu/ra-depth,41,RA-Depth: Resolution Adaptive Self-supervised Monocular Depth Estimation,"https://scholar.google.com/scholar?cluster=10155818425251370358&hl=en&as_sdt=0,5",3,2022
PolyphonicFormer: Unified Query Learning for Depth-Aware Video Panoptic Segmentation,15,eccv,3,1,2023-06-17 00:59:23.527000,https://github.com/harboryuan/polyphonicformer,45,Polyphonicformer: unified query learning for depth-aware video panoptic segmentation,"https://scholar.google.com/scholar?cluster=7127843590064680446&hl=en&as_sdt=0,5",13,2022
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds,47,eccv,10,15,2023-06-17 00:59:23.739000,https://github.com/QingyongHu/SQN,83,Sqn: Weakly-supervised semantic segmentation of large-scale 3d point clouds,"https://scholar.google.com/scholar?cluster=4460489745797601457&hl=en&as_sdt=0,50",14,2022
3D-PL: Domain Adaptive Depth Estimation with 3D-Aware Pseudo-Labeling,1,eccv,1,3,2023-06-17 00:59:23.951000,https://github.com/ccc870206/3d-pl,15,3D-PL: Domain Adaptive Depth Estimation with 3D-Aware Pseudo-Labeling,"https://scholar.google.com/scholar?cluster=15836829240967290716&hl=en&as_sdt=0,5",2,2022
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation,19,eccv,2,2,2023-06-17 00:59:24.162000,https://github.com/lxtgh/panoptic-partformer,46,Panoptic-partformer: Learning a unified model for panoptic part segmentation,"https://scholar.google.com/scholar?cluster=11513198882440237429&hl=en&as_sdt=0,5",4,2022
Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation,12,eccv,4,1,2023-06-17 00:59:24.374000,https://github.com/cwc1260/BiFlow,12,Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation,"https://scholar.google.com/scholar?cluster=12687214958834623027&hl=en&as_sdt=0,5",0,2022
3DG-STFM: 3D Geometric Guided Student-Teacher Feature Matching,0,eccv,2,0,2023-06-17 00:59:24.586000,https://github.com/ryan-prime/3dg-stfm,27,3DG-STFM: 3D Geometric Guided Student-Teacher Feature Matching,"https://scholar.google.com/scholar?cluster=15958247080770709007&hl=en&as_sdt=0,47",2,2022
MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud,1,eccv,0,0,2023-06-17 00:59:24.798000,https://github.com/michaelramamonjisoa/monteboxfinder,21,MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud,"https://scholar.google.com/scholar?cluster=7470902724279064123&hl=en&as_sdt=0,6",1,2022
Scene Text Recognition with Permuted Autoregressive Sequence Models,23,eccv,85,26,2023-06-17 00:59:25.010000,https://github.com/baudm/parseq,338,Scene text recognition with permuted autoregressive sequence models,"https://scholar.google.com/scholar?cluster=8935992213517493527&hl=en&as_sdt=0,26",12,2022
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition,4,eccv,44,16,2023-06-17 00:59:25.221000,https://github.com/lbh1024/can,285,When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition,"https://scholar.google.com/scholar?cluster=10414110543440415468&hl=en&as_sdt=0,15",23,2022
GLASS: Global to Local Attention for Scene-Text Spotting,7,eccv,7,9,2023-06-17 00:59:25.433000,https://github.com/amazon-research/glass-text-spotting,79,Glass: Global to local attention for scene-text spotting,"https://scholar.google.com/scholar?cluster=8076622804597824484&hl=en&as_sdt=0,22",4,2022
COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts,3,eccv,1,0,2023-06-17 00:59:25.645000,https://github.com/ku21fan/coo-comic-onomatopoeia,38,COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts,"https://scholar.google.com/scholar?cluster=1391865788632996555&hl=en&as_sdt=0,26",2,2022
Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition,4,eccv,11,3,2023-06-17 00:59:25.864000,https://github.com/xdxie/wordart,102,Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition,"https://scholar.google.com/scholar?cluster=6406452279700529821&hl=en&as_sdt=0,5",4,2022
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting,0,eccv,142,65,2023-06-17 00:59:26.076000,https://github.com/hikopensource/davar-lab-ocr,636,Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting,"https://scholar.google.com/scholar?cluster=16011258404988613521&hl=en&as_sdt=0,50",25,2022
CoMER: Modeling Coverage for Transformer-Based Handwritten Mathematical Expression Recognition,3,eccv,13,8,2023-06-17 00:59:26.288000,https://github.com/Green-Wood/CoMER,61,CoMER: Modeling Coverage for Transformer-Based Handwritten Mathematical Expression Recognition,"https://scholar.google.com/scholar?cluster=5646141492436593798&hl=en&as_sdt=0,10",3,2022
Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context,2,eccv,5,10,2023-06-17 00:59:26.500000,https://github.com/lcy0604/ctrnet,50,Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context,"https://scholar.google.com/scholar?cluster=8104354788716938084&hl=en&as_sdt=0,33",1,2022
TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers,1,eccv,1,1,2023-06-17 00:59:26.712000,https://github.com/amazon-research/textadain-robust-recognition,19,TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers,"https://scholar.google.com/scholar?cluster=11280375643907122561&hl=en&as_sdt=0,31",3,2022
Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features,13,eccv,6,0,2023-06-17 00:59:26.924000,https://github.com/wp03052/MATRN,56,Multi-modal text recognition networks: Interactive enhancements between visual and semantic features,"https://scholar.google.com/scholar?cluster=16909271202160367665&hl=en&as_sdt=0,10",3,2022
CAR: Class-Aware Regularizations for Semantic Segmentation,5,eccv,6,0,2023-06-17 00:59:27.136000,https://github.com/edwardyehuang/CAR,27,Car: Class-aware regularizations for semantic segmentation,"https://scholar.google.com/scholar?cluster=10799908460369282649&hl=en&as_sdt=0,47",3,2022
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation,10,eccv,3,1,2023-06-17 00:59:27.349000,https://github.com/helioszhao/shade,27,Style-hallucinated dual consistency learning for domain generalized semantic segmentation,"https://scholar.google.com/scholar?cluster=12065305653131607870&hl=en&as_sdt=0,47",2,2022
In Defense of Online Models for Video Instance Segmentation,35,eccv,49,38,2023-06-17 00:59:27.561000,https://github.com/wjf5203/vnext,547,In defense of online models for video instance segmentation,"https://scholar.google.com/scholar?cluster=16069829188377130053&hl=en&as_sdt=0,6",14,2022
Active Pointly-Supervised Instance Segmentation,2,eccv,0,0,2023-06-17 00:59:27.773000,https://github.com/chufengt/APIS,8,Active Pointly-Supervised Instance Segmentation,"https://scholar.google.com/scholar?cluster=10978471803996868329&hl=en&as_sdt=0,25",1,2022
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model,48,eccv,118,2,2023-06-17 00:59:27.989000,https://github.com/hkchengrex/XMem,1204,XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model,"https://scholar.google.com/scholar?cluster=4746998901966699571&hl=en&as_sdt=0,20",24,2022
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds,42,eccv,40,24,2023-06-17 00:59:28.204000,https://github.com/yanx27/2dpass,306,2dpass: 2d priors assisted semantic segmentation on lidar point clouds,"https://scholar.google.com/scholar?cluster=2558373953839539884&hl=en&as_sdt=0,6",15,2022
Extract Free Dense Labels from CLIP,52,eccv,21,8,2023-06-17 00:59:28.416000,https://github.com/chongzhou96/maskclip,244,Extract free dense labels from clip,"https://scholar.google.com/scholar?cluster=10784889589205919086&hl=en&as_sdt=0,44",7,2022
Box-Supervised Instance Segmentation with Level Set Evolution,14,eccv,24,6,2023-06-17 00:59:28.628000,https://github.com/liwentomng/boxlevelset,155,Box-supervised instance segmentation with level set evolution,"https://scholar.google.com/scholar?cluster=7955592219635477713&hl=en&as_sdt=0,1",5,2022
Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding,2,eccv,0,1,2023-06-17 00:59:28.840000,https://github.com/hoi4d/PPTr,6,Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding,"https://scholar.google.com/scholar?cluster=6712698866452693925&hl=en&as_sdt=0,31",1,2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation,11,eccv,3,2,2023-06-17 00:59:29.053000,https://github.com/damo-cv/transfgu,26,TransFGU: a top-down approach to fine-grained unsupervised semantic segmentation,"https://scholar.google.com/scholar?cluster=8246429810533346263&hl=en&as_sdt=0,7",2,2022
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation,20,eccv,14,1,2023-06-17 00:59:29.268000,https://github.com/Seokju-Cho/Volumetric-Aggregation-Transformer,129,Cost aggregation with 4d convolutional swin transformer for few-shot segmentation,"https://scholar.google.com/scholar?cluster=7354076544100243051&hl=en&as_sdt=0,5",1,2022
Perceptual Artifacts Localization for Inpainting,3,eccv,0,1,2023-06-17 00:59:29.480000,https://github.com/owenzlz/pal4inpaint,28,Perceptual artifacts localization for inpainting,"https://scholar.google.com/scholar?cluster=15640327239633342238&hl=en&as_sdt=0,10",2,2022
Data Efficient 3D Learner via Knowledge Transferred from 2D Model,2,eccv,0,0,2023-06-17 00:59:29.700000,https://github.com/bryanyu1997/data-efficient-3d-learner,15,Data Efficient 3D Learner via Knowledge Transferred from 2D Model,"https://scholar.google.com/scholar?cluster=10623808226901890539&hl=en&as_sdt=0,5",2,2022
Dense Gaussian Processes for Few-Shot Segmentation,8,eccv,3,1,2023-06-17 00:59:29.911000,https://github.com/joakimjohnander/dgpnet,41,Dense gaussian processes for few-shot segmentation,"https://scholar.google.com/scholar?cluster=9696467800979236699&hl=en&as_sdt=0,34",2,2022
3D Instances as 1D Kernels,4,eccv,4,1,2023-06-17 00:59:30.124000,https://github.com/w1zheng/dknet,45,3D Instances as 1D Kernels,"https://scholar.google.com/scholar?cluster=10539575323882110234&hl=en&as_sdt=0,6",3,2022
TransMatting: Enhancing Transparent Objects Matting with Transformers,6,eccv,2,1,2023-06-17 00:59:30.337000,https://github.com/acechq/transmatting,15,TransMatting: Enhancing Transparent Objects Matting with Transformers,"https://scholar.google.com/scholar?cluster=2970412112339223847&hl=en&as_sdt=0,47",8,2022
Abstracting Sketches through Simple Primitives,5,eccv,2,0,2023-06-17 00:59:30.549000,https://github.com/explainableml/sketch-primitives,15,Abstracting sketches through simple primitives,"https://scholar.google.com/scholar?cluster=12178522811290593447&hl=en&as_sdt=0,5",5,2022
Multi-Scale and Cross-Scale Contrastive Learning for Semantic Segmentation,1,eccv,2,1,2023-06-17 00:59:30.762000,https://github.com/rvimlab/ms_cs_contrseg,18,Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation,"https://scholar.google.com/scholar?cluster=2818282965941124519&hl=en&as_sdt=0,8",3,2022
One-Trimap Video Matting,4,eccv,6,6,2023-06-17 00:59:30.985000,https://github.com/hongje/otvm,70,One-Trimap Video Matting,"https://scholar.google.com/scholar?cluster=7588838291826563440&hl=en&as_sdt=0,5",6,2022
D2ADA: Dynamic Density-Aware Active Domain Adaptation for Semantic Segmentation,1,eccv,0,2,2023-06-17 00:59:31.207000,https://github.com/tsunghan-wu/d2ada,19,: Dynamic Density-Aware Active Domain Adaptation for Semantic Segmentation,"https://scholar.google.com/scholar?cluster=13096093290067916309&hl=en&as_sdt=0,5",2,2022
Learning Quality-Aware Dynamic Memory for Video Object Segmentation,10,eccv,17,0,2023-06-17 00:59:31.451000,https://github.com/workforai/qdmn,131,Learning quality-aware dynamic memory for video object segmentation,"https://scholar.google.com/scholar?cluster=14581578558335348283&hl=en&as_sdt=0,5",6,2022
Learning Implicit Feature Alignment Function for Semantic Segmentation,14,eccv,1,5,2023-06-17 00:59:31.664000,https://github.com/hzhupku/ifa,58,Learning implicit feature alignment function for semantic segmentation,"https://scholar.google.com/scholar?cluster=16350586248496262508&hl=en&as_sdt=0,5",3,2022
Instance As Identity: A Generic Online Paradigm for Video Instance Segmentation,4,eccv,3,0,2023-06-17 00:59:31.876000,https://github.com/zfonemore/iai,16,Instance as identity: A generic online paradigm for video instance segmentation,"https://scholar.google.com/scholar?cluster=2651342369418937109&hl=en&as_sdt=0,44",1,2022
Geodesic-Former: A Geodesic-Guided Few-Shot 3D Point Cloud Instance Segmenter,2,eccv,2,1,2023-06-17 00:59:32.088000,https://github.com/vinairesearch/geoformer,14,Geodesic-Former: A Geodesic-Guided Few-Shot 3D Point Cloud Instance Segmenter,"https://scholar.google.com/scholar?cluster=5545556338836437482&hl=en&as_sdt=0,19",3,2022
Union-Set Multi-source Model Adaptation for Semantic Segmentation,2,eccv,1,0,2023-06-17 00:59:32.301000,https://github.com/lzy7976/union-set-model-adaptation,9,Union-Set Multi-source Model Adaptation for Semantic Segmentation,"https://scholar.google.com/scholar?cluster=8783974652276072046&hl=en&as_sdt=0,5",2,2022
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection,19,eccv,3,1,2023-06-17 00:59:32.513000,https://github.com/Hydragon516/SPSN,32,Spsn: Superpixel prototype sampling network for rgb-d salient object detection,"https://scholar.google.com/scholar?cluster=961197252690730583&hl=en&as_sdt=0,5",2,2022
Global Spectral Filter Memory Network for Video Object Segmentation,8,eccv,2,1,2023-06-17 00:59:32.727000,https://github.com/workforai/gsfm,29,Global spectral filter memory network for video object segmentation,"https://scholar.google.com/scholar?cluster=6531040020135946124&hl=en&as_sdt=0,47",3,2022
Video Instance Segmentation via Multi-Scale Spatio-Temporal Split Attention Transformer,6,eccv,2,3,2023-06-17 00:59:32.946000,https://github.com/OmkarThawakar/MSSTS-VIS,36,Video Instance Segmentation via Multi-Scale Spatio-Temporal Split Attention Transformer,"https://scholar.google.com/scholar?cluster=971651369893003442&hl=en&as_sdt=0,23",8,2022
Learning Topological Interactions for Multi-Class Medical Image Segmentation,5,eccv,5,0,2023-06-17 00:59:33.162000,https://github.com/topoxlab/topointeraction,53,Learning Topological Interactions for Multi-Class Medical Image Segmentation,"https://scholar.google.com/scholar?cluster=7636749497701353644&hl=en&as_sdt=0,30",4,2022
Unsupervised Segmentation in Real-World Images via Spelke Object Inference,8,eccv,2,4,2023-06-17 00:59:33.382000,https://github.com/neuroailab/eisen,20,Unsupervised segmentation in real-world images via spelke object inference,"https://scholar.google.com/scholar?cluster=17744200822268427620&hl=en&as_sdt=0,5",4,2022
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model,23,eccv,10,6,2023-06-17 00:59:33.594000,https://github.com/mendelxu/zsseg.baseline,126,A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model,"https://scholar.google.com/scholar?cluster=1990243593035555434&hl=en&as_sdt=0,5",5,2022
Generative Subgraph Contrast for Self-Supervised Graph Representation Learning,1,eccv,1,0,2023-06-17 00:59:33.808000,https://github.com/yh-han/gsc,11,Generative Subgraph Contrast for Self-Supervised Graph Representation Learning,"https://scholar.google.com/scholar?cluster=17324044096784760749&hl=en&as_sdt=0,26",1,2022
SdAE: Self-Distillated Masked Autoencoder,18,eccv,1,2,2023-06-17 00:59:34.020000,https://github.com/abrahamyabo/sdae,36,Sdae: Self-distillated masked autoencoder,"https://scholar.google.com/scholar?cluster=6427547624181496716&hl=en&as_sdt=0,5",4,2022
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation,7,eccv,2,0,2023-06-17 00:59:34.232000,https://github.com/val-iisc/stickerda,17,Concurrent subsidiary supervision for unsupervised source-free domain adaptation,"https://scholar.google.com/scholar?cluster=16781913863489916282&hl=en&as_sdt=0,5",12,2022
Active Learning Strategies for Weakly-Supervised Object Detection,3,eccv,5,1,2023-06-17 00:59:34.445000,https://github.com/huyvvo/bib,25,Active Learning Strategies for Weakly-Supervised Object Detection,"https://scholar.google.com/scholar?cluster=6555341052977464243&hl=en&as_sdt=0,5",2,2022
Mc-BEiT: Multi-Choice Discretization for Image BERT Pre-training,18,eccv,1,0,2023-06-17 00:59:34.656000,https://github.com/lixiaotong97/mc-beit,21,mc-BEiT: Multi-choice Discretization for Image BERT Pre-training,"https://scholar.google.com/scholar?cluster=10612926957976727479&hl=en&as_sdt=0,5",2,2022
Bootstrapped Masked Autoencoders for Vision BERT Pretraining,18,eccv,6,0,2023-06-17 00:59:34.868000,https://github.com/lightdxy/bootmae,90,Bootstrapped Masked Autoencoders for Vision BERT Pretraining,"https://scholar.google.com/scholar?cluster=11908913569029309505&hl=en&as_sdt=0,10",3,2022
What to Hide from Your Students: Attention-Guided Masked Image Modeling,36,eccv,4,0,2023-06-17 00:59:35.089000,https://github.com/gkakogeorgiou/attmask,31,What to hide from your students: Attention-guided masked image modeling,"https://scholar.google.com/scholar?cluster=13621702207944750833&hl=en&as_sdt=0,5",5,2022
Pointly-Supervised Panoptic Segmentation,5,eccv,1,1,2023-06-17 00:59:35.307000,https://github.com/bravegroup/psps,19,Pointly-Supervised Panoptic Segmentation,"https://scholar.google.com/scholar?cluster=14167808655489374713&hl=en&as_sdt=0,1",4,2022
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation,43,eccv,25,4,2023-06-17 00:59:35.524000,https://github.com/lhoyer/hrda,188,HRDA: Context-aware high-resolution domain-adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=11500016484284904863&hl=en&as_sdt=0,5",5,2022
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning,6,eccv,3,1,2023-06-17 00:59:35.760000,https://github.com/TonyLianLong/UnsupervisedSelectiveLabeling,27,Unsupervised Selective Labeling for More Effective Semi-Supervised Learning,"https://scholar.google.com/scholar?cluster=2772643387348706767&hl=en&as_sdt=0,28",2,2022
Max Pooling with Vision Transformers Reconciles Class and Shape in Weakly Supervised Semantic Segmentation,10,eccv,1,0,2023-06-17 00:59:36.028000,https://github.com/deepplants/vit-pcm,14,Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentation,"https://scholar.google.com/scholar?cluster=13877065122591623844&hl=en&as_sdt=0,5",2,2022
Dense Siamese Network for Dense Unsupervised Learning,4,eccv,2,0,2023-06-17 00:59:36.298000,https://github.com/zwwwayne/densesiam,26,Dense Siamese Network for Dense Unsupervised Learning,"https://scholar.google.com/scholar?cluster=2962540697381771652&hl=en&as_sdt=0,25",1,2022
Multi-Granularity Distillation Scheme towards Lightweight Semi-Supervised Semantic Segmentation,2,eccv,1,3,2023-06-17 00:59:36.510000,https://github.com/jayqine/mgd-ssss,11,Multi-granularity Distillation Scheme Towards Lightweight Semi-supervised Semantic Segmentation,"https://scholar.google.com/scholar?cluster=14826363507378603600&hl=en&as_sdt=0,47",3,2022
CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation,10,eccv,1,0,2023-06-17 00:59:36.723000,https://github.com/wangf3014/cp2,6,CP: Copy-Paste Contrastive Pretraining for Semantic Segmentation,"https://scholar.google.com/scholar?cluster=9077524711445116563&hl=en&as_sdt=0,5",1,2022
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization,7,eccv,2,0,2023-06-17 00:59:36.934000,https://github.com/1998v7/self-filtering,19,Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization,"https://scholar.google.com/scholar?cluster=11503343963876701921&hl=en&as_sdt=0,36",1,2022
RDA: Reciprocal Distribution Alignment for Robust Semi-Supervised Learning,1,eccv,0,0,2023-06-17 00:59:37.155000,https://github.com/njuyued/rda4robustssl,7,RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning,"https://scholar.google.com/scholar?cluster=1893818676204532459&hl=en&as_sdt=0,11",1,2022
MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation,1,eccv,0,0,2023-06-17 00:59:37.368000,https://github.com/ViLab-UCSD/MemSAC_ECCV2022,6,MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation,"https://scholar.google.com/scholar?cluster=17292964986746280650&hl=en&as_sdt=0,5",2,2022
Synergistic Self-Supervised and Quantization Learning,2,eccv,4,0,2023-06-17 00:59:37.583000,https://github.com/megvii-research/ssql-eccv2022,67,Synergistic Self-supervised and Quantization Learning,"https://scholar.google.com/scholar?cluster=3701150918575417216&hl=en&as_sdt=0,23",4,2022
Semi-Supervised Vision Transformers,14,eccv,5,1,2023-06-17 00:59:37.798000,https://github.com/wengzejia1/semiformer,26,Semi-supervised vision transformers,"https://scholar.google.com/scholar?cluster=83081748366699225&hl=en&as_sdt=0,5",3,2022
Domain Adaptive Video Segmentation via Temporal Pseudo Supervision,5,eccv,6,9,2023-06-17 00:59:38.011000,https://github.com/xing0047/tps,28,Domain adaptive video segmentation via temporal pseudo supervision,"https://scholar.google.com/scholar?cluster=7231098623956259110&hl=en&as_sdt=0,5",2,2022
ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization,6,eccv,3,1,2023-06-17 00:59:38.224000,https://github.com/jiwoncocoder/conmatch,24,Conmatch: Semi-supervised learning with confidence-guided consistency regularization,"https://scholar.google.com/scholar?cluster=23511256883904024&hl=en&as_sdt=0,10",4,2022
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation,6,eccv,6,0,2023-06-17 00:59:38.436000,https://github.com/sungwon-han/fedx,47,FedX: Unsupervised Federated Learning with Cross Knowledge Distillation,"https://scholar.google.com/scholar?cluster=15024670483115601107&hl=en&as_sdt=0,44",3,2022
Decoupled Adversarial Contrastive Learning for Self-Supervised Adversarial Robustness,6,eccv,1,1,2023-06-17 00:59:38.649000,https://github.com/pantheon5100/deacl,12,Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness,"https://scholar.google.com/scholar?cluster=33793130511872188&hl=en&as_sdt=0,23",3,2022
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning,2,eccv,1,1,2023-06-17 00:59:38.861000,https://github.com/seleucia/goca,7,GOCA: guided online cluster assignment for self-supervised video representation Learning,"https://scholar.google.com/scholar?cluster=11832380063761473468&hl=en&as_sdt=0,47",1,2022
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning,1,eccv,2,0,2023-06-17 00:59:39.074000,https://github.com/ucdvision/cmsf,5,Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning,"https://scholar.google.com/scholar?cluster=3423733438227857877&hl=en&as_sdt=0,5",3,2022
Revisiting the Critical Factors of Augmentation-Invariant Representation Learning,3,eccv,0,0,2023-06-17 00:59:39.287000,https://github.com/megvii-research/revisitairl,11,Revisiting the Critical Factors of Augmentation-Invariant Representation Learning,"https://scholar.google.com/scholar?cluster=5620391103702962878&hl=en&as_sdt=0,33",3,2022
CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation,2,eccv,42,19,2023-06-17 00:59:39.498000,https://github.com/dvlab-research/Entity,449,CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation,"https://scholar.google.com/scholar?cluster=9702778436571703527&hl=en&as_sdt=0,31",20,2022
Semantic-Aware Fine-Grained Correspondence,2,eccv,1,1,2023-06-17 00:59:39.710000,https://github.com/alxead/sfc,13,Semantic-Aware Fine-Grained Correspondence,"https://scholar.google.com/scholar?cluster=9515469045960583745&hl=en&as_sdt=0,14",1,2022
Self-Supervised Classification Network,15,eccv,4,0,2023-06-17 00:59:39.924000,https://github.com/elad-amrani/self-classifier,31,Self-supervised classification network,"https://scholar.google.com/scholar?cluster=12911109870349597402&hl=en&as_sdt=0,31",1,2022
Semi-Supervised Object Detection via Virtual Category Learning,5,eccv,0,0,2023-06-17 00:59:40.135000,https://github.com/geoffreychen777/vc,6,Semi-supervised object detection via virtual category learning,"https://scholar.google.com/scholar?cluster=12705891433611100689&hl=en&as_sdt=0,10",1,2022
Completely Self-Supervised Crowd Counting via Distribution Matching,7,eccv,8,1,2023-06-17 00:59:40.348000,https://github.com/val-iisc/css-ccnn,25,Completely self-supervised crowd counting via distribution matching,"https://scholar.google.com/scholar?cluster=15996947716561762009&hl=en&as_sdt=0,5",14,2022
Coarse-to-Fine Incremental Few-Shot Learning,4,eccv,0,0,2023-06-17 00:59:40.561000,https://github.com/HAIV-Lab/Knowe,4,Coarse-to-fine incremental few-shot learning,"https://scholar.google.com/scholar?cluster=15208517894348282835&hl=en&as_sdt=0,5",0,2022
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling,0,eccv,1,0,2023-06-17 00:59:40.774000,https://github.com/puchapu/utep,2,Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling,"https://scholar.google.com/scholar?cluster=10772316129311859971&hl=en&as_sdt=0,5",1,2022
CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation,2,eccv,0,1,2023-06-17 00:59:40.986000,https://github.com/renwang435/cyborgs,3,Cyborgs: Contrastively bootstrapping object representations by grounding in segmentation,"https://scholar.google.com/scholar?cluster=4093202168544509121&hl=en&as_sdt=0,5",2,2022
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection,4,eccv,4,4,2023-06-17 00:59:41.207000,https://github.com/jinhseo/od-wscl,31,Object Discovery via Contrastive Learning for Weakly Supervised Object Detection,"https://scholar.google.com/scholar?cluster=4274032166116217116&hl=en&as_sdt=0,5",1,2022
Semi-Leak: Membership Inference Attacks against Semi-Supervised Learning,3,eccv,0,0,2023-06-17 00:59:41.420000,https://github.com/xinleihe/semi-leak,9,Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning,"https://scholar.google.com/scholar?cluster=9233507844643867685&hl=en&as_sdt=0,5",1,2022
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning,7,eccv,4,3,2023-06-17 00:59:41.632000,https://github.com/nayeemrizve/openldn,23,Openldn: Learning to discover novel classes for open-world semi-supervised learning,"https://scholar.google.com/scholar?cluster=12710236080905969514&hl=en&as_sdt=0,5",1,2022
Embedding Contrastive Unsupervised Features to Cluster in- and Out-of-Distribution Noise in Corrupted Image Datasets,2,eccv,0,1,2023-06-17 00:59:41.845000,https://github.com/paulalbert31/sncf,9,Embedding contrastive unsupervised features to cluster in-and out-of-distribution noise in corrupted image datasets,"https://scholar.google.com/scholar?cluster=6052251975448117268&hl=en&as_sdt=0,26",2,2022
Towards Realistic Semi-Supervised Learning,8,eccv,1,3,2023-06-17 00:59:42.058000,https://github.com/nayeemrizve/trssl,24,Towards realistic semi-supervised learning,"https://scholar.google.com/scholar?cluster=15906626340629740128&hl=en&as_sdt=0,5",1,2022
Masked Siamese Networks for Label-Efficient Learning,99,eccv,30,16,2023-06-17 00:59:42.271000,https://github.com/facebookresearch/msn,400,Masked siamese networks for label-efficient learning,"https://scholar.google.com/scholar?cluster=9235835052951341282&hl=en&as_sdt=0,23",13,2022
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization,17,eccv,5,1,2023-06-17 00:59:42.483000,https://github.com/hmsch/natural-synthetic-anomalies,29,Natural synthetic anomalies for self-supervised anomaly detection and localization,"https://scholar.google.com/scholar?cluster=7248162955817269145&hl=en&as_sdt=0,5",2,2022
Understanding Collapse in Non-Contrastive Siamese Representation Learning,10,eccv,1,1,2023-06-17 00:59:42.695000,https://github.com/alexlioralexli/noncontrastive-ssl,4,Understanding Collapse in Non-contrastive Siamese Representation Learning,"https://scholar.google.com/scholar?cluster=1641172093663463753&hl=en&as_sdt=0,44",1,2022
Federated Self-Supervised Learning for Video Understanding,2,eccv,1,0,2023-06-17 00:59:42.908000,https://github.com/yasar-rehman/fedvssl,15,Federated Self-supervised Learning for Video Understanding,"https://scholar.google.com/scholar?cluster=5203922739716905699&hl=en&as_sdt=0,4",6,2022
Towards Efficient and Effective Self-Supervised Learning of Visual Representations,3,eccv,1,0,2023-06-17 00:59:43.120000,https://github.com/val-iisc/effssl,4,Towards Efficient and Effective Self-Supervised Learning of Visual Representations,"https://scholar.google.com/scholar?cluster=9102827506777394507&hl=en&as_sdt=0,44",13,2022
MVSTER: Epipolar Transformer for Efficient Multi-View Stereo,20,eccv,12,10,2023-06-17 00:59:43.333000,https://github.com/jeffwang987/mvster,149,MVSTER: epipolar transformer for efficient multi-view stereo,"https://scholar.google.com/scholar?cluster=16748508301109808969&hl=en&as_sdt=0,5",6,2022
RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering,10,eccv,13,7,2023-06-17 00:59:43.545000,https://github.com/boese0601/rc-mvsnet,173,RC-MVSNet: unsupervised multi-view stereo with neural rendering,"https://scholar.google.com/scholar?cluster=7214458458085402585&hl=en&as_sdt=0,22",17,2022
ARF: Artistic Radiance Fields,30,eccv,41,7,2023-06-17 00:59:43.758000,https://github.com/Kai-46/ARF-svox2,435,Arf: Artistic radiance fields,"https://scholar.google.com/scholar?cluster=9612416165197735153&hl=en&as_sdt=0,16",18,2022
Multiview Stereo with Cascaded Epipolar RAFT,5,eccv,9,4,2023-06-17 00:59:43.970000,https://github.com/princeton-vl/cer-mvs,100,Multiview stereo with cascaded epipolar raft,"https://scholar.google.com/scholar?cluster=5026889917906841246&hl=en&as_sdt=0,43",9,2022
ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer,27,eccv,16,0,2023-06-17 00:59:44.241000,https://github.com/apple/ml-aspanformer,100,Aspanformer: Detector-free image matching with adaptive span transformer,"https://scholar.google.com/scholar?cluster=4389376922954725361&hl=en&as_sdt=0,37",12,2022
NDF: Neural Deformable Fields for Dynamic Human Modelling,4,eccv,4,1,2023-06-17 00:59:44.453000,https://github.com/hkbu-vscomputing/2022_eccv_ndf,14,NDF: Neural Deformable Fields for Dynamic Human Modelling,"https://scholar.google.com/scholar?cluster=10897766450583864581&hl=en&as_sdt=0,5",1,2022
Neural Density-Distance Fields,5,eccv,8,3,2023-06-17 00:59:44.665000,https://github.com/ueda0319/neddf,203,Neural Density-Distance Fields,"https://scholar.google.com/scholar?cluster=10169858113129806585&hl=en&as_sdt=0,5",13,2022
Learning Online Multi-sensor Depth Fusion,1,eccv,0,0,2023-06-17 00:59:44.878000,https://github.com/tfy14esa/senfunet,7,Learning online multi-sensor depth fusion,"https://scholar.google.com/scholar?cluster=12133624018619212262&hl=en&as_sdt=0,43",2,2022
Improving RGB-D Point Cloud Registration by Learning Multi-Scale Local Linear Transformation,1,eccv,0,0,2023-06-17 00:59:45.090000,https://github.com/514dna/llt,11,Improving rgb-d point cloud registration by learning multi-scale local linear transformation,"https://scholar.google.com/scholar?cluster=467163058867525578&hl=en&as_sdt=0,5",3,2022
Real-Time Neural Character Rendering with Pose-Guided Multiplane Images,6,eccv,5,1,2023-06-17 00:59:45.303000,https://github.com/ken-ouyang/PGMPI,46,Real-time neural character rendering with pose-guided multiplane images,"https://scholar.google.com/scholar?cluster=9080153091759921402&hl=en&as_sdt=0,5",3,2022
Disentangling Object Motion and Occlusion for Unsupervised Multi-Frame Monocular Depth,7,eccv,8,5,2023-06-17 00:59:45.514000,https://github.com/AutoAILab/DynamicDepth,96,Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth,"https://scholar.google.com/scholar?cluster=7095473850872719452&hl=en&as_sdt=0,5",3,2022
Context-Enhanced Stereo Transformer,2,eccv,3,3,2023-06-17 00:59:45.727000,https://github.com/guoweiyu/context-enhanced-stereo-transformer,30,Context-Enhanced Stereo Transformer,"https://scholar.google.com/scholar?cluster=3543640163219156828&hl=en&as_sdt=0,25",1,2022
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images,9,eccv,50,56,2023-06-17 00:59:45.940000,https://github.com/liuyuan-pal/gen6d,395,Gen6D: Generalizable model-free 6-DoF object pose estimation from RGB images,"https://scholar.google.com/scholar?cluster=5099493959917547229&hl=en&as_sdt=0,42",9,2022
Latency-Aware Collaborative Perception,20,eccv,1,2,2023-06-17 00:59:46.152000,https://github.com/mediabrain-sjtu/syncnet,13,Latency-aware collaborative perception,"https://scholar.google.com/scholar?cluster=12080385681051469958&hl=en&as_sdt=0,5",0,2022
TensoRF: Tensorial Radiance Fields,230,eccv,126,45,2023-06-17 00:59:46.364000,https://github.com/apchenstu/TensoRF,898,Tensorf: Tensorial radiance fields,"https://scholar.google.com/scholar?cluster=9392347583762409161&hl=en&as_sdt=0,29",20,2022
NeFSAC: Neurally Filtered Minimal Samples,5,eccv,1,1,2023-06-17 00:59:46.576000,https://github.com/cavalli1234/nefsac,36,NeFSAC: neurally filtered minimal samples,"https://scholar.google.com/scholar?cluster=13860320733798826375&hl=en&as_sdt=0,5",6,2022
HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields,4,eccv,7,3,2023-06-17 00:59:46.788000,https://github.com/postech-ami/hdr-plenoxels,90,HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields,"https://scholar.google.com/scholar?cluster=13393942085458083922&hl=en&as_sdt=0,39",9,2022
NeuMan: Neural Human Radiance Field from a Single Video,43,eccv,135,38,2023-06-17 00:59:47,https://github.com/apple/ml-neuman,1096,Neuman: Neural human radiance field from a single video,"https://scholar.google.com/scholar?cluster=4308511704577688456&hl=en&as_sdt=0,5",35,2022
TAVA: Template-Free Animatable Volumetric Actors,33,eccv,16,1,2023-06-17 00:59:47.217000,https://github.com/facebookresearch/tava,179,Tava: Template-free animatable volumetric actors,"https://scholar.google.com/scholar?cluster=7969304324450759992&hl=en&as_sdt=0,29",12,2022
EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching,2,eccv,0,0,2023-06-17 00:59:47.430000,https://github.com/hkbu-hpml/easnet,6,EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching,"https://scholar.google.com/scholar?cluster=15435889328607570391&hl=en&as_sdt=0,5",1,2022
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild,5,eccv,15,3,2023-06-17 00:59:47.642000,https://github.com/bytedance/particle-sfm,160,ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild,"https://scholar.google.com/scholar?cluster=649075297016253795&hl=en&as_sdt=0,5",14,2022
Approximate Differentiable Rendering with Algebraic Surfaces,2,eccv,2,2,2023-06-17 00:59:47.855000,https://github.com/leonidk/fuzzy-metaballs,48,Approximate Differentiable Rendering with Algebraic Surfaces,"https://scholar.google.com/scholar?cluster=14972940426953053796&hl=en&as_sdt=0,5",3,2022
GraphFit: Learning Multi-Scale Graph-Convolutional Representation for Point Cloud Normal Estimation,4,eccv,2,0,2023-06-17 00:59:48.069000,https://github.com/uestcjay/graphfit,21,GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation,"https://scholar.google.com/scholar?cluster=13505484244080411576&hl=en&as_sdt=0,38",1,2022
Point Scene Understanding via Disentangled Instance Mesh Reconstruction,6,eccv,3,4,2023-06-17 00:59:48.281000,https://github.com/ashawkey/dimr,25,Point scene understanding via disentangled instance mesh reconstruction,"https://scholar.google.com/scholar?cluster=2693964574751110058&hl=en&as_sdt=0,5",5,2022
Space-Partitioning RANSAC,3,eccv,79,7,2023-06-17 00:59:48.493000,https://github.com/danini/graph-cut-ransac,335,Space-Partitioning RANSAC,"https://scholar.google.com/scholar?cluster=7758768906343068839&hl=en&as_sdt=0,33",21,2022
What Matters for 3D Scene Flow Network,10,eccv,4,3,2023-06-17 00:59:48.705000,https://github.com/irmvlab/3dflow,36,What Matters for 3D Scene Flow Network,"https://scholar.google.com/scholar?cluster=15648120699613324906&hl=en&as_sdt=0,31",2,2022
GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs,3,eccv,1,3,2023-06-17 00:59:48.918000,https://github.com/xinliu20/graphcspn_eccv2022,15,GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs,"https://scholar.google.com/scholar?cluster=1840962488966960353&hl=en&as_sdt=0,43",2,2022
Language-Grounded Indoor 3D Semantic Segmentation in the Wild,5,eccv,14,1,2023-06-17 00:59:49.130000,https://github.com/RozDavid/LanguageGroundedSemseg,77,Language-grounded indoor 3D semantic segmentation in the wild,"https://scholar.google.com/scholar?cluster=4523012745679704845&hl=en&as_sdt=0,47",4,2022
FLEX: Extrinsic Parameters-Free Multi-View 3D Human Motion Reconstruction,7,eccv,4,10,2023-06-17 00:59:49.342000,https://github.com/BrianG13/FLEX,37,FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction,"https://scholar.google.com/scholar?cluster=9329272626865528352&hl=en&as_sdt=0,47",8,2022
ActiveNeRF: Learning Where to See with Uncertainty Estimation,7,eccv,3,4,2023-06-17 00:59:49.554000,https://github.com/leaplabthu/activenerf,58,ActiveNeRF: Learning Where to See with Uncertainty Estimation,"https://scholar.google.com/scholar?cluster=7696752785009743824&hl=en&as_sdt=0,5",6,2022
PoserNet: Refining Relative Camera Poses Exploiting Object Detections,1,eccv,0,0,2023-06-17 00:59:49.766000,https://github.com/iit-pavis/posernet,40,PoserNet: Refining Relative Camera Poses Exploiting Object Detections,"https://scholar.google.com/scholar?cluster=5415819577360667082&hl=en&as_sdt=0,39",6,2022
Class-Incremental Novel Class Discovery,9,eccv,6,2,2023-06-17 00:59:49.980000,https://github.com/oatmealliu/class-incd,55,Class-incremental Novel Class Discovery,"https://scholar.google.com/scholar?cluster=16320430811292329479&hl=en&as_sdt=0,5",4,2022
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation,7,eccv,1,1,2023-06-17 00:59:50.223000,https://github.com/hongbin98/proca,20,Prototype-guided continual adaptation for class-incremental unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=8804941606286373494&hl=en&as_sdt=0,39",3,2022
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation,17,eccv,2,0,2023-06-17 00:59:50.436000,https://github.com/dvlab-research/decouplenet,30,DecoupleNet: Decoupled network for domain adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=9009915321489245170&hl=en&as_sdt=0,5",3,2022
Mind the Gap in Distilling StyleGANs,4,eccv,1,3,2023-06-17 00:59:50.647000,https://github.com/xuguodong03/stylekd,22,Mind the Gap in Distilling StyleGANs,"https://scholar.google.com/scholar?cluster=5377121854905583705&hl=en&as_sdt=0,47",7,2022
Long-Tailed Class Incremental Learning,2,eccv,0,0,2023-06-17 00:59:50.859000,https://github.com/xialeiliu/long-tailed-cil,26,Long-Tailed Class Incremental Learning,"https://scholar.google.com/scholar?cluster=9618483384480829027&hl=en&as_sdt=0,14",6,2022
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation,5,eccv,1,0,2023-06-17 00:59:51.071000,https://github.com/saltoricristiano/gipso-sfouda,31,GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation,"https://scholar.google.com/scholar?cluster=13813760731976228346&hl=en&as_sdt=0,33",6,2022
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation,17,eccv,5,0,2023-06-17 00:59:51.284000,https://github.com/saltoricristiano/cosmix-uda,40,Cosmix: Compositional semantic mix for domain adaptation in 3d lidar segmentation,"https://scholar.google.com/scholar?cluster=6786759772054921592&hl=en&as_sdt=0,5",6,2022
A Unified Framework for Domain Adaptive Pose Estimation,9,eccv,4,1,2023-06-17 00:59:51.496000,https://github.com/visionlearninggroup/uda_poseestimation,11,A unified framework for domain adaptive pose estimation,"https://scholar.google.com/scholar?cluster=8665350708819483356&hl=en&as_sdt=0,5",2,2022
A Broad Study of Pre-training for Domain Generalization and Adaptation,22,eccv,2,1,2023-06-17 00:59:51.709000,https://github.com/visionlearninggroup/benchmark_domain_transfer,11,A broad study of pre-training for domain generalization and adaptation,"https://scholar.google.com/scholar?cluster=4743623741984149169&hl=en&as_sdt=0,5",2,2022
Prior Knowledge Guided Unsupervised Domain Adaptation,6,eccv,1,0,2023-06-17 00:59:51.920000,https://github.com/tsun/kuda,12,Prior Knowledge Guided Unsupervised Domain Adaptation,"https://scholar.google.com/scholar?cluster=16331707540385180374&hl=en&as_sdt=0,5",2,2022
AcroFOD: An Adaptive Method for Cross-Domain Few-Shot Object Detection,6,eccv,4,0,2023-06-17 00:59:52.133000,https://github.com/hlings/acrofod,30,AcroFOD: An Adaptive Method for Cross-Domain Few-Shot Object Detection,"https://scholar.google.com/scholar?cluster=8976863318013390692&hl=en&as_sdt=0,10",2,2022
Visual Prompt Tuning,249,eccv,63,7,2023-06-17 00:59:52.346000,https://github.com/KMnP/vpt,561,Visual prompt tuning,"https://scholar.google.com/scholar?cluster=14421942083121350206&hl=en&as_sdt=0,5",8,2022
Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap,3,eccv,2,0,2023-06-17 00:59:52.559000,https://github.com/gorilla-lab-scut/qs3,10,Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap,"https://scholar.google.com/scholar?cluster=5597206378696973800&hl=en&as_sdt=0,5",0,2022
TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation,4,eccv,0,1,2023-06-17 00:59:52.772000,https://github.com/ETHRuiGong/TADA,9,TACS: Taxonomy adaptive cross-domain semantic segmentation,"https://scholar.google.com/scholar?cluster=9748486556084036411&hl=en&as_sdt=0,10",1,2022
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation,23,eccv,5,1,2023-06-17 00:59:52.991000,https://github.com/jiangzhengkai/proca,36,Prototypical contrast adaptation for domain adaptive semantic segmentation,"https://scholar.google.com/scholar?cluster=18247261042350694594&hl=en&as_sdt=0,5",2,2022
Factorizing Knowledge in Neural Networks,43,eccv,4,2,2023-06-17 00:59:53.219000,https://github.com/adamdad/knowledgefactor,66,Factorizing knowledge in neural networks,"https://scholar.google.com/scholar?cluster=10840229611357646465&hl=en&as_sdt=0,11",1,2022
Contrastive Vicinal Space for Unsupervised Domain Adaptation,6,eccv,2,3,2023-06-17 00:59:53.431000,https://github.com/najaemin92/covi,7,Contrastive vicinal space for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=11822924811808351967&hl=en&as_sdt=0,19",1,2022
Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions,4,eccv,1,2,2023-06-17 00:59:53.643000,https://github.com/theo2021/onda,21,Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions,"https://scholar.google.com/scholar?cluster=13123083871593814171&hl=en&as_sdt=0,5",5,2022
Source-Free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition,12,eccv,2,0,2023-06-17 00:59:53.856000,https://github.com/xuyu0010/atcon,16,Source-free video domain adaptation by learning temporal consistency for action recognition,"https://scholar.google.com/scholar?cluster=16148930639028163885&hl=en&as_sdt=0,5",2,2022
BMD: A General Class-Balanced Multicentric Dynamic Prototype Strategy for Source-Free Domain Adaptation,14,eccv,0,0,2023-06-17 00:59:54.068000,https://github.com/ispc-lab/bmd,21,BMD: A general class-balanced multicentric dynamic prototype strategy for source-free domain adaptation,"https://scholar.google.com/scholar?cluster=14638148510749193557&hl=en&as_sdt=0,33",1,2022
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks,6,eccv,2,4,2023-06-17 00:59:54.281000,https://github.com/google-research/pactran_metrics,13,PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks,"https://scholar.google.com/scholar?cluster=12501188496378767771&hl=en&as_sdt=0,5",2,2022
Not All Models Are Equal: Predicting Model Transferability in a Self-Challenging Fisher Space,1,eccv,0,4,2023-06-17 00:59:54.493000,https://github.com/tencentarc/sfda,17,Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space,"https://scholar.google.com/scholar?cluster=5216001205389522548&hl=en&as_sdt=0,5",2,2022
Attention Diversification for Domain Generalization,10,eccv,4,0,2023-06-17 00:59:54.705000,https://github.com/hikvision-research/domaingeneralization,35,Attention diversification for domain generalization,"https://scholar.google.com/scholar?cluster=10487515382646340469&hl=en&as_sdt=0,10",5,2022
ESS: Learning Event-Based Semantic Segmentation from Still Images,19,eccv,7,3,2023-06-17 00:59:54.917000,https://github.com/uzh-rpg/ess,47,Ess: Learning event-based semantic segmentation from still images,"https://scholar.google.com/scholar?cluster=4136513580895924966&hl=en&as_sdt=0,5",6,2022
Human Trajectory Prediction via Neural Social Physics,13,eccv,13,12,2023-06-17 00:59:55.130000,https://github.com/realcrane/human-trajectory-prediction-via-neural-social-physics,69,Human trajectory prediction via neural social physics,"https://scholar.google.com/scholar?cluster=890292311615588687&hl=en&as_sdt=0,5",4,2022
ECLIPSE: Efficient Long-Range Video Retrieval Using Sight and Sound,11,eccv,6,2,2023-06-17 00:59:55.343000,https://github.com/GenjiB/ECLIPSE,21,EclipSE: Efficient Long-Range Video Retrieval Using Sight and Sound,"https://scholar.google.com/scholar?cluster=13579006365454724438&hl=en&as_sdt=0,5",1,2022
Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing,7,eccv,2,1,2023-06-17 00:59:55.556000,https://github.com/mcg-nju/jomold,25,Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing,"https://scholar.google.com/scholar?cluster=12645873220814826202&hl=en&as_sdt=0,5",2,2022
Real-Time Online Video Detection with Temporal Smoothing Transformers,2,eccv,4,3,2023-06-17 00:59:55.770000,https://github.com/zhaoyue-zephyrus/testra,70,Real-Time Online Video Detection with Temporal Smoothing Transformers,"https://scholar.google.com/scholar?cluster=18281896572010683419&hl=en&as_sdt=0,10",2,2022
TALLFormer: Temporal Action Localization with a Long-Memory Transformer,20,eccv,2,8,2023-06-17 00:59:55.982000,https://github.com/klauscc/tallformer,39,TallFormer: Temporal Action Localization with a Long-Memory Transformer,"https://scholar.google.com/scholar?cluster=15017107856472045012&hl=en&as_sdt=0,28",3,2022
Mining Relations among Cross-Frame Affinities for Video Semantic Segmentation,5,eccv,4,1,2023-06-17 00:59:56.219000,https://github.com/guoleisun/vss-mrcfa,21,Mining Relations Among Cross-Frame Affinities for Video Semantic Segmentation,"https://scholar.google.com/scholar?cluster=13563533975816670492&hl=en&as_sdt=0,31",1,2022
Rethinking Learning Approaches for Long-Term Action Anticipation,3,eccv,1,0,2023-06-17 00:59:56.431000,https://github.com/nmegha2601/anticipatr,7,Rethinking Learning Approaches for Long-Term Action Anticipation,"https://scholar.google.com/scholar?cluster=9860931350837211383&hl=en&as_sdt=0,5",3,2022
DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition,8,eccv,3,2,2023-06-17 00:59:56.644000,https://github.com/sail-sg/dualformer,23,Dualformer: Local-global stratified transformer for efficient video recognition,"https://scholar.google.com/scholar?cluster=5432221077116828342&hl=en&as_sdt=0,5",3,2022
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation,17,eccv,1,9,2023-06-17 00:59:56.856000,https://github.com/NUST-Machine-Intelligence-Laboratory/HFAN,20,Hierarchical feature alignment network for unsupervised video object segmentation,"https://scholar.google.com/scholar?cluster=9672488249865839397&hl=en&as_sdt=0,33",1,2022
How Severe Is Benchmark-Sensitivity in Video Self-Supervised Learning?,4,eccv,2,0,2023-06-17 00:59:57.068000,https://github.com/fmthoker/severe-benchmark,18,How Severe Is Benchmark-Sensitivity in Video Self-supervised Learning?,"https://scholar.google.com/scholar?cluster=17663216619245090292&hl=en&as_sdt=0,16",2,2022
Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation,9,eccv,6,0,2023-06-17 00:59:57.281000,https://github.com/boschresearch/uvast,19,Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation,"https://scholar.google.com/scholar?cluster=11800477731611681372&hl=en&as_sdt=0,47",6,2022
Efficient Video Transformers with Spatial-Temporal Token Selection,19,eccv,3,2,2023-06-17 00:59:57.493000,https://github.com/wangjk666/stts,26,Efficient video transformers with spatial-temporal token selection,"https://scholar.google.com/scholar?cluster=2538914770043000786&hl=en&as_sdt=0,33",3,2022
Long Movie Clip Classification with State-Space Video Models,16,eccv,2,6,2023-06-17 00:59:57.705000,https://github.com/md-mohaiminul/ViS4mer,32,Long movie clip classification with state-space video models,"https://scholar.google.com/scholar?cluster=6503165072649077877&hl=en&as_sdt=0,5",1,2022
Prompting Visual-Language Models for Efficient Video Understanding,93,eccv,6,4,2023-06-17 00:59:57.919000,https://github.com/ju-chen/Efficient-Prompt,143,Prompting visual-language models for efficient video understanding,"https://scholar.google.com/scholar?cluster=267484077532007560&hl=en&as_sdt=0,5",12,2022
Self-Supervised Social Relation Representation for Human Group Detection,1,eccv,0,0,2023-06-17 00:59:58.132000,https://github.com/jiaoma/shgd,3,Self-supervised Social Relation Representation for Human Group Detection,"https://scholar.google.com/scholar?cluster=14185808079610011178&hl=en&as_sdt=0,47",1,2022
A Deep Moving-Camera Background Model,0,eccv,5,1,2023-06-17 00:59:58.344000,https://github.com/bgu-cs-vil/deepmcbm,34,A Deep Moving-Camera Background Model,"https://scholar.google.com/scholar?cluster=14745114131372795526&hl=en&as_sdt=0,5",6,2022
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning,8,eccv,15,0,2023-06-17 00:59:58.556000,https://github.com/MTLab/MorphMLP,160,MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning,"https://scholar.google.com/scholar?cluster=15037267593155101529&hl=en&as_sdt=0,5",11,2022
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality,3,eccv,3,0,2023-06-17 00:59:58.769000,https://github.com/hongluzhou/composer,17,COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality,"https://scholar.google.com/scholar?cluster=3314305974342041018&hl=en&as_sdt=0,5",2,2022
E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context,3,eccv,2,0,2023-06-17 00:59:58.980000,https://github.com/kyleleey/e-nerv,21,E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context,"https://scholar.google.com/scholar?cluster=18039152217323552651&hl=en&as_sdt=0,39",2,2022
Semi-Supervised Learning of Optical Flow by Flow Supervisor,3,eccv,1,0,2023-06-17 00:59:59.218000,https://github.com/iwbn/flow-supervisor,17,Semi-Supervised Learning of Optical Flow by Flow Supervisor,"https://scholar.google.com/scholar?cluster=14986476376893472089&hl=en&as_sdt=0,43",3,2022
MaCLR: Motion-Aware Contrastive Learning of Representations for Videos,1,eccv,1,1,2023-06-17 00:59:59.431000,https://github.com/amazon-science/self-supervised-maclr,9,MaCLR: Motion-Aware Contrastive Learning of Representations for Videos,"https://scholar.google.com/scholar?cluster=8456383965807435758&hl=en&as_sdt=0,29",10,2022
Frozen CLIP Models Are Efficient Video Learners,34,eccv,13,18,2023-06-17 00:59:59.644000,https://github.com/opengvlab/efficient-video-recognition,120,Frozen clip models are efficient video learners,"https://scholar.google.com/scholar?cluster=16057670792750577500&hl=en&as_sdt=0,5",5,2022
Panoramic Vision Transformer for Saliency Detection in 360° Videos,4,eccv,3,2,2023-06-17 00:59:59.857000,https://github.com/hs-yn/paver,15,Panoramic Vision Transformer for Saliency Detection in 360 Videos,"https://scholar.google.com/scholar?cluster=3748316331390349636&hl=en&as_sdt=0,10",2,2022
Dynamic Temporal Filtering In Video Models,10,eccv,0,1,2023-06-17 01:00:00.079000,https://github.com/fuchenustc/dtf,12,Dynamic temporal filtering in video models,"https://scholar.google.com/scholar?cluster=17477329483444141468&hl=en&as_sdt=0,5",2,2022
Tip-Adapter: Training-Free Adaption of CLIP for Few-Shot Classification,30,eccv,24,10,2023-06-17 01:00:00.320000,https://github.com/gaopengcuhk/tip-adapter,283,Tip-adapter: Training-free adaption of clip for few-shot classification,"https://scholar.google.com/scholar?cluster=3256821949763414308&hl=en&as_sdt=0,5",6,2022
Temporal Lift Pooling for Continuous Sign Language Recognition,8,eccv,2,2,2023-06-17 01:00:00.551000,https://github.com/hulianyuyy/Temporal-Lift-Pooling,12,Temporal lift pooling for continuous sign language recognition,"https://scholar.google.com/scholar?cluster=16069408502002596313&hl=en&as_sdt=0,39",1,2022
MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes,9,eccv,3,0,2023-06-17 01:00:00.767000,https://github.com/SxJyJay/MORE,13,More: Multi-order relation mining for dense captioning in 3d scenes,"https://scholar.google.com/scholar?cluster=1604636413258715927&hl=en&as_sdt=0,5",1,2022
SiRi: A Simple Selective Retraining Mechanism for Transformer-Based Visual Grounding,5,eccv,0,1,2023-06-17 01:00:00.981000,https://github.com/qumengxue/siri-vg,4,SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Grounding,"https://scholar.google.com/scholar?cluster=11705649752847714960&hl=en&as_sdt=0,5",1,2022
Cross-Modal Prototype Driven Network for Radiology Report Generation,7,eccv,4,6,2023-06-17 01:00:01.207000,https://github.com/markin-wang/xpronet,29,Cross-modal prototype driven network for radiology report generation,"https://scholar.google.com/scholar?cluster=886158982898018569&hl=en&as_sdt=0,10",1,2022
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts,15,eccv,8,2,2023-06-17 01:00:01.421000,https://github.com/EricGuo5513/TM2T,54,Tm2t: Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts,"https://scholar.google.com/scholar?cluster=240678341877763939&hl=en&as_sdt=0,47",4,2022
SeqTR: A Simple Yet Universal Network for Visual Grounding,21,eccv,8,9,2023-06-17 01:00:01.634000,https://github.com/sean-zhuh/seqtr,98,Seqtr: A simple yet universal network for visual grounding,"https://scholar.google.com/scholar?cluster=16625342362223559486&hl=en&as_sdt=0,31",2,2022
FashionViL: Fashion-Focused Vision-and-Language Representation Learning,13,eccv,5,2,2023-06-17 01:00:01.848000,https://github.com/brandonhanx/mmf,45,FashionViL: Fashion-Focused Vision-and-Language Representation Learning,"https://scholar.google.com/scholar?cluster=5484876750844369889&hl=en&as_sdt=0,43",4,2022
Weakly Supervised Grounding for VQA in Vision-Language Transformers,0,eccv,2,1,2023-06-17 01:00:02.061000,https://github.com/aurooj/wsg-vqa-vltransformers,13,Weakly Supervised Grounding for VQA in Vision-Language Transformers,"https://scholar.google.com/scholar?cluster=2769282884337170315&hl=en&as_sdt=0,5",2,2022
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-Text Retrieval,18,eccv,15,9,2023-06-17 01:00:02.274000,https://github.com/tencentarc/mcq,115,Miles: visual bert pre-training with injected language semantics for video-text retrieval,"https://scholar.google.com/scholar?cluster=11340027733286940492&hl=en&as_sdt=0,5",4,2022
Video Graph Transformer for Video Question Answering,12,eccv,9,1,2023-06-17 01:00:02.496000,https://github.com/sail-sg/vgt,32,Video graph transformer for video question answering,"https://scholar.google.com/scholar?cluster=11691195303287369728&hl=en&as_sdt=0,5",4,2022
Rethinking Data Augmentation for Robust Visual Question Answering,12,eccv,0,2,2023-06-17 01:00:02.714000,https://github.com/itemzheng/kddaug,9,Rethinking data augmentation for robust visual question answering,"https://scholar.google.com/scholar?cluster=447635406700735492&hl=en&as_sdt=0,10",2,2022
Explicit Image Caption Editing,6,eccv,1,1,2023-06-17 01:00:02.940000,https://github.com/baaaad/ece,18,Explicit Image Caption Editing,"https://scholar.google.com/scholar?cluster=689303873355435212&hl=en&as_sdt=0,5",1,2022
Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding,5,eccv,4,0,2023-06-17 01:00:03.158000,https://github.com/haojc/shufflingvideosfortsg,25,Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding,"https://scholar.google.com/scholar?cluster=2849764449511253740&hl=en&as_sdt=0,33",2,2022
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly,9,eccv,5,0,2023-06-17 01:00:03.372000,https://github.com/facebookresearch/reliable_vqa,24,Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly,"https://scholar.google.com/scholar?cluster=15896341822502397294&hl=en&as_sdt=0,5",7,2022
GRIT: Faster and Better Image Captioning Transformer Using Dual Visual Features,26,eccv,15,7,2023-06-17 01:00:03.585000,https://github.com/davidnvq/grit,127,Grit: Faster and better image captioning transformer using dual visual features,"https://scholar.google.com/scholar?cluster=7449981441263246501&hl=en&as_sdt=0,5",3,2022
Object-Centric Unsupervised Image Captioning,4,eccv,2,2,2023-06-17 01:00:03.798000,https://github.com/zihangm/obj-centric-unsup-caption,8,Object-Centric Unsupervised Image Captioning,"https://scholar.google.com/scholar?cluster=13124646174189532262&hl=en&as_sdt=0,44",1,2022
Contrastive Vision-Language Pre-training with Limited Resources,6,eccv,4,4,2023-06-17 01:00:04.021000,https://github.com/zerovl/zerovl,35,Contrastive Vision-Language Pre-training with Limited Resources,"https://scholar.google.com/scholar?cluster=13621146277573179415&hl=en&as_sdt=0,33",3,2022
Word-Level Fine-Grained Story Visualization,5,eccv,0,1,2023-06-17 01:00:04.240000,https://github.com/mrlibw/word-level-story-visualization,7,Word-Level Fine-Grained Story Visualization,"https://scholar.google.com/scholar?cluster=8337966701810762036&hl=en&as_sdt=0,21",4,2022
Unifying Event Detection and Captioning as Sequence Generation via Pre-training,5,eccv,2,2,2023-06-17 01:00:04.453000,https://github.com/qiqang/uedvc,6,Unifying Event Detection and Captioning as Sequence Generation via Pre-training,"https://scholar.google.com/scholar?cluster=11398121099079320213&hl=en&as_sdt=0,14",1,2022
Multimodal Transformer with Variable-Length Memory for Vision-and-Language Navigation,10,eccv,0,0,2023-06-17 01:00:04.666000,https://github.com/clin1223/mtvm,17,Multimodal transformer with variable-length memory for vision-and-language navigation,"https://scholar.google.com/scholar?cluster=15245768438884240344&hl=en&as_sdt=0,7",1,2022
Fine-Grained Visual Entailment,0,eccv,0,0,2023-06-17 01:00:04.879000,https://github.com/skrighyz/fgve,11,Fine-Grained Visual Entailment,"https://scholar.google.com/scholar?cluster=16304184403087727361&hl=en&as_sdt=0,5",2,2022
Bottom Up Top down Detection Transformers for Language Grounding in Images and Point Clouds,11,eccv,7,1,2023-06-17 01:00:05.093000,https://github.com/nickgkan/butd_detr,51,Bottom up top down detection transformers for language grounding in images and point clouds,"https://scholar.google.com/scholar?cluster=5975344897755588707&hl=en&as_sdt=0,5",6,2022
Classification-Regression for Chart Comprehension,7,eccv,4,1,2023-06-17 01:00:05.306000,https://github.com/levymsn/cqa-crct,24,Classification-regression for chart comprehension,"https://scholar.google.com/scholar?cluster=1700240396983610337&hl=en&as_sdt=0,47",2,2022
AssistQ: Affordance-Centric Question-Driven Task Completion for Egocentric Assistant,8,eccv,5,1,2023-06-17 01:00:05.520000,https://github.com/showlab/Q2A,17,Assistq: Affordance-centric question-driven task completion for egocentric assistant,"https://scholar.google.com/scholar?cluster=7734678808440074882&hl=en&as_sdt=0,31",3,2022
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling,25,eccv,5,4,2023-06-17 01:00:05.734000,https://github.com/microsoft/UniTAB,68,Unitab: Unifying text and box outputs for grounded vision-language modeling,"https://scholar.google.com/scholar?cluster=4803373243514933274&hl=en&as_sdt=0,5",5,2022
Speaker-Adaptive Lip Reading with User-Dependent Padding,5,eccv,0,0,2023-06-17 01:00:05.952000,https://github.com/ms-dot-k/User-dependent-Padding,2,Speaker-adaptive Lip Reading with User-dependent Padding,"https://scholar.google.com/scholar?cluster=17763522654466182162&hl=en&as_sdt=0,5",1,2022
TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation,4,eccv,2,1,2023-06-17 01:00:06.166000,https://github.com/vinairesearch/tise-toolbox,31,TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation,"https://scholar.google.com/scholar?cluster=3627435139991791018&hl=en&as_sdt=0,34",3,2022
NewsStories: Illustrating Articles with Visual Summaries,5,eccv,1,3,2023-06-17 01:00:06.380000,https://github.com/newsstoriesdata/newsstories.github.io,19,NewsStories: Illustrating articles with visual summaries,"https://scholar.google.com/scholar?cluster=9816707230174674680&hl=en&as_sdt=0,33",0,2022
FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation,2,eccv,1,0,2023-06-17 01:00:06.618000,https://github.com/eric-ai-lab/fedvln,9,FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation,"https://scholar.google.com/scholar?cluster=11458385995379271180&hl=en&as_sdt=0,33",2,2022
Sports Video Analysis on Large-Scale Data,1,eccv,6,2,2023-06-17 01:00:06.832000,https://github.com/jackwu502/nsva,31,Sports Video Analysis on Large-Scale Data,"https://scholar.google.com/scholar?cluster=14505861862552262151&hl=en&as_sdt=0,5",2,2022
Grounding Visual Representations with Texts for Domain Generalization,4,eccv,6,0,2023-06-17 01:00:07.047000,https://github.com/mswzeus/gvrt,31,Grounding visual representations with texts for domain generalization,"https://scholar.google.com/scholar?cluster=3995014318717094250&hl=en&as_sdt=0,24",1,2022
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation,10,eccv,24,6,2023-06-17 01:00:07.275000,https://github.com/adymaharana/storydalle,288,StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation,"https://scholar.google.com/scholar?cluster=10699382869180186165&hl=en&as_sdt=0,36",8,2022
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance,132,eccv,37,6,2023-06-17 01:00:07.488000,https://github.com/eleutherai/vqgan-clip,327,Vqgan-clip: Open domain image generation and editing with natural language guidance,"https://scholar.google.com/scholar?cluster=525698880435488054&hl=en&as_sdt=0,11",9,2022
End-to-End Active Speaker Detection,8,eccv,0,1,2023-06-17 01:00:07.702000,https://github.com/fuankarion/end-to-end-asd,3,End-to-end active speaker detection,"https://scholar.google.com/scholar?cluster=2440596378456637356&hl=en&as_sdt=0,33",3,2022
Adaptive Fine-Grained Sketch-Based Image Retrieval,7,eccv,0,0,2023-06-17 01:00:07.922000,https://github.com/ayankumarbhunia/adaptive-fgsbir,5,Adaptive fine-grained sketch-based image retrieval,"https://scholar.google.com/scholar?cluster=13775672505240083357&hl=en&as_sdt=0,37",1,2022
Quantized GAN for Complex Music Generation from Dance Videos,8,eccv,13,1,2023-06-17 01:00:08.136000,https://github.com/l-yezhu/d2m-gan,65,Quantized gan for complex music generation from dance videos,"https://scholar.google.com/scholar?cluster=16389033554032178157&hl=en&as_sdt=0,5",6,2022
Localizing Visual Sounds the Easy Way,23,eccv,5,4,2023-06-17 01:00:08.351000,https://github.com/stonemo/ez-vsl,19,Localizing visual sounds the easy way,"https://scholar.google.com/scholar?cluster=9273108791941705691&hl=en&as_sdt=0,5",2,2022
PACS: A Dataset for Physical Audiovisual Commonsense Reasoning,5,eccv,1,0,2023-06-17 01:00:08.564000,https://github.com/samuelyu2002/pacs,8,PACS: A Dataset for Physical Audiovisual CommonSense Reasoning,"https://scholar.google.com/scholar?cluster=9586074467218759453&hl=en&as_sdt=0,23",2,2022
VoViT: Low Latency Graph-Based Audio-Visual Voice Separation Transformer,7,eccv,6,2,2023-06-17 01:00:08.776000,https://github.com/JuanFMontesinos/VoViT,25,Vovit: Low latency graph-based audio-visual voice separation transformer,"https://scholar.google.com/scholar?cluster=12817647739550983283&hl=en&as_sdt=0,20",4,2022
MultiMAE: Multi-modal Multi-task Masked Autoencoders,63,eccv,45,1,2023-06-17 01:00:09.005000,https://github.com/EPFL-VILAB/MultiMAE,440,Multimae: Multi-modal multi-task masked autoencoders,"https://scholar.google.com/scholar?cluster=7235983779434806126&hl=en&as_sdt=0,33",13,2022
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression,26,eccv,23,8,2023-06-17 01:00:09.250000,https://github.com/jinyeying/night-enhancement,216,Unsupervised night image enhancement: When layer decomposition meets light-effects suppression,"https://scholar.google.com/scholar?cluster=11260123721293405393&hl=en&as_sdt=0,5",2,2022
Relationformer: A Unified Framework for Image-to-Graph Generation,12,eccv,4,11,2023-06-17 01:00:09.470000,https://github.com/suprosanna/relationformer,65,Relationformer: A Unified Framework for Image-to-Graph Generation,"https://scholar.google.com/scholar?cluster=16116173940654779023&hl=en&as_sdt=0,5",7,2022
GAMa: Cross-view Video Geo-localization,2,eccv,2,1,2023-06-17 01:00:09.687000,https://github.com/svyas23/gama,12,GAMa: Cross-View Video Geo-Localization,"https://scholar.google.com/scholar?cluster=1999917487159106975&hl=en&as_sdt=0,10",2,2022
Geometric Representation Learning for Document Image Rectification,5,eccv,0,8,2023-06-17 01:00:09.904000,https://github.com/fh2019ustc/docgeonet,38,Geometric Representation Learning for Document Image Rectification,"https://scholar.google.com/scholar?cluster=10444079597645547222&hl=en&as_sdt=0,10",5,2022
Semantic-Guided Multi-Mask Image Harmonization,4,eccv,1,0,2023-06-17 01:00:10.118000,https://github.com/xuqianren/semantic-guided-multi-mask-image-harmonization,10,Semantic-Guided Multi-mask Image Harmonization,"https://scholar.google.com/scholar?cluster=740471268329692668&hl=en&as_sdt=0,19",1,2022
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models,32,eccv,4,0,2023-06-17 01:00:10.331000,https://github.com/chenfengxu714/image2point,100,Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models,"https://scholar.google.com/scholar?cluster=1887117743527395566&hl=en&as_sdt=0,32",14,2022
FAR: Fourier Aerial Video Recognition,4,eccv,0,0,2023-06-17 01:00:10.561000,https://github.com/divyakraman/ECCV2022_FARFourierAerialVideoRecognition,6,FAR: Fourier Aerial Video Recognition,"https://scholar.google.com/scholar?cluster=13274635461645923829&hl=en&as_sdt=0,47",1,2022
MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment,2,eccv,50,5,2023-06-17 01:00:10.783000,https://github.com/megviirobot/megba,361,MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment,"https://scholar.google.com/scholar?cluster=1343967457853026081&hl=en&as_sdt=0,26",23,2022
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information,8,eccv,1,1,2023-06-17 01:00:11.031000,https://github.com/surajkothawade/talisman,7,Talisman: targeted active learning for object detection with rare classes and slices using submodular mutual information,"https://scholar.google.com/scholar?cluster=18174068922113194553&hl=en&as_sdt=0,5",1,2022
An Efficient Person Clustering Algorithm for Open Checkout-Free Groceries,1,eccv,22,0,2023-06-17 01:00:11.338000,https://github.com/WuJunde/checkoutfree,149,An efficient person clustering algorithm for open checkout-free groceries,"https://scholar.google.com/scholar?cluster=8248893577780579601&hl=en&as_sdt=0,29",15,2022
POP: Mining POtential Performance of New Fashion Products via Webly Cross-Modal Query Expansion,2,eccv,1,0,2023-06-17 01:00:11.556000,https://github.com/humaticslab/pop-mining-potential-performance,5,POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion,"https://scholar.google.com/scholar?cluster=10986506715792902692&hl=en&as_sdt=0,5",1,2022
Pose Forecasting in Industrial Human-Robot Collaboration,3,eccv,5,2,2023-06-17 01:00:11.800000,https://github.com/alessiosam/chico-poseforecasting,15,Pose Forecasting in Industrial Human-Robot Collaboration,"https://scholar.google.com/scholar?cluster=5041748353955596330&hl=en&as_sdt=0,33",4,2022
Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment,2,eccv,11,0,2023-06-17 01:00:12.017000,https://github.com/ParitoshParmar/Fitness-AQA,16,Domain Knowledge-Informed Self-supervised Representations for Workout Form Assessment,"https://scholar.google.com/scholar?cluster=1771967292738028463&hl=en&as_sdt=0,5",5,2022
Addressing Heterogeneity in Federated Learning via Distributional Transformation,2,eccv,0,0,2023-06-17 01:00:12.288000,https://github.com/hyhmia/distrans,4,Addressing Heterogeneity in Federated Learning via Distributional Transformation,"https://scholar.google.com/scholar?cluster=7595000318226544497&hl=en&as_sdt=0,5",1,2022
Where in the World Is This Image? Transformer-Based Geo-Localization in the Wild,4,eccv,0,0,2023-06-17 01:00:12.523000,https://github.com/shramanpramanick/transformer_based_geo-localization,13,Where in the World is this Image? Transformer-based Geo-localization in the Wild,"https://scholar.google.com/scholar?cluster=12815785118431881387&hl=en&as_sdt=0,32",2,2022
Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection,8,eccv,7,4,2023-06-17 01:00:12.757000,https://github.com/mingyuyng/visual-selective-vio,64,Efficient deep visual and inertial odometry with adaptive visual modality selection,"https://scholar.google.com/scholar?cluster=10071301244979063127&hl=en&as_sdt=0,5",3,2022
AutoTransition: Learning to Recommend Video Transition Effects,2,eccv,6,4,2023-06-17 01:00:12.972000,https://github.com/acherstyx/autotransition,32,Autotransition: Learning to recommend video transition effects,"https://scholar.google.com/scholar?cluster=11848051264458533157&hl=en&as_sdt=0,26",1,2022
Online Segmentation of LiDAR Sequences: Dataset and Algorithm,1,eccv,6,0,2023-06-17 01:00:13.184000,https://github.com/romainloiseau/Helix4D,47,Online Segmentation of LiDAR Sequences: Dataset and Algorithm,"https://scholar.google.com/scholar?cluster=6780570625077556715&hl=en&as_sdt=0,5",3,2022
Open-World Semantic Segmentation for LIDAR Point Clouds,8,eccv,5,1,2023-06-17 01:00:13.396000,https://github.com/jun-cen/open_world_3d_semantic_segmentation,52,Open-world semantic segmentation for lidar point clouds,"https://scholar.google.com/scholar?cluster=1582819942034649369&hl=en&as_sdt=0,15",5,2022
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients,11,eccv,153,1,2023-06-17 01:00:13.610000,https://github.com/autonomousvision/transfuser,789,King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients,"https://scholar.google.com/scholar?cluster=10864165260336171731&hl=en&as_sdt=0,14",24,2022
Differentiable Raycasting for Self-Supervised Occupancy Forecasting,8,eccv,3,0,2023-06-17 01:00:13.823000,https://github.com/tarashakhurana/emergent-occ-forecasting,23,Differentiable raycasting for self-supervised occupancy forecasting,"https://scholar.google.com/scholar?cluster=16013699291387108751&hl=en&as_sdt=0,34",3,2022
CenterFormer: Center-based Transformer for 3D Object Detection,28,eccv,19,19,2023-06-17 01:00:14.035000,https://github.com/tusimple/centerformer,240,Centerformer: Center-based transformer for 3d object detection,"https://scholar.google.com/scholar?cluster=12072236336365895354&hl=en&as_sdt=0,22",10,2022
ST-P3: End-to-End Vision-Based Autonomous Driving via Spatial-Temporal Feature Learning,25,eccv,0,0,2023-06-17 01:00:14.258000,https://github.com/openperceptionx/st-p3,0,St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning,"https://scholar.google.com/scholar?cluster=6699869351633854350&hl=en&as_sdt=0,5",2,2022
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark,34,eccv,60,8,2023-06-17 01:00:14.470000,https://github.com/OpenDriveLab/PersFormer_3DLane,302,Persformer: 3d lane detection via perspective transformer and the openlane benchmark,"https://scholar.google.com/scholar?cluster=15083699818412600037&hl=en&as_sdt=0,34",13,2022
Context-Aware Streaming Perception in Dynamic Environments,1,eccv,0,1,2023-06-17 01:00:14.682000,https://github.com/eyalsel/contextual-streaming-perception,1,Context-Aware Streaming Perception in Dynamic Environments,"https://scholar.google.com/scholar?cluster=10274140893986242445&hl=en&as_sdt=0,32",1,2022
Multimodal Transformer for Automatic 3D Annotation and Object Detection,1,eccv,3,1,2023-06-17 01:00:14.894000,https://github.com/cliu2/mtrans,24,Multimodal Transformer for Automatic 3D Annotation and Object Detection,"https://scholar.google.com/scholar?cluster=969973501768812021&hl=en&as_sdt=0,31",4,2022
Dynamic 3D Scene Analysis by Point Cloud Accumulation,10,eccv,9,2,2023-06-17 01:00:15.120000,https://github.com/prs-eth/PCAccumulation,101,Dynamic 3D Scene Analysis by Point Cloud Accumulation,"https://scholar.google.com/scholar?cluster=5413156346707772700&hl=en&as_sdt=0,6",6,2022
Semi-Supervised 3D Object Detection with Proficient Teachers,19,eccv,0,7,2023-06-17 01:00:15.760000,https://github.com/yinjunbo/proficientteachers,16,Semi-supervised 3D object detection with proficient teachers,"https://scholar.google.com/scholar?cluster=14317699278856200063&hl=en&as_sdt=0,34",10,2022
ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection,32,eccv,2,2,2023-06-17 01:00:16.048000,https://github.com/yinjunbo/proposalcontrast,36,ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection,"https://scholar.google.com/scholar?cluster=18192635711712436925&hl=en&as_sdt=0,34",7,2022
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map,5,eccv,1,2,2023-06-17 01:00:16.308000,https://github.com/chenfengxu714/pretram,14,Pretram: Self-supervised pre-training via connecting trajectory and map,"https://scholar.google.com/scholar?cluster=15409489933369373248&hl=en&as_sdt=0,34",4,2022
Visual Cross-View Metric Localization with Dense Uncertainty Estimates,7,eccv,0,0,2023-06-17 01:00:16.548000,https://github.com/tudelft-iv/crossviewmetriclocalization,20,Visual cross-view metric localization with dense uncertainty estimates,"https://scholar.google.com/scholar?cluster=12330293739091912034&hl=en&as_sdt=0,31",2,2022
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer,79,eccv,25,2,2023-06-17 01:00:16.767000,https://github.com/DerrickXuNu/v2x-vit,197,V2X-ViT: Vehicle-to-everything cooperative perception with vision transformer,"https://scholar.google.com/scholar?cluster=15088728781552938978&hl=en&as_sdt=0,36",4,2022
DevNet: Self-Supervised Monocular Depth Learning via Density Volume Construction,5,eccv,0,1,2023-06-17 01:00:16.987000,https://github.com/gitkaichenzhou/devnet,9,Devnet: Self-supervised monocular depth learning via density volume construction,"https://scholar.google.com/scholar?cluster=13357376187012706198&hl=en&as_sdt=0,5",4,2022
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection,19,eccv,8,5,2023-06-17 01:00:17.200000,https://github.com/weiyithu/lidar-distillation,85,LiDAR distillation: bridging the beam-induced domain Gap for 3D object detection,"https://scholar.google.com/scholar?cluster=587459598663659263&hl=en&as_sdt=0,5",7,2022
Pixel-Wise Energy-Biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes,21,eccv,18,0,2023-06-17 01:00:17.414000,https://github.com/tianyu0207/pebal,125,Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes,"https://scholar.google.com/scholar?cluster=828162099730136684&hl=en&as_sdt=0,5",5,2022
Housekeep: Tidying Virtual Households Using Commonsense Reasoning,19,eccv,5,0,2023-06-17 01:00:17.628000,https://github.com/yashkant/housekeep,27,Housekeep: Tidying virtual households using commonsense reasoning,"https://scholar.google.com/scholar?cluster=17323819814788144115&hl=en&as_sdt=0,41",5,2022
Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects,3,eccv,4,2,2023-06-17 01:00:17.841000,https://github.com/pku-epic/dreds,69,Domain randomization-enhanced depth simulation and restoration for perceiving and grasping specular and transparent objects,"https://scholar.google.com/scholar?cluster=4212070645420757381&hl=en&as_sdt=0,33",2,2022
OPD: Single-View 3D Openable Part Detection,8,eccv,3,0,2023-06-17 01:00:18.054000,https://github.com/3dlg-hcvc/OPD,19,OPD: Single-view 3D openable part detection,"https://scholar.google.com/scholar?cluster=1442718459307012446&hl=en&as_sdt=0,25",2,2022
AirDet: Few-Shot Detection without Fine-Tuning for Autonomous Exploration,8,eccv,7,9,2023-06-17 01:00:18.270000,https://github.com/jaraxxus-me/airdet,56,Airdet: Few-shot detection without fine-tuning for autonomous exploration,"https://scholar.google.com/scholar?cluster=13310465653301998245&hl=en&as_sdt=0,33",4,2022
TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance,2,eccv,3,0,2023-06-17 01:00:18.483000,https://github.com/yanjh97/transgrasp,26,TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance,"https://scholar.google.com/scholar?cluster=3147703943715388736&hl=en&as_sdt=0,14",1,2022
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning,6,eccv,8,0,2023-06-17 01:00:18.697000,https://github.com/elicassion/StARformer,54,StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning,"https://scholar.google.com/scholar?cluster=14349342012592165154&hl=en&as_sdt=0,10",4,2022
Zero-Shot Category-Level Object Pose Estimation,10,eccv,1,1,2023-06-17 01:00:18.911000,https://github.com/applied-ai-lab/zero-shot-pose,43,Zero-shot category-level object pose estimation,"https://scholar.google.com/scholar?cluster=9047203948478183820&hl=en&as_sdt=0,44",6,2022
Style-Agnostic Reinforcement Learning,0,eccv,0,0,2023-06-17 01:00:19.127000,https://github.com/postech-cvlab/style-agnostic-rl,14,Style-Agnostic Reinforcement Learning,"https://scholar.google.com/scholar?cluster=4525773551309192298&hl=en&as_sdt=0,47",5,2022
Learning from Unlabeled 3D Environments for Vision-and-Language Navigation,8,eccv,1,2,2023-06-17 01:00:19.339000,https://github.com/cshizhe/HM3DAutoVLN,16,Learning from unlabeled 3d environments for vision-and-language navigation,"https://scholar.google.com/scholar?cluster=16234245640110224024&hl=en&as_sdt=0,10",1,2022
Video Dialog As Conversation about Objects Living in Space-Time,2,eccv,1,1,2023-06-17 01:00:19.551000,https://github.com/hoanganhpham1006/cost,29,Video Dialog as Conversation About Objects Living in Space-Time,"https://scholar.google.com/scholar?cluster=7360692861127476051&hl=en&as_sdt=0,31",1,2022
INSPECTRE: Privately Estimating the Unseen,24,icml,2,0,2023-06-17 02:59:19.495000,https://github.com/HuanyuZhang/INSPECTRE,4,Inspectre: Privately estimating the unseen,"https://scholar.google.com/scholar?cluster=17397677821917989513&hl=en&as_sdt=0,34",5,2018
Learning Representations and Generative Models for 3D Point Clouds,1057,icml,103,14,2023-06-17 02:59:19.714000,https://github.com/optas/latent_3d_points,468,Learning representations and generative models for 3d point clouds,"https://scholar.google.com/scholar?cluster=9902857073066842718&hl=en&as_sdt=0,30",15,2018
Accelerated Spectral Ranking,51,icml,0,0,2023-06-17 02:59:19.927000,https://github.com/agarpit/asr,0,Accelerated spectral ranking,"https://scholar.google.com/scholar?cluster=17059082101801262373&hl=en&as_sdt=0,5",1,2018
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches,41,icml,6,2,2023-06-17 02:59:20.141000,https://github.com/rdspring1/MISSION,12,Mission: Ultra large-scale feature selection using count-sketches,"https://scholar.google.com/scholar?cluster=7532201827567458901&hl=en&as_sdt=0,29",6,2018
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory,155,icml,5,1,2023-06-17 02:59:20.355000,https://github.com/ron-amit/meta-learning-adjusting-priors,22,Meta-learning by adjusting priors based on extended PAC-Bayes theory,"https://scholar.google.com/scholar?cluster=7282416635315381727&hl=en&as_sdt=0,21",2,2018
MAGAN: Aligning Biological Manifolds,71,icml,4,5,2023-06-17 02:59:20.569000,https://github.com/KrishnaswamyLab/MAGAN,17,MAGAN: Aligning biological manifolds,"https://scholar.google.com/scholar?cluster=2850609560851515473&hl=en&as_sdt=0,5",7,2018
Efficient Gradient-Free Variational Inference using Policy Search,30,icml,10,0,2023-06-17 02:59:20.783000,https://github.com/OlegArenz/VIPS,13,Efficient gradient-free variational inference using policy search,"https://scholar.google.com/scholar?cluster=15860909759042559191&hl=en&as_sdt=0,36",1,2018
Lipschitz Continuity in Model-based Reinforcement Learning,128,icml,1,0,2023-06-17 02:59:20.997000,https://github.com/kavosh8/Lip,11,Lipschitz continuity in model-based reinforcement learning,"https://scholar.google.com/scholar?cluster=7519868301941005316&hl=en&as_sdt=0,21",2,2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples,2845,icml,165,0,2023-06-17 02:59:21.212000,https://github.com/anishathalye/obfuscated-gradients,846,Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,"https://scholar.google.com/scholar?cluster=16371153415378772336&hl=en&as_sdt=0,5",51,2018
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing,81,icml,10,1,2023-06-17 02:59:21.425000,https://github.com/diningphil/CGMM,35,Contextual graph markov model: A deep and generative approach to graph processing,"https://scholar.google.com/scholar?cluster=11762309887012905485&hl=en&as_sdt=0,47",5,2018
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising,272,icml,17,0,2023-06-17 02:59:21.640000,https://github.com/BorjaBalle/analytic-gaussian-mechanism,38,Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising,"https://scholar.google.com/scholar?cluster=6616371088385060239&hl=en&as_sdt=0,15",5,2018
"Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients",128,icml,2,1,2023-06-17 02:59:21.855000,https://github.com/lballes/msvag,44,"Dissecting adam: The sign, magnitude and variance of stochastic gradients","https://scholar.google.com/scholar?cluster=7051163857828136426&hl=en&as_sdt=0,5",5,2018
Differentially Private Database Release via Kernel Mean Embeddings,34,icml,5,0,2023-06-17 02:59:22.073000,https://github.com/matejbalog/RKHS-private-database,9,Differentially private database release via kernel mean embeddings,"https://scholar.google.com/scholar?cluster=3884748492191157354&hl=en&as_sdt=0,47",3,2018
Classification from Pairwise Similarity and Unlabeled Data,70,icml,7,0,2023-06-17 02:59:22.287000,https://github.com/levelfour/SU_Classification,27,Classification from pairwise similarity and unlabeled data,"https://scholar.google.com/scholar?cluster=8079423244693933514&hl=en&as_sdt=0,23",4,2018
Bayesian Optimization of Combinatorial Structures,115,icml,27,1,2023-06-17 02:59:22.500000,https://github.com/baptistar/BOCS,89,Bayesian optimization of combinatorial structures,"https://scholar.google.com/scholar?cluster=1602326552169762893&hl=en&as_sdt=0,5",5,2018
signSGD: Compressed Optimisation for Non-Convex Problems,763,icml,17,2,2023-06-17 02:59:22.714000,https://github.com/jxbz/signSGD,68,signSGD: Compressed optimisation for non-convex problems,"https://scholar.google.com/scholar?cluster=2554335502701113649&hl=en&as_sdt=0,14",4,2018
Autoregressive Convolutional Neural Networks for Asynchronous Time Series,161,icml,66,6,2023-06-17 02:59:22.927000,https://github.com/mbinkowski/nntimeseries,207,Autoregressive convolutional neural networks for asynchronous time series,"https://scholar.google.com/scholar?cluster=16946741031490973459&hl=en&as_sdt=0,11",15,2018
Path-Level Network Transformation for Efficient Architecture Search,218,icml,21,5,2023-06-17 02:59:23.149000,https://github.com/han-cai/PathLevel-EAS,113,Path-level network transformation for efficient architecture search,"https://scholar.google.com/scholar?cluster=17606554867892755331&hl=en&as_sdt=0,9",5,2018
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent,113,icml,30,1,2023-06-17 02:59:23.364000,https://github.com/trevorcampbell/bayesian-coresets,124,Bayesian coreset construction via greedy iterative geodesic ascent,"https://scholar.google.com/scholar?cluster=662866254282688281&hl=en&as_sdt=0,33",8,2018
Adversarial Time-to-Event Modeling,101,icml,11,0,2023-06-17 02:59:23.577000,https://github.com/paidamoyo/adversarial_time_to_event,35,Adversarial time-to-event modeling,"https://scholar.google.com/scholar?cluster=2862325105848484148&hl=en&as_sdt=0,28",4,2018
Stein Points,99,icml,1,1,2023-06-17 02:59:23.791000,https://github.com/wilson-ye-chen/stein_points,2,Stein points,"https://scholar.google.com/scholar?cluster=9019835252196634623&hl=en&as_sdt=0,5",0,2018
PixelSNAIL: An Improved Autoregressive Generative Model,203,icml,23,4,2023-06-17 02:59:24.005000,https://github.com/neocxi/pixelsnail-public,122,Pixelsnail: An improved autoregressive generative model,"https://scholar.google.com/scholar?cluster=3510281947390800354&hl=en&as_sdt=0,31",5,2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation,455,icml,36,3,2023-06-17 02:59:24.219000,https://github.com/Jianbo-Lab/L2X,118,Learning to explain: An information-theoretic perspective on model interpretation,"https://scholar.google.com/scholar?cluster=8716068966978529202&hl=en&as_sdt=0,36",12,2018
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients,211,icml,11,2,2023-06-17 02:59:24.433000,https://github.com/hwang595/Draco,21,Draco: Byzantine-resilient distributed training via redundant gradients,"https://scholar.google.com/scholar?cluster=7533143184939579191&hl=en&as_sdt=0,19",8,2018
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms,146,icml,6,0,2023-06-17 02:59:24.646000,https://github.com/flowersteam/geppg,36,Gep-pg: Decoupling exploration and exploitation in deep reinforcement learning algorithms,"https://scholar.google.com/scholar?cluster=13798285446369315971&hl=en&as_sdt=0,47",11,2018
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation,63,icml,3,2,2023-06-17 02:59:24.861000,https://github.com/danecor/VaST,13,Efficient model-based deep reinforcement learning with variational state tabulation,"https://scholar.google.com/scholar?cluster=1130683550787496400&hl=en&as_sdt=0,33",3,2018
Asynchronous Byzantine Machine Learning (the case of SGD),99,icml,0,1,2023-06-17 02:59:25.075000,https://github.com/LPD-EPFL/kardam,0,Asynchronous Byzantine machine learning (the case of SGD),"https://scholar.google.com/scholar?cluster=7761726425458216568&hl=en&as_sdt=0,33",5,2018
Stochastic Video Generation with a Learned Prior,453,icml,54,15,2023-06-17 02:59:25.289000,https://github.com/edenton/svg,173,Stochastic video generation with a learned prior,"https://scholar.google.com/scholar?cluster=9440265505324516729&hl=en&as_sdt=0,5",6,2018
Probabilistic Recurrent State-Space Models,109,icml,19,3,2023-06-17 02:59:25.503000,https://github.com/andreasdoerr/PR-SSM,48,Probabilistic recurrent state-space models,"https://scholar.google.com/scholar?cluster=13376246686422250291&hl=en&as_sdt=0,46",14,2018
Essentially No Barriers in Neural Network Energy Landscape,299,icml,11,1,2023-06-17 02:59:25.717000,https://github.com/fdraxler/PyTorch-AutoNEB,45,Essentially no barriers in neural network energy landscape,"https://scholar.google.com/scholar?cluster=15426527759025848933&hl=en&as_sdt=0,5",4,2018
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures,1282,icml,162,14,2023-06-17 02:59:25.931000,https://github.com/deepmind/scalable_agent,948,Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures,"https://scholar.google.com/scholar?cluster=14673826846490570917&hl=en&as_sdt=0,10",36,2018
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF),28,icml,2,0,2023-06-17 02:59:26.145000,https://github.com/treforevans/gp_grief,22,Scalable Gaussian processes with grid-structured eigenfunctions (GP-GRIEF),"https://scholar.google.com/scholar?cluster=16145188730835971053&hl=en&as_sdt=0,10",6,2018
BOHB: Robust and Efficient Hyperparameter Optimization at Scale,913,icml,113,65,2023-06-17 02:59:26.358000,https://github.com/automl/HpBandSter,576,BOHB: Robust and efficient hyperparameter optimization at scale,"https://scholar.google.com/scholar?cluster=7414210775058292852&hl=en&as_sdt=0,5",27,2018
Nonparametric variable importance using an augmented neural network with multi-task learning,16,icml,1,2,2023-06-17 02:59:26.573000,https://github.com/jjfeng/nnet_var_import,2,Nonparametric variable importance using an augmented neural network with multi-task learning,"https://scholar.google.com/scholar?cluster=626109963873101656&hl=en&as_sdt=0,33",4,2018
DiCE: The Infinitely Differentiable Monte Carlo Estimator,78,icml,36,8,2023-06-17 02:59:26.787000,https://github.com/alshedivat/lola,133,Dice: The infinitely differentiable monte carlo estimator,"https://scholar.google.com/scholar?cluster=9790220931943601676&hl=en&as_sdt=0,5",12,2018
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning,93,icml,5,0,2023-06-17 02:59:27.001000,https://github.com/RonanFR/UCRL,25,Efficient bias-span-constrained exploration-exploitation in reinforcement learning,"https://scholar.google.com/scholar?cluster=10255005828513027230&hl=en&as_sdt=0,47",5,2018
Addressing Function Approximation Error in Actor-Critic Methods,3516,icml,393,4,2023-06-17 02:59:27.217000,https://github.com/sfujim/TD3,1371,Addressing function approximation error in actor-critic methods,"https://scholar.google.com/scholar?cluster=2930747733592680111&hl=en&as_sdt=0,5",19,2018
Clipped Action Policy Gradient,37,icml,1,0,2023-06-17 02:59:27.430000,https://github.com/pfnet-research/capg,26,Clipped action policy gradient,"https://scholar.google.com/scholar?cluster=14045811367797105459&hl=en&as_sdt=0,34",15,2018
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings,213,icml,11,1,2023-06-17 02:59:27.644000,https://github.com/dalab/hyperbolic_cones,122,Hyperbolic entailment cones for learning hierarchical embeddings,"https://scholar.google.com/scholar?cluster=18219062814600908733&hl=en&as_sdt=0,5",13,2018
Visualizing and Understanding Atari Agents,306,icml,34,5,2023-06-17 02:59:27.858000,https://github.com/greydanus/visualize_atari,114,Visualizing and understanding atari agents,"https://scholar.google.com/scholar?cluster=2974426333741298395&hl=en&as_sdt=0,5",2,2018
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,5442,icml,216,10,2023-06-17 02:59:28.073000,https://github.com/haarnoja/sac,802,Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,"https://scholar.google.com/scholar?cluster=13282174879342015249&hl=en&as_sdt=0,5",30,2018
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning,11,icml,4,0,2023-06-17 02:59:28.287000,https://github.com/jihunhamm/k-beam-minimax,11,K-beam minimax: Efficient optimization for deep adversarial learning,"https://scholar.google.com/scholar?cluster=17252205883133016426&hl=en&as_sdt=0,1",2,2018
Deep Models of Interactions Across Sets,137,icml,4,3,2023-06-17 02:59:28.501000,https://github.com/mravanba/deep_exchangeable_tensors,9,Deep models of interactions across sets,"https://scholar.google.com/scholar?cluster=9552429858443331211&hl=en&as_sdt=0,33",5,2018
Learning unknown ODE models with Gaussian processes,64,icml,3,0,2023-06-17 02:59:28.714000,https://github.com/cagatayyildiz/npode,19,Learning unknown ODE models with Gaussian processes,"https://scholar.google.com/scholar?cluster=5804235817829238713&hl=en&as_sdt=0,5",4,2018
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform,111,icml,1,1,2023-06-17 02:59:28.928000,https://github.com/SpartinStuff/scoRNN,10,Orthogonal recurrent neural networks with scaled Cayley transform,"https://scholar.google.com/scholar?cluster=10576322947857760953&hl=en&as_sdt=0,23",3,2018
CyCADA: Cycle-Consistent Adversarial Domain Adaptation,2682,icml,126,15,2023-06-17 02:59:29.142000,https://github.com/jhoffman/cycada_release,537,Cycada: Cycle-consistent adversarial domain adaptation,"https://scholar.google.com/scholar?cluster=13169730024102659375&hl=en&as_sdt=0,26",18,2018
Neural Autoregressive Flows,417,icml,28,2,2023-06-17 02:59:29.355000,https://github.com/CW-Huang/NAF,116,Neural autoregressive flows,"https://scholar.google.com/scholar?cluster=12117495056265504475&hl=en&as_sdt=0,33",12,2018
Topological mixture estimation,5,icml,0,0,2023-06-17 02:59:29.570000,https://github.com/SteveHuntsmanBAESystems/TopologicalMixtureEstimation,0,Topological mixture estimation,"https://scholar.google.com/scholar?cluster=5144775238736361207&hl=en&as_sdt=0,5",1,2018
Decoupled Parallel Backpropagation with Convergence Guarantee,69,icml,4,2,2023-06-17 02:59:29.783000,https://github.com/slowbull/DDG,28,Decoupled parallel backpropagation with convergence guarantee,"https://scholar.google.com/scholar?cluster=9542708515407168556&hl=en&as_sdt=0,43",4,2018
Deep Variational Reinforcement Learning for POMDPs,244,icml,26,5,2023-06-17 02:59:29.997000,https://github.com/maximilianigl/DVRL,123,Deep variational reinforcement learning for POMDPs,"https://scholar.google.com/scholar?cluster=12007406566032573768&hl=en&as_sdt=0,5",7,2018
Attention-based Deep Multiple Instance Learning,1102,icml,174,12,2023-06-17 02:59:30.211000,https://github.com/AMLab-Amsterdam/AttentionDeepMIL,651,Attention-based deep multiple instance learning,"https://scholar.google.com/scholar?cluster=10689360653942822671&hl=en&as_sdt=0,33",16,2018
Black-box Adversarial Attacks with Limited Queries and Information,966,icml,44,12,2023-06-17 02:59:30.425000,https://github.com/labsix/limited-blackbox-attacks,166,Black-box adversarial attacks with limited queries and information,"https://scholar.google.com/scholar?cluster=15556405409493863238&hl=en&as_sdt=0,5",9,2018
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model,25,icml,1,0,2023-06-17 02:59:30.640000,https://github.com/HideakiImamura/MinimaxErrorRate,5,Analysis of minimax error rate for crowdsourcing and its application to worker clustering model,"https://scholar.google.com/scholar?cluster=7703940495110454435&hl=en&as_sdt=0,5",1,2018
Anonymous Walk Embeddings,183,icml,22,3,2023-06-17 02:59:30.854000,https://github.com/nd7141/AWE,78,Anonymous walk embeddings,"https://scholar.google.com/scholar?cluster=14558299451586877033&hl=en&as_sdt=0,33",6,2018
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach,15,icml,0,0,2023-06-17 02:59:31.069000,https://github.com/arJaffe/BinaryLatentVariables,1,Learning binary latent variable models: A tensor eigenpair approach,"https://scholar.google.com/scholar?cluster=2293549350827967377&hl=en&as_sdt=0,14",1,2018
Efficient end-to-end learning for quantizable representations,14,icml,14,0,2023-06-17 02:59:31.283000,https://github.com/maestrojeong/Deep-Hash-Table-ICML18,66,Efficient end-to-end learning for quantizable representations,"https://scholar.google.com/scholar?cluster=14118214895723382983&hl=en&as_sdt=0,5",7,2018
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift,27,icml,20,1,2023-06-17 02:59:31.497000,https://github.com/google/quickshift,62,Quickshift++: Provably good initializations for sample-based mean shift,"https://scholar.google.com/scholar?cluster=9290981772171127937&hl=en&as_sdt=0,5",8,2018
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels,1228,icml,67,5,2023-06-17 02:59:31.710000,https://github.com/google/mentornet,308,Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels,"https://scholar.google.com/scholar?cluster=18276912967596258717&hl=en&as_sdt=0,33",13,2018
Junction Tree Variational Autoencoder for Molecular Graph Generation,1068,icml,182,30,2023-06-17 02:59:31.925000,https://github.com/wengong-jin/icml18-jtnn,439,Junction tree variational autoencoder for molecular graph generation,"https://scholar.google.com/scholar?cluster=14713480171095443338&hl=en&as_sdt=0,47",20,2018
Not All Samples Are Created Equal: Deep Learning with Importance Sampling,369,icml,59,9,2023-06-17 02:59:32.138000,https://github.com/idiap/importance-sampling,300,Not all samples are created equal: Deep learning with importance sampling,"https://scholar.google.com/scholar?cluster=6287347937947055060&hl=en&as_sdt=0,5",15,2018
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness,637,icml,10,1,2023-06-17 02:59:32.351000,https://github.com/algowatchpenn/GerryFair,31,Preventing fairness gerrymandering: Auditing and learning for subgroup fairness,"https://scholar.google.com/scholar?cluster=15519719606954445162&hl=en&as_sdt=0,23",7,2018
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam,231,icml,26,3,2023-06-17 02:59:32.565000,https://github.com/emtiyaz/vadam,107,Fast and scalable bayesian deep learning by weight-perturbation in adam,"https://scholar.google.com/scholar?cluster=11374390410783252644&hl=en&as_sdt=0,5",10,2018
Geometry Score: A Method For Comparing Generative Adversarial Networks,98,icml,21,1,2023-06-17 02:59:32.778000,https://github.com/KhrulkovV/geometry-score,113,Geometry score: A method for comparing generative adversarial networks,"https://scholar.google.com/scholar?cluster=3414107301309460899&hl=en&as_sdt=0,5",4,2018
Blind Justice: Fairness with Encrypted Sensitive Attributes,120,icml,3,1,2023-06-17 02:59:32.993000,https://github.com/nikikilbertus/blind-justice,14,Blind justice: Fairness with encrypted sensitive attributes,"https://scholar.google.com/scholar?cluster=7640712824806028167&hl=en&as_sdt=0,5",5,2018
Semi-Amortized Variational Autoencoders,246,icml,16,3,2023-06-17 02:59:33.207000,https://github.com/harvardnlp/sa-vae,153,Semi-amortized variational autoencoders,"https://scholar.google.com/scholar?cluster=15696369664604442539&hl=en&as_sdt=0,46",10,2018
Neural Relational Inference for Interacting Systems,710,icml,156,22,2023-06-17 02:59:33.430000,https://github.com/ethanfetaya/nri,681,Neural relational inference for interacting systems,"https://scholar.google.com/scholar?cluster=5985084190905139950&hl=en&as_sdt=0,5",25,2018
Nonconvex Optimization for Regression with Fairness Constraints,103,icml,1,1,2023-06-17 02:59:33.644000,https://github.com/jkomiyama/fairregresion,4,Nonconvex optimization for regression with fairness constraints,"https://scholar.google.com/scholar?cluster=9324671354987177692&hl=en&as_sdt=0,22",3,2018
Dynamic Evaluation of Neural Sequence Models,130,icml,21,1,2023-06-17 02:59:33.858000,https://github.com/benkrause/dynamic-evaluation,102,Dynamic evaluation of neural sequence models,"https://scholar.google.com/scholar?cluster=7171182301432620931&hl=en&as_sdt=0,5",5,2018
Semiparametric Contextual Bandits,42,icml,11,1,2023-06-17 02:59:34.071000,https://github.com/akshaykr/oracle_cb,28,Semiparametric contextual bandits,"https://scholar.google.com/scholar?cluster=8044014700167945410&hl=en&as_sdt=0,5",6,2018
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings,187,icml,5,0,2023-06-17 02:59:34.285000,https://github.com/aviralkumar2907/MMCE,15,Trainable calibration measures for neural networks from kernel mean embeddings,"https://scholar.google.com/scholar?cluster=3110087003136366065&hl=en&as_sdt=0,5",5,2018
Canonical Tensor Decomposition for Knowledge Base Completion,318,icml,40,2,2023-06-17 02:59:34.504000,https://github.com/facebookresearch/kbc,241,Canonical tensor decomposition for knowledge base completion,"https://scholar.google.com/scholar?cluster=9542404017825528876&hl=en&as_sdt=0,36",60,2018
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks,576,icml,28,1,2023-06-17 02:59:34.719000,https://github.com/brendenlake/SCAN,155,Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks,"https://scholar.google.com/scholar?cluster=11276348225798571948&hl=en&as_sdt=0,5",9,2018
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace,332,icml,8,5,2023-06-17 02:59:34.934000,https://github.com/yoonholee/MT-net,35,Gradient-based meta-learning with learned layerwise metric and subspace,"https://scholar.google.com/scholar?cluster=16589702021969633682&hl=en&as_sdt=0,5",5,2018
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling,54,icml,3,0,2023-06-17 02:59:35.154000,https://github.com/leekwoon/KR-DL-UCT,31,Deep reinforcement learning in continuous action spaces: a case study in the game of simulated curling,"https://scholar.google.com/scholar?cluster=6730862284084733221&hl=en&as_sdt=0,5",4,2018
Noise2Noise: Learning Image Restoration without Clean Data,1230,icml,301,5,2023-06-17 02:59:35.370000,https://github.com/NVlabs/noise2noise,1284,Noise2Noise: Learning image restoration without clean data,"https://scholar.google.com/scholar?cluster=16764673643469433149&hl=en&as_sdt=0,5",44,2018
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks,67,icml,7,0,2023-06-17 02:59:35.584000,https://github.com/LiQianxiao/discrete-MSA,20,An optimal control approach to deep learning and applications to discrete-weight neural networks,"https://scholar.google.com/scholar?cluster=6252296046431903031&hl=en&as_sdt=0,33",5,2018
Towards Binary-Valued Gates for Robust LSTM Training,55,icml,11,2,2023-06-17 02:59:35.799000,https://github.com/zhuohan123/g2-lstm,74,Towards binary-valued gates for robust lstm training,"https://scholar.google.com/scholar?cluster=9655995199891931380&hl=en&as_sdt=0,41",2,2018
"Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering",89,icml,1,0,2023-06-17 02:59:36.013000,https://github.com/lipan00123/IPM-for-submodular-hypergraphs,0,"Submodular hypergraphs: p-laplacians, cheeger inequalities and spectral clustering","https://scholar.google.com/scholar?cluster=3565527307250795946&hl=en&as_sdt=0,33",1,2018
RLlib: Abstractions for Distributed Reinforcement Learning,642,icml,4893,2935,2023-06-17 02:59:36.226000,https://github.com/ray-project/ray,26195,RLlib: Abstractions for distributed reinforcement learning,"https://scholar.google.com/scholar?cluster=9535249560181579239&hl=en&as_sdt=0,11",450,2018
On the Spectrum of Random Features Maps of High Dimensional Data,48,icml,5,0,2023-06-17 02:59:36.440000,https://github.com/Zhenyu-LIAO/RMT4RFM,7,On the spectrum of random features maps of high dimensional data,"https://scholar.google.com/scholar?cluster=4838372697610829936&hl=en&as_sdt=0,44",1,2018
Reviving and Improving Recurrent Back-Propagation,95,icml,4,2,2023-06-17 02:59:36.654000,https://github.com/lrjconan/RBP,36,Reviving and improving recurrent back-propagation,"https://scholar.google.com/scholar?cluster=8778638717316926195&hl=en&as_sdt=0,5",3,2018
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression,79,icml,4,1,2023-06-17 02:59:36.869000,https://github.com/LiuHaiTao01/GRBCM,9,Generalized robust Bayesian committee machine for large-scale Gaussian process regression,"https://scholar.google.com/scholar?cluster=8338496144713791124&hl=en&as_sdt=0,33",3,2018
Delayed Impact of Fair Machine Learning,410,icml,8,1,2023-06-17 02:59:37.082000,https://github.com/lydiatliu/delayedimpact,12,Delayed impact of fair machine learning,"https://scholar.google.com/scholar?cluster=5181623229195224544&hl=en&as_sdt=0,5",6,2018
Open Category Detection with PAC Guarantees,81,icml,1,0,2023-06-17 02:59:37.295000,https://github.com/liusi2019/ocd,6,Open category detection with PAC guarantees,"https://scholar.google.com/scholar?cluster=16442088261883676309&hl=en&as_sdt=0,3",1,2018
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design,101,icml,7,1,2023-06-17 02:59:37.510000,https://github.com/Alaya-in-Matrix/MACE,18,Batch Bayesian optimization via multi-objective acquisition ensemble for automated analog circuit design,"https://scholar.google.com/scholar?cluster=18060661280078108001&hl=en&as_sdt=0,41",2,2018
Celer: a Fast Solver for the Lasso with Dual Extrapolation,71,icml,29,23,2023-06-17 02:59:37.724000,https://github.com/mathurinm/celer,172,Celer: a fast solver for the lasso with dual extrapolation,"https://scholar.google.com/scholar?cluster=5377261088300700033&hl=en&as_sdt=0,5",11,2018
Dimensionality-Driven Learning with Noisy Labels,347,icml,13,7,2023-06-17 02:59:37.938000,https://github.com/xingjunm/dimensionality-driven-learning,54,Dimensionality-driven learning with noisy labels,"https://scholar.google.com/scholar?cluster=13671594748199391279&hl=en&as_sdt=0,5",6,2018
Orthogonal Machine Learning: Power and Limitations,32,icml,1,0,2023-06-17 02:59:38.152000,https://github.com/IliasZadik/double_orthogonal_ml,9,Orthogonal machine learning: Power and limitations,"https://scholar.google.com/scholar?cluster=5809392484253895551&hl=en&as_sdt=0,14",3,2018
Learning Adversarially Fair and Transferable Representations,552,icml,12,0,2023-06-17 02:59:38.366000,https://github.com/VectorInstitute/laftr,50,Learning adversarially fair and transferable representations,"https://scholar.google.com/scholar?cluster=6932272369084023440&hl=en&as_sdt=0,47",6,2018
Iterative Amortized Inference,139,icml,9,1,2023-06-17 02:59:38.579000,https://github.com/joelouismarino/iterative_inference,43,Iterative amortized inference,"https://scholar.google.com/scholar?cluster=11655024897433506011&hl=en&as_sdt=0,43",3,2018
"Optimization, fast and slow: optimally switching between local and Bayesian optimization",33,icml,9,2,2023-06-17 02:59:38.792000,https://github.com/markm541374/gpbo,25,"Optimization, fast and slow: optimally switching between local and Bayesian optimization","https://scholar.google.com/scholar?cluster=6241493477815440111&hl=en&as_sdt=0,5",2,2018
Which Training Methods for GANs do actually Converge?,1241,icml,115,12,2023-06-17 02:59:39.006000,https://github.com/LMescheder/GAN_stability,900,Which training methods for GANs do actually converge?,"https://scholar.google.com/scholar?cluster=11334901664651510839&hl=en&as_sdt=0,5",22,2018
prDeep: Robust Phase Retrieval with a Flexible Deep Network,151,icml,12,1,2023-06-17 02:59:39.219000,https://github.com/ricedsp/prDeep,37,prDeep: Robust phase retrieval with a flexible deep network,"https://scholar.google.com/scholar?cluster=13840213498750434607&hl=en&as_sdt=0,44",3,2018
One-Shot Segmentation in Clutter,39,icml,11,0,2023-06-17 02:59:39.433000,https://github.com/michaelisc/cluttered-omniglot,47,One-shot segmentation in clutter,"https://scholar.google.com/scholar?cluster=14253967975584352267&hl=en&as_sdt=0,5",4,2018
Differentiable plasticity: training plastic neural networks with backpropagation,151,icml,71,3,2023-06-17 02:59:39.646000,https://github.com/uber-common/differentiable-plasticity,389,Differentiable plasticity: training plastic neural networks with backpropagation,"https://scholar.google.com/scholar?cluster=16849084099727983459&hl=en&as_sdt=0,5",27,2018
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding,21,icml,1,1,2023-06-17 02:59:39.860000,https://github.com/tomMoral/dicod,12,Dicod: Distributed convolutional coordinate descent for convolutional sparse coding,"https://scholar.google.com/scholar?cluster=6841370809688839469&hl=en&as_sdt=0,25",3,2018
Nearly Optimal Robust Subspace Tracking,30,icml,4,0,2023-06-17 02:59:40.073000,https://github.com/praneethmurthy/NORST,7,Nearly optimal robust subspace tracking,"https://scholar.google.com/scholar?cluster=11197141106222317789&hl=en&as_sdt=0,14",3,2018
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry,341,icml,221,29,2023-06-17 02:59:40.286000,https://github.com/facebookresearch/poincare-embeddings,1592,Learning continuous hierarchies in the lorentz model of hyperbolic geometry,"https://scholar.google.com/scholar?cluster=5235601311596588081&hl=en&as_sdt=0,10",52,2018
SparseMAP: Differentiable Sparse Structured Inference,119,icml,9,3,2023-06-17 02:59:40.501000,https://github.com/vene/sparsemap,109,Sparsemap: Differentiable sparse structured inference,"https://scholar.google.com/scholar?cluster=16676407380618945031&hl=en&as_sdt=0,24",9,2018
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations,144,icml,0,0,2023-06-17 02:59:40.714000,https://github.com/weilinie/BackpropVis,5,A theoretical explanation for perplexing behaviors of backpropagation-based visualizations,"https://scholar.google.com/scholar?cluster=7254168770426119962&hl=en&as_sdt=0,47",2,2018
Self-Imitation Learning,274,icml,40,4,2023-06-17 02:59:40.928000,https://github.com/junhyukoh/self-imitation-learning,269,Self-imitation learning,"https://scholar.google.com/scholar?cluster=6282132634766578030&hl=en&as_sdt=0,31",16,2018
Learning Localized Spatio-Temporal Models From Streaming Data,1,icml,0,3,2023-06-17 02:59:41.142000,https://github.com/Muhammad-Osama/Localized-Spatio-temporal-Models,7,Learning localized spatio-temporal models from streaming data,"https://scholar.google.com/scholar?cluster=6273621603567429406&hl=en&as_sdt=0,33",1,2018
Efficient First-Order Algorithms for Adaptive Signal Denoising,5,icml,1,0,2023-06-17 02:59:41.355000,https://github.com/ostrodmit/AlgoRec,6,Efficient first-order algorithms for adaptive signal denoising,"https://scholar.google.com/scholar?cluster=16164313069281185033&hl=en&as_sdt=0,10",3,2018
Analyzing Uncertainty in Neural Machine Translation,748,icml,8,0,2023-06-17 02:59:41.569000,https://github.com/facebookresearch/analyzing-uncertainty-nmt,32,Analyzing uncertainty in neural machine translation,"https://scholar.google.com/scholar?cluster=1522001537063991105&hl=en&as_sdt=0,5",56,2018
Max-Mahalanobis Linear Discriminant Analysis Networks,46,icml,19,1,2023-06-17 02:59:41.783000,https://github.com/P2333/Max-Mahalanobis-Training,87,Max-mahalanobis linear discriminant analysis networks,"https://scholar.google.com/scholar?cluster=1310490945606447616&hl=en&as_sdt=0,33",4,2018
Stochastic Variance-Reduced Policy Gradient,145,icml,4,0,2023-06-17 02:59:41.997000,https://github.com/Dam930/rllab,3,Stochastic variance-reduced policy gradient,"https://scholar.google.com/scholar?cluster=10229080169981298445&hl=en&as_sdt=0,38",3,2018
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos,56,icml,2,0,2023-06-17 02:59:42.211000,https://github.com/proppo/pipps_demo,0,PIPPS: Flexible model-based policy search robust to the curse of chaos,"https://scholar.google.com/scholar?cluster=8640168252000745898&hl=en&as_sdt=0,5",2,2018
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration,38,icml,2,0,2023-06-17 02:59:42.426000,https://github.com/jliang993/Local-VRSGD,2,Local convergence properties of SAGA/Prox-SVRG and acceleration,"https://scholar.google.com/scholar?cluster=12517501002751750903&hl=en&as_sdt=0,33",3,2018
Learning Dynamics of Linear Denoising Autoencoders,24,icml,3,1,2023-06-17 02:59:42.640000,https://github.com/arnupretorius/lindaedynamics_icml2018,12,Learning dynamics of linear denoising autoencoders,"https://scholar.google.com/scholar?cluster=11573052296697932394&hl=en&as_sdt=0,6",3,2018
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets,40,icml,8,1,2023-06-17 02:59:42.856000,https://github.com/sdai654416/Joint-GAN,19,Jointgan: Multi-domain joint distribution learning with generative adversarial nets,"https://scholar.google.com/scholar?cluster=17442133177721066359&hl=en&as_sdt=0,25",1,2018
Selecting Representative Examples for Program Synthesis,30,icml,3,1,2023-06-17 02:59:43.071000,https://github.com/evanthebouncy/icml2018_selecting_representative_examples,11,Selecting representative examples for program synthesis,"https://scholar.google.com/scholar?cluster=18419281465561462811&hl=en&as_sdt=0,22",3,2018
DCFNet: Deep Neural Network with Decomposed Convolutional Filters,58,icml,1,0,2023-06-17 02:59:43.284000,https://github.com/xycheng/DCFNet,11,DCFNet: Deep neural network with decomposed convolutional filters,"https://scholar.google.com/scholar?cluster=6785841352849465563&hl=en&as_sdt=0,5",2,2018
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?,31,icml,0,0,2023-06-17 02:59:43.498000,https://github.com/rubai5/ESS_Game,7,Can deep reinforcement learning solve Erdos-Selfridge-Spencer games?,"https://scholar.google.com/scholar?cluster=5045759722516886464&hl=en&as_sdt=0,5",1,2018
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate,46,icml,3,0,2023-06-17 02:59:43.713000,https://github.com/tijana-zrnic/SAFFRONcode,7,SAFFRON: an adaptive algorithm for online control of the false discovery rate,"https://scholar.google.com/scholar?cluster=3162538214212248602&hl=en&as_sdt=0,5",0,2018
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning,1465,icml,360,54,2023-06-17 02:59:43.927000,https://github.com/oxwhirl/pymarl,1474,Monotonic value function factorisation for deep multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=3975132673723125155&hl=en&as_sdt=0,33",32,2018
Learning to Reweight Examples for Robust Deep Learning,1199,icml,53,8,2023-06-17 02:59:44.141000,https://github.com/uber-research/learning-to-reweight-examples,266,Learning to reweight examples for robust deep learning,"https://scholar.google.com/scholar?cluster=17871432661582272860&hl=en&as_sdt=0,5",11,2018
Fast Information-theoretic Bayesian Optimisation,48,icml,2,0,2023-06-17 02:59:44.355000,https://github.com/rubinxin/FITBO,16,Fast information-theoretic Bayesian optimisation,"https://scholar.google.com/scholar?cluster=12232335065092117172&hl=en&as_sdt=0,14",2,2018
Probabilistic Boolean Tensor Decomposition,18,icml,5,0,2023-06-17 02:59:44.570000,https://github.com/TammoR/LogicalFactorisationMachines,20,Probabilistic boolean tensor decomposition,"https://scholar.google.com/scholar?cluster=11732429422199282970&hl=en&as_sdt=0,5",3,2018
Black-Box Variational Inference for Stochastic Differential Equations,62,icml,10,0,2023-06-17 02:59:44.787000,https://github.com/Tom-Ryder/VIforSDEs,41,Black-box variational inference for stochastic differential equations,"https://scholar.google.com/scholar?cluster=771102464723698631&hl=en&as_sdt=0,33",6,2018
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks,254,icml,3,0,2023-06-17 02:59:45.001000,https://github.com/ItaySafran/OneLayerGDconvergence,1,Spurious local minima are common in two-layer relu neural networks,"https://scholar.google.com/scholar?cluster=2602196713819367782&hl=en&as_sdt=0,18",0,2018
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service,113,icml,6,0,2023-06-17 02:59:45.217000,https://github.com/amartya18x/tapas,16,TAPAS: Tricks to accelerate (encrypted) prediction as a service,"https://scholar.google.com/scholar?cluster=13862835131458070168&hl=en&as_sdt=0,5",8,2018
Learning with Abandonment,9,icml,0,0,2023-06-17 02:59:45.432000,https://github.com/schmit/learning-abandonment,1,Learning with abandonment,"https://scholar.google.com/scholar?cluster=5599696763306308098&hl=en&as_sdt=0,48",2,2018
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care,24,icml,3,1,2023-06-17 02:59:45.647000,https://github.com/d909b/DSMT-Nets,10,Not to cry wolf: Distantly supervised multitask learning in critical care,"https://scholar.google.com/scholar?cluster=13897538253893011334&hl=en&as_sdt=0,5",4,2018
Overcoming Catastrophic Forgetting with Hard Attention to the Task,699,icml,49,1,2023-06-17 02:59:45.862000,https://github.com/joansj/hat,174,Overcoming catastrophic forgetting with hard attention to the task,"https://scholar.google.com/scholar?cluster=11086231050694477723&hl=en&as_sdt=0,36",10,2018
First Order Generative Adversarial Networks,6,icml,12,1,2023-06-17 02:59:46.076000,https://github.com/zalandoresearch/first_order_gan,35,First order generative adversarial networks,"https://scholar.google.com/scholar?cluster=4229294235141796493&hl=en&as_sdt=0,5",7,2018
Finding Influential Training Samples for Gradient Boosted Decision Trees,38,icml,18,0,2023-06-17 02:59:46.294000,https://github.com/bsharchilev/influence_boosting,63,Finding influential training samples for gradient boosted decision trees,"https://scholar.google.com/scholar?cluster=16436473119957517587&hl=en&as_sdt=0,33",7,2018
Solving Partial Assignment Problems using Random Clique Complexes,1,icml,3,0,2023-06-17 02:59:46.513000,https://github.com/charusharma1991/RandomCliqueComplexes_ICML2018,2,Solving partial assignment problems using random clique complexes,"https://scholar.google.com/scholar?cluster=8378028426453482804&hl=en&as_sdt=0,36",2,2018
A Spectral Approach to Gradient Estimation for Implicit Distributions,78,icml,9,2,2023-06-17 02:59:46.734000,https://github.com/thjashin/spectral-stein-grad,33,A spectral approach to gradient estimation for implicit distributions,"https://scholar.google.com/scholar?cluster=34252178022681098&hl=en&as_sdt=0,9",4,2018
Accelerating Natural Gradient with Higher-Order Invariance,13,icml,8,0,2023-06-17 02:59:46.955000,https://github.com/ermongroup/higher_order_invariance,30,Accelerating natural gradient with higher-order invariance,"https://scholar.google.com/scholar?cluster=17686115985744822983&hl=en&as_sdt=0,33",6,2018
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search,92,icml,21,3,2023-06-17 02:59:47.175000,https://github.com/sg-nm/Evolutionary-Autoencoders,67,Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search,"https://scholar.google.com/scholar?cluster=4118394325034454915&hl=en&as_sdt=0,41",3,2018
Scalable approximate Bayesian inference for particle tracking data,12,icml,0,2,2023-06-17 02:59:47.390000,https://github.com/SunRuoxi/Single_Particle_Tracking,1,Scalable approximate Bayesian inference for particle tracking data,"https://scholar.google.com/scholar?cluster=8017063234741228178&hl=en&as_sdt=0,8",3,2018
Learning the Reward Function for a Misspecified Model,12,icml,1,0,2023-06-17 02:59:47.606000,https://github.com/etalvitie/hdaggermc,8,Learning the reward function for a misspecified model,"https://scholar.google.com/scholar?cluster=16036091820545871049&hl=en&as_sdt=0,5",1,2018
Chi-square Generative Adversarial Network,40,icml,0,3,2023-06-17 02:59:47.820000,https://github.com/chenyang-tao/chi2gan,6,Chi-square generative adversarial network,"https://scholar.google.com/scholar?cluster=3560140041128352974&hl=en&as_sdt=0,14",4,2018
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees,47,icml,1,0,2023-06-17 02:59:48.036000,https://github.com/QCGroup/quad-lyap-first-order,5,Lyapunov functions for first-order methods: Tight automated convergence guarantees,"https://scholar.google.com/scholar?cluster=1395570422835062279&hl=en&as_sdt=0,5",3,2018
Adversarial Regression with Multiple Learners,33,icml,1,0,2023-06-17 02:59:48.259000,https://github.com/marsplus/Adversarial-Regression-with-Multiple-Learners,2,Adversarial regression with multiple learners,"https://scholar.google.com/scholar?cluster=11851981725937878010&hl=en&as_sdt=0,33",2,2018
StrassenNets: Deep Learning with a Multiplication Budget,30,icml,10,0,2023-06-17 02:59:48.473000,https://github.com/mitscha/strassennets,45,StrassenNets: Deep learning with a multiplication budget,"https://scholar.google.com/scholar?cluster=9065345888211174353&hl=en&as_sdt=0,44",4,2018
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning,350,icml,85,6,2023-06-17 02:59:48.687000,https://github.com/Yunbo426/predrnn-pp,221,Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning,"https://scholar.google.com/scholar?cluster=16975551372418150051&hl=en&as_sdt=0,5",10,2018
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples,145,icml,1,0,2023-06-17 02:59:48.902000,https://github.com/EricYizhenWang/robust_nn_icml,6,Analyzing the robustness of nearest neighbors to adversarial examples,"https://scholar.google.com/scholar?cluster=15228068536645268692&hl=en&as_sdt=0,5",3,2018
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models,4,icml,0,1,2023-06-17 02:59:49.117000,https://github.com/QData/JEEK,1,A fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian graphical models,"https://scholar.google.com/scholar?cluster=12183443188962650844&hl=en&as_sdt=0,6",4,2018
Adversarial Distillation of Bayesian Neural Network Posteriors,59,icml,2,1,2023-06-17 02:59:49.330000,https://github.com/wangkua1/apd_public,14,Adversarial distillation of bayesian neural network posteriors,"https://scholar.google.com/scholar?cluster=8595967760145130464&hl=en&as_sdt=0,47",6,2018
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions,20,icml,2,1,2023-06-17 02:59:49.546000,https://github.com/wendazhou/alocv-package,6,Approximate leave-one-out for fast parameter tuning in high dimensions,"https://scholar.google.com/scholar?cluster=7517160253492394187&hl=en&as_sdt=0,7",4,2018
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples,179,icml,19,0,2023-06-17 02:59:49.797000,https://github.com/tech-srl/lstar_extraction,63,Extracting automata from recurrent neural networks using queries and counterexamples,"https://scholar.google.com/scholar?cluster=3812692831904479239&hl=en&as_sdt=0,31",7,2018
Towards Fast Computation of Certified Robustness for ReLU Networks,641,icml,5,1,2023-06-17 02:59:50.011000,https://github.com/huanzhang12/CertifiedReLURobustness,29,Towards fast computation of certified robustness for relu networks,"https://scholar.google.com/scholar?cluster=13154362274812885800&hl=en&as_sdt=0,39",5,2018
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope,1370,icml,83,8,2023-06-17 02:59:50.226000,https://github.com/locuslab/convex_adversarial,357,Provable defenses against adversarial examples via the convex outer adversarial polytope,"https://scholar.google.com/scholar?cluster=2593701021867797885&hl=en&as_sdt=0,47",16,2018
SQL-Rank: A Listwise Approach to Collaborative Ranking,42,icml,8,0,2023-06-17 02:59:50.441000,https://github.com/wuliwei9278/SQL-Rank,16,Sql-rank: A listwise approach to collaborative ranking,"https://scholar.google.com/scholar?cluster=3011153619955791541&hl=en&as_sdt=0,10",3,2018
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization,15,icml,0,1,2023-06-17 02:59:50.655000,https://github.com/hang-wu/VRCRM,2,Variance regularized counterfactual risk minimization via variational divergence minimization,"https://scholar.google.com/scholar?cluster=16906275230514657049&hl=en&as_sdt=0,10",2,2018
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions,122,icml,34,0,2023-06-17 02:59:50.870000,https://github.com/Sandbox3aster/Deep-K-Means,146,Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions,"https://scholar.google.com/scholar?cluster=5421215697510972919&hl=en&as_sdt=0,44",13,2018
"Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks",293,icml,8,0,2023-06-17 02:59:51.084000,https://github.com/brain-research/mean-field-cnns,35,"Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks","https://scholar.google.com/scholar?cluster=4327553153293253435&hl=en&as_sdt=0,47",7,2018
Learning Semantic Representations for Unsupervised Domain Adaptation,455,icml,38,3,2023-06-17 02:59:51.299000,https://github.com/Mid-Push/Moving-Semantic-Transfer-Network,105,Learning semantic representations for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=3795243851386744123&hl=en&as_sdt=0,47",5,2018
A Semantic Loss Function for Deep Learning with Symbolic Knowledge,362,icml,11,1,2023-06-17 02:59:51.513000,https://github.com/UCLA-StarAI/Semantic-Loss,52,A semantic loss function for deep learning with symbolic knowledge,"https://scholar.google.com/scholar?cluster=2687938736648965063&hl=en&as_sdt=0,44",11,2018
Mean Field Multi-Agent Reinforcement Learning,564,icml,92,20,2023-06-17 02:59:51.727000,https://github.com/mlii/mfrl,323,Mean field multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=18365585657208114611&hl=en&as_sdt=0,23",10,2018
"Yes, but Did It Work?: Evaluating Variational Inference",138,icml,2,1,2023-06-17 02:59:51.941000,https://github.com/yao-yl/Evaluating-Variational-Inference,12,"Yes, but did it work?: Evaluating variational inference","https://scholar.google.com/scholar?cluster=16612262779014542273&hl=en&as_sdt=0,31",3,2018
Semi-Implicit Variational Inference,122,icml,13,1,2023-06-17 02:59:52.156000,https://github.com/mingzhang-yin/SIVI,49,Semi-implicit variational inference,"https://scholar.google.com/scholar?cluster=952314383686625023&hl=en&as_sdt=0,5",5,2018
GAIN: Missing Data Imputation using Generative Adversarial Nets,784,icml,141,0,2023-06-17 02:59:52.371000,https://github.com/jsyoon0823/GAIN,307,Gain: Missing data imputation using generative adversarial nets,"https://scholar.google.com/scholar?cluster=6024113526841994005&hl=en&as_sdt=0,6",11,2018
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models,753,icml,109,6,2023-06-17 02:59:52.586000,https://github.com/snap-stanford/GraphRNN,387,Graphrnn: Generating realistic graphs with deep auto-regressive models,"https://scholar.google.com/scholar?cluster=18334516615969196433&hl=en&as_sdt=0,5",60,2018
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization,106,icml,3,1,2023-06-17 02:59:52.801000,https://github.com/zhangjiong724/spectral-RNN,12,Stabilizing gradients for deep neural networks via efficient svd parameterization,"https://scholar.google.com/scholar?cluster=10623363336533108811&hl=en&as_sdt=0,9",2,2018
Learning Long Term Dependencies via Fourier Recurrent Units,31,icml,8,0,2023-06-17 02:59:53.014000,https://github.com/limbo018/FRU,36,Learning long term dependencies via fourier recurrent units,"https://scholar.google.com/scholar?cluster=16150244378271641439&hl=en&as_sdt=0,5",7,2018
Inter and Intra Topic Structure Learning with Word Embeddings,16,icml,3,4,2023-06-17 02:59:53.228000,https://github.com/ethanhezhao/WEDTM,6,Inter and intra topic structure learning with word embeddings,"https://scholar.google.com/scholar?cluster=11048244315815532986&hl=en&as_sdt=0,5",4,2018
Adversarially Regularized Autoencoders,291,icml,93,19,2023-06-17 02:59:53.442000,https://github.com/jakezhaojb/ARAE,400,Adversarially regularized autoencoders,"https://scholar.google.com/scholar?cluster=5024716526871945774&hl=en&as_sdt=0,11",20,2018
Dynamic Weights in Multi-Objective Deep Reinforcement Learning,97,icml,16,0,2023-06-17 03:09:58.308000,https://github.com/axelabels/DynMORL,64,Dynamic weights in multi-objective deep reinforcement learning,"https://scholar.google.com/scholar?cluster=12040121315464946458&hl=en&as_sdt=0,39",2,2019
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing,600,icml,34,4,2023-06-17 03:09:58.523000,https://github.com/samihaija/mixhop,113,Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing,"https://scholar.google.com/scholar?cluster=8927230189965016671&hl=en&as_sdt=0,5",7,2019
Understanding the Impact of Entropy on Policy Optimization,182,icml,14,4,2023-06-17 03:09:58.738000,https://github.com/zafarali/emdp,47,Understanding the impact of entropy on policy optimization,"https://scholar.google.com/scholar?cluster=8905478721868235472&hl=en&as_sdt=0,36",5,2019
Fairwashing: the risk of rationalization,118,icml,2,1,2023-06-17 03:09:58.954000,https://github.com/aivodji/LaundryML,15,Fairwashing: the risk of rationalization,"https://scholar.google.com/scholar?cluster=2523692918696533409&hl=en&as_sdt=0,23",2,2019
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search,74,icml,12,2,2023-06-17 03:09:59.200000,https://github.com/shirakawas/ASNG-NAS,86,Adaptive stochastic natural gradient method for one-shot neural architecture search,"https://scholar.google.com/scholar?cluster=8278729461791344602&hl=en&as_sdt=0,44",12,2019
"Graph Element Networks: adaptive, structured computation and memory",73,icml,18,0,2023-06-17 03:09:59.417000,https://github.com/FerranAlet/graph_element_networks,54,"Graph element networks: adaptive, structured computation and memory","https://scholar.google.com/scholar?cluster=15635052566391015915&hl=en&as_sdt=0,47",4,2019
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation,43,icml,5,1,2023-06-17 03:09:59.631000,https://github.com/a5a/asynchronous-BO,9,Asynchronous batch Bayesian optimisation with improved local penalisation,"https://scholar.google.com/scholar?cluster=17891210137592442168&hl=en&as_sdt=0,11",2,2019
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data,9,icml,6,0,2023-06-17 03:09:59.848000,https://github.com/sergulaydore/Feature-Grouping-Regularizer,20,Feature grouping as a stochastic regularizer for high-dimensional structured data,"https://scholar.google.com/scholar?cluster=11613171711375782355&hl=en&as_sdt=0,47",3,2019
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior,11,icml,0,0,2023-06-17 03:10:00.065000,https://github.com/OxCSML-BayesNP/doublepowerlaw,0,Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior,"https://scholar.google.com/scholar?cluster=7805425707346893329&hl=en&as_sdt=0,5",4,2019
Scalable Fair Clustering,174,icml,6,0,2023-06-17 03:10:00.279000,https://github.com/talwagner/fair_clustering,16,Scalable fair clustering,"https://scholar.google.com/scholar?cluster=16665021693225941817&hl=en&as_sdt=0,14",2,2019
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs,18,icml,4,1,2023-06-17 03:10:00.494000,https://github.com/yogeshbalaji/EntropicGANs_meet_VAEs,8,Entropic gans meet vaes: A statistical approach to compute sample likelihoods in gans,"https://scholar.google.com/scholar?cluster=4502964466526434508&hl=en&as_sdt=0,10",2,2019
Provable Guarantees for Gradient-Based Meta-Learning,136,icml,2,0,2023-06-17 03:10:00.712000,https://github.com/mkhodak/FMRL,3,Provable guarantees for gradient-based meta-learning,"https://scholar.google.com/scholar?cluster=18333296959440727243&hl=en&as_sdt=0,33",3,2019
Learning to Route in Similarity Graphs,23,icml,15,3,2023-06-17 03:10:00.937000,https://github.com/dbaranchuk/learning-to-route,50,Learning to route in similarity graphs,"https://scholar.google.com/scholar?cluster=381431972230740194&hl=en&as_sdt=0,14",10,2019
Noise2Self: Blind Denoising by Self-Supervision,439,icml,67,5,2023-06-17 03:10:01.181000,https://github.com/czbiohub/noise2self,292,Noise2self: Blind denoising by self-supervision,"https://scholar.google.com/scholar?cluster=16484478987296907806&hl=en&as_sdt=0,43",16,2019
Efficient optimization of loops and limits with randomized telescoping sums,22,icml,4,1,2023-06-17 03:10:01.396000,https://github.com/PrincetonLIPS/randomized_telescopes,27,Efficient optimization of loops and limits with randomized telescoping sums,"https://scholar.google.com/scholar?cluster=3412668840791342029&hl=en&as_sdt=0,33",5,2019
Greedy Layerwise Learning Can Scale To ImageNet,136,icml,11,0,2023-06-17 03:10:01.612000,https://github.com/eugenium/layerCNN,17,Greedy layerwise learning can scale to imagenet,"https://scholar.google.com/scholar?cluster=17442726017389288785&hl=en&as_sdt=0,5",4,2019
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning,21,icml,1,0,2023-06-17 03:10:01.826000,https://github.com/marcelomatheusgauy/optimal_kronecker_approximation,5,Optimal kronecker-sum approximation of real time recurrent learning,"https://scholar.google.com/scholar?cluster=6902147836625554260&hl=en&as_sdt=0,50",3,2019
Analyzing Federated Learning through an Adversarial Lens,750,icml,34,4,2023-06-17 03:10:02.041000,https://github.com/inspire-group/ModelPoisoning,133,Analyzing federated learning through an adversarial lens,"https://scholar.google.com/scholar?cluster=16839948122426603319&hl=en&as_sdt=0,5",6,2019
A Kernel Perspective for Regularizing Deep Neural Networks,67,icml,6,0,2023-06-17 03:10:02.256000,https://github.com/albietz/kernel_reg,22,A kernel perspective for regularizing deep neural networks,"https://scholar.google.com/scholar?cluster=17149885341490741277&hl=en&as_sdt=0,5",3,2019
Online Variance Reduction with Mixtures,14,icml,1,0,2023-06-17 03:10:02.470000,https://github.com/zalanborsos/variance-reduction-mixtures,3,Online variance reduction with mixtures,"https://scholar.google.com/scholar?cluster=14403425847063612414&hl=en&as_sdt=0,10",2,2019
Compositional Fairness Constraints for Graph Embeddings,197,icml,18,2,2023-06-17 03:10:02.685000,https://github.com/joeybose/Flexible-Fairness-Constraints,44,Compositional fairness constraints for graph embeddings,"https://scholar.google.com/scholar?cluster=2983154672519525426&hl=en&as_sdt=0,5",4,2019
Active Manifolds: A non-linear analogue to Active Subspaces,21,icml,2,0,2023-06-17 03:10:02.900000,https://github.com/bridgesra/active-manifold-icml2019-code,4,Active Manifolds: A non-linear analogue to Active Subspaces,"https://scholar.google.com/scholar?cluster=12766453925065005709&hl=en&as_sdt=0,31",2,2019
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations,240,icml,21,3,2023-06-17 03:10:03.116000,https://github.com/hiwonjoon/ICML2019-TREX,71,Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations,"https://scholar.google.com/scholar?cluster=14944046691955331663&hl=en&as_sdt=0,10",6,2019
Understanding the Origins of Bias in Word Embeddings,168,icml,12,3,2023-06-17 03:10:03.331000,https://github.com/mebrunet/understanding-bias,21,Understanding the origins of bias in word embeddings,"https://scholar.google.com/scholar?cluster=18061585171680402541&hl=en&as_sdt=0,5",3,2019
Low Latency Privacy Preserving Inference,161,icml,68,0,2023-06-17 03:10:03.547000,https://github.com/microsoft/CryptoNets,242,Low latency privacy preserving inference,"https://scholar.google.com/scholar?cluster=86142108232916247&hl=en&as_sdt=0,5",13,2019
Active Embedding Search via Noisy Paired Comparisons,16,icml,3,0,2023-06-17 03:10:03.764000,https://github.com/siplab-gt/pairsearch,9,Active embedding search via noisy paired comparisons,"https://scholar.google.com/scholar?cluster=10123441327203003064&hl=en&as_sdt=0,23",3,2019
Dynamic Measurement Scheduling for Event Forecasting using Deep RL,12,icml,6,1,2023-06-17 03:10:03.978000,https://github.com/zzzace2000/autodiagnosis,9,Dynamic measurement scheduling for event forecasting using deep RL,"https://scholar.google.com/scholar?cluster=16682086403586827063&hl=en&as_sdt=0,5",3,2019
Stein Point Markov Chain Monte Carlo,55,icml,4,1,2023-06-17 03:10:04.193000,https://github.com/wilson-ye-chen/sp-mcmc,12,Stein point markov chain monte carlo,"https://scholar.google.com/scholar?cluster=6889028915730960186&hl=en&as_sdt=0,33",0,2019
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System,165,icml,30,4,2023-06-17 03:10:04.408000,https://github.com/xinshi-chen/GenerativeAdversarialUserModel,124,Generative adversarial user model for reinforcement learning based recommendation system,"https://scholar.google.com/scholar?cluster=18416272509453441398&hl=en&as_sdt=0,5",4,2019
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels,291,icml,23,2,2023-06-17 03:10:04.622000,https://github.com/chenpf1025/noisy_label_understanding_utilizing,82,Understanding and utilizing deep neural networks trained with noisy labels,"https://scholar.google.com/scholar?cluster=1459914703144318986&hl=en&as_sdt=0,33",6,2019
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation,358,icml,17,3,2023-06-17 03:10:04.837000,https://github.com/thuml/Batch-Spectral-Penalization,78,Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation,"https://scholar.google.com/scholar?cluster=8590630247063758749&hl=en&as_sdt=0,5",5,2019
"Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications",24,icml,6,0,2023-06-17 03:10:05.054000,https://github.com/pinyuchen/FINGER,6,"Fast incremental von neumann graph entropy computation: Theory, algorithm, and applications","https://scholar.google.com/scholar?cluster=15943782295657868941&hl=en&as_sdt=0,5",2,2019
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching,7,icml,1,0,2023-06-17 03:10:05.269000,https://github.com/MintYiqingchen/MMI-ALI,5,Multivariate-information adversarial ensemble for scalable joint distribution matching,"https://scholar.google.com/scholar?cluster=2407036909986043494&hl=en&as_sdt=0,5",0,2019
Robust Decision Trees Against Adversarial Examples,105,icml,11,3,2023-06-17 03:10:05.483000,https://github.com/chenhongge/RobustTrees,64,Robust decision trees against adversarial examples,"https://scholar.google.com/scholar?cluster=18298482644739407816&hl=en&as_sdt=0,31",8,2019
RaFM: Rank-Aware Factorization Machines,12,icml,6,1,2023-06-17 03:10:05.698000,https://github.com/cxsmarkchan/RaFM,12,RaFM: rank-aware factorization machines,"https://scholar.google.com/scholar?cluster=9961787920931572726&hl=en&as_sdt=0,11",4,2019
Control Regularization for Reduced Variance Reinforcement Learning,67,icml,5,0,2023-06-17 03:10:05.913000,https://github.com/rcheng805/CORE-RL,30,Control regularization for reduced variance reinforcement learning,"https://scholar.google.com/scholar?cluster=4210711157444974813&hl=en&as_sdt=0,5",1,2019
Predictor-Corrector Policy Optimization,21,icml,3,1,2023-06-17 03:10:06.127000,https://github.com/gtrll/rlfamily,3,Predictor-corrector policy optimization,"https://scholar.google.com/scholar?cluster=13913575152899689436&hl=en&as_sdt=0,31",4,2019
Neural Joint Source-Channel Coding,96,icml,13,2,2023-06-17 03:10:06.342000,https://github.com/ermongroup/necst,38,Neural joint source-channel coding,"https://scholar.google.com/scholar?cluster=13260217163651536800&hl=en&as_sdt=0,5",5,2019
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables,54,icml,14,0,2023-06-17 03:10:06.556000,https://github.com/IBM/online-alt-min,22,Beyond backprop: Online alternating minimization with auxiliary variables,"https://scholar.google.com/scholar?cluster=13143560607415133217&hl=en&as_sdt=0,5",9,2019
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization,161,icml,52,16,2023-06-17 03:10:06.771000,https://github.com/sosuperic/MeanSum,112,Meansum: A neural model for unsupervised multi-document abstractive summarization,"https://scholar.google.com/scholar?cluster=11126017598001925179&hl=en&as_sdt=0,33",10,2019
Quantifying Generalization in Reinforcement Learning,532,icml,84,3,2023-06-17 03:10:06.985000,https://github.com/openai/coinrun,361,Quantifying generalization in reinforcement learning,"https://scholar.google.com/scholar?cluster=9870113474300692969&hl=en&as_sdt=0,26",134,2019
Certified Adversarial Robustness via Randomized Smoothing,1382,icml,71,3,2023-06-17 03:10:07.200000,https://github.com/locuslab/smoothing,318,Certified adversarial robustness via randomized smoothing,"https://scholar.google.com/scholar?cluster=7039519782328477041&hl=en&as_sdt=0,14",11,2019
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning,174,icml,6,1,2023-06-17 03:10:07.415000,https://github.com/flowersteam/curious,26,Curious: intrinsically motivated modular multi-goal reinforcement learning,"https://scholar.google.com/scholar?cluster=329489517258350795&hl=en&as_sdt=0,48",12,2019
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets,17,icml,1,0,2023-06-17 03:10:07.629000,https://github.com/pjcv/smh,3,Scalable Metropolis-Hastings for exact Bayesian inference with large datasets,"https://scholar.google.com/scholar?cluster=10400262915897387298&hl=en&as_sdt=0,33",1,2019
Minimal Achievable Sufficient Statistic Learning,12,icml,4,0,2023-06-17 03:10:07.844000,https://github.com/mwcvitkovic/MASS-Learning,8,Minimal achievable sufficient statistic learning,"https://scholar.google.com/scholar?cluster=16216829176165913924&hl=en&as_sdt=0,33",3,2019
Open Vocabulary Learning on Source Code with a Graph-Structured Cache,42,icml,10,0,2023-06-17 03:10:08.060000,https://github.com/mwcvitkovic/Deep_Learning_On_Code_With_A_Graph_Vocabulary--Code_Preprocessor,21,Open vocabulary learning on source code with a graph-structured cache,"https://scholar.google.com/scholar?cluster=1145489630896909786&hl=en&as_sdt=0,41",2,2019
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations,76,icml,27,16,2023-06-17 03:10:08.277000,https://github.com/HazyResearch/butterfly,118,Learning fast algorithms for linear transforms using butterfly factorizations,"https://scholar.google.com/scholar?cluster=8670371133727236715&hl=en&as_sdt=0,10",20,2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization,99,icml,1,0,2023-06-17 03:10:08.492000,https://github.com/prolearner/onlineLTL,3,Learning-to-learn stochastic gradient descent with biased regularization,"https://scholar.google.com/scholar?cluster=5491276692157599761&hl=en&as_sdt=0,5",4,2019
Sever: A Robust Meta-Algorithm for Stochastic Optimization,251,icml,6,0,2023-06-17 03:10:08.709000,https://github.com/hoonose/sever,26,Sever: A robust meta-algorithm for stochastic optimization,"https://scholar.google.com/scholar?cluster=1735563344640957243&hl=en&as_sdt=0,32",4,2019
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization,104,icml,10,2,2023-06-17 03:10:08.929000,https://github.com/ShawnDing1994/AOFP,30,Approximated oracle filter pruning for destructive cnn width optimization,"https://scholar.google.com/scholar?cluster=979238780615518812&hl=en&as_sdt=0,5",3,2019
Trajectory-Based Off-Policy Deep Reinforcement Learning,5,icml,2,0,2023-06-17 03:10:09.157000,https://github.com/boschresearch/DD_OPG,11,Trajectory-based off-policy deep reinforcement learning,"https://scholar.google.com/scholar?cluster=3089333550231775288&hl=en&as_sdt=0,10",3,2019
Provably efficient RL with Rich Observations via Latent State Decoding,180,icml,14,1,2023-06-17 03:10:09.372000,https://github.com/Microsoft/StateDecoding,28,Provably efficient rl with rich observations via latent state decoding,"https://scholar.google.com/scholar?cluster=17139201255005810211&hl=en&as_sdt=0,14",5,2019
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning,43,icml,2,0,2023-06-17 03:10:09.587000,https://github.com/yilundu/task_agnostic_dynamics_prior,13,Task-agnostic dynamics priors for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=2869858217562916387&hl=en&as_sdt=0,5",1,2019
Autoregressive Energy Machines,52,icml,12,1,2023-06-17 03:10:09.802000,https://github.com/conormdurkan/autoregressive-energy-machines,79,Autoregressive energy machines,"https://scholar.google.com/scholar?cluster=6729811760374247021&hl=en&as_sdt=0,5",10,2019
Imitating Latent Policies from Observation,113,icml,21,1,2023-06-17 03:10:10.017000,https://github.com/ashedwards/ILPO,71,Imitating latent policies from observation,"https://scholar.google.com/scholar?cluster=16539609081927748607&hl=en&as_sdt=0,5",9,2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability,128,icml,1,0,2023-06-17 03:10:10.233000,https://github.com/cetmann/robustness-interpretability,15,On the connection between adversarial robustness and saliency map interpretability,"https://scholar.google.com/scholar?cluster=9006157315043198858&hl=en&as_sdt=0,47",1,2019
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap,31,icml,2,0,2023-06-17 03:10:10.450000,https://github.com/edfong/npl,8,Scalable nonparametric sampling from multimodal posteriors with the posterior bootstrap,"https://scholar.google.com/scholar?cluster=14627195645565170893&hl=en&as_sdt=0,5",2,2019
Approximating Orthogonal Matrices with Effective Givens Factorization,15,icml,3,0,2023-06-17 03:10:10.665000,https://github.com/tfrerix/givens-factorization,7,Approximating orthogonal matrices with effective Givens factorization,"https://scholar.google.com/scholar?cluster=16649468225264145943&hl=en&as_sdt=0,5",0,2019
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement,227,icml,30,12,2023-06-17 03:10:10.881000,https://github.com/JasonSWFu/MetricGAN,119,Metricgan: Generative adversarial networks based black-box metric scores optimization for speech enhancement,"https://scholar.google.com/scholar?cluster=10740262477107408585&hl=en&as_sdt=0,39",3,2019
Off-Policy Deep Reinforcement Learning without Exploration,944,icml,127,4,2023-06-17 03:10:11.105000,https://github.com/sfujim/BCQ,508,Off-policy deep reinforcement learning without exploration,"https://scholar.google.com/scholar?cluster=13735420516544008547&hl=en&as_sdt=0,33",6,2019
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation,100,icml,14,3,2023-06-17 03:10:11.320000,https://github.com/ShaniGam/RL-GAN,44,Transfer learning for related reinforcement learning tasks via image-to-image translation,"https://scholar.google.com/scholar?cluster=9611056051873190205&hl=en&as_sdt=0,5",4,2019
Graph U-Nets,839,icml,91,8,2023-06-17 03:10:11.537000,https://github.com/HongyangGao/gunet,445,Graph u-nets,"https://scholar.google.com/scholar?cluster=2250116536319373587&hl=en&as_sdt=0,5",11,2019
Deep Generative Learning via Variational Gradient Flow,21,icml,7,2,2023-06-17 03:10:11.756000,https://github.com/xjtuygao/VGrow,12,Deep generative learning via variational gradient flow,"https://scholar.google.com/scholar?cluster=13167225334345346820&hl=en&as_sdt=0,5",1,2019
Optimal Mini-Batch and Step Sizes for SAGA,35,icml,9,0,2023-06-17 03:10:11.975000,https://github.com/gowerrobert/StochOpt.jl,15,Optimal mini-batch and step sizes for SAGA,"https://scholar.google.com/scholar?cluster=14147185624190732996&hl=en&as_sdt=0,11",2,2019
SelectiveNet: A Deep Neural Network with an Integrated Reject Option,228,icml,13,4,2023-06-17 03:10:12.228000,https://github.com/geifmany/SelectiveNet,44,Selectivenet: A deep neural network with an integrated reject option,"https://scholar.google.com/scholar?cluster=3455752188101558663&hl=en&as_sdt=0,23",6,2019
Data Shapley: Equitable Valuation of Data for Machine Learning,441,icml,60,7,2023-06-17 03:10:12.445000,https://github.com/amiratag/DataShapley,212,Data shapley: Equitable valuation of data for machine learning,"https://scholar.google.com/scholar?cluster=7645060584356925514&hl=en&as_sdt=0,5",11,2019
Amortized Monte Carlo Integration,6,icml,1,0,2023-06-17 03:10:12.660000,https://github.com/talesa/amci,15,Amortized monte carlo integration,"https://scholar.google.com/scholar?cluster=7430114062861179606&hl=en&as_sdt=0,14",3,2019
A Statistical Investigation of Long Memory in Language and Music,19,icml,2,0,2023-06-17 03:10:12.877000,https://github.com/alecgt/RNN_long_memory,8,A statistical investigation of long memory in language and music,"https://scholar.google.com/scholar?cluster=3204260135600784159&hl=en&as_sdt=0,44",4,2019
Automatic Posterior Transformation for Likelihood-Free Inference,180,icml,29,0,2023-06-17 03:10:13.092000,https://github.com/mackelab/delfi,71,Automatic posterior transformation for likelihood-free inference,"https://scholar.google.com/scholar?cluster=9520658637115522401&hl=en&as_sdt=0,10",14,2019
Multi-Object Representation Learning with Iterative Variational Inference,385,icml,2436,170,2023-06-17 03:10:13.314000,https://github.com/deepmind/deepmind-research,11905,Multi-object representation learning with iterative variational inference,"https://scholar.google.com/scholar?cluster=213712144958725221&hl=en&as_sdt=0,11",336,2019
An Investigation of Model-Free Planning,79,icml,15,1,2023-06-17 03:10:13.533000,https://github.com/deepmind/boxoban-levels,54,An investigation of model-free planning,"https://scholar.google.com/scholar?cluster=7566080617462830679&hl=en&as_sdt=0,9",9,2019
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops,10,icml,2,0,2023-06-17 03:10:13.764000,https://github.com/limorigu/Cockamamie-Gobbledegook,6,Humor in word embeddings: Cockamamie gobbledegook for nincompoops,"https://scholar.google.com/scholar?cluster=13364498492064893478&hl=en&as_sdt=0,33",2,2019
Simple Black-box Adversarial Attacks,377,icml,54,0,2023-06-17 03:10:13.995000,https://github.com/cg563/simple-blackbox-attack,172,Simple black-box adversarial attacks,"https://scholar.google.com/scholar?cluster=14524309362525785070&hl=en&as_sdt=0,5",5,2019
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs,217,icml,17,0,2023-06-17 03:10:14.210000,https://github.com/nju-websoft/RSN,97,Learning to exploit long-term relational dependencies in knowledge graphs,"https://scholar.google.com/scholar?cluster=13843373750336430796&hl=en&as_sdt=0,5",8,2019
On The Power of Curriculum Learning in Training Deep Networks,330,icml,24,6,2023-06-17 03:10:14.426000,https://github.com/GuyHacohen/curriculum_learning,88,On the power of curriculum learning in training deep networks,"https://scholar.google.com/scholar?cluster=13645945393876441822&hl=en&as_sdt=0,39",3,2019
Learning Latent Dynamics for Planning from Pixels,1075,icml,203,4,2023-06-17 03:10:14.641000,https://github.com/google-research/planet,1130,Learning latent dynamics for planning from pixels,"https://scholar.google.com/scholar?cluster=17717536865000191198&hl=en&as_sdt=0,44",47,2019
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning,19,icml,3,0,2023-06-17 03:10:14.855000,https://github.com/seungyulhan/disc,8,Dimension-wise importance sampling weight clipping for sample-efficient reinforcement learning,"https://scholar.google.com/scholar?cluster=17087407211234698411&hl=en&as_sdt=0,33",1,2019
Importance Sampling Policy Evaluation with an Estimated Behavior Policy,57,icml,5,1,2023-06-17 03:10:15.071000,https://github.com/LARG/regression-importance-sampling,8,Importance sampling policy evaluation with an estimated behavior policy,"https://scholar.google.com/scholar?cluster=11718610357007396139&hl=en&as_sdt=0,23",4,2019
"Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications",79,icml,2,0,2023-06-17 03:10:15.312000,https://github.com/crharshaw/submodular-minus-linear,5,"Submodular maximization beyond non-negativity: Guarantees, fast algorithms, and applications","https://scholar.google.com/scholar?cluster=4032047436455480189&hl=en&as_sdt=0,5",2,2019
Provably Efficient Maximum Entropy Exploration,203,icml,0,0,2023-06-17 03:10:15.527000,https://github.com/abbyvansoest/maxent_ant,8,Provably efficient maximum entropy exploration,"https://scholar.google.com/scholar?cluster=7107307515820944527&hl=en&as_sdt=0,5",3,2019
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning,51,icml,2,0,2023-06-17 03:10:15.742000,https://github.com/nvedant07/effort_reward_fairness,3,On the long-term impact of algorithmic decision policies: Effort unfairness and feature segregation through social learning,"https://scholar.google.com/scholar?cluster=17715435590222166097&hl=en&as_sdt=0,5",2,2019
Using Pre-Training Can Improve Model Robustness and Uncertainty,563,icml,15,3,2023-06-17 03:10:15.958000,https://github.com/hendrycks/pre-training,92,Using pre-training can improve model robustness and uncertainty,"https://scholar.google.com/scholar?cluster=12052219296634461852&hl=en&as_sdt=0,39",6,2019
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules,353,icml,86,10,2023-06-17 03:10:16.194000,https://github.com/arcelien/pba,502,Population based augmentation: Efficient learning of augmentation policy schedules,"https://scholar.google.com/scholar?cluster=9297667920061606267&hl=en&as_sdt=0,34",20,2019
Connectivity-Optimized Representation Learning via Persistent Homology,65,icml,5,0,2023-06-17 03:10:16.408000,https://github.com/c-hofer/COREL_icml2019,10,Connectivity-optimized representation learning via persistent homology,"https://scholar.google.com/scholar?cluster=6723358631694302455&hl=en&as_sdt=0,33",4,2019
Emerging Convolutions for Generative Normalizing Flows,90,icml,4,2,2023-06-17 03:10:16.622000,https://github.com/ehoogeboom/emerging,39,Emerging convolutions for generative normalizing flows,"https://scholar.google.com/scholar?cluster=17212015756232898698&hl=en&as_sdt=0,11",4,2019
Parameter-Efficient Transfer Learning for NLP,1330,icml,39,7,2023-06-17 03:10:16.837000,https://github.com/google-research/adapter-bert,399,Parameter-efficient transfer learning for NLP,"https://scholar.google.com/scholar?cluster=18111543891993452201&hl=en&as_sdt=0,33",9,2019
Unsupervised Deep Learning by Neighbourhood Discovery,138,icml,19,1,2023-06-17 03:10:17.052000,https://github.com/raymond-sci/AND,148,Unsupervised deep learning by neighbourhood discovery,"https://scholar.google.com/scholar?cluster=2594287551241248539&hl=en&as_sdt=0,47",6,2019
Stable and Fair Classification,59,icml,0,3,2023-06-17 03:10:17.267000,https://github.com/huanglx12/Stable-Fair-Classification,1,Stable and fair classification,"https://scholar.google.com/scholar?cluster=6209492851752994222&hl=en&as_sdt=0,33",2,2019
HexaGAN: Generative Adversarial Nets for Real World Classification,43,icml,4,3,2023-06-17 03:10:17.483000,https://github.com/shinyflight/HexaGAN,20,Hexagan: Generative adversarial nets for real world classification,"https://scholar.google.com/scholar?cluster=9625100105337863533&hl=en&as_sdt=0,39",4,2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models,26,icml,4,0,2023-06-17 03:10:17.697000,https://github.com/ialong/GPt,19,Overcoming mean-field approximations in recurrent Gaussian process models,"https://scholar.google.com/scholar?cluster=13109450737746036374&hl=en&as_sdt=0,5",5,2019
"Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!",5,icml,1,0,2023-06-17 03:10:17.912000,https://github.com/nbip/ppca_ICML2019,1,"Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!","https://scholar.google.com/scholar?cluster=809292088427670370&hl=en&as_sdt=0,5",0,2019
Actor-Attention-Critic for Multi-Agent Reinforcement Learning,577,icml,152,10,2023-06-17 03:10:18.127000,https://github.com/shariqiqbal2810/MAAC,531,Actor-attention-critic for multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=241844530313281803&hl=en&as_sdt=0,14",7,2019
Complementary-Label Learning for Arbitrary Losses and Models,72,icml,16,0,2023-06-17 03:10:18.341000,https://github.com/takashiishida/comp,41,Complementary-label learning for arbitrary losses and models,"https://scholar.google.com/scholar?cluster=4663196775584030091&hl=en&as_sdt=0,5",1,2019
Learning What and Where to Transfer,118,icml,48,2,2023-06-17 03:10:18.558000,https://github.com/alinlab/L2T-ww,246,Learning what and where to transfer,"https://scholar.google.com/scholar?cluster=12979255639867638665&hl=en&as_sdt=0,5",8,2019
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning,337,icml,121,35,2023-06-17 03:10:18.775000,https://github.com/eugenevinitsky/sequential_social_dilemma_games,332,Social influence as intrinsic motivation for multi-agent deep reinforcement learning,"https://scholar.google.com/scholar?cluster=13693459800833279358&hl=en&as_sdt=0,44",13,2019
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement,28,icml,9,1,2023-06-17 03:10:18.992000,https://github.com/snu-mllab/DisentanglementICML19,22,Learning discrete and continuous factors of data via alternating disentanglement,"https://scholar.google.com/scholar?cluster=14742637203782847188&hl=en&as_sdt=0,33",4,2019
Neural Logic Reinforcement Learning,50,icml,27,1,2023-06-17 03:10:19.206000,https://github.com/ZhengyaoJiang/NLRL,71,Neural logic reinforcement learning,"https://scholar.google.com/scholar?cluster=18074632043038701502&hl=en&as_sdt=0,41",4,2019
Kernel Mean Matching for Content Addressability of GANs,8,icml,4,0,2023-06-17 03:10:19.434000,https://github.com/wittawatj/cadgan,22,Kernel mean matching for content addressability of GANs,"https://scholar.google.com/scholar?cluster=235365843120524307&hl=en&as_sdt=0,5",7,2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes,381,icml,9,2,2023-06-17 03:10:19.663000,https://github.com/epfml/error-feedback-SGD,24,Error feedback fixes signsgd and other gradient compression schemes,"https://scholar.google.com/scholar?cluster=15067189376913629578&hl=en&as_sdt=0,36",6,2019
Riemannian adaptive stochastic gradient algorithms on matrix manifolds,44,icml,19,0,2023-06-17 03:10:19.878000,https://github.com/hiroyuki-kasai/RSOpt,55,Riemannian adaptive stochastic gradient algorithms on matrix manifolds,"https://scholar.google.com/scholar?cluster=11814345447980112497&hl=en&as_sdt=0,39",5,2019
Neural Inverse Knitting: From Images to Manufacturing Instructions,27,icml,7,6,2023-06-17 03:10:20.093000,https://github.com/xionluhnis/neural_inverse_knitting,39,Neural inverse knitting: from images to manufacturing instructions,"https://scholar.google.com/scholar?cluster=15939506219703518176&hl=en&as_sdt=0,5",7,2019
Processing Megapixel Images with Deep Attention-Sampling Models,54,icml,18,15,2023-06-17 03:10:20.309000,https://github.com/idiap/attention-sampling,91,Processing megapixel images with deep attention-sampling models,"https://scholar.google.com/scholar?cluster=16495958235848738135&hl=en&as_sdt=0,5",9,2019
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking,183,icml,8,1,2023-06-17 03:10:20.524000,https://github.com/yigitcankaya/Shallow-Deep-Networks,33,Shallow-deep networks: Understanding and mitigating network overthinking,"https://scholar.google.com/scholar?cluster=6970216830123198900&hl=en&as_sdt=0,3",1,2019
Collaborative Evolutionary Reinforcement Learning,105,icml,23,3,2023-06-17 03:10:20.740000,https://github.com/intelai/cerl,71,Collaborative evolutionary reinforcement learning,"https://scholar.google.com/scholar?cluster=17431562445096471732&hl=en&as_sdt=0,43",12,2019
EMI: Exploration with Mutual Information,83,icml,11,0,2023-06-17 03:10:20.958000,https://github.com/snu-mllab/EMI,32,Emi: Exploration with mutual information,"https://scholar.google.com/scholar?cluster=13544760374723251277&hl=en&as_sdt=0,19",5,2019
FloWaveNet : A Generative Flow for Raw Audio,174,icml,113,4,2023-06-17 03:10:21.202000,https://github.com/ksw0306/FloWaveNet,494,FloWaveNet: A generative flow for raw audio,"https://scholar.google.com/scholar?cluster=6708907651291228140&hl=en&as_sdt=0,34",43,2019
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables,69,icml,40,4,2023-06-17 03:10:21.418000,https://github.com/fhkingma/bitswap,239,Bit-swap: Recursive bits-back coding for lossless compression with hierarchical latent variables,"https://scholar.google.com/scholar?cluster=12443881008782599419&hl=en&as_sdt=0,5",9,2019
CompILE: Compositional Imitation Learning and Execution,88,icml,33,0,2023-06-17 03:10:21.633000,https://github.com/tkipf/compile,104,Compile: Compositional imitation learning and execution,"https://scholar.google.com/scholar?cluster=12302759254570528216&hl=en&as_sdt=0,22",5,2019
Fair k-Center Clustering for Data Summarization,138,icml,4,0,2023-06-17 03:10:21.849000,https://github.com/matthklein/fair_k_center_clustering,10,Fair k-center clustering for data summarization,"https://scholar.google.com/scholar?cluster=10384783714256817355&hl=en&as_sdt=0,5",3,2019
Guarantees for Spectral Clustering with Fairness Constraints,122,icml,1,0,2023-06-17 03:10:22.065000,https://github.com/matthklein/fair_spectral_clustering,7,Guarantees for spectral clustering with fairness constraints,"https://scholar.google.com/scholar?cluster=10455657164331034065&hl=en&as_sdt=0,5",2,2019
POPQORN: Quantifying Robustness of Recurrent Neural Networks,86,icml,11,0,2023-06-17 03:10:22.280000,https://github.com/ZhaoyangLyu/POPQORN,45,POPQORN: Quantifying robustness of recurrent neural networks,"https://scholar.google.com/scholar?cluster=2942353004594500868&hl=en&as_sdt=0,5",5,2019
Robust Learning from Untrusted Sources,61,icml,4,0,2023-06-17 03:10:22.495000,https://github.com/NikolaKon1994/Robust-Learning-from-Untrusted-Sources,15,Robust learning from untrusted sources,"https://scholar.google.com/scholar?cluster=4366540847036601471&hl=en&as_sdt=0,5",2,2019
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement,128,icml,6,0,2023-06-17 03:10:22.711000,https://github.com/wouterkool/stochastic-beam-search,90,Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement,"https://scholar.google.com/scholar?cluster=13121847178128779153&hl=en&as_sdt=0,33",7,2019
Loss Landscapes of Regularized Linear Autoencoders,70,icml,12,2,2023-06-17 03:10:22.926000,https://github.com/danielkunin/Regularized-Linear-Autoencoders,139,Loss landscapes of regularized linear autoencoders,"https://scholar.google.com/scholar?cluster=15048938764743692524&hl=en&as_sdt=0,47",8,2019
A Large-Scale Study on Regularization and Normalization in GANs,174,icml,322,16,2023-06-17 03:10:23.141000,https://github.com/google/compare_gan,1814,A large-scale study on regularization and normalization in GANs,"https://scholar.google.com/scholar?cluster=2102263768032678612&hl=en&as_sdt=0,5",52,2019
Characterizing Well-Behaved vs. Pathological Deep Neural Networks,14,icml,1,2,2023-06-17 03:10:23.356000,https://github.com/alabatie/moments-dnns,5,Characterizing well-behaved vs. pathological deep neural networks,"https://scholar.google.com/scholar?cluster=3271469999043438586&hl=en&as_sdt=0,40",3,2019
Self-Attention Graph Pooling,846,icml,78,11,2023-06-17 03:10:23.570000,https://github.com/inyeoplee77/SAGPool,324,Self-attention graph pooling,"https://scholar.google.com/scholar?cluster=8950252210828065007&hl=en&as_sdt=0,10",8,2019
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks,777,icml,85,9,2023-06-17 03:10:23.785000,https://github.com/juho-lee/set_transformer,449,Set transformer: A framework for attention-based permutation-invariant neural networks,"https://scholar.google.com/scholar?cluster=564620061424738263&hl=en&as_sdt=0,34",13,2019
Robust Inference via Generative Classifiers for Handling Noisy Labels,93,icml,5,1,2023-06-17 03:10:23.999000,https://github.com/pokaxpoka/RoGNoisyLabel,29,Robust inference via generative classifiers for handling noisy labels,"https://scholar.google.com/scholar?cluster=14567604075585438767&hl=en&as_sdt=0,43",3,2019
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group,155,icml,19,1,2023-06-17 03:10:24.214000,https://github.com/Lezcano/expRNN,116,Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group,"https://scholar.google.com/scholar?cluster=17536814525953471769&hl=en&as_sdt=0,5",6,2019
Are Generative Classifiers More Robust to Adversarial Attacks?,85,icml,9,0,2023-06-17 03:10:24.429000,https://github.com/deepgenerativeclassifier/DeepBayes,22,Are generative classifiers more robust to adversarial attacks?,"https://scholar.google.com/scholar?cluster=10770378244624939531&hl=en&as_sdt=0,45",2,2019
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning,97,icml,24,3,2023-06-17 03:10:24.645000,https://github.com/likesiwell/LGM-Net,84,LGM-Net: Learning to generate matching networks for few-shot learning,"https://scholar.google.com/scholar?cluster=17373853660485197406&hl=en&as_sdt=0,5",4,2019
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks,217,icml,14,0,2023-06-17 03:10:24.859000,https://github.com/Cold-Winter/Nattack,46,Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks,"https://scholar.google.com/scholar?cluster=1133340624710172210&hl=en&as_sdt=0,5",5,2019
Bayesian Joint Spike-and-Slab Graphical Lasso,20,icml,2,0,2023-06-17 03:10:25.075000,https://github.com/richardli/SSJGL,7,Bayesian joint spike-and-slab graphical lasso,"https://scholar.google.com/scholar?cluster=11980207298770096957&hl=en&as_sdt=0,48",4,2019
Adversarial camera stickers: A physical camera-based attack on deep learning systems,115,icml,2,0,2023-06-17 03:10:25.290000,https://github.com/yoheikikuta/adversarial-camera-stickers,8,Adversarial camera stickers: A physical camera-based attack on deep learning systems,"https://scholar.google.com/scholar?cluster=8454184380086098103&hl=en&as_sdt=0,33",3,2019
Feature-Critic Networks for Heterogeneous Domain Generalization,200,icml,10,9,2023-06-17 03:10:25.505000,https://github.com/liyiying/Feature_Critic,42,Feature-critic networks for heterogeneous domain generalization,"https://scholar.google.com/scholar?cluster=15160705294700481017&hl=en&as_sdt=0,33",5,2019
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations,45,icml,6,0,2023-06-17 03:10:25.720000,https://github.com/yorkerlin/VB-MixEF,15,Fast and simple natural-gradient variational inference with mixture of exponential-family approximations,"https://scholar.google.com/scholar?cluster=9800018690635650774&hl=en&as_sdt=0,5",4,2019
Acceleration of SVRG and Katyusha X by Inexact Preconditioning,7,icml,3,0,2023-06-17 03:10:25.934000,https://github.com/uclaopt/IPSVRG,8,Acceleration of svrg and katyusha x by inexact preconditioning,"https://scholar.google.com/scholar?cluster=13059368819279986289&hl=en&as_sdt=0,45",5,2019
Rao-Blackwellized Stochastic Gradients for Discrete Distributions,33,icml,3,0,2023-06-17 03:10:26.161000,https://github.com/Runjing-Liu120/RaoBlackwellizedSGD,22,Rao-Blackwellized stochastic gradients for discrete distributions,"https://scholar.google.com/scholar?cluster=12116217648667930393&hl=en&as_sdt=0,23",2,2019
Understanding and Accelerating Particle-Based Variational Inference,75,icml,5,0,2023-06-17 03:10:26.375000,https://github.com/chang-ml-thu/AWGF,17,Understanding and accelerating particle-based variational inference,"https://scholar.google.com/scholar?cluster=7410249710967287826&hl=en&as_sdt=0,11",2,2019
Understanding MCMC Dynamics as Flows on the Wasserstein Space,21,icml,4,0,2023-06-17 03:10:26.592000,https://github.com/chang-ml-thu/FGH-flow,11,Understanding mcmc dynamics as flows on the wasserstein space,"https://scholar.google.com/scholar?cluster=16148000850438563191&hl=en&as_sdt=0,14",3,2019
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions,98,icml,5,1,2023-06-17 03:10:26.808000,https://github.com/aliutkus/swf,14,Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions,"https://scholar.google.com/scholar?cluster=7685202431169756099&hl=en&as_sdt=0,5",6,2019
CoT: Cooperative Training for Generative Modeling of Discrete Data,24,icml,28,1,2023-06-17 03:10:27.023000,https://github.com/desire2020/Cooperative-Training,75,Cot: Cooperative training for generative modeling of discrete data,"https://scholar.google.com/scholar?cluster=4231322493080735140&hl=en&as_sdt=0,10",11,2019
High-Fidelity Image Generation With Fewer Labels,145,icml,322,16,2023-06-17 03:10:27.237000,https://github.com/google/compare_gan,1814,High-fidelity image generation with fewer labels,"https://scholar.google.com/scholar?cluster=13622749687496052538&hl=en&as_sdt=0,11",52,2019
Variational Implicit Processes,57,icml,3,0,2023-06-17 03:10:27.452000,https://github.com/LaurantChao/VIP,8,Variational implicit processes,"https://scholar.google.com/scholar?cluster=11479270094313825180&hl=en&as_sdt=0,6",1,2019
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE,110,icml,15,0,2023-06-17 03:10:27.667000,https://github.com/microsoft/EDDI,37,Eddi: Efficient dynamic discovery of high-value information with partial vae,"https://scholar.google.com/scholar?cluster=7932877212524867960&hl=en&as_sdt=0,5",8,2019
Guided evolutionary strategies: augmenting random search with surrogate gradients,79,icml,25,2,2023-06-17 03:10:27.882000,https://github.com/brain-research/guided-evolutionary-strategies,262,Guided evolutionary strategies: Augmenting random search with surrogate gradients,"https://scholar.google.com/scholar?cluster=13097058951649931158&hl=en&as_sdt=0,5",15,2019
Adversarial Generation of Time-Frequency Features with application in audio synthesis,70,icml,13,5,2023-06-17 03:10:28.096000,https://github.com/tifgan/stftGAN,105,Adversarial generation of time-frequency features with application in audio synthesis,"https://scholar.google.com/scholar?cluster=7293234438017145749&hl=en&as_sdt=0,5",7,2019
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models,12,icml,4,0,2023-06-17 03:10:28.311000,https://github.com/kasparmartens/c-GPLVM,24,Decomposing feature-level variation with covariate Gaussian process latent variable models,"https://scholar.google.com/scholar?cluster=3291712378520398367&hl=en&as_sdt=0,15",2,2019
Disentangling Disentanglement in Variational Autoencoders,232,icml,13,0,2023-06-17 03:10:28.525000,https://github.com/iffsid/disentangling-disentanglement,86,Disentangling disentanglement in variational autoencoders,"https://scholar.google.com/scholar?cluster=4865252587822770331&hl=en&as_sdt=0,5",15,2019
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems,18,icml,18,0,2023-06-17 03:10:28.739000,https://github.com/Microsoft/vi-hds,45,Efficient amortised Bayesian inference for hierarchical and nonlinear dynamical systems,"https://scholar.google.com/scholar?cluster=1247732699292692241&hl=en&as_sdt=0,44",8,2019
Toward Controlling Discrimination in Online Ad Auctions,48,icml,4,0,2023-06-17 03:10:28.956000,https://github.com/AnayMehrotra/Fair-Online-Advertising,2,Toward controlling discrimination in online ad auctions,"https://scholar.google.com/scholar?cluster=3881350113786532991&hl=en&as_sdt=0,43",2,2019
Imputing Missing Events in Continuous-Time Event Streams,31,icml,17,2,2023-06-17 03:10:29.192000,https://github.com/HMEIatJHU/neural-hawkes-particle-smoothing,41,Imputing missing events in continuous-time event streams,"https://scholar.google.com/scholar?cluster=8012453208848277577&hl=en&as_sdt=0,14",4,2019
"Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks",125,icml,28,12,2023-06-17 03:10:29.407000,https://github.com/psg-mit/Ithemal,139,"Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks","https://scholar.google.com/scholar?cluster=6452183013544894818&hl=en&as_sdt=0,33",14,2019
On Dropout and Nuclear Norm Regularization,19,icml,2,0,2023-06-17 03:10:29.621000,https://github.com/r3831/dln_dropout,3,On dropout and nuclear norm regularization,"https://scholar.google.com/scholar?cluster=2540515501706995243&hl=en&as_sdt=0,40",2,2019
Flat Metric Minimization with Applications in Generative Modeling,3,icml,4,0,2023-06-17 03:10:29.836000,https://github.com/moellenh/flatgan,18,Flat metric minimization with applications in generative modeling,"https://scholar.google.com/scholar?cluster=16621113036066180234&hl=en&as_sdt=0,33",2,2019
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization,113,icml,14,0,2023-06-17 03:10:30.050000,https://github.com/snu-mllab/parsimonious-blackbox-attack,35,Parsimonious black-box adversarial attacks via efficient combinatorial optimization,"https://scholar.google.com/scholar?cluster=16009538798728740698&hl=en&as_sdt=0,5",6,2019
Relational Pooling for Graph Representations,185,icml,7,0,2023-06-17 03:10:30.265000,https://github.com/PurdueMINDS/RelationalPooling,34,Relational pooling for graph representations,"https://scholar.google.com/scholar?cluster=6145744994249893945&hl=en&as_sdt=0,31",6,2019
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning,81,icml,6,0,2023-06-17 03:10:30.481000,https://github.com/pfnet-research/hyperbolic_wrapped_distribution,28,A wrapped normal distribution on hyperbolic space for gradient-based learning,"https://scholar.google.com/scholar?cluster=11277639546038701066&hl=en&as_sdt=0,47",22,2019
Dropout as a Structured Shrinkage Prior,39,icml,5,0,2023-06-17 03:10:30.697000,https://github.com/enalisnick/dropout_icml2019,8,Dropout as a structured shrinkage prior,"https://scholar.google.com/scholar?cluster=16208195687877220296&hl=en&as_sdt=0,44",1,2019
Zero-Shot Knowledge Distillation in Deep Networks,182,icml,7,0,2023-06-17 03:10:30.910000,https://github.com/vcl-iisc/ZSKD,59,Zero-shot knowledge distillation in deep networks,"https://scholar.google.com/scholar?cluster=6513271489867205724&hl=en&as_sdt=0,23",7,2019
Safe Grid Search with Optimal Complexity,41,icml,3,0,2023-06-17 03:10:31.127000,https://github.com/EugeneNdiaye/safe_grid_search,7,Safe grid search with optimal complexity,"https://scholar.google.com/scholar?cluster=1378644094816844028&hl=en&as_sdt=0,5",5,2019
Rotation Invariant Householder Parameterization for Bayesian PCA,9,icml,3,10,2023-06-17 03:10:31.343000,https://github.com/RSNirwan/HouseholderBPCA,13,Rotation invariant householder parameterization for Bayesian PCA,"https://scholar.google.com/scholar?cluster=6089302904183911614&hl=en&as_sdt=0,41",5,2019
Training Neural Networks with Local Error Signals,169,icml,34,3,2023-06-17 03:10:31.558000,https://github.com/anokland/local-loss,150,Training neural networks with local error signals,"https://scholar.google.com/scholar?cluster=11332176056919584070&hl=en&as_sdt=0,5",10,2019
Remember and Forget for Experience Replay,80,icml,44,6,2023-06-17 03:10:31.777000,https://github.com/cselab/smarties,103,Remember and forget for experience replay,"https://scholar.google.com/scholar?cluster=13050806613216384530&hl=en&as_sdt=0,5",11,2019
Learning to Infer Program Sketches,92,icml,10,6,2023-06-17 03:10:31.992000,https://github.com/mtensor/neural_sketch,21,Learning to infer program sketches,"https://scholar.google.com/scholar?cluster=17303764643585588375&hl=en&as_sdt=0,23",8,2019
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models,118,icml,10,0,2023-06-17 03:10:32.207000,https://github.com/clinicalml/gumbel-max-scm,39,Counterfactual off-policy evaluation with gumbel-max structural causal models,"https://scholar.google.com/scholar?cluster=3302653893277553179&hl=en&as_sdt=0,26",19,2019
Orthogonal Random Forest for Causal Inference,79,icml,614,301,2023-06-17 03:10:32.422000,https://github.com/Microsoft/EconML,3004,Orthogonal random forest for causal inference,"https://scholar.google.com/scholar?cluster=1871181716543524277&hl=en&as_sdt=0,5",70,2019
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding,8,icml,2,0,2023-06-17 03:10:32.638000,https://github.com/Muhammad-Osama/Inferring-Heterogeneous-Causal-Effects-in-Presence-of-Spatial-Confounding,1,Inferring heterogeneous causal effects in presence of spatial confounding,"https://scholar.google.com/scholar?cluster=14041258610340953811&hl=en&as_sdt=0,5",0,2019
Improving Adversarial Robustness via Promoting Ensemble Diversity,348,icml,13,2,2023-06-17 03:10:32.853000,https://github.com/P2333/Adaptive-Diversity-Promoting,60,Improving adversarial robustness via promoting ensemble diversity,"https://scholar.google.com/scholar?cluster=16568032932303177237&hl=en&as_sdt=0,33",3,2019
Nonparametric Bayesian Deep Networks with Local Competition,30,icml,1,0,2023-06-17 03:10:33.067000,https://github.com/konpanousis/SB-LWTA,6,Nonparametric Bayesian deep networks with local competition,"https://scholar.google.com/scholar?cluster=6949349876007421452&hl=en&as_sdt=0,5",1,2019
Deep Residual Output Layers for Neural Language Generation,7,icml,3,0,2023-06-17 03:10:33.282000,https://github.com/idiap/drill,10,Deep residual output layers for neural language generation,"https://scholar.google.com/scholar?cluster=6336276005436023906&hl=en&as_sdt=0,1",8,2019
Self-Supervised Exploration via Disagreement,272,icml,23,3,2023-06-17 03:10:33.496000,https://github.com/pathak22/exploration-by-disagreement,120,Self-supervised exploration via disagreement,"https://scholar.google.com/scholar?cluster=13780996231531586358&hl=en&as_sdt=0,47",4,2019
Domain Agnostic Learning with Disentangled Representations,194,icml,28,3,2023-06-17 03:10:33.710000,https://github.com/VisionLearningGroup/DAL,133,Domain agnostic learning with disentangled representations,"https://scholar.google.com/scholar?cluster=10085135045247935679&hl=en&as_sdt=0,33",8,2019
Temporal Gaussian Mixture Layer for Videos,77,icml,14,5,2023-06-17 03:10:33.924000,https://github.com/piergiaj/tgm-icml19,99,Temporal gaussian mixture layer for videos,"https://scholar.google.com/scholar?cluster=7515216755463628280&hl=en&as_sdt=0,47",5,2019
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss,343,icml,95,8,2023-06-17 03:10:34.139000,https://github.com/liusongxiang/StarGAN-Voice-Conversion,460,Autovc: Zero-shot voice style transfer with only autoencoder loss,"https://scholar.google.com/scholar?cluster=16861313448156905141&hl=en&as_sdt=0,33",20,2019
On the Spectral Bias of Neural Networks,653,icml,18,0,2023-06-17 03:10:34.355000,https://github.com/nasimrahaman/SpectralBias,87,On the spectral bias of neural networks,"https://scholar.google.com/scholar?cluster=6023723620228240592&hl=en&as_sdt=0,5",5,2019
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables,487,icml,116,10,2023-06-17 03:10:34.571000,https://github.com/katerakelly/oyster,422,Efficient off-policy meta-reinforcement learning via probabilistic context variables,"https://scholar.google.com/scholar?cluster=15379570585451726919&hl=en&as_sdt=0,31",22,2019
Topological Data Analysis of Decision Boundaries with Application to Model Selection,45,icml,5,1,2023-06-17 03:10:34.786000,https://github.com/nrkarthikeyan/topology-decision-boundaries,25,Topological data analysis of decision boundaries with application to model selection,"https://scholar.google.com/scholar?cluster=16310684424372533537&hl=en&as_sdt=0,47",3,2019
Do ImageNet Classifiers Generalize to ImageNet?,1011,icml,19,3,2023-06-17 03:10:35.002000,https://github.com/modestyachts/ImageNetV2,200,Do imagenet classifiers generalize to imagenet?,"https://scholar.google.com/scholar?cluster=9642974458829870490&hl=en&as_sdt=0,5",9,2019
Separating value functions across time-scales,20,icml,8,1,2023-06-17 03:10:35.217000,https://github.com/facebookresearch/td-delta,17,Separating value functions across time-scales,"https://scholar.google.com/scholar?cluster=4770640199000017982&hl=en&as_sdt=0,23",5,2019
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples,162,icml,12,4,2023-06-17 03:10:35.432000,https://github.com/yk/icml19_public,21,The odds are odd: A statistical test for detecting adversarial examples,"https://scholar.google.com/scholar?cluster=6673355422445965167&hl=en&as_sdt=0,10",2,2019
A Contrastive Divergence for Combining Variational Inference and MCMC,69,icml,7,0,2023-06-17 03:10:35.647000,https://github.com/franrruiz/vcd_divergence,27,A contrastive divergence for combining variational inference and mcmc,"https://scholar.google.com/scholar?cluster=10765853948406678619&hl=en&as_sdt=0,43",5,2019
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers,243,icml,18,4,2023-06-17 03:10:35.863000,https://github.com/uclaopt/Provable_Plug_and_Play,60,Plug-and-play methods provably converge with properly trained denoisers,"https://scholar.google.com/scholar?cluster=11121192984446474149&hl=en&as_sdt=0,5",6,2019
Deep Gaussian Processes with Importance-Weighted Variational Inference,43,icml,4,1,2023-06-17 03:10:36.078000,https://github.com/hughsalimbeni/DGPs_with_IWVI,36,Deep Gaussian processes with importance-weighted variational inference,"https://scholar.google.com/scholar?cluster=17591045211502754804&hl=en&as_sdt=0,11",4,2019
Exploration Conscious Reinforcement Learning Revisited,11,icml,4,0,2023-06-17 03:10:36.293000,https://github.com/shanlior/ExplorationConsciousRL,6,Exploration conscious reinforcement learning revisited,"https://scholar.google.com/scholar?cluster=2069086734091208368&hl=en&as_sdt=0,5",2,2019
Mixture Models for Diverse Machine Translation: Tricks of the Trade,101,icml,5878,1031,2023-06-17 03:10:36.509000,https://github.com/pytorch/fairseq,26482,Mixture models for diverse machine translation: Tricks of the trade,"https://scholar.google.com/scholar?cluster=10713606322116851955&hl=en&as_sdt=0,50",411,2019
Replica Conditional Sequential Monte Carlo,2,icml,1,0,2023-06-17 03:10:36.723000,https://github.com/ayshestopaloff/replicacsmc,2,Replica Conditional Sequential Monte Carlo,"https://scholar.google.com/scholar?cluster=8937563905514647283&hl=en&as_sdt=0,36",0,2019
Scalable Training of Inference Networks for Gaussian-Process Models,19,icml,4,1,2023-06-17 03:10:36.938000,https://github.com/thjashin/gp-infer-net,41,Scalable training of inference networks for gaussian-process models,"https://scholar.google.com/scholar?cluster=18315311533765480343&hl=en&as_sdt=0,5",4,2019
Model-Based Active Exploration,157,icml,16,1,2023-06-17 03:10:37.167000,https://github.com/nnaisense/max,72,Model-based active exploration,"https://scholar.google.com/scholar?cluster=4949040749673510686&hl=en&as_sdt=0,5",5,2019
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension,127,icml,6,0,2023-06-17 03:10:37.382000,https://github.com/facebookresearch/AdversarialAndDimensionality,16,First-order adversarial vulnerability of neural networks and input dimension,"https://scholar.google.com/scholar?cluster=577929050796401765&hl=en&as_sdt=0,36",4,2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation,42,icml,3,0,2023-06-17 03:10:37.597000,https://github.com/singlasahil14/CASO,13,Understanding impacts of high-order loss approximations and features in deep learning interpretation,"https://scholar.google.com/scholar?cluster=17624808507201697872&hl=en&as_sdt=0,22",3,2019
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects,90,icml,12,1,2023-06-17 03:10:37.814000,https://github.com/EdwardSmith1884/GEOMetrics,117,Geometrics: Exploiting geometric structure for graph-encoded objects,"https://scholar.google.com/scholar?cluster=15300382945837912303&hl=en&as_sdt=0,5",9,2019
The Evolved Transformer,420,icml,3290,589,2023-06-17 03:10:38.029000,https://github.com/tensorflow/tensor2tensor,13764,The evolved transformer,"https://scholar.google.com/scholar?cluster=12069106626021161148&hl=en&as_sdt=0,38",461,2019
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning,540,icml,15,3,2023-06-17 03:10:38.244000,https://github.com/Sonkyunghwan/QTRAN,64,Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=8081563128599106489&hl=en&as_sdt=0,44",1,2019
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective,46,icml,3,0,2023-06-17 03:10:38.461000,https://github.com/zhao-song/Softmax-DQN,6,Revisiting the softmax bellman operator: New benefits and new perspective,"https://scholar.google.com/scholar?cluster=12009633864988483522&hl=en&as_sdt=0,39",1,2019
MASS: Masked Sequence to Sequence Pre-training for Language Generation,910,icml,209,67,2023-06-17 03:10:38.676000,https://github.com/microsoft/MASS,1103,Mass: Masked sequence to sequence pre-training for language generation,"https://scholar.google.com/scholar?cluster=9265562426073523323&hl=en&as_sdt=0,26",37,2019
Compressing Gradient Optimizers via Count-Sketches,29,icml,13,0,2023-06-17 03:10:38.891000,https://github.com/rdspring1/Count-Sketch-Optimizers,26,Compressing gradient optimizers via count-sketches,"https://scholar.google.com/scholar?cluster=1104222702149426557&hl=en&as_sdt=0,5",4,2019
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning,192,icml,25,1,2023-06-17 03:10:39.107000,https://github.com/AsaCooperStickland/Bert-n-Pals,74,Bert and pals: Projected attention layers for efficient adaptation in multi-task learning,"https://scholar.google.com/scholar?cluster=3136454913064441910&hl=en&as_sdt=0,38",3,2019
Provably Efficient Imitation Learning from Observation Alone,82,icml,5,0,2023-06-17 03:10:39.322000,https://github.com/wensun/Imitation-Learning-from-Observation,20,Provably efficient imitation learning from observation alone,"https://scholar.google.com/scholar?cluster=12068954688266237988&hl=en&as_sdt=0,5",3,2019
Hyperbolic Disk Embeddings for Directed Acyclic Graphs,41,icml,5,0,2023-06-17 03:10:39.537000,https://github.com/lapras-inc/disk-embedding,17,Hyperbolic disk embeddings for directed acyclic graphs,"https://scholar.google.com/scholar?cluster=15999788633415414766&hl=en&as_sdt=0,34",18,2019
Equivariant Transformer Networks,67,icml,7,1,2023-06-17 03:10:39.760000,https://github.com/stanford-futuredata/equivariant-transformers,82,Equivariant transformer networks,"https://scholar.google.com/scholar?cluster=740882376854558881&hl=en&as_sdt=0,36",10,2019
Correlated Variational Auto-Encoders,19,icml,4,0,2023-06-17 03:10:39.976000,https://github.com/datang1992/Correlated-VAEs,14,Correlated variational auto-encoders,"https://scholar.google.com/scholar?cluster=14520356175099829641&hl=en&as_sdt=0,33",5,2019
The Variational Predictive Natural Gradient,3,icml,1,0,2023-06-17 03:10:40.191000,https://github.com/datang1992/VPNG,8,The variational predictive natural gradient,"https://scholar.google.com/scholar?cluster=6073859204913275725&hl=en&as_sdt=0,47",2,2019
Adaptive Neural Trees,151,icml,22,2,2023-06-17 03:10:40.406000,https://github.com/rtanno21609/AdaptiveNeuralTrees,140,Adaptive neural trees,"https://scholar.google.com/scholar?cluster=10252139245277017232&hl=en&as_sdt=0,20",8,2019
Combating Label Noise in Deep Learning using Abstention,146,icml,9,6,2023-06-17 03:10:40.621000,https://github.com/thulas/dac-label-noise,56,Combating label noise in deep learning using abstention,"https://scholar.google.com/scholar?cluster=13352196764325122860&hl=en&as_sdt=0,5",5,2019
ELF OpenGo: an analysis and open reimplementation of AlphaZero,101,icml,577,44,2023-06-17 03:10:40.836000,https://github.com/pytorch/ELF,3316,Elf opengo: An analysis and open reimplementation of alphazero,"https://scholar.google.com/scholar?cluster=9736512126040760893&hl=en&as_sdt=0,5",191,2019
Metropolis-Hastings Generative Adversarial Networks,85,icml,24,4,2023-06-17 03:10:41.051000,https://github.com/uber-research/metropolis-hastings-gans,112,Metropolis-hastings generative adversarial networks,"https://scholar.google.com/scholar?cluster=18080915212804537296&hl=en&as_sdt=0,26",7,2019
Model Comparison for Semantic Grouping,1,icml,4,0,2023-06-17 03:10:41.266000,https://github.com/Babylonpartners/MCSG,8,Model comparison for semantic grouping,"https://scholar.google.com/scholar?cluster=18345833118099808380&hl=en&as_sdt=0,5",10,2019
Manifold Mixup: Better Representations by Interpolating Hidden States,889,icml,65,8,2023-06-17 03:10:41.482000,https://github.com/vikasverma1077/manifold_mixup,457,Manifold mixup: Better representations by interpolating hidden states,"https://scholar.google.com/scholar?cluster=5005853392111011711&hl=en&as_sdt=0,15",12,2019
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation,60,icml,6,2,2023-06-17 03:10:41.697000,https://github.com/RuohanW/RED,28,Random expert distillation: Imitation learning via expert policy support estimation,"https://scholar.google.com/scholar?cluster=2838461363780817206&hl=en&as_sdt=0,44",2,2019
Improving Neural Language Modeling via Adversarial Training,93,icml,3,3,2023-06-17 03:10:41.913000,https://github.com/ChengyueGongR/advsoft,40,Improving neural language modeling via adversarial training,"https://scholar.google.com/scholar?cluster=13673209609848344447&hl=en&as_sdt=0,39",3,2019
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis,84,icml,18,1,2023-06-17 03:10:42.127000,https://github.com/alecwangcq/EigenDamage-Pytorch,108,Eigendamage: Structured pruning in the kronecker-factored eigenbasis,"https://scholar.google.com/scholar?cluster=15048467937573583684&hl=en&as_sdt=0,48",5,2019
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions,66,icml,2,1,2023-06-17 03:10:42.342000,https://github.com/ustunb/ctfdist,10,Repairing without retraining: Avoiding disparate impact with counterfactual distributions,"https://scholar.google.com/scholar?cluster=16561986856093629430&hl=en&as_sdt=0,5",5,2019
Non-Monotonic Sequential Text Generation,105,icml,11,2,2023-06-17 03:10:42.557000,https://github.com/wellecks/nonmonotonic_text,73,Non-monotonic sequential text generation,"https://scholar.google.com/scholar?cluster=16018486661840997659&hl=en&as_sdt=0,5",7,2019
Learning deep kernels for exponential family densities,70,icml,2,0,2023-06-17 03:10:42.791000,https://github.com/kevin-w-li/deep-kexpfam,22,Learning deep kernels for exponential family densities,"https://scholar.google.com/scholar?cluster=18438114656627425154&hl=en&as_sdt=0,43",3,2019
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation,28,icml,1,0,2023-06-17 03:10:43.006000,https://github.com/SamuelWiqvist/PENs-and-ABC,5,Partially exchangeable networks and architectures for learning summary statistics in approximate Bayesian computation,"https://scholar.google.com/scholar?cluster=16942332521272083058&hl=en&as_sdt=0,44",5,2019
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations,197,icml,13,1,2023-06-17 03:10:43.223000,https://github.com/locuslab/projected_sinkhorn,86,Wasserstein adversarial examples via projected sinkhorn iterations,"https://scholar.google.com/scholar?cluster=4087808921541648707&hl=en&as_sdt=0,33",7,2019
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling,55,icml,5,1,2023-06-17 03:10:43.439000,https://github.com/wushanshan/L1AE,18,Learning a compressed sensing measurement matrix via gradient unrolling,"https://scholar.google.com/scholar?cluster=7047806265254435189&hl=en&as_sdt=0,5",4,2019
Simplifying Graph Convolutional Networks,2063,icml,146,1,2023-06-17 03:10:43.654000,https://github.com/Tiiiger/SGC,766,Simplifying graph convolutional networks,"https://scholar.google.com/scholar?cluster=17348071344751182786&hl=en&as_sdt=0,23",19,2019
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance,158,icml,5,0,2023-06-17 03:10:43.870000,https://github.com/xcgoner/icml2019_zeno,13,Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance,"https://scholar.google.com/scholar?cluster=10331500453771682409&hl=en&as_sdt=0,14",2,2019
Differentiable Linearized ADMM,53,icml,9,0,2023-06-17 03:10:44.085000,https://github.com/zzs1994/D-LADMM,27,Differentiable linearized ADMM,"https://scholar.google.com/scholar?cluster=7429496083508800871&hl=en&as_sdt=0,41",4,2019
Gromov-Wasserstein Learning for Graph Matching and Node Embedding,181,icml,17,0,2023-06-17 03:10:44.301000,https://github.com/HongtengXu/gwl,63,Gromov-wasserstein learning for graph matching and node embedding,"https://scholar.google.com/scholar?cluster=17323824579705471287&hl=en&as_sdt=0,10",5,2019
Supervised Hierarchical Clustering with Exponential Linkage,27,icml,6,0,2023-06-17 03:10:44.517000,https://github.com/iesl/expLinkage,9,Supervised hierarchical clustering with exponential linkage,"https://scholar.google.com/scholar?cluster=14591272843062718088&hl=en&as_sdt=0,5",12,2019
Learning to Prove Theorems via Interacting with Proof Assistants,79,icml,46,0,2023-06-17 03:10:44.731000,https://github.com/princeton-vl/CoqGym,319,Learning to prove theorems via interacting with proof assistants,"https://scholar.google.com/scholar?cluster=14925207938076962028&hl=en&as_sdt=0,4",17,2019
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation,145,icml,10,0,2023-06-17 03:10:44.946000,https://github.com/YyzHarry/ME-Net,51,Me-net: Towards effective adversarial robustness with matrix estimation,"https://scholar.google.com/scholar?cluster=15543482510654180189&hl=en&as_sdt=0,34",3,2019
Hierarchically Structured Meta-learning,197,icml,13,1,2023-06-17 03:10:45.197000,https://github.com/huaxiuyao/HSML,48,Hierarchically structured meta-learning,"https://scholar.google.com/scholar?cluster=3487980416117206371&hl=en&as_sdt=0,31",5,2019
Rademacher Complexity for Adversarially Robust Generalization,234,icml,1,0,2023-06-17 03:10:45.412000,https://github.com/dongyin92/adversarially-robust-generalization,9,Rademacher complexity for adversarially robust generalization,"https://scholar.google.com/scholar?cluster=3771850404643054723&hl=en&as_sdt=0,5",1,2019
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables,24,icml,10,0,2023-06-17 03:10:45.628000,https://github.com/ARM-gradient/ARSM,18,ARSM: Augment-REINFORCE-swap-merge estimator for gradient backpropagation through categorical variables,"https://scholar.google.com/scholar?cluster=18117321206953712314&hl=en&as_sdt=0,5",1,2019
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning,221,icml,12,4,2023-06-17 03:10:45.844000,https://github.com/istarjun/TapNet,52,Tapnet: Neural network augmented with task-adaptive projection for few-shot learning,"https://scholar.google.com/scholar?cluster=12575801957058912486&hl=en&as_sdt=0,5",3,2019
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation,110,icml,10,2,2023-06-17 03:10:46.060000,https://github.com/thuml/Deep-Embedded-Validation,58,Towards accurate model selection in deep unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=2565642679287912484&hl=en&as_sdt=0,39",3,2019
Position-aware Graph Neural Networks,386,icml,75,11,2023-06-17 03:10:46.276000,https://github.com/JiaxuanYou/P-GNN,367,Position-aware graph neural networks,"https://scholar.google.com/scholar?cluster=2886623965746954945&hl=en&as_sdt=0,5",15,2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks,277,icml,55,21,2023-06-17 03:10:46.492000,https://github.com/fishmoon1234/DAG-GNN,233,DAG-GNN: DAG structure learning with graph neural networks,"https://scholar.google.com/scholar?cluster=12962909633357312064&hl=en&as_sdt=0,34",8,2019
Multi-Agent Adversarial Inverse Reinforcement Learning,90,icml,26,6,2023-06-17 03:10:46.723000,https://github.com/ermongroup/MA-AIRL,153,Multi-agent adversarial inverse reinforcement learning,"https://scholar.google.com/scholar?cluster=13913946030309510400&hl=en&as_sdt=0,5",16,2019
Online Adaptive Principal Component Analysis and Its extensions,4,icml,1,0,2023-06-17 03:10:46.938000,https://github.com/yuanx270/online-adaptive-PCA,7,Online adaptive principal component analysis and its extensions,"https://scholar.google.com/scholar?cluster=11284462216308687300&hl=en&as_sdt=0,34",2,2019
Bayesian Nonparametric Federated Learning of Neural Networks,394,icml,30,2,2023-06-17 03:10:47.156000,https://github.com/IBM/probabilistic-federated-neural-matching,120,Bayesian nonparametric federated learning of neural networks,"https://scholar.google.com/scholar?cluster=14489502397862024393&hl=en&as_sdt=0,21",15,2019
Dirichlet Simplex Nest and Geometric Inference,5,icml,1,0,2023-06-17 03:10:47.371000,https://github.com/moonfolk/VLAD,3,Dirichlet simplex nest and geometric inference,"https://scholar.google.com/scholar?cluster=3107204927758089702&hl=en&as_sdt=0,36",1,2019
Making Convolutional Networks Shift-Invariant Again,669,icml,206,14,2023-06-17 03:10:47.587000,https://github.com/adobe/antialiased-cnns,1613,Making convolutional networks shift-invariant again,"https://scholar.google.com/scholar?cluster=6405795848737680233&hl=en&as_sdt=0,5",39,2019
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback,27,icml,2,0,2023-06-17 03:10:47.802000,https://github.com/zcc1307/warmcb_scripts,4,Warm-starting contextual bandits: Robustly combining supervised and bandit feedback,"https://scholar.google.com/scholar?cluster=13381714542277312288&hl=en&as_sdt=0,5",2,2019
Self-Attention Generative Adversarial Networks,3723,icml,173,17,2023-06-17 03:10:48.018000,https://github.com/brain-research/self-attention-gan,967,Self-attention generative adversarial networks,"https://scholar.google.com/scholar?cluster=7330853420568873733&hl=en&as_sdt=0,31",38,2019
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition,78,icml,14,2,2023-06-17 03:10:48.233000,https://github.com/latentgnn/LatentGNN-V1-PyTorch,74,Latentgnn: Learning efficient non-local relations for visual recognition,"https://scholar.google.com/scholar?cluster=7578360606999759452&hl=en&as_sdt=0,5",6,2019
Bridging Theory and Algorithm for Domain Adaptation,530,icml,27,5,2023-06-17 03:10:48.449000,https://github.com/thuml/MDD,121,Bridging theory and algorithm for domain adaptation,"https://scholar.google.com/scholar?cluster=12036658661059863941&hl=en&as_sdt=0,5",5,2019
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning,234,icml,18,5,2023-06-17 03:10:48.665000,https://github.com/sharadmv/parasol,67,Solar: Deep structured representations for model-based reinforcement learning,"https://scholar.google.com/scholar?cluster=3160286257401504607&hl=en&as_sdt=0,33",3,2019
Theoretically Principled Trade-off between Robustness and Accuracy,1779,icml,119,3,2023-06-17 03:10:48.880000,https://github.com/yaodongyu/TRADES,474,Theoretically principled trade-off between robustness and accuracy,"https://scholar.google.com/scholar?cluster=3311622924435738798&hl=en&as_sdt=0,5",10,2019
Interpreting Adversarially Trained Convolutional Neural Networks,120,icml,9,0,2023-06-17 03:10:49.095000,https://github.com/PKUAI26/AT-CNN,62,Interpreting adversarially trained convolutional neural networks,"https://scholar.google.com/scholar?cluster=6664229559742953811&hl=en&as_sdt=0,5",7,2019
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits,15,icml,3,0,2023-06-17 03:10:49.311000,https://github.com/martinjzhang/AMT,4,Adaptive monte carlo multiple testing via multi-armed bandits,"https://scholar.google.com/scholar?cluster=17419761528871683302&hl=en&as_sdt=0,44",0,2019
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning,73,icml,6,1,2023-06-17 03:10:49.527000,https://github.com/ruizhaogit/mep,21,Maximum entropy-regularized multi-goal reinforcement learning,"https://scholar.google.com/scholar?cluster=12004531622883216435&hl=en&as_sdt=0,5",3,2019
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization,9,icml,3,0,2023-06-17 03:10:49.742000,https://github.com/baojianzhou/graph-sto-iht,3,Stochastic iterative hard thresholding for graph-structured sparsity optimization,"https://scholar.google.com/scholar?cluster=4121937272467164287&hl=en&as_sdt=0,26",2,2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets,227,icml,10,4,2023-06-17 03:10:49.964000,https://github.com/zhuchen03/ConvexPolytopePosioning,28,Transferable clean-label poisoning attacks on deep neural nets,"https://scholar.google.com/scholar?cluster=457598797512585014&hl=en&as_sdt=0,5",3,2019
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects,197,icml,0,0,2023-06-17 03:10:50.205000,https://github.com/uuujf/SGDNoise,11,The anisotropic noise in stochastic gradient descent: Its behavior of escaping from sharp minima and regularization effects,"https://scholar.google.com/scholar?cluster=8530319537943237114&hl=en&as_sdt=0,5",2,2019
Latent Normalizing Flows for Discrete Sequences,103,icml,15,4,2023-06-17 03:10:50.420000,https://github.com/harvardnlp/TextFlow,113,Latent normalizing flows for discrete sequences,"https://scholar.google.com/scholar?cluster=14468956623112090674&hl=en&as_sdt=0,36",11,2019
Fast Context Adaptation via Meta-Learning,342,icml,39,1,2023-06-17 03:10:50.635000,https://github.com/lmzintgraf/cavia,126,Fast context adaptation via meta-learning,"https://scholar.google.com/scholar?cluster=731845317332872337&hl=en&as_sdt=0,36",6,2019
A distributional view on multi-objective policy optimization,51,icml,613,69,2023-06-17 03:56:43.582000,https://github.com/deepmind/dm_control,3200,A distributional view on multi-objective policy optimization,"https://scholar.google.com/scholar?cluster=8438162900583355554&hl=en&as_sdt=0,11",127,2020
An Optimistic Perspective on Offline Reinforcement Learning,366,icml,72,9,2023-06-17 03:56:43.785000,https://github.com/google-research/batch_rl,441,An optimistic perspective on offline reinforcement learning,"https://scholar.google.com/scholar?cluster=199235154784983919&hl=en&as_sdt=0,37",12,2020
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments,8,icml,0,0,2023-06-17 03:56:43.987000,https://github.com/teshnizi/LazyIter,7,Lazyiter: a fast algorithm for counting Markov equivalent DAGs and designing experiments,"https://scholar.google.com/scholar?cluster=11588857558630683059&hl=en&as_sdt=0,5",1,2020
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay,16,icml,0,0,2023-06-17 03:56:44.190000,https://github.com/Ralami1859/Restarted-BOCPD,2,Restarted Bayesian online change-point detector achieves optimal detection delay,"https://scholar.google.com/scholar?cluster=12357062813763301915&hl=en&as_sdt=0,5",2,2020
Structural Language Models of Code,82,icml,7,6,2023-06-17 03:56:44.392000,https://github.com/tech-srl/slm-code-generation,75,Structural language models of code,"https://scholar.google.com/scholar?cluster=12400277411486589122&hl=en&as_sdt=0,44",11,2020
LowFER: Low-rank Bilinear Pooling for Link Prediction,31,icml,5,0,2023-06-17 03:56:44.595000,https://github.com/suamin/LowFER,12,LowFER: Low-rank bilinear pooling for link prediction,"https://scholar.google.com/scholar?cluster=6369643568974944132&hl=en&as_sdt=0,5",0,2020
Discount Factor as a Regularizer in Reinforcement Learning,42,icml,2,1,2023-06-17 03:56:44.797000,https://github.com/ron-amit/Discount_as_Regularizer,5,Discount factor as a regularizer in reinforcement learning,"https://scholar.google.com/scholar?cluster=4222677586854479535&hl=en&as_sdt=0,33",2,2020
The Differentiable Cross-Entropy Method,45,icml,10,0,2023-06-17 03:56:44.998000,https://github.com/facebookresearch/dcem,118,The differentiable cross-entropy method,"https://scholar.google.com/scholar?cluster=5207717261153832790&hl=en&as_sdt=0,5",9,2020
Fairwashing explanations with off-manifold detergent,73,icml,3,0,2023-06-17 03:56:45.201000,https://github.com/fairwashing/fairwashing,10,Fairwashing explanations with off-manifold detergent,"https://scholar.google.com/scholar?cluster=869145400827496969&hl=en&as_sdt=0,14",2,2020
Online metric algorithms with untrusted predictions,96,icml,1,0,2023-06-17 03:56:45.403000,https://github.com/adampolak/mts-with-predictions,1,Online metric algorithms with untrusted predictions,"https://scholar.google.com/scholar?cluster=8779637967313325541&hl=en&as_sdt=0,24",4,2020
Invertible generative models for inverse problems: mitigating representation error and dataset bias,113,icml,14,3,2023-06-17 03:56:45.605000,https://github.com/CACTuS-AI/GlowIP,17,Invertible generative models for inverse problems: mitigating representation error and dataset bias,"https://scholar.google.com/scholar?cluster=18360186920065669378&hl=en&as_sdt=0,5",5,2020
Forecasting Sequential Data Using Consistent Koopman Autoencoders,78,icml,17,2,2023-06-17 03:56:45.807000,https://github.com/erichson/koopmanAE,45,Forecasting sequential data using consistent koopman autoencoders,"https://scholar.google.com/scholar?cluster=604581388291751037&hl=en&as_sdt=0,5",4,2020
Learning De-biased Representations with Biased Representations,178,icml,28,0,2023-06-17 03:56:46.008000,https://github.com/clovaai/rebias,152,Learning de-biased representations with biased representations,"https://scholar.google.com/scholar?cluster=2454950202861832490&hl=en&as_sdt=0,33",7,2020
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training,281,icml,1868,365,2023-06-17 03:56:46.210000,https://github.com/microsoft/unilm,12786,Unilmv2: Pseudo-masked language models for unified language model pre-training,"https://scholar.google.com/scholar?cluster=17252701423323416900&hl=en&as_sdt=0,5",260,2020
Option Discovery in the Absence of Rewards with Manifold Analysis,5,icml,0,0,2023-06-17 03:56:46.412000,https://github.com/amitaybar/Diffusion-options,0,Option discovery in the absence of rewards with manifold analysis,"https://scholar.google.com/scholar?cluster=5097986500723178583&hl=en&as_sdt=0,33",1,2020
Decoupled Greedy Learning of CNNs,74,icml,4,1,2023-06-17 03:56:46.614000,https://github.com/eugenium/DGL,24,Decoupled greedy learning of cnns,"https://scholar.google.com/scholar?cluster=984410843298404679&hl=en&as_sdt=0,41",7,2020
Efficient Policy Learning from Surrogate-Loss Classification Reductions,16,icml,1,0,2023-06-17 03:56:46.816000,https://github.com/CausalML/ESPRM,2,Efficient policy learning from surrogate-loss classification reductions,"https://scholar.google.com/scholar?cluster=17482295204063069180&hl=en&as_sdt=0,33",3,2020
Training Neural Networks for and by Interpolation,35,icml,5,0,2023-06-17 03:56:47.018000,https://github.com/oval-group/ali-g,22,Training neural networks for and by interpolation,"https://scholar.google.com/scholar?cluster=12646838748171851359&hl=en&as_sdt=0,26",3,2020
Implicit differentiation of Lasso-type models for hyperparameter optimization,50,icml,14,20,2023-06-17 03:56:47.220000,https://github.com/QB3/sparse-ho,37,Implicit differentiation of lasso-type models for hyperparameter optimization,"https://scholar.google.com/scholar?cluster=9364706080727749786&hl=en&as_sdt=0,5",6,2020
The Boomerang Sampler,32,icml,0,0,2023-06-17 03:56:47.421000,https://github.com/jbierkens/ICML-boomerang,7,The boomerang sampler,"https://scholar.google.com/scholar?cluster=8538965772361697464&hl=en&as_sdt=0,5",4,2020
Fast Differentiable Sorting and Ranking,132,icml,40,11,2023-06-17 03:56:47.623000,https://github.com/google-research/fast-soft-sort,483,Fast differentiable sorting and ranking,"https://scholar.google.com/scholar?cluster=1601300606865471199&hl=en&as_sdt=0,10",15,2020
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization?,12,icml,0,0,2023-06-17 03:56:47.826000,https://github.com/yanivbl6/BeyondSigProp,2,Beyond signal propagation: is feature diversity necessary in deep neural network initialization?,"https://scholar.google.com/scholar?cluster=12443428565734084047&hl=en&as_sdt=0,5",1,2020
Deep Coordination Graphs,130,icml,20,9,2023-06-17 03:56:48.027000,https://github.com/wendelinboehmer/dcg,65,Deep coordination graphs,"https://scholar.google.com/scholar?cluster=8113641514627174064&hl=en&as_sdt=0,23",5,2020
Lorentz Group Equivariant Neural Network for Particle Physics,97,icml,6,0,2023-06-17 03:56:48.229000,https://github.com/fizisist/LorentzGroupNetwork,40,Lorentz group equivariant neural network for particle physics,"https://scholar.google.com/scholar?cluster=354482020847877812&hl=en&as_sdt=0,33",5,2020
Proper Network Interpretability Helps Adversarial Robustness in Classification,49,icml,3,0,2023-06-17 03:56:48.431000,https://github.com/AkhilanB/Proper-Interpretability,11,Proper network interpretability helps adversarial robustness in classification,"https://scholar.google.com/scholar?cluster=9035074662025671292&hl=en&as_sdt=0,15",4,2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks,106,icml,3,5,2023-06-17 03:56:48.632000,https://github.com/Pehlevan-Group/NTK_Learning_Curves,3,Spectrum dependent learning curves in kernel regression and wide neural networks,"https://scholar.google.com/scholar?cluster=3712020461682803664&hl=en&as_sdt=0,5",4,2020
Latent Variable Modelling with Hyperbolic Normalizing Flows,40,icml,7,23,2023-06-17 03:56:48.834000,https://github.com/joeybose/HyperbolicNF,52,Latent variable modelling with hyperbolic normalizing flows,"https://scholar.google.com/scholar?cluster=16943766719750515886&hl=en&as_sdt=0,15",3,2020
Preference Modeling with Context-Dependent Salient Features,9,icml,0,0,2023-06-17 03:56:49.052000,https://github.com/Amandarg/salient_features,1,Preference modeling with context-dependent salient features,"https://scholar.google.com/scholar?cluster=14377795205947287878&hl=en&as_sdt=0,14",2,2020
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference,12,icml,0,0,2023-06-17 03:56:49.254000,https://github.com/vmasrani/tvo_all_in,0,All in the exponential family: Bregman duality in thermodynamic variational inference,"https://scholar.google.com/scholar?cluster=6653952944869299139&hl=en&as_sdt=0,47",0,2020
Estimating the Number and Effect Sizes of Non-null Hypotheses,8,icml,0,0,2023-06-17 03:56:49.455000,https://github.com/jenniferbrennan/CountingDiscoveries,1,Estimating the number and effect sizes of non-null hypotheses,"https://scholar.google.com/scholar?cluster=13761193891605574377&hl=en&as_sdt=0,44",1,2020
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation,83,icml,229,10,2023-06-17 03:56:49.658000,https://github.com/microsoft/tf-gnn-samples,877,Gnn-film: Graph neural networks with feature-wise linear modulation,"https://scholar.google.com/scholar?cluster=17006226546313472447&hl=en&as_sdt=0,1",35,2020
TaskNorm: Rethinking Batch Normalization for Meta-Learning,82,icml,22,1,2023-06-17 03:56:49.860000,https://github.com/cambridge-mlg/cnaps,152,Tasknorm: Rethinking batch normalization for meta-learning,"https://scholar.google.com/scholar?cluster=5780176448524951533&hl=en&as_sdt=0,5",11,2020
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences,78,icml,4,0,2023-06-17 03:56:50.083000,https://github.com/dsbrown1331/bayesianrex,11,Safe imitation learning via fast bayesian reward inference from preferences,"https://scholar.google.com/scholar?cluster=7057495303121096550&hl=en&as_sdt=0,5",3,2020
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models,21,icml,1,0,2023-06-17 03:56:50.284000,https://github.com/clinicalml/overparam,6,Empirical study of the benefits of overparameterization in learning latent variable models,"https://scholar.google.com/scholar?cluster=18021082651755132236&hl=en&as_sdt=0,5",2,2020
DeBayes: a Bayesian Method for Debiasing Network Embeddings,54,icml,1,1,2023-06-17 03:56:50.486000,https://github.com/aida-ugent/DeBayes,7,Debayes: a bayesian method for debiasing network embeddings,"https://scholar.google.com/scholar?cluster=12507703931590961178&hl=en&as_sdt=0,47",2,2020
Online Learned Continual Compression with Adaptive Quantization Modules,55,icml,5,0,2023-06-17 03:56:50.691000,https://github.com/pclucas14/adaptive-quantization-modules,26,Online learned continual compression with adaptive quantization modules,"https://scholar.google.com/scholar?cluster=4962059148023200241&hl=en&as_sdt=0,5",4,2020
Near-linear time Gaussian process optimization with adaptive batching and resparsification,19,icml,1,0,2023-06-17 03:56:50.893000,https://github.com/luigicarratino/batch-bkb,11,Near-linear time Gaussian process optimization with adaptive batching and resparsification,"https://scholar.google.com/scholar?cluster=9965392032053007731&hl=en&as_sdt=0,44",3,2020
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates,55,icml,19,0,2023-06-17 03:56:51.095000,https://github.com/jwcalder/GraphLearning,62,Poisson learning: Graph based semi-supervised learning at very low label rates,"https://scholar.google.com/scholar?cluster=11788739359346189749&hl=en&as_sdt=0,43",3,2020
"Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills",85,icml,0,0,2023-06-17 03:56:51.297000,https://github.com/imatge-upc/edl,3,"Explore, discover and learn: Unsupervised discovery of state-covering skills","https://scholar.google.com/scholar?cluster=6344383621952136699&hl=en&as_sdt=0,5",2,2020
Data preprocessing to mitigate bias: A maximum entropy based approach,29,icml,1,2,2023-06-17 03:56:51.499000,https://github.com/vijaykeswani/Fair-Max-Entropy-Distributions,8,Data preprocessing to mitigate bias: A maximum entropy based approach,"https://scholar.google.com/scholar?cluster=1389448522545210547&hl=en&as_sdt=0,10",3,2020
Concise Explanations of Neural Networks using Adversarial Training,40,icml,1,17,2023-06-17 03:56:51.701000,https://github.com/jfc43/advex,5,Concise explanations of neural networks using adversarial training,"https://scholar.google.com/scholar?cluster=13018632630820208929&hl=en&as_sdt=0,10",3,2020
Optimizing for the Future in Non-Stationary MDPs,48,icml,0,3,2023-06-17 03:56:51.903000,https://github.com/yashchandak/OptFuture_NSMDP,7,Optimizing for the future in non-stationary mdps,"https://scholar.google.com/scholar?cluster=2732891290707774950&hl=en&as_sdt=0,33",2,2020
Learning to Simulate and Design for Structural Engineering,27,icml,0,0,2023-06-17 03:56:52.105000,https://github.com/AutodeskAILab/LSDSE-Dataset,7,Learning to simulate and design for structural engineering,"https://scholar.google.com/scholar?cluster=3089482596592308925&hl=en&as_sdt=0,33",3,2020
Invariant Rationalization,119,icml,4,11,2023-06-17 03:56:52.306000,https://github.com/code-terminator/invariant_rationalization,43,Invariant rationalization,"https://scholar.google.com/scholar?cluster=2718521387879023599&hl=en&as_sdt=0,10",4,2020
Explainable and Discourse Topic-aware Neural Language Understanding,5,icml,2,4,2023-06-17 03:56:52.508000,https://github.com/YatinChaudhary/NCLM,9,Explainable and discourse topic-aware neural language understanding,"https://scholar.google.com/scholar?cluster=159060864795495099&hl=en&as_sdt=0,31",2,2020
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training,61,icml,13,0,2023-06-17 03:56:52.710000,https://github.com/TAMU-VITA/Self-PU,51,Self-pu: Self boosted and calibrated positive-unlabeled training,"https://scholar.google.com/scholar?cluster=10514971696768538295&hl=en&as_sdt=0,5",15,2020
Graph Optimal Transport for Cross-Domain Alignment,106,icml,21,3,2023-06-17 03:56:52.912000,https://github.com/LiqunChen0606/Graph-Optimal-Transport,131,Graph optimal transport for cross-domain alignment,"https://scholar.google.com/scholar?cluster=13506984443465445309&hl=en&as_sdt=0,5",6,2020
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization,141,icml,12,1,2023-06-17 03:56:53.114000,https://github.com/xiangning-chen/SmoothDARTS,70,Stabilizing differentiable architecture search via perturbation-based regularization,"https://scholar.google.com/scholar?cluster=16658085005261012709&hl=en&as_sdt=0,34",3,2020
Convolutional Kernel Networks for Graph-Structured Data,47,icml,9,2,2023-06-17 03:56:53.316000,https://github.com/claying/GCKN,47,Convolutional kernel networks for graph-structured data,"https://scholar.google.com/scholar?cluster=6544343038344215140&hl=en&as_sdt=0,14",5,2020
A Simple Framework for Contrastive Learning of Visual Representations,10491,icml,570,69,2023-06-17 03:56:53.522000,https://github.com/google-research/simclr,3562,A simple framework for contrastive learning of visual representations,"https://scholar.google.com/scholar?cluster=13219652991368821610&hl=en&as_sdt=0,23",46,2020
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search,66,icml,20,11,2023-06-17 03:56:53.723000,https://github.com/binghong-ml/retro_star,101,Retro*: learning retrosynthetic planning with neural guided A* search,"https://scholar.google.com/scholar?cluster=6946559653071134529&hl=en&as_sdt=0,5",4,2020
Differentiable Product Quantization for End-to-End Embedding Compression,37,icml,10,3,2023-06-17 03:56:53.924000,https://github.com/chentingpc/dpq_embedding_compression,52,Differentiable product quantization for end-to-end embedding compression,"https://scholar.google.com/scholar?cluster=15237200124504416658&hl=en&as_sdt=0,34",4,2020
VFlow: More Expressive Generative Flows with Variational Data Augmentation,46,icml,3,0,2023-06-17 03:56:54.126000,https://github.com/thu-ml/vflow,34,Vflow: More expressive generative flows with variational data augmentation,"https://scholar.google.com/scholar?cluster=3780987304943068813&hl=en&as_sdt=0,26",10,2020
Generative Pretraining From Pixels,1046,icml,362,13,2023-06-17 03:56:54.329000,https://github.com/openai/image-gpt,1909,Generative pretraining from pixels,"https://scholar.google.com/scholar?cluster=7981583694904172555&hl=en&as_sdt=0,5",81,2020
Simple and Deep Graph Convolutional Networks,828,icml,64,12,2023-06-17 03:56:54.531000,https://github.com/chennnM/GCNII,270,Simple and deep graph convolutional networks,"https://scholar.google.com/scholar?cluster=16283804483876681464&hl=en&as_sdt=0,18",6,2020
On Breaking Deep Generative Model-based Defenses and Beyond,5,icml,2,0,2023-06-17 03:56:54.734000,https://github.com/cyz-ai/attack_DGM,7,On breaking deep generative model-based defenses and beyond,"https://scholar.google.com/scholar?cluster=13887603208363837628&hl=en&as_sdt=0,39",2,2020
Automated Synthetic-to-Real Generalization,63,icml,4,3,2023-06-17 03:56:54.935000,https://github.com/NVlabs/ASG,30,Automated synthetic-to-real generalization,"https://scholar.google.com/scholar?cluster=14261788417891163581&hl=en&as_sdt=0,3",16,2020
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information,147,icml,35,7,2023-06-17 03:56:55.137000,https://github.com/Linear95/CLUB,226,Club: A contrastive log-ratio upper bound of mutual information,"https://scholar.google.com/scholar?cluster=384230567728582843&hl=en&as_sdt=0,43",7,2020
Streaming Coresets for Symmetric Tensor Factorization,9,icml,0,0,2023-06-17 03:56:55.339000,https://github.com/supratim05/Streaming-Coresets-for-Symmetric-Tensor-Factorization,0,Streaming coresets for symmetric tensor factorization,"https://scholar.google.com/scholar?cluster=17659645573901290217&hl=en&as_sdt=0,18",2,2020
Fair Generative Modeling via Weak Supervision,69,icml,7,6,2023-06-17 03:56:55.540000,https://github.com/ermongroup/fairgen,15,Fair generative modeling via weak supervision,"https://scholar.google.com/scholar?cluster=17083056249871731008&hl=en&as_sdt=0,36",4,2020
Distance Metric Learning with Joint Representation Diversification,7,icml,0,0,2023-06-17 03:56:55.742000,https://github.com/YangLin122/JRD,1,Distance metric learning with joint representation diversification,"https://scholar.google.com/scholar?cluster=1557397264873578069&hl=en&as_sdt=0,33",1,2020
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations,32,icml,2,0,2023-06-17 03:56:55.944000,https://github.com/chingyaoc/estimating-generalization,21,Estimating generalization under distribution shifts via domain-invariant representations,"https://scholar.google.com/scholar?cluster=2002502648003109319&hl=en&as_sdt=0,10",3,2020
Boosting Frank-Wolfe by Chasing Gradients,24,icml,2,0,2023-06-17 03:56:56.145000,https://github.com/cyrillewcombettes/boostfw,3,Boosting Frank-Wolfe by chasing gradients,"https://scholar.google.com/scholar?cluster=3076591881269921139&hl=en&as_sdt=0,5",2,2020
Learnable Group Transform For Time-Series,14,icml,4,0,2023-06-17 03:56:56.347000,https://github.com/Koldh/LearnableGroupTransform-TimeSeries,7,Learnable group transform for time-series,"https://scholar.google.com/scholar?cluster=11923042673090742544&hl=en&as_sdt=0,5",3,2020
Causal Modeling for Fairness In Dynamical Systems,42,icml,4,5,2023-06-17 03:56:56.549000,https://github.com/ecreager/causal-dyna-fair,8,Causal modeling for fairness in dynamical systems,"https://scholar.google.com/scholar?cluster=12839359629093476958&hl=en&as_sdt=0,44",4,2020
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack,307,icml,8,0,2023-06-17 03:56:56.751000,https://github.com/fra31/fab-attack,32,Minimally distorted adversarial examples with a fast adaptive boundary attack,"https://scholar.google.com/scholar?cluster=11433432412885384423&hl=en&as_sdt=0,25",2,2020
Scalable Deep Generative Modeling for Sparse Graphs,40,icml,7322,1026,2023-06-17 03:56:56.953000,https://github.com/google-research/google-research,29791,Scalable deep generative modeling for sparse graphs,"https://scholar.google.com/scholar?cluster=13017453490963979295&hl=en&as_sdt=0,34",727,2020
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting,16,icml,1,2,2023-06-17 03:56:57.155000,https://github.com/Mr8ND/ACORE-LFI,9,Confidence sets and hypothesis testing in a likelihood-free inference setting,"https://scholar.google.com/scholar?cluster=14385524652709102879&hl=en&as_sdt=0,5",2,2020
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models,20,icml,11,7,2023-06-17 03:56:57.356000,https://github.com/eth-sri/probabilistic-forecasts-attacks,29,Adversarial attacks on probabilistic autoregressive forecasting models,"https://scholar.google.com/scholar?cluster=1773916962694787403&hl=en&as_sdt=0,5",8,2020
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction,116,icml,31,5,2023-06-17 03:56:57.558000,https://github.com/locuslab/cfd-gcn,91,Combining differentiable PDE solvers and graph neural networks for fluid flow prediction,"https://scholar.google.com/scholar?cluster=5822388869556870864&hl=en&as_sdt=0,23",9,2020
Randomly Projected Additive Gaussian Processes for Regression,24,icml,3,1,2023-06-17 03:56:57.759000,https://github.com/idelbrid/Randomly-Projected-Additive-GPs,24,Randomly projected additive Gaussian processes for regression,"https://scholar.google.com/scholar?cluster=11838391975313028153&hl=en&as_sdt=0,5",4,2020
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC,27,icml,4,0,2023-06-17 03:56:57.961000,https://github.com/gaoliyao/Replica_Exchange_Stochastic_Gradient_MCMC,22,Non-convex learning via replica exchange stochastic gradient mcmc,"https://scholar.google.com/scholar?cluster=6979152849103979749&hl=en&as_sdt=0,5",4,2020
A Swiss Army Knife for Minimax Optimal Transport,15,icml,1,0,2023-06-17 03:56:58.163000,https://github.com/sofiendhouib/minimax_OT,6,A swiss army knife for minimax optimal transport,"https://scholar.google.com/scholar?cluster=2500404421772704612&hl=en&as_sdt=0,5",2,2020
Margin-aware Adversarial Domain Adaptation with Optimal Transport,22,icml,3,0,2023-06-17 03:56:58.365000,https://github.com/sofiendhouib/MADAOT,14,Margin-aware adversarial domain adaptation with optimal transport,"https://scholar.google.com/scholar?cluster=5511163225310216545&hl=en&as_sdt=0,1",1,2020
Growing Adaptive Multi-hyperplane Machines,1,icml,1,0,2023-06-17 03:56:58.567000,https://github.com/djurikom/BudgetedSVM,6,Growing adaptive multi-hyperplane machines,"https://scholar.google.com/scholar?cluster=8685157416945290118&hl=en&as_sdt=0,43",1,2020
Towards Adaptive Residual Network Training: A Neural-ODE Perspective,22,icml,0,0,2023-06-17 03:56:58.769000,https://github.com/shwinshaker/LipGrow,14,Towards adaptive residual network training: A neural-ode perspective,"https://scholar.google.com/scholar?cluster=790808977072857265&hl=en&as_sdt=0,5",4,2020
On the Expressivity of Neural Networks for Deep Reinforcement Learning,21,icml,5,0,2023-06-17 03:56:58.971000,https://github.com/roosephu/boots,11,On the expressivity of neural networks for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=10031650459091105952&hl=en&as_sdt=0,10",4,2020
NGBoost: Natural Gradient Boosting for Probabilistic Prediction,197,icml,207,47,2023-06-17 03:56:59.173000,https://github.com/stanfordmlgroup/ngboost,1440,Ngboost: Natural gradient boosting for probabilistic prediction,"https://scholar.google.com/scholar?cluster=4894543059596757711&hl=en&as_sdt=0,33",45,2020
Familywise Error Rate Control by Interactive Unmasking,9,icml,0,0,2023-06-17 03:56:59.375000,https://github.com/duanby/i-FWER,0,Familywise error rate control by interactive unmasking,"https://scholar.google.com/scholar?cluster=4720846503563749113&hl=en&as_sdt=0,41",1,2020
On Contrastive Learning for Likelihood-free Inference,82,icml,10,0,2023-06-17 03:56:59.577000,https://github.com/conormdurkan/lfi,36,On contrastive learning for likelihood-free inference,"https://scholar.google.com/scholar?cluster=2331371123661181745&hl=en&as_sdt=0,10",4,2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors,155,icml,79,73,2023-06-17 03:56:59.782000,https://github.com/google/edward2,644,Efficient and scalable bayesian neural nets with rank-1 factors,"https://scholar.google.com/scholar?cluster=14999664725860521004&hl=en&as_sdt=0,5",20,2020
Self-Concordant Analysis of Frank-Wolfe Algorithms,19,icml,1,0,2023-06-17 03:56:59.984000,https://github.com/kamil-safin/SCFW,3,Self-concordant analysis of Frank-Wolfe algorithms,"https://scholar.google.com/scholar?cluster=10274753710668333699&hl=en&as_sdt=0,5",2,2020
Decision Trees for Decision-Making under the Predict-then-Optimize Framework,88,icml,16,0,2023-06-17 03:57:00.187000,https://github.com/rtm2130/SPOTree,21,Decision trees for decision-making under the predict-then-optimize framework,"https://scholar.google.com/scholar?cluster=2000494760504517215&hl=en&as_sdt=0,5",2,2020
Identifying Statistical Bias in Dataset Replication,47,icml,5,0,2023-06-17 03:57:00.390000,https://github.com/MadryLab/dataset-replication-analysis,25,Identifying statistical bias in dataset replication,"https://scholar.google.com/scholar?cluster=16322569355368565071&hl=en&as_sdt=0,5",9,2020
Latent Bernoulli Autoencoder,5,icml,2,2,2023-06-17 03:57:00.591000,https://github.com/ok1zjf/lbae,18,Latent bernoulli autoencoder,"https://scholar.google.com/scholar?cluster=8997104581865575542&hl=en&as_sdt=0,5",4,2020
Growing Action Spaces,26,icml,128,12,2023-06-17 03:57:00.795000,https://github.com/TorchCraft/TorchCraftAI,640,Growing action spaces,"https://scholar.google.com/scholar?cluster=2822509827640565136&hl=en&as_sdt=0,5",49,2020
Why Are Learned Indexes So Effective?,25,icml,4,0,2023-06-17 03:57:00.997000,https://github.com/gvinciguerra/Learned-indexes-effectiveness,15,Why are learned indexes so effective?,"https://scholar.google.com/scholar?cluster=10615073257658129787&hl=en&as_sdt=0,33",4,2020
"Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?",125,icml,34,10,2023-06-17 03:57:01.200000,https://github.com/OATML/oatomobile,176,"Can autonomous vehicles identify, recover from, and adapt to distribution shifts?","https://scholar.google.com/scholar?cluster=12116616826636126634&hl=en&as_sdt=0,5",12,2020
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data,199,icml,28,2,2023-06-17 03:57:01.402000,https://github.com/mfinzi/LieConv,239,Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data,"https://scholar.google.com/scholar?cluster=5464981001229463744&hl=en&as_sdt=0,5",10,2020
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains,24,icml,4,2,2023-06-17 03:57:01.604000,https://github.com/johannes-fischer/icml2020_ipft,9,Information particle filter tree: An online algorithm for pomdps with belief-based rewards on continuous domains,"https://scholar.google.com/scholar?cluster=12906174048753061788&hl=en&as_sdt=0,21",2,2020
p-Norm Flow Diffusion for Local Graph Clustering,12,icml,1,0,2023-06-17 03:57:01.806000,https://github.com/s-h-yang/pNormFlowDiffusion,1,p-Norm flow diffusion for local graph clustering,"https://scholar.google.com/scholar?cluster=13045214522176891757&hl=en&as_sdt=0,44",1,2020
Stochastic Latent Residual Video Prediction,110,icml,16,0,2023-06-17 03:57:02.010000,https://github.com/edouardelasalles/srvp,75,Stochastic latent residual video prediction,"https://scholar.google.com/scholar?cluster=13364014516718772272&hl=en&as_sdt=0,34",3,2020
Leveraging Frequency Analysis for Deep Fake Image Recognition,247,icml,21,9,2023-06-17 03:57:02.213000,https://github.com/RUB-SysSec/GANDCTAnalysis,141,Leveraging frequency analysis for deep fake image recognition,"https://scholar.google.com/scholar?cluster=15424504685179897985&hl=en&as_sdt=0,33",8,2020
No-Regret and Incentive-Compatible Online Learning,9,icml,0,0,2023-06-17 03:57:02.422000,https://github.com/charapod/noregr-and-ic,3,No-regret and incentive-compatible online learning,"https://scholar.google.com/scholar?cluster=10101414388050703329&hl=en&as_sdt=0,3",3,2020
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods,79,icml,21,2,2023-06-17 03:57:02.630000,https://github.com/HazyResearch/flyingsquid,302,Fast and three-rious: Speeding up weak supervision with triplet methods,"https://scholar.google.com/scholar?cluster=13381739478195543351&hl=en&as_sdt=0,21",26,2020
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks,78,icml,19,0,2023-06-17 03:57:02.832000,https://github.com/TAMU-VITA/AGD,101,Autogan-distiller: Searching to compress generative adversarial networks,"https://scholar.google.com/scholar?cluster=1452214065033971023&hl=en&as_sdt=0,5",16,2020
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths,4,icml,5,0,2023-06-17 03:57:03.034000,https://github.com/DessiLBI2020/DessiLBI,35,Dessilbi: Exploring structural sparsity of deep networks via differential inclusion paths,"https://scholar.google.com/scholar?cluster=10194996533073442340&hl=en&as_sdt=0,5",1,2020
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs,14,icml,0,0,2023-06-17 03:57:03.237000,https://github.com/syanga/dglearn,6,Characterizing distribution equivalence and structure learning for cyclic and acyclic directed graphs,"https://scholar.google.com/scholar?cluster=4341241833833873634&hl=en&as_sdt=0,10",2,2020
Gradient Temporal-Difference Learning with Regularized Corrections,35,icml,9,1,2023-06-17 03:57:03.439000,https://github.com/rlai-lab/Regularized-GradientTD,32,Gradient temporal-difference learning with regularized corrections,"https://scholar.google.com/scholar?cluster=8254675597355502028&hl=en&as_sdt=0,44",10,2020
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks,62,icml,5,0,2023-06-17 03:57:03.641000,https://github.com/goldblum/FeatureClustering,12,Unraveling meta-learning: Understanding feature representations for few-shot tasks,"https://scholar.google.com/scholar?cluster=17583362370632834127&hl=en&as_sdt=0,5",2,2020
Towards a General Theory of Infinite-Width Limits of Neural Classifiers,9,icml,0,0,2023-06-17 03:57:03.843000,https://github.com/deepmipt/infinite-width_nets,3,Towards a general theory of infinite-width limits of neural classifiers,"https://scholar.google.com/scholar?cluster=6182164811378755380&hl=en&as_sdt=0,5",4,2020
Differentially Private Set Union,26,icml,1,0,2023-06-17 03:57:04.072000,https://github.com/heyyjudes/differentially-private-set-union,6,Differentially private set union,"https://scholar.google.com/scholar?cluster=2482149851439545745&hl=en&as_sdt=0,41",3,2020
The continuous categorical: a novel simplex-valued exponential family,15,icml,7,0,2023-06-17 03:57:04.273000,https://github.com/cunningham-lab/cb_and_cc,31,The continuous categorical: a novel simplex-valued exponential family,"https://scholar.google.com/scholar?cluster=17174456964236691188&hl=en&as_sdt=0,44",4,2020
Automatic Reparameterisation of Probabilistic Programs,21,icml,3,1,2023-06-17 03:57:04.475000,https://github.com/mgorinova/autoreparam,33,Automatic reparameterisation of probabilistic programs,"https://scholar.google.com/scholar?cluster=1767777764184099722&hl=en&as_sdt=0,5",6,2020
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning,82,icml,10,1,2023-06-17 03:57:04.677000,https://github.com/99andBeyond/Apollo1060,62,Learning to navigate the synthetically accessible chemical space using reinforcement learning,"https://scholar.google.com/scholar?cluster=12254018210357831699&hl=en&as_sdt=0,5",7,2020
Ordinal Non-negative Matrix Factorization for Recommendation,14,icml,5,0,2023-06-17 03:57:04.879000,https://github.com/Oligou/OrdNMF,9,Ordinal non-negative matrix factorization for recommendation,"https://scholar.google.com/scholar?cluster=3251477194660112209&hl=en&as_sdt=0,33",3,2020
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination,33,icml,13,3,2023-06-17 03:57:05.080000,https://github.com/IBM/PoWER-BERT,53,PoWER-BERT: Accelerating BERT inference via progressive word-vector elimination,"https://scholar.google.com/scholar?cluster=306627104113108298&hl=en&as_sdt=0,33",7,2020
PackIt: A Virtual Environment for Geometric Planning,7,icml,4,4,2023-06-17 03:57:05.282000,https://github.com/princeton-vl/PackIt,45,Packit: A virtual environment for geometric planning,"https://scholar.google.com/scholar?cluster=5535871935242220151&hl=en&as_sdt=0,33",6,2020
DROCC: Deep Robust One-Class Classification,121,icml,369,28,2023-06-17 03:57:05.484000,https://github.com/Microsoft/EdgeML,1453,DROCC: Deep robust one-class classification,"https://scholar.google.com/scholar?cluster=3986505951359290998&hl=en&as_sdt=0,29",87,2020
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling,63,icml,5,1,2023-06-17 03:57:05.686000,https://github.com/wgrathwohl/LSD,43,Learning the stein discrepancy for training and evaluating energy-based models without sampling,"https://scholar.google.com/scholar?cluster=12824935271809632059&hl=en&as_sdt=0,5",2,2020
On the Iteration Complexity of Hypergradient Computation,116,icml,17,1,2023-06-17 03:57:05.887000,https://github.com/prolearner/hypertorch,112,On the iteration complexity of hypergradient computation,"https://scholar.google.com/scholar?cluster=3451320004072265708&hl=en&as_sdt=0,36",6,2020
Robust Learning with the Hilbert-Schmidt Independence Criterion,33,icml,6,0,2023-06-17 03:57:06.088000,https://github.com/danielgreenfeld3/XIC,34,Robust learning with the hilbert-schmidt independence criterion,"https://scholar.google.com/scholar?cluster=13054295788524587578&hl=en&as_sdt=0,29",2,2020
Implicit Geometric Regularization for Learning Shapes,408,icml,35,5,2023-06-17 03:57:06.291000,https://github.com/amosgropp/IGR,331,Implicit geometric regularization for learning shapes,"https://scholar.google.com/scholar?cluster=18082545558132742834&hl=en&as_sdt=0,33",7,2020
Recurrent Hierarchical Topic-Guided RNN for Language Generation,22,icml,2,5,2023-06-17 03:57:06.493000,https://github.com/Dan123dan/rGBN-RNN,6,Recurrent hierarchical topic-guided RNN for language generation,"https://scholar.google.com/scholar?cluster=11674844584780363467&hl=en&as_sdt=0,4",1,2020
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search,52,icml,7,1,2023-06-17 03:57:06.695000,https://github.com/guoyongcs/CNAS,17,Breaking the curse of space explosion: Towards efficient nas with curriculum search,"https://scholar.google.com/scholar?cluster=5489996847363496431&hl=en&as_sdt=0,10",4,2020
Certified Data Removal from Machine Learning Models,163,icml,8,0,2023-06-17 03:57:06.897000,https://github.com/facebookresearch/certified-removal,39,Certified data removal from machine learning models,"https://scholar.google.com/scholar?cluster=5421394926787368463&hl=en&as_sdt=0,33",8,2020
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks,28,icml,0,0,2023-06-17 03:57:07.099000,https://github.com/ZhishuaiGuo/DistributedAUC,2,Communication-efficient distributed stochastic auc maximization with deep neural networks,"https://scholar.google.com/scholar?cluster=992924762353556583&hl=en&as_sdt=0,48",1,2020
Neural Topic Modeling with Continual Lifelong Learning,29,icml,3,1,2023-06-17 03:57:07.301000,https://github.com/pgcool/Lifelong-Neural-Topic-Modeling,23,Neural topic modeling with continual lifelong learning,"https://scholar.google.com/scholar?cluster=5694355012238035603&hl=en&as_sdt=0,5",2,2020
Optimal approximation for unconstrained non-submodular minimization,22,icml,0,0,2023-06-17 03:57:07.503000,https://github.com/marwash25/non-sub-min,0,Optimal approximation for unconstrained non-submodular minimization,"https://scholar.google.com/scholar?cluster=16541151635330995478&hl=en&as_sdt=0,31",2,2020
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix,7,icml,0,1,2023-06-17 03:57:07.705000,https://github.com/insuhan/polytensorsketch,2,Polynomial tensor sketch for element-wise function of low-rank matrix,"https://scholar.google.com/scholar?cluster=15937632034353153696&hl=en&as_sdt=0,48",1,2020
Improving generalization by controlling label-noise information in neural network weights,39,icml,8,0,2023-06-17 03:57:07.906000,https://github.com/hrayrhar/limit-label-memorization,37,Improving generalization by controlling label-noise information in neural network weights,"https://scholar.google.com/scholar?cluster=8186840532226802329&hl=en&as_sdt=0,11",5,2020
Contrastive Multi-View Representation Learning on Graphs,683,icml,46,11,2023-06-17 03:57:08.108000,https://github.com/kavehhassani/mvgrl,225,Contrastive multi-view representation learning on graphs,"https://scholar.google.com/scholar?cluster=11131425815493661687&hl=en&as_sdt=0,47",9,2020
Nested Subspace Arrangement for Representation of Relational Data,3,icml,0,0,2023-06-17 03:57:08.311000,https://github.com/KyushuUniversityMathematics/DANCAR,2,Nested subspace arrangement for representation of relational data,"https://scholar.google.com/scholar?cluster=5195931229921461485&hl=en&as_sdt=0,5",4,2020
The Tree Ensemble Layer: Differentiability meets Conditional Computation,48,icml,7322,1026,2023-06-17 03:57:08.513000,https://github.com/google-research/google-research,29791,The tree ensemble layer: Differentiability meets conditional computation,"https://scholar.google.com/scholar?cluster=4646704514802719017&hl=en&as_sdt=0,22",727,2020
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation,59,icml,2,0,2023-06-17 03:57:08.715000,https://github.com/MLI-lab/cs_deep_decoder,15,Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation,"https://scholar.google.com/scholar?cluster=17885213602830705754&hl=en&as_sdt=0,5",2,2020
Hierarchically Decoupled Imitation For Morphological Transfer,21,icml,5,3,2023-06-17 03:57:08.917000,https://github.com/jhejna/hierarchical_morphology_transfer,16,Hierarchically decoupled imitation for morphological transfer,"https://scholar.google.com/scholar?cluster=7821488667980467803&hl=en&as_sdt=0,5",3,2020
Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD),23,icml,2,1,2023-06-17 03:57:09.128000,https://github.com/FabianHinder/DAWIDD,7,Towards non-parametric drift detection via dynamic adapting window independence drift detection (DAWIDD),"https://scholar.google.com/scholar?cluster=4763047039028062564&hl=en&as_sdt=0,7",2,2020
Topologically Densified Distributions,13,icml,1,0,2023-06-17 03:57:09.331000,https://github.com/c-hofer/topologically_densified_distributions,2,Topologically densified distributions,"https://scholar.google.com/scholar?cluster=18143439633922765637&hl=en&as_sdt=0,10",2,2020
Graph Filtration Learning,63,icml,8,0,2023-06-17 03:57:09.534000,https://github.com/c-hofer/graph_filtration_learning,14,Graph filtration learning,"https://scholar.google.com/scholar?cluster=16680082495324217816&hl=en&as_sdt=0,44",3,2020
Set Functions for Time Series,73,icml,26,2,2023-06-17 03:57:09.736000,https://github.com/BorgwardtLab/Set_Functions_for_Time_Series,104,Set functions for time series,"https://scholar.google.com/scholar?cluster=11653676919176974096&hl=en&as_sdt=0,45",7,2020
Lifted Disjoint Paths with Application in Multiple Object Tracking,112,icml,8,2,2023-06-17 03:57:09.938000,https://github.com/AndreaHor/LifT_Solver,51,Lifted disjoint paths with application in multiple object tracking,"https://scholar.google.com/scholar?cluster=7450982728927056524&hl=en&as_sdt=0,5",7,2020
Infinite attention: NNGP and NTK for deep attention networks,71,icml,227,58,2023-06-17 03:57:10.141000,https://github.com/google/neural-tangents,2024,Infinite attention: NNGP and NTK for deep attention networks,"https://scholar.google.com/scholar?cluster=8612471018033907356&hl=en&as_sdt=0,5",64,2020
The Non-IID Data Quagmire of Decentralized Machine Learning,373,icml,8,0,2023-06-17 03:57:10.342000,https://github.com/kevinhsieh/non_iid_dml,26,The non-iid data quagmire of decentralized machine learning,"https://scholar.google.com/scholar?cluster=6995419568802932569&hl=en&as_sdt=0,5",1,2020
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation,633,icml,108,27,2023-06-17 03:57:10.544000,https://github.com/google-research/xtreme,583,Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation,"https://scholar.google.com/scholar?cluster=3128313942238375094&hl=en&as_sdt=0,10",21,2020
Momentum-Based Policy Gradient Methods,29,icml,2,0,2023-06-17 03:57:10.746000,https://github.com/gaosh/MBPG,6,Momentum-based policy gradient methods,"https://scholar.google.com/scholar?cluster=12318216464045418856&hl=en&as_sdt=0,47",2,2020
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control,97,icml,28,12,2023-06-17 03:57:10.948000,https://github.com/huangwl18/modular-rl,194,One policy to control them all: Shared modular policies for agent-agnostic control,"https://scholar.google.com/scholar?cluster=14540777310694580207&hl=en&as_sdt=0,33",11,2020
Generating Programmatic Referring Expressions via Program Synthesis,8,icml,3,0,2023-06-17 03:57:11.150000,https://github.com/moqingyan/object_reference_synthesis,5,Generating programmatic referring expressions via program synthesis,"https://scholar.google.com/scholar?cluster=6959334433424014581&hl=en&as_sdt=0,33",2,2020
Accelerated Stochastic Gradient-free and Projection-free Methods,16,icml,0,0,2023-06-17 03:57:11.352000,https://github.com/TLMichael/Acc-SZOFW,3,Accelerated stochastic gradient-free and projection-free methods,"https://scholar.google.com/scholar?cluster=9296344013020465952&hl=en&as_sdt=0,6",2,2020
Multigrid Neural Memory,5,icml,1,0,2023-06-17 03:57:11.554000,https://github.com/trihuynh88/multigrid_mem,7,Multigrid neural memory,"https://scholar.google.com/scholar?cluster=15687545930604068210&hl=en&as_sdt=0,34",4,2020
Meta-Learning with Shared Amortized Variational Inference,15,icml,0,2,2023-06-17 03:57:11.756000,https://github.com/katafeya/samovar,3,Meta-learning with shared amortized variational inference,"https://scholar.google.com/scholar?cluster=5105160592289559562&hl=en&as_sdt=0,21",6,2020
Do We Need Zero Training Loss After Achieving Zero Training Error?,90,icml,6,0,2023-06-17 03:57:11.959000,https://github.com/takashiishida/flooding,82,Do we need zero training loss after achieving zero training error?,"https://scholar.google.com/scholar?cluster=6131533147705685027&hl=en&as_sdt=0,33",5,2020
Semi-Supervised Learning with Normalizing Flows,77,icml,12,1,2023-06-17 03:57:12.160000,https://github.com/izmailovpavel/flowgmm,129,Semi-supervised learning with normalizing flows,"https://scholar.google.com/scholar?cluster=9421035999149534110&hl=en&as_sdt=0,5",10,2020
Source Separation with Deep Generative Priors,28,icml,5,3,2023-06-17 03:57:12.362000,https://github.com/jthickstun/basis-separation,33,Source separation with deep generative priors,"https://scholar.google.com/scholar?cluster=17132907753659598254&hl=en&as_sdt=0,5",7,2020
T-GD: Transferable GAN-generated Images Detection Framework,31,icml,4,0,2023-06-17 03:57:12.564000,https://github.com/cutz-j/T-GD,16,T-gd: Transferable gan-generated images detection framework,"https://scholar.google.com/scholar?cluster=17021668985815827092&hl=en&as_sdt=0,5",2,2020
Information-Theoretic Local Minima Characterization and Regularization,9,icml,0,0,2023-06-17 03:57:12.766000,https://github.com/SeanJia/InfoMCR,5,Information-theoretic local minima characterization and regularization,"https://scholar.google.com/scholar?cluster=16854698489852164998&hl=en&as_sdt=0,11",2,2020
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation,89,icml,9,3,2023-06-17 03:57:12.968000,https://github.com/xiangdal/implicit_alignment,87,Implicit class-conditioned domain alignment for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=17175487218857833755&hl=en&as_sdt=0,5",6,2020
Multi-Objective Molecule Generation using Interpretable Substructures,124,icml,42,8,2023-06-17 03:57:13.171000,https://github.com/wengong-jin/multiobj-rationale,119,Multi-objective molecule generation using interpretable substructures,"https://scholar.google.com/scholar?cluster=7786133206388752764&hl=en&as_sdt=0,44",3,2020
On Relativistic f-Divergences,18,icml,17,0,2023-06-17 03:57:13.373000,https://github.com/AlexiaJM/relativistic-f-divergences,85,On relativistic f-divergences,"https://scholar.google.com/scholar?cluster=17068494214467307697&hl=en&as_sdt=0,5",8,2020
Being Bayesian about Categorical Probability,44,icml,4,2,2023-06-17 03:57:13.575000,https://github.com/tjoo512/belief-matching-framework,33,Being bayesian about categorical probability,"https://scholar.google.com/scholar?cluster=6426225307727814668&hl=en&as_sdt=0,5",5,2020
Evaluating the Performance of Reinforcement Learning Algorithms,46,icml,0,2,2023-06-17 03:57:13.777000,https://github.com/ScottJordan/EvaluationOfRLAlgs,26,Evaluating the performance of reinforcement learning algorithms,"https://scholar.google.com/scholar?cluster=4785496300749883115&hl=en&as_sdt=0,5",2,2020
Stochastic Differential Equations with Variational Wishart Diffusions,7,icml,1,0,2023-06-17 03:57:13.979000,https://github.com/JorgensenMart/Wishart-priored-SDE,8,Stochastic differential equations with variational wishart diffusions,"https://scholar.google.com/scholar?cluster=8080141843979887658&hl=en&as_sdt=0,5",1,2020
Partial Trace Regression and Low-Rank Kraus Decomposition,6,icml,1,0,2023-06-17 03:57:14.182000,https://github.com/Stef-hub/partial_trace_kraus,0,Partial trace regression and low-rank kraus decomposition,"https://scholar.google.com/scholar?cluster=794742801432087247&hl=en&as_sdt=0,47",3,2020
Operation-Aware Soft Channel Pruning using Differentiable Masks,96,icml,0,0,2023-06-17 03:57:14.384000,https://github.com/kminsoo/SCP,6,Operation-aware soft channel pruning using differentiable masks,"https://scholar.google.com/scholar?cluster=6963448174836272281&hl=en&as_sdt=0,31",1,2020
Non-autoregressive Machine Translation with Disentangled Context Transformer,71,icml,9,3,2023-06-17 03:57:14.586000,https://github.com/facebookresearch/DisCo,78,Non-autoregressive machine translation with disentangled context transformer,"https://scholar.google.com/scholar?cluster=8958608366652830932&hl=en&as_sdt=0,25",11,2020
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention,705,icml,162,28,2023-06-17 03:57:14.788000,https://github.com/idiap/fast-transformers,1434,Transformers are rnns: Fast autoregressive transformers with linear attention,"https://scholar.google.com/scholar?cluster=15303739914785429862&hl=en&as_sdt=0,6",27,2020
Entropy Minimization In Emergent Languages,20,icml,98,7,2023-06-17 03:57:14.991000,https://github.com/facebookresearch/EGG,261,Entropy minimization in emergent languages,"https://scholar.google.com/scholar?cluster=9085278772671430646&hl=en&as_sdt=0,6",16,2020
What can I do here? A Theory of Affordances in Reinforcement Learning,49,icml,1,0,2023-06-17 03:57:15.193000,https://github.com/deepmind/affordances_option_models,21,What can I do here? A Theory of Affordances in Reinforcement Learning,"https://scholar.google.com/scholar?cluster=2336774470554893443&hl=en&as_sdt=0,9",4,2020
FACT: A Diagnostic for Group Fairness Trade-offs,28,icml,0,1,2023-06-17 03:57:15.395000,https://github.com/wnstlr/FACT,4,FACT: A diagnostic for group fairness trade-offs,"https://scholar.google.com/scholar?cluster=17087884984751620008&hl=en&as_sdt=0,34",4,2020
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup,208,icml,17,0,2023-06-17 03:57:15.598000,https://github.com/snu-mllab/PuzzleMix,144,Puzzle mix: Exploiting saliency and local statistics for optimal mixup,"https://scholar.google.com/scholar?cluster=58056101510275173&hl=en&as_sdt=0,47",10,2020
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation,52,icml,2,0,2023-06-17 03:57:15.799000,https://github.com/stevenkleinegesse/minebed,7,Bayesian experimental design for implicit models by mutual information neural estimation,"https://scholar.google.com/scholar?cluster=18098257663902816323&hl=en&as_sdt=0,5",1,2020
Learning Similarity Metrics for Numerical Simulations,14,icml,4,0,2023-06-17 03:57:16.001000,https://github.com/tum-pbs/LSIM,28,Learning similarity metrics for numerical simulations,"https://scholar.google.com/scholar?cluster=16424748636461420663&hl=en&as_sdt=0,31",6,2020
Online Learning for Active Cache Synchronization,4,icml,15,0,2023-06-17 03:57:16.204000,https://github.com/microsoft/Optimal-Freshness-Crawl-Scheduling,34,Online learning for active cache synchronization,"https://scholar.google.com/scholar?cluster=17855139660047402604&hl=en&as_sdt=0,10",12,2020
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates,72,icml,17,3,2023-06-17 03:57:16.406000,https://github.com/Lingkai-Kong/SDE-Net,86,Sde-net: Equipping deep neural networks with uncertainty estimates,"https://scholar.google.com/scholar?cluster=8672163591192600750&hl=en&as_sdt=0,5",5,2020
"Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks",171,icml,13,1,2023-06-17 03:57:16.608000,https://github.com/wiseodd/last_layer_laplace,68,"Being bayesian, even just a bit, fixes overconfidence in relu networks","https://scholar.google.com/scholar?cluster=12071417821093265788&hl=en&as_sdt=0,10",3,2020
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness,77,icml,2,0,2023-06-17 03:57:16.810000,https://github.com/alevine0/smoothingGenGaussian,3,Curse of dimensionality on randomized smoothing for certifiable robustness,"https://scholar.google.com/scholar?cluster=3011754801302314262&hl=en&as_sdt=0,22",3,2020
Two Routes to Scalable Credit Assignment without Weight Symmetry,27,icml,3,0,2023-06-17 03:57:17.012000,https://github.com/neuroailab/Neural-Alignment,22,Two routes to scalable credit assignment without weight symmetry,"https://scholar.google.com/scholar?cluster=5596776573115388882&hl=en&as_sdt=0,21",6,2020
Soft Threshold Weight Reparameterization for Learnable Sparsity,134,icml,9,5,2023-06-17 03:57:17.214000,https://github.com/RAIVNLab/STR,78,Soft threshold weight reparameterization for learnable sparsity,"https://scholar.google.com/scholar?cluster=6875882562671228073&hl=en&as_sdt=0,21",6,2020
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics,89,icml,10,1,2023-06-17 03:57:17.416000,https://github.com/bayesgroup/tqc_pytorch,63,Controlling overestimation bias with truncated mixture of continuous distributional quantile critics,"https://scholar.google.com/scholar?cluster=17490032530609728383&hl=en&as_sdt=0,5",10,2020
Principled learning method for Wasserstein distributionally robust optimization with local perturbations,6,icml,2,2,2023-06-17 03:57:17.618000,https://github.com/ykwon0407/wdro_local_perturbation,19,Principled learning method for Wasserstein distributionally robust optimization with local perturbations,"https://scholar.google.com/scholar?cluster=2114088593646438168&hl=en&as_sdt=0,22",2,2020
Bidirectional Model-based Policy Optimization,35,icml,0,1,2023-06-17 03:57:17.820000,https://github.com/hanglai/bmpo,21,Bidirectional model-based policy optimization,"https://scholar.google.com/scholar?cluster=8899413271083643198&hl=en&as_sdt=0,5",3,2020
CURL: Contrastive Unsupervised Representations for Reinforcement Learning,724,icml,84,12,2023-06-17 03:57:18.023000,https://github.com/MishaLaskin/curl,519,Curl: Contrastive unsupervised representations for reinforcement learning,"https://scholar.google.com/scholar?cluster=10576608792458329488&hl=en&as_sdt=0,5",11,2020
Self-Attentive Associative Memory,43,icml,7,0,2023-06-17 03:57:18.226000,https://github.com/thaihungle/SAM,39,Self-attentive associative memory,"https://scholar.google.com/scholar?cluster=10962782688418035731&hl=en&as_sdt=0,5",4,2020
Self-supervised Label Augmentation via Input Transformations,110,icml,14,0,2023-06-17 03:57:18.428000,https://github.com/hankook/SLA,100,Self-supervised label augmentation via input transformations,"https://scholar.google.com/scholar?cluster=8322322680850611064&hl=en&as_sdt=0,33",5,2020
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning,73,icml,6,1,2023-06-17 03:57:18.630000,https://github.com/younggyoseo/CaDM,51,Context-aware dynamics model for generalization in model-based reinforcement learning,"https://scholar.google.com/scholar?cluster=4703047670466451533&hl=en&as_sdt=0,33",6,2020
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression,37,icml,16,3,2023-06-17 03:57:18.832000,https://github.com/chl8856/AC_TPC,40,Temporal phenotyping using deep predictive clustering of disease progression,"https://scholar.google.com/scholar?cluster=529698419891828395&hl=en&as_sdt=0,43",2,2020
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks,18,icml,2,0,2023-06-17 03:57:19.033000,https://github.com/Karbo123/AnalyticMesh,51,Analytic marching: An analytic meshing solution from deep implicit surface networks,"https://scholar.google.com/scholar?cluster=13457623400866526866&hl=en&as_sdt=0,5",5,2020
ACFlow: Flow Models for Arbitrary Conditional Likelihoods,17,icml,0,4,2023-06-17 03:57:19.235000,https://github.com/lupalab/ACFlow,11,ACFlow: Flow models for arbitrary conditional likelihoods,"https://scholar.google.com/scholar?cluster=4436891943483900806&hl=en&as_sdt=0,9",3,2020
Manifold Identification for Ultimately Communication-Efficient Distributed Optimization,4,icml,0,0,2023-06-17 03:57:19.438000,https://github.com/leepei/madpqn,0,Manifold identification for ultimately communication-efficient distributed optimization,"https://scholar.google.com/scholar?cluster=7891580359300327237&hl=en&as_sdt=0,47",4,2020
PENNI: Pruned Kernel Sharing for Efficient CNN Inference,12,icml,4,4,2023-06-17 03:57:19.640000,https://github.com/timlee0212/PENNI,7,Penni: Pruned kernel sharing for efficient CNN inference,"https://scholar.google.com/scholar?cluster=15394571534654834943&hl=en&as_sdt=0,33",3,2020
"Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning",54,icml,5,1,2023-06-17 03:57:19.841000,https://github.com/liqing-ustc/NGS,50,"Closed loop neural-symbolic learning via integrating neural perception, grammar parsing, and symbolic reasoning","https://scholar.google.com/scholar?cluster=9257372000778020812&hl=en&as_sdt=0,47",3,2020
Latent Space Factorisation and Manipulation via Matrix Subspace Projection,30,icml,3,2,2023-06-17 03:57:20.043000,https://github.com/lissomx/MSP,10,Latent space factorisation and manipulation via matrix subspace projection,"https://scholar.google.com/scholar?cluster=9592355331559392684&hl=en&as_sdt=0,45",2,2020
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective,40,icml,11,1,2023-06-17 03:57:20.246000,https://github.com/steveli/partial-encoder-decoder,34,Learning from irregularly-sampled time series: A missing data perspective,"https://scholar.google.com/scholar?cluster=9259999612636522766&hl=en&as_sdt=0,5",2,2020
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation,618,icml,77,2,2023-06-17 03:57:20.448000,https://github.com/tim-learn/SHOT,340,Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=2414062070271265691&hl=en&as_sdt=0,31",7,2020
Variable Skipping for Autoregressive Range Density Estimation,7,icml,3,0,2023-06-17 03:57:20.651000,https://github.com/var-skip/var-skip,6,Variable skipping for autoregressive range density estimation,"https://scholar.google.com/scholar?cluster=16617388741966363068&hl=en&as_sdt=0,5",2,2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule,19,icml,1,1,2023-06-17 03:57:20.854000,https://github.com/yorkerlin/iBayesLRule,4,Handling the positive-definite constraint in the Bayesian learning rule,"https://scholar.google.com/scholar?cluster=14519338791070687660&hl=en&as_sdt=0,26",4,2020
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs,62,icml,8,0,2023-06-17 03:57:21.073000,https://github.com/fjxmlzn/InfoGAN-CR,40,Infogan-cr and modelcentrality: Self-supervised model training and selection for disentangling gans,"https://scholar.google.com/scholar?cluster=4410576608706121212&hl=en&as_sdt=0,5",6,2020
Generalized and Scalable Optimal Sparse Decision Trees,93,icml,29,11,2023-06-17 03:57:21.275000,https://github.com/Jimmy-Lin/GeneralizedOptimalSparseDecisionTrees,48,Generalized and scalable optimal sparse decision trees,"https://scholar.google.com/scholar?cluster=15979140727083888111&hl=en&as_sdt=0,14",5,2020
Time-aware Large Kernel Convolutions,23,icml,6,0,2023-06-17 03:57:21.478000,https://github.com/lioutasb/TaLKConvolutions,28,Time-aware large kernel convolutions,"https://scholar.google.com/scholar?cluster=2978340010054806540&hl=en&as_sdt=0,5",4,2020
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors,19,icml,2,0,2023-06-17 03:57:21.680000,https://github.com/selwyn96/Quant_CS,1,Sample complexity bounds for 1-bit compressive sensing and binary stable embeddings with generative priors,"https://scholar.google.com/scholar?cluster=14332918764703179344&hl=en&as_sdt=0,5",2,2020
An Imitation Learning Approach for Cache Replacement,53,icml,7322,1026,2023-06-17 03:57:21.882000,https://github.com/google-research/google-research,29791,An imitation learning approach for cache replacement,"https://scholar.google.com/scholar?cluster=14524866221937250156&hl=en&as_sdt=0,5",727,2020
Hallucinative Topological Memory for Zero-Shot Visual Planning,33,icml,6,0,2023-06-17 03:57:22.084000,https://github.com/thanard/hallucinative-topological-memory,12,Hallucinative topological memory for zero-shot visual planning,"https://scholar.google.com/scholar?cluster=2366589002127869836&hl=en&as_sdt=0,5",2,2020
Learning Deep Kernels for Non-Parametric Two-Sample Tests,125,icml,9,0,2023-06-17 03:57:22.286000,https://github.com/fengliu90/DK-for-TST,38,Learning deep kernels for non-parametric two-sample tests,"https://scholar.google.com/scholar?cluster=11419051350787047758&hl=en&as_sdt=0,10",5,2020
Finding trainable sparse networks through Neural Tangent Transfer,21,icml,8,1,2023-06-17 03:57:22.487000,https://github.com/fmi-basel/neural-tangent-transfer,14,Finding trainable sparse networks through neural tangent transfer,"https://scholar.google.com/scholar?cluster=4513428362784750127&hl=en&as_sdt=0,5",4,2020
Weakly-Supervised Disentanglement Without Compromises,212,icml,199,20,2023-06-17 03:57:22.689000,https://github.com/google-research/disentanglement_lib,1301,Weakly-supervised disentanglement without compromises,"https://scholar.google.com/scholar?cluster=17730117604231114120&hl=en&as_sdt=0,11",35,2020
Too Relaxed to Be Fair,45,icml,3,0,2023-06-17 03:57:22.892000,https://github.com/mlohaus/SearchFair,9,Too relaxed to be fair,"https://scholar.google.com/scholar?cluster=8729544437248973666&hl=en&as_sdt=0,34",2,2020
Differentiating through the Fréchet Mean,52,icml,2,4,2023-06-17 03:57:23.095000,https://github.com/CUAI/Differentiable-Frechet-Mean,50,Differentiating through the fréchet mean,"https://scholar.google.com/scholar?cluster=1425573169014829533&hl=en&as_sdt=0,5",7,2020
Progressive Graph Learning for Open-Set Domain Adaptation,73,icml,5,2,2023-06-17 03:57:23.296000,https://github.com/BUserName/PGL,28,Progressive graph learning for open-set domain adaptation,"https://scholar.google.com/scholar?cluster=2624735787669105317&hl=en&as_sdt=0,5",4,2020
Learning Algebraic Multigrid Using Graph Neural Networks,43,icml,3,0,2023-06-17 03:57:23.497000,https://github.com/ilayluz/learning-amg,12,Learning algebraic multigrid using graph neural networks,"https://scholar.google.com/scholar?cluster=9215058872113912967&hl=en&as_sdt=0,5",4,2020
Progressive Identification of True Labels for Partial-Label Learning,99,icml,5,0,2023-06-17 03:57:23.700000,https://github.com/Lvcrezia77/PRODEN,41,Progressive identification of true labels for partial-label learning,"https://scholar.google.com/scholar?cluster=17946181753810073887&hl=en&as_sdt=0,5",1,2020
Efficient Continuous Pareto Exploration in Multi-Task Learning,54,icml,27,1,2023-06-17 03:57:23.901000,https://github.com/mit-gfx/ContinuousParetoMTL,117,Efficient continuous pareto exploration in multi-task learning,"https://scholar.google.com/scholar?cluster=14510629090081206490&hl=en&as_sdt=0,5",20,2020
Normalized Loss Functions for Deep Learning with Noisy Labels,239,icml,25,1,2023-06-17 03:57:24.103000,https://github.com/HanxunH/Active-Passive-Losses,106,Normalized loss functions for deep learning with noisy labels,"https://scholar.google.com/scholar?cluster=15594415410821742634&hl=en&as_sdt=0,5",4,2020
Adversarial Neural Pruning with Latent Vulnerability Suppression,37,icml,1,0,2023-06-17 03:57:24.305000,https://github.com/divyam3897/ANP_VS,14,Adversarial neural pruning with latent vulnerability suppression,"https://scholar.google.com/scholar?cluster=14781666760584022356&hl=en&as_sdt=0,5",3,2020
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization,75,icml,11,3,2023-06-17 03:57:24.507000,https://github.com/dbmptr/EPOSearch,41,Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization,"https://scholar.google.com/scholar?cluster=14380308074302940199&hl=en&as_sdt=0,5",1,2020
Adversarial Robustness Against the Union of Multiple Perturbation Models,124,icml,3,1,2023-06-17 03:57:24.709000,https://github.com/locuslab/robust_union,23,Adversarial robustness against the union of multiple perturbation models,"https://scholar.google.com/scholar?cluster=7466169251019166105&hl=en&as_sdt=0,14",7,2020
Adaptive Gradient Descent without Descent,51,icml,5,0,2023-06-17 03:57:24.911000,https://github.com/ymalitsky/adaptive_gd,39,Adaptive gradient descent without descent,"https://scholar.google.com/scholar?cluster=9121623366075061608&hl=en&as_sdt=0,5",5,2020
Emergence of Separable Manifolds in Deep Language Representations,31,icml,3,0,2023-06-17 03:57:25.114000,https://github.com/schung039/contextual-repr-manifolds,5,Emergence of separable manifolds in deep language representations,"https://scholar.google.com/scholar?cluster=5179476739222728970&hl=en&as_sdt=0,5",2,2020
Minimax Pareto Fairness: A Multi Objective Perspective,126,icml,5,0,2023-06-17 03:57:25.316000,https://github.com/natalialmg/MMPF,21,Minimax pareto fairness: A multi objective perspective,"https://scholar.google.com/scholar?cluster=7690434188548585535&hl=en&as_sdt=0,31",3,2020
Predictive Multiplicity in Classification,75,icml,2,2,2023-06-17 03:57:25.519000,https://github.com/charliemarx/pmtools,9,Predictive multiplicity in classification,"https://scholar.google.com/scholar?cluster=12971902900115271261&hl=en&as_sdt=0,5",3,2020
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification,17,icml,5,1,2023-06-17 03:57:25.720000,https://github.com/HMEIatJHU/neural-datalog-through-time,30,Neural Datalog through time: Informed temporal modeling via logical specification,"https://scholar.google.com/scholar?cluster=13196809524951928440&hl=en&as_sdt=0,5",1,2020
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM,8,icml,1,1,2023-06-17 03:57:25.923000,https://github.com/sisl/CEEM,8,Scalable identification of partially observed systems with certainty-equivalent EM,"https://scholar.google.com/scholar?cluster=12141244862224511768&hl=en&as_sdt=0,32",17,2020
Training Binary Neural Networks using the Bayesian Learning Rule,32,icml,5,1,2023-06-17 03:57:26.124000,https://github.com/team-approx-bayes/BayesBiNN,33,Training binary neural networks using the bayesian learning rule,"https://scholar.google.com/scholar?cluster=8866131573979767036&hl=en&as_sdt=0,33",7,2020
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning,23,icml,1,0,2023-06-17 03:57:26.327000,https://github.com/albertometelli/pfqi,3,Control frequency adaptation via action persistence in batch reinforcement learning,"https://scholar.google.com/scholar?cluster=6884047998353070413&hl=en&as_sdt=0,33",3,2020
Projective Preferential Bayesian Optimization,7,icml,0,1,2023-06-17 03:57:26.530000,https://github.com/AaltoPML/PPBO,10,Projective preferential bayesian optimization,"https://scholar.google.com/scholar?cluster=16344312867654899507&hl=en&as_sdt=0,5",8,2020
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing,0,icml,0,0,2023-06-17 03:57:26.732000,https://github.com/srph25/videoonenet,0,VideoOneNet: bidirectional convolutional recurrent onenet with trainable data steps for video processing,"https://scholar.google.com/scholar?cluster=1084769805460535145&hl=en&as_sdt=0,5",2,2020
Learning Reasoning Strategies in End-to-End Differentiable Proving,63,icml,17,3,2023-06-17 03:57:26.935000,https://github.com/uclnlp/ctp,47,Learning reasoning strategies in end-to-end differentiable proving,"https://scholar.google.com/scholar?cluster=16334802341623350418&hl=en&as_sdt=0,10",2,2020
Coresets for Data-efficient Training of Machine Learning Models,137,icml,18,4,2023-06-17 03:57:27.137000,https://github.com/baharanm/craig,47,Coresets for data-efficient training of machine learning models,"https://scholar.google.com/scholar?cluster=15062918067238617199&hl=en&as_sdt=0,14",1,2020
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules,53,icml,4,0,2023-06-17 03:57:27.339000,https://github.com/sarthmit/BRIMs,27,Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules,"https://scholar.google.com/scholar?cluster=15085852194314811643&hl=en&as_sdt=0,37",3,2020
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time,8,icml,0,0,2023-06-17 03:57:27.539000,https://github.com/DurstewitzLab/contPLRNN,0,Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time,"https://scholar.google.com/scholar?cluster=8416515873686618077&hl=en&as_sdt=0,44",1,2020
An end-to-end approach for the verification problem: learning the right distance,12,icml,1,0,2023-06-17 03:57:27.742000,https://github.com/joaomonteirof/e2e_verification,6,An end-to-end approach for the verification problem: learning the right distance,"https://scholar.google.com/scholar?cluster=18311458565256398722&hl=en&as_sdt=0,5",4,2020
Confidence-Aware Learning for Deep Neural Networks,89,icml,11,2,2023-06-17 03:57:27.943000,https://github.com/daintlab/confidence-aware-learning,62,Confidence-aware learning for deep neural networks,"https://scholar.google.com/scholar?cluster=7136169408479402844&hl=en&as_sdt=0,36",6,2020
Topological Autoencoders,111,icml,26,0,2023-06-17 03:57:28.145000,https://github.com/BorgwardtLab/topological-autoencoders,105,Topological autoencoders,"https://scholar.google.com/scholar?cluster=11510547932502602061&hl=en&as_sdt=0,10",7,2020
Fair Learning with Private Demographic Data,48,icml,1,0,2023-06-17 03:57:28.347000,https://github.com/husseinmozannar/fairlearn_private_data,4,Fair learning with private demographic data,"https://scholar.google.com/scholar?cluster=16497841133836187682&hl=en&as_sdt=0,5",2,2020
Consistent Estimators for Learning to Defer to an Expert,101,icml,7,15,2023-06-17 03:57:28.548000,https://github.com/clinicalml/learn-to-defer,9,Consistent estimators for learning to defer to an expert,"https://scholar.google.com/scholar?cluster=3621001929696373512&hl=en&as_sdt=0,5",3,2020
Missing Data Imputation using Optimal Transport,62,icml,11,1,2023-06-17 03:57:28.750000,https://github.com/BorisMuzellec/MissingDataOT,73,Missing data imputation using optimal transport,"https://scholar.google.com/scholar?cluster=1517478488560941748&hl=en&as_sdt=0,5",4,2020
Voice Separation with an Unknown Number of Multiple Speakers,145,icml,159,27,2023-06-17 03:57:28.952000,https://github.com/facebookresearch/svoice,1030,Voice separation with an unknown number of multiple speakers,"https://scholar.google.com/scholar?cluster=8245320586171214224&hl=en&as_sdt=0,21",24,2020
Reliable Fidelity and Diversity Metrics for Generative Models,147,icml,28,7,2023-06-17 03:57:29.153000,https://github.com/clovaai/generative-evaluation-prdc,207,Reliable fidelity and diversity metrics for generative models,"https://scholar.google.com/scholar?cluster=6046067727543252873&hl=en&as_sdt=0,5",9,2020
Bayesian Sparsification of Deep C-valued Networks,10,icml,25,7,2023-06-17 03:57:29.357000,https://github.com/ivannz/cplxmodule,119,Bayesian sparsification of deep c-valued networks,"https://scholar.google.com/scholar?cluster=17209924131548214610&hl=en&as_sdt=0,33",11,2020
Oracle Efficient Private Non-Convex Optimization,7,icml,1,0,2023-06-17 03:57:29.559000,https://github.com/giusevtr/private_objective_perturbation,3,Oracle efficient private non-convex optimization,"https://scholar.google.com/scholar?cluster=7786612400665657488&hl=en&as_sdt=0,5",0,2020
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization,25,icml,35,22,2023-06-17 03:57:29.761000,https://github.com/openopt/copt,125,Stochastic Frank-Wolfe for constrained finite-sum minimization,"https://scholar.google.com/scholar?cluster=611899428047262705&hl=en&as_sdt=0,14",12,2020
Aggregation of Multiple Knockoffs,15,icml,7,5,2023-06-17 03:57:29.963000,https://github.com/ja-che/hidimstat,20,Aggregation of multiple knockoffs,"https://scholar.google.com/scholar?cluster=656849439593762318&hl=en&as_sdt=0,5",7,2020
Knowing The What But Not The Where in Bayesian Optimization,34,icml,3,0,2023-06-17 03:57:30.166000,https://github.com/ntienvu/KnownOptimum_BO,13,Knowing the what but not the where in Bayesian optimization,"https://scholar.google.com/scholar?cluster=16424117469518186156&hl=en&as_sdt=0,33",1,2020
Robust Bayesian Classification Using An Optimistic Score Ratio,11,icml,0,0,2023-06-17 03:57:30.368000,https://github.com/nian-si/bsc,0,Robust bayesian classification using an optimistic score ratio,"https://scholar.google.com/scholar?cluster=7833733923868334694&hl=en&as_sdt=0,33",1,2020
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction,13,icml,7,2,2023-06-17 03:57:30.572000,https://github.com/deep-spin/lp-sparsemap,39,Lp-sparsemap: Differentiable relaxed optimization for sparse structured prediction,"https://scholar.google.com/scholar?cluster=13952332112683207065&hl=en&as_sdt=0,36",7,2020
Consistent Structured Prediction with Max-Min Margin Markov Networks,12,icml,5,1,2023-06-17 03:57:30.777000,https://github.com/alexnowakvila/maxminloss,7,Consistent structured prediction with max-min margin markov networks,"https://scholar.google.com/scholar?cluster=10738021504710900469&hl=en&as_sdt=0,10",2,2020
T-Basis: a Compact Representation for Neural Networks,22,icml,1,0,2023-06-17 03:57:30.992000,https://github.com/toshas/tbasis,8,T-basis: a compact representation for neural networks,"https://scholar.google.com/scholar?cluster=12293196328367856783&hl=en&as_sdt=0,5",1,2020
Interferometric Graph Transform: a Deep Unsupervised Graph Representation,6,icml,1,0,2023-06-17 03:57:31.205000,https://github.com/edouardoyallon/interferometric-graph-transform,9,Interferometric graph transform: a deep unsupervised graph representation,"https://scholar.google.com/scholar?cluster=7788892344484265680&hl=en&as_sdt=0,5",2,2020
Learning to Score Behaviors for Guided Policy Optimization,26,icml,7,0,2023-06-17 03:57:31.409000,https://github.com/behaviorguidedRL/BGRL,23,Learning to score behaviors for guided policy optimization,"https://scholar.google.com/scholar?cluster=7653224630549423499&hl=en&as_sdt=0,5",4,2020
Adversarial Mutual Information for Text Generation,4,icml,1,2,2023-06-17 03:57:31.657000,https://github.com/ZJULearning/AMI,7,Adversarial mutual information for text generation,"https://scholar.google.com/scholar?cluster=5510716302378812620&hl=en&as_sdt=0,32",3,2020
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis,14,icml,9,0,2023-06-17 03:57:31.877000,https://github.com/Rose-STL-Lab/mrtl,11,Multiresolution tensor learning for efficient and interpretable spatial analysis,"https://scholar.google.com/scholar?cluster=15097484700920257271&hl=en&as_sdt=0,10",4,2020
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning,46,icml,80,9,2023-06-17 03:57:32.105000,https://github.com/alex-petrenko/sample-factory,593,Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning,"https://scholar.google.com/scholar?cluster=7436378038868807375&hl=en&as_sdt=0,5",17,2020
Scalable Differential Privacy with Certified Robustness in Adversarial Learning,34,icml,1,0,2023-06-17 03:57:32.315000,https://github.com/haiphanNJIT/StoBatch,6,Scalable differential privacy with certified robustness in adversarial learning,"https://scholar.google.com/scholar?cluster=11508415782067363031&hl=en&as_sdt=0,48",3,2020
WaveFlow: A Compact Flow-based Model for Raw Audio,95,icml,82,0,2023-06-17 03:57:32.518000,https://github.com/PaddlePaddle/Parakeet,584,Waveflow: A compact flow-based model for raw audio,"https://scholar.google.com/scholar?cluster=15645705670677592172&hl=en&as_sdt=0,39",29,2020
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition,129,icml,7,0,2023-06-17 03:57:32.721000,https://github.com/vihari/csd,43,Efficient domain generalization via common-specific low-rank decomposition,"https://scholar.google.com/scholar?cluster=11307656152978308596&hl=en&as_sdt=0,47",3,2020
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning,82,icml,21,8,2023-06-17 03:57:32.923000,https://github.com/spitis/mrl,95,Maximum entropy gain exploration for long horizon multi-goal reinforcement learning,"https://scholar.google.com/scholar?cluster=11035896371402538645&hl=en&as_sdt=0,47",5,2020
Explaining Groups of Points in Low-Dimensional Representations,16,icml,2,1,2023-06-17 03:57:33.151000,https://github.com/GDPlumb/ELDR,7,Explaining groups of points in low-dimensional representations,"https://scholar.google.com/scholar?cluster=2769965454437760669&hl=en&as_sdt=0,24",4,2020
SoftSort: A Continuous Relaxation for the argsort Operator,30,icml,5,4,2023-06-17 03:57:33.353000,https://github.com/sprillo/softsort,31,Softsort: A continuous relaxation for the argsort operator,"https://scholar.google.com/scholar?cluster=16358906798054657773&hl=en&as_sdt=0,5",5,2020
Graph-based Nearest Neighbor Search: From Practice to Theory,34,icml,2,0,2023-06-17 03:57:33.554000,https://github.com/Shekhale/gbnns_theory,15,Graph-based nearest neighbor search: From practice to theory,"https://scholar.google.com/scholar?cluster=13724716068024753657&hl=en&as_sdt=0,5",0,2020
Deep Isometric Learning for Visual Recognition,42,icml,21,0,2023-06-17 03:57:33.757000,https://github.com/HaozhiQi/ISONet,143,Deep isometric learning for visual recognition,"https://scholar.google.com/scholar?cluster=11095100806384225671&hl=en&as_sdt=0,14",9,2020
Unsupervised Speech Decomposition via Triple Information Bottleneck,131,icml,93,27,2023-06-17 03:57:33.960000,https://github.com/auspicious3000/SpeechSplit,529,Unsupervised speech decomposition via triple information bottleneck,"https://scholar.google.com/scholar?cluster=6104818093122244998&hl=en&as_sdt=0,44",23,2020
DeepCoDA: personalized interpretability for compositional health data,8,icml,1,0,2023-06-17 03:57:34.162000,https://github.com/nphdang/DeepCoDA,6,Deepcoda: personalized interpretability for compositional health data,"https://scholar.google.com/scholar?cluster=1822616617548782028&hl=en&as_sdt=0,5",3,2020
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning,82,icml,2,0,2023-06-17 03:57:34.364000,https://github.com/adishs/icml2020_rl-policy-teaching_code,8,Policy teaching via environment poisoning: Training-time adversarial attacks against reinforcement learning,"https://scholar.google.com/scholar?cluster=2440833771930412039&hl=en&as_sdt=0,47",1,2020
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons,10,icml,0,0,2023-06-17 03:57:34.564000,https://github.com/WenboRen/Topk-Ranking-from-Pairwise-Comparisons,1,The Sample Complexity of Best- Items Selection from Pairwise Comparisons,"https://scholar.google.com/scholar?cluster=5765760591952820635&hl=en&as_sdt=0,5",1,2020
Overfitting in adversarially robust deep learning,555,icml,30,2,2023-06-17 03:57:34.771000,https://github.com/locuslab/robust_overfitting,145,Overfitting in adversarially robust deep learning,"https://scholar.google.com/scholar?cluster=3283552716843896977&hl=en&as_sdt=0,34",8,2020
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge,147,icml,13,1,2023-06-17 03:57:34.973000,https://github.com/laura-rieger/deep-explanation-penalization,120,Interpretations are useful: penalizing explanations to align neural networks with prior knowledge,"https://scholar.google.com/scholar?cluster=15865202666417121360&hl=en&as_sdt=0,33",8,2020
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training,53,icml,4,0,2023-06-17 03:57:35.176000,https://github.com/yuji-roh/fr-train,12,Fr-train: A mutual information-based approach to fair and robust training,"https://scholar.google.com/scholar?cluster=13680487688009337153&hl=en&as_sdt=0,33",3,2020
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning,20,icml,1,0,2023-06-17 03:57:35.378000,https://github.com/estherrolf/multi-objective-impact,5,Balancing competing objectives with noisy data: Score-based classifiers for welfare-aware machine learning,"https://scholar.google.com/scholar?cluster=13495786516885485801&hl=en&as_sdt=0,33",8,2020
Attentive Group Equivariant Convolutional Networks,61,icml,3,0,2023-06-17 03:57:35.579000,https://github.com/dwromero/att_gconvs,46,Attentive group equivariant convolutional networks,"https://scholar.google.com/scholar?cluster=7532982364611268025&hl=en&as_sdt=0,5",3,2020
Bayesian Optimisation over Multiple Continuous and Categorical Inputs,63,icml,5,2,2023-06-17 03:57:35.782000,https://github.com/rubinxin/CoCaBO_code,38,Bayesian optimisation over multiple continuous and categorical inputs,"https://scholar.google.com/scholar?cluster=6939944017464158601&hl=en&as_sdt=0,5",3,2020
Bounding the fairness and accuracy of classifiers from population statistics,12,icml,1,0,2023-06-17 03:57:35.985000,https://github.com/sivansabato/bfa,0,Bounding the fairness and accuracy of classifiers from population statistics,"https://scholar.google.com/scholar?cluster=2023767612415868273&hl=en&as_sdt=0,15",2,2020
Radioactive data: tracing through training,47,icml,9,3,2023-06-17 03:57:36.186000,https://github.com/facebookresearch/radioactive_data,37,Radioactive data: tracing through training,"https://scholar.google.com/scholar?cluster=10544737846821362051&hl=en&as_sdt=0,48",7,2020
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics,51,icml,0,0,2023-06-17 03:57:36.389000,https://github.com/saharaja/ICML2020-fairness,0,Measuring non-expert comprehension of machine learning fairness metrics,"https://scholar.google.com/scholar?cluster=9761297825118487455&hl=en&as_sdt=0,44",2,2020
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models,22,icml,6,2,2023-06-17 03:57:36.590000,https://github.com/usaito/counterfactual-cv,29,Counterfactual cross-validation: Stable model selection procedure for causal inference models,"https://scholar.google.com/scholar?cluster=10053699039608727761&hl=en&as_sdt=0,39",2,2020
Learning to Simulate Complex Physics with Graph Networks,658,icml,2436,170,2023-06-17 03:57:36.792000,https://github.com/deepmind/deepmind-research,11905,Learning to simulate complex physics with graph networks,"https://scholar.google.com/scholar?cluster=7841761417368333272&hl=en&as_sdt=0,5",336,2020
Discriminative Adversarial Search for Abstractive Summarization,24,icml,1868,365,2023-06-17 03:57:36.994000,https://github.com/microsoft/unilm,12786,Discriminative adversarial search for abstractive summarization,"https://scholar.google.com/scholar?cluster=2830447746758496884&hl=en&as_sdt=0,5",260,2020
Planning to Explore via Self-Supervised World Models,237,icml,26,12,2023-06-17 03:57:37.196000,https://github.com/ramanans1/plan2explore,201,Planning to explore via self-supervised world models,"https://scholar.google.com/scholar?cluster=804828726250878727&hl=en&as_sdt=0,33",14,2020
Lookahead-Bounded Q-learning,6,icml,1,0,2023-06-17 03:57:37.398000,https://github.com/ibrahim-elshar/LBQL_ICML2020,4,Lookahead-bounded q-learning,"https://scholar.google.com/scholar?cluster=15722192187033607775&hl=en&as_sdt=0,39",1,2020
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions,27,icml,5,2,2023-06-17 03:57:37.600000,https://github.com/shenzy08/PDO-eConvs,13,Pdo-econvs: Partial differential operator based equivariant convolutions,"https://scholar.google.com/scholar?cluster=8875071450506377272&hl=en&as_sdt=0,14",1,2020
Educating Text Autoencoders: Latent Representation Guidance via Denoising,44,icml,39,3,2023-06-17 03:57:37.801000,https://github.com/shentianxiao/text-autoencoders,185,Educating text autoencoders: Latent representation guidance via denoising,"https://scholar.google.com/scholar?cluster=3322516432269705271&hl=en&as_sdt=0,31",9,2020
PowerNorm: Rethinking Batch Normalization in Transformers,55,icml,16,2,2023-06-17 03:57:38.004000,https://github.com/sIncerass/powernorm,107,Powernorm: Rethinking batch normalization in transformers,"https://scholar.google.com/scholar?cluster=11876493237600488243&hl=en&as_sdt=0,5",8,2020
Incremental Sampling Without Replacement for Sequence Models,14,icml,3,0,2023-06-17 03:57:38.206000,https://github.com/google-research/unique-randomizer,6,Incremental sampling without replacement for sequence models,"https://scholar.google.com/scholar?cluster=570267648910120463&hl=en&as_sdt=0,5",6,2020
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective,74,icml,6,24,2023-06-17 03:57:38.407000,https://github.com/bfshi/InfoDrop,121,Informative dropout for robust representation learning: A shape-bias perspective,"https://scholar.google.com/scholar?cluster=14939290265495016487&hl=en&as_sdt=0,11",10,2020
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation,18,icml,2,0,2023-06-17 03:57:38.609000,https://github.com/wenxianxian/demvae,25,Dispersed exponential family mixture vaes for interpretable text generation,"https://scholar.google.com/scholar?cluster=8941211277689628269&hl=en&as_sdt=0,5",3,2020
Predictive Coding for Locally-Linear Control,11,icml,3,0,2023-06-17 03:57:38.810000,https://github.com/VinAIResearch/PC3-pytorch,16,Predictive coding for locally-linear control,"https://scholar.google.com/scholar?cluster=8037643226796861111&hl=en&as_sdt=0,5",3,2020
A Generative Model for Molecular Distance Geometry,68,icml,13,5,2023-06-17 03:57:39.013000,https://github.com/gncs/graphdg,32,A generative model for molecular distance geometry,"https://scholar.google.com/scholar?cluster=11522427677669311015&hl=en&as_sdt=0,5",2,2020
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics,82,icml,22,7,2023-06-17 03:57:39.218000,https://github.com/gncs/molgym,94,Reinforcement learning for molecular design guided by quantum mechanics,"https://scholar.google.com/scholar?cluster=2647402113412769429&hl=en&as_sdt=0,7",5,2020
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise,40,icml,0,0,2023-06-17 03:57:39.421000,https://github.com/umutsimsekli/fuld,0,Fractional underdamped langevin dynamics: Retargeting sgd with momentum under heavy-tailed gradient noise,"https://scholar.google.com/scholar?cluster=12546091337586051753&hl=en&as_sdt=0,5",1,2020
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis,22,icml,2,0,2023-06-17 03:57:39.627000,https://github.com/travelbureau/f0_icml_code,5,FormulaZero: Distributionally robust online adaptation via offline population synthesis,"https://scholar.google.com/scholar?cluster=4155022533808347163&hl=en&as_sdt=0,47",4,2020
"Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure",45,icml,21,1,2023-06-17 03:57:39.829000,https://github.com/google/madi,62,"Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure","https://scholar.google.com/scholar?cluster=3739930474828740815&hl=en&as_sdt=0,33",10,2020
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation,10,icml,0,1,2023-06-17 03:57:40.031000,https://github.com/GeorgiosSmyrnis/multiclass_minimization_icml2020,1,Multiclass neural network minimization via tropical newton polytope approximation,"https://scholar.google.com/scholar?cluster=2547708256108168456&hl=en&as_sdt=0,31",2,2020
Bridging the Gap Between f-GANs and Wasserstein GANs,36,icml,4,0,2023-06-17 03:57:40.234000,https://github.com/ermongroup/f-wgan,14,Bridging the gap between f-gans and wasserstein gans,"https://scholar.google.com/scholar?cluster=15572821134317773979&hl=en&as_sdt=0,44",6,2020
Hypernetwork approach to generating point clouds,25,icml,4,1,2023-06-17 03:57:40.435000,https://github.com/gmum/3d-point-clouds-HyperCloud,26,Hypernetwork approach to generating point clouds,"https://scholar.google.com/scholar?cluster=1381462816428622645&hl=en&as_sdt=0,10",7,2020
Which Tasks Should Be Learned Together in Multi-task Learning?,333,icml,13,7,2023-06-17 03:57:40.637000,https://github.com/tstandley/taskgrouping,89,Which tasks should be learned together in multi-task learning?,"https://scholar.google.com/scholar?cluster=11792880914150945674&hl=en&as_sdt=0,5",2,2020
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information,8,icml,1,0,2023-06-17 03:57:40.839000,https://github.com/karlstratos/ammi,11,Learning discrete structured representations by adversarially maximizing mutual information,"https://scholar.google.com/scholar?cluster=10269620235757517949&hl=en&as_sdt=0,10",2,2020
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks,101,icml,0,0,2023-06-17 03:57:41.041000,https://github.com/davidstutz/icml2020-confidence-calibrated-adversarial-training,9,Confidence-calibrated adversarial training: Generalizing to unseen attacks,"https://scholar.google.com/scholar?cluster=14154958119332735093&hl=en&as_sdt=0,5",4,2020
Adaptive Estimator Selection for Off-Policy Evaluation,23,icml,2,0,2023-06-17 03:57:41.249000,https://github.com/VowpalWabbit/slope-experiments,3,Adaptive estimator selection for off-policy evaluation,"https://scholar.google.com/scholar?cluster=578911518697866009&hl=en&as_sdt=0,49",4,2020
Multi-Agent Routing Value Iteration Network,33,icml,14,0,2023-06-17 03:57:41.451000,https://github.com/uber/MARVIN,50,Multi-agent routing value iteration network,"https://scholar.google.com/scholar?cluster=16960600258669760447&hl=en&as_sdt=0,5",5,2020
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery,18,icml,2,0,2023-06-17 03:57:41.652000,https://github.com/tagas/bQCD,2,Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery,"https://scholar.google.com/scholar?cluster=15617920136874649205&hl=en&as_sdt=0,5",1,2020
DropNet: Reducing Neural Network Complexity via Iterative Pruning,25,icml,7,0,2023-06-17 03:57:41.854000,https://github.com/tanchongmin/DropNet,14,Dropnet: Reducing neural network complexity via iterative pruning,"https://scholar.google.com/scholar?cluster=5847979658470311835&hl=en&as_sdt=0,5",1,2020
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies,13,icml,3,0,2023-06-17 03:57:42.056000,https://github.com/MLD3/RL-Set-Valued-Policy,12,Clinician-in-the-loop decision making: Reinforcement learning with near-optimal set-valued policies,"https://scholar.google.com/scholar?cluster=2625470057202017453&hl=en&as_sdt=0,5",2,2020
Variational Imitation Learning with Diverse-quality Demonstrations,26,icml,3,0,2023-06-17 03:57:42.258000,https://github.com/voot-t/vild_code,13,Variational imitation learning with diverse-quality demonstrations,"https://scholar.google.com/scholar?cluster=17459982405311544718&hl=en&as_sdt=0,5",2,2020
Inductive Relation Prediction by Subgraph Reasoning,213,icml,50,9,2023-06-17 03:57:42.460000,https://github.com/kkteru/grail,166,Inductive relation prediction by subgraph reasoning,"https://scholar.google.com/scholar?cluster=14042316464156946923&hl=en&as_sdt=0,33",4,2020
Few-shot Domain Adaptation by Causal Mechanism Transfer,71,icml,13,41,2023-06-17 03:57:42.662000,https://github.com/takeshi-teshima/few-shot-domain-adaptation-by-causal-mechanism-transfer,34,Few-shot domain adaptation by causal mechanism transfer,"https://scholar.google.com/scholar?cluster=15173839596303603057&hl=en&as_sdt=0,5",3,2020
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions,22,icml,1,0,2023-06-17 03:57:42.864000,https://github.com/ds2p/dea,2,Convolutional dictionary learning based auto-encoders for natural exponential-family distributions,"https://scholar.google.com/scholar?cluster=17717998361857407154&hl=en&as_sdt=0,47",3,2020
Choice Set Optimization Under Discrete Choice Models of Group Decisions,6,icml,1,0,2023-06-17 03:57:43.086000,https://github.com/tomlinsonk/choice-set-opt,9,Choice set optimization under discrete choice models of group decisions,"https://scholar.google.com/scholar?cluster=9509628446146574324&hl=en&as_sdt=0,5",5,2020
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics,69,icml,12,6,2023-06-17 03:57:43.288000,https://github.com/KrishnaswamyLab/TrajectoryNet,72,Trajectorynet: A dynamic optimal transport network for modeling cellular dynamics,"https://scholar.google.com/scholar?cluster=13927969516648778690&hl=en&as_sdt=0,33",8,2020
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances,29,icml,9,3,2023-06-17 03:57:43.490000,https://github.com/tgcsaba/GPSig,37,Bayesian learning from sequential data using gaussian processes with signature covariances,"https://scholar.google.com/scholar?cluster=5665279431482036771&hl=en&as_sdt=0,33",3,2020
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations,75,icml,5,0,2023-06-17 03:57:43.693000,https://github.com/ftramer/Excessive-Invariance,25,Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations,"https://scholar.google.com/scholar?cluster=12838198146332206865&hl=en&as_sdt=0,47",6,2020
Bayesian Differential Privacy for Machine Learning,58,icml,4,0,2023-06-17 03:57:43.895000,https://github.com/AlekseiTriastcyn/bayesian-differential-privacy,16,Bayesian differential privacy for machine learning,"https://scholar.google.com/scholar?cluster=2037504457051740866&hl=en&as_sdt=0,5",2,2020
Single Point Transductive Prediction,2,icml,0,0,2023-06-17 03:57:44.098000,https://github.com/nileshtrip/SPTransducPredCode,3,Single point transductive prediction,"https://scholar.google.com/scholar?cluster=4391877212575021385&hl=en&as_sdt=0,36",2,2020
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks,111,icml,2,0,2023-06-17 03:57:44.299000,https://github.com/MadryLab/ImageNetMultiLabel,28,From imagenet to image classification: Contextualizing progress on benchmarks,"https://scholar.google.com/scholar?cluster=17622651192510371827&hl=en&as_sdt=0,5",9,2020
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network,11,icml,0,0,2023-06-17 03:57:44.502000,https://github.com/TuKo/dRNN,5,Approximating stacked and bidirectional recurrent architectures with the delayed recurrent neural network,"https://scholar.google.com/scholar?cluster=1436978091908679295&hl=en&as_sdt=0,14",3,2020
Uncertainty Estimation Using a Single Deep Deterministic Neural Network,304,icml,32,2,2023-06-17 03:57:44.703000,https://github.com/y0ast/deterministic-uncertainty-quantification,239,Uncertainty estimation using a single deep deterministic neural network,"https://scholar.google.com/scholar?cluster=16222536793080297152&hl=en&as_sdt=0,32",7,2020
Born-Again Tree Ensembles,50,icml,5,6,2023-06-17 03:57:44.937000,https://github.com/vidalt/BA-Trees,56,Born-again tree ensembles,"https://scholar.google.com/scholar?cluster=16560127278940498393&hl=en&as_sdt=0,5",4,2020
New Oracle-Efficient Algorithms for Private Synthetic Data Release,45,icml,2,0,2023-06-17 03:57:45.141000,https://github.com/giusevtr/fem,7,New oracle-efficient algorithms for private synthetic data release,"https://scholar.google.com/scholar?cluster=18163576365323257065&hl=en&as_sdt=0,36",2,2020
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space,275,icml,53,16,2023-06-17 03:57:45.343000,https://github.com/anvoynov/GANLatentDiscovery,406,Unsupervised discovery of interpretable directions in the gan latent space,"https://scholar.google.com/scholar?cluster=13408893088338762457&hl=en&as_sdt=0,5",10,2020
Safe Reinforcement Learning in Constrained Markov Decision Processes,87,icml,8,0,2023-06-17 03:57:45.552000,https://github.com/akifumi-wachi-4/safe_near_optimal_mdp,38,Safe reinforcement learning in constrained Markov decision processes,"https://scholar.google.com/scholar?cluster=13376476556539351032&hl=en&as_sdt=0,44",1,2020
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching,76,icml,7,0,2023-06-17 03:57:45.755000,https://github.com/PeisongWang/BitSplit,38,Towards accurate post-training network quantization via bit-split and stitching,"https://scholar.google.com/scholar?cluster=958273940309910649&hl=en&as_sdt=0,5",2,2020
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles,137,icml,32,14,2023-06-17 03:57:45.958000,https://github.com/TonghanWang/ROMA,136,Roma: Multi-agent reinforcement learning with emergent roles,"https://scholar.google.com/scholar?cluster=10158010923788252116&hl=en&as_sdt=0,5",4,2020
Continuously Indexed Domain Adaptation,77,icml,18,3,2023-06-17 03:57:46.161000,https://github.com/hehaodele/CIDA,108,Continuously indexed domain adaptation,"https://scholar.google.com/scholar?cluster=3441708260891083426&hl=en&as_sdt=0,33",6,2020
Frustratingly Simple Few-Shot Object Detection,306,icml,215,56,2023-06-17 03:57:46.362000,https://github.com/ucbdrive/few-shot-object-detection,961,Frustratingly simple few-shot object detection,"https://scholar.google.com/scholar?cluster=13847197306360708920&hl=en&as_sdt=0,5",28,2020
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere,946,icml,34,0,2023-06-17 03:57:46.578000,https://github.com/SsnL/align_uniform,354,Understanding contrastive representation learning through alignment and uniformity on the hypersphere,"https://scholar.google.com/scholar?cluster=5122266742982340747&hl=en&as_sdt=0,3",11,2020
Enhanced POET: Open-ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions,73,icml,51,5,2023-06-17 03:57:46.781000,https://github.com/uber-research/poet,233,Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions,"https://scholar.google.com/scholar?cluster=17583648324422024748&hl=en&as_sdt=0,44",15,2020
Haar Graph Pooling,62,icml,5,6,2023-06-17 03:57:46.983000,https://github.com/YuGuangWang/HaarPool,9,Haar graph pooling,"https://scholar.google.com/scholar?cluster=196487871230108211&hl=en&as_sdt=0,34",2,2020
Deep Streaming Label Learning,29,icml,2,1,2023-06-17 03:57:47.187000,https://github.com/DSLLcode/DSLL,5,Deep streaming label learning,"https://scholar.google.com/scholar?cluster=13962185185630699460&hl=en&as_sdt=0,5",1,2020
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates,7,icml,0,0,2023-06-17 03:57:47.389000,https://github.com/BoXHED/BoXHED1.0,6,BoXHED: Boosted eXact hazard estimator with dynamic covariates,"https://scholar.google.com/scholar?cluster=4269847056654945250&hl=en&as_sdt=0,3",1,2020
Optimizing Data Usage via Differentiable Rewards,41,icml,0,0,2023-06-17 03:57:47.591000,https://github.com/cindyxinyiwang/DataSelection,2,Optimizing data usage via differentiable rewards,"https://scholar.google.com/scholar?cluster=4407582239871274683&hl=en&as_sdt=0,11",1,2020
Loss Function Search for Face Recognition,45,icml,8,5,2023-06-17 03:57:47.794000,https://github.com/tiandunx/loss_function_search,37,Loss function search for face recognition,"https://scholar.google.com/scholar?cluster=4661570129688704480&hl=en&as_sdt=0,31",3,2020
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling,20,icml,6,2,2023-06-17 03:57:47.996000,https://github.com/AutumnWu/Streamlined-Off-Policy-Learning,18,Striving for simplicity and performance in off-policy DRL: Output normalization and non-uniform sampling,"https://scholar.google.com/scholar?cluster=11197578875286418478&hl=en&as_sdt=0,5",4,2020
Thompson Sampling via Local Uncertainty,16,icml,2,1,2023-06-17 03:57:48.199000,https://github.com/Zhendong-Wang/Thompson-Sampling-via-Local-Uncertainty,3,Thompson sampling via local uncertainty,"https://scholar.google.com/scholar?cluster=15106467344904481899&hl=en&as_sdt=0,10",1,2020
The Implicit and Explicit Regularization Effects of Dropout,91,icml,2,0,2023-06-17 03:57:48.400000,https://github.com/cwein3/dropout-analytical,4,The implicit and explicit regularization effects of dropout,"https://scholar.google.com/scholar?cluster=7315580872864689276&hl=en&as_sdt=0,44",2,2020
How Good is the Bayes Posterior in Deep Neural Networks Really?,274,icml,7322,1026,2023-06-17 03:57:48.601000,https://github.com/google-research/google-research,29791,How good is the bayes posterior in deep neural networks really?,"https://scholar.google.com/scholar?cluster=11185773961293705941&hl=en&as_sdt=0,36",727,2020
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes,12,icml,12,2,2023-06-17 03:57:48.804000,https://github.com/AaltoML/kalman-jax,86,State space expectation propagation: Efficient inference schemes for temporal Gaussian processes,"https://scholar.google.com/scholar?cluster=3634962580178312612&hl=en&as_sdt=0,5",10,2020
Efficiently sampling functions from Gaussian process posteriors,107,icml,16,0,2023-06-17 03:57:49.006000,https://github.com/j-wilson/GPflowSampling,57,Efficiently sampling functions from Gaussian process posteriors,"https://scholar.google.com/scholar?cluster=15698699983460471132&hl=en&as_sdt=0,39",3,2020
Obtaining Adjustable Regularization for Free via Iterate Averaging,4,icml,1,0,2023-06-17 03:57:49.208000,https://github.com/uuujf/IterAvg,3,Obtaining adjustable regularization for free via iterate averaging,"https://scholar.google.com/scholar?cluster=8907876046676470481&hl=en&as_sdt=0,23",1,2020
DeltaGrad: Rapid retraining of machine learning models,94,icml,1,1,2023-06-17 03:57:49.410000,https://github.com/thuwuyinjun/DeltaGrad,19,Deltagrad: Rapid retraining of machine learning models,"https://scholar.google.com/scholar?cluster=5989632010826923243&hl=en&as_sdt=0,5",1,2020
On the Noisy Gradient Descent that Generalizes as SGD,66,icml,2,0,2023-06-17 03:57:49.612000,https://github.com/uuujf/MultiNoise,4,On the noisy gradient descent that generalizes as sgd,"https://scholar.google.com/scholar?cluster=7998772173539396288&hl=en&as_sdt=0,5",2,2020
Stronger and Faster Wasserstein Adversarial Attacks,18,icml,9,1,2023-06-17 03:57:49.813000,https://github.com/watml/fast-wasserstein-adversarial,21,Stronger and faster wasserstein adversarial attacks,"https://scholar.google.com/scholar?cluster=5877536134148697532&hl=en&as_sdt=0,31",5,2020
On the Generalization Effects of Linear Transformations in Data Augmentation,57,icml,6,3,2023-06-17 03:57:50.016000,https://github.com/SenWu/dauphin,28,On the generalization effects of linear transformations in data augmentation,"https://scholar.google.com/scholar?cluster=18304073580439494047&hl=en&as_sdt=0,5",5,2020
Generative Flows with Matrix Exponential,4,icml,0,0,2023-06-17 03:57:50.218000,https://github.com/changyi7231/MEF,10,Generative flows with matrix exponential,"https://scholar.google.com/scholar?cluster=5544738884567808407&hl=en&as_sdt=0,5",1,2020
Maximum-and-Concatenation Networks,1,icml,0,0,2023-06-17 03:57:50.422000,https://github.com/XingyuXie/Maximum-and-Concatenation-Networks,3,Maximum-and-concatenation networks,"https://scholar.google.com/scholar?cluster=6894098060248560789&hl=en&as_sdt=0,24",3,2020
Zeno++: Robust Fully Asynchronous SGD,74,icml,2,0,2023-06-17 03:57:50.623000,https://github.com/xcgoner/iclr2020_zeno_async,11,Zeno++: Robust fully asynchronous SGD,"https://scholar.google.com/scholar?cluster=6498141081528459239&hl=en&as_sdt=0,44",3,2020
On Variational Learning of Controllable Representations for Text without Supervision,42,icml,7,2,2023-06-17 03:57:50.825000,https://github.com/BorealisAI/CP-VAE,26,On variational learning of controllable representations for text without supervision,"https://scholar.google.com/scholar?cluster=2089630781496630830&hl=en&as_sdt=0,7",5,2020
Class-Weighted Classification: Trade-offs and Robust Approaches,27,icml,1,0,2023-06-17 03:57:51.027000,https://github.com/neilzxu/robust_weighted_classification,6,Class-weighted classification: Trade-offs and robust approaches,"https://scholar.google.com/scholar?cluster=11254113557179327347&hl=en&as_sdt=0,33",3,2020
Learning Autoencoders with Relational Regularization,42,icml,5,1,2023-06-17 03:57:51.230000,https://github.com/HongtengXu/Relational-AutoEncoders,39,Learning autoencoders with relational regularization,"https://scholar.google.com/scholar?cluster=12327328629265717488&hl=en&as_sdt=0,5",3,2020
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control,61,icml,22,2,2023-06-17 03:57:51.434000,https://github.com/mit-gfx/PGMORL,75,Prediction-guided multi-objective reinforcement learning for continuous robot control,"https://scholar.google.com/scholar?cluster=7336223321111703903&hl=en&as_sdt=0,21",18,2020
MetaFun: Meta-Learning with Iterative Functional Updates,53,icml,1,0,2023-06-17 03:57:51.637000,https://github.com/jinxu06/metafun-tensorflow,15,Metafun: Meta-learning with iterative functional updates,"https://scholar.google.com/scholar?cluster=4986964761080027704&hl=en&as_sdt=0,5",3,2020
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization,29,icml,3,1,2023-06-17 03:57:51.839000,https://github.com/tianjuxue/AmorFEA,10,Amortized finite element analysis for fast pde-constrained optimization,"https://scholar.google.com/scholar?cluster=14411842717926650131&hl=en&as_sdt=0,44",3,2020
Feature Selection using Stochastic Gates,83,icml,20,4,2023-06-17 03:57:52.041000,https://github.com/runopti/stg,74,Feature selection using stochastic gates,"https://scholar.google.com/scholar?cluster=3895875359750859329&hl=en&as_sdt=0,34",4,2020
Energy-Based Processes for Exchangeable Data,8,icml,7322,1026,2023-06-17 03:57:52.244000,https://github.com/google-research/google-research,29791,Energy-based processes for exchangeable data,"https://scholar.google.com/scholar?cluster=11717820488260195326&hl=en&as_sdt=0,5",727,2020
Randomized Smoothing of All Shapes and Sizes,141,icml,6,1,2023-06-17 03:57:52.446000,https://github.com/tonyduan/rs4a,48,Randomized smoothing of all shapes and sizes,"https://scholar.google.com/scholar?cluster=4321255830555154678&hl=en&as_sdt=0,21",2,2020
Improving Molecular Design by Stochastic Iterative Target Augmentation,14,icml,4,0,2023-06-17 03:57:52.648000,https://github.com/yangkevin2/icml2020-stochastic-iterative-target-augmentation,8,Improving molecular design by stochastic iterative target augmentation,"https://scholar.google.com/scholar?cluster=13262578872318506866&hl=en&as_sdt=0,5",3,2020
Multi-Agent Determinantal Q-Learning,60,icml,7,12,2023-06-17 03:57:52.850000,https://github.com/QDPP-GitHub/QDPP,40,Multi-agent determinantal q-learning,"https://scholar.google.com/scholar?cluster=15130986787127087305&hl=en&as_sdt=0,33",2,2020
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks,135,icml,7,2,2023-06-17 03:57:53.052000,https://github.com/yaodongyu/Rethink-BiasVariance-Tradeoff,51,Rethinking bias-variance trade-off for generalization of neural networks,"https://scholar.google.com/scholar?cluster=7345683172232852767&hl=en&as_sdt=0,25",4,2020
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks,20,icml,4,1,2023-06-17 03:57:53.254000,https://github.com/thuml/transferable-memory,20,Unsupervised transfer learning for spatiotemporal predictive networks,"https://scholar.google.com/scholar?cluster=11334443058124456085&hl=en&as_sdt=0,21",4,2020
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification,30,icml,2,3,2023-06-17 03:57:53.457000,https://github.com/huiyegit/APLC_XLNet,14,Pretrained generalized autoregressive model with adaptive probabilistic label clusters for extreme multi-label text classification,"https://scholar.google.com/scholar?cluster=11309810770103233080&hl=en&as_sdt=0,5",1,2020
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection,81,icml,7,1,2023-06-17 03:57:53.660000,https://github.com/lushleaf/Network-Pruning-Greedy-Forward-Selection,20,Good subnetworks provably exist: Pruning via greedy forward selection,"https://scholar.google.com/scholar?cluster=9077539701453917687&hl=en&as_sdt=0,5",2,2020
Data Valuation using Reinforcement Learning,109,icml,7322,1026,2023-06-17 03:57:53.862000,https://github.com/google-research/google-research,29791,Data valuation using reinforcement learning,"https://scholar.google.com/scholar?cluster=12792068149668296468&hl=en&as_sdt=0,5",727,2020
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning,40,icml,8,2,2023-06-17 03:57:54.063000,https://github.com/EdwinKim3069/XtarNet,27,Xtarnet: Learning to extract task-adaptive representation for incremental few-shot learning,"https://scholar.google.com/scholar?cluster=14540039022540446073&hl=en&as_sdt=0,5",3,2020
When Does Self-Supervision Help Graph Convolutional Networks?,161,icml,26,0,2023-06-17 03:57:54.266000,https://github.com/Shen-Lab/SS-GCNs,105,When does self-supervision help graph convolutional networks?,"https://scholar.google.com/scholar?cluster=8359089573172587095&hl=en&as_sdt=0,33",4,2020
Graph Structure of Neural Networks,108,icml,33,0,2023-06-17 03:57:54.469000,https://github.com/facebookresearch/graph2nn,142,Graph structure of neural networks,"https://scholar.google.com/scholar?cluster=4649234253279793186&hl=en&as_sdt=0,5",15,2020
Intrinsic Reward Driven Imitation Learning via Generative Model,33,icml,4,0,2023-06-17 03:57:54.671000,https://github.com/xingruiyu/GIRIL,12,Intrinsic reward driven imitation learning via generative model,"https://scholar.google.com/scholar?cluster=3469994683333919574&hl=en&as_sdt=0,16",3,2020
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters,63,icml,22,5,2023-06-17 03:57:54.873000,https://github.com/Wenhui-Yu/LCFN,67,Graph convolutional network for recommendation with low-pass collaborative filters,"https://scholar.google.com/scholar?cluster=1889227241401545976&hl=en&as_sdt=0,44",1,2020
Training Deep Energy-Based Models with f-Divergence Minimization,34,icml,6,4,2023-06-17 03:57:55.093000,https://github.com/ermongroup/f-EBM,35,Training deep energy-based models with f-divergence minimization,"https://scholar.google.com/scholar?cluster=2539049001962282394&hl=en&as_sdt=0,45",7,2020
Graph Random Neural Features for Distance-Preserving Graph Representations,11,icml,0,0,2023-06-17 03:57:55.295000,https://github.com/dzambon/graph-random-neural-features,6,Graph random neural features for distance-preserving graph representations,"https://scholar.google.com/scholar?cluster=2137393059005426125&hl=en&as_sdt=0,34",2,2020
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing,9,icml,0,0,2023-06-17 03:57:55.497000,https://github.com/UCLA-StarAI/mpwmi,4,Scaling up hybrid probabilistic inference with logical and arithmetic constraints via message passing,"https://scholar.google.com/scholar?cluster=11266053605918005936&hl=en&as_sdt=0,5",5,2020
Learning Calibratable Policies using Programmatic Style-Consistency,12,icml,3,0,2023-06-17 03:57:55.702000,https://github.com/ezhan94/calibratable-style-consistency,7,Learning calibratable policies using programmatic style-consistency,"https://scholar.google.com/scholar?cluster=14384068625001787252&hl=en&as_sdt=0,14",3,2020
Robustness to Programmable String Transformations via Augmented Abstract Training,12,icml,1,0,2023-06-17 03:57:55.905000,https://github.com/ForeverZyh/A3T,2,Robustness to programmable string transformations via augmented abstract training,"https://scholar.google.com/scholar?cluster=8464081788378179758&hl=en&as_sdt=0,5",2,2020
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning,119,icml,4,2,2023-06-17 03:57:56.107000,https://github.com/zhang64-llnl/Mix-n-Match-Calibration,28,Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning,"https://scholar.google.com/scholar?cluster=11733441465519935785&hl=en&as_sdt=0,5",4,2020
Self-Attentive Hawkes Process,135,icml,13,4,2023-06-17 03:57:56.310000,https://github.com/QiangAIResearcher/sahp_repo,41,Self-attentive Hawkes process,"https://scholar.google.com/scholar?cluster=10015751221024050727&hl=en&as_sdt=0,47",2,2020
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values,69,icml,658,6,2023-06-17 03:57:56.512000,https://github.com/ShangtongZhang/DeepRL,2943,Gradientdice: Rethinking generalized offline estimation of stationary values,"https://scholar.google.com/scholar?cluster=13399124962585883315&hl=en&as_sdt=0,5",93,2020
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation,39,icml,658,6,2023-06-17 03:57:56.714000,https://github.com/ShangtongZhang/DeepRL,2943,Provably convergent two-timescale off-policy actor-critic with function approximation,"https://scholar.google.com/scholar?cluster=13566441396966994806&hl=en&as_sdt=0,44",93,2020
Invariant Causal Prediction for Block MDPs,82,icml,9,0,2023-06-17 03:57:56.916000,https://github.com/facebookresearch/icp-block-mdp,43,Invariant causal prediction for block mdps,"https://scholar.google.com/scholar?cluster=18252595177085256687&hl=en&as_sdt=0,5",8,2020
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods,28,icml,8,4,2023-06-17 03:57:57.119000,https://github.com/razhangwei/CAUSE,22,Cause: Learning granger causality from event sequences using attribution methods,"https://scholar.google.com/scholar?cluster=1620742205028282603&hl=en&as_sdt=0,5",1,2020
Perceptual Generative Autoencoders,28,icml,1,0,2023-06-17 03:57:57.321000,https://github.com/zj10/PGA,23,Perceptual generative autoencoders,"https://scholar.google.com/scholar?cluster=8244017166037108075&hl=en&as_sdt=0,5",2,2020
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization,1245,icml,309,101,2023-06-17 03:57:57.524000,https://github.com/google-research/pegasus,1505,Pegasus: Pre-training with extracted gap-sentences for abstractive summarization,"https://scholar.google.com/scholar?cluster=6497734628006555281&hl=en&as_sdt=0,23",49,2020
On Leveraging Pretrained GANs for Generation with Limited Data,65,icml,6,2,2023-06-17 03:57:57.726000,https://github.com/MiaoyunZhao/GANTransferLimitedData,59,On leveraging pretrained GANs for generation with limited data,"https://scholar.google.com/scholar?cluster=16391058196447072580&hl=en&as_sdt=0,10",3,2020
Feature Quantization Improves GAN Training,33,icml,30,6,2023-06-17 03:57:57.930000,https://github.com/YangNaruto/FQ-GAN,169,Feature quantization improves gan training,"https://scholar.google.com/scholar?cluster=18271199409635968326&hl=en&as_sdt=0,31",11,2020
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion,11,icml,1,0,2023-06-17 03:57:58.132000,https://github.com/enosair/gdp-edgeworth,1,Sharp composition bounds for Gaussian differential privacy via edgeworth expansion,"https://scholar.google.com/scholar?cluster=9890314862207483858&hl=en&as_sdt=0,33",2,2020
Error-Bounded Correction of Noisy Labels,76,icml,5,3,2023-06-17 03:57:58.334000,https://github.com/pingqingsheng/LRT,15,Error-bounded correction of noisy labels,"https://scholar.google.com/scholar?cluster=16003512579511208211&hl=en&as_sdt=0,33",2,2020
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time,11,icml,6,3,2023-06-17 03:57:58.536000,https://github.com/CQUlearningsystemgroup/YicongPeng,35,Monet3d: Towards accurate monocular 3d object localization in real time,"https://scholar.google.com/scholar?cluster=16905032404731743832&hl=en&as_sdt=0,11",6,2020
Nonparametric Score Estimators,20,icml,1,0,2023-06-17 03:57:58.738000,https://github.com/miskcoo/kscore,34,Nonparametric score estimators,"https://scholar.google.com/scholar?cluster=497538758665413874&hl=en&as_sdt=0,14",5,2020
Robust Outlier Arm Identification,2,icml,0,0,2023-06-17 03:57:58.941000,https://github.com/yinglunz/ROAI_ICML2020,1,Robust outlier arm identification,"https://scholar.google.com/scholar?cluster=11900711973456670658&hl=en&as_sdt=0,11",1,2020
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health,9,icml,1,0,2023-06-17 03:57:59.144000,https://github.com/lz2379/Mhealth,1,Causal effect estimation and optimal dose suggestions in mobile health,"https://scholar.google.com/scholar?cluster=15932963727789756281&hl=en&as_sdt=0,39",1,2020
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization,21,icml,4,1,2023-06-17 03:57:59.346000,https://github.com/schzhu/learning-adversarially-robust-representations,20,Learning adversarially robust representations via worst-case mutual information maximization,"https://scholar.google.com/scholar?cluster=16073902151794610018&hl=en&as_sdt=0,5",4,2020
Laplacian Regularized Few-Shot Learning,123,icml,8,2,2023-06-17 03:57:59.547000,https://github.com/imtiazziko/LaplacianShot,76,Laplacian regularized few-shot learning,"https://scholar.google.com/scholar?cluster=1752522898167620276&hl=en&as_sdt=0,5",4,2020
Transformer Hawkes Process,153,icml,43,14,2023-06-17 03:57:59.749000,https://github.com/SimiaoZuo/Transformer-Hawkes-Process,129,Transformer hawkes process,"https://scholar.google.com/scholar?cluster=16348815210194084709&hl=en&as_sdt=0,33",7,2020
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling,33,icml,2,2,2023-06-17 04:13:07.614000,https://github.com/cair/PyTsetlinMachineCUDA,37,Massively parallel and asynchronous tsetlin machine architecture supporting almost constant-time scaling,"https://scholar.google.com/scholar?cluster=14399815899714278833&hl=en&as_sdt=0,5",8,2021
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning,19,icml,7,3,2023-06-17 04:13:07.817000,https://github.com/IdanAchituve/GP-Tree,27,Gp-tree: A gaussian process classifier for few-shot incremental learning,"https://scholar.google.com/scholar?cluster=3252666331118779321&hl=en&as_sdt=0,5",1,2021
Towards Rigorous Interpretations: a Formalisation of Feature Attribution,9,icml,1,0,2023-06-17 04:13:08.019000,https://github.com/DariusAf/functional_attribution,6,Towards rigorous interpretations: a formalisation of feature attribution,"https://scholar.google.com/scholar?cluster=6443235161573305083&hl=en&as_sdt=0,5",3,2021
Sparse Bayesian Learning via Stepwise Regression,3,icml,1,1,2023-06-17 04:13:08.222000,https://github.com/SebastianAment/CompressedSensing.jl,21,Sparse bayesian learning via stepwise regression,"https://scholar.google.com/scholar?cluster=14029385398750356286&hl=en&as_sdt=0,14",2,2021
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards,9,icml,3,0,2023-06-17 04:13:08.424000,https://github.com/h-aboutalebi/SparseBaseline,2,Locally persistent exploration in continuous control tasks with sparse rewards,"https://scholar.google.com/scholar?cluster=15739830429970028692&hl=en&as_sdt=0,41",3,2021
Preferential Temporal Difference Learning,1,icml,3,0,2023-06-17 04:13:08.627000,https://github.com/NishanthVAnand/Preferential-Temporal-Difference-Learning,4,Preferential temporal difference learning,"https://scholar.google.com/scholar?cluster=17314820173846745739&hl=en&as_sdt=0,45",0,2021
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification,7,icml,1,0,2023-06-17 04:13:08.829000,https://github.com/ranaa-b/OOCS,3,On-off center-surround receptive fields for accurate and robust image classification,"https://scholar.google.com/scholar?cluster=14788977888396220864&hl=en&as_sdt=0,10",1,2021
Stabilizing Equilibrium Models by Jacobian Regularization,36,icml,75,5,2023-06-17 04:13:09.032000,https://github.com/locuslab/deq,650,Stabilizing equilibrium models by jacobian regularization,"https://scholar.google.com/scholar?cluster=7648841566854588035&hl=en&as_sdt=0,21",20,2021
Principled Exploration via Optimistic Bootstrapping and Backward Induction,25,icml,1,0,2023-06-17 04:13:09.235000,https://github.com/Baichenjia/OB2I,7,Principled exploration via optimistic bootstrapping and backward induction,"https://scholar.google.com/scholar?cluster=732043823350828929&hl=en&as_sdt=0,33",2,2021
Breaking the Limits of Message Passing Graph Neural Networks,69,icml,3,0,2023-06-17 04:13:09.436000,https://github.com/balcilar/gnn-matlang,30,Breaking the limits of message passing graph neural networks,"https://scholar.google.com/scholar?cluster=7981688691402609281&hl=en&as_sdt=0,33",3,2021
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers,12,icml,1,0,2023-06-17 04:13:09.638000,https://github.com/YujiaBao/Predict-then-Interpolate,16,Predict then interpolate: A simple algorithm to learn stable classifiers,"https://scholar.google.com/scholar?cluster=2357278583556296891&hl=en&as_sdt=0,5",1,2021
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models,7,icml,2,0,2023-06-17 04:13:09.841000,https://github.com/baofff/VaGES,5,Variational (gradient) estimate of the score function in energy-based latent variable models,"https://scholar.google.com/scholar?cluster=17355652803431034105&hl=en&as_sdt=0,21",1,2021
Compositional Video Synthesis with Action Graphs,22,icml,3,5,2023-06-17 04:13:10.043000,https://github.com/roeiherz/AG2Video,28,Compositional video synthesis with action graphs,"https://scholar.google.com/scholar?cluster=835836297893492143&hl=en&as_sdt=0,5",6,2021
Optimal Thompson Sampling strategies for support-aware CVaR bandits,26,icml,1,0,2023-06-17 04:13:10.246000,https://github.com/rgautron/DssatBanditEnv,5,Optimal thompson sampling strategies for support-aware cvar bandits,"https://scholar.google.com/scholar?cluster=13964455175632716086&hl=en&as_sdt=0,10",1,2021
On Limited-Memory Subsampling Strategies for Bandits,8,icml,2,0,2023-06-17 04:13:10.448000,https://github.com/YRussac/LB-SDA,3,On Limited-Memory Subsampling Strategies for Bandits,"https://scholar.google.com/scholar?cluster=4227884458802378115&hl=en&as_sdt=0,34",2,2021
Directional Graph Networks,104,icml,13,3,2023-06-17 04:13:10.650000,https://github.com/Saro00/DGN,109,Directional graph networks,"https://scholar.google.com/scholar?cluster=6256455976929564913&hl=en&as_sdt=0,6",3,2021
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling,32,icml,9,3,2023-06-17 04:13:10.852000,https://github.com/g-benton/loss-surface-simplexes,96,Loss surface simplexes for mode connecting volumes and fast ensembling,"https://scholar.google.com/scholar?cluster=11311661921259603537&hl=en&as_sdt=0,5",5,2021
Is Space-Time Attention All You Need for Video Understanding?,959,icml,187,61,2023-06-17 04:13:11.054000,https://github.com/facebookresearch/TimeSformer,1187,Is space-time attention all you need for video understanding?,"https://scholar.google.com/scholar?cluster=6828425192739736056&hl=en&as_sdt=0,5",22,2021
Size-Invariant Graph Representations for Graph Classification Extrapolations,51,icml,1,0,2023-06-17 04:13:11.257000,https://github.com/PurdueMINDS/size-invariant-GNNs,18,Size-invariant graph representations for graph classification extrapolations,"https://scholar.google.com/scholar?cluster=18387285677592946358&hl=en&as_sdt=0,10",4,2021
Principal Bit Analysis: Autoencoding with Schur-Concave Loss,0,icml,0,0,2023-06-17 04:13:11.459000,https://github.com/SourbhBh/PBA,0,Principal Bit Analysis: Autoencoding with Schur-Concave Loss,"https://scholar.google.com/scholar?cluster=11365886742546689505&hl=en&as_sdt=0,5",1,2021
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries,5,icml,1,0,2023-06-17 04:13:11.661000,https://github.com/arjunbhagoji/log-loss-lower-bounds,4,Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries,"https://scholar.google.com/scholar?cluster=9078439186014463953&hl=en&as_sdt=0,36",2,2021
TempoRL: Learning When to Act,14,icml,4,0,2023-06-17 04:13:11.863000,https://github.com/automl/TempoRL,14,TempoRL: Learning when to act,"https://scholar.google.com/scholar?cluster=16276824665719650733&hl=en&as_sdt=0,5",8,2021
Neural Symbolic Regression that scales,59,icml,7,2,2023-06-17 04:13:12.073000,https://github.com/SymposiumOrganization/NeuralSymbolicRegressionThatScales,44,Neural symbolic regression that scales,"https://scholar.google.com/scholar?cluster=13426541991949181353&hl=en&as_sdt=0,26",2,2021
Multiplying Matrices Without Multiplying,23,icml,171,19,2023-06-17 04:13:12.275000,https://github.com/dblalock/bolt,2397,Multiplying matrices without multiplying,"https://scholar.google.com/scholar?cluster=16672894839769153249&hl=en&as_sdt=0,41",47,2021
"One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning",18,icml,0,0,2023-06-17 04:13:12.483000,https://github.com/rlphilli/Collaborative-Incentives,5,"One for one, or all for all: Equilibria and optimality of collaboration in federated learning","https://scholar.google.com/scholar?cluster=3850848411825917524&hl=en&as_sdt=0,33",2,2021
Black-box density function estimation using recursive partitioning,4,icml,1,0,2023-06-17 04:13:12.697000,https://github.com/bodin-e/defer,4,Black-box density function estimation using recursive partitioning,"https://scholar.google.com/scholar?cluster=17001427494872038467&hl=en&as_sdt=0,5",1,2021
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks,128,icml,20,0,2023-06-17 04:13:12.899000,https://github.com/twitter-research/cwn,124,Weisfeiler and lehman go topological: Message passing simplicial networks,"https://scholar.google.com/scholar?cluster=8275189776192061574&hl=en&as_sdt=0,5",7,2021
Offline Contextual Bandits with Overparameterized Models,6,icml,0,0,2023-06-17 04:13:13.110000,https://github.com/davidbrandfonbrener/deep-offline-bandits,1,Offline contextual bandits with overparameterized models,"https://scholar.google.com/scholar?cluster=11852183431002924037&hl=en&as_sdt=0,5",2,2021
Value Alignment Verification,19,icml,0,0,2023-06-17 04:13:13.346000,https://github.com/dsbrown1331/vav-icml,1,Value alignment verification,"https://scholar.google.com/scholar?cluster=5318002618951129429&hl=en&as_sdt=0,46",3,2021
Lenient Regret and Good-Action Identification in Gaussian Process Bandits,2,icml,0,0,2023-06-17 04:13:13.552000,https://github.com/caitree/GoodAction,1,Lenient regret and good-action identification in Gaussian process bandits,"https://scholar.google.com/scholar?cluster=13998414945788250067&hl=en&as_sdt=0,33",1,2021
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization,13,icml,1,0,2023-06-17 04:13:13.761000,https://github.com/YuchenLou/ZO-BCD,4,A zeroth-order block coordinate descent algorithm for huge-scale black-box optimization,"https://scholar.google.com/scholar?cluster=10394095959262689530&hl=en&as_sdt=0,33",2,2021
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections,11,icml,0,0,2023-06-17 04:13:13.964000,https://github.com/alexander-camuto/asym-heavy-tails-bias-GNI,1,Asymmetric heavy tails and implicit bias in gaussian noise injections,"https://scholar.google.com/scholar?cluster=6154175937826979347&hl=en&as_sdt=0,5",1,2021
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design,18,icml,8,5,2023-06-17 04:13:14.166000,https://github.com/IBM/fold2seq,46,Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design,"https://scholar.google.com/scholar?cluster=9442126458531954169&hl=en&as_sdt=0,5",4,2021
Optimizing persistent homology based functions,29,icml,2,0,2023-06-17 04:13:14.368000,https://github.com/MathieuCarriere/difftda,15,Optimizing persistent homology based functions,"https://scholar.google.com/scholar?cluster=1795374418354954800&hl=en&as_sdt=0,5",4,2021
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research,56,icml,8,1,2023-06-17 04:13:14.570000,https://github.com/JohanSamir/revisiting_rainbow,72,Revisiting rainbow: Promoting more insightful and inclusive deep reinforcement learning research,"https://scholar.google.com/scholar?cluster=12882829322787597157&hl=en&as_sdt=0,33",1,2021
GRAND: Graph Neural Diffusion,115,icml,42,4,2023-06-17 04:13:14.773000,https://github.com/twitter-research/graph-neural-pde,254,Grand: Graph neural diffusion,"https://scholar.google.com/scholar?cluster=6075394870168508131&hl=en&as_sdt=0,5",12,2021
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection,22,icml,2,1,2023-06-17 04:13:14.975000,https://github.com/NVlabs/RIO,17,Image-level or object-level? a tale of two resampling strategies for long-tailed detection,"https://scholar.google.com/scholar?cluster=121160204477537085&hl=en&as_sdt=0,14",6,2021
DeepWalking Backwards: From Embeddings Back to Graphs,5,icml,3,0,2023-06-17 04:13:15.178000,https://github.com/konsotirop/Invert_Embeddings,6,Deepwalking backwards: from embeddings back to graphs,"https://scholar.google.com/scholar?cluster=367308941848540342&hl=en&as_sdt=0,5",1,2021
Unsupervised Learning of Visual 3D Keypoints for Control,22,icml,7,0,2023-06-17 04:13:15.380000,https://github.com/buoyancy99/unsup-3d-keypoints,37,Unsupervised learning of visual 3d keypoints for control,"https://scholar.google.com/scholar?cluster=7013737531012764740&hl=en&as_sdt=0,5",5,2021
Integer Programming for Causal Structure Learning in the Presence of Latent Variables,3,icml,1,0,2023-06-17 04:13:15.582000,https://github.com/rchen234/IP4AncADMG,1,Integer programming for causal structure learning in the presence of latent variables,"https://scholar.google.com/scholar?cluster=14082497365746391672&hl=en&as_sdt=0,5",1,2021
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks,33,icml,1,0,2023-06-17 04:13:15.784000,https://github.com/sbyebss/scalable-wasserstein-barycenter,8,Scalable computations of wasserstein barycenter via input convex neural networks,"https://scholar.google.com/scholar?cluster=7480420834678810462&hl=en&as_sdt=0,44",2,2021
Decentralized Riemannian Gradient Descent on the Stiefel Manifold,20,icml,2,1,2023-06-17 04:13:15.987000,https://github.com/chenshixiang/Decentralized_Riemannian_gradient_descent_on_Stiefel_manifold,7,Decentralized riemannian gradient descent on the stiefel manifold,"https://scholar.google.com/scholar?cluster=10235515881899160189&hl=en&as_sdt=0,5",2,2021
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation,7,icml,1,0,2023-06-17 04:13:16.189000,https://github.com/cchao0116/CTSMA-ICML21,10,Learning self-modulating attention in continuous time space with applications to sequential recommendation,"https://scholar.google.com/scholar?cluster=16476778005591065966&hl=en&as_sdt=0,22",1,2021
Mandoline: Model Evaluation under Distribution Shift,25,icml,4,0,2023-06-17 04:13:16.390000,https://github.com/HazyResearch/mandoline,30,Mandoline: Model evaluation under distribution shift,"https://scholar.google.com/scholar?cluster=3421066091815040064&hl=en&as_sdt=0,5",18,2021
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation,16,icml,6,1,2023-06-17 04:13:16.593000,https://github.com/tufts-ml/graph-generation-vi,20,Order matters: Probabilistic modeling of node sequence for graph generation,"https://scholar.google.com/scholar?cluster=10391803537150156085&hl=en&as_sdt=0,5",9,2021
CARTL: Cooperative Adversarially-Robust Transfer Learning,5,icml,1,1,2023-06-17 04:13:16.795000,https://github.com/NISP-official/CARTL,5,CARTL: Cooperative Adversarially-Robust Transfer Learning,"https://scholar.google.com/scholar?cluster=16986605262499697725&hl=en&as_sdt=0,5",1,2021
SpreadsheetCoder: Formula Prediction from Semi-structured Context,15,icml,7322,1026,2023-06-17 04:13:16.997000,https://github.com/google-research/google-research,29791,Spreadsheetcoder: Formula prediction from semi-structured context,"https://scholar.google.com/scholar?cluster=422033345602932532&hl=en&as_sdt=0,25",727,2021
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting,38,icml,8,2,2023-06-17 04:13:17.200000,https://github.com/Z-GCNETs/Z-GCNETs,29,Z-GCNETs: Time zigzags at graph convolutional networks for time series forecasting,"https://scholar.google.com/scholar?cluster=7480163184753342890&hl=en&as_sdt=0,5",2,2021
A Unified Lottery Ticket Hypothesis for Graph Neural Networks,82,icml,13,4,2023-06-17 04:13:17.402000,https://github.com/VITA-Group/Unified-LTH-GNN,45,A unified lottery ticket hypothesis for graph neural networks,"https://scholar.google.com/scholar?cluster=14150091349849211712&hl=en&as_sdt=0,33",10,2021
Analysis of stochastic Lanczos quadrature for spectrum approximation,11,icml,0,0,2023-06-17 04:13:17.604000,https://github.com/chentyl/SLQ_analysis,0,Analysis of stochastic Lanczos quadrature for spectrum approximation,"https://scholar.google.com/scholar?cluster=3718766219336547017&hl=en&as_sdt=0,19",1,2021
Cyclically Equivariant Neural Decoders for Cyclic Codes,11,icml,4,0,2023-06-17 04:13:17.807000,https://github.com/cyclicallyneuraldecoder/CyclicallyEquivariantNeuralDecoders,7,Cyclically equivariant neural decoders for cyclic codes,"https://scholar.google.com/scholar?cluster=14253987085025630344&hl=en&as_sdt=0,5",2,2021
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training,36,icml,29,9,2023-06-17 04:13:18.009000,https://github.com/ucbrise/actnn,186,Actnn: Reducing training memory footprint via 2-bit activation compressed training,"https://scholar.google.com/scholar?cluster=3861965596155884920&hl=en&as_sdt=0,37",6,2021
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation,3,icml,2,2,2023-06-17 04:13:18.211000,https://github.com/Feng-Research/SPADE,6,Spade: A spectral method for black-box adversarial robustness evaluation,"https://scholar.google.com/scholar?cluster=174985207826748384&hl=en&as_sdt=0,5",0,2021
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning,9,icml,2,0,2023-06-17 04:13:18.413000,https://github.com/gchers/exact-cp-optimization,6,Exact optimization of conformal predictors via incremental and decremental learning,"https://scholar.google.com/scholar?cluster=9789883793705911412&hl=en&as_sdt=0,5",2,2021
Understanding and Mitigating Accuracy Disparity in Regression,12,icml,1,0,2023-06-17 04:13:18.615000,https://github.com/JFChi/Understanding-and-Mitigating-Accuracy-Disparity-in-Regression,3,Understanding and mitigating accuracy disparity in regression,"https://scholar.google.com/scholar?cluster=9962646376890451048&hl=en&as_sdt=0,5",2,2021
Robust Learning-Augmented Caching: An Experimental Study,9,icml,1,0,2023-06-17 04:13:18.818000,https://github.com/chledowski/Robust-Learning-Augmented-Caching-An-Experimental-Study-Datasets,0,Robust learning-augmented caching: An experimental study,"https://scholar.google.com/scholar?cluster=7732162850430458310&hl=en&as_sdt=0,14",2,2021
Unifying Vision-and-Language Tasks via Text Generation,262,icml,55,14,2023-06-17 04:13:19.020000,https://github.com/j-min/VL-T5,317,Unifying vision-and-language tasks via text generation,"https://scholar.google.com/scholar?cluster=17951690001214387773&hl=en&as_sdt=0,5",9,2021
Label-Only Membership Inference Attacks,225,icml,6,6,2023-06-17 04:13:19.223000,https://github.com/cchoquette/membership-inference,48,Label-only membership inference attacks,"https://scholar.google.com/scholar?cluster=18421653793757811360&hl=en&as_sdt=0,5",4,2021
Modeling Hierarchical Structures with Continuous Recursive Neural Networks,4,icml,1,0,2023-06-17 04:13:19.424000,https://github.com/JRC1995/Continuous-RvNN,10,Modeling hierarchical structures with continuous recursive neural networks,"https://scholar.google.com/scholar?cluster=12633108093638083396&hl=en&as_sdt=0,5",3,2021
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing,45,icml,1,0,2023-06-17 04:13:19.626000,https://github.com/uoe-agents/seps,11,Scaling multi-agent reinforcement learning with selective parameter sharing,"https://scholar.google.com/scholar?cluster=5803292243518473578&hl=en&as_sdt=0,5",1,2021
Phasic Policy Gradient,90,icml,51,5,2023-06-17 04:13:19.830000,https://github.com/openai/phasic-policy-gradient,224,Phasic policy gradient,"https://scholar.google.com/scholar?cluster=10786895332065637304&hl=en&as_sdt=0,5",7,2021
Riemannian Convex Potential Maps,11,icml,4,1,2023-06-17 04:13:20.032000,https://github.com/facebookresearch/rcpm,64,Riemannian convex potential maps,"https://scholar.google.com/scholar?cluster=8877178841663842639&hl=en&as_sdt=0,5",7,2021
Scaling Properties of Deep Residual Networks,14,icml,0,0,2023-06-17 04:13:20.234000,https://github.com/instadeepai/scaling-resnets,5,Scaling properties of deep residual networks,"https://scholar.google.com/scholar?cluster=8302805439596916242&hl=en&as_sdt=0,33",3,2021
Exploiting Shared Representations for Personalized Federated Learning,214,icml,25,4,2023-06-17 04:13:20.437000,https://github.com/lgcollins/FedRep,98,Exploiting shared representations for personalized federated learning,"https://scholar.google.com/scholar?cluster=15594469304978697146&hl=en&as_sdt=0,44",1,2021
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport,47,icml,3,1,2023-06-17 04:13:20.638000,https://github.com/JTT94/filterflow,33,Differentiable particle filtering via entropy-regularized optimal transport,"https://scholar.google.com/scholar?cluster=6170897491109878876&hl=en&as_sdt=0,21",4,2021
Explaining Time Series Predictions with Dynamic Masks,28,icml,15,5,2023-06-17 04:13:20.840000,https://github.com/JonathanCrabbe/Dynamask,55,Explaining time series predictions with dynamic masks,"https://scholar.google.com/scholar?cluster=3877310140943578440&hl=en&as_sdt=0,14",2,2021
Environment Inference for Invariant Learning,184,icml,8,0,2023-06-17 04:13:21.042000,https://github.com/ecreager/eiil,44,Environment inference for invariant learning,"https://scholar.google.com/scholar?cluster=7012730739761324020&hl=en&as_sdt=0,5",3,2021
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability,6,icml,0,0,2023-06-17 04:13:21.246000,https://github.com/modestyachts/stochastic-rec-reachability,5,Quantifying availability and discovery in recommender systems via stochastic reachability,"https://scholar.google.com/scholar?cluster=6680880425324910585&hl=en&as_sdt=0,44",4,2021
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases,435,icml,52,4,2023-06-17 04:13:21.449000,https://github.com/facebookresearch/convit,440,Convit: Improving vision transformers with soft convolutional inductive biases,"https://scholar.google.com/scholar?cluster=817698272872287436&hl=en&as_sdt=0,41",17,2021
Sliced Iterative Normalizing Flows,20,icml,10,3,2023-06-17 04:13:21.661000,https://github.com/biweidai/SIG,32,Sliced iterative normalizing flows,"https://scholar.google.com/scholar?cluster=2467748158069488227&hl=en&as_sdt=0,31",4,2021
Re-understanding Finite-State Representations of Recurrent Policy Networks,12,icml,0,2,2023-06-17 04:13:21.869000,https://github.com/modanesh/Differential_IG,10,Re-understanding finite-state representations of recurrent policy networks,"https://scholar.google.com/scholar?cluster=2835459084556077542&hl=en&as_sdt=0,5",2,2021
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models,54,icml,11,2,2023-06-17 04:13:22.073000,https://github.com/giannisdaras/ilo,115,Intermediate layer optimization for inverse problems using deep generative models,"https://scholar.google.com/scholar?cluster=10888680252420581266&hl=en&as_sdt=0,3",5,2021
Measuring Robustness in Deep Learning Based Compressive Sensing,50,icml,4,0,2023-06-17 04:13:22.275000,https://github.com/MLI-lab/Robustness-CS,25,Measuring robustness in deep learning based compressive sensing,"https://scholar.google.com/scholar?cluster=15924992003782305417&hl=en&as_sdt=0,23",2,2021
Lipschitz normalization for self-attention layers with application to graph neural networks,13,icml,0,1,2023-06-17 04:13:22.478000,https://github.com/gdasoulas/lipschitznorm,9,Lipschitz normalization for self-attention layers with application to graph neural networks,"https://scholar.google.com/scholar?cluster=11996902541195607773&hl=en&as_sdt=0,5",2,2021
Bayesian Deep Learning via Subnetwork Inference,55,icml,45,45,2023-06-17 04:13:22.713000,https://github.com/AlexImmer/Laplace,327,Bayesian deep learning via subnetwork inference,"https://scholar.google.com/scholar?cluster=4967391317568444060&hl=en&as_sdt=0,5",9,2021
Adversarial Robustness Guarantees for Random Deep Neural Networks,4,icml,0,0,2023-06-17 04:13:22.915000,https://github.com/bkiani/Adversarial-robustness-guarantees-for-random-deep-neural-networks,1,Adversarial robustness guarantees for random deep neural networks,"https://scholar.google.com/scholar?cluster=2504173380091047222&hl=en&as_sdt=0,5",1,2021
Kernel Continual Learning,20,icml,0,1,2023-06-17 04:13:23.118000,https://github.com/mmderakhshani/KCL,7,Kernel continual learning,"https://scholar.google.com/scholar?cluster=16309190237334513251&hl=en&as_sdt=0,33",2,2021
Bayesian Optimization over Hybrid Spaces,24,icml,5,0,2023-06-17 04:13:23.321000,https://github.com/aryandeshwal/HyBO,18,Bayesian optimization over hybrid spaces,"https://scholar.google.com/scholar?cluster=10724416920548508977&hl=en&as_sdt=0,3",1,2021
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation,15,icml,2,0,2023-06-17 04:13:23.525000,https://github.com/microsoft/NTT,11,Navigation turing test (NTT): Learning to evaluate human-like navigation,"https://scholar.google.com/scholar?cluster=1633562910551633122&hl=en&as_sdt=0,39",3,2021
Versatile Verification of Tree Ensembles,9,icml,3,1,2023-06-17 04:13:23.754000,https://github.com/laudv/veritas,12,Versatile verification of tree ensembles,"https://scholar.google.com/scholar?cluster=16419931013195180348&hl=en&as_sdt=0,47",3,2021
A Wasserstein Minimax Framework for Mixed Linear Regression,4,icml,0,0,2023-06-17 04:13:23.957000,https://github.com/tjdiamandis/WMLR,0,A Wasserstein minimax framework for mixed linear regression,"https://scholar.google.com/scholar?cluster=3546795848288703283&hl=en&as_sdt=0,14",1,2021
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables,7,icml,1,0,2023-06-17 04:13:24.162000,https://github.com/alekdimi/arms,3,ARMS: Antithetic-REINFORCE-Multi-Sample gradient for binary variables,"https://scholar.google.com/scholar?cluster=546385727654075781&hl=en&as_sdt=0,5",1,2021
On Energy-Based Models with Overparametrized Shallow Neural Networks,7,icml,0,0,2023-06-17 04:13:24.366000,https://github.com/CDEnrich/ebms_shallow_nn,4,On energy-based models with overparametrized shallow neural networks,"https://scholar.google.com/scholar?cluster=2626488009584096909&hl=en&as_sdt=0,31",2,2021
Kernel-Based Reinforcement Learning: A Finite-Time Analysis,17,icml,1,1,2023-06-17 04:13:24.571000,https://github.com/omardrwch/kernel_ucbvi_experiments,3,Kernel-based reinforcement learning: A finite-time analysis,"https://scholar.google.com/scholar?cluster=1350124438767928735&hl=en&as_sdt=0,34",2,2021
Attention is not all you need: pure attention loses rank doubly exponentially with depth,158,icml,10,0,2023-06-17 04:13:24.775000,https://github.com/twistedcubic/attention-rank-collapse,138,Attention is not all you need: Pure attention loses rank doubly exponentially with depth,"https://scholar.google.com/scholar?cluster=6882435683900456661&hl=en&as_sdt=0,5",7,2021
How rotational invariance of common kernels prevents generalization in high dimensions,10,icml,0,0,2023-06-17 04:13:24.977000,https://github.com/DonhauserK/High-dim-kernel-paper,2,How rotational invariance of common kernels prevents generalization in high dimensions,"https://scholar.google.com/scholar?cluster=15941159767452882886&hl=en&as_sdt=0,5",1,2021
Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation,53,icml,3,1,2023-06-17 04:13:25.180000,https://github.com/tencent-ailab/ICML21_OAXE,21,Order-agnostic cross entropy for non-autoregressive machine translation,"https://scholar.google.com/scholar?cluster=10622606881880564341&hl=en&as_sdt=0,23",6,2021
Learning Diverse-Structured Networks for Adversarial Robustness,15,icml,2,0,2023-06-17 04:13:25.384000,https://github.com/d12306/dsnet,6,Learning diverse-structured networks for adversarial robustness,"https://scholar.google.com/scholar?cluster=4158996356819287139&hl=en&as_sdt=0,39",1,2021
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network,19,icml,3,0,2023-06-17 04:13:25.592000,https://github.com/BoChenGroup/SawETM,6,Sawtooth factorial topic embeddings guided gamma belief network,"https://scholar.google.com/scholar?cluster=14933868730567582356&hl=en&as_sdt=0,5",3,2021
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics,4,icml,1,0,2023-06-17 04:13:25.798000,https://github.com/lanl-ansi/learning-ising-dynamics,2,Exponential reduction in sample complexity with learning of ising model dynamics,"https://scholar.google.com/scholar?cluster=14788105086389586758&hl=en&as_sdt=0,50",4,2021
Reinforcement Learning Under Moral Uncertainty,21,icml,2,0,2023-06-17 04:13:26.002000,https://github.com/uber-research/normative-uncertainty,15,Reinforcement learning under moral uncertainty,"https://scholar.google.com/scholar?cluster=2905901650161533369&hl=en&as_sdt=0,44",2,2021
Self-Paced Context Evaluation for Contextual Reinforcement Learning,14,icml,1,0,2023-06-17 04:13:26.207000,https://github.com/automl/SPaCE,2,Self-paced context evaluation for contextual reinforcement learning,"https://scholar.google.com/scholar?cluster=18295369493204614247&hl=en&as_sdt=0,36",8,2021
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations,31,icml,3,0,2023-06-17 04:13:26.410000,https://github.com/pemami4911/EfficientMORL,22,Efficient iterative amortized inference for learning symmetric and disentangled multi-object representations,"https://scholar.google.com/scholar?cluster=7263217510523036363&hl=en&as_sdt=0,14",3,2021
Whitening for Self-Supervised Representation Learning,177,icml,28,0,2023-06-17 04:13:26.614000,https://github.com/htdt/self-supervised,112,Whitening for self-supervised representation learning,"https://scholar.google.com/scholar?cluster=14222215050873553089&hl=en&as_sdt=0,5",3,2021
Graph Mixture Density Networks,11,icml,2,0,2023-06-17 04:13:26.815000,https://github.com/diningphil/graph-mixture-density-networks,20,Graph mixture density networks,"https://scholar.google.com/scholar?cluster=13606441826263868149&hl=en&as_sdt=0,5",2,2021
Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data,27,icml,2,1,2023-06-17 04:13:27.018000,https://github.com/yasesf93/CrossGradientAggregation,6,Cross-gradient aggregation for decentralized learning from non-iid data,"https://scholar.google.com/scholar?cluster=13501782840884499288&hl=en&as_sdt=0,5",2,2021
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling,11,icml,1,1,2023-06-17 04:13:27.220000,https://github.com/yingfan-bot/mbpsrl,5,Model-based reinforcement learning for continuous control with posterior sampling,"https://scholar.google.com/scholar?cluster=9782112597540480270&hl=en&as_sdt=0,15",1,2021
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies,35,icml,6,0,2023-06-17 04:13:27.423000,https://github.com/LinxiFan/SECANT,37,Secant: Self-expert cloning for zero-shot generalization of visual policies,"https://scholar.google.com/scholar?cluster=16889342839830358284&hl=en&as_sdt=0,6",4,2021
Learning Bounds for Open-Set Learning,34,icml,5,0,2023-06-17 04:13:27.626000,https://github.com/Anjin-Liu/Openset_Learning_AOSR,33,Learning bounds for open-set learning,"https://scholar.google.com/scholar?cluster=5726822076204238537&hl=en&as_sdt=0,5",1,2021
Provably Correct Optimization and Exploration with Non-linear Policies,11,icml,0,0,2023-06-17 04:13:27.833000,https://github.com/FlorenceFeng/ENIAC,2,Provably correct optimization and exploration with non-linear policies,"https://scholar.google.com/scholar?cluster=5246454033177283474&hl=en&as_sdt=0,5",2,2021
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation,52,icml,10,0,2023-06-17 04:13:28.035000,https://github.com/FengHZ/KD3A,98,KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation.,"https://scholar.google.com/scholar?cluster=14984342689086286396&hl=en&as_sdt=0,10",3,2021
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings,69,icml,23,16,2023-06-17 04:13:28.238000,https://github.com/rusty1s/pyg_autoscale,148,Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings,"https://scholar.google.com/scholar?cluster=4526974256428451675&hl=en&as_sdt=0,5",4,2021
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning,10,icml,1,0,2023-06-17 04:13:28.441000,https://github.com/filangelos/social_rl,6,Psiphi-learning: Reinforcement learning with demonstrations using successor features and inverse temporal difference learning,"https://scholar.google.com/scholar?cluster=673567895573287554&hl=en&as_sdt=0,5",4,2021
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups,110,icml,17,3,2023-06-17 04:13:28.661000,https://github.com/mfinzi/equivariant-MLP,222,A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups,"https://scholar.google.com/scholar?cluster=7699207538683831568&hl=en&as_sdt=0,5",9,2021
Few-Shot Conformal Prediction with Auxiliary Tasks,24,icml,2,0,2023-06-17 04:13:28.864000,https://github.com/ajfisch/few-shot-cp,5,Few-shot conformal prediction with auxiliary tasks,"https://scholar.google.com/scholar?cluster=10162141541577160393&hl=en&as_sdt=0,5",0,2021
Scalable Certified Segmentation via Randomized Smoothing,18,icml,1,0,2023-06-17 04:13:29.072000,https://github.com/eth-sri/segmentation-smoothing,9,Scalable certified segmentation via randomized smoothing,"https://scholar.google.com/scholar?cluster=9847674407340584512&hl=en&as_sdt=0,10",7,2021
Online Learning with Optimism and Delay,17,icml,2,0,2023-06-17 04:13:29.275000,https://github.com/geflaspohler/poold,9,Online learning with optimism and delay,"https://scholar.google.com/scholar?cluster=3051720071690017995&hl=en&as_sdt=0,33",3,2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design,40,icml,9,0,2023-06-17 04:13:29.478000,https://github.com/ae-foster/dad,22,Deep adaptive design: Amortizing sequential bayesian experimental design,"https://scholar.google.com/scholar?cluster=8507220836791345595&hl=en&as_sdt=0,36",4,2021
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning,62,icml,22,2,2023-06-17 04:13:29.738000,https://github.com/Accenture/Labs-Federated-Learning,50,Clustered sampling: Low-variance and improved representativity for clients selection in federated learning,"https://scholar.google.com/scholar?cluster=1617025297400599136&hl=en&as_sdt=0,33",16,2021
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise,15,icml,0,0,2023-06-17 04:13:29.940000,https://github.com/spencerfrei/nn_generalization_agnostic_noise,0,Provable generalization of sgd-trained neural networks of any width in the presence of adversarial label noise,"https://scholar.google.com/scholar?cluster=10029653979209669660&hl=en&as_sdt=0,14",1,2021
Post-selection inference with HSIC-Lasso,6,icml,1,0,2023-06-17 04:13:30.143000,https://github.com/tobias-freidling/hsic-lasso-psi,3,Post-selection inference with HSIC-Lasso,"https://scholar.google.com/scholar?cluster=10354725144319499088&hl=en&as_sdt=0,10",1,2021
Variational Data Assimilation with a Learned Inverse Observation Operator,15,icml,4,0,2023-06-17 04:13:30.351000,https://github.com/googleinterns/invobs-data-assimilation,28,Variational data assimilation with a learned inverse observation operator,"https://scholar.google.com/scholar?cluster=9123657318704968381&hl=en&as_sdt=0,5",3,2021
Bayesian Quadrature on Riemannian Data Manifolds,4,icml,2,0,2023-06-17 04:13:30.561000,https://github.com/froec/BQonRDM,8,Bayesian quadrature on Riemannian data manifolds,"https://scholar.google.com/scholar?cluster=14587892748613209913&hl=en&as_sdt=0,22",1,2021
Learning Task Informed Abstractions,27,icml,2,2,2023-06-17 04:13:30.764000,https://github.com/kyonofx/tia,11,Learning task informed abstractions,"https://scholar.google.com/scholar?cluster=2332386988369186148&hl=en&as_sdt=0,41",1,2021
"Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators",11,icml,2,0,2023-06-17 04:13:30.972000,https://github.com/RICE-EIC/Auto-NBA,12,"Auto-NBA: Efficient and effective search over the joint space of networks, bitwidths, and accelerators","https://scholar.google.com/scholar?cluster=860563000728112413&hl=en&as_sdt=0,47",4,2021
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation,10,icml,2,0,2023-06-17 04:13:31.177000,https://github.com/sfujim/SR-DICE,14,A deep reinforcement learning approach to marginalized importance sampling with the successor representation,"https://scholar.google.com/scholar?cluster=2623436752996151694&hl=en&as_sdt=0,5",1,2021
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning,15,icml,2,0,2023-06-17 04:13:31.379000,https://github.com/frt03/pic,8,Policy information capacity: Information-theoretic measure for task complexity in deep reinforcement learning,"https://scholar.google.com/scholar?cluster=4831959598163320466&hl=en&as_sdt=0,5",0,2021
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks,32,icml,5,2,2023-06-17 04:13:31.582000,https://github.com/Sjtubrian/SAMMD,16,Maximum mean discrepancy test is aware of adversarial attacks,"https://scholar.google.com/scholar?cluster=5133700864957699812&hl=en&as_sdt=0,5",2,2021
Unsupervised Co-part Segmentation through Assembly,11,icml,6,0,2023-06-17 04:13:31.784000,https://github.com/Talegqz/unsupervised_co_part_segmentation,40,Unsupervised co-part segmentation through assembly,"https://scholar.google.com/scholar?cluster=11164401170119653450&hl=en&as_sdt=0,5",1,2021
RATT: Leveraging Unlabeled Data to Guarantee Generalization,17,icml,0,0,2023-06-17 04:13:32.002000,https://github.com/acmi-lab/ratt_generalization_bound,6,Ratt: Leveraging unlabeled data to guarantee generalization,"https://scholar.google.com/scholar?cluster=5614969385611278866&hl=en&as_sdt=0,5",2,2021
What does LIME really see in images?,22,icml,1,0,2023-06-17 04:13:32.206000,https://github.com/dgarreau/image_lime_theory,4,What does LIME really see in images?,"https://scholar.google.com/scholar?cluster=8275490801192083940&hl=en&as_sdt=0,5",1,2021
Strategic Classification in the Dark,29,icml,2,0,2023-06-17 04:13:32.410000,https://github.com/staretgicclfdark/strategic_rep,0,Strategic classification in the dark,"https://scholar.google.com/scholar?cluster=15886223975765131668&hl=en&as_sdt=0,1",1,2021
Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective,25,icml,3,1,2023-06-17 04:13:32.612000,https://github.com/floringogianu/snrl,9,Spectral normalisation for deep reinforcement learning: an optimisation perspective,"https://scholar.google.com/scholar?cluster=1887962783436917172&hl=en&as_sdt=0,3",2,2021
Active Slices for Sliced Stein Discrepancy,2,icml,1,0,2023-06-17 04:13:32.815000,https://github.com/WenboGong/Sliced_Kernelized_Stein_Discrepancy,1,Active Slices for Sliced Stein Discrepancy,"https://scholar.google.com/scholar?cluster=9280564173167932948&hl=en&as_sdt=0,5",1,2021
On the Problem of Underranking in Group-Fair Ranking,10,icml,1,0,2023-06-17 04:13:33.018000,https://github.com/sruthigorantla/FIGR,0,On the problem of underranking in group-fair ranking,"https://scholar.google.com/scholar?cluster=15412568586111712326&hl=en&as_sdt=0,44",1,2021
MARINA: Faster Non-Convex Distributed Learning with Compression,56,icml,1,0,2023-06-17 04:13:33.221000,https://github.com/burlachenkok/marina,5,MARINA: Faster non-convex distributed learning with compression,"https://scholar.google.com/scholar?cluster=6014843650767988680&hl=en&as_sdt=0,5",2,2021
Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline,115,icml,22,0,2023-06-17 04:13:33.424000,https://github.com/princeton-vl/SimpleView,129,Revisiting point cloud shape classification with a simple and effective baseline,"https://scholar.google.com/scholar?cluster=17283112957651231327&hl=en&as_sdt=0,39",8,2021
Dissecting Supervised Contrastive Learning,61,icml,0,1,2023-06-17 04:13:33.627000,https://github.com/plus-rkwitt/py_supcon_vs_ce,0,Dissecting supervised contrastive learning,"https://scholar.google.com/scholar?cluster=15842603334888826339&hl=en&as_sdt=0,25",2,2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions,52,icml,12,2,2023-06-17 04:13:33.831000,https://github.com/wgrathwohl/GWG_release,43,Oops i took a gradient: Scalable sampling for discrete distributions,"https://scholar.google.com/scholar?cluster=6540555600529946476&hl=en&as_sdt=0,39",4,2021
Detecting Rewards Deterioration in Episodic Reinforcement Learning,7,icml,0,0,2023-06-17 04:13:34.033000,https://github.com/ido90/Rewards-Deterioration-Detection,2,Detecting rewards deterioration in episodic reinforcement learning,"https://scholar.google.com/scholar?cluster=6107338977661068725&hl=en&as_sdt=0,14",1,2021
Operationalizing Complex Causes: A Pragmatic View of Mediation,4,icml,0,0,2023-06-17 04:13:34.236000,https://github.com/limorigu/ComplexCauses,4,Operationalizing complex causes: A pragmatic view of mediation,"https://scholar.google.com/scholar?cluster=15565452123708375262&hl=en&as_sdt=0,5",2,2021
Distribution-Free Calibration Guarantees for Histogram Binning without Sample Splitting,22,icml,5,0,2023-06-17 04:13:34.439000,https://github.com/aigen/df-posthoc-calibration,31,Distribution-free calibration guarantees for histogram binning without sample splitting,"https://scholar.google.com/scholar?cluster=1595974871643501822&hl=en&as_sdt=0,44",1,2021
Correcting Exposure Bias for Link Recommendation,21,icml,1,0,2023-06-17 04:13:34.642000,https://github.com/shantanu95/exposure-bias-link-rec,6,Correcting exposure bias for link recommendation,"https://scholar.google.com/scholar?cluster=8695845050687290736&hl=en&as_sdt=0,5",2,2021
The Heavy-Tail Phenomenon in SGD,65,icml,1,0,2023-06-17 04:13:34.845000,https://github.com/umutsimsekli/sgd_ht,1,The heavy-tail phenomenon in SGD,"https://scholar.google.com/scholar?cluster=11485380306468946114&hl=en&as_sdt=0,5",1,2021
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks,17,icml,3,0,2023-06-17 04:13:35.048000,https://github.com/AI-secure/Knowledge-Enhanced-Machine-Learning-Pipeline,10,Knowledge enhanced machine learning pipeline against diverse adversarial attacks,"https://scholar.google.com/scholar?cluster=7636701886743640050&hl=en&as_sdt=0,33",2,2021
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration,14,icml,0,1,2023-06-17 04:13:35.250000,https://github.com/seungyulhan/dac,4,Diversity actor-critic: Sample-aware entropy regularization for sample-efficient exploration,"https://scholar.google.com/scholar?cluster=1891726031922597340&hl=en&as_sdt=0,11",1,2021
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning,22,icml,4,0,2023-06-17 04:13:35.453000,https://github.com/ahjwang/messenger-emma,17,Grounding language to entities and dynamics for generalization in reinforcement learning,"https://scholar.google.com/scholar?cluster=14975248165561232256&hl=en&as_sdt=0,5",1,2021
SPECTRE: defending against backdoor attacks using robust statistics,47,icml,5,4,2023-06-17 04:13:35.657000,https://github.com/SewoongLab/spectre-defense,15,Spectre: Defending against backdoor attacks using robust statistics,"https://scholar.google.com/scholar?cluster=17952878874994811152&hl=en&as_sdt=0,15",2,2021
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity,26,icml,0,0,2023-06-17 04:13:35.859000,https://github.com/bayer-science-for-a-better-life/graph-attribution,7,Improving molecular graph neural network explainability with orthonormalization and induced sparsity,"https://scholar.google.com/scholar?cluster=2317141663535501848&hl=en&as_sdt=0,5",1,2021
Optimizing Black-box Metrics with Iterative Example Weighting,5,icml,0,0,2023-06-17 04:13:36.063000,https://github.com/koyejolab/fweg,2,Optimizing black-box metrics with iterative example weighting,"https://scholar.google.com/scholar?cluster=2459105363066716864&hl=en&as_sdt=0,47",1,2021
Trees with Attention for Set Prediction Tasks,0,icml,1,2,2023-06-17 04:13:36.267000,https://github.com/TAU-MLwell/Set-Tree,10,Trees with Attention for Set Prediction Tasks,"https://scholar.google.com/scholar?cluster=8916867411595092231&hl=en&as_sdt=0,5",3,2021
MC-LSTM: Mass-Conserving LSTM,41,icml,12,0,2023-06-17 04:13:36.470000,https://github.com/ml-jku/mc-lstm,32,Mc-lstm: Mass-conserving lstm,"https://scholar.google.com/scholar?cluster=4541460761992496905&hl=en&as_sdt=0,46",4,2021
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes,20,icml,0,0,2023-06-17 04:13:36.674000,https://github.com/anonymous-code-0/SteerableCNP,0,Equivariant learning of stochastic fields: Gaussian processes and steerable conditional neural processes,"https://scholar.google.com/scholar?cluster=12538236800312580419&hl=en&as_sdt=0,48",1,2021
Off-Belief Learning,42,icml,7,3,2023-06-17 04:13:36.877000,https://github.com/facebookresearch/off-belief-learning,36,Off-belief learning,"https://scholar.google.com/scholar?cluster=9880359834919449179&hl=en&as_sdt=0,39",9,2021
Generalizable Episodic Memory for Deep Reinforcement Learning,22,icml,4,1,2023-06-17 04:13:37.079000,https://github.com/MouseHu/GEM,10,Generalizable episodic memory for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=2172996156668096387&hl=en&as_sdt=0,47",2,2021
STRODE: Stochastic Boundary Ordinary Differential Equation,5,icml,2,1,2023-06-17 04:13:37.282000,https://github.com/Waffle-Liu/STRODE,13,Strode: Stochastic boundary ordinary differential equation,"https://scholar.google.com/scholar?cluster=3501265210663364162&hl=en&as_sdt=0,5",3,2021
Generative Adversarial Transformers,128,icml,142,14,2023-06-17 04:13:37.484000,https://github.com/dorarad/gansformer,1272,Generative adversarial transformers,"https://scholar.google.com/scholar?cluster=2292407280859337870&hl=en&as_sdt=0,15",38,2021
Selecting Data Augmentation for Simulating Interventions,47,icml,3,0,2023-06-17 04:13:37.713000,https://github.com/AMLab-Amsterdam/DataAugmentationInterventions,25,Selecting data augmentation for simulating interventions,"https://scholar.google.com/scholar?cluster=3812556752145273819&hl=en&as_sdt=0,11",6,2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning,62,icml,0,0,2023-06-17 04:13:37.916000,https://github.com/AlexImmer/marglik,5,Scalable marginal likelihood estimation for model selection in deep learning,"https://scholar.google.com/scholar?cluster=11062863403728072122&hl=en&as_sdt=0,5",3,2021
Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization,3,icml,1,0,2023-06-17 04:13:38.118000,https://github.com/HeddaCohenIndelman/PerturbedStructuredPredictorsDirect,4,Learning randomly perturbed structured predictors for direct loss minimization,"https://scholar.google.com/scholar?cluster=6521871878208082553&hl=en&as_sdt=0,50",1,2021
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning,33,icml,11,1,2023-06-17 04:13:38.320000,https://github.com/shariqiqbal2810/REFIL,50,Randomized entity-wise factorization for multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=4592647130622480373&hl=en&as_sdt=0,25",2,2021
Instance-Optimal Compressed Sensing via Posterior Sampling,28,icml,5,1,2023-06-17 04:13:38.523000,https://github.com/ajiljalal/code-cs-fairness,17,Instance-optimal compressed sensing via posterior sampling,"https://scholar.google.com/scholar?cluster=13669430670080066426&hl=en&as_sdt=0,28",3,2021
Fairness for Image Generation with Uncertain Sensitive Attributes,22,icml,5,1,2023-06-17 04:13:38.733000,https://github.com/ajiljalal/code-cs-fairness,17,Fairness for image generation with uncertain sensitive attributes,"https://scholar.google.com/scholar?cluster=8101927413528099299&hl=en&as_sdt=0,32",3,2021
In-Database Regression in Input Sparsity Time,9,icml,0,0,2023-06-17 04:13:38.935000,https://github.com/AnonymousFireman/ICML_code,0,In-Database Regression in Input Sparsity Time,"https://scholar.google.com/scholar?cluster=4719057238276619749&hl=en&as_sdt=0,5",1,2021
Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics,18,icml,4,1,2023-06-17 04:13:39.137000,https://github.com/vivjay30/pnf-sampling,19,Parallel and flexible sampling from autoregressive models via langevin dynamics,"https://scholar.google.com/scholar?cluster=6113516044812949338&hl=en&as_sdt=0,5",3,2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding,32,icml,2,1,2023-06-17 04:13:39.339000,https://github.com/anndvision/quince,20,Quantifying ignorance in individual-level causal-effect estimates under hidden confounding,"https://scholar.google.com/scholar?cluster=4021084687511550592&hl=en&as_sdt=0,34",1,2021
Bilevel Optimization: Convergence Analysis and Enhanced Design,95,icml,12,0,2023-06-17 04:13:39.541000,https://github.com/junjieyang97/stocbio_hp,35,Bilevel optimization: Convergence analysis and enhanced design,"https://scholar.google.com/scholar?cluster=14240180646297063660&hl=en&as_sdt=0,7",1,2021
Self-Damaging Contrastive Learning,35,icml,5,1,2023-06-17 04:13:39.743000,https://github.com/VITA-Group/SDCLR,56,Self-damaging contrastive learning,"https://scholar.google.com/scholar?cluster=16794370267246676640&hl=en&as_sdt=0,5",3,2021
Prioritized Level Replay,80,icml,15,2,2023-06-17 04:13:39.945000,https://github.com/facebookresearch/level-replay,67,Prioritized level replay,"https://scholar.google.com/scholar?cluster=18011658212512846682&hl=en&as_sdt=0,44",9,2021
Streaming and Distributed Algorithms for Robust Column Subset Selection,3,icml,0,0,2023-06-17 04:13:40.148000,https://github.com/11hifish/robust_css,0,Streaming and distributed algorithms for robust column subset selection,"https://scholar.google.com/scholar?cluster=14557967983043893613&hl=en&as_sdt=0,31",1,2021
Adversarial Option-Aware Hierarchical Imitation Learning,8,icml,4,1,2023-06-17 04:13:40.351000,https://github.com/id9502/Option-GAIL,12,Adversarial option-aware hierarchical imitation learning,"https://scholar.google.com/scholar?cluster=15905939393304829332&hl=en&as_sdt=0,23",2,2021
Provable Lipschitz Certification for Generative Models,11,icml,0,5,2023-06-17 04:13:40.555000,https://github.com/revbucket/lipMIP,12,Provable Lipschitz certification for generative models,"https://scholar.google.com/scholar?cluster=12680803124320000894&hl=en&as_sdt=0,33",2,2021
A Differentiable Point Process with Its Application to Spiking Neural Networks,2,icml,0,0,2023-06-17 04:13:40.757000,https://github.com/ibm-research-tokyo/diffsnn,18,A differentiable point process with its application to spiking neural networks,"https://scholar.google.com/scholar?cluster=18295729593563933234&hl=en&as_sdt=0,47",4,2021
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes,8,icml,2,1,2023-06-17 04:13:40.959000,https://github.com/activatedgeek/simplex-gp,6,Skiing on simplices: Kernel interpolation on the permutohedral lattice for scalable gaussian processes,"https://scholar.google.com/scholar?cluster=612518699030619789&hl=en&as_sdt=0,5",4,2021
Variational Auto-Regressive Gaussian Processes for Continual Learning,16,icml,4,0,2023-06-17 04:13:41.161000,https://github.com/uber-research/vargp,21,Variational auto-regressive gaussian processes for continual learning,"https://scholar.google.com/scholar?cluster=11399430121097777886&hl=en&as_sdt=0,34",3,2021
Learning from History for Byzantine Robust Optimization,82,icml,2,0,2023-06-17 04:13:41.364000,https://github.com/epfml/byzantine-robust-optimizer,15,Learning from history for byzantine robust optimization,"https://scholar.google.com/scholar?cluster=3091706733962162017&hl=en&as_sdt=0,10",6,2021
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation,20,icml,2,1,2023-06-17 04:13:41.566000,https://github.com/MasaKat0/D3RE,1,Non-negative bregman divergence minimization for deep direct density ratio estimation,"https://scholar.google.com/scholar?cluster=10575793668423594372&hl=en&as_sdt=0,24",2,2021
Prior Image-Constrained Reconstruction using Style-Based Generative Models,17,icml,2,0,2023-06-17 04:13:41.769000,https://github.com/comp-imaging-sci/pic-recon,8,Prior image-constrained reconstruction using style-based generative models,"https://scholar.google.com/scholar?cluster=11782166038775253980&hl=en&as_sdt=0,44",1,2021
Self Normalizing Flows,6,icml,8,0,2023-06-17 04:13:41.970000,https://github.com/akandykeller/SelfNormalizingFlows,66,Self normalizing flows,"https://scholar.google.com/scholar?cluster=16907220136527385464&hl=en&as_sdt=0,5",3,2021
Markpainting: Adversarial Machine Learning meets Inpainting,8,icml,2,3,2023-06-17 04:13:42.173000,https://github.com/iliaishacked/markpainting,20,Markpainting: Adversarial machine learning meets inpainting,"https://scholar.google.com/scholar?cluster=7879607124420125546&hl=en&as_sdt=0,32",3,2021
Neural SDEs as Infinite-Dimensional GANs,64,icml,157,16,2023-06-17 04:13:42.375000,https://github.com/google-research/torchsde,1277,Neural sdes as infinite-dimensional gans,"https://scholar.google.com/scholar?cluster=5987016743553578663&hl=en&as_sdt=0,33",35,2021
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training,84,icml,44,27,2023-06-17 04:13:42.577000,https://github.com/decile-team/cords,272,Grad-match: Gradient matching based data subset selection for efficient deep model training,"https://scholar.google.com/scholar?cluster=8588416693456815954&hl=en&as_sdt=0,33",10,2021
Self-Improved Retrosynthetic Planning,13,icml,0,2,2023-06-17 04:13:42.779000,https://github.com/junsu-kim97/self_improved_retro,18,Self-improved retrosynthetic planning,"https://scholar.google.com/scholar?cluster=18216216524696929776&hl=en&as_sdt=0,33",1,2021
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech,258,icml,911,108,2023-06-17 04:13:42.983000,https://github.com/jaywalnut310/vits,4387,Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,"https://scholar.google.com/scholar?cluster=12414540587288194560&hl=en&as_sdt=0,22",42,2021
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning,38,icml,5,1,2023-06-17 04:13:43.186000,https://github.com/dkkim93/meta-mapg,25,A policy gradient algorithm for learning to learn in multiagent reinforcement learning,"https://scholar.google.com/scholar?cluster=9520170531989775101&hl=en&as_sdt=0,5",2,2021
Unsupervised Skill Discovery with Bottleneck Option Learning,16,icml,2,0,2023-06-17 04:13:43.388000,https://github.com/jaekyeom/IBOL,27,Unsupervised skill discovery with bottleneck option learning,"https://scholar.google.com/scholar?cluster=2474291061858386960&hl=en&as_sdt=0,33",2,2021
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision,665,icml,181,51,2023-06-17 04:13:43.591000,https://github.com/dandelin/vilt,1094,Vilt: Vision-and-language transformer without convolution or region supervision,"https://scholar.google.com/scholar?cluster=12987945369444025427&hl=en&as_sdt=0,44",15,2021
"CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients",62,icml,9,2,2023-06-17 04:13:43.794000,https://github.com/danikiyasseh/CLOCS,27,"Clocs: Contrastive learning of cardiac signals across space, time, and patients","https://scholar.google.com/scholar?cluster=16333919134757348473&hl=en&as_sdt=0,14",4,2021
WILDS: A Benchmark of in-the-Wild Distribution Shifts,719,icml,109,5,2023-06-17 04:13:43.996000,https://github.com/p-lambda/wilds,482,Wilds: A benchmark of in-the-wild distribution shifts,"https://scholar.google.com/scholar?cluster=11557463912604627857&hl=en&as_sdt=0,5",20,2021
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?,30,icml,0,0,2023-06-17 04:13:44.198000,https://github.com/TUM-DAML/dbu-robustness,21,Evaluating robustness of predictive uncertainty estimation: Are Dirichlet-based models reliable?,"https://scholar.google.com/scholar?cluster=5773054947592188875&hl=en&as_sdt=0,5",2,2021
Kernel Stein Discrepancy Descent,19,icml,2,0,2023-06-17 04:13:44.400000,https://github.com/pierreablin/ksddescent,12,Kernel stein discrepancy descent,"https://scholar.google.com/scholar?cluster=5389096233704622104&hl=en&as_sdt=0,33",2,2021
Active Testing: Sample-Efficient Model Evaluation,22,icml,6,1,2023-06-17 04:13:44.602000,https://github.com/jlko/active-testing,20,Active testing: Sample-efficient model evaluation,"https://scholar.google.com/scholar?cluster=9561072418583325722&hl=en&as_sdt=0,5",1,2021
Offline Reinforcement Learning with Fisher Divergence Critic Regularization,154,icml,7322,1026,2023-06-17 04:13:44.804000,https://github.com/google-research/google-research,29791,Offline reinforcement learning with fisher divergence critic regularization,"https://scholar.google.com/scholar?cluster=4410288794309638335&hl=en&as_sdt=0,5",727,2021
Out-of-Distribution Generalization via Risk Extrapolation (REx),443,icml,4,1,2023-06-17 04:13:45.007000,https://github.com/capybaralet/REx_code_release,60,Out-of-distribution generalization via risk extrapolation (rex),"https://scholar.google.com/scholar?cluster=10054528338033032937&hl=en&as_sdt=0,25",2,2021
Near-Optimal Confidence Sequences for Bounded Random Variables,3,icml,1,0,2023-06-17 04:13:45.210000,https://github.com/enosair/bentkus_conf_seq,1,Near-optimal confidence sequences for bounded random variables,"https://scholar.google.com/scholar?cluster=1224018117329927923&hl=en&as_sdt=0,10",3,2021
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples,11,icml,7,0,2023-06-17 04:13:45.412000,https://github.com/ckuemmerle/MatrixIRLS,10,A scalable second order method for ill-conditioned matrix completion from few samples,"https://scholar.google.com/scholar?cluster=9201585357486239881&hl=en&as_sdt=0,15",2,2021
ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks,118,icml,16,4,2023-06-17 04:13:45.615000,https://github.com/SamsungLabs/ASAM,111,Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks,"https://scholar.google.com/scholar?cluster=8550448363439632053&hl=en&as_sdt=0,31",5,2021
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix,28,icml,0,0,2023-06-17 04:13:45.844000,https://github.com/gdisag/gradient_disaggregation,12,Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix,"https://scholar.google.com/scholar?cluster=1910992678848824138&hl=en&as_sdt=0,5",1,2021
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification,14,icml,0,0,2023-06-17 04:13:46.046000,https://github.com/movinghoon/ESFR,12,Unsupervised embedding adaptation via early-stage feature reconstruction for few-shot classification,"https://scholar.google.com/scholar?cluster=16796057083006115935&hl=en&as_sdt=0,5",1,2021
Continual Learning in the Teacher-Student Setup: Impact of Task Similarity,29,icml,0,1,2023-06-17 04:13:46.248000,https://github.com/seblee97/student_teacher_catastrophic,4,Continual learning in the teacher-student setup: Impact of task similarity,"https://scholar.google.com/scholar?cluster=4325632592050646056&hl=en&as_sdt=0,11",2,2021
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning,137,icml,27,1,2023-06-17 04:13:46.451000,https://github.com/pokaxpoka/sunrise,110,Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning,"https://scholar.google.com/scholar?cluster=8840831494454574191&hl=en&as_sdt=0,5",6,2021
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training,76,icml,17,6,2023-06-17 04:13:46.654000,https://github.com/rll-research/bpref,76,Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training,"https://scholar.google.com/scholar?cluster=9254305801075741995&hl=en&as_sdt=0,43",0,2021
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems,21,icml,0,0,2023-06-17 04:13:46.856000,https://github.com/zhenhuan-yang/minimax-stability,5,Stability and generalization of stochastic gradient methods for minimax problems,"https://scholar.google.com/scholar?cluster=5282146573067352151&hl=en&as_sdt=0,32",1,2021
Better Training using Weight-Constrained Stochastic Dynamics,2,icml,0,0,2023-06-17 04:13:47.058000,https://github.com/TiffanyVlaar/ConstrainedNNtraining,4,Better Training using Weight-Constrained Stochastic Dynamics,"https://scholar.google.com/scholar?cluster=16942829728118781879&hl=en&as_sdt=0,5",2,2021
Globally-Robust Neural Networks,72,icml,4,1,2023-06-17 04:13:47.261000,https://github.com/klasleino/gloro,25,Globally-robust neural networks,"https://scholar.google.com/scholar?cluster=8564874255784830612&hl=en&as_sdt=0,5",2,2021
Strategic Classification Made Practical,25,icml,0,0,2023-06-17 04:13:47.462000,https://github.com/SagiLevanon1/scmp,5,Strategic classification made practical,"https://scholar.google.com/scholar?cluster=6308861918899589533&hl=en&as_sdt=0,10",1,2021
"Improved, Deterministic Smoothing for L_1 Certified Robustness",20,icml,0,0,2023-06-17 04:13:47.665000,https://github.com/alevine0/smoothingSplittingNoise,3,"Improved, deterministic smoothing for L_1 certified robustness","https://scholar.google.com/scholar?cluster=4413252390109069610&hl=en&as_sdt=0,33",1,2021
"BASE Layers: Simplifying Training of Large, Sparse Models",86,icml,5878,1031,2023-06-17 04:13:47.868000,https://github.com/pytorch/fairseq,26483,"Base layers: Simplifying training of large, sparse models","https://scholar.google.com/scholar?cluster=10892687538376450252&hl=en&as_sdt=0,19",411,2021
"A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration",84,icml,12,5,2023-06-17 04:13:48.070000,https://github.com/yhhhli/SNN_Calibration,70,"A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration","https://scholar.google.com/scholar?cluster=15407151931731425738&hl=en&as_sdt=0,11",3,2021
Ditto: Fair and Robust Federated Learning Through Personalization,340,icml,28,1,2023-06-17 04:13:48.272000,https://github.com/litian96/ditto,100,Ditto: Fair and robust federated learning through personalization,"https://scholar.google.com/scholar?cluster=11515326237813489969&hl=en&as_sdt=0,5",2,2021
Provably End-to-end Label-noise Learning without Anchor Points,58,icml,0,0,2023-06-17 04:13:48.474000,https://github.com/xuefeng-li1/Provably-end-to-end-label-noise-learning-without-anchor-points,10,Provably end-to-end label-noise learning without anchor points,"https://scholar.google.com/scholar?cluster=9258083582460233447&hl=en&as_sdt=0,15",1,2021
Mixed Cross Entropy Loss for Neural Machine Translation,5,icml,1,0,2023-06-17 04:13:48.677000,https://github.com/haorannlp/mix,17,Mixed cross entropy loss for neural machine translation,"https://scholar.google.com/scholar?cluster=16791533551271975512&hl=en&as_sdt=0,5",1,2021
Distributionally Robust Optimization with Markovian Data,6,icml,0,0,2023-06-17 04:13:48.878000,https://github.com/mkvdro/DRO_Markov,2,Distributionally robust optimization with Markovian data,"https://scholar.google.com/scholar?cluster=13967502296963435329&hl=en&as_sdt=0,5",1,2021
Communication-Efficient Distributed SVD via Local Power Iterations,10,icml,0,0,2023-06-17 04:13:49.080000,https://github.com/lx10077/LocalPower,0,Communication-efficient distributed SVD via local power iterations,"https://scholar.google.com/scholar?cluster=1741371435444323515&hl=en&as_sdt=0,5",1,2021
FILTRA: Rethinking Steerable CNN by Filter Transform,2,icml,1,0,2023-06-17 04:13:49.284000,https://github.com/prclibo/filtra,7,Filtra: Rethinking steerable CNN by filter transform,"https://scholar.google.com/scholar?cluster=12773800134537729615&hl=en&as_sdt=0,5",1,2021
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models,40,icml,3,5,2023-06-17 04:13:49.486000,https://github.com/zhuohan123/terapipe,45,Terapipe: Token-level pipeline parallelism for training large-scale language models,"https://scholar.google.com/scholar?cluster=9109745061137409325&hl=en&as_sdt=0,6",3,2021
Towards Understanding and Mitigating Social Biases in Language Models,123,icml,8,0,2023-06-17 04:13:49.689000,https://github.com/pliang279/LM_bias,48,Towards understanding and mitigating social biases in language models,"https://scholar.google.com/scholar?cluster=16764320017418997560&hl=en&as_sdt=0,5",4,2021
Information Obfuscation of Graph Neural Networks,27,icml,7,2,2023-06-17 04:13:49.891000,https://github.com/liaopeiyuan/GAL,35,Information obfuscation of graph neural networks,"https://scholar.google.com/scholar?cluster=17996715912972296815&hl=en&as_sdt=0,5",5,2021
Guided Exploration with Proximal Policy Optimization using a Single Demonstration,7,icml,1,0,2023-06-17 04:13:50.094000,https://github.com/compsciencelab/ppo_D,10,Guided exploration with proximal policy optimization using a single demonstration,"https://scholar.google.com/scholar?cluster=1058578842192260735&hl=en&as_sdt=0,26",2,2021
Debiasing a First-order Heuristic for Approximate Bi-level Optimization,3,icml,1,0,2023-06-17 04:13:50.297000,https://github.com/xingyousong/ufom,3,Debiasing a first-order heuristic for approximate bi-level optimization,"https://scholar.google.com/scholar?cluster=11037305189679806516&hl=en&as_sdt=0,50",2,2021
Making transport more robust and interpretable by moving data through a small number of anchor points,13,icml,3,1,2023-06-17 04:13:50.498000,https://github.com/nerdslab/latentOT,13,Making transport more robust and interpretable by moving data through a small number of anchor points,"https://scholar.google.com/scholar?cluster=14045713528225441550&hl=en&as_sdt=0,47",2,2021
Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation,8,icml,4,0,2023-06-17 04:13:50.700000,https://github.com/shawnlimn/ScaleGrad,13,Straight to the gradient: Learning to use novel tokens for neural text generation,"https://scholar.google.com/scholar?cluster=743520526432802506&hl=en&as_sdt=0,36",1,2021
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data,57,icml,3,1,2023-06-17 04:13:50.903000,https://github.com/epfml/quasi-global-momentum,7,Quasi-global momentum: Accelerating decentralized deep learning on heterogeneous data,"https://scholar.google.com/scholar?cluster=11090813795485624273&hl=en&as_sdt=0,5",5,2021
Learning by Turning: Neural Architecture Aware Optimisation,11,icml,6,1,2023-06-17 04:13:51.106000,https://github.com/jxbz/nero,19,Learning by turning: Neural architecture aware optimisation,"https://scholar.google.com/scholar?cluster=9218227008920600415&hl=en&as_sdt=0,15",3,2021
Just Train Twice: Improving Group Robustness without Training Group Information,213,icml,14,1,2023-06-17 04:13:51.308000,https://github.com/anniesch/jtt,58,Just train twice: Improving group robustness without training group information,"https://scholar.google.com/scholar?cluster=13173846618257909762&hl=en&as_sdt=0,5",1,2021
Event Outlier Detection in Continuous Time,7,icml,0,0,2023-06-17 04:13:51.520000,https://github.com/siqil/CPPOD,9,Event outlier detection in continuous time,"https://scholar.google.com/scholar?cluster=11315185602040849494&hl=en&as_sdt=0,7",1,2021
Heterogeneous Risk Minimization,58,icml,8,0,2023-06-17 04:13:51.733000,https://github.com/ljsthu/hrm,19,Heterogeneous risk minimization,"https://scholar.google.com/scholar?cluster=12299879840182415633&hl=en&as_sdt=0,5",1,2021
Elastic Graph Neural Networks,70,icml,7,0,2023-06-17 04:13:51.936000,https://github.com/lxiaorui/ElasticGNN,35,Elastic graph neural networks,"https://scholar.google.com/scholar?cluster=7978714464929950404&hl=en&as_sdt=0,5",4,2021
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition,22,icml,5,1,2023-06-17 04:13:52.138000,https://github.com/cranial-xix/marl-copa,13,Coach-player multi-agent reinforcement learning for dynamic team composition,"https://scholar.google.com/scholar?cluster=16222834590436839078&hl=en&as_sdt=0,47",3,2021
Selfish Sparse RNN Training,29,icml,3,3,2023-06-17 04:13:52.340000,https://github.com/Shiweiliuiiiiiii/Selfish-RNN,10,Selfish sparse rnn training,"https://scholar.google.com/scholar?cluster=14857851775115975297&hl=en&as_sdt=0,5",1,2021
Leveraging Public Data for Practical Private Query Release,36,icml,0,0,2023-06-17 04:13:52.542000,https://github.com/terranceliu/pmw-pub,3,Leveraging public data for practical private query release,"https://scholar.google.com/scholar?cluster=10819180564771632569&hl=en&as_sdt=0,22",2,2021
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training,56,icml,6,0,2023-06-17 04:13:52.744000,https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization,38,Do we actually need dense over-parameterization? in-time over-parameterization in sparse training,"https://scholar.google.com/scholar?cluster=17950677328551432354&hl=en&as_sdt=0,5",2,2021
Group Fisher Pruning for Practical Network Compression,66,icml,12,5,2023-06-17 04:13:52.947000,https://github.com/jshilong/FisherPruning,138,Group fisher pruning for practical network compression,"https://scholar.google.com/scholar?cluster=7436704720048829343&hl=en&as_sdt=0,44",6,2021
Relative Positional Encoding for Transformers with Linear Complexity,22,icml,7,3,2023-06-17 04:13:53.150000,https://github.com/aliutkus/spe,58,Relative positional encoding for transformers with linear complexity,"https://scholar.google.com/scholar?cluster=16520451235518396778&hl=en&as_sdt=0,37",4,2021
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach,8,icml,7,0,2023-06-17 04:13:53.352000,https://github.com/fedelopez77/sympa,25,Symmetric spaces for graph embeddings: A finsler-riemannian approach,"https://scholar.google.com/scholar?cluster=12337649232069613673&hl=en&as_sdt=0,33",2,2021
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification,9,icml,0,0,2023-06-17 04:13:53.555000,https://github.com/leishida/Um-Classification,5,Binary classification from multiple unlabeled datasets via surrogate set classification,"https://scholar.google.com/scholar?cluster=8249584082478727878&hl=en&as_sdt=0,7",1,2021
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking,15,icml,2,0,2023-06-17 04:13:53.758000,https://github.com/maxc01/metacal,6,Meta-cal: Well-controlled post-hoc calibration by ranking,"https://scholar.google.com/scholar?cluster=4779443102063826651&hl=en&as_sdt=0,14",2,2021
Local Algorithms for Finding Densely Connected Clusters,5,icml,3,0,2023-06-17 04:13:53.960000,https://github.com/pmacg/local-densely-connected-clusters,4,Local algorithms for finding densely connected clusters,"https://scholar.google.com/scholar?cluster=2599205940153817748&hl=en&as_sdt=0,25",1,2021
Learning to Generate Noise for Multi-Attack Robustness,11,icml,2,1,2023-06-17 04:13:54.163000,https://github.com/divyam3897/MNG_AC,8,Learning to generate noise for multi-attack robustness,"https://scholar.google.com/scholar?cluster=10029031126071377800&hl=en&as_sdt=0,5",1,2021
Domain Generalization using Causal Matching,153,icml,30,11,2023-06-17 04:13:54.365000,https://github.com/microsoft/robustdg,159,Domain generalization using causal matching,"https://scholar.google.com/scholar?cluster=7680827305765663856&hl=en&as_sdt=0,5",10,2021
Nonparametric Hamiltonian Monte Carlo,5,icml,2,1,2023-06-17 04:13:54.567000,https://github.com/fzaiser/nonparametric-hmc,12,Nonparametric Hamiltonian Monte Carlo,"https://scholar.google.com/scholar?cluster=15980590487021793124&hl=en&as_sdt=0,26",1,2021
KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning,21,icml,8,1,2023-06-17 04:13:54.771000,https://github.com/deepcomm/kocodes,12,Ko codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning,"https://scholar.google.com/scholar?cluster=6409739785381196000&hl=en&as_sdt=0,31",4,2021
Inverse Constrained Reinforcement Learning,24,icml,3,2,2023-06-17 04:13:54.974000,https://github.com/shehryar-malik/icrl,13,Inverse constrained reinforcement learning,"https://scholar.google.com/scholar?cluster=6882447057123293006&hl=en&as_sdt=0,5",2,2021
A Sampling-Based Method for Tensor Ring Decomposition,20,icml,1,0,2023-06-17 04:13:55.177000,https://github.com/OsmanMalik/tr-als-sampled,6,A sampling-based method for tensor ring decomposition,"https://scholar.google.com/scholar?cluster=9925150278480736841&hl=en&as_sdt=0,10",2,2021
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers,18,icml,820,36,2023-06-17 04:13:55.380000,https://github.com/deepmind/open_spiel,3698,Multi-agent training beyond zero-sum with correlated equilibrium meta-solvers,"https://scholar.google.com/scholar?cluster=13991149676180937828&hl=en&as_sdt=0,9",106,2021
Neural Architecture Search without Training,214,icml,58,7,2023-06-17 04:13:55.583000,https://github.com/BayesWatch/nas-without-training,432,Neural architecture search without training,"https://scholar.google.com/scholar?cluster=12821590639566718193&hl=en&as_sdt=0,34",15,2021
UCB Momentum Q-learning: Correcting the bias without forgetting,28,icml,1,1,2023-06-17 04:13:55.791000,https://github.com/omardrwch/ucbmq_code,2,UCB Momentum Q-learning: Correcting the bias without forgetting,"https://scholar.google.com/scholar?cluster=13418224994694979040&hl=en&as_sdt=0,5",2,2021
An Integer Linear Programming Framework for Mining Constraints from Data,4,icml,0,0,2023-06-17 04:13:55.994000,https://github.com/uclanlp/ILPLearning,2,An Integer Linear Programming Framework for Mining Constraints from Data,"https://scholar.google.com/scholar?cluster=15134580706124032020&hl=en&as_sdt=0,26",7,2021
Signatured Deep Fictitious Play for Mean Field Games with Common Noise,18,icml,2,0,2023-06-17 04:13:56.196000,https://github.com/mmin0/SigDFP,1,Signatured deep fictitious play for mean field games with common noise,"https://scholar.google.com/scholar?cluster=5737626410689821885&hl=en&as_sdt=0,5",2,2021
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation,67,icml,37,10,2023-06-17 04:13:56.405000,https://github.com/KevinMIN95/StyleSpeech,196,Meta-stylespeech: Multi-speaker adaptive text-to-speech generation,"https://scholar.google.com/scholar?cluster=9200152829644981336&hl=en&as_sdt=0,33",8,2021
Offline Meta-Reinforcement Learning with Advantage Weighting,57,icml,9,1,2023-06-17 04:13:56.607000,https://github.com/eric-mitchell/macaw,34,Offline meta-reinforcement learning with advantage weighting,"https://scholar.google.com/scholar?cluster=17977945892617234025&hl=en&as_sdt=0,47",2,2021
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization,2,icml,1,0,2023-06-17 04:13:56.810000,https://github.com/TaikiMiyagawa/MSPRT-TANDEM,3,The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization,"https://scholar.google.com/scholar?cluster=8968954885886250341&hl=en&as_sdt=0,14",2,2021
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games,19,icml,2,0,2023-06-17 04:13:57.013000,https://github.com/dmorrill10/hr_edl_experiments,3,Efficient deviation types and learning for hindsight rationality in extensive-form games,"https://scholar.google.com/scholar?cluster=2350651197115820142&hl=en&as_sdt=0,33",2,2021
Connecting Interpretability and Robustness in Decision Trees through Separation,16,icml,0,0,2023-06-17 04:13:57.215000,https://github.com/yangarbiter/interpretable-robust-trees,12,Connecting interpretability and robustness in decision trees through separation,"https://scholar.google.com/scholar?cluster=2331497214666374393&hl=en&as_sdt=0,37",3,2021
Oblivious Sketching for Logistic Regression,10,icml,1,0,2023-06-17 04:13:57.418000,https://github.com/cxan96/oblivious-sketching-logreg,3,Oblivious sketching for logistic regression,"https://scholar.google.com/scholar?cluster=16316892732322711108&hl=en&as_sdt=0,5",1,2021
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search,23,icml,5,3,2023-06-17 04:13:57.621000,https://github.com/Alibaba-MIIL/HardCoReNAS,30,Hardcore-nas: Hard constrained differentiable neural architecture search,"https://scholar.google.com/scholar?cluster=12851686551366341896&hl=en&as_sdt=0,5",6,2021
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information,17,icml,6,0,2023-06-17 04:13:57.824000,https://github.com/willieneis/bayesian-algorithm-execution,40,Bayesian algorithm execution: Estimating computable properties of black-box functions using mutual information,"https://scholar.google.com/scholar?cluster=10668214102939988393&hl=en&as_sdt=0,18",6,2021
Incentivizing Compliance with Algorithmic Instruments,2,icml,0,0,2023-06-17 04:13:58.026000,https://github.com/DanielNgo207/Incentivizing-Compliance-with-Algorithmic-Instruments,0,Incentivizing compliance with algorithmic instruments,"https://scholar.google.com/scholar?cluster=8032953671879607459&hl=en&as_sdt=0,39",1,2021
Cross-model Back-translated Distillation for Unsupervised Machine Translation,7,icml,3,1,2023-06-17 04:13:58.228000,https://github.com/nxphi47/multiagent_crosstranslate,4,Cross-model back-translated distillation for unsupervised machine translation,"https://scholar.google.com/scholar?cluster=12269896059746732525&hl=en&as_sdt=0,10",1,2021
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search,24,icml,3,0,2023-06-17 04:13:58.430000,https://github.com/ntienvu/TW_NAS,4,Optimal transport kernels for sequential and parallel neural architecture search,"https://scholar.google.com/scholar?cluster=12662732608463413645&hl=en&as_sdt=0,5",2,2021
Interactive Learning from Activity Description,20,icml,0,0,2023-06-17 04:13:58.633000,https://github.com/khanhptnk/iliad,6,Interactive learning from activity description,"https://scholar.google.com/scholar?cluster=6188595152759271430&hl=en&as_sdt=0,47",2,2021
Data Augmentation for Meta-Learning,57,icml,5,0,2023-06-17 04:13:58.836000,https://github.com/RenkunNi/MetaAug,25,Data augmentation for meta-learning,"https://scholar.google.com/scholar?cluster=2872867843367483483&hl=en&as_sdt=0,5",1,2021
Improved Denoising Diffusion Probabilistic Models,754,icml,332,68,2023-06-17 04:13:59.039000,https://github.com/openai/improved-diffusion,1891,Improved denoising diffusion probabilistic models,"https://scholar.google.com/scholar?cluster=2227179395488568184&hl=en&as_sdt=0,5",99,2021
AdaXpert: Adapting Neural Architecture for Growing Data,8,icml,2,2,2023-06-17 04:13:59.242000,https://github.com/mr-eggplant/adaxpert0,13,Adaxpert: Adapting neural architecture for growing data,"https://scholar.google.com/scholar?cluster=1668694704547918132&hl=en&as_sdt=0,37",1,2021
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points,2,icml,0,0,2023-06-17 04:13:59.445000,https://github.com/sehyunkwon/Infinite-WGAN,3,Wgan with an infinitely wide generator has no spurious stationary points,"https://scholar.google.com/scholar?cluster=2540862355442244934&hl=en&as_sdt=0,5",2,2021
"Accuracy, Interpretability, and Differential Privacy via Explainable Boosting",16,icml,678,58,2023-06-17 04:13:59.648000,https://github.com/interpretml/interpret,5546,"Accuracy, interpretability, and differential privacy via explainable boosting","https://scholar.google.com/scholar?cluster=3909488782505274678&hl=en&as_sdt=0,32",142,2021
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes,35,icml,6,2,2023-06-17 04:13:59.850000,https://github.com/LaurenceA/bayesfunc,12,Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes,"https://scholar.google.com/scholar?cluster=8024621603786330099&hl=en&as_sdt=0,14",3,2021
Regularizing towards Causal Invariance: Linear Models with Proxies,18,icml,2,0,2023-06-17 04:14:00.054000,https://github.com/clinicalml/proxy-anchor-regression,9,Regularizing towards causal invariance: Linear models with proxies,"https://scholar.google.com/scholar?cluster=5547608297314715512&hl=en&as_sdt=0,33",9,2021
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting,15,icml,4,0,2023-06-17 04:14:00.257000,https://github.com/networkslab/rnn_flow,18,RNN with particle flow for probabilistic spatio-temporal forecasting,"https://scholar.google.com/scholar?cluster=16256105255072962985&hl=en&as_sdt=0,43",4,2021
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification,17,icml,3,1,2023-06-17 04:14:00.460000,https://github.com/bpucla/ibebm,8,Latent space energy-based model of symbol-vector coupling for text generation and classification,"https://scholar.google.com/scholar?cluster=18132333076288060504&hl=en&as_sdt=0,5",2,2021
Unsupervised Representation Learning via Neural Activation Coding,2,icml,1,0,2023-06-17 04:14:00.663000,https://github.com/yookoon/nac,5,Unsupervised Representation Learning via Neural Activation Coding,"https://scholar.google.com/scholar?cluster=3527585526812184622&hl=en&as_sdt=0,22",2,2021
Optimal Counterfactual Explanations in Tree Ensembles,30,icml,5,0,2023-06-17 04:14:00.866000,https://github.com/vidalt/OCEAN,16,Optimal counterfactual explanations in tree ensembles,"https://scholar.google.com/scholar?cluster=1410339152566950271&hl=en&as_sdt=0,23",2,2021
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints,32,icml,10,1,2023-06-17 04:14:01.083000,https://github.com/martius-lab/CombOptNet,67,Comboptnet: Fit the right np-hard problem by learning integer programming constraints,"https://scholar.google.com/scholar?cluster=13237034191144507355&hl=en&as_sdt=0,11",4,2021
How could Neural Networks understand Programs?,37,icml,14,3,2023-06-17 04:14:01.286000,https://github.com/pdlan/OSCAR,116,How could neural networks understand programs?,"https://scholar.google.com/scholar?cluster=16362826083131548815&hl=en&as_sdt=0,44",4,2021
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length,13,icml,0,0,2023-06-17 04:14:01.489000,https://github.com/ethanjperez/rda,33,Rissanen data analysis: Examining dataset characteristics via description length,"https://scholar.google.com/scholar?cluster=5428264289372921149&hl=en&as_sdt=0,33",1,2021
Megaverse: Simulating Embodied Agents at One Million Experiences per Second,12,icml,19,4,2023-06-17 04:14:01.691000,https://github.com/alex-petrenko/megaverse,201,Megaverse: Simulating embodied agents at one million experiences per second,"https://scholar.google.com/scholar?cluster=3066110392358323524&hl=en&as_sdt=0,3",8,2021
Towards Practical Mean Bounds for Small Samples,4,icml,1,0,2023-06-17 04:14:01.894000,https://github.com/myphan9/small_sample_mean_bounds,2,Towards practical mean bounds for small samples,"https://scholar.google.com/scholar?cluster=108164015875257038&hl=en&as_sdt=0,5",2,2021
GeomCA: Geometric Evaluation of Data Representations,8,icml,2,0,2023-06-17 04:14:02.098000,https://github.com/petrapoklukar/GeomCA,10,Geomca: Geometric evaluation of data representations,"https://scholar.google.com/scholar?cluster=1763637443737261657&hl=en&as_sdt=0,5",1,2021
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech,167,icml,93,15,2023-06-17 04:14:02.300000,https://github.com/huawei-noah/Speech-Backbones,396,Grad-tts: A diffusion probabilistic model for text-to-speech,"https://scholar.google.com/scholar?cluster=6905767521784147251&hl=en&as_sdt=0,5",26,2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations,13,icml,0,0,2023-06-17 04:14:02.503000,https://github.com/cunningham-lab/RTGPS,7,Bias-free scalable gaussian processes via randomized truncations,"https://scholar.google.com/scholar?cluster=5236118263143002712&hl=en&as_sdt=0,5",4,2021
Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset,5,icml,0,0,2023-06-17 04:14:02.705000,https://github.com/IlanPrice/DCTpS,12,Dense for the price of sparse: Improved performance of sparsely initialized networks via a subspace offset,"https://scholar.google.com/scholar?cluster=17879749331929716913&hl=en&as_sdt=0,36",1,2021
Neural Transformation Learning for Deep Anomaly Detection Beyond Images,54,icml,11,0,2023-06-17 04:14:02.908000,https://github.com/boschresearch/NeuTraL-AD,35,Neural transformation learning for deep anomaly detection beyond images,"https://scholar.google.com/scholar?cluster=1292087033558963213&hl=en&as_sdt=0,5",4,2021
Optimization Planning for 3D ConvNets,8,icml,0,0,2023-06-17 04:14:03.111000,https://github.com/zhaofanqiu/optimization-planning-for-3d-convnets,2,Optimization planning for 3d convnets,"https://scholar.google.com/scholar?cluster=17965785653886460675&hl=en&as_sdt=0,15",2,2021
Learning Transferable Visual Models From Natural Language Supervision,5987,icml,2336,151,2023-06-17 04:14:03.314000,https://github.com/openai/CLIP,15759,Learning transferable visual models from natural language supervision,"https://scholar.google.com/scholar?cluster=15031020161691567042&hl=en&as_sdt=0,48",268,2021
A General Framework For Detecting Anomalous Inputs to DNN Classifiers,21,icml,3,3,2023-06-17 04:14:03.516000,https://github.com/jayaram-r/adversarial-detection,16,A general framework for detecting anomalous inputs to dnn classifiers,"https://scholar.google.com/scholar?cluster=7846344670241873650&hl=en&as_sdt=0,5",5,2021
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning,27,icml,6,0,2023-06-17 04:14:03.730000,https://github.com/uoe-agents/GPL,25,Towards open ad hoc teamwork using graph-based policy learning,"https://scholar.google.com/scholar?cluster=13446293545265914898&hl=en&as_sdt=0,5",4,2021
Decoupling Value and Policy for Generalization in Reinforcement Learning,62,icml,13,0,2023-06-17 04:14:03.932000,https://github.com/rraileanu/idaac,52,Decoupling value and policy for generalization in reinforcement learning,"https://scholar.google.com/scholar?cluster=12990450966698605101&hl=en&as_sdt=0,5",3,2021
Differentially Private Sliced Wasserstein Distance,9,icml,3,2,2023-06-17 04:14:04.136000,https://github.com/arakotom/dp_swd,5,Differentially private sliced wasserstein distance,"https://scholar.google.com/scholar?cluster=11153564524741628543&hl=en&as_sdt=0,33",1,2021
Zero-Shot Text-to-Image Generation,1797,icml,1904,65,2023-06-17 04:14:04.338000,https://github.com/openai/DALL-E,10322,Zero-shot text-to-image generation,"https://scholar.google.com/scholar?cluster=18428055834209091582&hl=en&as_sdt=0,5",230,2021
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting,78,icml,168,63,2023-06-17 04:14:04.541000,https://github.com/zalandoresearch/pytorch-ts,1006,Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting,"https://scholar.google.com/scholar?cluster=11453532699552258037&hl=en&as_sdt=0,14",24,2021
Implicit Regularization in Tensor Factorization,26,icml,0,0,2023-06-17 04:14:04.744000,https://github.com/noamrazin/imp_reg_in_tf,3,Implicit regularization in tensor factorization,"https://scholar.google.com/scholar?cluster=4594323532805369080&hl=en&as_sdt=0,5",2,2021
"Align, then memorise: the dynamics of learning with feedback alignment",17,icml,2,0,2023-06-17 04:14:04.947000,https://github.com/sdascoli/dfa-dynamics,8,"Align, then memorise: the dynamics of learning with feedback alignment","https://scholar.google.com/scholar?cluster=10115011183031848291&hl=en&as_sdt=0,11",3,2021
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed,42,icml,0,0,2023-06-17 04:14:05.150000,https://github.com/mariaref/rfvs2lnn_GMM_online,5,Classifying high-dimensional gaussian mixtures: Where kernel methods fail and neural networks succeed,"https://scholar.google.com/scholar?cluster=2175676811548405487&hl=en&as_sdt=0,5",2,2021
Solving high-dimensional parabolic PDEs using the tensor train format,32,icml,6,1,2023-06-17 04:14:05.353000,https://github.com/lorenzrichter/PDE-backward-solver,11,Solving high-dimensional parabolic PDEs using the tensor train format,"https://scholar.google.com/scholar?cluster=11792660313798176886&hl=en&as_sdt=0,5",2,2021
Principled Simplicial Neural Networks for Trajectory Prediction,37,icml,2,0,2023-06-17 04:14:05.555000,https://github.com/nglaze00/SCoNe_GCN,9,Principled simplicial neural networks for trajectory prediction,"https://scholar.google.com/scholar?cluster=4466528152103096087&hl=en&as_sdt=0,4",2,2021
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data,13,icml,1,0,2023-06-17 04:14:05.757000,https://github.com/estherrolf/representation-matters,3,Representation matters: Assessing the importance of subgroup allocations in training data,"https://scholar.google.com/scholar?cluster=9213574703320829677&hl=en&as_sdt=0,11",3,2021
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL,14,icml,4,2,2023-06-17 04:14:05.960000,https://github.com/flowersteam/TeachMyAgent,56,Teachmyagent: a benchmark for automatic curriculum learning in deep rl,"https://scholar.google.com/scholar?cluster=11016662361926634008&hl=en&as_sdt=0,5",9,2021
Discretization Drift in Two-Player Games,6,icml,2436,170,2023-06-17 04:14:06.162000,https://github.com/deepmind/deepmind-research,11905,Discretization drift in two-player games,"https://scholar.google.com/scholar?cluster=5098459478601130257&hl=en&as_sdt=0,5",336,2021
"Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement",8,icml,1,1,2023-06-17 04:14:06.364000,https://github.com/dtak/hierarchical-disentanglement,5,"Benchmarks, algorithms, and metrics for hierarchical disentanglement","https://scholar.google.com/scholar?cluster=9234964175960458338&hl=en&as_sdt=0,5",2,2021
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees,80,icml,12,4,2023-06-17 04:14:06.566000,https://github.com/jonasrothfuss/meta_learning_pacoh,23,PACOH: Bayes-optimal meta-learning with PAC-guarantees,"https://scholar.google.com/scholar?cluster=12050746952935759142&hl=en&as_sdt=0,30",5,2021
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding,20,icml,0,0,2023-06-17 04:14:06.768000,https://github.com/ryoungj/mcbits,13,Improving lossless compression rates via monte carlo bits-back coding,"https://scholar.google.com/scholar?cluster=1052321349567422387&hl=en&as_sdt=0,5",2,2021
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes,1,icml,1,0,2023-06-17 04:14:06.971000,https://github.com/timrudner/snr_issues_in_deep_gps,5,On signal-to-noise ratio issues in variational inference for deep Gaussian processes,"https://scholar.google.com/scholar?cluster=16244183498083641614&hl=en&as_sdt=0,16",3,2021
Tilting the playing field: Dynamical loss functions for machine learning,13,icml,0,2,2023-06-17 04:14:07.174000,https://github.com/miguel-rg/dynamical-loss-functions,3,Tilting the playing field: Dynamical loss functions for machine learning,"https://scholar.google.com/scholar?cluster=1722474778051641263&hl=en&as_sdt=0,33",1,2021
UnICORNN: A recurrent model for learning very long time dependencies,35,icml,3,0,2023-06-17 04:14:07.376000,https://github.com/tk-rusch/unicornn,23,UnICORNN: A recurrent model for learning very long time dependencies,"https://scholar.google.com/scholar?cluster=16728515819525304575&hl=en&as_sdt=0,5",2,2021
Simple and Effective VAE Training with Calibrated Decoders,52,icml,1,0,2023-06-17 04:14:07.579000,https://github.com/orybkin/sigma-vae,25,Simple and effective VAE training with calibrated decoders,"https://scholar.google.com/scholar?cluster=16943299314546110740&hl=en&as_sdt=0,26",3,2021
Model-Based Reinforcement Learning via Latent-Space Collocation,17,icml,0,0,2023-06-17 04:14:07.783000,https://github.com/zchuning/latco,26,Model-based reinforcement learning via latent-space collocation,"https://scholar.google.com/scholar?cluster=2726935776109554696&hl=en&as_sdt=0,5",4,2021
Training Data Subset Selection for Regression with Controlled Generalization Error,8,icml,1,0,2023-06-17 04:14:07.985000,https://github.com/abir-de/SELCON,7,Training data subset selection for regression with controlled generalization error,"https://scholar.google.com/scholar?cluster=8877772987506172355&hl=en&as_sdt=0,5",2,2021
Momentum Residual Neural Networks,39,icml,17,6,2023-06-17 04:14:08.188000,https://github.com/michaelsdr/momentumnet,204,Momentum residual neural networks,"https://scholar.google.com/scholar?cluster=195539269682246494&hl=en&as_sdt=0,10",8,2021
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks,12,icml,2,2,2023-06-17 04:14:08.390000,https://github.com/keynans/HypeRL,16,Recomposing the reinforcement learning building blocks with hypernetworks,"https://scholar.google.com/scholar?cluster=11431615300192492432&hl=en&as_sdt=0,7",2,2021
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning,12,icml,1,0,2023-06-17 04:14:08.593000,https://github.com/nsaunshi/meta_tr_val_split,2,A representation learning perspective on the importance of train-validation splitting in meta-learning,"https://scholar.google.com/scholar?cluster=15485330124938854681&hl=en&as_sdt=0,14",2,2021
Linear Transformers Are Secretly Fast Weight Programmers,77,icml,9,0,2023-06-17 04:14:08.796000,https://github.com/ischlag/fast-weight-transformers,80,Linear transformers are secretly fast weight programmers,"https://scholar.google.com/scholar?cluster=7929763198773172485&hl=en&as_sdt=0,39",5,2021
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks,95,icml,19,0,2023-06-17 04:14:08.998000,https://github.com/aks2203/poisoning-benchmark,127,Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks,"https://scholar.google.com/scholar?cluster=15855049854905847899&hl=en&as_sdt=0,34",6,2021
Learning Intra-Batch Connections for Deep Metric Learning,33,icml,8,2,2023-06-17 04:14:09.202000,https://github.com/dvl-tum/intra_batch,43,Learning intra-batch connections for deep metric learning,"https://scholar.google.com/scholar?cluster=10851391941882516865&hl=en&as_sdt=0,33",3,2021
Personalized Federated Learning using Hypernetworks,124,icml,24,0,2023-06-17 04:14:09.405000,https://github.com/AvivSham/pFedHN,138,Personalized federated learning using hypernetworks,"https://scholar.google.com/scholar?cluster=9364037892005853502&hl=en&as_sdt=0,5",4,2021
Backdoor Scanning for Deep Neural Networks through K-Arm Optimization,49,icml,4,0,2023-06-17 04:14:09.607000,https://github.com/PurduePAML/K-ARM_Backdoor_Optimization,11,Backdoor scanning for deep neural networks through k-arm optimization,"https://scholar.google.com/scholar?cluster=18424002237979010229&hl=en&as_sdt=0,5",9,2021
State Relevance for Off-Policy Evaluation,2,icml,0,0,2023-06-17 04:14:09.810000,https://github.com/dtak/osiris,1,State relevance for off-policy evaluation,"https://scholar.google.com/scholar?cluster=1184988858503207705&hl=en&as_sdt=0,25",2,2021
Learning Gradient Fields for Molecular Conformation Generation,96,icml,28,7,2023-06-17 04:14:10.013000,https://github.com/DeepGraphLearning/ConfGF,131,Learning gradient fields for molecular conformation generation,"https://scholar.google.com/scholar?cluster=1418815604364379894&hl=en&as_sdt=0,47",9,2021
Deeply-Debiased Off-Policy Interval Estimation,22,icml,3,0,2023-06-17 04:14:10.216000,https://github.com/RunzheStat/D2OPE,9,Deeply-debiased off-policy interval estimation,"https://scholar.google.com/scholar?cluster=16793961424384021624&hl=en&as_sdt=0,33",2,2021
On Characterizing GAN Convergence Through Proximal Duality Gap,5,icml,2,1,2023-06-17 04:14:10.421000,https://github.com/proximal-dg/proximal_dg,9,On characterizing gan convergence through proximal duality gap,"https://scholar.google.com/scholar?cluster=16988175738385537443&hl=en&as_sdt=0,44",3,2021
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers,1,icml,0,0,2023-06-17 04:14:10.626000,https://github.com/cjsg/PopSkipJump,4,Popskipjump: Decision-based attack for probabilistic classifiers,"https://scholar.google.com/scholar?cluster=8512283764080476060&hl=en&as_sdt=0,39",1,2021
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances,31,icml,1,0,2023-06-17 04:14:10.856000,https://github.com/jbrea/symmetrysaddles.jl,0,Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances,"https://scholar.google.com/scholar?cluster=6069341273217919605&hl=en&as_sdt=0,5",2,2021
Skew Orthogonal Convolutions,34,icml,6,1,2023-06-17 04:14:11.067000,https://github.com/singlasahil14/SOC,12,Skew orthogonal convolutions,"https://scholar.google.com/scholar?cluster=17464482494309423430&hl=en&as_sdt=0,39",1,2021
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks,2,icml,3,0,2023-06-17 04:14:11.274000,https://github.com/srsohn/shortest-path-rl,11,Shortest-path constrained reinforcement learning for sparse reward tasks,"https://scholar.google.com/scholar?cluster=5761539218622911437&hl=en&as_sdt=0,10",5,2021
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving,9,icml,0,0,2023-06-17 04:14:11.476000,https://github.com/ermongroup/fast_feedforward_computation,18,Accelerating feedforward computation via parallel nonlinear equation solving,"https://scholar.google.com/scholar?cluster=9587891109353811026&hl=en&as_sdt=0,11",7,2021
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration,13,icml,3,0,2023-06-17 04:14:11.686000,https://github.com/yudasong/PCMLP,3,Pc-mlp: Model-based reinforcement learning with policy cover guided exploration,"https://scholar.google.com/scholar?cluster=8561706312159715447&hl=en&as_sdt=0,36",2,2021
Decoupling Representation Learning from Reinforcement Learning,220,icml,326,63,2023-06-17 04:14:11.889000,https://github.com/astooke/rlpyt,2141,Decoupling representation learning from reinforcement learning,"https://scholar.google.com/scholar?cluster=4351064812627090102&hl=en&as_sdt=0,47",53,2021
Not All Memories are Created Equal: Learning to Forget by Expiring,24,icml,17,2,2023-06-17 04:14:12.093000,https://github.com/facebookresearch/transformer-sequential,134,Not all memories are created equal: Learning to forget by expiring,"https://scholar.google.com/scholar?cluster=18323176449983399592&hl=en&as_sdt=0,11",10,2021
Nondeterminism and Instability in Neural Network Optimization,17,icml,0,1,2023-06-17 04:14:12.304000,https://github.com/ceciliaresearch/nondeterminism_instability,1,Nondeterminism and instability in neural network optimization,"https://scholar.google.com/scholar?cluster=3721428237004074314&hl=en&as_sdt=0,44",1,2021
What Makes for End-to-End Object Detection?,79,icml,74,3,2023-06-17 04:14:12.506000,https://github.com/PeizeSun/OneNet,633,What makes for end-to-end object detection?,"https://scholar.google.com/scholar?cluster=17182921757850029040&hl=en&as_sdt=0,4",20,2021
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning,29,icml,2,0,2023-06-17 04:14:12.709000,https://github.com/j3soon/dfac,22,DFAC framework: Factorizing the value function via quantile mixture for multi-agent distributional Q-learning,"https://scholar.google.com/scholar?cluster=13269837837943676067&hl=en&as_sdt=0,5",3,2021
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition,5,icml,0,0,2023-06-17 04:14:12.911000,https://github.com/ssydasheng/Harmonic-Kernel-Decomposition,9,Scalable variational gaussian processes via harmonic kernel decomposition,"https://scholar.google.com/scholar?cluster=5527723102830248655&hl=en&as_sdt=0,44",1,2021
Model-Targeted Poisoning Attacks with Provable Convergence,24,icml,4,3,2023-06-17 04:14:13.114000,https://github.com/suyeecav/model-targeted-poisoning,9,Model-targeted poisoning attacks with provable convergence,"https://scholar.google.com/scholar?cluster=1651990358981165914&hl=en&as_sdt=0,5",3,2021
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap,21,icml,4,1,2023-06-17 04:14:13.316000,https://github.com/gkswamy98/pillbox,16,Of moments and matching: A game-theoretic framework for closing the imitation gap,"https://scholar.google.com/scholar?cluster=7938694148424637226&hl=en&as_sdt=0,49",2,2021
Parallel tempering on optimized paths,10,icml,2,0,2023-06-17 04:14:13.520000,https://github.com/vittrom/PT-pathoptim,2,Parallel tempering on optimized paths,"https://scholar.google.com/scholar?cluster=14697506612657062549&hl=en&as_sdt=0,5",1,2021
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training,19,icml,3,0,2023-06-17 04:14:13.736000,https://github.com/stanford-futuredata/sinkhorn-label-allocation,50,Sinkhorn label allocation: Semi-supervised classification via annealed self-training,"https://scholar.google.com/scholar?cluster=13645843302447766832&hl=en&as_sdt=0,44",7,2021
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts,10,icml,2,0,2023-06-17 04:14:13.939000,https://github.com/RAO-EPFL/DR-DA,3,Sequential domain adaptation by synthesizing distributionally robust experts,"https://scholar.google.com/scholar?cluster=6930689921879255394&hl=en&as_sdt=0,13",2,2021
T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP,4,icml,0,0,2023-06-17 04:14:14.141000,https://github.com/thutzr/Cox,1,T-sci: A two-stage conformal inference algorithm with guaranteed coverage for cox-mlp,"https://scholar.google.com/scholar?cluster=1012431253971456969&hl=en&as_sdt=0,39",2,2021
Moreau-Yosida $f$-divergences,2,icml,0,0,2023-06-17 04:14:14.358000,https://github.com/renyi-ai/moreau-yosida-f-divergences,2,Moreau-Yosida -divergences,"https://scholar.google.com/scholar?cluster=3652869154522690970&hl=en&as_sdt=0,5",2,2021
Training data-efficient image transformers & distillation through attention,3348,icml,516,12,2023-06-17 04:14:14.561000,https://github.com/facebookresearch/deit,3450,Training data-efficient image transformers & distillation through attention,"https://scholar.google.com/scholar?cluster=16235705232339507184&hl=en&as_sdt=0,48",48,2021
Conservative Objective Models for Effective Offline Model-Based Optimization,36,icml,7,2,2023-06-17 04:14:14.764000,https://github.com/brandontrabucco/design-baselines,42,Conservative objective models for effective offline model-based optimization,"https://scholar.google.com/scholar?cluster=10951629581873877852&hl=en&as_sdt=0,10",4,2021
On Disentangled Representations Learned from Correlated Data,71,icml,5,0,2023-06-17 04:14:14.966000,https://github.com/ftraeuble/disentanglement_lib,10,On disentangled representations learned from correlated data,"https://scholar.google.com/scholar?cluster=10644866140945749570&hl=en&as_sdt=0,33",0,2021
"A New Formalism, Method and Open Issues for Zero-Shot Coordination",16,icml,0,0,2023-06-17 04:14:15.169000,https://github.com/johannestreutlein/op-tie-breaking,4,"A new formalism, method and open issues for zero-shot coordination","https://scholar.google.com/scholar?cluster=7081499741440160815&hl=en&as_sdt=0,10",1,2021
Learning a Universal Template for Few-shot Dataset Generalization,49,icml,136,44,2023-06-17 04:14:15.371000,https://github.com/google-research/meta-dataset,698,Learning a universal template for few-shot dataset generalization,"https://scholar.google.com/scholar?cluster=1180369253723418240&hl=en&as_sdt=0,45",24,2021
Provable Meta-Learning of Linear Representations,127,icml,1,0,2023-06-17 04:14:15.573000,https://github.com/nileshtrip/MTL,2,Provable meta-learning of linear representations,"https://scholar.google.com/scholar?cluster=14454744225976907789&hl=en&as_sdt=0,36",2,2021
LTL2Action: Generalizing LTL Instructions for Multi-Task RL,43,icml,4,0,2023-06-17 04:14:15.777000,https://github.com/LTL2Action/LTL2Action,16,Ltl2action: Generalizing ltl instructions for multi-task rl,"https://scholar.google.com/scholar?cluster=14511888964718858114&hl=en&as_sdt=0,5",1,2021
Online Graph Dictionary Learning,32,icml,6,0,2023-06-17 04:14:15.981000,https://github.com/cedricvincentcuaz/GDL,12,Online graph dictionary learning,"https://scholar.google.com/scholar?cluster=7527452774562329300&hl=en&as_sdt=0,33",1,2021
Efficient Training of Robust Decision Trees Against Adversarial Examples,18,icml,8,0,2023-06-17 04:14:16.184000,https://github.com/tudelft-cda-lab/GROOT,18,Efficient training of robust decision trees against adversarial examples,"https://scholar.google.com/scholar?cluster=9227298780298647203&hl=en&as_sdt=0,33",5,2021
Object Segmentation Without Labels with Large-Scale Generative Models,28,icml,30,3,2023-06-17 04:14:16.387000,https://github.com/anvoynov/BigGANsAreWatching,118,Object segmentation without labels with large-scale generative models,"https://scholar.google.com/scholar?cluster=7466808437204273550&hl=en&as_sdt=0,5",7,2021
Principal Component Hierarchy for Sparse Quadratic Programs,3,icml,0,1,2023-06-17 04:14:16.591000,https://github.com/RVreugdenhil/sparseQP,3,Principal component hierarchy for sparse quadratic programs,"https://scholar.google.com/scholar?cluster=2335943370788592099&hl=en&as_sdt=0,5",2,2021
Safe Reinforcement Learning Using Advantage-Based Intervention,26,icml,5,1,2023-06-17 04:14:16.794000,https://github.com/nolanwagener/safe_rl,18,Safe reinforcement learning using advantage-based intervention,"https://scholar.google.com/scholar?cluster=5048043466827651236&hl=en&as_sdt=0,33",1,2021
Learning and Planning in Average-Reward Markov Decision Processes,38,icml,0,0,2023-06-17 04:14:16.998000,https://github.com/abhisheknaik96/average-reward-methods,12,Learning and planning in average-reward markov decision processes,"https://scholar.google.com/scholar?cluster=750901868273869826&hl=en&as_sdt=0,47",1,2021
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces,27,icml,5,0,2023-06-17 04:14:17.202000,https://github.com/xingchenwan/Casmopolitan,21,Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces,"https://scholar.google.com/scholar?cluster=6765216544866118683&hl=en&as_sdt=0,47",1,2021
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model,24,icml,1,2,2023-06-17 04:14:17.405000,https://github.com/zwang84/zsdb3kd,10,Zero-shot knowledge distillation from a decision-based black-box model,"https://scholar.google.com/scholar?cluster=7908835679548457764&hl=en&as_sdt=0,33",3,2021
Fast Algorithms for Stackelberg Prediction Game with Least Squares Loss,5,icml,2,0,2023-06-17 04:14:17.607000,https://github.com/JialiWang12/SPGLS,2,Fast algorithms for stackelberg prediction game with least squares loss,"https://scholar.google.com/scholar?cluster=2550353303659094230&hl=en&as_sdt=0,5",2,2021
Self-Tuning for Data-Efficient Deep Learning,35,icml,14,6,2023-06-17 04:14:17.810000,https://github.com/thuml/Self-Tuning,108,Self-tuning for data-efficient deep learning,"https://scholar.google.com/scholar?cluster=3161082086338934038&hl=en&as_sdt=0,5",4,2021
AlphaNet: Improved Training of Supernets with Alpha-Divergence,41,icml,13,0,2023-06-17 04:14:18.013000,https://github.com/facebookresearch/AlphaNet,90,Alphanet: Improved training of supernets with alpha-divergence,"https://scholar.google.com/scholar?cluster=16040812221590233106&hl=en&as_sdt=0,5",10,2021
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model,6,icml,0,0,2023-06-17 04:14:18.217000,https://github.com/ywa136/sg-palm,0,Sg-palm: a fast physically interpretable tensor graphical model,"https://scholar.google.com/scholar?cluster=15846965999647833426&hl=en&as_sdt=0,39",2,2021
Robust Inference for High-Dimensional Linear Models via Residual Randomization,2,icml,0,0,2023-06-17 04:14:18.419000,https://github.com/atechnicolorskye/rrHDI,0,Robust inference for high-dimensional linear models via residual randomization,"https://scholar.google.com/scholar?cluster=7848775259409033077&hl=en&as_sdt=0,5",4,2021
Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method,12,icml,0,0,2023-06-17 04:14:18.623000,https://github.com/peng8wang/ICML2021-PPM-SBM,1,Optimal non-convex exact recovery in stochastic block model via projected power method,"https://scholar.google.com/scholar?cluster=2598400261123150872&hl=en&as_sdt=0,5",1,2021
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks,14,icml,0,0,2023-06-17 04:14:18.826000,https://github.com/bhwangfy/ICML-2021-Adaptive-Bias,0,The implicit bias for adaptive optimization algorithms on homogeneous neural networks,"https://scholar.google.com/scholar?cluster=6329455504055217085&hl=en&as_sdt=0,6",1,2021
Directional Bias Amplification,30,icml,1,0,2023-06-17 04:14:19.029000,https://github.com/princetonvisualai/directional-bias-amp,12,Directional bias amplification,"https://scholar.google.com/scholar?cluster=16389460185229956032&hl=en&as_sdt=0,5",3,2021
An exact solver for the Weston-Watkins SVM subproblem,1,icml,1,0,2023-06-17 04:14:19.233000,https://github.com/YutongWangUMich/liblinear,1,An exact solver for the Weston-Watkins SVM subproblem,"https://scholar.google.com/scholar?cluster=3159763216882198120&hl=en&as_sdt=0,33",1,2021
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data,68,icml,1,0,2023-06-17 04:14:19.435000,https://github.com/cywang97/unispeech,6,Unispeech: Unified speech representation learning with labeled and unlabeled data,"https://scholar.google.com/scholar?cluster=13435266557122878220&hl=en&as_sdt=0,10",0,2021
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach,14,icml,0,0,2023-06-17 04:14:19.638000,https://github.com/Kolin96/learning-to-learn,5,Guarantees for tuning the step size using a learning-to-learn approach,"https://scholar.google.com/scholar?cluster=14011148372183922163&hl=en&as_sdt=0,47",1,2021
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation,48,icml,9,1,2023-06-17 04:14:19.840000,https://github.com/AI-secure/multi-task-learning,61,Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation,"https://scholar.google.com/scholar?cluster=5814522177483838670&hl=en&as_sdt=0,5",2,2021
Robust Asymmetric Learning in POMDPs,18,icml,0,0,2023-06-17 04:14:20.043000,https://github.com/plai-group/a2d,6,Robust asymmetric learning in pomdps,"https://scholar.google.com/scholar?cluster=3140825517966878728&hl=en&as_sdt=0,11",6,2021
Thinking Like Transformers,23,icml,18,1,2023-06-17 04:14:20.250000,https://github.com/tech-srl/RASP,204,Thinking like transformers,"https://scholar.google.com/scholar?cluster=18191652199606300845&hl=en&as_sdt=0,33",9,2021
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations,4,icml,0,4,2023-06-17 04:14:20.453000,https://github.com/mateuszwilinski/dynamic-message-passing,2,Prediction-centric learning of independent cascade dynamics from partial observations,"https://scholar.google.com/scholar?cluster=10502404999928524540&hl=en&as_sdt=0,5",2,2021
Learning Neural Network Subspaces,40,icml,17,1,2023-06-17 04:14:20.656000,https://github.com/apple/learning-subspaces,124,Learning neural network subspaces,"https://scholar.google.com/scholar?cluster=10251875714480398754&hl=en&as_sdt=0,5",10,2021
Making Paper Reviewing Robust to Bid Manipulation Attacks,17,icml,5,0,2023-06-17 04:14:20.858000,https://github.com/facebookresearch/secure-paper-bidding,9,Making paper reviewing robust to bid manipulation attacks,"https://scholar.google.com/scholar?cluster=3106264104832629742&hl=en&as_sdt=0,5",9,2021
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning,25,icml,4,0,2023-06-17 04:14:21.061000,https://github.com/tonywu95/lime,16,Lime: Learning inductive bias for primitives of mathematical reasoning,"https://scholar.google.com/scholar?cluster=6631886312737976055&hl=en&as_sdt=0,18",2,2021
ChaCha for Online AutoML,5,icml,379,173,2023-06-17 04:14:21.263000,https://github.com/microsoft/FLAML,2517,Chacha for online automl,"https://scholar.google.com/scholar?cluster=15774579199663385941&hl=en&as_sdt=0,39",45,2021
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach,20,icml,6,0,2023-06-17 04:14:21.465000,https://github.com/qitianwu/IDCF,24,Towards open-world recommendation: An inductive model-based collaborative filtering approach,"https://scholar.google.com/scholar?cluster=13656226067206698249&hl=en&as_sdt=0,5",2,2021
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty,24,icml,5,1,2023-06-17 04:14:21.668000,https://github.com/zzzx1224/A-Bit-More-Bayesian,10,A bit more bayesian: Domain-invariant learning with uncertainty,"https://scholar.google.com/scholar?cluster=8533759072554466832&hl=en&as_sdt=0,5",1,2021
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks,75,icml,12,3,2023-06-17 04:14:21.872000,https://github.com/AI-secure/CRFL,56,Crfl: Certifiably robust federated learning against backdoor attacks,"https://scholar.google.com/scholar?cluster=566297691223350385&hl=en&as_sdt=0,47",3,2021
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization,21,icml,6,0,2023-06-17 04:14:22.074000,https://github.com/zeke-xie/Positive-Negative-Momentum,25,Positive-negative momentum: Manipulating stochastic gradient noise to improve generalization,"https://scholar.google.com/scholar?cluster=9647717968624963089&hl=en&as_sdt=0,23",3,2021
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming,42,icml,12,0,2023-06-17 04:14:22.277000,https://github.com/MinkaiXu/ConfVAE-ICML21,47,An end-to-end framework for molecular conformation generation via bilevel programming,"https://scholar.google.com/scholar?cluster=914718927564575831&hl=en&as_sdt=0,5",3,2021
Self-supervised Graph-level Representation Learning with Local and Global Structure,96,icml,15,3,2023-06-17 04:14:22.479000,https://github.com/DeepGraphLearning/GraphLoG,57,Self-supervised graph-level representation learning with local and global structure,"https://scholar.google.com/scholar?cluster=15360735332012817623&hl=en&as_sdt=0,5",7,2021
Conformal prediction interval for dynamic time-series,38,icml,18,0,2023-06-17 04:14:22.682000,https://github.com/hamrel-cxu/EnbPI,42,Conformal prediction interval for dynamic time-series,"https://scholar.google.com/scholar?cluster=9397887507156986767&hl=en&as_sdt=0,33",2,2021
KNAS: Green Neural Architecture Search,25,icml,15,1,2023-06-17 04:14:22.885000,https://github.com/jingjing-nlp/knas,90,KNAS: green neural architecture search,"https://scholar.google.com/scholar?cluster=636730090425787241&hl=en&as_sdt=0,36",2,2021
Structured Convolutional Kernel Networks for Airline Crew Scheduling,8,icml,3,0,2023-06-17 04:14:23.087000,https://github.com/Yaakoubi/Struct-CKN,5,Structured convolutional kernel networks for airline crew scheduling,"https://scholar.google.com/scholar?cluster=6467944180520163376&hl=en&as_sdt=0,47",1,2021
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences,1,icml,0,0,2023-06-17 04:14:23.289000,https://github.com/i-yamane/mediated_uncoupled_learning,2,Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences,"https://scholar.google.com/scholar?cluster=17617652020684598053&hl=en&as_sdt=0,21",2,2021
EL-Attention: Memory Efficient Lossless Attention for Generation,2,icml,41,11,2023-06-17 04:14:23.492000,https://github.com/microsoft/fastseq,416,El-attention: Memory efficient lossless attention for generation,"https://scholar.google.com/scholar?cluster=1375858256863771464&hl=en&as_sdt=0,47",15,2021
Link Prediction with Persistent Homology: An Interactive View,23,icml,1,1,2023-06-17 04:14:23.695000,https://github.com/pkuyzy/TLC-GNN,8,Link prediction with persistent homology: An interactive view,"https://scholar.google.com/scholar?cluster=6988958697269886780&hl=en&as_sdt=0,44",2,2021
CATE: Computation-aware Neural Architecture Encoding with Transformers,12,icml,5,1,2023-06-17 04:14:23.897000,https://github.com/MSU-MLSys-Lab/CATE,17,Cate: Computation-aware neural architecture encoding with transformers,"https://scholar.google.com/scholar?cluster=8641165479167437291&hl=en&as_sdt=0,41",4,2021
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework,12,icml,2,0,2023-06-17 04:14:24.100000,https://github.com/ZeyuYan/Perceptual-Lossy-Compression,10,On perceptual lossy compression: The cost of perceptual reconstruction and an optimal training framework,"https://scholar.google.com/scholar?cluster=3982169689811841911&hl=en&as_sdt=0,36",1,2021
Graph Neural Networks Inspired by Classical Iterative Algorithms,43,icml,5,0,2023-06-17 04:14:24.302000,https://github.com/FFTYYY/TWIRLS,35,Graph neural networks inspired by classical iterative algorithms,"https://scholar.google.com/scholar?cluster=7834297008396631458&hl=en&as_sdt=0,5",2,2021
Voice2Series: Reprogramming Acoustic Models for Time Series Classification,49,icml,8,3,2023-06-17 04:14:24.504000,https://github.com/huckiyang/Voice2Series-Reprogramming,54,Voice2series: Reprogramming acoustic models for time series classification,"https://scholar.google.com/scholar?cluster=436573915483653789&hl=en&as_sdt=0,38",2,2021
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss,166,icml,178,21,2023-06-17 04:14:24.706000,https://github.com/yangxue0827/RotationDetection,1013,Rethinking rotated object detection with gaussian wasserstein distance loss,"https://scholar.google.com/scholar?cluster=9458084216549029781&hl=en&as_sdt=0,33",21,2021
Delving into Deep Imbalanced Regression,114,icml,111,3,2023-06-17 04:14:24.909000,https://github.com/YyzHarry/imbalanced-regression,642,Delving into deep imbalanced regression,"https://scholar.google.com/scholar?cluster=14041915448985010978&hl=en&as_sdt=0,31",18,2021
"SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks",189,icml,33,9,2023-06-17 04:14:25.111000,https://github.com/ZjjConan/SimAM,257,"Simam: A simple, parameter-free attention module for convolutional neural networks","https://scholar.google.com/scholar?cluster=6748424654077587327&hl=en&as_sdt=0,47",5,2021
Improving Generalization in Meta-learning via Task Augmentation,53,icml,4,0,2023-06-17 04:14:25.314000,https://github.com/huaxiuyao/MetaMix,25,Improving generalization in meta-learning via task augmentation,"https://scholar.google.com/scholar?cluster=756197262814969387&hl=en&as_sdt=0,47",1,2021
Deep Learning for Functional Data Analysis with Adaptive Basis Layers,8,icml,4,0,2023-06-17 04:14:25.516000,https://github.com/jwyyy/AdaFNN,18,Deep learning for functional data analysis with adaptive basis layers,"https://scholar.google.com/scholar?cluster=17144943362411304273&hl=en&as_sdt=0,14",1,2021
Addressing Catastrophic Forgetting in Few-Shot Problems,10,icml,1,0,2023-06-17 04:14:25.719000,https://github.com/pauchingyap/boml,4,Addressing catastrophic forgetting in few-shot problems,"https://scholar.google.com/scholar?cluster=5331519649661500119&hl=en&as_sdt=0,11",1,2021
Break-It-Fix-It: Unsupervised Learning for Program Repair,52,icml,19,5,2023-06-17 04:14:25.922000,https://github.com/michiyasunaga/bifi,97,Break-it-fix-it: Unsupervised learning for program repair,"https://scholar.google.com/scholar?cluster=4368697690139646578&hl=en&as_sdt=0,25",2,2021
Neighborhood Contrastive Learning Applied to Online Patient Monitoring,14,icml,1,0,2023-06-17 04:14:26.125000,https://github.com/ratschlab/ncl,15,Neighborhood contrastive learning applied to online patient monitoring,"https://scholar.google.com/scholar?cluster=4664115316667000917&hl=en&as_sdt=0,5",7,2021
Continuous-time Model-based Reinforcement Learning,25,icml,9,0,2023-06-17 04:14:26.328000,https://github.com/cagatayyildiz/oderl,29,Continuous-time model-based reinforcement learning,"https://scholar.google.com/scholar?cluster=14746718008006143630&hl=en&as_sdt=0,33",3,2021
Path Planning using Neural A* Search,48,icml,39,0,2023-06-17 04:14:26.588000,https://github.com/omron-sinicx/neural-astar,112,Path planning using neural a* search,"https://scholar.google.com/scholar?cluster=997109174991202847&hl=en&as_sdt=0,34",8,2021
SinIR: Efficient General Image Manipulation with Single Image Reconstruction,15,icml,6,2,2023-06-17 04:14:26.873000,https://github.com/YooJiHyeong/SinIR,49,Sinir: Efficient general image manipulation with single image reconstruction,"https://scholar.google.com/scholar?cluster=10599627975062939893&hl=en&as_sdt=0,5",4,2021
Conditional Temporal Neural Processes with Covariance Loss,6,icml,3,0,2023-06-17 04:14:27.077000,https://github.com/boseon-ai/Conditional-Temporal-Neural-Processes-with-Covariance-Loss,4,Conditional temporal neural processes with covariance loss,"https://scholar.google.com/scholar?cluster=11587001317959077781&hl=en&as_sdt=0,5",1,2021
Adversarial Purification with Score-based Generative Models,44,icml,3,2,2023-06-17 04:14:27.280000,https://github.com/jmyoon1/adp,19,Adversarial purification with score-based generative models,"https://scholar.google.com/scholar?cluster=1510322463041774819&hl=en&as_sdt=0,44",1,2021
Federated Continual Learning with Weighted Inter-client Transfer,76,icml,22,0,2023-06-17 04:14:27.484000,https://github.com/wyjeong/FedWeIT,67,Federated continual learning with weighted inter-client transfer,"https://scholar.google.com/scholar?cluster=6346174361267860505&hl=en&as_sdt=0,21",2,2021
Autoencoding Under Normalization Constraints,16,icml,13,0,2023-06-17 04:14:27.694000,https://github.com/swyoon/normalized-autoencoders,37,Autoencoding under normalization constraints,"https://scholar.google.com/scholar?cluster=1297005004772257313&hl=en&as_sdt=0,47",5,2021
Lower-Bounded Proper Losses for Weakly Supervised Classification,2,icml,0,0,2023-06-17 04:14:27.897000,https://github.com/yoshum/lower-bounded-proper-losses,2,Lower-Bounded Proper Losses for Weakly Supervised Classification,"https://scholar.google.com/scholar?cluster=17541047076253957367&hl=en&as_sdt=0,5",1,2021
Graph Contrastive Learning Automated,196,icml,8,4,2023-06-17 04:14:28.101000,https://github.com/Shen-Lab/GraphCL_Automated,85,Graph contrastive learning automated,"https://scholar.google.com/scholar?cluster=4319391299971749370&hl=en&as_sdt=0,33",3,2021
LogME: Practical Assessment of Pre-trained Models for Transfer Learning,69,icml,15,6,2023-06-17 04:14:28.303000,https://github.com/thuml/LogME,172,Logme: Practical assessment of pre-trained models for transfer learning,"https://scholar.google.com/scholar?cluster=7398435047749789865&hl=en&as_sdt=0,33",5,2021
DAGs with No Curl: An Efficient DAG Structure Learning Approach,30,icml,5,1,2023-06-17 04:14:28.506000,https://github.com/fishmoon1234/DAG-NoCurl,16,Dags with no curl: An efficient dag structure learning approach,"https://scholar.google.com/scholar?cluster=3161455728562313506&hl=en&as_sdt=0,5",2,2021
Large Scale Private Learning via Low-rank Reparametrization,41,icml,17,0,2023-06-17 04:14:28.710000,https://github.com/dayu11/Differentially-Private-Deep-Learning,72,Large scale private learning via low-rank reparametrization,"https://scholar.google.com/scholar?cluster=10646842759761842433&hl=en&as_sdt=0,33",2,2021
Federated Composite Optimization,38,icml,3,0,2023-06-17 04:14:28.914000,https://github.com/hongliny/FCO-ICML21,9,Federated composite optimization,"https://scholar.google.com/scholar?cluster=10805982907996173478&hl=en&as_sdt=0,34",1,2021
Three Operator Splitting with a Nonconvex Loss Function,5,icml,0,0,2023-06-17 04:14:29.117000,https://github.com/alpyurtsever/NonconvexTOS,1,Three operator splitting with a nonconvex loss function,"https://scholar.google.com/scholar?cluster=14275996016492090770&hl=en&as_sdt=0,14",1,2021
Learning Binary Decision Trees by Argmin Differentiation,10,icml,1,2,2023-06-17 04:14:29.320000,https://github.com/vzantedeschi/LatentTrees,11,Learning binary decision trees by argmin differentiation,"https://scholar.google.com/scholar?cluster=8235159658077202682&hl=en&as_sdt=0,5",1,2021
Barlow Twins: Self-Supervised Learning via Redundancy Reduction,1208,icml,122,8,2023-06-17 04:14:29.523000,https://github.com/facebookresearch/barlowtwins,886,Barlow twins: Self-supervised learning via redundancy reduction,"https://scholar.google.com/scholar?cluster=5159677840794766125&hl=en&as_sdt=0,47",28,2021
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling,10,icml,3,2,2023-06-17 04:14:29.769000,https://github.com/mlpen/yoso,13,You only sample (almost) once: Linear cost self-attention via bernoulli sampling,"https://scholar.google.com/scholar?cluster=11877607783928250360&hl=en&as_sdt=0,10",2,2021
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning,67,icml,477,18,2023-06-17 04:14:29.972000,https://github.com/kwai/DouZero,3324,Douzero: Mastering doudizhu with self-play deep reinforcement learning,"https://scholar.google.com/scholar?cluster=10717987879996790788&hl=en&as_sdt=0,33",44,2021
DORO: Distributional and Outlier Robust Optimization,27,icml,4,0,2023-06-17 04:14:30.174000,https://github.com/RuntianZ/doro,25,Doro: Distributional and outlier robust optimization,"https://scholar.google.com/scholar?cluster=7792478456437572549&hl=en&as_sdt=0,6",2,2021
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons,31,icml,6,0,2023-06-17 04:14:30.377000,https://github.com/zbh2047/L_inf-dist-net,38,Towards certifying l-infinity robustness using neural networks with l-inf-dist neurons,"https://scholar.google.com/scholar?cluster=6201420149183682924&hl=en&as_sdt=0,5",2,2021
Efficient Lottery Ticket Finding: Less Data is More,38,icml,4,0,2023-06-17 04:14:30.580000,https://github.com/VITA-Group/PrAC-LTH,24,Efficient lottery ticket finding: Less data is more,"https://scholar.google.com/scholar?cluster=9030177952981756712&hl=en&as_sdt=0,14",8,2021
Robust Policy Gradient against Strong Data Corruption,22,icml,0,0,2023-06-17 04:14:30.783000,https://github.com/zhangxz1123/FilteredPolicyGradient,4,Robust policy gradient against strong data corruption,"https://scholar.google.com/scholar?cluster=5709291198914313258&hl=en&as_sdt=0,47",2,2021
PAPRIKA: Private Online False Discovery Rate Control,5,icml,0,0,2023-06-17 04:14:30.985000,https://github.com/wanrongz/PAPRIKA,6,Paprika: Private online false discovery rate control,"https://scholar.google.com/scholar?cluster=16053819406696763043&hl=en&as_sdt=0,44",2,2021
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation,12,icml,1,1,2023-06-17 04:14:31.187000,https://github.com/AI-secure/PSBA,5,Progressive-scale boundary blackbox attack via projective gradient estimation,"https://scholar.google.com/scholar?cluster=2561734592069193549&hl=en&as_sdt=0,11",2,2021
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization,44,icml,0,0,2023-06-17 04:14:31.389000,https://github.com/YivanZhang/lio,9,Learning noise transition matrix from only noisy labels via total variation regularization,"https://scholar.google.com/scholar?cluster=14671082055157503187&hl=en&as_sdt=0,34",2,2021
Quantile Bandits for Best Arms Identification,9,icml,0,0,2023-06-17 04:14:31.591000,https://github.com/Mengyanz/QSAR,0,Quantile bandits for best arms identification,"https://scholar.google.com/scholar?cluster=6809249853640844054&hl=en&as_sdt=0,5",2,2021
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients,32,icml,2,2,2023-06-17 04:14:31.794000,https://github.com/MiaoZhang0525/iDARTS,9,idarts: Differentiable architecture search with stochastic implicit gradients,"https://scholar.google.com/scholar?cluster=2918201960391178882&hl=en&as_sdt=0,47",2,2021
Average-Reward Off-Policy Policy Evaluation with Function Approximation,23,icml,658,6,2023-06-17 04:14:31.997000,https://github.com/ShangtongZhang/DeepRL,2943,Average-reward off-policy policy evaluation with function approximation,"https://scholar.google.com/scholar?cluster=12042728594024517731&hl=en&as_sdt=0,10",93,2021
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration,16,icml,3,0,2023-06-17 04:14:32.201000,https://github.com/NagisaZj/MetaCURE-Public,12,Metacure: Meta reinforcement learning with empowerment-driven exploration,"https://scholar.google.com/scholar?cluster=8017350448991384435&hl=en&as_sdt=0,5",2,2021
World Model as a Graph: Learning Latent Landmarks for Planning,41,icml,2,0,2023-06-17 04:14:32.404000,https://github.com/LunjunZhang/world-model-as-a-graph,53,World model as a graph: Learning latent landmarks for planning,"https://scholar.google.com/scholar?cluster=11617385762396360333&hl=en&as_sdt=0,5",1,2021
Breaking the Deadly Triad with a Target Network,29,icml,658,6,2023-06-17 04:14:32.607000,https://github.com/ShangtongZhang/DeepRL,2943,Breaking the deadly triad with a target network,"https://scholar.google.com/scholar?cluster=3294420755935359524&hl=en&as_sdt=0,5",93,2021
Dataset Condensation with Differentiable Siamese Augmentation,82,icml,73,0,2023-06-17 04:14:32.809000,https://github.com/VICO-UoE/DatasetCondensation,331,Dataset condensation with differentiable siamese augmentation,"https://scholar.google.com/scholar?cluster=14949848395042620640&hl=en&as_sdt=0,5",9,2021
Calibrate Before Use: Improving Few-shot Performance of Language Models,366,icml,42,3,2023-06-17 04:14:33.012000,https://github.com/tonyzhaozh/few-shot-learning,273,Calibrate before use: Improving few-shot performance of language models,"https://scholar.google.com/scholar?cluster=8877771337173887679&hl=en&as_sdt=0,5",5,2021
Few-Shot Neural Architecture Search,71,icml,7,3,2023-06-17 04:14:33.215000,https://github.com/aoiang/few-shot-NAS,39,Few-shot neural architecture search,"https://scholar.google.com/scholar?cluster=668653762741709836&hl=en&as_sdt=0,5",4,2021
How Framelets Enhance Graph Neural Networks,41,icml,13,0,2023-06-17 04:14:33.416000,https://github.com/YuGuangWang/UFG,30,How framelets enhance graph neural networks,"https://scholar.google.com/scholar?cluster=13922049936410780570&hl=en&as_sdt=0,44",2,2021
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions,6,icml,0,0,2023-06-17 04:14:33.619000,https://github.com/zixinzh/2021-ICML,0,Probabilistic sequential shrinking: A best arm identification algorithm for stochastic bandits with corruptions,"https://scholar.google.com/scholar?cluster=17868833179563071427&hl=en&as_sdt=0,47",1,2021
Asymmetric Loss Functions for Learning with Noisy Labels,25,icml,4,3,2023-06-17 04:14:33.822000,https://github.com/hitcszx/ALFs,28,Asymmetric loss functions for learning with noisy labels,"https://scholar.google.com/scholar?cluster=425870196210326248&hl=en&as_sdt=0,3",3,2021
Examining and Combating Spurious Features under Distribution Shift,34,icml,3,0,2023-06-17 04:14:34.026000,https://github.com/violet-zct/group-conditional-DRO,14,Examining and combating spurious features under distribution shift,"https://scholar.google.com/scholar?cluster=14520135804314510635&hl=en&as_sdt=0,14",1,2021
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm,11,icml,3,1,2023-06-17 04:14:34.228000,https://github.com/VITA-Group/SparseADV_Homotopy,7,Sparse and imperceptible adversarial attack via a homotopy algorithm,"https://scholar.google.com/scholar?cluster=18221995160833723432&hl=en&as_sdt=0,14",8,2021
Data-Free Knowledge Distillation for Heterogeneous Federated Learning,218,icml,61,12,2023-06-17 04:14:34.431000,https://github.com/zhuangdizhu/FedGen,185,Data-free knowledge distillation for heterogeneous federated learning,"https://scholar.google.com/scholar?cluster=7623989304932004124&hl=en&as_sdt=0,6",2,2021
Commutative Lie Group VAE for Disentanglement Learning,13,icml,0,0,2023-06-17 04:14:34.633000,https://github.com/zhuxinqimac/CommutativeLieGroupVAE-Pytorch,21,Commutative lie group vae for disentanglement learning,"https://scholar.google.com/scholar?cluster=13512230477271020552&hl=en&as_sdt=0,3",2,2021
Contrastive Learning Inverts the Data Generating Process,118,icml,8,3,2023-06-17 04:14:34.835000,https://github.com/brendel-group/cl-ica,76,Contrastive learning inverts the data generating process,"https://scholar.google.com/scholar?cluster=6297973976914221052&hl=en&as_sdt=0,6",7,2021
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning,30,icml,2,0,2023-06-17 04:14:35.040000,https://github.com/lmzintgraf/hyperx,12,Exploration in approximate hyper-state space for meta reinforcement learning,"https://scholar.google.com/scholar?cluster=598880115896472356&hl=en&as_sdt=0,22",2,2021
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning,21,icml,6,0,2023-06-17 04:54:22.079000,https://github.com/mominabbass/sharp-maml,23,Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning,"https://scholar.google.com/scholar?cluster=14950420836477699137&hl=en&as_sdt=0,22",1,2022
Active Sampling for Min-Max Fairness,15,icml,1,1,2023-06-17 04:54:22.293000,https://github.com/amazon-research/active-sampling-for-minmax-fairness,4,Active sampling for min-max fairness,"https://scholar.google.com/scholar?cluster=7250212054919979465&hl=en&as_sdt=0,5",6,2022
Meaningfully debugging model mistakes using conceptual counterfactual explanations,20,icml,5,1,2023-06-17 04:54:22.498000,https://github.com/mertyg/debug-mistakes-cce,70,Meaningfully debugging model mistakes using conceptual counterfactual explanations,"https://scholar.google.com/scholar?cluster=2849569429175172034&hl=en&as_sdt=0,5",8,2022
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games,6,icml,0,0,2023-06-17 04:54:22.703000,https://github.com/XinyiYS/Parametric-Bayesian-Learning-Games,1,On the convergence of the Shapley value in parametric Bayesian learning games,"https://scholar.google.com/scholar?cluster=7727281335591886084&hl=en&as_sdt=0,5",1,2022
Individual Preference Stability for Clustering,2,icml,1,0,2023-06-17 04:54:22.909000,https://github.com/amazon-research/ip-stability-for-clustering,0,Individual Preference Stability for Clustering,"https://scholar.google.com/scholar?cluster=5704874975941768336&hl=en&as_sdt=0,30",6,2022
Minimum Cost Intervention Design for Causal Effect Identification,2,icml,0,0,2023-06-17 04:54:23.115000,https://github.com/sinaakbarii/min_cost_intervention,1,Minimum cost intervention design for causal effect identification,"https://scholar.google.com/scholar?cluster=8464705336757566822&hl=en&as_sdt=0,44",1,2022
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models,60,icml,4,0,2023-06-17 04:54:23.322000,https://github.com/vanderschaarlab/evaluating-generative-models,22,How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models,"https://scholar.google.com/scholar?cluster=15840878488291944826&hl=en&as_sdt=0,33",5,2022
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations,2,icml,0,1,2023-06-17 04:54:23.528000,https://github.com/neuromorphiccomputationresearchprogram/connectionist-symbolic-pseudo-secrets,3,Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations,"https://scholar.google.com/scholar?cluster=7363780369551842627&hl=en&as_sdt=0,44",1,2022
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer,8,icml,1,0,2023-06-17 04:54:23.734000,https://github.com/lucasalegre/sfols,6,Optimistic linear support and successor features as a basis for optimal policy transfer,"https://scholar.google.com/scholar?cluster=130731457432112857&hl=en&as_sdt=0,5",2,2022
Structured Stochastic Gradient MCMC,4,icml,1,0,2023-06-17 04:54:23.940000,https://github.com/ajboyd2/pytorch_lvi,1,Structured stochastic gradient MCMC,"https://scholar.google.com/scholar?cluster=8097612641869986343&hl=en&as_sdt=0,5",2,2022
XAI for Transformers: Better Explanations through Conservative Propagation,16,icml,12,5,2023-06-17 04:54:24.145000,https://github.com/ameenali/xai_transformers,33,XAI for transformers: Better explanations through conservative propagation,"https://scholar.google.com/scholar?cluster=8318067021687688094&hl=en&as_sdt=0,5",2,2022
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees,1,icml,0,0,2023-06-17 04:54:24.349000,https://github.com/machinelearningbcam/amrc-for-concept-drift-icml-2022,6,Minimax classification under concept drift with multidimensional adaptation and performance guarantees,"https://scholar.google.com/scholar?cluster=6492087255845076443&hl=en&as_sdt=0,5",1,2022
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation,2,icml,3,8,2023-06-17 04:54:24.555000,https://github.com/sebastianament/covariancefunctions.jl,17,Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation,"https://scholar.google.com/scholar?cluster=17864781963029193260&hl=en&as_sdt=0,11",2,2022
Towards Understanding Sharpness-Aware Minimization,42,icml,3,0,2023-06-17 04:54:24.761000,https://github.com/tml-epfl/understanding-sam,24,Towards understanding sharpness-aware minimization,"https://scholar.google.com/scholar?cluster=18222527206389875127&hl=en&as_sdt=0,3",2,2022
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging,27,icml,5,0,2023-06-17 04:54:24.966000,https://github.com/aangelopoulos/im2im-uq,33,Image-to-image regression with distribution-free uncertainty quantification and applications in imaging,"https://scholar.google.com/scholar?cluster=3321497325155679298&hl=en&as_sdt=0,5",4,2022
Online Balanced Experimental Design,0,icml,1,1,2023-06-17 04:54:25.171000,https://github.com/ddimmery/balancer-package,0,Online Balanced Experimental Design,"https://scholar.google.com/scholar?cluster=9578642124774969527&hl=en&as_sdt=0,33",1,2022
Thresholded Lasso Bandit,11,icml,0,0,2023-06-17 04:54:25.386000,https://github.com/cyberagentailab/thresholded-lasso-bandit,5,Thresholded lasso bandit,"https://scholar.google.com/scholar?cluster=2549693999294336180&hl=en&as_sdt=0,44",1,2022
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model,1,icml,1,0,2023-06-17 04:54:25.592000,https://github.com/BaeHeeSun/NPC,16,From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model,"https://scholar.google.com/scholar?cluster=8277956937717286777&hl=en&as_sdt=0,5",3,2022
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification,3,icml,4,0,2023-06-17 04:54:25.798000,https://github.com/junwenbai/c-gmvae,23,Gaussian mixture variational autoencoder with contrastive learning for multi-label classification,"https://scholar.google.com/scholar?cluster=9275720515589327599&hl=en&as_sdt=0,3",2,2022
Certified Neural Network Watermarks with Randomized Smoothing,6,icml,2,0,2023-06-17 04:54:26.004000,https://github.com/arpitbansal297/certified_watermarks,9,Certified Neural Network Watermarks with Randomized Smoothing,"https://scholar.google.com/scholar?cluster=2567091061635643130&hl=en&as_sdt=0,22",2,2022
Learning Stable Classifiers by Transferring Unstable Features,5,icml,0,0,2023-06-17 04:54:26.215000,https://github.com/YujiaBao/Tofu,7,Learning stable classifiers by transferring unstable features,"https://scholar.google.com/scholar?cluster=13001665395610981653&hl=en&as_sdt=0,5",1,2022
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models,30,icml,7,1,2023-06-17 04:54:26.425000,https://github.com/baofff/Extended-Analytic-DPM,87,Estimating the optimal covariance with imperfect mean in diffusion probabilistic models,"https://scholar.google.com/scholar?cluster=2323665209976347341&hl=en&as_sdt=0,5",2,2022
On the Surrogate Gap between Contrastive and Supervised Losses,8,icml,0,0,2023-06-17 04:54:26.631000,https://github.com/nzw0301/gap-contrastive-and-supervised-losses,7,On the surrogate gap between contrastive and supervised losses,"https://scholar.google.com/scholar?cluster=17468865477895467662&hl=en&as_sdt=0,33",3,2022
Imitation Learning by Estimating Expertise of Demonstrators,11,icml,1,0,2023-06-17 04:54:26.838000,https://github.com/stanford-iliad/ileed,4,Imitation learning by estimating expertise of demonstrators,"https://scholar.google.com/scholar?cluster=13040919863635608534&hl=en&as_sdt=0,5",4,2022
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes,1,icml,8,2,2023-06-17 04:54:27.046000,https://github.com/g-benton/volt,39,Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes,"https://scholar.google.com/scholar?cluster=445432332886185125&hl=en&as_sdt=0,5",3,2022
Gradient Descent on Neurons and its Link to Approximate Second-order Optimization,3,icml,2,0,2023-06-17 04:54:27.253000,https://github.com/freedbee/neuron_descent_and_kfac,1,Gradient descent on neurons and its link to approximate second-order optimization,"https://scholar.google.com/scholar?cluster=4847605706007812580&hl=en&as_sdt=0,14",1,2022
Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification,9,icml,2,0,2023-06-17 04:54:27.465000,https://github.com/pbevan1/Skin-Deep-Unlearning,5,Skin deep unlearning: artefact and instrument debiasing in the context of melanoma classification,"https://scholar.google.com/scholar?cluster=13843943708217895697&hl=en&as_sdt=0,5",1,2022
Approximate Bayesian Computation with Domain Expert in the Loop,4,icml,0,0,2023-06-17 04:54:27.671000,https://github.com/lfilstro/hitl-abc,1,Approximate Bayesian Computation with Domain Expert in the Loop,"https://scholar.google.com/scholar?cluster=17515613516089862675&hl=en&as_sdt=0,33",1,2022
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning,6,icml,1,0,2023-06-17 04:54:27.878000,https://github.com/albietz/ppsgd,5,Personalization improves privacy-accuracy tradeoffs in federated learning,"https://scholar.google.com/scholar?cluster=2803924388956334708&hl=en&as_sdt=0,5",1,2022
Non-Vacuous Generalisation Bounds for Shallow Neural Networks,11,icml,0,0,2023-06-17 04:54:28.087000,https://github.com/biggs/shallow-nets,0,Non-vacuous generalisation bounds for shallow neural networks,"https://scholar.google.com/scholar?cluster=11560382540049939968&hl=en&as_sdt=0,33",2,2022
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities,12,icml,1,0,2023-06-17 04:54:28.293000,https://github.com/j-cb/breaking_down_ood_detection,10,Breaking down out-of-distribution detection: Many methods based on ood training data estimate a combination of the same core quantities,"https://scholar.google.com/scholar?cluster=3629472061640674656&hl=en&as_sdt=0,11",1,2022
Optimizing Sequential Experimental Design with Deep Reinforcement Learning,13,icml,5,0,2023-06-17 04:54:28.499000,https://github.com/csiro-mlai/RL-BOED,5,Optimizing Sequential Experimental Design with Deep Reinforcement Learning,"https://scholar.google.com/scholar?cluster=17698300138792965088&hl=en&as_sdt=0,21",3,2022
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective,4,icml,1,0,2023-06-17 04:54:28.704000,https://github.com/fietelab/wide-network-alignment,2,How to train your wide neural network without backprop: An input-weight alignment perspective,"https://scholar.google.com/scholar?cluster=9130275033770297216&hl=en&as_sdt=0,33",2,2022
Lie Point Symmetry Data Augmentation for Neural PDE Solvers,17,icml,5,1,2023-06-17 04:54:28.916000,https://github.com/brandstetter-johannes/lpsda,28,Lie point symmetry data augmentation for neural pde solvers,"https://scholar.google.com/scholar?cluster=6135726084743263275&hl=en&as_sdt=0,5",2,2022
An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees,4,icml,0,0,2023-06-17 04:54:29.125000,https://github.com/glmbraun/csbm,2,An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees,"https://scholar.googleusercontent.com/scholar?q=cache:6omcJTzt9pMJ:scholar.google.com/+An+iterative+clustering+algorithm+for+the+Contextual+Stochastic+Block+Model+with+optimality+guarantees&hl=en&as_sdt=0,5",1,2022
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems,10,icml,6,0,2023-06-17 04:54:29.332000,https://github.com/durstewitzlab/dendplrnn,6,Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems,"https://scholar.google.com/scholar?cluster=8212489607836330678&hl=en&as_sdt=0,10",1,2022
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters,7,icml,2,0,2023-06-17 04:54:29.543000,https://github.com/lmotte/graph-prediction-with-fused-gromov-wasserstein,10,Learning to predict graphs with fused Gromov-Wasserstein barycenters,"https://scholar.google.com/scholar?cluster=449987462895486157&hl=en&as_sdt=0,10",1,2022
Measuring dissimilarity with diffeomorphism invariance,1,icml,1,0,2023-06-17 04:54:29.752000,https://github.com/theophilec/diffy,5,Measuring dissimilarity with diffeomorphism invariance,"https://scholar.google.com/scholar?cluster=9356741545436506583&hl=en&as_sdt=0,11",1,2022
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications,7,icml,3,0,2023-06-17 04:54:29.960000,https://github.com/aCapone1/gauss_proc_unknown_hyp,0,Gaussian process uniform error bounds with unknown hyperparameters for safety-critical applications,"https://scholar.google.com/scholar?cluster=10619138412695371190&hl=en&as_sdt=0,5",1,2022
Burst-Dependent Plasticity and Dendritic Amplification Support Target-Based Learning and Hierarchical Imitation Learning,3,icml,1,0,2023-06-17 04:54:30.167000,https://github.com/cristianocapone/lttb,1,Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning,"https://scholar.google.com/scholar?cluster=8004952254033817821&hl=en&as_sdt=0,33",1,2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization,9,icml,0,0,2023-06-17 04:54:30.374000,https://github.com/yaircarmon/recapp,0,Recapp: Crafting a more efficient catalyst for convex optimization,"https://scholar.google.com/scholar?cluster=7906072571653012949&hl=en&as_sdt=0,5",4,2022
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for Everyone,59,icml,1642,38,2023-06-17 04:54:30.586000,https://github.com/coqui-ai/TTS,12544,Yourtts: Towards zero-shot multi-speaker tts and zero-shot voice conversion for everyone,"https://scholar.google.com/scholar?cluster=8575580251111777245&hl=en&as_sdt=0,5",169,2022
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels,7,icml,0,1,2023-06-17 04:54:30.794000,https://github.com/aladoro/stabilizing-off-policy-rl,8,Stabilizing off-policy deep reinforcement learning from pixels,"https://scholar.google.com/scholar?cluster=14839229722928778219&hl=en&as_sdt=0,5",2,2022
Robust Imitation Learning against Variations in Environment Dynamics,3,icml,2,0,2023-06-17 04:54:31.011000,https://github.com/jongseongchae/rime,4,Robust imitation learning against variations in environment dynamics,"https://scholar.google.com/scholar?cluster=16698148577673896615&hl=en&as_sdt=0,19",1,2022
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction,21,icml,3,2,2023-06-17 04:54:31.221000,https://github.com/facebookresearch/unirex,21,Unirex: A unified learning framework for language model rationale extraction,"https://scholar.google.com/scholar?cluster=7352055260763393065&hl=en&as_sdt=0,21",8,2022
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?,15,icml,5,1,2023-06-17 04:54:31.456000,https://github.com/sutd-visual-computing-group/LS-KD-compatibility,9,Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?,"https://scholar.google.com/scholar?cluster=7014741791819212008&hl=en&as_sdt=0,39",1,2022
Learning Bellman Complete Representations for Offline Policy Evaluation,2,icml,0,0,2023-06-17 04:54:31.662000,https://github.com/causalml/bcrl,7,Learning Bellman Complete Representations for Offline Policy Evaluation,"https://scholar.google.com/scholar?cluster=6803502920630786381&hl=en&as_sdt=0,33",1,2022
Sample Efficient Learning of Predictors that Complement Humans,5,icml,2,0,2023-06-17 04:54:31.869000,https://github.com/clinicalml/active_learn_to_defer,4,Sample efficient learning of predictors that complement humans,"https://scholar.google.com/scholar?cluster=14604138868272717546&hl=en&as_sdt=0,5",9,2022
Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets,18,icml,4,0,2023-06-17 04:54:32.076000,https://github.com/vita-group/structure-lth,20,Coarsening the granularity: Towards structurally sparse lottery tickets,"https://scholar.google.com/scholar?cluster=11130219439194607083&hl=en&as_sdt=0,5",7,2022
Learning Domain Adaptive Object Detection with Probabilistic Teacher,14,icml,7,4,2023-06-17 04:54:32.283000,https://github.com/hikvision-research/probabilisticteacher,52,Learning domain adaptive object detection with probabilistic teacher,"https://scholar.google.com/scholar?cluster=17755903452096200771&hl=en&as_sdt=0,34",5,2022
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning,18,icml,2,2,2023-06-17 04:54:32.489000,https://github.com/HazyResearch/thanos-code,16,Perfectly balanced: Improving transfer and robustness of supervised contrastive learning,"https://scholar.google.com/scholar?cluster=4069781946979626386&hl=en&as_sdt=0,5",17,2022
On Collective Robustness of Bagging Against Data Poisoning,5,icml,1,0,2023-06-17 04:54:32.695000,https://github.com/emiyalzn/icml22-crb,2,On Collective Robustness of Bagging Against Data Poisoning,"https://scholar.google.com/scholar?cluster=7671982562316508504&hl=en&as_sdt=0,5",1,2022
Structure-Aware Transformer for Graph Representation Learning,51,icml,25,0,2023-06-17 04:54:32.900000,https://github.com/borgwardtlab/sat,149,Structure-aware transformer for graph representation learning,"https://scholar.google.com/scholar?cluster=4875324713433840142&hl=en&as_sdt=0,5",6,2022
Optimization-Induced Graph Implicit Nonlinear Diffusion,10,icml,0,0,2023-06-17 04:54:33.110000,https://github.com/7qchen/gind,15,Optimization-induced graph implicit nonlinear diffusion,"https://scholar.google.com/scholar?cluster=1600506523476072350&hl=en&as_sdt=0,14",2,2022
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile,2,icml,0,0,2023-06-17 04:54:33.326000,https://github.com/anfeather/eigen-reptile,8,Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile,"https://scholar.google.com/scholar?cluster=8530355739289210050&hl=en&as_sdt=0,5",1,2022
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training,2,icml,0,0,2023-06-17 04:54:33.531000,https://github.com/vita-group/double-win-lth,9,Data-Efficient Double-Win Lottery Tickets from Robust Pre-training,"https://scholar.google.com/scholar?cluster=2999471991915534947&hl=en&as_sdt=0,15",8,2022
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness,3,icml,3,0,2023-06-17 04:54:33.737000,https://github.com/vita-group/linearity-grafting,14,Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness,"https://scholar.google.com/scholar?cluster=2944620875879702886&hl=en&as_sdt=0,5",9,2022
Task-aware Privacy Preservation for Multi-dimensional Data,4,icml,1,0,2023-06-17 04:54:33.943000,https://github.com/chengjiangnan/task_aware_privacy,2,Task-aware privacy preservation for multi-dimensional data,"https://scholar.google.com/scholar?cluster=12634725104863101184&hl=en&as_sdt=0,14",1,2022
Adversarially Trained Actor Critic for Offline Reinforcement Learning,44,icml,6,0,2023-06-17 04:54:34.149000,https://github.com/microsoft/atac,48,Adversarially trained actor critic for offline reinforcement learning,"https://scholar.google.com/scholar?cluster=8385322441763797566&hl=en&as_sdt=0,22",8,2022
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests,13,icml,1,0,2023-06-17 04:54:34.355000,https://github.com/victor5as/rieszlearning,7,Riesznet and forestriesz: Automatic debiased machine learning with neural nets and random forests,"https://scholar.google.com/scholar?cluster=9961128829212907766&hl=en&as_sdt=0,48",1,2022
Selective Network Linearization for Efficient Private Inference,9,icml,1,0,2023-06-17 04:54:34.560000,https://github.com/nyu-dice-lab/selective_network_linearization,3,Selective network linearization for efficient private inference,"https://scholar.google.com/scholar?cluster=14016452576504224756&hl=en&as_sdt=0,11",5,2022
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers,8,icml,4,0,2023-06-17 04:54:34.766000,https://github.com/hl-hanlin/gkat,7,From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers,"https://scholar.google.com/scholar?cluster=1399080390715291897&hl=en&as_sdt=0,33",1,2022
Context-Aware Drift Detection,7,icml,180,127,2023-06-17 04:54:34.971000,https://github.com/SeldonIO/alibi-detect,1843,Context-aware drift detection,"https://scholar.google.com/scholar?cluster=9993193813631773645&hl=en&as_sdt=0,5",35,2022
Diffusion bridges vector quantized variational autoencoders,5,icml,1,0,2023-06-17 04:54:35.176000,https://github.com/maxjcohen/diffusion-bridges,14,Diffusion bridges vector quantized Variational AutoEncoders,"https://scholar.google.com/scholar?cluster=15768272528622480760&hl=en&as_sdt=0,21",4,2022
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model,5,icml,0,0,2023-06-17 04:54:35.383000,https://github.com/JRConti/EthicalModule_vMF,1,Mitigating gender bias in face recognition using the von mises-fisher mixture model,"https://scholar.google.com/scholar?cluster=11800206203871099663&hl=en&as_sdt=0,10",1,2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses,28,icml,1,0,2023-06-17 04:54:35.590000,https://github.com/fra31/evaluating-adaptive-test-time-defenses,14,Evaluating the adversarial robustness of adaptive test-time defenses,"https://scholar.google.com/scholar?cluster=9007385894917173233&hl=en&as_sdt=0,23",2,2022
Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers,6,icml,3,1,2023-06-17 04:54:35.796000,https://github.com/fra31/robust-finetuning,14,Adversarial Robustness against Multiple and Single -Threat Models via Quick Fine-Tuning of Robust Classifiers,"https://scholar.google.com/scholar?cluster=14798100310510930510&hl=en&as_sdt=0,5",2,2022
Continuous Control with Action Quantization from Demonstrations,4,icml,7322,1026,2023-06-17 04:54:36.002000,https://github.com/google-research/google-research,29792,Continuous Control with Action Quantization from Demonstrations,"https://scholar.google.com/scholar?cluster=18354958382752460493&hl=en&as_sdt=0,5",727,2022
Dialog Inpainting: Turning Documents into Dialogs,17,icml,2,2,2023-06-17 04:54:36.208000,https://github.com/google-research/dialog-inpainting,85,Dialog inpainting: Turning documents into dialogs,"https://scholar.google.com/scholar?cluster=13888132119591432248&hl=en&as_sdt=0,44",8,2022
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training,22,icml,8,1,2023-06-17 04:54:36.421000,https://github.com/rong-dai/dispfl,34,Dispfl: Towards communication-efficient personalized federated learning via decentralized sparse training,"https://scholar.google.com/scholar?cluster=13590903827423118545&hl=en&as_sdt=0,5",2,2022
Unsupervised Image Representation Learning with Deep Latent Particles,1,icml,1,0,2023-06-17 04:54:36.626000,https://github.com/taldatech/deep-latent-particles-pytorch,21,Unsupervised Image Representation Learning with Deep Latent Particles,"https://scholar.google.com/scholar?cluster=8443981998714808027&hl=en&as_sdt=0,24",3,2022
Monarch: Expressive Structured Matrices for Efficient and Accurate Training,21,icml,17,11,2023-06-17 04:54:36.831000,https://github.com/hazyresearch/monarch,127,Monarch: Expressive structured matrices for efficient and accurate training,"https://scholar.google.com/scholar?cluster=908299519413693348&hl=en&as_sdt=0,48",22,2022
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing,8,icml,2,0,2023-06-17 04:54:37.037000,https://github.com/mli-lab/ttt_for_deep_learning_cs,9,Test-time training can close the natural distribution shift performance gap in deep learning based compressed sensing,"https://scholar.google.com/scholar?cluster=17586372982715627644&hl=en&as_sdt=0,33",1,2022
Knowledge Base Question Answering by Case-based Reasoning over Subgraphs,19,icml,5,4,2023-06-17 04:54:37.243000,https://github.com/rajarshd/cbr-subg,28,Knowledge base question answering by case-based reasoning over subgraphs,"https://scholar.google.com/scholar?cluster=9521902592444277767&hl=en&as_sdt=0,33",4,2022
Robust Multi-Objective Bayesian Optimization Under Input Noise,15,icml,1,0,2023-06-17 04:54:37.448000,https://github.com/facebookresearch/robust_mobo,36,Robust multi-objective bayesian optimization under input noise,"https://scholar.google.com/scholar?cluster=14538783621300673718&hl=en&as_sdt=0,5",13,2022
Attentional Meta-learners for Few-shot Polythetic Classification,1,icml,1,0,2023-06-17 04:54:37.654000,https://github.com/rvinas/polythetic_metalearning,7,Attentional Meta-learners for Few-shot Polythetic Classification,"https://scholar.google.com/scholar?cluster=5360824455580624680&hl=en&as_sdt=0,47",3,2022
Adversarial Vulnerability of Randomized Ensembles,1,icml,1,0,2023-06-17 04:54:37.859000,https://github.com/hsndbk4/arc,9,Adversarial Vulnerability of Randomized Ensembles,"https://scholar.google.com/scholar?cluster=2408757977511355426&hl=en&as_sdt=0,5",1,2022
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization,4,icml,4,0,2023-06-17 04:54:38.066000,https://github.com/gbdl/bbi,5,Born-Infeld (BI) for AI: energy-conserving descent (ECD) for optimization,"https://scholar.google.com/scholar?cluster=11927103073322066327&hl=en&as_sdt=0,44",2,2022
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass,7,icml,2,0,2023-06-17 04:54:38.271000,https://github.com/giorgiad/pepita,16,Error-driven input modulation: solving the credit assignment problem without a backward pass,"https://scholar.google.com/scholar?cluster=12440766337737848620&hl=en&as_sdt=0,5",1,2022
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations,20,icml,4,0,2023-06-17 04:54:38.477000,https://github.com/fdeng18/dreamer-pro,26,Dreamerpro: Reconstruction-free model-based reinforcement learning with prototypical representations,"https://scholar.google.com/scholar?cluster=11064573461444670693&hl=en&as_sdt=0,34",1,2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks,9,icml,1,0,2023-06-17 04:54:38.683000,https://github.com/thudzj/neuraleigenfunction,10,Neuralef: Deconstructing kernels by deep neural networks,"https://scholar.google.com/scholar?cluster=14961387103388663924&hl=en&as_sdt=0,47",2,2022
Generalization and Robustness Implications in Object-Centric Learning,20,icml,2,0,2023-06-17 04:54:38.889000,https://github.com/addtt/object-centric-library,61,Generalization and robustness implications in object-centric learning,"https://scholar.google.com/scholar?cluster=9362373326387424526&hl=en&as_sdt=0,33",3,2022
Fair Generalized Linear Models with a Convex Penalty,1,icml,1,1,2023-06-17 04:54:39.095000,https://github.com/hyungrok-do/fair-glm-cvx,0,Fair Generalized Linear Models with a Convex Penalty,"https://scholar.google.com/scholar?cluster=11693304205339987181&hl=en&as_sdt=0,33",3,2022
On the Adversarial Robustness of Causal Algorithmic Recourse,28,icml,0,0,2023-06-17 04:54:39.300000,https://github.com/ricardodominguez/adversariallyrobustrecourse,5,On the adversarial robustness of causal algorithmic recourse,"https://scholar.google.com/scholar?cluster=16011924534958641945&hl=en&as_sdt=0,14",1,2022
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks,4,icml,1,0,2023-06-17 04:54:39.505000,https://github.com/RunpeiDong/DGMS,5,Finding the task-optimal low-bit sub-distribution in deep neural networks,"https://scholar.google.com/scholar?cluster=7264575101488982108&hl=en&as_sdt=0,47",2,2022
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs,2,icml,0,0,2023-06-17 04:54:39.711000,https://github.com/zehao-dong/pace,7,PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs,"https://scholar.google.com/scholar?cluster=11354614986119464774&hl=en&as_sdt=0,5",1,2022
Adapting to Mixing Time in Stochastic Optimization with Markovian Data,8,icml,8,0,2023-06-17 04:54:39.916000,https://github.com/Rondorf/BOReL,20,Adapting to mixing time in stochastic optimization with markovian data,"https://scholar.google.com/scholar?cluster=4133641935390571413&hl=en&as_sdt=0,45",3,2022
TACTiS: Transformer-Attentional Copulas for Time Series,11,icml,11,3,2023-06-17 04:54:40.121000,https://github.com/ServiceNow/tactis,72,Tactis: Transformer-attentional copulas for time series,"https://scholar.google.com/scholar?cluster=5604382526172400005&hl=en&as_sdt=0,33",8,2022
Learning Iterative Reasoning through Energy Minimization,4,icml,6,4,2023-06-17 04:54:40.327000,https://github.com/yilundu/irem_code_release,38,Learning iterative reasoning through energy minimization,"https://scholar.google.com/scholar?cluster=1554477033097529382&hl=en&as_sdt=0,7",3,2022
SE(3) Equivariant Graph Neural Networks with Complete Local Frames,10,icml,6,1,2023-06-17 04:54:40.534000,https://github.com/mouthful/ClofNet,11,SE (3) Equivariant Graph Neural Networks with Complete Local Frames,"https://scholar.google.com/scholar?cluster=14602440346377958112&hl=en&as_sdt=0,33",2,2022
A Context-Integrated Transformer-Based Neural Network for Auction Design,10,icml,1,0,2023-06-17 04:54:40.739000,https://github.com/zjduan/CITransNet,10,A context-integrated transformer-based neural network for auction design,"https://scholar.google.com/scholar?cluster=9850607820011561614&hl=en&as_sdt=0,5",1,2022
From data to functa: Your data point is a function and you can treat it like one,33,icml,4,3,2023-06-17 04:54:40.944000,https://github.com/deepmind/functa,101,From data to functa: Your data point is a function and you can treat it like one,"https://scholar.google.com/scholar?cluster=4550089326904681331&hl=en&as_sdt=0,39",8,2022
On the Difficulty of Defending Self-Supervised Learning against Model Extraction,7,icml,0,0,2023-06-17 04:54:41.150000,https://github.com/cleverhans-lab/ssl-attacks-defenses,1,On the difficulty of defending self-supervised learning against model extraction,"https://scholar.google.com/scholar?cluster=16145224211258754535&hl=en&as_sdt=0,33",1,2022
LIMO: Latent Inceptionism for Targeted Molecule Generation,8,icml,14,9,2023-06-17 04:54:41.356000,https://github.com/rose-stl-lab/limo,44,LIMO: Latent Inceptionism for Targeted Molecule Generation,"https://scholar.google.com/scholar?cluster=12167942813454300503&hl=en&as_sdt=0,10",3,2022
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning,5,icml,1,1,2023-06-17 04:54:41.561000,https://github.com/aelgabli/fednew,9,FedNew: A communication-efficient and privacy-preserving Newton-type method for federated learning,"https://scholar.google.com/scholar?cluster=13605239667986344129&hl=en&as_sdt=0,5",1,2022
"For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria",1,icml,0,0,2023-06-17 04:54:41.767000,https://github.com/scottemmons/coordination,0,"For learning in symmetric teams, local optima are global nash equilibria","https://scholar.google.com/scholar?cluster=16109782432543935692&hl=en&as_sdt=0,33",2,2022
Towards Scaling Difference Target Propagation by Learning Backprop Targets,11,icml,0,0,2023-06-17 04:54:41.973000,https://github.com/bptargetdtp/scalabledtp,1,Towards scaling difference target propagation by learning backprop targets,"https://scholar.google.com/scholar?cluster=16976057052458549832&hl=en&as_sdt=0,5",2,2022
Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information,18,icml,8,0,2023-06-17 04:54:42.180000,https://github.com/kawine/dataset_difficulty,58,Understanding Dataset Difficulty with $\mathcalV $-Usable Information,"https://scholar.google.com/scholar?cluster=446878521601081307&hl=en&as_sdt=0,5",1,2022
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning,36,icml,9,1,2023-06-17 04:54:42.386000,https://github.com/google-research/head2toe,71,Head2toe: Utilizing intermediate representations for better transfer learning,"https://scholar.google.com/scholar?cluster=12027550380073751806&hl=en&as_sdt=0,33",6,2022
Variational Sparse Coding with Learned Thresholding,0,icml,1,0,2023-06-17 04:54:42.593000,https://github.com/kfallah/variational-sparse-coding,7,Variational Sparse Coding with Learned Thresholding,"https://scholar.google.com/scholar?cluster=10401057138019982209&hl=en&as_sdt=0,43",2,2022
Training Discrete Deep Generative Models via Gapped Straight-Through Estimator,4,icml,0,0,2023-06-17 04:54:42.798000,https://github.com/chijames/gst,8,Training Discrete Deep Generative Models via Gapped Straight-Through Estimator,"https://scholar.google.com/scholar?cluster=3212785124198988357&hl=en&as_sdt=0,50",1,2022
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck,16,icml,2,1,2023-06-17 04:54:43.003000,https://github.com/BU-DEPEND-Lab/DRIBO,4,Dribo: Robust deep reinforcement learning via multi-view information bottleneck,"https://scholar.google.com/scholar?cluster=17795910493641193453&hl=en&as_sdt=0,10",1,2022
Variational Wasserstein gradient flow,20,icml,0,0,2023-06-17 04:54:43.209000,https://github.com/sbyebss/variational_wgf,9,Variational wasserstein gradient flow,"https://scholar.google.com/scholar?cluster=4247639090058922494&hl=en&as_sdt=0,34",1,2022
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP),38,icml,1,0,2023-06-17 04:54:43.428000,https://github.com/mlfoundations/imagenet-captions,33,Data determines distributional robustness in contrastive language image pre-training (clip),"https://scholar.google.com/scholar?cluster=12568254041342889008&hl=en&as_sdt=0,5",5,2022
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks,5,icml,1,0,2023-06-17 04:54:43.634000,https://github.com/lpd-epfl/attack_equivalence,1,An equivalence between data poisoning and byzantine gradient attacks,"https://scholar.google.com/scholar?cluster=15814948581438408162&hl=en&as_sdt=0,5",1,2022
Investigating Generalization by Controlling Normalized Margin,3,icml,0,0,2023-06-17 04:54:43.839000,https://github.com/alexfarhang/margin,1,Investigating Generalization by Controlling Normalized Margin,"https://scholar.google.com/scholar?cluster=715638377527231014&hl=en&as_sdt=0,34",1,2022
Private frequency estimation via projective geometry,6,icml,0,0,2023-06-17 04:54:44.044000,https://github.com/minilek/private_frequency_oracles,3,Private frequency estimation via projective geometry,"https://scholar.google.com/scholar?cluster=5605547034926514625&hl=en&as_sdt=0,33",1,2022
Coordinated Double Machine Learning,0,icml,1,0,2023-06-17 04:54:44.250000,https://github.com/nitaifingerhut/c-dml,3,Coordinated Double Machine Learning,"https://scholar.google.com/scholar?cluster=3098806630799952921&hl=en&as_sdt=0,10",2,2022
Conformal Prediction Sets with Limited False Positives,4,icml,0,1,2023-06-17 04:54:44.457000,https://github.com/ajfisch/conformal-fp,0,Conformal prediction sets with limited false positives,"https://scholar.google.com/scholar?cluster=3023340906965759657&hl=en&as_sdt=0,36",1,2022
"Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness",1,icml,1,0,2023-06-17 04:54:44.662000,https://github.com/benevolentai/comp,7,"Contrastive mixture of posteriors for counterfactual inference, data integration and fairness","https://scholar.google.com/scholar?cluster=7874050188706328624&hl=en&as_sdt=0,5",3,2022
A Neural Tangent Kernel Perspective of GANs,13,icml,2,0,2023-06-17 04:54:44.869000,https://github.com/emited/gantk2,13,A neural tangent kernel perspective of gans,"https://scholar.google.com/scholar?cluster=4606779800346786718&hl=en&as_sdt=0,5",4,2022
SPDY: Accurate Pruning with Speedup Guarantees,7,icml,4,3,2023-06-17 04:54:45.075000,https://github.com/ist-daslab/spdy,11,SPDY: Accurate pruning with speedup guarantees,"https://scholar.google.com/scholar?cluster=9481477632006628831&hl=en&as_sdt=0,32",5,2022
Scaling Structured Inference with Randomization,2,icml,3,0,2023-06-17 04:54:45.280000,https://github.com/franxyao/rdp,13,Scaling structured inference with randomization,"https://scholar.google.com/scholar?cluster=13234676438098295868&hl=en&as_sdt=0,38",2,2022
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks,5,icml,1,1,2023-06-17 04:54:45.488000,https://github.com/rice-eic/depthshrinker,36,DepthShrinker: a new compression paradigm towards boosting real-hardware efficiency of compact neural networks,"https://scholar.google.com/scholar?cluster=13003128521759488248&hl=en&as_sdt=0,33",10,2022
$p$-Laplacian Based Graph Neural Networks,7,icml,2,0,2023-06-17 04:54:45.693000,https://github.com/guoji-fu/pgnns,21,-Laplacian Based Graph Neural Networks,"https://scholar.google.com/scholar?cluster=15123165040444629585&hl=en&as_sdt=0,33",2,2022
Generalizing Gaussian Smoothing for Random Search,2,icml,0,0,2023-06-17 04:54:45.898000,https://github.com/isl-org/generalized-smoothing,3,Generalizing Gaussian Smoothing for Random Search,"https://scholar.google.com/scholar?cluster=2545306041243695019&hl=en&as_sdt=0,5",4,2022
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems,2,icml,1,0,2023-06-17 04:54:46.105000,https://github.com/wi-pi/rethinking-image-scaling-attacks,3,Rethinking image-scaling attacks: The interplay between vulnerabilities in machine learning systems,"https://scholar.google.com/scholar?cluster=9730023948978190760&hl=en&as_sdt=0,11",2,2022
Lazy Estimation of Variable Importance for Large Neural Networks,1,icml,0,0,2023-06-17 04:54:46.313000,https://github.com/willett-group/lazyvi,0,Lazy Estimation of Variable Importance for Large Neural Networks,"https://scholar.google.com/scholar?cluster=11646154414177168250&hl=en&as_sdt=0,3",2,2022
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack,3,icml,2,0,2023-06-17 04:54:46.526000,https://github.com/sjtubrian/mm-attack,4,Fast and reliable evaluation of adversarial robustness with minimum-margin attack,"https://scholar.google.com/scholar?cluster=16577119936016409064&hl=en&as_sdt=0,5",1,2022
Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems,9,icml,3,0,2023-06-17 04:54:46.731000,https://github.com/sustech-optimization/vf-idca,2,Value function based difference-of-convex algorithm for bilevel hyperparameter selection problems,"https://scholar.google.com/scholar?cluster=5559492833861486776&hl=en&as_sdt=0,10",1,2022
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization,1,icml,1,1,2023-06-17 04:54:46.937000,https://github.com/xianggao1102/learning-to-incorporate-texture-saliency-adaptive-attention-to-image-cartoonization,4,Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization,"https://scholar.google.com/scholar?cluster=11484326183315995757&hl=en&as_sdt=0,33",1,2022
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification,3,icml,0,2,2023-06-17 04:54:47.142000,https://github.com/garcinc/noised-topk,10,Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification,"https://scholar.google.com/scholar?cluster=16642060329776900644&hl=en&as_sdt=0,47",2,2022
A Functional Information Perspective on Model Interpretation,1,icml,0,0,2023-06-17 04:54:47.347000,https://github.com/nitaytech/functionalexplanation,5,A Functional Information Perspective on Model Interpretation,"https://scholar.google.com/scholar?cluster=5647868257497386951&hl=en&as_sdt=0,33",1,2022
Inducing Causal Structure for Interpretable Neural Networks,20,icml,0,0,2023-06-17 04:54:47.554000,https://github.com/frankaging/interchange-intervention-training,7,Inducing causal structure for interpretable neural networks,"https://scholar.google.com/scholar?cluster=3318078853003855419&hl=en&as_sdt=0,5",2,2022
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning,9,icml,5,0,2023-06-17 04:54:47.760000,https://github.com/jmaces/aapm-ct-challenge,34,Near-exact recovery for tomographic inverse problems via deep learning,"https://scholar.google.com/scholar?cluster=10012619344494620426&hl=en&as_sdt=0,5",3,2022
Equivariance versus Augmentation for Spherical Images,8,icml,1,0,2023-06-17 04:54:47.966000,https://github.com/janegerken/sem_seg_s2cnn,2,Equivariance versus augmentation for spherical images,"https://scholar.google.com/scholar?cluster=2388075100052458630&hl=en&as_sdt=0,33",0,2022
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations,1,icml,0,1,2023-06-17 04:54:48.170000,https://github.com/youranonymousefriend/plugininversion,9,Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations,"https://scholar.google.com/scholar?cluster=3783911125052785325&hl=en&as_sdt=0,5",1,2022
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation,2,icml,0,0,2023-06-17 04:54:48.377000,https://github.com/georgosgeorgos/hierarchical-few-shot-generative-models,10,Scha-vae: Hierarchical context aggregation for few-shot generation,"https://scholar.google.com/scholar?cluster=18154128388289892262&hl=en&as_sdt=0,23",1,2022
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression,8,icml,5,2,2023-06-17 04:54:48.585000,https://github.com/BorealisAI/ranksim-imbalanced-regression,27,RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression,"https://scholar.google.com/scholar?cluster=2649008384099907500&hl=en&as_sdt=0,5",2,2022
Causal Inference Through the Structural Causal Marginal Problem,6,icml,3,0,2023-06-17 04:54:48.791000,https://github.com/lgresele/structural-causal-marginal,2,Causal inference through the structural causal marginal problem,"https://scholar.google.com/scholar?cluster=2256399104999533783&hl=en&as_sdt=0,47",1,2022
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics,3,icml,1,2,2023-06-17 04:54:48.997000,https://github.com/welch-lab/velovae,21,Variational mixtures of ODEs for inferring cellular gene expression dynamics,"https://scholar.google.com/scholar?cluster=5570506012304975998&hl=en&as_sdt=0,47",5,2022
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity,7,icml,1,0,2023-06-17 04:54:49.202000,https://github.com/GuanSuns/ASGRL,11,Leveraging approximate symbolic models for reinforcement learning via skill diversity,"https://scholar.google.com/scholar?cluster=9607066569965060600&hl=en&as_sdt=0,29",1,2022
Bounding Training Data Reconstruction in Private (Deep) Learning,14,icml,0,0,2023-06-17 04:54:49.411000,https://github.com/facebookresearch/bounding_data_reconstruction,10,Bounding training data reconstruction in private (deep) learning,"https://scholar.google.com/scholar?cluster=3008455373482985083&hl=en&as_sdt=0,23",4,2022
NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks,8,icml,2,0,2023-06-17 04:54:49.617000,https://github.com/burakgurbuz97/nispa,15,Nispa: Neuro-inspired stability-plasticity adaptation for continual learning in sparse networks,"https://scholar.google.com/scholar?cluster=17073314745146797398&hl=en&as_sdt=0,5",2,2022
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets,25,icml,4,0,2023-06-17 04:54:49.823000,https://github.com/avihu111/typiclust,44,Active learning on a budget: Opposite strategies suit high and low budgets,"https://scholar.google.com/scholar?cluster=7933856557848734665&hl=en&as_sdt=0,36",4,2022
You Only Cut Once: Boosting Data Augmentation with a Single Cut,9,icml,10,3,2023-06-17 04:54:50.032000,https://github.com/junlinhan/yoco,93,You only cut once: Boosting data augmentation with a single cut,"https://scholar.google.com/scholar?cluster=501111593877482032&hl=en&as_sdt=0,24",3,2022
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes,1,icml,0,0,2023-06-17 04:54:50.238000,https://github.com/insuhan/ndpp-mcmc-sampling,0,Scalable mcmc sampling for nonsymmetric determinantal point processes,"https://scholar.google.com/scholar?cluster=280717695600419200&hl=en&as_sdt=0,5",1,2022
Adversarial Attacks on Gaussian Process Bandits,2,icml,0,0,2023-06-17 04:54:50.443000,https://github.com/eric-vader/attack-bo,1,Adversarial attacks on Gaussian process bandits,"https://scholar.google.com/scholar?cluster=13292319437654740768&hl=en&as_sdt=0,5",2,2022
Temporal Difference Learning for Model Predictive Control,35,icml,40,1,2023-06-17 04:54:50.650000,https://github.com/nicklashansen/tdmpc,201,Temporal difference learning for model predictive control,"https://scholar.google.com/scholar?cluster=10762661949285432757&hl=en&as_sdt=0,34",4,2022
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses,13,icml,1,0,2023-06-17 04:54:50.855000,https://github.com/logan-stapleton/strategic-instrumental-variable-regression,0,Strategic instrumental variable regression: Recovering causal relationships from strategic responses,"https://scholar.google.com/scholar?cluster=5426296166892217767&hl=en&as_sdt=0,29",2,2022
"General-purpose, long-context autoregressive modeling with Perceiver AR",22,icml,18,16,2023-06-17 04:54:51.061000,https://github.com/google-research/perceiver-ar,202,"General-purpose, long-context autoregressive modeling with perceiver ar","https://scholar.google.com/scholar?cluster=1307821423265105144&hl=en&as_sdt=0,1",12,2022
On Distribution Shift in Learning-based Bug Detectors,10,icml,4,2,2023-06-17 04:54:51.266000,https://github.com/eth-sri/learning-real-bug-detector,12,On distribution shift in learning-based bug detectors,"https://scholar.google.com/scholar?cluster=16187870824460798751&hl=en&as_sdt=0,1",8,2022
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks,6,icml,8,0,2023-06-17 04:54:51.472000,https://github.com/sherylhyx/gnnrank,39,GNNRank: Learning global rankings from pairwise comparisons via directed graph neural networks,"https://scholar.google.com/scholar?cluster=4446473441491315248&hl=en&as_sdt=0,5",2,2022
Sparse Double Descent: Where Network Pruning Aggravates Overfitting,7,icml,1,0,2023-06-17 04:54:51.677000,https://github.com/hezheug/sparse-double-descent,14,Sparse Double Descent: Where Network Pruning Aggravates Overfitting,"https://scholar.google.com/scholar?cluster=13575634226332267218&hl=en&as_sdt=0,5",2,2022
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors,2,icml,0,0,2023-06-17 04:54:51.883000,https://github.com/uds-lsv/premise,2,Label-descriptive patterns and their application to characterizing classification errors,"https://scholar.google.com/scholar?cluster=17151062876326396641&hl=en&as_sdt=0,5",5,2022
NOMU: Neural Optimization-based Model Uncertainty,10,icml,5,1,2023-06-17 04:54:52.089000,https://github.com/marketdesignresearch/NOMU,7,Nomu: Neural optimization-based model uncertainty,"https://scholar.google.com/scholar?cluster=17483969048738577269&hl=en&as_sdt=0,39",1,2022
Scaling Out-of-Distribution Detection for Real-World Settings,137,icml,19,0,2023-06-17 04:54:52.295000,https://github.com/hendrycks/anomaly-seg,144,Scaling out-of-distribution detection for real-world settings,"https://scholar.google.com/scholar?cluster=8919172731066658800&hl=en&as_sdt=0,10",9,2022
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology,0,icml,0,0,2023-06-17 04:54:52.501000,https://github.com/valentinhofmann/unsupervised_bias,0,Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology,"https://scholar.google.com/scholar?cluster=11219729475628655718&hl=en&as_sdt=0,5",1,2022
Equivariant Diffusion for Molecule Generation in 3D,145,icml,64,13,2023-06-17 04:54:52.707000,https://github.com/ehoogeboom/e3_diffusion_for_molecules,260,Equivariant diffusion for molecule generation in 3d,"https://scholar.google.com/scholar?cluster=9412014854490527272&hl=en&as_sdt=0,14",7,2022
Conditional GANs with Auxiliary Discriminative Classifier,7,icml,4,0,2023-06-17 04:54:52.912000,https://github.com/houliangict/adcgan,15,Conditional GANs with auxiliary discriminative classifier,"https://scholar.google.com/scholar?cluster=868024013198158367&hl=en&as_sdt=0,5",1,2022
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents,162,icml,20,3,2023-06-17 04:54:53.118000,https://github.com/huangwl18/language-planner,163,Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,"https://scholar.google.com/scholar?cluster=11998123682359381476&hl=en&as_sdt=0,3",4,2022
Going Deeper into Permutation-Sensitive Graph Neural Networks,11,icml,4,0,2023-06-17 04:54:53.323000,https://github.com/zhongyu1998/pg-gnn,20,Going Deeper into Permutation-Sensitive Graph Neural Networks,"https://scholar.google.com/scholar?cluster=14997369349376020515&hl=en&as_sdt=0,5",1,2022
Directed Acyclic Transformer for Non-Autoregressive Machine Translation,15,icml,10,6,2023-06-17 04:54:53.529000,https://github.com/thu-coai/da-transformer,89,Directed acyclic transformer for non-autoregressive machine translation,"https://scholar.google.com/scholar?cluster=12752123369496105828&hl=en&as_sdt=0,33",7,2022
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors,2,icml,0,0,2023-06-17 04:54:53.734000,https://github.com/gjhuizing/wsingular,7,Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors,"https://scholar.google.com/scholar?cluster=15888088169122917171&hl=en&as_sdt=0,5",2,2022
Robust Kernel Density Estimation with Median-of-Means principle,8,icml,3,3,2023-06-17 04:54:53.940000,https://github.com/lminvielle/mom-kde,6,Robust kernel density estimation with median-of-means principle,"https://scholar.google.com/scholar?cluster=14673811907284819215&hl=en&as_sdt=0,5",3,2022
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization,17,icml,3,0,2023-06-17 04:54:54.145000,https://github.com/samuro95/prox-pnp,4,Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization,"https://scholar.google.com/scholar?cluster=12256965087281375600&hl=en&as_sdt=0,5",2,2022
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation,4,icml,2,0,2023-06-17 04:54:54.350000,https://github.com/davidireland3/lense,9,LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation,"https://scholar.google.com/scholar?cluster=7267816984726307573&hl=en&as_sdt=0,26",3,2022
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention,12,icml,1,1,2023-06-17 04:54:54.555000,https://github.com/robertcsordas/linear_layer_as_attention,14,The dual form of neural networks revisited: Connecting test time predictions to training patterns via spotlights of attention,"https://scholar.google.com/scholar?cluster=11337857580515349157&hl=en&as_sdt=0,5",2,2022
A Modern Self-Referential Weight Matrix That Learns to Modify Itself,19,icml,17,3,2023-06-17 04:54:54.761000,https://github.com/idsia/modern-srwm,148,A modern self-referential weight matrix that learns to modify itself,"https://scholar.google.com/scholar?cluster=10630456414832460528&hl=en&as_sdt=0,33",8,2022
A deep convolutional neural network that is invariant to time rescaling,2,icml,0,1,2023-06-17 04:54:54.967000,https://github.com/compmem/SITHCon,2,A deep convolutional neural network that is invariant to time rescaling,"https://scholar.google.com/scholar?cluster=731774651536846779&hl=en&as_sdt=0,5",4,2022
Biological Sequence Design with GFlowNets,31,icml,14,7,2023-06-17 04:54:55.172000,https://github.com/mj10/bioseq-gfn-al,51,Biological sequence design with gflownets,"https://scholar.google.com/scholar?cluster=13153301030980981497&hl=en&as_sdt=0,39",1,2022
Combining Diverse Feature Priors,5,icml,0,0,2023-06-17 04:54:55.378000,https://github.com/MadryLab/copriors,7,Combining diverse feature priors,"https://scholar.google.com/scholar?cluster=3431368394631636693&hl=en&as_sdt=0,33",5,2022
Training Your Sparse Neural Network Better with Any Mask,5,icml,3,0,2023-06-17 04:54:55.584000,https://github.com/vita-group/tost,20,Training your sparse neural network better with any mask,"https://scholar.google.com/scholar?cluster=17434761620518064417&hl=en&as_sdt=0,11",10,2022
Planning with Diffusion for Flexible Behavior Synthesis,64,icml,56,8,2023-06-17 04:54:55.789000,https://github.com/jannerm/diffuser,441,Planning with diffusion for flexible behavior synthesis,"https://scholar.google.com/scholar?cluster=17441916079353459921&hl=en&as_sdt=0,44",8,2022
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection,9,icml,4,0,2023-06-17 04:54:55.997000,https://github.com/vanderschaarlab/hyperimpute,94,Hyperimpute: Generalized iterative imputation with automatic model selection,"https://scholar.google.com/scholar?cluster=7345905181972151816&hl=en&as_sdt=0,33",3,2022
Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization,5,icml,0,0,2023-06-17 04:54:56.204000,https://github.com/adrianjav/impartial-vaes,3,Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization,"https://scholar.google.com/scholar?cluster=14600839373536938661&hl=en&as_sdt=0,11",1,2022
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer,9,icml,4,3,2023-06-17 04:54:56.452000,https://github.com/jiwonjeon9603/maser,11,Maser: Multi-agent reinforcement learning with subgoals generated from experience replay buffer,"https://scholar.google.com/scholar?cluster=3511041100939657281&hl=en&as_sdt=0,45",2,2022
Improving Policy Optimization with Generalist-Specialist Learning,5,icml,0,0,2023-06-17 04:54:56.658000,https://github.com/seanjia/gsl,3,Improving policy optimization with generalist-specialist learning,"https://scholar.google.com/scholar?cluster=14525219330814535505&hl=en&as_sdt=0,23",1,2022
Supervised Off-Policy Ranking,6,icml,1,1,2023-06-17 04:54:56.864000,https://github.com/SOPR-T/SOPR-T,5,Supervised off-policy ranking,"https://scholar.google.com/scholar?cluster=12930957527069555602&hl=en&as_sdt=0,10",1,2022
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations,47,icml,17,2,2023-06-17 04:54:57.069000,https://github.com/harryjo97/gdss,81,Score-based generative modeling of graphs via the system of stochastic differential equations,"https://scholar.google.com/scholar?cluster=4163972994004543532&hl=en&as_sdt=0,5",2,2022
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees,7,icml,0,0,2023-06-17 04:54:57.275000,https://github.com/neu-statsml-research/robust-fine-tuning,2,Robust fine-tuning of deep neural networks with hessian-based generalization guarantees,"https://scholar.google.com/scholar?cluster=6709344473214339936&hl=en&as_sdt=0,5",1,2022
Flashlight: Enabling Innovation in Tools for Machine Learning,11,icml,468,106,2023-06-17 04:54:57.481000,https://github.com/flashlight/flashlight,4858,Flashlight: Enabling innovation in tools for machine learning,"https://scholar.google.com/scholar?cluster=13806487547053815832&hl=en&as_sdt=0,5",123,2022
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning,3,icml,1,0,2023-06-17 04:54:57.688000,https://github.com/kkalais/stochlwta-ml,2,Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning,"https://scholar.google.com/scholar?cluster=12812982432289049616&hl=en&as_sdt=0,22",1,2022
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning,8,icml,1,0,2023-06-17 04:54:57.893000,https://github.com/causalml/doubly-robust-dropel,4,Doubly robust distributionally robust off-policy evaluation and learning,"https://scholar.google.com/scholar?cluster=3538177620069646339&hl=en&as_sdt=0,5",0,2022
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning,3,icml,0,1,2023-06-17 04:54:58.099000,https://github.com/kamigaito/icml2022,9,Comprehensive analysis of negative sampling in knowledge graph representation learning,"https://scholar.google.com/scholar?cluster=4661195844634999621&hl=en&as_sdt=0,5",2,2022
Composing Partial Differential Equations with Physics-Aware Neural Networks,6,icml,10,0,2023-06-17 04:54:58.304000,https://github.com/cognitivemodeling/finn,25,Composing partial differential equations with physics-aware neural networks,"https://scholar.google.com/scholar?cluster=5219761110162787549&hl=en&as_sdt=0,44",5,2022
FOCUS: Familiar Objects in Common and Uncommon Settings,5,icml,0,0,2023-06-17 04:54:58.509000,https://github.com/priyathamkat/focus,4,Focus: Familiar objects in common and uncommon settings,"https://scholar.google.com/scholar?cluster=2485805129814216346&hl=en&as_sdt=0,5",2,2022
Training OOD Detectors in their Natural Habitats,18,icml,1,0,2023-06-17 04:54:58.715000,https://github.com/jkatzsam/woods_ood,13,Training ood detectors in their natural habitats,"https://scholar.google.com/scholar?cluster=8582043463264170613&hl=en&as_sdt=0,5",1,2022
Secure Quantized Training for Deep Learning,18,icml,9,3,2023-06-17 04:54:58.921000,https://github.com/csiro-mlai/deep-mpc,26,Secure quantized training for deep learning,"https://scholar.google.com/scholar?cluster=15154157227965198183&hl=en&as_sdt=0,5",3,2022
A Convergent and Dimension-Independent Min-Max Optimization Algorithm,3,icml,0,0,2023-06-17 04:54:59.126000,https://github.com/vijaykeswani/min-max-optimization-algorithm,1,A convergent and dimension-independent first-order algorithm for min-max optimization,"https://scholar.google.com/scholar?cluster=1442030372277689222&hl=en&as_sdt=0,5",2,2022
Multi-Level Branched Regularization for Federated Learning,3,icml,4,1,2023-06-17 04:54:59.332000,https://github.com/jinkyu032/FedMLB,13,Multi-level branched regularization for federated learning,"https://scholar.google.com/scholar?cluster=2425993830334019201&hl=en&as_sdt=0,5",1,2022
Learning fair representation with a parametric integral probability metric,5,icml,1,0,2023-06-17 04:54:59.545000,https://github.com/kwkimonline/sipm-lfr,3,Learning fair representation with a parametric integral probability metric,"https://scholar.google.com/scholar?cluster=7724112263757302618&hl=en&as_sdt=0,47",1,2022
Dataset Condensation via Efficient Synthetic-Data Parameterization,28,icml,12,1,2023-06-17 04:54:59.750000,https://github.com/snu-mllab/efficient-dataset-condensation,65,Dataset condensation via efficient synthetic-data parameterization,"https://scholar.google.com/scholar?cluster=13062983297577274052&hl=en&as_sdt=0,5",2,2022
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder,14,icml,4,4,2023-06-17 04:54:59.956000,https://github.com/jumpsnack/ViT-NeT,21,Vit-net: Interpretable vision transformers with neural tree decoder,"https://scholar.google.com/scholar?cluster=7284110818114269396&hl=en&as_sdt=0,33",2,2022
Sanity Simulations for Saliency Methods,10,icml,0,0,2023-06-17 04:55:00.162000,https://github.com/wnstlr/SMERF,3,Sanity simulations for saliency methods,"https://scholar.google.com/scholar?cluster=7944058318921349973&hl=en&as_sdt=0,10",2,2022
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation,26,icml,6,0,2023-06-17 04:55:00.367000,https://github.com/Kim-Dongjun/Soft-Truncation,43,Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation,"https://scholar.google.com/scholar?cluster=547732243097530529&hl=en&as_sdt=0,5",4,2022
Rotting Infinitely Many-Armed Bandits,0,icml,0,0,2023-06-17 04:55:00.573000,https://github.com/junghunkim7786/rotting_infinite_armed_bandits,0,Rotting infinitely many-armed bandits,"https://scholar.google.com/scholar?cluster=7431943945679360181&hl=en&as_sdt=0,23",1,2022
Generalizing to New Physical Systems via Context-Informed Dynamics Model,10,icml,1,0,2023-06-17 04:55:00.778000,https://github.com/yuan-yin/coda,12,Generalizing to new physical systems via context-informed dynamics model,"https://scholar.google.com/scholar?cluster=9987364402754968813&hl=en&as_sdt=0,31",2,2022
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups,9,icml,1,0,2023-06-17 04:55:00.983000,https://github.com/david-knigge/separable-group-convolutional-networks,9,Exploiting redundancy: Separable group convolutional networks on lie groups,"https://scholar.google.com/scholar?cluster=15152080644760721791&hl=en&as_sdt=0,5",2,2022
Controlling Conditional Language Models without Catastrophic Forgetting,8,icml,21,0,2023-06-17 04:55:01.189000,https://github.com/naver/gdc,108,Controlling Conditional Language Models without Catastrophic Forgetting,"https://scholar.google.com/scholar?cluster=13215553222930646661&hl=en&as_sdt=0,11",10,2022
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series,10,icml,4,0,2023-06-17 04:55:01.394000,https://github.com/durstewitzlab/mmplrnn,1,Reconstructing nonlinear dynamical systems from multi-modal time series,"https://scholar.google.com/scholar?cluster=17080536605245199937&hl=en&as_sdt=0,14",1,2022
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions,4,icml,1,0,2023-06-17 04:55:01.600000,https://github.com/heinerkremer/functional-gel,1,Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions,"https://scholar.google.com/scholar?cluster=4926746325545340155&hl=en&as_sdt=0,34",2,2022
Balancing Discriminability and Transferability for Source-Free Domain Adaptation,26,icml,0,0,2023-06-17 04:55:01.805000,https://github.com/val-iisc/MixupDA,6,Balancing discriminability and transferability for source-free domain adaptation,"https://scholar.google.com/scholar?cluster=9320809919166954591&hl=en&as_sdt=0,5",11,2022
Large Batch Experience Replay,8,icml,1,1,2023-06-17 04:55:02.011000,https://github.com/sureli/laber,6,Large batch experience replay,"https://scholar.google.com/scholar?cluster=7195743594836265223&hl=en&as_sdt=0,36",1,2022
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale,64,icml,101,39,2023-06-17 04:55:02.233000,https://github.com/SymbioticLab/FedScale,302,Fedscale: Benchmarking model and system performance of federated learning at scale,"https://scholar.google.com/scholar?cluster=9366536104914467915&hl=en&as_sdt=0,5",10,2022
Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses,4,icml,0,0,2023-06-17 04:55:02.448000,https://github.com/allambert/foreg,4,Functional Output Regression with Infimal Convolution: Exploring the Huber and -insensitive Losses,"https://scholar.google.com/scholar?cluster=13118582575057878063&hl=en&as_sdt=0,31",2,2022
Marginal Tail-Adaptive Normalizing Flows,1,icml,2,0,2023-06-17 04:55:02.655000,https://github.com/mikelasz/marginaltailadaptiveflow,0,Marginal tail-adaptive normalizing flows,"https://scholar.google.com/scholar?cluster=3241792279775112520&hl=en&as_sdt=0,5",1,2022
Implicit Bias of Linear Equivariant Networks,11,icml,0,0,2023-06-17 04:55:02.862000,https://github.com/kristian-georgiev/implicit-bias-of-linear-equivariant-networks,0,Implicit bias of linear equivariant networks,"https://scholar.google.com/scholar?cluster=5414336386133292832&hl=en&as_sdt=0,7",1,2022
Differentially Private Maximal Information Coefficients,0,icml,0,0,2023-06-17 04:55:03.069000,https://github.com/jlazarsfeld/dp-mic,4,Differentially Private Maximal Information Coefficients,"https://scholar.google.com/scholar?cluster=14074773669133605205&hl=en&as_sdt=0,32",2,2022
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks,2,icml,0,0,2023-06-17 04:55:03.275000,https://github.com/lthilnklover/deep-narrow-ntk,1,Neural tangent kernel analysis of deep narrow neural networks,"https://scholar.google.com/scholar?cluster=11344426025520591295&hl=en&as_sdt=0,11",2,2022
Dataset Condensation with Contrastive Signals,18,icml,0,1,2023-06-17 04:55:03.481000,https://github.com/saehyung-lee/dcc,12,Dataset condensation with contrastive signals,"https://scholar.google.com/scholar?cluster=7694046388594127798&hl=en&as_sdt=0,11",1,2022
Confidence Score for Source-Free Unsupervised Domain Adaptation,16,icml,1,0,2023-06-17 04:55:03.686000,https://github.com/jhyun17/cowa-jmds,15,Confidence score for source-free unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=10361966623265648313&hl=en&as_sdt=0,5",1,2022
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization,8,icml,0,0,2023-06-17 04:55:03.892000,https://github.com/snu-mllab/discreteblockbayesattack,17,Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization,"https://scholar.google.com/scholar?cluster=10043868339521505770&hl=en&as_sdt=0,10",1,2022
Least Squares Estimation using Sketched Data with Heteroskedastic Errors,2,icml,0,0,2023-06-17 04:55:04.098000,https://github.com/sokbae/replication-leeng-2022-icml,0,Least Squares Estimation Using Sketched Data with Heteroskedastic Errors,"https://scholar.google.com/scholar?cluster=2973545111138164523&hl=en&as_sdt=0,47",1,2022
Generalized Strategic Classification and the Case of Aligned Incentives,6,icml,0,0,2023-06-17 04:55:04.304000,https://github.com/SagiLevanon1/GSC,1,Generalized strategic classification and the case of aligned incentives,"https://scholar.google.com/scholar?cluster=5634368728411242394&hl=en&as_sdt=0,5",1,2022
Neural Inverse Transform Sampler,1,icml,0,0,2023-06-17 04:55:04.510000,https://github.com/lihenryhfl/nits,1,Neural Inverse Transform Sampler,"https://scholar.google.com/scholar?cluster=3014954787029992873&hl=en&as_sdt=0,5",2,2022
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information,1,icml,1,3,2023-06-17 04:55:04.715000,https://github.com/hugo101/platinum,1,Platinum: Semi-supervised model agnostic meta-learning using submodular mutual information,"https://scholar.google.com/scholar?cluster=1070646536780297100&hl=en&as_sdt=0,5",2,2022
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation,483,icml,504,186,2023-06-17 04:55:04.922000,https://github.com/salesforce/lavis,5513,Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,"https://scholar.google.com/scholar?cluster=7770442917120891581&hl=en&as_sdt=0,5",75,2022
Achieving Fairness at No Utility Cost via Data Reweighing with Influence,9,icml,2,0,2023-06-17 04:55:05.127000,https://github.com/brandeis-machine-learning/influence-fairness,5,Achieving fairness at no utility cost via data reweighing with influence,"https://scholar.google.com/scholar?cluster=1481946580804842338&hl=en&as_sdt=0,10",0,2022
MetAug: Contrastive Learning via Meta Feature Augmentation,10,icml,2,1,2023-06-17 04:55:05.333000,https://github.com/lionellee9089/metaug,15,Metaug: Contrastive learning via meta feature augmentation,"https://scholar.google.com/scholar?cluster=13342110327075124099&hl=en&as_sdt=0,33",1,2022
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration,6,icml,1,1,2023-06-17 04:55:05.539000,https://github.com/yeshenpy/pmic,7,PMIC: Improving multi-agent reinforcement learning with progressive mutual information collaboration,"https://scholar.google.com/scholar?cluster=2755470732694105502&hl=en&as_sdt=0,5",3,2022
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning,30,icml,1,1,2023-06-17 04:55:05.746000,https://github.com/lsh0520/rgcl,23,Let invariant rationale discovery inspire graph contrastive learning,"https://scholar.google.com/scholar?cluster=13286040992676917455&hl=en&as_sdt=0,19",2,2022
Private Adaptive Optimization with Side information,14,icml,1,0,2023-06-17 04:55:05.953000,https://github.com/litian96/adadps,12,Private adaptive optimization with side information,"https://scholar.google.com/scholar?cluster=15603924695620252408&hl=en&as_sdt=0,18",1,2022
Permutation Search of Tensor Network Structures via Local Sampling,4,icml,1,0,2023-06-17 04:55:06.158000,https://github.com/chaoliatriken/tnls,2,Permutation search of tensor network structures via local sampling,"https://scholar.google.com/scholar?cluster=14266729648210963776&hl=en&as_sdt=0,5",1,2022
Double Sampling Randomized Smoothing,5,icml,2,0,2023-06-17 04:55:06.364000,https://github.com/llylly/dsrs,5,Double sampling randomized smoothing,"https://scholar.google.com/scholar?cluster=13905428147766407509&hl=en&as_sdt=0,5",1,2022
HousE: Knowledge Graph Embedding with Householder Parameterization,10,icml,2,0,2023-06-17 04:55:06.571000,https://github.com/anrep/house,15,House: Knowledge graph embedding with householder parameterization,"https://scholar.google.com/scholar?cluster=15337285257575958816&hl=en&as_sdt=0,34",1,2022
Learning Multiscale Transformer Models for Sequence Generation,4,icml,2,1,2023-06-17 04:55:06.778000,https://github.com/libeineu/umst,10,Learning multiscale transformer models for sequence generation,"https://scholar.google.com/scholar?cluster=10490177289793431927&hl=en&as_sdt=0,5",1,2022
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily,38,icml,3,0,2023-06-17 04:55:06.984000,https://github.com/recklessronan/glognn,26,Finding global homophily in graph neural networks when meeting heterophily,"https://scholar.google.com/scholar?cluster=881393506933530763&hl=en&as_sdt=0,5",1,2022
Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling,0,icml,0,0,2023-06-17 04:55:07.191000,https://github.com/byronliang8/hubnessgansampling,8,Exploring and exploiting hubness priors for high-quality GAN latent sampling,"https://scholar.google.com/scholar?cluster=12825471375795704979&hl=en&as_sdt=0,5",1,2022
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks,4,icml,1,0,2023-06-17 04:55:07.396000,https://github.com/indylab/meanq,8,Reducing variance in temporal-difference value estimation via ensemble of deep networks,"https://scholar.google.com/scholar?cluster=5733035201533168571&hl=en&as_sdt=0,5",0,2022
Order Constraints in Optimal Transport,1,icml,294,54,2023-06-17 04:55:07.603000,https://github.com/Trusted-AI/AIX360,1340,Order Constraints in Optimal Transport,"https://scholar.google.com/scholar?cluster=1063075229818760095&hl=en&as_sdt=0,5",51,2022
Flow-Guided Sparse Transformer for Video Deblurring,23,icml,12,1,2023-06-17 04:55:07.808000,https://github.com/linjing7/VR-Baseline,122,Flow-guided sparse transformer for video deblurring,"https://scholar.google.com/scholar?cluster=14219657862279161517&hl=en&as_sdt=0,5",13,2022
Federated Learning with Positive and Unlabeled Data,8,icml,1,2,2023-06-17 04:55:08.013000,https://github.com/littlesunlxy/fedpu-torch,7,Federated Learning with Positive and Unlabeled Data,"https://scholar.google.com/scholar?cluster=5808543531345013860&hl=en&as_sdt=0,29",1,2022
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration,2,icml,12,1,2023-06-17 04:55:08.223000,https://github.com/linjing7/VR-Baseline,122,Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration,"https://scholar.google.com/scholar?cluster=11447631455312360639&hl=en&as_sdt=0,5",13,2022
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks,0,icml,0,0,2023-06-17 04:55:08.433000,https://github.com/linweiran/CGD,1,Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks,"https://scholar.google.com/scholar?cluster=14082433359159261518&hl=en&as_sdt=0,5",1,2022
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments,8,icml,0,0,2023-06-17 04:55:08.639000,https://github.com/lazycal/ame,1,Measuring the effect of training data on deep learning predictions via randomized experiments,"https://scholar.google.com/scholar?cluster=7808395865683583052&hl=en&as_sdt=0,43",1,2022
Interactively Learning Preference Constraints in Linear Bandits,1,icml,0,0,2023-06-17 04:55:08.847000,https://github.com/lasgroup/adaptive-constraint-learning,3,Interactively Learning Preference Constraints in Linear Bandits,"https://scholar.google.com/scholar?cluster=10442761554995680158&hl=en&as_sdt=0,2",2,2022
CITRIS: Causal Identifiability from Temporal Intervened Sequences,31,icml,5,1,2023-06-17 04:55:09.055000,https://github.com/phlippe/citris,38,Citris: Causal identifiability from temporal intervened sequences,"https://scholar.google.com/scholar?cluster=9740161650140858183&hl=en&as_sdt=0,36",6,2022
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models,7,icml,0,1,2023-06-17 04:55:09.261000,https://github.com/deepmind/streamingqa,35,Streamingqa: A benchmark for adaptation to new knowledge over time in question answering models,"https://scholar.google.com/scholar?cluster=14847402247915330134&hl=en&as_sdt=0,5",3,2022
Constrained Variational Policy Optimization for Safe Reinforcement Learning,19,icml,6,2,2023-06-17 04:55:09.466000,https://github.com/liuzuxin/cvpo-safe-rl,42,Constrained variational policy optimization for safe reinforcement learning,"https://scholar.google.com/scholar?cluster=13833315390800713597&hl=en&as_sdt=0,48",3,2022
Boosting Graph Structure Learning with Dummy Nodes,4,icml,3,0,2023-06-17 04:55:09.672000,https://github.com/hkust-knowcomp/dummynode4graphlearning,14,Boosting graph structure learning with dummy nodes,"https://scholar.google.com/scholar?cluster=11720456442737654498&hl=en&as_sdt=0,5",2,2022
Rethinking Attention-Model Explainability through Faithfulness Violation Test,6,icml,2,0,2023-06-17 04:55:09.878000,https://github.com/BierOne/Attention-Faithfulness,15,Rethinking attention-model explainability through faithfulness violation test,"https://scholar.google.com/scholar?cluster=2225803020950336962&hl=en&as_sdt=0,10",1,2022
Generating 3D Molecules for Target Protein Binding,33,icml,22,0,2023-06-17 04:55:10.084000,https://github.com/divelab/graphbp,82,Generating 3d molecules for target protein binding,"https://scholar.google.com/scholar?cluster=5832718815392405433&hl=en&as_sdt=0,23",4,2022
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer,8,icml,1,0,2023-06-17 04:55:10.290000,https://github.com/xingyul/revolver,21,Revolver: Continuous evolutionary models for robot-to-robot policy transfer,"https://scholar.google.com/scholar?cluster=4925772100401553485&hl=en&as_sdt=0,5",0,2022
Local Augmentation for Graph Neural Networks,27,icml,10,0,2023-06-17 04:55:10.496000,https://github.com/songtaoliu0823/lagnn,49,Local augmentation for graph neural networks,"https://scholar.google.com/scholar?cluster=1477899180662383839&hl=en&as_sdt=0,33",2,2022
GACT: Activation Compressed Training for Generic Network Architectures,6,icml,7,0,2023-06-17 04:55:10.702000,https://github.com/LiuXiaoxuanPKU/GACT-ICML,25,GACT: Activation compressed training for generic network architectures,"https://scholar.google.com/scholar?cluster=12961558979640169971&hl=en&as_sdt=0,11",1,2022
Robust Training under Label Noise by Over-parameterization,32,icml,6,2,2023-06-17 04:55:10.911000,https://github.com/shengliu66/sop,45,Robust training under label noise by over-parameterization,"https://scholar.google.com/scholar?cluster=7351288537652812990&hl=en&as_sdt=0,5",4,2022
"Bayesian Model Selection, the Marginal Likelihood, and Generalization",22,icml,2,0,2023-06-17 04:55:11.124000,https://github.com/sanaelotfi/bayesian_model_comparison,29,"Bayesian model selection, the marginal likelihood, and generalization","https://scholar.google.com/scholar?cluster=9966221610854779885&hl=en&as_sdt=0,10",2,2022
Additive Gaussian Processes Revisited,5,icml,3,2,2023-06-17 04:55:11.347000,https://github.com/amzn/orthogonal-additive-gaussian-processes,27,Additive Gaussian Processes Revisited,"https://scholar.google.com/scholar?cluster=6171646250259596364&hl=en&as_sdt=0,7",1,2022
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias,3,icml,0,0,2023-06-17 04:55:11.555000,https://github.com/YupuLu/ModLaNets,3,Modlanets: Learning generalisable dynamics via modularity and physical inductive bias,"https://scholar.google.com/scholar?cluster=13273673478017721155&hl=en&as_sdt=0,5",1,2022
Model-Free Opponent Shaping,16,icml,3,0,2023-06-17 04:55:11.762000,https://github.com/luchris429/model-free-opponent-shaping,8,Model-free opponent shaping,"https://scholar.google.com/scholar?cluster=2936183608022340062&hl=en&as_sdt=0,6",1,2022
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering,13,icml,8,1,2023-06-17 04:55:11.968000,https://github.com/akhilmathurs/orchestra,34,Orchestra: Unsupervised federated learning via globally consistent clustering,"https://scholar.google.com/scholar?cluster=12370876234487104592&hl=en&as_sdt=0,5",3,2022
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions,13,icml,1,0,2023-06-17 04:55:12.173000,https://github.com/optimization-for-data-driven-science/xai,0,A rigorous study of integrated gradients method and extensions to internal neuron attributions,"https://scholar.google.com/scholar?cluster=2734810007243082678&hl=en&as_sdt=0,14",3,2022
Channel Importance Matters in Few-Shot Image Classification,9,icml,6,0,2023-06-17 04:55:12.378000,https://github.com/Frankluox/Channel_Importance_FSL,41,Channel importance matters in few-shot image classification,"https://scholar.google.com/scholar?cluster=11800681644277658610&hl=en&as_sdt=0,5",3,2022
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching,5,icml,2,0,2023-06-17 04:55:12.584000,https://github.com/jasonma2016/smodice,20,Versatile offline imitation from observations and examples via regularized state-occupancy matching,"https://scholar.google.com/scholar?cluster=11179690746522153663&hl=en&as_sdt=0,5",2,2022
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding,0,icml,0,1,2023-06-17 04:55:12.790000,https://github.com/haotiansustc/deepinfo,3,Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding,"https://scholar.google.com/scholar?cluster=17376169416462148944&hl=en&as_sdt=0,5",1,2022
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings,9,icml,0,0,2023-06-17 04:55:12.995000,https://github.com/zib-iol/fw-rde,4,Interpretable neural networks with frank-wolfe: Sparse relevance maps and relevance orderings,"https://scholar.google.com/scholar?cluster=1124674536822867580&hl=en&as_sdt=0,21",2,2022
"A Tighter Analysis of Spectral Clustering, and Beyond",4,icml,0,0,2023-06-17 04:55:13.201000,https://github.com/pmacg/spectral-clustering-meta-graphs,3,"A Tighter Analysis of Spectral Clustering, and Beyond","https://scholar.google.com/scholar?cluster=7116468291147711017&hl=en&as_sdt=0,10",1,2022
Feature selection using e-values,1,icml,0,0,2023-06-17 04:55:13.407000,https://github.com/shubhobm/e-values,2,Feature Selection using e-values,"https://scholar.google.com/scholar?cluster=14169974284290385503&hl=en&as_sdt=0,5",2,2022
Nonparametric Involutive Markov Chain Monte Carlo,0,icml,2,1,2023-06-17 04:55:13.612000,https://github.com/fzaiser/nonparametric-hmc,12,Nonparametric Involutive Markov Chain Monte Carlo,"https://scholar.google.com/scholar?cluster=17862750245568901583&hl=en&as_sdt=0,25",1,2022
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees,9,icml,0,0,2023-06-17 04:55:13.818000,https://github.com/osmanmalik/td-als-es,3,More efficient sampling for tensor decomposition with worst-case guarantees,"https://scholar.google.com/scholar?cluster=18131307988891143062&hl=en&as_sdt=0,5",1,2022
Unaligned Supervision for Automatic Music Transcription in The Wild,4,icml,1,1,2023-06-17 04:55:14.024000,https://github.com/benadar293/benadar293.github.io,16,Unaligned supervision for automatic music transcription in the wild,"https://scholar.google.com/scholar?cluster=7612759621426730574&hl=en&as_sdt=0,43",1,2022
Decision-Focused Learning: Through the Lens of Learning to Rank,7,icml,1,0,2023-06-17 04:55:14.230000,https://github.com/jayman91/ltr-predopt,5,Decision-Focused Learning: Through the Lens of Learning to Rank,"https://scholar.google.com/scholar?cluster=68474757504279365&hl=en&as_sdt=0,5",1,2022
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models,5,icml,0,0,2023-06-17 04:55:14.440000,https://github.com/tmanole/refined-mixture-rates,1,Refined convergence rates for maximum likelihood estimation under finite mixture models,"https://scholar.google.com/scholar?cluster=15536015401615707970&hl=en&as_sdt=0,34",2,2022
On the Effects of Artificial Data Modification,0,icml,0,0,2023-06-17 04:55:14.646000,https://github.com/antoniamarcu/data-modification,1,On the Effects of Artificial Data Modification,"https://scholar.google.com/scholar?cluster=5171301994487774624&hl=en&as_sdt=0,33",2,2022
Personalized Federated Learning through Local Memorization,15,icml,11,1,2023-06-17 04:55:14.851000,https://github.com/omarfoq/knn-per,32,Personalized federated learning through local memorization,"https://scholar.google.com/scholar?cluster=1735959565667819081&hl=en&as_sdt=0,5",1,2022
Closed-Form Diffeomorphic Transformations for Time Series Alignment,0,icml,1,0,2023-06-17 04:55:15.058000,https://github.com/imartinezl/difw,12,Closed-Form Diffeomorphic Transformations for Time Series Alignment,"https://scholar.google.com/scholar?cluster=15344236423757479416&hl=en&as_sdt=0,5",2,2022
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators,17,icml,4,0,2023-06-17 04:55:15.264000,https://github.com/karolismart/spectre,18,Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators,"https://scholar.google.com/scholar?cluster=12175380990160510944&hl=en&as_sdt=0,14",2,2022
Continual Repeated Annealed Flow Transport Monte Carlo,8,icml,10,0,2023-06-17 04:55:15.470000,https://github.com/deepmind/annealed_flow_transport,35,Continual repeated annealed flow transport Monte Carlo,"https://scholar.google.com/scholar?cluster=15272534120760724190&hl=en&as_sdt=0,33",4,2022
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection,3,icml,3,1,2023-06-17 04:55:15.675000,https://github.com/mmazeika/model-stealing-defenses,2,How to steer your adversary: Targeted and efficient model stealing defenses with gradient redirection,"https://scholar.google.com/scholar?cluster=12763327756240287958&hl=en&as_sdt=0,5",1,2022
Causal Transformer for Estimating Counterfactual Outcomes,15,icml,10,3,2023-06-17 04:55:15.882000,https://github.com/Valentyn1997/CausalTransformer,48,Causal transformer for estimating counterfactual outcomes,"https://scholar.google.com/scholar?cluster=15562561940840223837&hl=en&as_sdt=0,5",2,2022
Steerable 3D Spherical Neurons,2,icml,0,0,2023-06-17 04:55:16.088000,https://github.com/pavlo-melnyk/steerable-3d-neurons,0,Steerable 3D Spherical Neurons,"https://scholar.google.com/scholar?cluster=12172638513685585373&hl=en&as_sdt=0,23",2,2022
Transformers are Meta-Reinforcement Learners,15,icml,4,3,2023-06-17 04:55:16.294000,https://github.com/luckeciano/transformers-metarl,32,Transformers are meta-reinforcement learners,"https://scholar.google.com/scholar?cluster=4334650228414799916&hl=en&as_sdt=0,33",4,2022
Stochastic Rising Bandits,4,icml,0,0,2023-06-17 04:55:16.500000,https://github.com/albertometelli/stochastic-rising-bandits,4,Stochastic Rising Bandits,"https://scholar.google.com/scholar?cluster=15697580060507911770&hl=en&as_sdt=0,5",1,2022
Minimizing Control for Credit Assignment with Strong Feedback,4,icml,3,0,2023-06-17 04:55:16.706000,https://github.com/mariacer/strong_dfc,8,Minimizing control for credit assignment with strong feedback,"https://scholar.google.com/scholar?cluster=4546119476247760219&hl=en&as_sdt=0,33",1,2022
Distribution Regression with Sliced Wasserstein Kernels,4,icml,0,0,2023-06-17 04:55:16.912000,https://github.com/dimsum2k/drswk,4,Distribution Regression with Sliced Wasserstein Kernels,"https://scholar.google.com/scholar?cluster=6056433376162861662&hl=en&as_sdt=0,33",1,2022
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism,32,icml,15,0,2023-06-17 04:55:17.118000,https://github.com/Graph-COM/GSAT,112,Interpretable and generalizable graph learning via stochastic attention mechanism,"https://scholar.google.com/scholar?cluster=15869188404391034141&hl=en&as_sdt=0,5",2,2022
Modeling Structure with Undirected Neural Networks,0,icml,0,0,2023-06-17 04:55:17.323000,https://github.com/deep-spin/unn,5,Modeling Structure with Undirected Neural Networks,"https://scholar.google.com/scholar?cluster=2812799179011776020&hl=en&as_sdt=0,33",4,2022
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models,10,icml,2,0,2023-06-17 04:55:17.529000,https://github.com/BerenMillidge/Theory_Associative_Memory,12,Universal hopfield networks: A general framework for single-shot associative memory models,"https://scholar.google.com/scholar?cluster=11661827262437868518&hl=en&as_sdt=0,5",3,2022
"Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt",24,icml,18,2,2023-06-17 04:55:17.735000,https://github.com/oatml/rho-loss,158,"Prioritized training on points that are learnable, worth learning, and not yet learnt","https://scholar.google.com/scholar?cluster=5784378723216835078&hl=en&as_sdt=0,33",6,2022
POEM: Out-of-Distribution Detection with Posterior Sampling,20,icml,1,1,2023-06-17 04:55:17.940000,https://github.com/deeplearning-wisc/poem,22,Poem: Out-of-distribution detection with posterior sampling,"https://scholar.google.com/scholar?cluster=14373980882186283690&hl=en&as_sdt=0,33",4,2022
Proximal and Federated Random Reshuffling,21,icml,2,0,2023-06-17 04:55:18.146000,https://github.com/konstmish/rr_prox_fed,2,Proximal and federated random reshuffling,"https://scholar.google.com/scholar?cluster=4410848419822485671&hl=en&as_sdt=0,33",2,2022
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions,14,icml,2,0,2023-06-17 04:55:18.353000,https://github.com/pilancilab/scnn,5,Fast convex optimization for two-layer relu networks: Equivalent model classes and cone decompositions,"https://scholar.google.com/scholar?cluster=7077031077028119954&hl=en&as_sdt=0,21",3,2022
Invariant Ancestry Search,1,icml,0,0,2023-06-17 04:55:18.559000,https://github.com/phillipmogensen/invariantancestrysearch,0,Invariant Ancestry Search,"https://scholar.google.com/scholar?cluster=7085135570627495556&hl=en&as_sdt=0,10",1,2022
SpeqNets: Sparsity-aware permutation-equivariant graph networks,21,icml,3,0,2023-06-17 04:55:18.765000,https://github.com/chrsmrrs/speqnets,9,Speqnets: Sparsity-aware permutation-equivariant graph networks,"https://scholar.google.com/scholar?cluster=18273879943488078405&hl=en&as_sdt=0,1",1,2022
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer,4,icml,2,1,2023-06-17 04:55:18.970000,https://github.com/YaoMarkMu/CtrlFormer_robotic,26,Ctrlformer: Learning transferable state representation for visual control via transformer,"https://scholar.google.com/scholar?cluster=15994281746681133957&hl=en&as_sdt=0,5",2,2022
AutoSNN: Towards Energy-Efficient Spiking Neural Networks,19,icml,1,0,2023-06-17 04:55:19.177000,https://github.com/nabk89/autosnn,11,AutoSNN: towards energy-efficient spiking neural networks,"https://scholar.google.com/scholar?cluster=4509781886252984486&hl=en&as_sdt=0,44",1,2022
Overcoming Oscillations in Quantization-Aware Training,12,icml,6,4,2023-06-17 04:55:19.383000,https://github.com/qualcomm-ai-research/oscillations-qat,35,Overcoming oscillations in quantization-aware training,"https://scholar.google.com/scholar?cluster=7420900147449297727&hl=en&as_sdt=0,33",6,2022
Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation,3,icml,1,0,2023-06-17 04:55:19.589000,https://github.com/cs-giung/distill-latentbe,2,Improving ensemble distillation with weight averaging and diversifying perturbation,"https://scholar.google.com/scholar?cluster=15634605277253421377&hl=en&as_sdt=0,5",1,2022
Measuring Representational Robustness of Neural Networks Through Shared Invariances,2,icml,0,0,2023-06-17 04:55:19.796000,https://github.com/nvedant07/stir,5,Measuring Representational Robustness of Neural Networks Through Shared Invariances,"https://scholar.google.com/scholar?cluster=11535296107699738994&hl=en&as_sdt=0,5",2,2022
Multi-Task Learning as a Bargaining Game,20,icml,16,0,2023-06-17 04:55:20.002000,https://github.com/avivnavon/nash-mtl,116,Multi-task learning as a bargaining game,"https://scholar.google.com/scholar?cluster=3841743488607196482&hl=en&as_sdt=0,5",4,2022
Variational Inference for Infinitely Deep Neural Networks,2,icml,0,1,2023-06-17 04:55:20.208000,https://github.com/anazaret/unbounded-depth-neural-networks,12,Variational Inference for Infinitely Deep Neural Networks,"https://scholar.google.com/scholar?cluster=15923008707496019552&hl=en&as_sdt=0,5",1,2022
Stable Conformal Prediction Sets,7,icml,0,0,2023-06-17 04:55:20.414000,https://github.com/EugeneNdiaye/stable_conformal_prediction,3,Stable conformal prediction sets,"https://scholar.google.com/scholar?cluster=1322086183676915267&hl=en&as_sdt=0,36",2,2022
Sublinear-Time Clustering Oracle for Signed Graphs,0,icml,0,0,2023-06-17 04:55:20.621000,https://github.com/stefanresearch/signed-oracle,0,Sublinear-Time Clustering Oracle for Signed Graphs,"https://scholar.google.com/scholar?cluster=11680644385251401321&hl=en&as_sdt=0,5",1,2022
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling,21,icml,6,0,2023-06-17 04:55:20.827000,https://github.com/tung-nd/tnp-pytorch,42,Transformer neural processes: Uncertainty-aware meta learning via sequence modeling,"https://scholar.google.com/scholar?cluster=8314226561470238527&hl=en&as_sdt=0,39",2,2022
Improving Transformers with Probabilistic Attention Keys,9,icml,6,1,2023-06-17 04:55:21.033000,https://github.com/minhtannguyen/transformer-mgk,20,Improving transformers with probabilistic attention keys,"https://scholar.google.com/scholar?cluster=15369073464631209004&hl=en&as_sdt=0,33",1,2022
Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs,16,icml,29,1,2023-06-17 04:55:21.239000,https://github.com/twni2016/pomdp-baselines,212,Recurrent model-free rl can be a strong baseline for many pomdps,"https://scholar.google.com/scholar?cluster=10952850493674011457&hl=en&as_sdt=0,39",5,2022
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models,742,icml,457,23,2023-06-17 04:55:21.445000,https://github.com/openai/glide-text2im,3226,Glide: Towards photorealistic image generation and editing with text-guided diffusion models,"https://scholar.google.com/scholar?cluster=15472303808406531445&hl=en&as_sdt=0,34",142,2022
Diffusion Models for Adversarial Purification,72,icml,22,0,2023-06-17 04:55:21.653000,https://github.com/NVlabs/DiffPure,163,Diffusion models for adversarial purification,"https://scholar.google.com/scholar?cluster=9166244005732160404&hl=en&as_sdt=0,5",5,2022
The Primacy Bias in Deep Reinforcement Learning,23,icml,6,0,2023-06-17 04:55:21.859000,https://github.com/evgenii-nikishin/rl_with_resets,82,The primacy bias in deep reinforcement learning,"https://scholar.google.com/scholar?cluster=11620338198970862085&hl=en&as_sdt=0,48",3,2022
Efficient Test-Time Model Adaptation without Forgetting,40,icml,5,0,2023-06-17 04:55:22.065000,https://github.com/mr-eggplant/eata,65,Efficient test-time model adaptation without forgetting,"https://scholar.google.com/scholar?cluster=17499416478096807711&hl=en&as_sdt=0,5",2,2022
Utilizing Expert Features for Contrastive Learning of Time-Series Representations,5,icml,2,2,2023-06-17 04:55:22.270000,https://github.com/boschresearch/expclr,14,Utilizing expert features for contrastive learning of time-series representations,"https://scholar.google.com/scholar?cluster=16790455232498977165&hl=en&as_sdt=0,33",6,2022
Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval,33,icml,21,4,2023-06-17 04:55:22.477000,https://github.com/oatml-markslab/tranception,88,Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval,"https://scholar.google.com/scholar?cluster=13139855140556717827&hl=en&as_sdt=0,44",5,2022
Scalable Deep Gaussian Markov Random Fields for General Graphs,2,icml,3,0,2023-06-17 04:55:22.684000,https://github.com/joeloskarsson/graph-dgmrf,4,Scalable Deep Gaussian Markov Random Fields for General Graphs,"https://scholar.google.com/scholar?cluster=16619238478793238405&hl=en&as_sdt=0,48",3,2022
Zero-shot AutoML with Pretrained Models,2,icml,2,0,2023-06-17 04:55:22.890000,https://github.com/automl/zero-shot-automl-with-pretrained-models,35,Zero-Shot AutoML with Pretrained Models,"https://scholar.google.com/scholar?cluster=4155086096102443249&hl=en&as_sdt=0,21",9,2022
History Compression via Language Models in Reinforcement Learning,8,icml,4,0,2023-06-17 04:55:23.096000,https://github.com/ml-jku/helm,38,History compression via language models in reinforcement learning,"https://scholar.google.com/scholar?cluster=3335833011258515063&hl=en&as_sdt=0,19",6,2022
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks,2,icml,9,2,2023-06-17 04:55:23.302000,https://github.com/tnbar/tednet,64,A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks,"https://scholar.google.com/scholar?cluster=2601266852558996821&hl=en&as_sdt=0,22",3,2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition,31,icml,7,0,2023-06-17 04:55:23.509000,https://github.com/p2333/score,58,Robustness and accuracy could be reconcilable by (proper) definition,"https://scholar.google.com/scholar?cluster=12573058517676493723&hl=en&as_sdt=0,5",2,2022
Learning Symmetric Embeddings for Equivariant World Models,16,icml,0,1,2023-06-17 04:55:23.717000,https://github.com/jypark0/sen,4,Learning symmetric embeddings for equivariant world models,"https://scholar.google.com/scholar?cluster=17517971134760315540&hl=en&as_sdt=0,33",1,2022
"Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness",7,icml,7,0,2023-06-17 04:55:23.924000,https://github.com/xxxnell/spatial-smoothing,70,"Blurs behave like ensembles: Spatial smoothings to improve accuracy, uncertainty, and robustness","https://scholar.google.com/scholar?cluster=11971703868153296298&hl=en&as_sdt=0,33",2,2022
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution,24,icml,3,1,2023-06-17 04:55:24.130000,https://github.com/ml-jku/align-rudder,18,Align-rudder: Learning from few demonstrations by reward redistribution,"https://scholar.google.com/scholar?cluster=17099796649634976721&hl=en&as_sdt=0,36",6,2022
POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging,8,icml,11,7,2023-06-17 04:55:24.336000,https://github.com/shishirpatil/poet,127,POET: Training neural networks on tiny devices with integrated rematerialization and paging,"https://scholar.google.com/scholar?cluster=5184430437455623817&hl=en&as_sdt=0,6",9,2022
Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding,32,icml,1936,479,2023-06-17 04:55:24.542000,https://github.com/espnet/espnet,6692,Branchformer: Parallel mlp-attention architectures to capture local and global context for speech recognition and understanding,"https://scholar.google.com/scholar?cluster=8709670323739096599&hl=en&as_sdt=0,33",179,2022
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets,30,icml,46,9,2023-06-17 04:55:24.751000,https://github.com/pengxingang/pocket2mol,155,Pocket2mol: Efficient molecular sampling based on 3d protein pockets,"https://scholar.google.com/scholar?cluster=5422392293509643070&hl=en&as_sdt=0,33",8,2022
Differentiable Top-k Classification Learning,8,icml,0,1,2023-06-17 04:55:24.977000,https://github.com/felix-petersen/difftopk,48,Differentiable top-k classification learning,"https://scholar.google.com/scholar?cluster=2888939572667326983&hl=en&as_sdt=0,33",3,2022
Multi-scale Feature Learning Dynamics: Insights for Double Descent,8,icml,2,0,2023-06-17 04:55:25.183000,https://github.com/nndoubledescent/doubledescent,0,Multi-scale feature learning dynamics: Insights for double descent,"https://scholar.google.com/scholar?cluster=15892651020867127021&hl=en&as_sdt=0,33",1,2022
A Differential Entropy Estimator for Training Neural Networks,13,icml,4,0,2023-06-17 04:55:25.390000,https://github.com/g-pichler/knife,9,A differential entropy estimator for training neural networks,"https://scholar.google.com/scholar?cluster=5856117255578319314&hl=en&as_sdt=0,33",1,2022
Federated Learning with Partial Model Personalization,30,icml,0,0,2023-06-17 04:55:25.596000,https://github.com/krishnap25/fl_partial_personalization,1,Federated learning with partial model personalization,"https://scholar.google.com/scholar?cluster=4750968691898857474&hl=en&as_sdt=0,11",3,2022
Geometric Multimodal Contrastive Representation Learning,7,icml,4,0,2023-06-17 04:55:25.801000,https://github.com/miguelsvasco/gmc,17,Geometric Multimodal Contrastive Representation Learning,"https://scholar.google.com/scholar?cluster=1723737180667149201&hl=en&as_sdt=0,50",2,2022
On the Practicality of Deterministic Epistemic Uncertainty,15,icml,178,119,2023-06-17 04:55:26.007000,https://github.com/google/uncertainty-baselines,1244,On the practicality of deterministic epistemic uncertainty,"https://scholar.google.com/scholar?cluster=10237983835645354047&hl=en&as_sdt=0,33",20,2022
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers,22,icml,19,4,2023-06-17 04:55:26.214000,https://github.com/auspicious3000/contentvec,277,Contentvec: An improved self-supervised speech representation by disentangling speakers,"https://scholar.google.com/scholar?cluster=16442143470536354603&hl=en&as_sdt=0,26",7,2022
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder,3,icml,4,0,2023-06-17 04:55:26.440000,https://github.com/wonderseven/lssae,19,Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder,"https://scholar.google.com/scholar?cluster=8021731201291301386&hl=en&as_sdt=0,22",3,2022
Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence,3,icml,1,0,2023-06-17 04:55:26.646000,https://github.com/zhqiu/ndcg-optimization,2,Large-scale stochastic optimization of ndcg surrogates for deep learning with provable convergence,"https://scholar.google.com/scholar?cluster=9377138316635213561&hl=en&as_sdt=0,33",1,2022
Latent Outlier Exposure for Anomaly Detection with Contaminated Data,15,icml,8,1,2023-06-17 04:55:26.853000,https://github.com/boschresearch/LatentOE-AD,34,Latent outlier exposure for anomaly detection with contaminated data,"https://scholar.google.com/scholar?cluster=3679566789459312121&hl=en&as_sdt=0,33",4,2022
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning,7,icml,0,0,2023-06-17 04:55:27.059000,https://github.com/baichenjia/contrastive-ucb,9,Contrastive ucb: Provably efficient contrastive self-supervised learning in online reinforcement learning,"https://scholar.google.com/scholar?cluster=4487688180752876620&hl=en&as_sdt=0,5",2,2022
Particle Transformer for Jet Tagging,10,icml,25,1,2023-06-17 04:55:27.265000,https://github.com/jet-universe/particle_transformer,43,Particle transformer for jet tagging,"https://scholar.google.com/scholar?cluster=12329206017907212560&hl=en&as_sdt=0,23",3,2022
Winning the Lottery Ahead of Time: Efficient Early Network Pruning,5,icml,2,0,2023-06-17 04:55:27.480000,https://github.com/johnrachwan123/Early-Cropression-via-Gradient-Flow-Preservation,15,Winning the lottery ahead of time: Efficient early network pruning,"https://scholar.google.com/scholar?cluster=3167787605705434615&hl=en&as_sdt=0,41",2,2022
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale,59,icml,3110,886,2023-06-17 04:55:27.718000,https://github.com/microsoft/DeepSpeed,25974,Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale,"https://scholar.google.com/scholar?cluster=6450094276419504510&hl=en&as_sdt=0,22",290,2022
A Closer Look at Smoothness in Domain Adversarial Training,20,icml,4,2,2023-06-17 04:55:27.927000,https://github.com/val-iisc/sdat,40,A closer look at smoothness in domain adversarial training,"https://scholar.google.com/scholar?cluster=11164597139581450427&hl=en&as_sdt=0,33",14,2022
Linear Adversarial Concept Erasure,25,icml,3,2,2023-06-17 04:55:28.133000,https://github.com/shauli-ravfogel/rlace-icml,23,Linear adversarial concept erasure,"https://scholar.google.com/scholar?cluster=157683061025883774&hl=en&as_sdt=0,31",1,2022
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks,15,icml,0,0,2023-06-17 04:55:28.339000,https://github.com/asafmaman101/imp_reg_htf,4,Implicit regularization in hierarchical tensor factorization and deep convolutional neural networks,"https://scholar.google.com/scholar?cluster=12909622448171060632&hl=en&as_sdt=0,33",2,2022
"The dynamics of representation learning in shallow, non-linear autoencoders",2,icml,0,0,2023-06-17 04:55:28.548000,https://github.com/mariaref/nonlinearshallowae,5,"The dynamics of representation learning in shallow, non-linear autoencoders","https://scholar.google.com/scholar?cluster=14118431460184328977&hl=en&as_sdt=0,31",2,2022
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs,1,icml,0,0,2023-06-17 04:55:28.755000,https://github.com/sjtu-xai-lab/transformation-complexity,1,Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs,"https://scholar.google.com/scholar?cluster=1146425504680188001&hl=en&as_sdt=0,33",1,2022
Benchmarking and Analyzing Point Cloud Classification under Corruptions,26,icml,3,0,2023-06-17 04:55:28.962000,https://github.com/jiawei-ren/modelnetc,50,Benchmarking and analyzing point cloud classification under corruptions,"https://scholar.google.com/scholar?cluster=4434116773940428233&hl=en&as_sdt=0,33",6,2022
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning,2,icml,1,0,2023-06-17 04:55:29.168000,https://github.com/juliusberner/robust_kolmogorov,2,Robust SDE-based variational formulations for solving linear PDEs via deep learning,"https://scholar.google.com/scholar?cluster=5839668907631655505&hl=en&as_sdt=0,16",1,2022
LyaNet: A Lyapunov Framework for Training Neural ODEs,17,icml,3,0,2023-06-17 04:55:29.382000,https://github.com/ivandariojr/lyapunovlearning,27,LyaNet: A Lyapunov framework for training neural ODEs,"https://scholar.google.com/scholar?cluster=11176249487221195122&hl=en&as_sdt=0,33",3,2022
Short-Term Plasticity Neurons Learning to Learn and Forget,8,icml,1,0,2023-06-17 04:55:29.589000,https://github.com/neuromorphiccomputing/stpn,17,Short-term plasticity neurons learning to learn and forget,"https://scholar.google.com/scholar?cluster=13353176637859953693&hl=en&as_sdt=0,5",4,2022
Function-space Inference with Sparse Implicit Processes,2,icml,2,0,2023-06-17 04:55:29.800000,https://github.com/simonrsantana/sparse-implicit-processes,1,Function-space Inference with Sparse Implicit Processes,"https://scholar.google.com/scholar?cluster=3087914783084308149&hl=en&as_sdt=0,50",1,2022
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images,3,icml,3,0,2023-06-17 04:55:30.007000,https://github.com/tomron27/dd_med,2,Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images,"https://scholar.google.com/scholar?cluster=10465544337215443782&hl=en&as_sdt=0,14",1,2022
A Consistent and Efficient Evaluation Strategy for Attribution Methods,18,icml,4,3,2023-06-17 04:55:30.216000,https://github.com/tleemann/road_evaluation,12,A consistent and efficient evaluation strategy for attribution methods,"https://scholar.google.com/scholar?cluster=16933534039020294474&hl=en&as_sdt=0,44",1,2022
Direct Behavior Specification via Constrained Reinforcement Learning,14,icml,2,0,2023-06-17 04:55:30.424000,https://github.com/ubisoft/directbehaviorspecification,8,Direct behavior specification via constrained reinforcement learning,"https://scholar.google.com/scholar?cluster=12930072295285422644&hl=en&as_sdt=0,18",2,2022
Graph-Coupled Oscillator Networks,26,icml,7,1,2023-06-17 04:55:30.631000,https://github.com/tk-rusch/graphcon,39,Graph-coupled oscillator networks,"https://scholar.google.com/scholar?cluster=9009434155878040135&hl=en&as_sdt=0,5",3,2022
Hindering Adversarial Attacks with Implicit Neural Representations,1,icml,0,0,2023-06-17 04:55:30.837000,https://github.com/deepmind/linac,8,Hindering Adversarial Attacks with Implicit Neural Representations,"https://scholar.google.com/scholar?cluster=14287948960663739347&hl=en&as_sdt=0,33",2,2022
Exploiting Independent Instruments: Identification and Distribution Generalization,5,icml,0,0,2023-06-17 04:55:31.043000,https://github.com/sorawitj/hsic-x,5,Exploiting independent instruments: Identification and distribution generalization,"https://scholar.google.com/scholar?cluster=7573181679595557794&hl=en&as_sdt=0,31",1,2022
LSB: Local Self-Balancing MCMC in Discrete Spaces,5,icml,0,0,2023-06-17 04:55:31.250000,https://github.com/emsansone/lsb,2,Lsb: Local self-balancing mcmc in discrete spaces,"https://scholar.google.com/scholar?cluster=4624892797012274460&hl=en&as_sdt=0,11",2,2022
PoF: Post-Training of Feature Extractor for Improving Generalization,1,icml,0,0,2023-06-17 04:55:31.463000,https://github.com/densoitlab/pof-v1,3,PoF: Post-Training of Feature Extractor for Improving Generalization,"https://scholar.google.com/scholar?cluster=1799078834754218861&hl=en&as_sdt=0,31",2,2022
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings,2,icml,1,0,2023-06-17 04:55:31.670000,https://github.com/meyerscetbon/lp-ci-test,0,An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings,"https://scholar.google.com/scholar?cluster=14026015450757796884&hl=en&as_sdt=0,33",3,2022
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs,19,icml,0,0,2023-06-17 04:55:31.877000,https://github.com/meyerscetbon/lineargromov,1,Linear-time gromov wasserstein distances using low rank couplings and costs,"https://scholar.google.com/scholar?cluster=883418138428344777&hl=en&as_sdt=0,14",2,2022
Modeling Irregular Time Series with Continuous Recurrent Units,15,icml,9,6,2023-06-17 04:55:32.085000,https://github.com/boschresearch/continuous-recurrent-units,32,Modeling irregular time series with continuous recurrent units,"https://scholar.google.com/scholar?cluster=7564792311041526490&hl=en&as_sdt=0,19",7,2022
Data-SUITE: Data-centric identification of in-distribution incongruous examples,3,icml,4,0,2023-06-17 04:55:32.291000,https://github.com/seedatnabeel/data-suite,7,Data-SUITE: Data-centric identification of in-distribution incongruous examples,"https://scholar.google.com/scholar?cluster=11485689307897239676&hl=en&as_sdt=0,33",2,2022
Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization,5,icml,0,0,2023-06-17 04:55:32.497000,https://github.com/mselezniova/ntk_beyond_limit,0,Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization,"https://scholar.google.com/scholar?cluster=16495366436833298314&hl=en&as_sdt=0,3",1,2022
Reinforcement Learning with Action-Free Pre-Training from Videos,34,icml,5,0,2023-06-17 04:55:32.703000,https://github.com/younggyoseo/apv,46,Reinforcement learning with action-free pre-training from videos,"https://scholar.google.com/scholar?cluster=6676654951334590185&hl=en&as_sdt=0,5",4,2022
Selective Regression under Fairness Criteria,3,icml,0,0,2023-06-17 04:55:32.909000,https://github.com/abhin02/fair-selective-regression,4,Selective regression under fairness criteria,"https://scholar.google.com/scholar?cluster=11829060385063117064&hl=en&as_sdt=0,33",1,2022
A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning,8,icml,1,0,2023-06-17 04:55:33.115000,https://github.com/architsharma97/medal,4,A state-distribution matching approach to non-episodic reinforcement learning,"https://scholar.google.com/scholar?cluster=14448955307324292158&hl=en&as_sdt=0,31",2,2022
Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold,4,icml,0,0,2023-06-17 04:55:33.322000,https://github.com/fietelab/mesh,1,Content addressable memory without catastrophic forgetting by heteroassociation with a fixed scaffold,"https://scholar.google.com/scholar?cluster=16874084475877050820&hl=en&as_sdt=0,5",4,2022
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning,2,icml,0,0,2023-06-17 04:55:33.528000,https://github.com/IntelLabs/DNS,1,DNS: Determinantal point process based neural network sampler for ensemble reinforcement learning,"https://scholar.google.com/scholar?cluster=16987143666282140914&hl=en&as_sdt=0,34",2,2022
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs,3,icml,0,1,2023-06-17 04:55:33.734000,https://github.com/shenzy08/PDO-s3DCNN,3,Pdo-s3dcnns: Partial differential operator based steerable 3d cnns,"https://scholar.google.com/scholar?cluster=7127988507569489900&hl=en&as_sdt=0,24",1,2022
Staged Training for Transformer Language Models,3,icml,1,1,2023-06-17 04:55:33.941000,https://github.com/allenai/staged-training,19,Staged training for transformer language models,"https://scholar.google.com/scholar?cluster=4204701598187830659&hl=en&as_sdt=0,5",5,2022
Adversarial Masking for Self-Supervised Learning,32,icml,5,2,2023-06-17 04:55:34.147000,https://github.com/yugeten/adios,50,Adversarial masking for self-supervised learning,"https://scholar.google.com/scholar?cluster=3881185449721325576&hl=en&as_sdt=0,5",3,2022
Visual Attention Emerges from Recurrent Sparse Reconstruction,4,icml,2,0,2023-06-17 04:55:34.353000,https://github.com/bfshi/vars,24,Visual attention emerges from recurrent sparse reconstruction,"https://scholar.google.com/scholar?cluster=626547526031635836&hl=en&as_sdt=0,44",1,2022
Robust Group Synchronization via Quadratic Programming,1,icml,0,1,2023-06-17 04:55:34.559000,https://github.com/colewyeth/desc,6,Robust Group Synchronization via Quadratic Programming,"https://scholar.google.com/scholar?cluster=14329242327668843280&hl=en&as_sdt=0,39",3,2022
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets,3,icml,1,0,2023-06-17 04:55:34.764000,https://github.com/shnitzer/les-distance,4,Log-euclidean signatures for intrinsic distances between unaligned datasets,"https://scholar.google.com/scholar?cluster=528448898197574004&hl=en&as_sdt=0,24",1,2022
Demystifying the Adversarial Robustness of Random Transformation Defenses,7,icml,0,0,2023-06-17 04:55:34.970000,https://github.com/wagner-group/demystify-random-transform,5,Demystifying the adversarial robustness of random transformation defenses,"https://scholar.google.com/scholar?cluster=6394427111079703523&hl=en&as_sdt=0,23",1,2022
Communicating via Markov Decision Processes,4,icml,0,3,2023-06-17 04:55:35.176000,https://github.com/schroederdewitt/meme,1,Communicating via Markov Decision Processes,"https://scholar.google.com/scholar?cluster=1909863582927997201&hl=en&as_sdt=0,5",3,2022
The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks,3,icml,1,0,2023-06-17 04:55:35.381000,https://github.com/ideaslabut/multivariate-community-hawkes,1,The multivariate community hawkes model for dependent relational events in continuous-time networks,"https://scholar.google.com/scholar?cluster=16117758994538292993&hl=en&as_sdt=0,33",3,2022
A General Recipe for Likelihood-free Bayesian Optimization,8,icml,2,0,2023-06-17 04:55:35.587000,https://github.com/lfbo-ml/lfbo,39,A general recipe for likelihood-free Bayesian optimization,"https://scholar.google.com/scholar?cluster=2199690906597156790&hl=en&as_sdt=0,37",3,2022
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation,15,icml,271,7,2023-06-17 04:55:35.793000,https://github.com/huawei-noah/hebo,1285,Sauté rl: Almost surely safe reinforcement learning using state augmentation,"https://scholar.google.com/scholar?cluster=12545517423097788852&hl=en&as_sdt=0,22",130,2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders,24,icml,15,1,2023-06-17 04:55:35.999000,https://github.com/samuelstanton/lambo,50,Accelerating bayesian optimization for biological sequence design with denoising autoencoders,"https://scholar.google.com/scholar?cluster=2506639909996415595&hl=en&as_sdt=0,33",2,2022
3D Infomax improves GNNs for Molecular Property Prediction,66,icml,29,4,2023-06-17 04:55:36.206000,https://github.com/hannesstark/3dinfomax,116,3d infomax improves gnns for molecular property prediction,"https://scholar.google.com/scholar?cluster=18195860750409632321&hl=en&as_sdt=0,5",3,2022
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction,83,icml,99,5,2023-06-17 04:55:36.413000,https://github.com/HannesStark/EquiBind,397,Equibind: Geometric deep learning for drug binding structure prediction,"https://scholar.google.com/scholar?cluster=2579310543705352041&hl=en&as_sdt=0,5",9,2022
Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks,6,icml,4,1,2023-06-17 04:55:36.620000,https://github.com/LukasStruppek/Plug-and-Play-Attacks,16,Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks,"https://scholar.google.com/scholar?cluster=10382805845190184141&hl=en&as_sdt=0,20",2,2022
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection,10,icml,32,9,2023-06-17 04:55:36.826000,https://github.com/alibaba/lightweight-neural-architecture-search,266,Mae-det: Revisiting maximum entropy principle in zero-shot nas for efficient object detection,"https://scholar.google.com/scholar?cluster=9429584722885379910&hl=en&as_sdt=0,33",10,2022
Out-of-Distribution Detection with Deep Nearest Neighbors,79,icml,14,1,2023-06-17 04:55:37.032000,https://github.com/deeplearning-wisc/knn-ood,118,Out-of-distribution detection with deep nearest neighbors,"https://scholar.google.com/scholar?cluster=8587930909818673494&hl=en&as_sdt=0,33",2,2022
Black-Box Tuning for Language-Model-as-a-Service,52,icml,28,4,2023-06-17 04:55:37.248000,https://github.com/txsun1997/black-box-tuning,223,Black-box tuning for language-model-as-a-service,"https://scholar.google.com/scholar?cluster=6566630989334663783&hl=en&as_sdt=0,22",7,2022
Causal Imitation Learning under Temporally Correlated Noise,13,icml,0,0,2023-06-17 04:55:37.461000,https://github.com/gkswamy98/causal_il,6,Causal imitation learning under temporally correlated noise,"https://scholar.google.com/scholar?cluster=3778588231646817630&hl=en&as_sdt=0,5",2,2022
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization,10,icml,14,1,2023-06-17 04:55:37.667000,https://github.com/sony/sqvae,132,SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization,"https://scholar.google.com/scholar?cluster=13353459274510421570&hl=en&as_sdt=0,10",6,2022
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources,18,icml,1,0,2023-06-17 04:55:37.872000,https://github.com/ellenxtan/ifedtree,8,A tree-based model averaging approach for personalized treatment effect estimation from heterogeneous data sources,"https://scholar.google.com/scholar?cluster=602189476639254582&hl=en&as_sdt=0,5",3,2022
Rethinking Graph Neural Networks for Anomaly Detection,24,icml,20,0,2023-06-17 04:55:38.077000,https://github.com/squareroot3/rethinking-anomaly-detection,118,Rethinking graph neural networks for anomaly detection,"https://scholar.google.com/scholar?cluster=15800828162221381866&hl=en&as_sdt=0,33",1,2022
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning,15,icml,9,5,2023-06-17 04:55:38.284000,https://github.com/wizard1203/vhl,29,Virtual homogeneity learning: Defending against data heterogeneity in federated learning,"https://scholar.google.com/scholar?cluster=5551753342557173221&hl=en&as_sdt=0,34",2,2022
"FedNest: Federated Bilevel, Minimax, and Compositional Optimization",23,icml,1,0,2023-06-17 04:55:38.491000,https://github.com/ucr-optml/FedNest,8,"FedNest: Federated bilevel, minimax, and compositional optimization","https://scholar.google.com/scholar?cluster=7138561365880400777&hl=en&as_sdt=0,24",2,2022
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood,8,icml,1,1,2023-06-17 04:55:38.697000,https://github.com/opium-sh/lidl,7,Lidl: Local intrinsic dimension estimation using approximate likelihood,"https://scholar.google.com/scholar?cluster=9636618006452252616&hl=en&as_sdt=0,11",3,2022
Quantifying and Learning Linear Symmetry-Based Disentanglement,7,icml,0,0,2023-06-17 04:55:38.902000,https://github.com/luis-armando-perez-rey/lsbd-vae,0,Quantifying and learning linear symmetry-based disentanglement,"https://scholar.google.com/scholar?cluster=11951723712936247797&hl=en&as_sdt=0,33",2,2022
A Temporal-Difference Approach to Policy Gradient Estimation,1,icml,0,0,2023-06-17 04:55:39.110000,https://github.com/samuelepolimi/temporal-difference-gradient,3,A Temporal-Difference Approach to Policy Gradient Estimation,"https://scholar.google.com/scholar?cluster=12213929390329707477&hl=en&as_sdt=0,40",2,2022
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization,5,icml,0,0,2023-06-17 04:55:39.317000,https://github.com/htt-trangtran/nasg,0,Nesterov accelerated shuffling gradient method for convex optimization,"https://scholar.google.com/scholar?cluster=14735125807077653853&hl=en&as_sdt=0,5",1,2022
Tackling covariate shift with node-based Bayesian neural networks,4,icml,0,0,2023-06-17 04:55:39.523000,https://github.com/aaltopml/node-bnn-covariate-shift,6,Tackling covariate shift with node-based Bayesian neural networks,"https://scholar.google.com/scholar?cluster=8088780476336589916&hl=en&as_sdt=0,33",7,2022
Prototype Based Classification from Hierarchy to Fairness,1,icml,0,0,2023-06-17 04:55:39.729000,https://github.com/mycal-tucker/csn,1,Prototype Based Classification from Hierarchy to Fairness,"https://scholar.google.com/scholar?cluster=11530419927101336822&hl=en&as_sdt=0,5",2,2022
Path-Gradient Estimators for Continuous Normalizing Flows,2,icml,2,0,2023-06-17 04:55:39.935000,https://github.com/lenz3000/ffjord-path,5,Path-gradient estimators for continuous normalizing flows,"https://scholar.google.com/scholar?cluster=102102598474391702&hl=en&as_sdt=0,33",0,2022
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning,10,icml,1,0,2023-06-17 04:55:40.140000,https://github.com/amitport/eden-distributed-mean-estimation,7,Eden: Communication-efficient and robust distributed mean estimation for federated learning,"https://scholar.google.com/scholar?cluster=3209500586717789200&hl=en&as_sdt=0,34",2,2022
Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds,6,icml,1,0,2023-06-17 04:55:40.346000,https://github.com/nveldt/fastcc-via-stc,0,Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds,"https://scholar.google.com/scholar?cluster=18023293593694212775&hl=en&as_sdt=0,23",1,2022
The CLRS Algorithmic Reasoning Benchmark,15,icml,48,4,2023-06-17 04:55:40.552000,https://github.com/deepmind/clrs,304,The CLRS algorithmic reasoning benchmark,"https://scholar.google.com/scholar?cluster=9181302241653376962&hl=en&as_sdt=0,5",13,2022
Bregman Power k-Means for Clustering Exponential Family Data,3,icml,1,0,2023-06-17 04:55:40.759000,https://github.com/avellal14/bregman_power_kmeans,3,Bregman power k-means for clustering exponential family data,"https://scholar.google.com/scholar?cluster=10416936130963333532&hl=en&as_sdt=0,33",2,2022
Calibrated Learning to Defer with One-vs-All Classifiers,8,icml,1,0,2023-06-17 04:55:40.965000,https://github.com/rajevv/ova-l2d,1,Calibrated learning to defer with one-vs-all classifiers,"https://scholar.google.com/scholar?cluster=8829480964232923072&hl=en&as_sdt=0,33",1,2022
Bayesian Nonparametrics for Offline Skill Discovery,2,icml,1,0,2023-06-17 04:55:41.171000,https://github.com/layer6ai-labs/bnpo,4,Bayesian nonparametrics for offline skill discovery,"https://scholar.google.com/scholar?cluster=5074347961003664860&hl=en&as_sdt=0,33",4,2022
Hermite Polynomial Features for Private Data Generation,5,icml,1,1,2023-06-17 04:55:41.376000,https://github.com/parklabml/dp-hp,3,Hermite polynomial features for private data generation,"https://scholar.google.com/scholar?cluster=16485118791106646859&hl=en&as_sdt=0,31",2,2022
Multirate Training of Neural Networks,3,icml,3,0,2023-06-17 04:55:41.583000,https://github.com/tiffanyvlaar/multiratetrainingofnns,3,Multirate training of neural networks,"https://scholar.google.com/scholar?cluster=14672109036130949413&hl=en&as_sdt=0,33",2,2022
Provably Adversarially Robust Nearest Prototype Classifiers,1,icml,0,0,2023-06-17 04:55:41.788000,https://github.com/vvoracek/provably-adversarially-robust-nearest-prototype-classifiers,4,Provably Adversarially Robust Nearest Prototype Classifiers,"https://scholar.google.com/scholar?cluster=12783036933914721155&hl=en&as_sdt=0,21",1,2022
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods,4,icml,0,0,2023-06-17 04:55:41.994000,https://github.com/chandar-lab/LoCA2,2,Towards evaluating adaptivity of model-based reinforcement learning methods,"https://scholar.google.com/scholar?cluster=8278156303366460605&hl=en&as_sdt=0,50",3,2022
Fast Lossless Neural Compression with Integer-Only Discrete Flows,3,icml,2,0,2023-06-17 04:55:42.200000,https://github.com/thu-ml/iodf,15,Fast Lossless Neural Compression with Integer-Only Discrete Flows,"https://scholar.google.com/scholar?cluster=9606476142959964204&hl=en&as_sdt=0,39",8,2022
Accelerating Shapley Explanation via Contributive Cooperator Selection,3,icml,1,0,2023-06-17 04:55:42.406000,https://github.com/guanchuwang/shear,10,Accelerating Shapley Explanation via Contributive Cooperator Selection,"https://scholar.google.com/scholar?cluster=2493376524235633954&hl=en&as_sdt=0,5",2,2022
Denoised MDPs: Learning World Models Better Than the World Itself,11,icml,8,0,2023-06-17 04:55:42.612000,https://github.com/facebookresearch/denoised_mdp,118,Denoised mdps: Learning world models better than the world itself,"https://scholar.google.com/scholar?cluster=4094945741122544681&hl=en&as_sdt=0,33",138,2022
Robust Models Are More Interpretable Because Attributions Look Normal,5,icml,1,1,2023-06-17 04:55:42.818000,https://github.com/zifanw/boundary,6,Robust models are more interpretable because attributions look normal,"https://scholar.google.com/scholar?cluster=14430069598728045155&hl=en&as_sdt=0,5",1,2022
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix,13,icml,0,1,2023-06-17 04:55:43.024000,https://github.com/ttengwang/vlmixer,14,Vlmixer: Unpaired vision-language pre-training via cross-modal cutmix,"https://scholar.google.com/scholar?cluster=6137962123845990063&hl=en&as_sdt=0,5",6,2022
DynaMixer: A Vision MLP Architecture with Dynamic Mixing,14,icml,1,1,2023-06-17 04:55:43.229000,https://github.com/ziyuwwang/dynamixer,19,Dynamixer: a vision MLP architecture with dynamic mixing,"https://scholar.google.com/scholar?cluster=9756910838903336255&hl=en&as_sdt=0,5",1,2022
Improving Screening Processes via Calibrated Subset Selection,7,icml,2,0,2023-06-17 04:55:43.445000,https://github.com/LequnWang/Improve-Screening-via-Calibrated-Subset-Selection,2,Improving screening processes via calibrated subset selection,"https://scholar.google.com/scholar?cluster=9485317495432772346&hl=en&as_sdt=0,19",1,2022
What Dense Graph Do You Need for Self-Attention?,1,icml,3,0,2023-06-17 04:55:43.651000,https://github.com/yxzwang/normalized-information-payload,7,What Dense Graph Do You Need for Self-Attention?,"https://scholar.google.com/scholar?cluster=6817431716045479667&hl=en&as_sdt=0,33",2,2022
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation,21,icml,0,0,2023-06-17 04:55:43.858000,https://github.com/wangwenxiao/FiniteAggregation,5,Improved certified defenses against data poisoning with (deterministic) finite aggregation,"https://scholar.google.com/scholar?cluster=13385935402210758494&hl=en&as_sdt=0,33",1,2022
"Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond",8,icml,0,0,2023-06-17 04:55:44.063000,https://github.com/Haoxiang-Wang/gradual-domain-adaptation,6,"Understanding gradual domain adaptation: Improved analysis, optimal path and beyond","https://scholar.google.com/scholar?cluster=8368642919883535588&hl=en&as_sdt=0,33",3,2022
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering,2,icml,0,0,2023-06-17 04:55:44.268000,https://github.com/peng8wang/icml2022-k-subspaces,1,Convergence and recovery guarantees of the k-subspaces method for subspace clustering,"https://scholar.google.com/scholar?cluster=4190201275040810423&hl=en&as_sdt=0,15",1,2022
NP-Match: When Neural Processes meet Semi-Supervised Learning,11,icml,20,0,2023-06-17 04:55:44.475000,https://github.com/jianf-wang/np-match,126,Np-match: When neural processes meet semi-supervised learning,"https://scholar.google.com/scholar?cluster=13863868059773263765&hl=en&as_sdt=0,5",14,2022
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution,11,icml,0,0,2023-06-17 04:55:44.680000,https://github.com/joey-wang123/DRO-Task-free,10,Improving task-free continual learning by distributionally robust memory evolution,"https://scholar.google.com/scholar?cluster=14894776006626228965&hl=en&as_sdt=0,47",1,2022
Provable Domain Generalization via Invariant-Feature Subspace Recovery,10,icml,3,0,2023-06-17 04:55:44.887000,https://github.com/haoxiang-wang/isr,15,Provable domain generalization via invariant-feature subspace recovery,"https://scholar.google.com/scholar?cluster=16846223791215545357&hl=en&as_sdt=0,46",3,2022
"ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training",15,icml,5,1,2023-06-17 04:55:45.093000,https://github.com/a514514772/progfed,14,"ProgFed: effective, communication, and computation efficient federated learning by progressive training","https://scholar.google.com/scholar?cluster=14093452975120098193&hl=en&as_sdt=0,5",2,2022
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics,25,icml,0,0,2023-06-17 04:55:45.299000,https://github.com/rose-stl-lab/approximately-equivariant-nets,7,Approximately equivariant networks for imperfectly symmetric dynamics,"https://scholar.google.com/scholar?cluster=5872423159806810171&hl=en&as_sdt=0,10",1,2022
Understanding Instance-Level Impact of Fairness Constraints,6,icml,0,1,2023-06-17 04:55:45.505000,https://github.com/ucsc-real/fairinfl,5,Understanding instance-level impact of fairness constraints,"https://scholar.google.com/scholar?cluster=3186856282017277340&hl=en&as_sdt=0,4",1,2022
Causal Dynamics Learning for Task-Independent State Abstraction,11,icml,4,2,2023-06-17 04:55:45.711000,https://github.com/wangzizhao/causaldynamicslearning,16,Causal dynamics learning for task-independent state abstraction,"https://scholar.google.com/scholar?cluster=7092132108841275612&hl=en&as_sdt=0,33",1,2022
Generative Coarse-Graining of Molecular Conformations,14,icml,5,0,2023-06-17 04:55:45.918000,https://github.com/wwang2/coarsegrainingvae,22,Generative coarse-graining of molecular conformations,"https://scholar.google.com/scholar?cluster=6589570772523921711&hl=en&as_sdt=0,44",4,2022
How Powerful are Spectral Graph Neural Networks,34,icml,9,0,2023-06-17 04:55:46.123000,https://github.com/graphpku/jacobiconv,56,How powerful are spectral graph neural networks,"https://scholar.google.com/scholar?cluster=17960766448265380456&hl=en&as_sdt=0,33",1,2022
Thompson Sampling for Robust Transfer in Multi-Task Bandits,1,icml,0,0,2023-06-17 04:55:46.329000,https://github.com/zhiwang123/eps-mpmab-ts,0,Thompson Sampling for Robust Transfer in Multi-Task Bandits,"https://scholar.google.com/scholar?cluster=9498764153726193190&hl=en&as_sdt=0,15",2,2022
Removing Batch Normalization Boosts Adversarial Training,12,icml,0,1,2023-06-17 04:55:46.534000,https://github.com/amazon-research/normalizer-free-robust-training,17,Removing batch normalization boosts adversarial training,"https://scholar.google.com/scholar?cluster=4233277386290159249&hl=en&as_sdt=0,39",4,2022
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition,11,icml,5,5,2023-06-17 04:55:46.740000,https://github.com/amazon-research/long-tailed-ood-detection,29,Partial and asymmetric contrastive learning for out-of-distribution detection in long-tailed recognition,"https://scholar.google.com/scholar?cluster=14212057730611759763&hl=en&as_sdt=0,33",6,2022
Certifying Out-of-Domain Generalization for Blackbox Functions,7,icml,0,0,2023-06-17 04:55:46.946000,https://github.com/ds3lab/certified-generalization,2,Certifying out-of-domain generalization for blackbox functions,"https://scholar.google.com/scholar?cluster=5540253257951212310&hl=en&as_sdt=0,5",6,2022
To Smooth or Not? When Label Smoothing Meets Noisy Labels,11,icml,9,2,2023-06-17 04:55:47.152000,https://github.com/ucsc-real/negative-label-smoothing,75,To smooth or not? when label smoothing meets noisy labels,"https://scholar.google.com/scholar?cluster=18297648993704774023&hl=en&as_sdt=0,5",10,2022
Mitigating Neural Network Overconfidence with Logit Normalization,45,icml,12,3,2023-06-17 04:55:47.359000,https://github.com/hongxin001/logitnorm_ood,113,Mitigating neural network overconfidence with logit normalization,"https://scholar.google.com/scholar?cluster=3765768230173383060&hl=en&as_sdt=0,19",1,2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification,27,icml,37,0,2023-06-17 04:55:47.565000,https://github.com/JonasGeiping/breaching,178,Fishing for user data in large-batch federated learning via gradient magnification,"https://scholar.google.com/scholar?cluster=11388041584211331417&hl=en&as_sdt=0,34",3,2022
Measure Estimation in the Barycentric Coding Model,2,icml,0,0,2023-06-17 04:55:47.771000,https://github.com/mattwerenski/bcm,2,Measure Estimation in the Barycentric Coding Model,"https://scholar.google.com/scholar?cluster=3529680784651732155&hl=en&as_sdt=0,3",2,2022
COLA: Consistent Learning with Opponent-Learning Awareness,19,icml,0,0,2023-06-17 04:55:47.977000,https://github.com/aidandos/cola,5,COLA: consistent learning with opponent-learning awareness,"https://scholar.google.com/scholar?cluster=14450342073245803366&hl=en&as_sdt=0,33",2,2022
Easy Variational Inference for Categorical Models via an Independent Binary Approximation,0,icml,0,0,2023-06-17 04:55:48.184000,https://github.com/tufts-ml/categorical-from-binary,2,Easy Variational Inference for Categorical Models via an Independent Binary Approximation,"https://scholar.google.com/scholar?cluster=13180457782658047792&hl=en&as_sdt=0,36",4,2022
Continual Learning with Guarantees via Weight Interval Constraints,1,icml,0,0,2023-06-17 04:55:48.390000,https://github.com/gmum/intercontinet,2,Continual Learning with Guarantees via Weight Interval Constraints,"https://scholar.google.com/scholar?cluster=12644818321484154250&hl=en&as_sdt=0,33",5,2022
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications,0,icml,0,0,2023-06-17 04:55:48.596000,https://github.com/lu-wo/detrtime,12,A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications,"https://scholar.google.com/scholar?cluster=561665774245262907&hl=en&as_sdt=0,10",2,2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time,221,icml,21,2,2023-06-17 04:55:48.820000,https://github.com/mlfoundations/model-soups,236,Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time,"https://scholar.google.com/scholar?cluster=16922194924900565989&hl=en&as_sdt=0,5",10,2022
Structural Entropy Guided Graph Hierarchical Pooling,11,icml,5,3,2023-06-17 04:55:49.030000,https://github.com/wu-junran/sep,20,Structural entropy guided graph hierarchical pooling,"https://scholar.google.com/scholar?cluster=15391796189805731538&hl=en&as_sdt=0,26",1,2022
Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks,15,icml,1,1,2023-06-17 04:55:49.236000,https://github.com/nyukat/greedy_multimodal_learning,19,Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks,"https://scholar.google.com/scholar?cluster=12235200636315362810&hl=en&as_sdt=0,44",2,2022
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum,4,icml,0,0,2023-06-17 04:55:49.457000,https://github.com/jlwu002/bcl,4,Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum,"https://scholar.google.com/scholar?cluster=6530213985097280080&hl=en&as_sdt=0,31",1,2022
Flowformer: Linearizing Transformers with Conservation Flows,13,icml,27,0,2023-06-17 04:55:49.663000,https://github.com/thuml/Flowformer,237,Flowformer: Linearizing transformers with conservation flows,"https://scholar.google.com/scholar?cluster=13534095276250575794&hl=en&as_sdt=0,5",8,2022
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning,29,icml,2,4,2023-06-17 04:55:49.872000,https://github.com/junxia97/progcl,32,Progcl: Rethinking hard negative mining in graph contrastive learning,"https://scholar.google.com/scholar?cluster=3134502444981244972&hl=en&as_sdt=0,33",1,2022
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations,18,icml,0,0,2023-06-17 04:55:50.079000,https://github.com/ryanxhr/dwbc,25,Discriminator-weighted offline imitation learning from suboptimal demonstrations,"https://scholar.google.com/scholar?cluster=12184701455253705252&hl=en&as_sdt=0,21",1,2022
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests,1,icml,0,0,2023-06-17 04:55:50.285000,https://github.com/godxuxilie/robust-tst,3,Adversarial Attack and Defense for Non-Parametric Two-Sample Tests,"https://scholar.google.com/scholar?cluster=16006347209208499674&hl=en&as_sdt=0,5",2,2022
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization,4,icml,0,0,2023-06-17 04:55:50.492000,https://github.com/windxrz/independence-driven-iw,9,A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization,"https://scholar.google.com/scholar?cluster=14134137266916397351&hl=en&as_sdt=0,5",1,2022
Langevin Monte Carlo for Contextual Bandits,6,icml,3,0,2023-06-17 04:55:50.699000,https://github.com/devzhk/lmcts,8,Langevin monte carlo for contextual bandits,"https://scholar.google.com/scholar?cluster=17947059462373456392&hl=en&as_sdt=0,5",1,2022
Diversified Adversarial Attacks based on Conjugate Gradient Method,6,icml,2,0,2023-06-17 04:55:50.906000,https://github.com/yamamura-k/ACG,5,Diversified Adversarial Attacks based on Conjugate Gradient Method,"https://scholar.google.com/scholar?cluster=13855220363786968422&hl=en&as_sdt=0,33",2,2022
Cycle Representation Learning for Inductive Relation Prediction,4,icml,2,2,2023-06-17 04:55:51.112000,https://github.com/pkuyzy/cbgnn,4,Cycle Representation Learning for Inductive Relation Prediction,"https://scholar.google.com/scholar?cluster=2061126116449549118&hl=en&as_sdt=0,41",1,2022
Optimally Controllable Perceptual Lossy Compression,2,icml,2,1,2023-06-17 04:55:51.318000,https://github.com/zeyuyan/controllable-perceptual-compression,9,Optimally Controllable Perceptual Lossy Compression,"https://scholar.google.com/scholar?cluster=15214339197144115082&hl=en&as_sdt=0,32",3,2022
Self-Organized Polynomial-Time Coordination Graphs,3,icml,0,0,2023-06-17 04:55:51.524000,https://github.com/yanQval/SOP-CG,3,Self-organized polynomial-time coordination graphs,"https://scholar.google.com/scholar?cluster=10295867697115976866&hl=en&as_sdt=0,19",1,2022
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning,4,icml,0,0,2023-06-17 04:55:51.730000,https://github.com/shentao-yang/sdm-gan_icml2022,2,Regularizing a model-based policy stationary distribution to stabilize offline reinforcement learning,"https://scholar.google.com/scholar?cluster=1188226225988660555&hl=en&as_sdt=0,33",1,2022
Does the Data Induce Capacity Control in Deep Learning?,13,icml,0,0,2023-06-17 04:55:51.935000,https://github.com/grasp-lyrl/sloppy,1,Does the data induce capacity control in deep learning?,"https://scholar.google.com/scholar?cluster=884919534291840762&hl=en&as_sdt=0,36",0,2022
A New Perspective on the Effects of Spectrum in Graph Neural Networks,5,icml,6,0,2023-06-17 04:55:52.141000,https://github.com/qslim/gnn-spectrum,16,A new perspective on the effects of spectrum in graph neural networks,"https://scholar.google.com/scholar?cluster=12355104145181167707&hl=en&as_sdt=0,5",1,2022
A Study of Face Obfuscation in ImageNet,90,icml,12,1,2023-06-17 04:55:52.349000,https://github.com/princetonvisualai/imagenet-face-obfuscation,40,A study of face obfuscation in imagenet,"https://scholar.google.com/scholar?cluster=18170664845630332563&hl=en&as_sdt=0,33",7,2022
Improving Out-of-Distribution Robustness via Selective Augmentation,49,icml,5,2,2023-06-17 04:55:52.555000,https://github.com/huaxiuyao/LISA,35,Improving out-of-distribution robustness via selective augmentation,"https://scholar.google.com/scholar?cluster=4894079975600009568&hl=en&as_sdt=0,31",1,2022
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework,22,icml,21,8,2023-06-17 04:55:52.761000,https://github.com/yaoxingcheng/TLM,240,Nlp from scratch without large-scale pretraining: A simple and efficient framework,"https://scholar.google.com/scholar?cluster=3254978626719045112&hl=en&as_sdt=0,5",5,2022
Feature Space Particle Inference for Neural Network Ensembles,4,icml,0,0,2023-06-17 04:55:52.966000,https://github.com/densoitlab/featurepi,4,Feature space particle inference for neural network ensembles,"https://scholar.google.com/scholar?cluster=11870961066098934714&hl=en&as_sdt=0,44",3,2022
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks,5,icml,1,1,2023-06-17 04:55:53.172000,https://github.com/rice-eic/shiftaddnas,11,ShiftAddNAS: Hardware-inspired search for more accurate and efficient neural networks,"https://scholar.google.com/scholar?cluster=17026416337828414455&hl=en&as_sdt=0,33",2,2022
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks,7,icml,10,0,2023-06-17 04:55:53.378000,https://github.com/zhaoningyu1996/hm-gnn,23,Molecular representation learning via heterogeneous motif graph neural networks,"https://scholar.google.com/scholar?cluster=16142260161361576450&hl=en&as_sdt=0,33",2,2022
Understanding Robust Overfitting of Adversarial Training and Beyond,13,icml,0,1,2023-06-17 04:55:53.584000,https://github.com/chaojianyu/understanding-robust-overfitting,10,Understanding robust overfitting of adversarial training and beyond,"https://scholar.google.com/scholar?cluster=4696544864566467358&hl=en&as_sdt=0,6",1,2022
Reachability Constrained Reinforcement Learning,10,icml,2,0,2023-06-17 04:55:53.791000,https://github.com/mahaitongdae/Reachability_Constrained_RL,13,Reachability constrained reinforcement learning,"https://scholar.google.com/scholar?cluster=2404570936990332675&hl=en&as_sdt=0,31",3,2022
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning,12,icml,10,1,2023-06-17 04:55:53.996000,https://github.com/yusx-swapp/gnn-rl-model-compression,36,Topology-aware network pruning using multi-stage graph embedding and reinforcement learning,"https://scholar.google.com/scholar?cluster=9807843131373835884&hl=en&as_sdt=0,47",2,2022
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks,14,icml,1,0,2023-06-17 04:55:54.202000,https://github.com/yuxwind/cbs,8,The combinatorial brain surgeon: Pruning weights that cancel one another in neural networks,"https://scholar.google.com/scholar?cluster=2256443788852509146&hl=en&as_sdt=0,11",1,2022
GraphFM: Improving Large-Scale GNN Training via Feature Momentum,8,icml,239,19,2023-06-17 04:55:54.407000,https://github.com/divelab/DIG,1503,GraphFM: Improving large-scale GNN training via feature momentum,"https://scholar.google.com/scholar?cluster=14093235266162728639&hl=en&as_sdt=0,33",33,2022
Predicting Out-of-Distribution Error with the Projection Norm,9,icml,0,0,2023-06-17 04:55:54.613000,https://github.com/yaodongyu/projnorm,13,Predicting out-of-distribution error with the projection norm,"https://scholar.google.com/scholar?cluster=14580458746203726066&hl=en&as_sdt=0,14",2,2022
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning,5,icml,3,3,2023-06-17 04:55:54.819000,https://github.com/pku-ai-edge/corro,15,Robust task representations for offline meta-reinforcement learning via contrastive learning,"https://scholar.google.com/scholar?cluster=5539110127380539643&hl=en&as_sdt=0,34",0,2022
Time Is MattEr: Temporal Self-supervision for Video Transformers,3,icml,4,1,2023-06-17 04:55:55.024000,https://github.com/alinlab/temporal-selfsupervision,26,Time is matter: Temporal self-supervision for video transformers,"https://scholar.google.com/scholar?cluster=10001737047837090145&hl=en&as_sdt=0,33",2,2022
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images,5,icml,0,3,2023-06-17 04:55:55.231000,https://github.com/shiranzada/pure-noise,9,Pure noise to the rescue of insufficient data: Improving imbalanced classification by training on random noise images,"https://scholar.google.com/scholar?cluster=13535908408356605995&hl=en&as_sdt=0,5",2,2022
Adaptive Conformal Predictions for Time Series,28,icml,10,1,2023-06-17 04:55:55.437000,https://github.com/mzaffran/adaptiveconformalpredictionstimeseries,30,Adaptive conformal predictions for time series,"https://scholar.google.com/scholar?cluster=6242332424381793143&hl=en&as_sdt=0,33",1,2022
Multi Resolution Analysis (MRA) for Approximate Self-Attention,2,icml,2,0,2023-06-17 04:55:55.643000,https://github.com/mlpen/mra-attention,6,Multi Resolution Analysis (MRA) for Approximate Self-Attention,"https://scholar.google.com/scholar?cluster=184055539633336213&hl=en&as_sdt=0,44",1,2022
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts,91,icml,49,15,2023-06-17 04:55:55.850000,https://github.com/zengyan-97/x-vlm,365,Multi-grained vision language pre-training: Aligning texts with visual concepts,"https://scholar.google.com/scholar?cluster=8119995839638175849&hl=en&as_sdt=0,5",5,2022
PDE-Based Optimal Strategy for Unconstrained Online Learning,5,icml,0,0,2023-06-17 04:55:56.056000,https://github.com/zhiyuzz/icml2022-pde-potential,0,PDE-based optimal strategy for unconstrained online learning,"https://scholar.google.com/scholar?cluster=2664380085986514830&hl=en&as_sdt=0,44",1,2022
Revisiting End-to-End Speech-to-Text Translation From Scratch,11,icml,21,0,2023-06-17 04:55:56.269000,https://github.com/bzhangGo/zero,135,Revisiting end-to-end speech-to-text translation from scratch,"https://scholar.google.com/scholar?cluster=1521111115547925534&hl=en&as_sdt=0,34",6,2022
GALAXY: Graph-based Active Learning at the Extreme,5,icml,0,0,2023-06-17 04:55:56.476000,https://github.com/jifanz/GALAXY,6,GALAXY: graph-based active learning at the extreme,"https://scholar.google.com/scholar?cluster=10022632741658948627&hl=en&as_sdt=0,33",1,2022
A Langevin-like Sampler for Discrete Distributions,9,icml,3,0,2023-06-17 04:55:56.682000,https://github.com/ruqizhang/discrete-langevin,18,A Langevin-like sampler for discrete distributions,"https://scholar.google.com/scholar?cluster=3541239242626478838&hl=en&as_sdt=0,33",3,2022
Rich Feature Construction for the Optimization-Generalization Dilemma,13,icml,1,1,2023-06-17 04:55:56.889000,https://github.com/tjujianyu/rfc,8,Rich feature construction for the optimization-generalization dilemma,"https://scholar.google.com/scholar?cluster=4651591858912243934&hl=en&as_sdt=0,33",2,2022
Generative Flow Networks for Discrete Probabilistic Modeling,21,icml,16,0,2023-06-17 04:55:57.094000,https://github.com/zdhnarsil/eb_gfn,62,Generative flow networks for discrete probabilistic modeling,"https://scholar.google.com/scholar?cluster=5719959167998853445&hl=en&as_sdt=0,43",2,2022
Neurotoxin: Durable Backdoors in Federated Learning,19,icml,3,5,2023-06-17 04:55:57.300000,https://github.com/jhcknzzm/federated-learning-backdoor,39,Neurotoxin: Durable backdoors in federated learning,"https://scholar.google.com/scholar?cluster=15130248935781363426&hl=en&as_sdt=0,5",3,2022
Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations,40,icml,4,0,2023-06-17 04:55:57.506000,https://github.com/HazyResearch/correct-n-contrast,14,Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations,"https://scholar.google.com/scholar?cluster=8960959356014477531&hl=en&as_sdt=0,33",19,2022
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach,25,icml,1,0,2023-06-17 04:55:57.712000,https://github.com/yudasong/briee,11,Efficient reinforcement learning in block mdps: A model-free representation learning approach,"https://scholar.google.com/scholar?cluster=10850889224658556483&hl=en&as_sdt=0,33",2,2022
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets,0,icml,2,0,2023-06-17 04:55:57.918000,https://github.com/rajesh-lab/deep_permutation_invariant,10,Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets,"https://scholar.google.com/scholar?cluster=8359318767015654610&hl=en&as_sdt=0,31",2,2022
Learning to Estimate and Refine Fluid Motion with Physical Dynamics,6,icml,4,1,2023-06-17 04:55:58.125000,https://github.com/erizmr/learn-to-estimate-fluid-motion,11,Learning to estimate and refine fluid motion with physical dynamics,"https://scholar.google.com/scholar?cluster=7117659598027113757&hl=en&as_sdt=0,31",2,2022
Low-Precision Stochastic Gradient Langevin Dynamics,2,icml,1,0,2023-06-17 04:55:58.332000,https://github.com/ruqizhang/low-precision-sgld,5,Low-Precision Stochastic Gradient Langevin Dynamics,"https://scholar.google.com/scholar?cluster=5250731865302553140&hl=en&as_sdt=0,34",2,2022
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control,10,icml,3,0,2023-06-17 04:55:58.545000,https://github.com/LiangZhang1996/Advanced_XLight,14,Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control,"https://scholar.google.com/scholar?cluster=995321608406249380&hl=en&as_sdt=0,33",1,2022
Building Robust Ensembles via Margin Boosting,8,icml,0,1,2023-06-17 04:55:58.751000,https://github.com/zdhnarsil/margin-boosting,7,Building robust ensembles via margin boosting,"https://scholar.google.com/scholar?cluster=13608655782211931186&hl=en&as_sdt=0,47",2,2022
ROCK: Causal Inference Principles for Reasoning about Commonsense Causality,2,icml,1,1,2023-06-17 04:55:58.958000,https://github.com/zjiayao/ccr_rock,7,ROCK: Causal Inference Principles for Reasoning about Commonsense Causality,"https://scholar.google.com/scholar?cluster=4757630172142505662&hl=en&as_sdt=0,41",1,2022
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance,14,icml,4,1,2023-06-17 04:55:59.168000,https://github.com/qingruzhang/platon,21,Platon: Pruning large transformer models with upper confidence bound of weight importance,"https://scholar.google.com/scholar?cluster=17654209064614422018&hl=en&as_sdt=0,34",2,2022
Learning from Counterfactual Links for Link Prediction,31,icml,6,1,2023-06-17 04:55:59.378000,https://github.com/DM2-ND/CFLP,49,Learning from counterfactual links for link prediction,"https://scholar.google.com/scholar?cluster=12649708640262432051&hl=en&as_sdt=0,33",2,2022
Certified Robustness Against Natural Language Attacks by Causal Intervention,4,icml,3,0,2023-06-17 04:55:59.591000,https://github.com/zhao-ht/convexcertify,7,Certified Robustness Against Natural Language Attacks by Causal Intervention,"https://scholar.google.com/scholar?cluster=16167491038280669708&hl=en&as_sdt=0,10",1,2022
Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning,26,icml,1,0,2023-06-17 04:55:59.798000,https://github.com/zhaoyang-0204/gnp,21,Penalizing gradient norm for efficiently improving generalization in deep learning,"https://scholar.google.com/scholar?cluster=9350049289748522587&hl=en&as_sdt=0,3",1,2022
Online Decision Transformer,59,icml,18,0,2023-06-17 04:56:00.005000,https://github.com/facebookresearch/online-dt,127,Online decision transformer,"https://scholar.google.com/scholar?cluster=11549184825048973545&hl=en&as_sdt=0,34",4,2022
Describing Differences between Text Distributions with Natural Language,6,icml,3,0,2023-06-17 04:56:00.212000,https://github.com/ruiqi-zhong/describedistributionaldifferences,32,Describing differences between text distributions with natural language,"https://scholar.google.com/scholar?cluster=12276789524717856994&hl=en&as_sdt=0,36",3,2022
Model Agnostic Sample Reweighting for Out-of-Distribution Learning,15,icml,2,0,2023-06-17 04:56:00.430000,https://github.com/x-zho14/maple,30,Model agnostic sample reweighting for out-of-distribution learning,"https://scholar.google.com/scholar?cluster=4328634809674273852&hl=en&as_sdt=0,48",3,2022
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting,151,icml,78,1,2023-06-17 04:56:00.639000,https://github.com/MAZiqing/FEDformer,371,Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting,"https://scholar.google.com/scholar?cluster=447506194635826863&hl=en&as_sdt=0,36",4,2022
Improving Adversarial Robustness via Mutual Information Estimation,1,icml,0,0,2023-06-17 04:56:00.845000,https://github.com/dwdavidxd/miat,0,Improving Adversarial Robustness via Mutual Information Estimation,"https://scholar.google.com/scholar?cluster=18303472399739418322&hl=en&as_sdt=0,3",1,2022
Modeling Adversarial Noise for Adversarial Training,0,icml,0,0,2023-06-17 04:56:01.052000,https://github.com/dwdavidxd/man,2,Modeling Adversarial Noise for Adversarial Training,"https://scholar.google.com/scholar?cluster=6688229047921425158&hl=en&as_sdt=0,33",1,2022
Contrastive Learning with Boosted Memorization,5,icml,7,0,2023-06-17 04:56:01.270000,https://github.com/MediaBrain-SJTU/BCL,106,Contrastive learning with boosted memorization,"https://scholar.google.com/scholar?cluster=1426610895759607761&hl=en&as_sdt=0,33",4,2022
Understanding The Robustness in Vision Transformers,66,icml,24,11,2023-06-17 04:56:01.481000,https://github.com/nvlabs/fan,421,Understanding the robustness in vision transformers,"https://scholar.google.com/scholar?cluster=3041067607452518927&hl=en&as_sdt=0,5",22,2022
Contextual Bandits with Large Action Spaces: Made Practical,8,icml,0,0,2023-06-17 04:56:01.687000,https://github.com/pmineiro/linrepcb,1,Contextual bandits with large action spaces: Made practical,"https://scholar.google.com/scholar?cluster=5763648014002570810&hl=en&as_sdt=0,44",0,2022
Neural-Symbolic Models for Logical Queries on Knowledge Graphs,21,icml,5,4,2023-06-17 04:56:01.894000,https://github.com/DeepGraphLearning/GNN-QE,70,Neural-symbolic models for logical queries on knowledge graphs,"https://scholar.google.com/scholar?cluster=2755509975751664011&hl=en&as_sdt=0,5",4,2022
Topology-aware Generalization of Decentralized SGD,8,icml,2,0,2023-06-17 04:56:02.100000,https://github.com/raiden-zhu/generalization-of-dsgd,24,Topology-aware generalization of decentralized sgd,"https://scholar.google.com/scholar?cluster=17709285400263398599&hl=en&as_sdt=0,10",3,2022
On Numerical Integration in Neural Ordinary Differential Equations,7,icml,0,0,2023-06-17 04:56:02.306000,https://github.com/aiqing-zhu/imde,2,On numerical integration in neural ordinary differential equations,"https://scholar.google.com/scholar?cluster=1480049561976484832&hl=en&as_sdt=0,47",1,2022
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces,5,icml,0,0,2023-06-17 04:56:02.513000,https://github.com/pmineiro/smoothcb,2,Contextual bandits with smooth regret: Efficient learning in continuous action spaces,"https://scholar.google.com/scholar?cluster=2237234303144765537&hl=en&as_sdt=0,39",0,2022
Region-Based Semantic Factorization in GANs,14,icml,3,6,2023-06-17 04:56:02.718000,https://github.com/zhujiapeng/resefa,67,Region-based semantic factorization in GANs,"https://scholar.google.com/scholar?cluster=15967827822215112166&hl=en&as_sdt=0,15",5,2022
Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm,4,icml,1,0,2023-06-17 04:56:02.925000,https://github.com/pizilber/IMC,1,Inductive matrix completion: No bad local minima and a fast algorithm,"https://scholar.google.com/scholar?cluster=1576217126267485656&hl=en&as_sdt=0,3",1,2022
Synthetic and Natural Noise Both Break Neural Machine Translation,633,iclr,8,2,2023-06-18 08:50:41.202000,https://github.com/ybisk/charNMT-noise,28,Synthetic and natural noise both break neural machine translation,"https://scholar.google.com/scholar?cluster=10493132199224079445&hl=en&as_sdt=0,5",4,2018
Training and Inference with Integers in Deep Neural Networks,413,iclr,38,4,2023-06-18 08:50:41.409000,https://github.com/boluoweifenda/WAGE,143,Training and inference with integers in deep neural networks,"https://scholar.google.com/scholar?cluster=15215054387477750278&hl=en&as_sdt=0,44",10,2018
Spherical CNNs,888,iclr,170,17,2023-06-18 08:50:41.611000,https://github.com/jonas-koehler/s2cnn,908,Spherical cnns,"https://scholar.google.com/scholar?cluster=6361332838540502667&hl=en&as_sdt=0,36",28,2018
On the insufficiency of existing momentum schemes for Stochastic Optimization,103,iclr,27,1,2023-06-18 08:50:41.814000,https://github.com/rahulkidambi/AccSGD,207,On the insufficiency of existing momentum schemes for stochastic optimization,"https://scholar.google.com/scholar?cluster=6907311906014063619&hl=en&as_sdt=0,3",5,2018
Wasserstein Auto-Encoders,1044,iclr,92,7,2023-06-18 08:50:42.021000,https://github.com/tolstikhin/wae,490,Wasserstein auto-encoders,"https://scholar.google.com/scholar?cluster=1669877132293977025&hl=en&as_sdt=0,5",21,2018
Spectral Normalization for Generative Adversarial Networks,4106,iclr,200,26,2023-06-18 08:50:42.223000,https://github.com/pfnet-research/sngan_projection,1045,Spectral normalization for generative adversarial networks,"https://scholar.google.com/scholar?cluster=973410365172845184&hl=en&as_sdt=0,5",34,2018
Learning to Represent Programs with Graphs,764,iclr,37,4,2023-06-18 08:50:42.514000,https://github.com/Microsoft/graph-based-code-modelling,157,Learning to represent programs with graphs,"https://scholar.google.com/scholar?cluster=9342740598325165289&hl=en&as_sdt=0,5",13,2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality,633,iclr,38,0,2023-06-18 08:50:42.773000,https://github.com/xingjunm/lid_adversarial_subspace_detection,112,Characterizing adversarial subspaces using local intrinsic dimensionality,"https://scholar.google.com/scholar?cluster=17134144151462669065&hl=en&as_sdt=0,23",4,2018
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model,349,iclr,84,6,2023-06-18 08:50:42.979000,https://github.com/zihangdai/mos,391,Breaking the softmax bottleneck: A high-rank RNN language model,"https://scholar.google.com/scholar?cluster=15538946355362697879&hl=en&as_sdt=0,23",14,2018
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments,348,iclr,73,5,2023-06-18 08:50:43.181000,https://github.com/openai/robosumo,283,Continuous adaptation via meta-learning in nonstationary and competitive environments,"https://scholar.google.com/scholar?cluster=10800934967753473866&hl=en&as_sdt=0,5",20,2018
Neural Sketch Learning for Conditional Program Generation,137,iclr,83,33,2023-06-18 08:50:43.381000,https://github.com/capergroup/bayou,276,Neural sketch learning for conditional program generation,"https://scholar.google.com/scholar?cluster=11134234129920472875&hl=en&as_sdt=0,5",43,2018
"Progressive Growing of GANs for Improved Quality, Stability, and Variation",6379,iclr,1102,11,2023-06-18 08:50:43.589000,https://github.com/tkarras/progressive_growing_of_gans,5932,"Progressive growing of gans for improved quality, stability, and variation","https://scholar.google.com/scholar?cluster=11486098150916361186&hl=en&as_sdt=0,5",273,2018
Zero-Shot Visual Imitation,264,iclr,43,6,2023-06-18 08:50:43.790000,https://github.com/pathak22/zeroshot-imitation,201,Zero-shot visual imitation,"https://scholar.google.com/scholar?cluster=15276541363750863723&hl=en&as_sdt=0,5",15,2018
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs,205,iclr,11,0,2023-06-18 08:50:43.991000,https://github.com/jamie-murdoch/ContextualDecomposition,56,Beyond word importance: Contextual decomposition to extract interactions from lstms,"https://scholar.google.com/scholar?cluster=9223539489272553209&hl=en&as_sdt=0,5",10,2018
Model-Ensemble Trust-Region Policy Optimization,422,iclr,26,1,2023-06-18 08:50:44.192000,https://github.com/thanard/me-trpo,85,Model-ensemble trust-region policy optimization,"https://scholar.google.com/scholar?cluster=5763230631763342838&hl=en&as_sdt=0,22",4,2018
Learning Latent Permutations with Gumbel-Sinkhorn Networks,199,iclr,21,0,2023-06-18 08:50:44.394000,https://github.com/google/gumbel_sinkhorn,69,Learning latent permutations with gumbel-sinkhorn networks,"https://scholar.google.com/scholar?cluster=17995429437153045101&hl=en&as_sdt=0,29",4,2018
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration,88,iclr,1,1,2023-06-18 08:50:44.596000,https://github.com/flowersteam/Unsupervised_Goal_Space_Learning,19,Unsupervised learning of goal spaces for intrinsically motivated goal exploration,"https://scholar.google.com/scholar?cluster=17844977813077230695&hl=en&as_sdt=0,5",16,2018
Multi-View Data Generation Without View Supervision,22,iclr,1,0,2023-06-18 08:50:44.799000,https://github.com/mickaelChen/GMV,12,Multi-view data generation without view supervision,"https://scholar.google.com/scholar?cluster=15286827840377806140&hl=en&as_sdt=0,5",3,2018
Hyperparameter optimization: a spectral approach,131,iclr,32,2,2023-06-18 08:50:45.001000,https://github.com/callowbird/Harmonica,173,Hyperparameter optimization: A spectral approach,"https://scholar.google.com/scholar?cluster=11236398750787903780&hl=en&as_sdt=0,3",8,2018
Efficient Sparse-Winograd Convolutional Neural Networks,140,iclr,50,1,2023-06-18 08:50:45.203000,https://github.com/xingyul/Sparse-Winograd-CNN,179,Efficient sparse-winograd convolutional neural networks,"https://scholar.google.com/scholar?cluster=5437414522331578688&hl=en&as_sdt=0,33",13,2018
Polar Transformer Networks,174,iclr,19,3,2023-06-18 08:50:45.407000,https://github.com/daniilidis-group/polar-transformer-networks,54,Polar transformer networks,"https://scholar.google.com/scholar?cluster=15618354521274654533&hl=en&as_sdt=0,5",7,2018
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks,1398,iclr,101,15,2023-06-18 08:50:45.613000,https://github.com/facebookresearch/odin,485,Enhancing the reliability of out-of-distribution image detection in neural networks,"https://scholar.google.com/scholar?cluster=7536099354022278878&hl=en&as_sdt=0,7",14,2018
Stabilizing Adversarial Nets with Prediction Methods,106,iclr,0,1,2023-06-18 08:50:45.821000,https://github.com/jaiabhayk/stableGAN,1,Stabilizing adversarial nets with prediction methods,"https://scholar.google.com/scholar?cluster=1304972437215881711&hl=en&as_sdt=0,5",3,2018
Graph Attention Networks,6447,iclr,618,31,2023-06-18 08:50:46.032000,https://github.com/PetarV-/GAT,2775,Graph attention networks,"https://scholar.google.com/scholar?cluster=5609128480281463225&hl=en&as_sdt=0,5",47,2018
Generalizing Hamiltonian Monte Carlo with Neural Networks,127,iclr,42,2,2023-06-18 08:50:46.235000,https://github.com/brain-research/l2hmc,179,Generalizing hamiltonian monte carlo with neural networks,"https://scholar.google.com/scholar?cluster=6189563132756829558&hl=en&as_sdt=0,10",20,2018
Divide and Conquer Networks,16,iclr,8,1,2023-06-18 08:50:46.436000,https://github.com/alexnowakvila/DiCoNet,11,Divide and conquer networks,"https://scholar.google.com/scholar?cluster=13506472853038229205&hl=en&as_sdt=0,5",3,2018
Meta Learning Shared Hierarchies,361,iclr,164,16,2023-06-18 08:50:46.644000,https://github.com/openai/mlsh,588,Meta learning shared hierarchies,"https://scholar.google.com/scholar?cluster=8366113293045727240&hl=en&as_sdt=0,5",44,2018
Deep Neural Networks as Gaussian Processes,914,iclr,52,2,2023-06-18 08:50:46.845000,https://github.com/brain-research/nngp,178,Deep neural networks as gaussian processes,"https://scholar.google.com/scholar?cluster=6709509064500094656&hl=en&as_sdt=0,18",12,2018
Syntax-Directed Variational Autoencoder for Structured Data,315,iclr,19,2,2023-06-18 08:50:47.049000,https://github.com/Hanjun-Dai/sdvae,75,Syntax-directed variational autoencoder for structured data,"https://scholar.google.com/scholar?cluster=7991796845235005593&hl=en&as_sdt=0,14",9,2018
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering,181,iclr,19,2,2023-06-18 08:50:47.252000,https://github.com/shuohangwang/mprc,83,Evidence aggregation for answer re-ranking in open-domain question answering,"https://scholar.google.com/scholar?cluster=5917321946590508860&hl=en&as_sdt=0,5",14,2018
MGAN: Training Generative Adversarial Nets with Multiple Generators,221,iclr,19,2,2023-06-18 08:50:47.453000,https://github.com/qhoangdl/MGAN,38,MGAN: Training generative adversarial nets with multiple generators,"https://scholar.google.com/scholar?cluster=15083973924521420990&hl=en&as_sdt=0,47",8,2018
SEARNN: Training RNNs with global-local losses,42,iclr,9,1,2023-06-18 08:50:47.656000,https://github.com/RemiLeblond/SeaRNN-open,50,SEARNN: Training RNNs with global-local losses,"https://scholar.google.com/scholar?cluster=10552754146488713829&hl=en&as_sdt=0,22",6,2018
Unsupervised Representation Learning by Predicting Image Rotations,2632,iclr,123,14,2023-06-18 08:50:47.859000,https://github.com/gidariss/FeatureLearningRotNet,490,Unsupervised representation learning by predicting image rotations,"https://scholar.google.com/scholar?cluster=12748509220929577948&hl=en&as_sdt=0,44",14,2018
"Emergent Communication in a Multi-Modal, Multi-Step Referential Game",113,iclr,21,1,2023-06-18 08:50:48.059000,https://github.com/nyu-dl/MultimodalGame,55,"Emergent communication in a multi-modal, multi-step referential game","https://scholar.google.com/scholar?cluster=6581857213563474520&hl=en&as_sdt=0,19",9,2018
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling,1329,iclr,110,25,2023-06-18 08:50:48.261000,https://github.com/matenure/FastGCN,504,Fastgcn: fast learning with graph convolutional networks via importance sampling,"https://scholar.google.com/scholar?cluster=18054036108684442257&hl=en&as_sdt=0,14",12,2018
Demystifying MMD GANs,873,iclr,14,0,2023-06-18 08:50:48.462000,https://github.com/mbinkowski/MMD-GAN,79,Demystifying mmd gans,"https://scholar.google.com/scholar?cluster=10236052458128513824&hl=en&as_sdt=0,5",5,2018
Smooth Loss Functions for Deep Top-k Classification,97,iclr,31,1,2023-06-18 08:50:48.663000,https://github.com/oval-group/smooth-topk,237,Smooth loss functions for deep top-k classification,"https://scholar.google.com/scholar?cluster=2261810241418874442&hl=en&as_sdt=0,3",14,2018
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem,25,iclr,6,0,2023-06-18 08:50:48.865000,https://github.com/afriesen/ftprop,27,Deep learning as a mixed convex-combinatorial optimization problem,"https://scholar.google.com/scholar?cluster=14079107033151501838&hl=en&as_sdt=0,5",3,2018
Model compression via distillation and quantization,627,iclr,77,1,2023-06-18 08:50:49.066000,https://github.com/antspy/quantized_distillation,317,Model compression via distillation and quantization,"https://scholar.google.com/scholar?cluster=9862176539747361028&hl=en&as_sdt=0,5",10,2018
Variational Message Passing with Structured Inference Networks,44,iclr,16,3,2023-06-18 08:50:49.267000,https://github.com/emtiyaz/vmp-for-svae,41,Variational message passing with structured inference networks,"https://scholar.google.com/scholar?cluster=4788714492758509312&hl=en&as_sdt=0,5",6,2018
Learning from Between-class Examples for Deep Sound Recognition,240,iclr,23,8,2023-06-18 08:50:49.470000,https://github.com/mil-tokyo/bc_learning_sound,84,Learning from between-class examples for deep sound recognition,"https://scholar.google.com/scholar?cluster=13221046760066147561&hl=en&as_sdt=0,19",18,2018
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples,735,iclr,47,4,2023-06-18 08:50:49.672000,https://github.com/alinlab/Confident_classifier,173,Training confidence-calibrated classifiers for detecting out-of-distribution samples,"https://scholar.google.com/scholar?cluster=14294577348397503039&hl=en&as_sdt=0,5",11,2018
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop,167,iclr,162,18,2023-06-18 08:50:49.874000,https://github.com/facebookresearch/loop,874,Voiceloop: Voice fitting and synthesis via a phonological loop,"https://scholar.google.com/scholar?cluster=14159878382438547497&hl=en&as_sdt=0,34",68,2018
Generating Wikipedia by Summarizing Long Sequences,727,iclr,3290,589,2023-06-18 08:50:50.075000,https://github.com/tensorflow/tensor2tensor,13768,Generating wikipedia by summarizing long sequences,"https://scholar.google.com/scholar?cluster=9480555348664414627&hl=en&as_sdt=0,5",461,2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training,1212,iclr,39,3,2023-06-18 08:50:50.276000,https://github.com/synxlin/deep-gradient-compression,186,Deep gradient compression: Reducing the communication bandwidth for distributed training,"https://scholar.google.com/scholar?cluster=2485379403852124678&hl=en&as_sdt=0,44",8,2018
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models,1148,iclr,415,34,2023-06-18 08:50:50.478000,https://github.com/bethgelab/foolbox,2503,Decision-based adversarial attacks: Reliable attacks against black-box machine learning models,"https://scholar.google.com/scholar?cluster=1222517566911879461&hl=en&as_sdt=0,47",46,2018
Unbiased Online Recurrent Optimization,80,iclr,1,0,2023-06-18 08:50:50.682000,https://github.com/ctallec/uoro,9,Unbiased online recurrent optimization,"https://scholar.google.com/scholar?cluster=3493841590728342658&hl=en&as_sdt=0,10",5,2018
Measuring the Intrinsic Dimension of Objective Landscapes,234,iclr,36,4,2023-06-18 08:50:50.884000,https://github.com/uber-research/intrinsic-dimension,223,Measuring the intrinsic dimension of objective landscapes,"https://scholar.google.com/scholar?cluster=17182266159657033387&hl=en&as_sdt=0,5",12,2018
Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks,38,iclr,8,0,2023-06-18 08:50:51.086000,https://github.com/whyjay/memoryGAN,47,Memorization precedes generation: Learning unsupervised gans with memory networks,"https://scholar.google.com/scholar?cluster=7548592689214672445&hl=en&as_sdt=0,5",8,2018
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control,112,iclr,46278,1207,2023-06-18 08:50:51.287000,https://github.com/tensorflow/models,75928,Trust-pcl: An off-policy trust region method for continuous control,"https://scholar.google.com/scholar?cluster=11034633680493566157&hl=en&as_sdt=0,47",2774,2018
Towards better understanding of gradient-based attribution methods for Deep Neural Networks,852,iclr,158,41,2023-06-18 08:50:51.512000,https://github.com/kundajelab/deeplift,735,Towards better understanding of gradient-based attribution methods for deep neural networks,"https://scholar.google.com/scholar?cluster=7129422820232184089&hl=en&as_sdt=0,3",38,2018
Countering Adversarial Images using Input Transformations,1237,iclr,74,0,2023-06-18 08:50:51.715000,https://github.com/facebookresearch/adversarial_image_defenses,478,Countering adversarial images using input transformations,"https://scholar.google.com/scholar?cluster=3375700876994648267&hl=en&as_sdt=0,26",19,2018
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks,230,iclr,41,1,2023-06-18 08:50:51.918000,https://github.com/imatge-upc/skiprnn-2017-telecombcn,123,Skip rnn: Learning to skip state updates in recurrent neural networks,"https://scholar.google.com/scholar?cluster=4452574796134429216&hl=en&as_sdt=0,31",10,2018
Twin Networks: Matching the Future for Sequence Generation,56,iclr,2,0,2023-06-18 08:50:52.122000,https://github.com/dmitriy-serdyuk/twin-net,14,Twin networks: Matching the future for sequence generation,"https://scholar.google.com/scholar?cluster=18040787837429694230&hl=en&as_sdt=0,7",3,2018
Interactive Grounded Language Acquisition and Generalization in a 2D World,78,iclr,31,1,2023-06-18 08:50:52.324000,https://github.com/PaddlePaddle/XWorld,84,Interactive grounded language acquisition and generalization in a 2d world,"https://scholar.google.com/scholar?cluster=4696587271474463712&hl=en&as_sdt=0,5",17,2018
Emergent Complexity via Multi-Agent Competition,396,iclr,151,12,2023-06-18 08:50:52.561000,https://github.com/openai/multiagent-competition,761,Emergent complexity via multi-agent competition,"https://scholar.google.com/scholar?cluster=12865596457557919071&hl=en&as_sdt=0,21",46,2018
Learning to Count Objects in Natural Images for Visual Question Answering,203,iclr,45,1,2023-06-18 08:50:52.771000,https://github.com/Cyanogenoid/vqa-counting,197,Learning to count objects in natural images for visual question answering,"https://scholar.google.com/scholar?cluster=5291501502665174038&hl=en&as_sdt=0,24",10,2018
i-RevNet: Deep Invertible Networks,304,iclr,46,3,2023-06-18 08:50:52.973000,https://github.com/jhjacobsen/pytorch-i-revnet,385,i-revnet: Deep invertible networks,"https://scholar.google.com/scholar?cluster=14608880224467079528&hl=en&as_sdt=0,5",20,2018
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,411,iclr,19,8,2023-06-18 08:50:53.174000,https://github.com/huanzhang12/CLEVER,55,Evaluating the robustness of neural networks: An extreme value theory approach,"https://scholar.google.com/scholar?cluster=2078120094241692942&hl=en&as_sdt=0,5",6,2018
HexaConv,82,iclr,12,5,2023-06-18 08:50:53.375000,https://github.com/ehoogeboom/hexaconv,57,Hexaconv,"https://scholar.google.com/scholar?cluster=3503620825946735449&hl=en&as_sdt=0,14",7,2018
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge,266,iclr,17,3,2023-06-18 08:50:53.611000,https://github.com/emited/flow,51,Deep learning for physical processes: Incorporating prior scientific knowledge,"https://scholar.google.com/scholar?cluster=339008717685681020&hl=en&as_sdt=0,15",4,2018
Communication Algorithms via Deep Learning,213,iclr,48,0,2023-06-18 08:50:53.812000,https://github.com/yihanjiang/Sequential-RNN-Decoder,56,Communication algorithms via deep learning,"https://scholar.google.com/scholar?cluster=3745511757842142493&hl=en&as_sdt=0,6",7,2018
Unsupervised Cipher Cracking Using Discrete GANs,71,iclr,24,6,2023-06-18 08:50:54.014000,https://github.com/for-ai/ciphergan,122,Unsupervised cipher cracking using discrete gans,"https://scholar.google.com/scholar?cluster=3064134608179971225&hl=en&as_sdt=0,21",8,2018
Towards Neural Phrase-based Machine Translation,95,iclr,28,0,2023-06-18 08:50:54.214000,https://github.com/posenhuang/NPMT,175,Towards neural phrase-based machine translation,"https://scholar.google.com/scholar?cluster=14839462711165509564&hl=en&as_sdt=0,34",22,2018
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples,750,iclr,6,2,2023-06-18 08:50:54.415000,https://github.com/Microsoft/PixelDefend,19,Pixeldefend: Leveraging generative models to understand and defend against adversarial examples,"https://scholar.google.com/scholar?cluster=9269726813530152599&hl=en&as_sdt=0,10",5,2018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models,1154,iclr,62,13,2023-06-18 08:50:54.616000,https://github.com/kabkabm/defensegan,218,Defense-gan: Protecting classifiers against adversarial attacks using generative models,"https://scholar.google.com/scholar?cluster=4356922002684962280&hl=en&as_sdt=0,22",8,2018
Fraternal Dropout,48,iclr,12,1,2023-06-18 08:50:54.817000,https://github.com/kondiz/fraternal-dropout,65,Fraternal dropout,"https://scholar.google.com/scholar?cluster=4593127166702636404&hl=en&as_sdt=0,5",4,2018
Attacking Binarized Neural Networks,102,iclr,2,1,2023-06-18 08:50:55.018000,https://github.com/AngusG/cleverhans-attacking-bnns,21,Attacking binarized neural networks,"https://scholar.google.com/scholar?cluster=4964512256521124807&hl=en&as_sdt=0,24",3,2018
Depthwise Separable Convolutions for Neural Machine Translation,294,iclr,3290,589,2023-06-18 08:50:55.219000,https://github.com/tensorflow/tensor2tensor,13768,Depthwise separable convolutions for neural machine translation,"https://scholar.google.com/scholar?cluster=7520360878420709403&hl=en&as_sdt=0,10",461,2018
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization,19,iclr,4,0,2023-06-18 08:50:55.420000,https://github.com/BorealisAI/bre-gan,20,Improving GAN training via binarized representation entropy (BRE) regularization,"https://scholar.google.com/scholar?cluster=14467671840316463321&hl=en&as_sdt=0,3",6,2018
Generative networks as inverse problems with Scattering transforms,32,iclr,9,0,2023-06-18 08:50:55.622000,https://github.com/tomas-angles/generative-scattering-networks,25,Generative networks as inverse problems with scattering transforms,"https://scholar.google.com/scholar?cluster=2488553421180641259&hl=en&as_sdt=0,5",3,2018
On the Expressive Power of Overlapping Architectures of Deep Learning,40,iclr,0,0,2023-06-18 08:50:55.824000,https://github.com/HUJI-Deep/OverlapsAndExpressiveness,1,On the expressive power of overlapping architectures of deep learning,"https://scholar.google.com/scholar?cluster=17865700268037263115&hl=en&as_sdt=0,5",2,2018
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers,401,iclr,9,2,2023-06-18 08:50:56.025000,https://github.com/bobye/batchnorm_prune,30,Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers,"https://scholar.google.com/scholar?cluster=17821725364773859726&hl=en&as_sdt=0,5",2,2018
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting,2227,iclr,379,21,2023-06-18 08:50:56.226000,https://github.com/liyaguang/DCRNN,1011,Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,"https://scholar.google.com/scholar?cluster=6301301566407555232&hl=en&as_sdt=0,5",21,2018
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions,279,iclr,19,10,2023-06-18 08:50:56.427000,https://github.com/sjoerdvansteenkiste/Relational-NEM,70,Relational neural expectation maximization: Unsupervised discovery of objects and their interactions,"https://scholar.google.com/scholar?cluster=11323622217846680222&hl=en&as_sdt=0,5",3,2018
Hierarchical Density Order Embeddings,52,iclr,8,2,2023-06-18 08:50:56.628000,https://github.com/benathi/density-order-emb,32,Hierarchical density order embeddings,"https://scholar.google.com/scholar?cluster=12427920250451702495&hl=en&as_sdt=0,33",5,2018
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks,124,iclr,19,1,2023-06-18 08:50:56.829000,https://github.com/chrisdonahue/sdgan,94,Semantically decomposing the latent spaces of generative adversarial networks,"https://scholar.google.com/scholar?cluster=8664262583947148240&hl=en&as_sdt=0,44",8,2018
A Framework for the Quantitative Evaluation of Disentangled Representations,356,iclr,9,0,2023-06-18 08:50:57.030000,https://github.com/cianeastwood/qedr,56,A framework for the quantitative evaluation of disentangled representations,"https://scholar.google.com/scholar?cluster=3224087322020629595&hl=en&as_sdt=0,5",2,2018
Meta-Learning for Semi-Supervised Few-Shot Classification,1205,iclr,99,12,2023-06-18 08:50:57.231000,https://github.com/renmengye/few-shot-ssl-public,514,Meta-learning for semi-supervised few-shot classification,"https://scholar.google.com/scholar?cluster=798380540199769906&hl=en&as_sdt=0,44",18,2018
A DIRT-T Approach to Unsupervised Domain Adaptation,541,iclr,35,1,2023-06-18 08:50:57.432000,https://github.com/RuiShu/dirt-t,171,A dirt-t approach to unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=8960716763873957731&hl=en&as_sdt=0,3",7,2018
Generalizing Across Domains via Cross-Gradient Training,394,iclr,5,1,2023-06-18 08:50:57.632000,https://github.com/vihari/crossgrad,21,Generalizing across domains via cross-gradient training,"https://scholar.google.com/scholar?cluster=4167124586655060881&hl=en&as_sdt=0,5",5,2018
Deep Complex Networks,327,iclr,268,22,2023-06-18 08:50:57.832000,https://github.com/ChihebTrabelsi/deep_complex_networks,655,Deep complex networks,"https://scholar.google.com/scholar?cluster=18218729763326747000&hl=en&as_sdt=0,48",40,2018
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling,157,iclr,27,0,2023-06-18 08:50:58.034000,https://github.com/taoshen58/BiBloSA,123,Bi-directional block self-attention for fast and memory-efficient sequence modeling,"https://scholar.google.com/scholar?cluster=7203374430207428965&hl=en&as_sdt=0,33",7,2018
Training wide residual networks for deployment using a single bit for each weight,74,iclr,10,0,2023-06-18 08:50:58.234000,https://github.com/McDonnell-Lab/1-bit-per-weight,35,Training wide residual networks for deployment using a single bit for each weight,"https://scholar.google.com/scholar?cluster=7686605623349914581&hl=en&as_sdt=0,33",7,2018
Proximal Backpropagation,139,iclr,6,0,2023-06-18 08:50:58.437000,https://github.com/tfrerix/proxprop,41,Proximal backpropagation,"https://scholar.google.com/scholar?cluster=13919472914722495778&hl=en&as_sdt=0,3",15,2018
The Implicit Bias of Gradient Descent on Separable Data,739,iclr,1,0,2023-06-18 08:50:58.638000,https://github.com/paper-submissions/MaxMargin,3,The implicit bias of gradient descent on separable data,"https://scholar.google.com/scholar?cluster=8363232294125339657&hl=en&as_sdt=0,5",2,2018
Regularizing and Optimizing LSTM Language Models,1144,iclr,502,63,2023-06-18 08:50:58.839000,https://github.com/salesforce/awd-lstm-lm,1912,Regularizing and optimizing LSTM language models,"https://scholar.google.com/scholar?cluster=10613038919449342432&hl=en&as_sdt=0,39",70,2018
Word translation without parallel data,1567,iclr,544,79,2023-06-18 08:50:59.040000,https://github.com/facebookresearch/MUSE,3099,Word translation without parallel data,"https://scholar.google.com/scholar?cluster=10646845124593498896&hl=en&as_sdt=0,5",99,2018
Natural Language Inference over Interaction Space,291,iclr,58,11,2023-06-18 08:50:59.241000,https://github.com/YichenGong/Densely-Interactive-Inference-Network,243,Natural language inference over interaction space,"https://scholar.google.com/scholar?cluster=3763530184208671433&hl=en&as_sdt=0,5",8,2018
Multi-Task Learning for Document Ranking and Query Suggestion,57,iclr,31,0,2023-06-18 08:50:59.442000,https://github.com/wasiahmad/mnsrf_ranking_suggestion,110,Multi-task learning for document ranking and query suggestion,"https://scholar.google.com/scholar?cluster=14352356705152132006&hl=en&as_sdt=0,3",9,2018
Cascade Adversarial Machine Learning Regularized with a Unified Embedding,107,iclr,3,1,2023-06-18 08:50:59.644000,https://github.com/taesikna/cascade_adv_training,5,Cascade adversarial machine learning regularized with a unified embedding,"https://scholar.google.com/scholar?cluster=11749941240097246023&hl=en&as_sdt=0,33",2,2018
Mitigating Adversarial Effects Through Randomization,948,iclr,19,5,2023-06-18 08:50:59.845000,https://github.com/cihangxie/NIPS2017_adv_challenge_defense,109,Mitigating adversarial effects through randomization,"https://scholar.google.com/scholar?cluster=1119418123159333221&hl=en&as_sdt=0,5",6,2018
Decision Boundary Analysis of Adversarial Examples,126,iclr,6,1,2023-06-18 08:51:00.046000,https://github.com/sunblaze-ucb/decision-boundaries,24,Decision boundary analysis of adversarial examples,"https://scholar.google.com/scholar?cluster=14822232947259136601&hl=en&as_sdt=0,47",10,2018
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training,198,iclr,23,5,2023-06-18 08:51:00.248000,https://github.com/mkocaoglu/CausalGAN,122,Causalgan: Learning causal implicit generative models with adversarial training,"https://scholar.google.com/scholar?cluster=16773515662718074217&hl=en&as_sdt=0,25",9,2018
Activation Maximization Generative Adversarial Nets,96,iclr,1,1,2023-06-18 08:51:00.448000,https://github.com/ZhimingZhou/AM-GAN,15,Activation maximization generative adversarial nets,"https://scholar.google.com/scholar?cluster=5158804099762139876&hl=en&as_sdt=0,15",2,2018
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields,78,iclr,13,0,2023-06-18 08:51:00.650000,https://github.com/bioinf-jku/coulomb_gan,62,Coulomb gans: Provably optimal nash equilibria via potential fields,"https://scholar.google.com/scholar?cluster=14788505867309328713&hl=en&as_sdt=0,24",12,2018
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect,260,iclr,15,4,2023-06-18 08:51:00.850000,https://github.com/biuyq/CT-GAN,47,Improving the improved training of wasserstein gans: A consistency term and its dual effect,"https://scholar.google.com/scholar?cluster=3155067773578991569&hl=en&as_sdt=0,5",3,2018
FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension,196,iclr,39,5,2023-06-18 08:51:01.051000,https://github.com/momohuang/FusionNet-NLI,134,Fusionnet: Fusing via fully-aware attention with application to machine comprehension,"https://scholar.google.com/scholar?cluster=17073455781225282077&hl=en&as_sdt=0,5",10,2018
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning,453,iclr,83,8,2023-06-18 08:51:01.252000,https://github.com/shehzaadzd/MINERVA,287,Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,"https://scholar.google.com/scholar?cluster=4820794446342808007&hl=en&as_sdt=0,5",11,2018
Compositional Attention Networks for Machine Reasoning,510,iclr,124,15,2023-06-18 08:51:01.454000,https://github.com/stanfordnlp/mac-network,483,Compositional attention networks for machine reasoning,"https://scholar.google.com/scholar?cluster=6263143180991689473&hl=en&as_sdt=0,47",32,2018
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs,36,iclr,8,2,2023-06-18 08:51:01.655000,https://github.com/ForoughA/neuralMath,31,Combining symbolic expressions and black-box function evaluations in neural programs,"https://scholar.google.com/scholar?cluster=12704807079952611027&hl=en&as_sdt=0,5",4,2018
Active Learning for Convolutional Neural Networks: A Core-Set Approach,1218,iclr,43,0,2023-06-18 08:51:01.857000,https://github.com/ozansener/active_learning_coreset,218,Active learning for convolutional neural networks: A core-set approach,"https://scholar.google.com/scholar?cluster=11951024346317000591&hl=en&as_sdt=0,5",4,2018
Loss-aware Weight Quantization of Deep Networks,135,iclr,6,0,2023-06-18 08:51:02.060000,https://github.com/houlu369/Loss-aware-weight-quantization,24,Loss-aware weight quantization of deep networks,"https://scholar.google.com/scholar?cluster=17603219917891692242&hl=en&as_sdt=0,3",3,2018
SpectralNet: Spectral Clustering using Deep Neural Networks,269,iclr,104,15,2023-06-18 08:51:02.261000,https://github.com/kstant0725/SpectralNet,299,Spectralnet: Spectral clustering using deep neural networks,"https://scholar.google.com/scholar?cluster=4554119900285680620&hl=en&as_sdt=0,5",13,2018
Not-So-Random Features,22,iclr,0,1,2023-06-18 08:51:02.462000,https://github.com/yz-ignescent/Not-So-Random-Features,3,Not-so-random features,"https://scholar.google.com/scholar?cluster=16622124799980351573&hl=en&as_sdt=0,5",1,2018
Generating Natural Adversarial Examples,560,iclr,43,3,2023-06-18 08:51:02.664000,https://github.com/zhengliz/natural-adversary,138,Generating natural adversarial examples,"https://scholar.google.com/scholar?cluster=6487263081764376046&hl=en&as_sdt=0,15",5,2018
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation,265,iclr,29,3,2023-06-18 08:51:02.865000,https://github.com/duvenaud/relax,156,Backpropagation through the void: Optimizing control variates for black-box gradient estimation,"https://scholar.google.com/scholar?cluster=14404204871710653077&hl=en&as_sdt=0,3",21,2018
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference,47,iclr,8,0,2023-06-18 08:51:03.066000,https://github.com/Microsoft/jackknife-variational-inference,21,Debiasing evidence approximations: On importance-weighted autoencoders and jackknife variational inference,"https://scholar.google.com/scholar?cluster=9069832931054868249&hl=en&as_sdt=0,5",5,2018
Learning a Generative Model for Validity in Complex Discrete Structures,21,iclr,1,2,2023-06-18 08:51:03.267000,https://github.com/DavidJanz/molecule_grammar_rnn,2,Learning a generative model for validity in complex discrete structures,"https://scholar.google.com/scholar?cluster=5246820158519363051&hl=en&as_sdt=0,33",2,2018
Understanding Short-Horizon Bias in Stochastic Meta-Optimization,111,iclr,7,1,2023-06-18 08:51:03.469000,https://github.com/renmengye/meta-optim-public,37,Understanding short-horizon bias in stochastic meta-optimization,"https://scholar.google.com/scholar?cluster=10519066902248713180&hl=en&as_sdt=0,5",3,2018
Self-ensembling for visual domain adaptation,492,iclr,36,6,2023-06-18 08:51:03.670000,https://github.com/Britefury/self-ensemble-visual-domain-adapt,187,Self-ensembling for visual domain adaptation,"https://scholar.google.com/scholar?cluster=9203351470159334271&hl=en&as_sdt=0,1",5,2018
Gradient Estimators for Implicit Models,85,iclr,4,0,2023-06-18 08:51:03.872000,https://github.com/YingzhenLi/SteinGrad,19,Gradient estimators for implicit models,"https://scholar.google.com/scholar?cluster=29993418784277680&hl=en&as_sdt=0,5",2,2018
An image representation based convolutional network for DNA classification,30,iclr,7,5,2023-06-18 08:51:04.073000,https://github.com/Bojian/Hilbert-CNN,21,An image representation based convolutional network for DNA classification,"https://scholar.google.com/scholar?cluster=4721638019752473074&hl=en&as_sdt=0,5",0,2018
SMASH: One-Shot Model Architecture Search through HyperNetworks,697,iclr,59,4,2023-06-18 08:51:04.275000,https://github.com/ajbrock/SMASH,481,Smash: one-shot model architecture search through hypernetworks,"https://scholar.google.com/scholar?cluster=10456857144668119976&hl=en&as_sdt=0,5",20,2018
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks,223,iclr,8,0,2023-06-18 08:51:04.477000,https://github.com/manuelmolano/Spike-GAN,20,Synthesizing realistic neural population activity patterns using generative adversarial networks,"https://scholar.google.com/scholar?cluster=3292717005509087968&hl=en&as_sdt=0,3",2,2018
PixelNN: Example-based Image Synthesis,109,iclr,0,0,2023-06-18 08:51:04.680000,https://github.com/aayushbansal/PixelNN-Code,3,Pixelnn: Example-based image synthesis,"https://scholar.google.com/scholar?cluster=16832087782645647806&hl=en&as_sdt=0,5",2,2018
Non-Autoregressive Neural Machine Translation,640,iclr,49,3,2023-06-18 08:51:04.881000,https://github.com/salesforce/nonauto-nmt,263,Non-autoregressive neural machine translation,"https://scholar.google.com/scholar?cluster=3482831974828539059&hl=en&as_sdt=0,5",18,2018
mixup: Beyond Empirical Risk Minimization,6796,iclr,217,15,2023-06-18 08:51:05.082000,https://github.com/facebookresearch/mixup-cifar10,1077,mixup: Beyond empirical risk minimization,"https://scholar.google.com/scholar?cluster=12669856454801555406&hl=en&as_sdt=0,31",22,2018
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning,19,iclr,6,0,2023-06-18 08:51:05.283000,https://github.com/lmb-freiburg/td-or-not-td,12,TD or not TD: Analyzing the role of temporal differencing in deep reinforcement learning,"https://scholar.google.com/scholar?cluster=17309732018163861252&hl=en&as_sdt=0,33",12,2018
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection,56,iclr,2,0,2023-06-18 08:51:05.485000,https://github.com/borgr/DORA,6,Dora the explorer: Directed outreaching reinforcement action-selection,"https://scholar.google.com/scholar?cluster=10658112327839471119&hl=en&as_sdt=0,5",4,2018
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning,130,iclr,17,2,2023-06-18 08:51:05.687000,https://github.com/oxwhirl/treeqn,86,Treeqn and atreec: Differentiable tree-structured models for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=10647768083329764430&hl=en&as_sdt=0,18",10,2018
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback,5,iclr,2,0,2023-06-18 08:51:05.888000,https://github.com/hal3/reslope,4,Residual loss prediction: Reinforcement learning with no incremental feedback,"https://scholar.google.com/scholar?cluster=11251280234880641754&hl=en&as_sdt=0,44",2,2018
Guide Actor-Critic for Continuous Control,24,iclr,5,0,2023-06-18 08:51:06.089000,https://github.com/voot-t/guide-actor-critic,10,Guide actor-critic for continuous control,"https://scholar.google.com/scholar?cluster=6316181617581438246&hl=en&as_sdt=0,47",1,2018
Online Learning Rate Adaptation with Hypergradient Descent,202,iclr,17,7,2023-06-18 08:51:06.291000,https://github.com/gbaydin/hypergradient-descent,121,Online learning rate adaptation with hypergradient descent,"https://scholar.google.com/scholar?cluster=2792585694661059835&hl=en&as_sdt=0,36",10,2018
On the regularization of Wasserstein GANs,244,iclr,5,0,2023-06-18 08:51:06.492000,https://github.com/lukovnikov/improved_wgan_training,6,On the regularization of wasserstein gans,"https://scholar.google.com/scholar?cluster=16449463251581049938&hl=en&as_sdt=0,5",2,2018
Divide-and-Conquer Reinforcement Learning,111,iclr,12,1,2023-06-18 08:51:06.694000,https://github.com/dibyaghosh/dnc,56,Divide-and-conquer reinforcement learning,"https://scholar.google.com/scholar?cluster=8527540948926777430&hl=en&as_sdt=0,51",4,2018
A New Method of Region Embedding for Text Classification,58,iclr,13,0,2023-06-18 08:51:06.895000,https://github.com/text-representation/local-context-unit,56,A New Method of Region Embedding for Text Classification.,"https://scholar.google.com/scholar?cluster=4730426859617818868&hl=en&as_sdt=0,3",7,2018
Fix your classifier: the marginal value of training the last weight layer,90,iclr,7,1,2023-06-18 08:51:07.096000,https://github.com/eladhoffer/fix_your_classifier,34,Fix your classifier: the marginal value of training the last weight layer,"https://scholar.google.com/scholar?cluster=10161515370917941482&hl=en&as_sdt=0,5",3,2018
Temporally Efficient Deep Learning with Spikes,18,iclr,5,1,2023-06-18 08:51:07.297000,https://github.com/petered/pdnn,17,Temporally efficient deep learning with spikes,"https://scholar.google.com/scholar?cluster=10962726962539033469&hl=en&as_sdt=0,32",4,2018
Training GANs with Optimism,452,iclr,6,1,2023-06-18 08:51:07.498000,https://github.com/vsyrgkanis/optimistic_GAN_training,42,Training gans with optimism,"https://scholar.google.com/scholar?cluster=721555332302459217&hl=en&as_sdt=0,14",5,2018
Learning From Noisy Singly-labeled Data,151,iclr,5,3,2023-06-18 08:51:07.699000,https://github.com/khetan2/MBEM,20,Learning from noisy singly-labeled data,"https://scholar.google.com/scholar?cluster=1761205373572122420&hl=en&as_sdt=0,33",4,2018
Gaussian Process Behaviour in Wide Deep Neural Networks,365,iclr,10,1,2023-06-18 08:51:07.900000,https://github.com/widedeepnetworks/widedeepnetworks,47,Gaussian process behaviour in wide deep neural networks,"https://scholar.google.com/scholar?cluster=14179398766282481068&hl=en&as_sdt=0,5",5,2018
On the Information Bottleneck Theory of Deep Learning,469,iclr,44,0,2023-06-18 08:51:08.102000,https://github.com/artemyk/ibsgd,127,On the information bottleneck theory of deep learning,"https://scholar.google.com/scholar?cluster=12271240925674881982&hl=en&as_sdt=0,22",9,2018
Deterministic Variational Inference for Robust Bayesian Neural Networks,174,iclr,21,0,2023-06-18 08:57:43.942000,https://github.com/Microsoft/deterministic-variational-inference,94,Deterministic variational inference for robust bayesian neural networks,"https://scholar.google.com/scholar?cluster=180186411545863756&hl=en&as_sdt=0,44",7,2019
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks,326,iclr,101,8,2023-06-18 08:57:44.143000,https://github.com/yikangshen/Ordered-Neurons,572,Ordered neurons: Integrating tree structures into recurrent neural networks,"https://scholar.google.com/scholar?cluster=18012332994072296158&hl=en&as_sdt=0,3",15,2019
Learning deep representations by mutual information estimation and maximization,2178,iclr,103,18,2023-06-18 08:57:44.346000,https://github.com/rdevon/DIM,774,Learning deep representations by mutual information estimation and maximization,"https://scholar.google.com/scholar?cluster=9102831258285751412&hl=en&as_sdt=0,36",21,2019
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,2124,iclr,63,1,2023-06-18 08:57:44.546000,https://github.com/rgeirhos/Stylized-ImageNet,469,ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,"https://scholar.google.com/scholar?cluster=14190455085351957023&hl=en&as_sdt=0,5",13,2019
Meta-Learning Update Rules for Unsupervised Representation Learning,104,iclr,46278,1207,2023-06-18 08:57:44.748000,https://github.com/tensorflow/models,75928,Meta-learning update rules for unsupervised representation learning,"https://scholar.google.com/scholar?cluster=5989711063339819997&hl=en&as_sdt=0,24",2774,2019
Transferring Knowledge across Learning Processes,58,iclr,60,6,2023-06-18 08:57:44.950000,https://github.com/amzn/xfer,250,Transferring knowledge across learning processes,"https://scholar.google.com/scholar?cluster=12789436144351549005&hl=en&as_sdt=0,21",19,2019
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs,75,iclr,13,0,2023-06-18 08:57:45.154000,https://github.com/ganguli-lab/RetinalResources,47,A unified theory of early visual representations from retina to cortex through anatomically constrained deep CNNs,"https://scholar.google.com/scholar?cluster=2073469512347644047&hl=en&as_sdt=0,5",15,2019
Pay Less Attention with Lightweight and Dynamic Convolutions,538,iclr,5883,1031,2023-06-18 08:57:45.356000,https://github.com/pytorch/fairseq,26500,Pay less attention with lightweight and dynamic convolutions,"https://scholar.google.com/scholar?cluster=3358231780148394025&hl=en&as_sdt=0,3",411,2019
"Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware",317,iclr,40,6,2023-06-18 08:57:45.558000,https://github.com/ftramer/slalom,147,"Slalom: Fast, verifiable and private execution of neural networks in trusted hardware","https://scholar.google.com/scholar?cluster=7461531422951047390&hl=en&as_sdt=0,5",10,2019
"The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision",577,iclr,91,7,2023-06-18 08:57:45.759000,https://github.com/vacancy/NSCL-PyTorch-Release,383,"The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision","https://scholar.google.com/scholar?cluster=8837128214653317831&hl=en&as_sdt=0,18",20,2019
How Powerful are Graph Neural Networks?,4871,iclr,211,17,2023-06-18 08:57:45.959000,https://github.com/weihua916/powerful-gnns,1038,How powerful are graph neural networks?,"https://scholar.google.com/scholar?cluster=9955904491400591671&hl=en&as_sdt=0,5",25,2019
Variance Networks: When Expectation Does Not Meet Your Expectations,26,iclr,3,1,2023-06-18 08:57:46.161000,https://github.com/da-molchanov/variance-networks,39,Variance networks: When expectation does not meet your expectations,"https://scholar.google.com/scholar?cluster=3938870273847182783&hl=en&as_sdt=0,5",2,2019
Explaining Image Classifiers by Counterfactual Generation,214,iclr,1,0,2023-06-18 08:57:46.361000,https://github.com/zzzace2000/FIDO-saliency,27,Explaining image classifiers by counterfactual generation,"https://scholar.google.com/scholar?cluster=6313449476805696850&hl=en&as_sdt=0,33",4,2019
Snip: single-Shot Network Pruning based on Connection sensitivity,792,iclr,18,1,2023-06-18 08:57:46.562000,https://github.com/namhoonlee/snip-public,97,Snip: Single-shot network pruning based on connection sensitivity,"https://scholar.google.com/scholar?cluster=9820036975414969048&hl=en&as_sdt=0,11",8,2019
Diagnosing and Enhancing VAE Models,328,iclr,33,11,2023-06-18 08:57:46.765000,https://github.com/daib13/TwoStageVAE,223,Diagnosing and enhancing VAE models,"https://scholar.google.com/scholar?cluster=15377413262741867924&hl=en&as_sdt=0,47",13,2019
Automatically Composing Representation Transformations as a Means for Generalization,76,iclr,5,1,2023-06-18 08:57:46.966000,https://github.com/mbchang/crl,22,Automatically composing representation transformations as a means for generalization,"https://scholar.google.com/scholar?cluster=2301953604663446405&hl=en&as_sdt=0,44",6,2019
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference,534,iclr,33,2,2023-06-18 08:57:47.168000,https://github.com/mattriemer/mer,136,Learning to learn without forgetting by maximizing transfer and minimizing interference,"https://scholar.google.com/scholar?cluster=1577299111936747730&hl=en&as_sdt=0,10",5,2019
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data,73,iclr,4,1,2023-06-18 08:57:47.368000,https://github.com/lunanbit/UUlearning,22,On the minimal supervision for training any binary classifier from only unlabeled data,"https://scholar.google.com/scholar?cluster=12632779449090033610&hl=en&as_sdt=0,1",1,2019
Neural Speed Reading with Structural-Jump-LSTM,30,iclr,5,0,2023-06-18 08:57:47.569000,https://github.com/Varyn/Neural-Speed-Reading-with-Structural-Jump-LSTM,25,Neural speed reading with structural-jump-lstm,"https://scholar.google.com/scholar?cluster=10699754124824317847&hl=en&as_sdt=0,33",5,2019
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees,172,iclr,7,4,2023-06-18 08:57:47.771000,https://github.com/roosephu/slbo,53,Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees,"https://scholar.google.com/scholar?cluster=3175696566467828309&hl=en&as_sdt=0,43",6,2019
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability,182,iclr,5,6,2023-06-18 08:57:47.972000,https://github.com/MadryLab/relu_stable,26,Training for faster adversarial robustness verification via inducing relu stability,"https://scholar.google.com/scholar?cluster=11696009804149879522&hl=en&as_sdt=0,5",5,2019
Unsupervised Adversarial Image Reconstruction,30,iclr,3,4,2023-06-18 08:57:48.173000,https://github.com/UNIR-Anonymous/UNIR,15,Unsupervised adversarial image reconstruction,"https://scholar.google.com/scholar?cluster=552778780795437052&hl=en&as_sdt=0,39",0,2019
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds,34,iclr,1,0,2023-06-18 08:57:48.375000,https://github.com/Newbeeer/Max-MIG,23,Max-mig: an information theoretic approach for joint learning from crowds,"https://scholar.google.com/scholar?cluster=14993809510724823282&hl=en&as_sdt=0,5",3,2019
Meta-Learning with Latent Embedding Optimization,1251,iclr,57,1,2023-06-18 08:57:48.576000,https://github.com/deepmind/leo,292,Meta-learning with latent embedding optimization,"https://scholar.google.com/scholar?cluster=11552536411545683614&hl=en&as_sdt=0,22",14,2019
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach,155,iclr,4,0,2023-06-18 08:57:48.779000,https://github.com/wendazhou/nnet-compression-generalization,25,Non-vacuous generalization bounds at the imagenet scale: a PAC-bayesian compression approach,"https://scholar.google.com/scholar?cluster=12180551458196751211&hl=en&as_sdt=0,33",4,2019
Learning to Represent Edits,98,iclr,10,0,2023-06-18 08:57:48.980000,https://github.com/Microsoft/msrc-dpu-learning-to-represent-edits,27,Learning to represent edits,"https://scholar.google.com/scholar?cluster=15643648406405720624&hl=en&as_sdt=0,3",9,2019
An Empirical Study of Example Forgetting during Deep Neural Network Learning,348,iclr,26,3,2023-06-18 08:57:49.182000,https://github.com/mtoneva/example_forgetting,151,An empirical study of example forgetting during deep neural network learning,"https://scholar.google.com/scholar?cluster=14912040563601232331&hl=en&as_sdt=0,33",6,2019
RNNs implicitly implement tensor-product representations,46,iclr,3,0,2023-06-18 08:57:49.384000,https://github.com/tommccoy1/tpdn,18,RNNs implicitly implement tensor product representations,"https://scholar.google.com/scholar?cluster=8578120166770522666&hl=en&as_sdt=0,33",7,2019
Dynamic Channel Pruning: Feature Boosting and Suppression,290,iclr,20,4,2023-06-18 08:57:49.585000,https://github.com/deep-fry/mayo,109,Dynamic channel pruning: Feature boosting and suppression,"https://scholar.google.com/scholar?cluster=1895104173020407133&hl=en&as_sdt=0,50",11,2019
Towards Metamerism via Foveated Style Transfer,33,iclr,0,0,2023-06-18 08:57:49.787000,https://github.com/ArturoDeza/NeuroFovea,18,Towards metamerism via foveated style transfer,"https://scholar.google.com/scholar?cluster=17935865817929282522&hl=en&as_sdt=0,44",3,2019
Generative Code Modeling with Graphs,154,iclr,37,4,2023-06-18 08:57:49.988000,https://github.com/Microsoft/graph-based-code-modelling,157,Generative code modeling with graphs,"https://scholar.google.com/scholar?cluster=2376600485661149991&hl=en&as_sdt=0,34",13,2019
CEM-RL: Combining evolutionary and gradient-based methods for policy search,130,iclr,17,1,2023-06-18 08:57:50.190000,https://github.com/apourchot/CEM-RL,88,CEM-RL: Combining evolutionary and gradient-based methods for policy search,"https://scholar.google.com/scholar?cluster=11981496156929972562&hl=en&as_sdt=0,5",4,2019
LanczosNet: Multi-Scale Deep Graph Convolutional Networks,221,iclr,64,4,2023-06-18 08:57:50.393000,https://github.com/lrjconan/LanczosNetwork,307,Lanczosnet: Multi-scale deep graph convolutional networks,"https://scholar.google.com/scholar?cluster=4668385491596284189&hl=en&as_sdt=0,5",8,2019
No Training Required: Exploring Random Encoders for Sentence Classification,111,iclr,28,1,2023-06-18 08:57:50.594000,https://github.com/facebookresearch/randsent,183,No training required: Exploring random encoders for sentence classification,"https://scholar.google.com/scholar?cluster=12787240152315433650&hl=en&as_sdt=0,5",12,2019
Neural Graph Evolution: Towards Efficient Automatic Robot Design,46,iclr,12,5,2023-06-18 08:57:50.794000,https://github.com/WilsonWangTHU/neural_graph_evolution,43,Neural graph evolution: Towards efficient automatic robot design,"https://scholar.google.com/scholar?cluster=2252025967426248193&hl=en&as_sdt=0,5",2,2019
Function Space Particle Optimization for Bayesian Neural Networks,52,iclr,7,2,2023-06-18 08:57:50.995000,https://github.com/thu-ml/fpovi,16,Function space particle optimization for bayesian neural networks,"https://scholar.google.com/scholar?cluster=3265058804151062573&hl=en&as_sdt=0,3",8,2019
Structured Adversarial Attack: Towards General Implementation and Better Interpretability,160,iclr,7,1,2023-06-18 08:57:51.196000,https://github.com/KaidiXu/StrAttack,30,Structured adversarial attack: Towards general implementation and better interpretability,"https://scholar.google.com/scholar?cluster=2416957312060244972&hl=en&as_sdt=0,5",4,2019
Spherical CNNs on Unstructured Grids,154,iclr,24,6,2023-06-18 08:57:51.398000,https://github.com/maxjiang93/ugscnn,157,Spherical CNNs on unstructured grids,"https://scholar.google.com/scholar?cluster=8988090417232263617&hl=en&as_sdt=0,5",15,2019
Selfless Sequential Learning,109,iclr,5,0,2023-06-18 08:57:51.600000,https://github.com/rahafaljundi/Selfless-Sequential-Learning,23,Selfless sequential learning,"https://scholar.google.com/scholar?cluster=11518728044683719539&hl=en&as_sdt=0,5",4,2019
The Deep Weight Prior,37,iclr,8,0,2023-06-18 08:57:51.803000,https://github.com/bayesgroup/deep-weight-prior,44,The deep weight prior,"https://scholar.google.com/scholar?cluster=15422497541572460475&hl=en&as_sdt=0,44",11,2019
Adversarial Audio Synthesis,579,iclr,269,50,2023-06-18 08:57:52.008000,https://github.com/chrisdonahue/wavegan,1225,Adversarial audio synthesis,"https://scholar.google.com/scholar?cluster=5918610073287101746&hl=en&as_sdt=0,11",49,2019
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module,80,iclr,13,0,2023-06-18 08:57:52.210000,https://github.com/cogentlabs/apl,46,Adaptive posterior learning: few-shot learning with a surprise-based memory module,"https://scholar.google.com/scholar?cluster=3877086335539241291&hl=en&as_sdt=0,33",5,2019
DHER: Hindsight Experience Replay for Dynamic Goals,72,iclr,6,0,2023-06-18 08:57:52.427000,https://github.com/mengf1/DHER,63,DHER: Hindsight experience replay for dynamic goals,"https://scholar.google.com/scholar?cluster=810824099491823319&hl=en&as_sdt=0,5",4,2019
FlowQA: Grasping Flow in History for Conversational Machine Comprehension,115,iclr,58,19,2023-06-18 08:57:52.674000,https://github.com/momohuang/FlowQA,198,Flowqa: Grasping flow in history for conversational machine comprehension,"https://scholar.google.com/scholar?cluster=13021094548556076955&hl=en&as_sdt=0,36",10,2019
Learning to Design RNA,55,iclr,14,1,2023-06-18 08:57:52.889000,https://github.com/automl/learna,50,Learning to design RNA,"https://scholar.google.com/scholar?cluster=17240520904353756155&hl=en&as_sdt=0,3",12,2019
Robust Conditional Generative Adversarial Networks,128,iclr,2,1,2023-06-18 08:57:53.090000,https://github.com/grigorisg9gr/rocgan,15,Robust conditional generative adversarial networks,"https://scholar.google.com/scholar?cluster=15862016331433813666&hl=en&as_sdt=0,3",3,2019
Cost-Sensitive Robustness against Adversarial Examples,21,iclr,2,0,2023-06-18 08:57:53.290000,https://github.com/xiaozhanguva/Cost-Sensitive-Robustness,20,Cost-sensitive robustness against adversarial examples,"https://scholar.google.com/scholar?cluster=16169861265468560490&hl=en&as_sdt=0,50",4,2019
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking,40,iclr,5,0,2023-06-18 08:57:53.491000,https://github.com/hyang1990/model_based_energy_constrained_compression,17,Energy-constrained compression for deep neural networks via weighted sparse projection and layer input masking,"https://scholar.google.com/scholar?cluster=6237094978821638350&hl=en&as_sdt=0,37",3,2019
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure,10,iclr,1,0,2023-06-18 08:57:53.691000,https://github.com/StanfordAI4HI/ICLR2019_evaluating_discrete_temporal_structure,3,Learning procedural abstractions and evaluating discrete latent temporal structure,"https://scholar.google.com/scholar?cluster=11760620653931209024&hl=en&as_sdt=0,5",6,2019
Adversarial Attacks on Graph Neural Networks via Meta Learning,81,iclr,25,0,2023-06-18 08:57:53.893000,https://github.com/danielzuegner/gnn-meta-attack,125,Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach,"https://scholar.google.com/scholar?cluster=15469142668663053021&hl=en&as_sdt=0,5",5,2019
Information-Directed Exploration for Deep Reinforcement Learning,72,iclr,23,0,2023-06-18 08:57:54.093000,https://github.com/nikonikolov/rltf,80,Information-directed exploration for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=12419979613667846761&hl=en&as_sdt=0,5",13,2019
Improving Generalization and Stability of Generative Adversarial Networks,126,iclr,7,0,2023-06-18 08:57:54.295000,https://github.com/htt210/GeneralizationAndStabilityInGANs,36,Improving generalization and stability of generative adversarial networks,"https://scholar.google.com/scholar?cluster=13499019185526283919&hl=en&as_sdt=0,15",3,2019
Adaptive Input Representations for Neural Language Modeling,317,iclr,5883,1031,2023-06-18 08:57:54.497000,https://github.com/pytorch/fairseq,26500,Adaptive input representations for neural language modeling,"https://scholar.google.com/scholar?cluster=9932684582274973195&hl=en&as_sdt=0,32",411,2019
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology,102,iclr,6,0,2023-06-18 08:57:54.698000,https://github.com/BorgwardtLab/Neural-Persistence,24,Neural persistence: A complexity measure for deep neural networks using algebraic topology,"https://scholar.google.com/scholar?cluster=12286997751595249495&hl=en&as_sdt=0,44",9,2019
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model,10,iclr,6,1,2023-06-18 08:57:54.900000,https://github.com/florianmai/word2mat,20,CBOW is not all you need: Combining CBOW with the compositional matrix space model,"https://scholar.google.com/scholar?cluster=6038502138949255694&hl=en&as_sdt=0,43",1,2019
Stochastic Optimization of Sorting Networks via Continuous Relaxations,110,iclr,24,6,2023-06-18 08:57:55.101000,https://github.com/ermongroup/neuralsort,119,Stochastic optimization of sorting networks via continuous relaxations,"https://scholar.google.com/scholar?cluster=10619362619006891050&hl=en&as_sdt=0,44",10,2019
Generating Multiple Objects at Spatially Distinct Locations,105,iclr,14,7,2023-06-18 08:57:55.303000,https://github.com/tohinz/multiple-objects-gan,111,Generating multiple objects at spatially distinct locations,"https://scholar.google.com/scholar?cluster=13574885695794039292&hl=en&as_sdt=0,14",7,2019
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning,176,iclr,46278,1207,2023-06-18 08:57:55.504000,https://github.com/tensorflow/models,75928,Near-optimal representation learning for hierarchical reinforcement learning,"https://scholar.google.com/scholar?cluster=17682749665983906973&hl=en&as_sdt=0,14",2774,2019
Understanding Composition of Word Embeddings via Tensor Decomposition,6,iclr,1,0,2023-06-18 08:57:55.708000,https://github.com/abefrandsen/syntactic-rand-walk,5,Understanding composition of word embeddings via tensor decomposition,"https://scholar.google.com/scholar?cluster=9072436238425463642&hl=en&as_sdt=0,34",4,2019
Structured Neural Summarization,204,iclr,26,11,2023-06-18 08:57:55.910000,https://github.com/CoderPat/structured-neural-summarization,74,Structured neural summarization,"https://scholar.google.com/scholar?cluster=5961913139611201410&hl=en&as_sdt=0,5",3,2019
Supervised Community Detection with Line Graph Neural Networks,271,iclr,18,0,2023-06-18 08:57:56.111000,https://github.com/zhengdao-chen/GNN4CD,76,Supervised community detection with line graph neural networks,"https://scholar.google.com/scholar?cluster=5008209229610559765&hl=en&as_sdt=0,5",3,2019
code2seq: Generating Sequences from Structured Representations of Code,605,iclr,152,11,2023-06-18 08:57:56.312000,https://github.com/tech-srl/code2seq,494,code2seq: Generating sequences from structured representations of code,"https://scholar.google.com/scholar?cluster=14844338714783082531&hl=en&as_sdt=0,5",16,2019
Predict then Propagate: Graph Neural Networks meet Personalized PageRank,1085,iclr,53,0,2023-06-18 08:57:56.513000,https://github.com/klicperajo/ppnp,298,Predict then propagate: Graph neural networks meet personalized pagerank,"https://scholar.google.com/scholar?cluster=12842465886565513517&hl=en&as_sdt=0,4",9,2019
Slimmable Neural Networks,477,iclr,131,11,2023-06-18 08:57:56.716000,https://github.com/JiahuiYu/slimmable_networks,883,Slimmable neural networks,"https://scholar.google.com/scholar?cluster=15212173000600372424&hl=en&as_sdt=0,14",30,2019
Exploration by random network distillation,982,iclr,153,17,2023-06-18 08:57:56.917000,https://github.com/openai/random-network-distillation,811,Exploration by random network distillation,"https://scholar.google.com/scholar?cluster=126098205768710278&hl=en&as_sdt=0,10",26,2019
Latent Convolutional Models,30,iclr,5,2,2023-06-18 08:57:57.118000,https://github.com/srxdev0619/Latent_Convolutional_Models,17,Latent convolutional models,"https://scholar.google.com/scholar?cluster=1201013501878383620&hl=en&as_sdt=0,5",5,2019
A Universal Music Translation Network,137,iclr,73,9,2023-06-18 08:57:57.318000,https://github.com/facebookresearch/music-translation,446,A universal music translation network,"https://scholar.google.com/scholar?cluster=6168332349111008894&hl=en&as_sdt=0,3",21,2019
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition,79,iclr,13,1,2023-06-18 08:57:57.519000,https://github.com/IBM/BigLittleNet,55,Big-little net: An efficient multi-scale feature representation for visual and speech recognition,"https://scholar.google.com/scholar?cluster=555905086227832192&hl=en&as_sdt=0,38",9,2019
Active Learning with Partial Feedback,55,iclr,4,1,2023-06-18 08:57:57.720000,https://github.com/peiyunh/alpf,11,Active learning with partial feedback,"https://scholar.google.com/scholar?cluster=2828167692054854631&hl=en&as_sdt=0,34",2,2019
DOM-Q-NET: Grounded RL on Structured Language,20,iclr,10,1,2023-06-18 08:57:57.922000,https://github.com/Sheng-J/DOM-Q-NET,44,Dom-q-net: Grounded rl on structured language,"https://scholar.google.com/scholar?cluster=10126688324952353090&hl=en&as_sdt=0,47",2,2019
Predicting the Generalization Gap in Deep Networks with Margin Distributions,169,iclr,7332,1026,2023-06-18 08:57:58.123000,https://github.com/google-research/google-research,29803,Predicting the generalization gap in deep networks with margin distributions,"https://scholar.google.com/scholar?cluster=13633337648471293543&hl=en&as_sdt=0,14",728,2019
Measuring Compositionality in Representation Learning,119,iclr,6,3,2023-06-18 08:57:58.324000,https://github.com/jacobandreas/tre,67,Measuring compositionality in representation learning,"https://scholar.google.com/scholar?cluster=36884338001216785&hl=en&as_sdt=0,5",2,2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations,2199,iclr,138,9,2023-06-18 08:57:58.526000,https://github.com/hendrycks/robustness,846,Benchmarking neural network robustness to common corruptions and perturbations,"https://scholar.google.com/scholar?cluster=4440880036617273374&hl=en&as_sdt=0,24",12,2019
Learning Recurrent Binary/Ternary Weights,30,iclr,2,2,2023-06-18 08:57:58.727000,https://github.com/arashardakani/Learning-Recurrent-Binary-Ternary-Weights,12,Learning recurrent binary/ternary weights,"https://scholar.google.com/scholar?cluster=14324986620118227094&hl=en&as_sdt=0,5",1,2019
Residual Non-local Attention Networks for Image Restoration,573,iclr,55,17,2023-06-18 08:57:58.929000,https://github.com/yulunzhang/RNAN,327,Residual non-local attention networks for image restoration,"https://scholar.google.com/scholar?cluster=5425381515618577679&hl=en&as_sdt=0,10",15,2019
Meta-Learning For Stochastic Gradient MCMC,43,iclr,4,0,2023-06-18 08:57:59.130000,https://github.com/WenboGong/MetaSGMCMC,23,Meta-learning for stochastic gradient MCMC,"https://scholar.google.com/scholar?cluster=5266885862075190072&hl=en&as_sdt=0,11",7,2019
Systematic Generalization: What Is Required and Can It Be Learned?,169,iclr,11,2,2023-06-18 08:57:59.331000,https://github.com/rizar/systematic-generalization-sqoop,38,Systematic generalization: What is required and can it be learned?,"https://scholar.google.com/scholar?cluster=376953749686735892&hl=en&as_sdt=0,5",6,2019
Efficient Lifelong Learning with A-GEM,920,iclr,41,6,2023-06-18 08:57:59.531000,https://github.com/facebookresearch/agem,188,Efficient lifelong learning with a-gem,"https://scholar.google.com/scholar?cluster=14191909055509326948&hl=en&as_sdt=0,33",11,2019
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering,175,iclr,15,5,2023-06-18 08:57:59.733000,https://github.com/rajarshd/Multi-Step-Reasoning,118,Multi-step retriever-reader interaction for scalable open-domain question answering,"https://scholar.google.com/scholar?cluster=17865791345794061973&hl=en&as_sdt=0,5",7,2019
Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision,27,iclr,0,1,2023-06-18 08:57:59.934000,https://github.com/jlezama/disentangling-jacobian,24,Overcoming the disentanglement vs reconstruction trade-off via Jacobian supervision,"https://scholar.google.com/scholar?cluster=72617481773116679&hl=en&as_sdt=0,23",3,2019
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space,1517,iclr,250,7,2023-06-18 08:58:00.136000,https://github.com/DeepGraphLearning/KnowledgeGraphEmbedding,1051,Rotate: Knowledge graph embedding by relational rotation in complex space,"https://scholar.google.com/scholar?cluster=9820389801132772086&hl=en&as_sdt=0,5",24,2019
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications,11,iclr,0,0,2023-06-18 08:58:00.336000,https://github.com/ceisenach/MPG,3,Marginal policy gradients: A unified family of estimators for bounded action spaces with applications,"https://scholar.google.com/scholar?cluster=14825352687327812567&hl=en&as_sdt=0,36",4,2019
On Self Modulation for Generative Adversarial Networks,104,iclr,322,16,2023-06-18 08:58:00.537000,https://github.com/google/compare_gan,1814,On self modulation for generative adversarial networks,"https://scholar.google.com/scholar?cluster=14481067201346722037&hl=en&as_sdt=0,44",52,2019
Subgradient Descent Learns Orthogonal Dictionaries,49,iclr,1,0,2023-06-18 08:58:00.738000,https://github.com/sunju/ODL_L1,1,Subgradient descent learns orthogonal dictionaries,"https://scholar.google.com/scholar?cluster=3757427846147866582&hl=en&as_sdt=0,26",4,2019
A Closer Look at Few-shot Classification,1513,iclr,271,60,2023-06-18 08:58:00.939000,https://github.com/wyharveychen/CloserLookFewShot,1064,A closer look at few-shot classification,"https://scholar.google.com/scholar?cluster=10436738309048088927&hl=en&as_sdt=0,4",20,2019
Meta-Learning Probabilistic Inference for Prediction,230,iclr,13,0,2023-06-18 08:58:01.140000,https://github.com/Gordonjo/versa,68,Meta-learning probabilistic inference for prediction,"https://scholar.google.com/scholar?cluster=18291407046711557858&hl=en&as_sdt=0,5",7,2019
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling,65,iclr,6,0,2023-06-18 08:58:01.341000,https://github.com/catniplab/tree_structured_rslds,30,Tree-structured recurrent switching linear dynamical systems for multi-scale modeling,"https://scholar.google.com/scholar?cluster=10945679458649765039&hl=en&as_sdt=0,6",4,2019
"Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control",35,iclr,7,1,2023-06-18 08:58:01.542000,https://github.com/robertcsordas/dnc,25,"Improving differentiable neural computers through memory masking, de-allocation, and link distribution sharpness control","https://scholar.google.com/scholar?cluster=9465849868631633208&hl=en&as_sdt=0,45",1,2019
Evaluating Robustness of Neural Networks with Mixed Integer Programming,665,iclr,30,7,2023-06-18 08:58:01.743000,https://github.com/vtjeng/MIPVerify.jl,106,Evaluating robustness of neural networks with mixed integer programming,"https://scholar.google.com/scholar?cluster=18154476008132424293&hl=en&as_sdt=0,48",4,2019
Random mesh projectors for inverse problems,7,iclr,4,0,2023-06-18 08:58:01.945000,https://github.com/swing-research/deepmesh,23,Random mesh projectors for inverse problems,"https://scholar.google.com/scholar?cluster=1149610136001098856&hl=en&as_sdt=0,5",9,2019
Complement Objective Training,49,iclr,9,2,2023-06-18 08:58:02.146000,https://github.com/henry8527/COT,74,Complement objective training,"https://scholar.google.com/scholar?cluster=63949908447902569&hl=en&as_sdt=0,2",7,2019
Trellis Networks for Sequence Modeling,125,iclr,63,1,2023-06-18 08:58:02.347000,https://github.com/locuslab/trellisnet,464,Trellis networks for sequence modeling,"https://scholar.google.com/scholar?cluster=13782940196634240151&hl=en&as_sdt=0,5",24,2019
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks,49,iclr,0,0,2023-06-18 08:58:02.549000,https://github.com/uhlerlab/unbalanced_ot,1,Scalable unbalanced optimal transport using generative adversarial networks,"https://scholar.google.com/scholar?cluster=14112773597586866494&hl=en&as_sdt=0,21",3,2019
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic,118,iclr,56,6,2023-06-18 08:58:02.750000,https://github.com/Atcold/pytorch-PPUU,188,Model-predictive policy learning with uncertainty regularization for driving in dense traffic,"https://scholar.google.com/scholar?cluster=5048415252406845644&hl=en&as_sdt=0,5",25,2019
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks,472,iclr,286,15,2023-06-18 08:58:02.952000,https://github.com/CSAILVision/gandissect,1749,Gan dissection: Visualizing and understanding generative adversarial networks,"https://scholar.google.com/scholar?cluster=197925763027882731&hl=en&as_sdt=0,5",75,2019
Improving MMD-GAN Training with Repulsive Loss Function,60,iclr,19,2,2023-06-18 08:58:03.154000,https://github.com/richardwth/MMD-GAN,82,Improving MMD-GAN training with repulsive loss function,"https://scholar.google.com/scholar?cluster=5981776109708607840&hl=en&as_sdt=0,49",5,2019
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware,1665,iclr,281,2,2023-06-18 08:58:03.354000,https://github.com/MIT-HAN-LAB/ProxylessNAS,1379,Proxylessnas: Direct neural architecture search on target task and hardware,"https://scholar.google.com/scholar?cluster=18033301425061747520&hl=en&as_sdt=0,5",73,2019
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization,34,iclr,5,0,2023-06-18 08:58:03.555000,https://github.com/TakaOsa/adInfoHRL,6,Hierarchical reinforcement learning via advantage-weighted information maximization,"https://scholar.google.com/scholar?cluster=8371143208721459013&hl=en&as_sdt=0,15",2,2019
Generalizable Adversarial Training via Spectral Normalization,122,iclr,4,0,2023-06-18 08:58:03.757000,https://github.com/jessemzhang/dl_spectral_normalization,13,Generalizable adversarial training via spectral normalization,"https://scholar.google.com/scholar?cluster=16959420457208400665&hl=en&as_sdt=0,14",3,2019
Deep Anomaly Detection with Outlier Exposure,1070,iclr,102,3,2023-06-18 08:58:03.960000,https://github.com/hendrycks/outlier-exposure,498,Deep anomaly detection with outlier exposure,"https://scholar.google.com/scholar?cluster=13915279318347653817&hl=en&as_sdt=0,5",19,2019
Context-adaptive Entropy Model for End-to-end Optimized Image Compression,318,iclr,29,1,2023-06-18 08:58:04.161000,https://github.com/JooyoungLeeETRI/CA_Entropy_Model,134,Context-adaptive entropy model for end-to-end optimized image compression,"https://scholar.google.com/scholar?cluster=17458297235582784877&hl=en&as_sdt=0,5",3,2019
ProxQuant: Quantized Neural Networks via Proximal Operators,96,iclr,3,3,2023-06-18 08:58:04.363000,https://github.com/allenbai01/ProxQuant,23,Proxquant: Quantized neural networks via proximal operators,"https://scholar.google.com/scholar?cluster=13740367040689029941&hl=en&as_sdt=0,47",3,2019
Universal Transformers,698,iclr,3290,589,2023-06-18 08:58:04.564000,https://github.com/tensorflow/tensor2tensor,13768,Universal transformers,"https://scholar.google.com/scholar?cluster=8443376534582904234&hl=en&as_sdt=0,44",461,2019
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data,170,iclr,4,1,2023-06-18 08:58:04.766000,https://github.com/Jianbo-Lab/LCShapley,15,L-shapley and c-shapley: Efficient model interpretation for structured data,"https://scholar.google.com/scholar?cluster=13478206087371335896&hl=en&as_sdt=0,14",8,2019
Discovery of Natural Language Concepts in Individual Units of CNNs,19,iclr,1,0,2023-06-18 08:58:04.971000,https://github.com/seilna/CNN-Units-in-NLP,27,Discovery of natural language concepts in individual units of cnns,"https://scholar.google.com/scholar?cluster=16647657304104807726&hl=en&as_sdt=0,10",3,2019
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams,31,iclr,16,0,2023-06-18 08:58:05.172000,https://github.com/mkachuee/Opportunistic,10,Opportunistic learning: Budgeted cost-sensitive learning from data streams,"https://scholar.google.com/scholar?cluster=926797319762361897&hl=en&as_sdt=0,5",3,2019
DARTS: Differentiable Architecture Search,3734,iclr,831,92,2023-06-18 08:58:05.374000,https://github.com/quark0/darts,3757,Darts: Differentiable architecture search,"https://scholar.google.com/scholar?cluster=895422516420751823&hl=en&as_sdt=0,22",92,2019
The relativistic discriminator: a key element missing from standard GAN,966,iclr,106,1,2023-06-18 08:58:05.575000,https://github.com/AlexiaJM/RelativisticGAN,706,The relativistic discriminator: a key element missing from standard GAN,"https://scholar.google.com/scholar?cluster=9348243398459465041&hl=en&as_sdt=0,6",26,2019
Quasi-hyperbolic momentum and Adam for deep learning,118,iclr,15,2,2023-06-18 08:58:05.776000,https://github.com/facebookresearch/qhoptim,99,Quasi-hyperbolic momentum and Adam for deep learning,"https://scholar.google.com/scholar?cluster=4018448922538302075&hl=en&as_sdt=0,5",10,2019
Multilingual Neural Machine Translation with Knowledge Distillation,205,iclr,18,5,2023-06-18 08:58:05.978000,https://github.com/RayeRen/multilingual-kd-pytorch,69,Multilingual neural machine translation with knowledge distillation,"https://scholar.google.com/scholar?cluster=5753623392275205285&hl=en&as_sdt=0,33",4,2019
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks,177,iclr,18,2,2023-06-18 08:58:06.180000,https://github.com/steveli/misgan,77,Misgan: Learning from incomplete data with generative adversarial networks,"https://scholar.google.com/scholar?cluster=4415656656646533426&hl=en&as_sdt=0,47",3,2019
A Direct Approach to Robust Deep Learning Using Adversarial Networks,62,iclr,3,3,2023-06-18 08:58:06.380000,https://github.com/whxbergkamp/RobustDL_GAN,20,A direct approach to robust deep learning using adversarial networks,"https://scholar.google.com/scholar?cluster=2332293430655643076&hl=en&as_sdt=0,5",2,2019
ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks,60,iclr,9,0,2023-06-18 08:58:06.582000,https://github.com/mingzhang-yin/ARM-gradient,28,ARM: Augment-REINFORCE-merge gradient for stochastic binary networks,"https://scholar.google.com/scholar?cluster=1199474822347449770&hl=en&as_sdt=0,34",2,2019
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer,100,iclr,0,3,2023-06-18 08:58:06.783000,https://github.com/huangsicong/TimbreTron,43,Timbretron: A wavenet (cyclegan (cqt (audio))) pipeline for musical timbre transfer,"https://scholar.google.com/scholar?cluster=11196022310662002190&hl=en&as_sdt=0,33",19,2019
Whitening and Coloring Batch Transform for GANs,51,iclr,10,0,2023-06-18 08:58:06.985000,https://github.com/AliaksandrSiarohin/wc-gan,34,Whitening and coloring batch transform for gans,"https://scholar.google.com/scholar?cluster=8343777033924906329&hl=en&as_sdt=0,39",5,2019
Learnable Embedding Space for Efficient Neural Architecture Compression,46,iclr,3,1,2023-06-18 08:58:07.189000,https://github.com/Friedrich1006/ESNAC,28,Learnable embedding space for efficient neural architecture compression,"https://scholar.google.com/scholar?cluster=117198627951999316&hl=en&as_sdt=0,33",4,2019
A Statistical Approach to Assessing Neural Network Robustness,68,iclr,9,0,2023-06-18 08:58:07.390000,https://github.com/oval-group/statistical-robustness,8,A statistical approach to assessing neural network robustness,"https://scholar.google.com/scholar?cluster=7897732150648450452&hl=en&as_sdt=0,5",12,2019
Supervised Policy Update for Deep Reinforcement Learning,20,iclr,2,21,2023-06-18 08:58:07.592000,https://github.com/quanvuong/Supervised_Policy_Update,17,Supervised policy update for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=9669638111330201224&hl=en&as_sdt=0,3",3,2019
Learning to Schedule Communication in Multi-agent Reinforcement Learning,154,iclr,27,2,2023-06-18 08:58:07.794000,https://github.com/rhoowd/sched_net,68,Learning to schedule communication in multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=2430706253185717368&hl=en&as_sdt=0,10",5,2019
Multi-class classification without multi-class labels,118,iclr,49,3,2023-06-18 08:58:07.994000,https://github.com/GT-RIPL/L2C,306,Multi-class classification without multi-class labels,"https://scholar.google.com/scholar?cluster=15660059153270341215&hl=en&as_sdt=0,18",20,2019
Spectral Inference Networks: Unifying Deep and Spectral Learning,30,iclr,27,2,2023-06-18 08:58:08.195000,https://github.com/deepmind/spectral_inference_networks,165,Spectral inference networks: Unifying deep and spectral learning,"https://scholar.google.com/scholar?cluster=16660579419089969631&hl=en&as_sdt=0,10",14,2019
Attentive Neural Processes,312,iclr,146,8,2023-06-18 08:58:08.397000,https://github.com/deepmind/neural-processes,929,Attentive neural processes,"https://scholar.google.com/scholar?cluster=6519833436864425356&hl=en&as_sdt=0,5",42,2019
Hierarchical interpretations for neural network predictions,126,iclr,21,2,2023-06-18 08:58:08.598000,https://github.com/csinva/hierarchical-dnn-interpretations,114,Hierarchical interpretations for neural network predictions,"https://scholar.google.com/scholar?cluster=14523630218994203463&hl=en&as_sdt=0,33",10,2019
Spreading vectors for similarity search,70,iclr,37,0,2023-06-18 08:58:08.799000,https://github.com/facebookresearch/spreadingvectors,308,Spreading vectors for similarity search,"https://scholar.google.com/scholar?cluster=7912762574684423820&hl=en&as_sdt=0,5",15,2019
Episodic Curiosity through Reachability,249,iclr,34,6,2023-06-18 08:58:09.001000,https://github.com/google-research/episodic-curiosity,188,Episodic curiosity through reachability,"https://scholar.google.com/scholar?cluster=3202653392377789217&hl=en&as_sdt=0,34",12,2019
Multilingual Neural Machine Translation With Soft Decoupled Encoding,53,iclr,3,0,2023-06-18 08:58:09.202000,https://github.com/cindyxinyiwang/SDE,28,Multilingual neural machine translation with soft decoupled encoding,"https://scholar.google.com/scholar?cluster=1841872742547049658&hl=en&as_sdt=0,5",2,2019
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet,523,iclr,45,6,2023-06-18 08:58:09.403000,https://github.com/wielandbrendel/bag-of-local-features-models,304,Approximating cnns with bag-of-local-features models works surprisingly well on imagenet,"https://scholar.google.com/scholar?cluster=13421262728275736184&hl=en&as_sdt=0,5",11,2019
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length,82,iclr,2,8,2023-06-18 08:58:09.612000,https://github.com/kudkudak/dnn_sharpest_directions,11,On the relation between the sharpest directions of DNN loss and the SGD step length,"https://scholar.google.com/scholar?cluster=3857357074541596262&hl=en&as_sdt=0,33",3,2019
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos,9,iclr,0,0,2023-06-18 08:58:09.813000,https://github.com/EKirschbaum/LeMoNADe,3,LeMoNADe: Learned motif and neuronal assembly detection in calcium imaging videos,"https://scholar.google.com/scholar?cluster=16794354699308703573&hl=en&as_sdt=0,36",2,2019
Multi-Domain Adversarial Learning,63,iclr,6,2,2023-06-18 08:58:10.015000,https://github.com/AltschulerWu-Lab/MuLANN,38,Multi-domain adversarial learning,"https://scholar.google.com/scholar?cluster=12918642192245741417&hl=en&as_sdt=0,10",5,2019
ProMP: Proximal Meta-Policy Search,193,iclr,50,8,2023-06-18 08:58:10.217000,https://github.com/jonasrothfuss/promp,222,Promp: Proximal meta-policy search,"https://scholar.google.com/scholar?cluster=5271959514847376578&hl=en&as_sdt=0,33",14,2019
"Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors",47,iclr,4,1,2023-06-18 08:58:10.418000,https://github.com/Babylonpartners/fuzzymax,43,"Don't settle for average, go for the max: fuzzy sets and max-pooled word vectors","https://scholar.google.com/scholar?cluster=17199150617564073243&hl=en&as_sdt=0,5",118,2019
Learning Exploration Policies for Navigation,177,iclr,17,2,2023-06-18 08:58:10.620000,https://github.com/taochenshh/exp4nav,83,Learning exploration policies for navigation,"https://scholar.google.com/scholar?cluster=1526633576375251578&hl=en&as_sdt=0,47",3,2019
Deep Frank-Wolfe For Neural Network Optimization,40,iclr,10,0,2023-06-18 08:58:10.821000,https://github.com/oval-group/dfw,57,Deep Frank-Wolfe for neural network optimization,"https://scholar.google.com/scholar?cluster=17584931574409094808&hl=en&as_sdt=0,33",12,2019
Learning protein sequence embeddings using information from structure,242,iclr,72,3,2023-06-18 08:58:11.022000,https://github.com/tbepler/protein-sequence-embedding-iclr2019,239,Learning protein sequence embeddings using information from structure,"https://scholar.google.com/scholar?cluster=15164585032422536283&hl=en&as_sdt=0,5",11,2019
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets,58,iclr,4,0,2023-06-18 08:58:11.224000,https://github.com/willwx/sign-symmetry,24,Biologically-plausible learning algorithms can scale to large datasets,"https://scholar.google.com/scholar?cluster=10952740218459903429&hl=en&as_sdt=0,36",0,2019
Learning to Make Analogies by Contrasting Abstract Relational Structure,78,iclr,36,5,2023-06-18 08:58:11.426000,https://github.com/deepmind/abstract-reasoning-matrices,162,Learning to make analogies by contrasting abstract relational structure,"https://scholar.google.com/scholar?cluster=15521573039503233138&hl=en&as_sdt=0,5",24,2019
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion,28,iclr,2,0,2023-06-18 08:58:11.627000,https://github.com/ruiqigao/GridCell,16,Learning grid cells as vector representation of self-position coupled with matrix representation of self-motion,"https://scholar.google.com/scholar?cluster=1267366913161335013&hl=en&as_sdt=0,33",4,2019
Feature Intertwiner for Object Detection,19,iclr,15,5,2023-06-18 08:58:11.828000,https://github.com/hli2020/feature_intertwiner,106,Feature intertwiner for object detection,"https://scholar.google.com/scholar?cluster=1331733591833237522&hl=en&as_sdt=0,5",8,2019
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation,220,iclr,17,9,2023-06-18 08:58:12.029000,https://github.com/chihyaoma/selfmonitoring-agent,113,Self-monitoring navigation agent via auxiliary progress estimation,"https://scholar.google.com/scholar?cluster=5431855784757864150&hl=en&as_sdt=0,33",6,2019
Kernel Change-point Detection with Auxiliary Deep Generative Models,65,iclr,13,3,2023-06-18 08:58:12.230000,https://github.com/OctoberChang/klcpd_code,46,Kernel change-point detection with auxiliary deep generative models,"https://scholar.google.com/scholar?cluster=15362141737124631231&hl=en&as_sdt=0,46",2,2019
Auxiliary Variational MCMC,26,iclr,2,0,2023-06-18 08:58:12.431000,https://github.com/AVMCMC/AuxiliaryVariationalMCMC,17,Auxiliary variational MCMC,"https://scholar.google.com/scholar?cluster=16399175938915448128&hl=en&as_sdt=0,46",1,2019
Interpolation-Prediction Networks for Irregularly Sampled Time Series,114,iclr,14,5,2023-06-18 08:58:12.632000,https://github.com/mlds-lab/interp-net,75,Interpolation-prediction networks for irregularly sampled time series,"https://scholar.google.com/scholar?cluster=15477406781147246766&hl=en&as_sdt=0,5",9,2019
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters,49,iclr,4,0,2023-06-18 08:58:12.848000,https://github.com/cambridge-mlg/miracle,18,Minimal random code learning: Getting bits back from compressed model parameters,"https://scholar.google.com/scholar?cluster=17962712491875468296&hl=en&as_sdt=0,5",3,2019
Equi-normalization of Neural Networks,369,iclr,13,0,2023-06-18 08:58:13.050000,https://github.com/facebookresearch/enorm,114,Data-free quantization through weight equalization and bias correction,"https://scholar.google.com/scholar?cluster=7650143789920544723&hl=en&as_sdt=0,33",10,2019
A Variational Inequality Perspective on Generative Adversarial Networks,341,iclr,11,0,2023-06-18 08:58:13.251000,https://github.com/GauthierGidel/Variational-Inequality-GAN,36,A variational inequality perspective on generative adversarial networks,"https://scholar.google.com/scholar?cluster=6445881932716952872&hl=en&as_sdt=0,24",5,2019
GamePad: A Learning Environment for Theorem Proving,83,iclr,15,12,2023-06-18 08:58:13.453000,https://github.com/ml4tp/gamepad,66,Gamepad: A learning environment for theorem proving,"https://scholar.google.com/scholar?cluster=10460600857870546205&hl=en&as_sdt=0,5",9,2019
Large-Scale Study of Curiosity-Driven Learning,677,iclr,178,14,2023-06-18 08:58:13.661000,https://github.com/openai/large-scale-curiosity,783,Large-scale study of curiosity-driven learning,"https://scholar.google.com/scholar?cluster=6931272873542879959&hl=en&as_sdt=0,5",63,2019
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning,138,iclr,135,9,2023-06-18 08:58:13.866000,https://github.com/mila-iqia/babyai,609,Babyai: A platform to study the sample efficiency of grounded language learning,"https://scholar.google.com/scholar?cluster=16615836502291630253&hl=en&as_sdt=0,33",36,2019
An Empirical study of Binary Neural Networks' Optimisation,72,iclr,10,0,2023-06-18 08:58:14.067000,https://github.com/mi-lad/studying-binary-neural-networks,51,An empirical study of binary neural networks' optimisation,"https://scholar.google.com/scholar?cluster=9499204720789675846&hl=en&as_sdt=0,31",5,2019
DeepOBS: A Deep Learning Optimizer Benchmark Suite,47,iclr,34,16,2023-06-18 08:58:14.269000,https://github.com/fsschneider/deepobs,97,DeepOBS: A deep learning optimizer benchmark suite,"https://scholar.google.com/scholar?cluster=10657953635405668036&hl=en&as_sdt=0,5",4,2019
Learning Implicitly Recurrent CNNs Through Parameter Sharing,57,iclr,13,1,2023-06-18 08:58:14.472000,https://github.com/lolemacs/soft-sharing,65,Learning implicitly recurrent CNNs through parameter sharing,"https://scholar.google.com/scholar?cluster=15123734257747528548&hl=en&as_sdt=0,33",4,2019
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs,67,iclr,18,2,2023-06-18 08:58:14.673000,https://github.com/Sachin19/seq2seq-con,76,Von mises-fisher loss for training sequence to sequence models with continuous outputs,"https://scholar.google.com/scholar?cluster=1822338940984352644&hl=en&as_sdt=0,5",9,2019
Rethinking the Value of Network Pruning,1281,iclr,307,23,2023-06-18 08:58:14.874000,https://github.com/Eric-mingjie/rethinking-network-pruning,1452,Rethinking the value of network pruning,"https://scholar.google.com/scholar?cluster=3601827758437367761&hl=en&as_sdt=0,33",35,2019
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network,158,iclr,13,3,2023-06-18 08:58:15.075000,https://github.com/xuanqing94/BayesianDefense,61,Adv-bnn: Improved adversarial defense through robust bayesian neural network,"https://scholar.google.com/scholar?cluster=16111397550296660225&hl=en&as_sdt=0,10",5,2019
Caveats for information bottleneck in deterministic scenarios,62,iclr,1,0,2023-06-18 08:58:15.276000,https://github.com/artemyk/ibcurve,9,Caveats for information bottleneck in deterministic scenarios,"https://scholar.google.com/scholar?cluster=8561375002982335569&hl=en&as_sdt=0,23",5,2019
Preferences Implicit in the State of the World,54,iclr,7,0,2023-06-18 08:58:15.477000,https://github.com/HumanCompatibleAI/rlsp,40,Preferences implicit in the state of the world,"https://scholar.google.com/scholar?cluster=9659325123261489202&hl=en&as_sdt=0,10",8,2019
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average,227,iclr,34,5,2023-06-18 08:58:15.677000,https://github.com/benathi/fastswa-semi-sup,180,There are many consistent explanations of unlabeled data: Why you should average,"https://scholar.google.com/scholar?cluster=16133183473908875555&hl=en&as_sdt=0,47",11,2019
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation,12,iclr,22,1,2023-06-18 08:58:15.878000,https://github.com/naver/aqm-plus,50,Large-scale answerer in questioner's mind for visual dialog question generation,"https://scholar.google.com/scholar?cluster=7353352535802475325&hl=en&as_sdt=0,43",8,2019
Delta: Deep Learning Transfer using Feature Map with Attention for Convolutional Networks,126,iclr,12,2,2023-06-18 08:58:16.079000,https://github.com/lixingjian/DELTA,63,Delta: Deep learning transfer using feature map with attention for convolutional networks,"https://scholar.google.com/scholar?cluster=1065820725505324380&hl=en&as_sdt=0,3",1,2019
Texttovec: Deep Contextualized Neural autoregressive Topic Models of Language with Distributed Compositional Prior,10,iclr,5,3,2023-06-18 08:58:16.280000,https://github.com/pgcool/textTOvec,24,Texttovec: Deep contextualized neural autoregressive topic models of language with distributed compositional prior,"https://scholar.google.com/scholar?cluster=16604775897027080889&hl=en&as_sdt=0,14",3,2019
Deep Graph Infomax,1328,iclr,128,10,2023-06-18 08:58:16.482000,https://github.com/PetarV-/DGI,533,Deep graph infomax.,"https://scholar.google.com/scholar?cluster=6675561854020696633&hl=en&as_sdt=0,11",11,2019
Practical lossless compression with latent variables using bits back coding,105,iclr,20,2,2023-06-18 08:58:16.684000,https://github.com/bits-back/bits-back,131,Practical lossless compression with latent variables using bits back coding,"https://scholar.google.com/scholar?cluster=1443052248345328520&hl=en&as_sdt=0,22",5,2019
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks,214,iclr,50,10,2023-06-18 08:58:16.885000,https://github.com/IC3Net/IC3Net,185,Learning when to communicate at scale in multiagent cooperative and competitive tasks,"https://scholar.google.com/scholar?cluster=12298395236200633957&hl=en&as_sdt=0,5",4,2019
GO Gradient for Expectation-Based Objectives,22,iclr,0,0,2023-06-18 08:58:17.085000,https://github.com/YulaiCong/GOgradient,4,GO gradient for expectation-based objectives,"https://scholar.google.com/scholar?cluster=13295613950307692271&hl=en&as_sdt=0,23",1,2019
h-detach: Modifying the LSTM Gradient Towards Better Optimization,40,iclr,3,0,2023-06-18 08:58:17.286000,https://github.com/bhargav104/h-detach,11,h-detach: Modifying the LSTM gradient towards better optimization,"https://scholar.google.com/scholar?cluster=762520068872474914&hl=en&as_sdt=0,14",3,2019
SOM-VAE: Interpretable Discrete Representation Learning on Time Series,136,iclr,33,2,2023-06-18 08:58:17.488000,https://github.com/ratschlab/SOM-VAE,179,Som-vae: Interpretable discrete representation learning on time series,"https://scholar.google.com/scholar?cluster=9836294528958312436&hl=en&as_sdt=0,5",11,2019
Learning Factorized Multimodal Representations,273,iclr,9,4,2023-06-18 08:58:17.689000,https://github.com/pliang279/factorized,56,Learning factorized multimodal representations,"https://scholar.google.com/scholar?cluster=2626823666054989533&hl=en&as_sdt=0,5",7,2019
Human-level Protein Localization with Convolutional Neural Networks,21,iclr,2,1,2023-06-18 08:58:17.890000,https://github.com/ml-jku/gapnet-pl,8,Human-level protein localization with convolutional neural networks,"https://scholar.google.com/scholar?cluster=9993156504734443423&hl=en&as_sdt=0,5",6,2019
Environment Probing Interaction Policies,54,iclr,1,1,2023-06-18 08:58:18.091000,https://github.com/Wenxuan-Zhou/EPI,27,Environment probing interaction policies,"https://scholar.google.com/scholar?cluster=2903789960714905866&hl=en&as_sdt=0,47",2,2019
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders,278,iclr,33,2,2023-06-18 08:58:18.292000,https://github.com/jxhe/vae-lagging-encoder,183,Lagging inference networks and posterior collapse in variational autoencoders,"https://scholar.google.com/scholar?cluster=5286759698670808442&hl=en&as_sdt=0,5",4,2019
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks,235,iclr,26,0,2023-06-18 08:58:18.492000,https://github.com/reinhardh/supplement_deep_decoder,81,Deep decoder: Concise image representations from untrained non-convolutional networks,"https://scholar.google.com/scholar?cluster=5031846359818705791&hl=en&as_sdt=0,5",6,2019
SNAS: stochastic neural architecture search,877,iclr,24,3,2023-06-18 08:58:18.693000,https://github.com/SNAS-Series/SNAS-Series,133,SNAS: stochastic neural architecture search,"https://scholar.google.com/scholar?cluster=13328811299154907405&hl=en&as_sdt=0,33",5,2019
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue,151,iclr,25,1,2023-06-18 08:58:18.894000,https://github.com/jasonwu0731/GLMP,159,Global-to-local memory pointer networks for task-oriented dialogue,"https://scholar.google.com/scholar?cluster=8042905846859720405&hl=en&as_sdt=0,5",14,2019
InstaGAN: Instance-aware Image-to-Image Translation,172,iclr,161,11,2023-06-18 08:58:19.096000,https://github.com/sangwoomo/instagan,836,Instagan: Instance-aware image-to-image translation,"https://scholar.google.com/scholar?cluster=14041898124180765737&hl=en&as_sdt=0,5",34,2019
Learning Multi-Level Hierarchies with Hindsight,199,iclr,59,0,2023-06-18 08:58:19.297000,https://github.com/andrew-j-levy/Hierarchical-Actor-Critc-HAC-,239,Learning multi-level hierarchies with hindsight,"https://scholar.google.com/scholar?cluster=11558193958091287134&hl=en&as_sdt=0,33",12,2019
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation,143,iclr,19,4,2023-06-18 09:09:50.832000,https://github.com/hugochan/RL-based-Graph2Seq-for-NQG,114,Reinforcement learning based graph-to-sequence model for natural question generation,"https://scholar.google.com/scholar?cluster=5519507630710292821&hl=en&as_sdt=0,5",7,2020
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification,475,iclr,76,4,2023-06-18 09:09:51.035000,https://github.com/yxgeee/MMT,450,Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification,"https://scholar.google.com/scholar?cluster=5921437976740591026&hl=en&as_sdt=0,47",10,2020
Automatically Discovering and Learning New Visual Categories with Ranking Statistics,112,iclr,20,4,2023-06-18 09:09:51.251000,https://github.com/k-han/AutoNovel,211,Automatically discovering and learning new visual categories with ranking statistics,"https://scholar.google.com/scholar?cluster=6046841849136229502&hl=en&as_sdt=0,5",7,2020
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning,104,iclr,11,1,2023-06-18 09:09:51.455000,https://github.com/qlan3/Explorer,72,Maxmin q-learning: Controlling the estimation bias of q-learning,"https://scholar.google.com/scholar?cluster=7792637153572320374&hl=en&as_sdt=0,5",4,2020
DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures,89,iclr,3,2,2023-06-18 09:09:51.657000,https://github.com/yanghr/DeepHoyer,29,Deephoyer: Learning sparser neural network with differentiable scale-invariant sparsity measures,"https://scholar.google.com/scholar?cluster=9357831330077087953&hl=en&as_sdt=0,33",3,2020
Evaluating The Search Phase of Neural Architecture Search,226,iclr,8,2,2023-06-18 09:09:51.860000,https://github.com/kcyu2014/eval-nas,49,Evaluating the search phase of neural architecture search,"https://scholar.google.com/scholar?cluster=14035367419965317698&hl=en&as_sdt=0,10",4,2020
LAMOL: LAnguage MOdeling for Lifelong Language Learning,122,iclr,10,1,2023-06-18 09:09:52.074000,https://github.com/jojotenya/LAMOL,82,Lamol: Language modeling for lifelong language learning,"https://scholar.google.com/scholar?cluster=16454938344621096337&hl=en&as_sdt=0,5",7,2020
Automated Relational Meta-learning,83,iclr,5,4,2023-06-18 09:09:52.280000,https://github.com/huaxiuyao/ARML,41,Automated relational meta-learning,"https://scholar.google.com/scholar?cluster=12701522525812856519&hl=en&as_sdt=0,33",5,2020
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition,98,iclr,2,0,2023-06-18 09:09:52.483000,https://github.com/iclr2020-apd/anonymous_iclr2020_apd_code,7,Scalable and order-robust continual learning with additive parameter decomposition,"https://scholar.google.com/scholar?cluster=1824460160917131841&hl=en&as_sdt=0,34",1,2020
A Learning-based Iterative Method for Solving Vehicle Routing Problems,169,iclr,23,5,2023-06-18 09:09:52.685000,https://github.com/rlopt/l2i,84,A learning-based iterative method for solving vehicle routing problems,"https://scholar.google.com/scholar?cluster=17783279286650305146&hl=en&as_sdt=0,47",11,2020
Ranking Policy Gradient,12,iclr,1,0,2023-06-18 09:09:52.888000,https://github.com/illidanlab/rpg,22,Ranking policy gradient,"https://scholar.google.com/scholar?cluster=15054324663691805917&hl=en&as_sdt=0,36",3,2020
On Mutual Information Maximization for Representation Learning,390,iclr,7332,1026,2023-06-18 09:09:53.091000,https://github.com/google-research/google-research,29803,On mutual information maximization for representation learning,"https://scholar.google.com/scholar?cluster=13497843317340085742&hl=en&as_sdt=0,24",728,2020
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks,198,iclr,48,17,2023-06-18 09:09:53.295000,https://github.com/yhhhli/APoT_Quantization,217,Additive powers-of-two quantization: An efficient non-uniform discretization for neural networks,"https://scholar.google.com/scholar?cluster=15761551970233038392&hl=en&as_sdt=0,5",5,2020
TabFact: A Large-scale Dataset for Table-based Fact Verification,228,iclr,51,0,2023-06-18 09:09:53.497000,https://github.com/wenhuchen/Table-Fact-Checking,324,Tabfact: A large-scale dataset for table-based fact verification,"https://scholar.google.com/scholar?cluster=17043210713635846770&hl=en&as_sdt=0,5",10,2020
Neural Stored-program Memory,29,iclr,3,23,2023-06-18 09:09:53.700000,https://github.com/thaihungle/NSM,14,Neural stored-program memory,"https://scholar.google.com/scholar?cluster=15969516798219653164&hl=en&as_sdt=0,11",3,2020
Multi-agent Reinforcement Learning for Networked System Control,71,iclr,82,3,2023-06-18 09:09:53.902000,https://github.com/cts198859/deeprl_network,309,Multi-agent reinforcement learning for networked system control,"https://scholar.google.com/scholar?cluster=8406297615890251928&hl=en&as_sdt=0,33",9,2020
FSPool: Learning Set Representations with Featurewise Sort Pooling,66,iclr,8,1,2023-06-18 09:09:54.105000,https://github.com/Cyanogenoid/fspool,42,Fspool: Learning set representations with featurewise sort pooling,"https://scholar.google.com/scholar?cluster=3929630154366081815&hl=en&as_sdt=0,5",4,2020
Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction,77,iclr,5,3,2023-06-18 09:09:54.308000,https://github.com/galsang/trees_from_transformers,28,Are pre-trained language models aware of phrases? simple but strong baselines for grammar induction,"https://scholar.google.com/scholar?cluster=12987326770571285349&hl=en&as_sdt=0,6",3,2020
Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning,54,iclr,3,1,2023-06-18 09:09:54.512000,https://github.com/netpaladinx/DPMPN,20,Dynamically pruned message passing networks for large-scale knowledge graph reasoning,"https://scholar.google.com/scholar?cluster=6314488797301074088&hl=en&as_sdt=0,5",3,2020
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks,101,iclr,10,1,2023-06-18 09:09:54.714000,https://github.com/P2333/Mixup-Inference,58,Mixup inference: Better exploiting mixup to defend adversarial attacks,"https://scholar.google.com/scholar?cluster=17489632663330060721&hl=en&as_sdt=0,34",3,2020
Theory and Evaluation Metrics for Learning Disentangled Representations,66,iclr,2,0,2023-06-18 09:09:54.917000,https://github.com/clarken92/DisentanglementMetrics,15,Theory and evaluation metrics for learning disentangled representations,"https://scholar.google.com/scholar?cluster=7456690520633127745&hl=en&as_sdt=0,41",2,2020
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness,133,iclr,19,1,2023-06-18 09:09:55.119000,https://github.com/P2333/Max-Mahalanobis-Training,87,Rethinking softmax cross-entropy loss for adversarial robustness,"https://scholar.google.com/scholar?cluster=12978417581755318851&hl=en&as_sdt=0,5",4,2020
The Implicit Bias of Depth: How Incremental Learning Drives Generalization,38,iclr,0,0,2023-06-18 09:09:55.322000,https://github.com/dsgissin/Incremental-Learning,7,The implicit bias of depth: How incremental learning drives generalization,"https://scholar.google.com/scholar?cluster=13677656727804857978&hl=en&as_sdt=0,5",3,2020
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget,20,iclr,530,16,2023-06-18 09:09:55.524000,https://github.com/maximecb/gym-minigrid,1810,The variational bandwidth bottleneck: Stochastic evaluation on an information budget,"https://scholar.google.com/scholar?cluster=5182568436909686711&hl=en&as_sdt=0,8",39,2020
Robust Local Features for Improving the Generalization of Adversarial Training,66,iclr,3,1,2023-06-18 09:09:55.727000,https://github.com/JHL-HUST/RLFAT,13,Robust local features for improving the generalization of adversarial training,"https://scholar.google.com/scholar?cluster=11695646506050122270&hl=en&as_sdt=0,5",3,2020
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification,5,iclr,2,0,2023-06-18 09:09:55.930000,https://github.com/Benji4/zebrafish-learning,7,Analysis of video feature learning in two-stream CNNs on the example of zebrafish swim bout classification,"https://scholar.google.com/scholar?cluster=7291111967926344032&hl=en&as_sdt=0,14",1,2020
Logic and the 2-Simplicial Transformer,2,iclr,4,0,2023-06-18 09:09:56.134000,https://github.com/dmurfet/2simplicialtransformer,15,Logic and the -Simplicial Transformer,"https://scholar.google.com/scholar?cluster=3081517893804157897&hl=en&as_sdt=0,33",2,2020
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking,59,iclr,9,2,2023-06-18 09:09:56.337000,https://github.com/anonymousjack/hijacking,42,Fooling detection alone is not enough: Adversarial attack against multiple object tracking,"https://scholar.google.com/scholar?cluster=15515055522275518315&hl=en&as_sdt=0,47",2,2020
DivideMix: Learning with Noisy Labels as Semi-supervised Learning,599,iclr,76,8,2023-06-18 09:09:56.540000,https://github.com/LiJunnan1992/DivideMix,456,Dividemix: Learning with noisy labels as semi-supervised learning,"https://scholar.google.com/scholar?cluster=11967955085227307195&hl=en&as_sdt=0,33",10,2020
Accelerating SGD with momentum for over-parameterized learning,62,iclr,1,0,2023-06-18 09:09:56.744000,https://github.com/ts66395/MaSS,5,Accelerating sgd with momentum for over-parameterized learning,"https://scholar.google.com/scholar?cluster=15634725943352892277&hl=en&as_sdt=0,36",1,2020
"A critical analysis of self-supervision, or what we can learn from a single image",129,iclr,8,3,2023-06-18 09:09:56.947000,https://github.com/yukimasano/linear-probes,36,"A critical analysis of self-supervision, or what we can learn from a single image","https://scholar.google.com/scholar?cluster=1196793253523325509&hl=en&as_sdt=0,31",2,2020
Progressive Memory Banks for Incremental Domain Adaptation,23,iclr,2,0,2023-06-18 09:09:57.151000,https://github.com/nabihach/IDA,13,Progressive memory banks for incremental domain adaptation,"https://scholar.google.com/scholar?cluster=16171132848868692146&hl=en&as_sdt=0,33",2,2020
Exploring Model-based Planning with Policy Networks,124,iclr,12,4,2023-06-18 09:09:57.356000,https://github.com/WilsonWangTHU/POPLIN,93,Exploring model-based planning with policy networks,"https://scholar.google.com/scholar?cluster=5788425518026701179&hl=en&as_sdt=0,34",4,2020
Few-shot Text Classification with Distributional Signatures,141,iclr,57,0,2023-06-18 09:09:57.559000,https://github.com/YujiaBao/Distributional-Signatures,248,Few-shot text classification with distributional signatures,"https://scholar.google.com/scholar?cluster=4872590605106254296&hl=en&as_sdt=0,14",6,2020
Adversarial Policies: Attacking Deep Reinforcement Learning,299,iclr,41,7,2023-06-18 09:09:57.762000,https://github.com/HumanCompatibleAI/adversarial-policies,241,Adversarial policies: Attacking deep reinforcement learning,"https://scholar.google.com/scholar?cluster=1203868559900085227&hl=en&as_sdt=0,11",15,2020
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation,81,iclr,3290,589,2023-06-18 09:09:57.965000,https://github.com/tensorflow/tensor2tensor,13768,Videoflow: A conditional flow-based model for stochastic video generation,"https://scholar.google.com/scholar?cluster=13005087974871140727&hl=en&as_sdt=0,14",461,2020
GLAD: Learning Sparse Graph Recovery,23,iclr,5,0,2023-06-18 09:09:58.168000,https://github.com/Harshs27/GLAD,13,GLAD: Learning sparse graph recovery,"https://scholar.google.com/scholar?cluster=17323993038772593390&hl=en&as_sdt=0,47",2,2020
FasterSeg: Searching for Faster Real-time Semantic Segmentation,150,iclr,110,11,2023-06-18 09:09:58.370000,https://github.com/TAMU-VITA/FasterSeg,515,Fasterseg: Searching for faster real-time semantic segmentation,"https://scholar.google.com/scholar?cluster=11587095836376020772&hl=en&as_sdt=0,5",27,2020
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth,161,iclr,242,79,2023-06-18 09:09:58.573000,https://github.com/TRI-ML/packnet-sfm,1140,Semantically-guided representation learning for self-supervised monocular depth,"https://scholar.google.com/scholar?cluster=17082069917027724929&hl=en&as_sdt=0,5",56,2020
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius,123,iclr,7,3,2023-06-18 09:09:58.776000,https://github.com/RuntianZ/macer,27,Macer: Attack-free and scalable robust training via maximizing certified radius,"https://scholar.google.com/scholar?cluster=17692253363082747545&hl=en&as_sdt=0,5",4,2020
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification,40,iclr,0,0,2023-06-18 09:09:58.979000,https://github.com/xuwangyin/GAT-Generative-Adversarial-Training,3,Gat: Generative adversarial training for adversarial example detection and robust classification,"https://scholar.google.com/scholar?cluster=11402188250493503654&hl=en&as_sdt=0,5",2,2020
Variational Recurrent Models for Solving Partially Observable Control Tasks,45,iclr,13,0,2023-06-18 09:09:59.181000,https://github.com/oist-cnru/Variational-Recurrent-Models,41,Variational recurrent models for solving partially observable control tasks,"https://scholar.google.com/scholar?cluster=10619641407453895242&hl=en&as_sdt=0,5",6,2020
Population-Guided Parallel Policy Search for Reinforcement Learning,36,iclr,6,1,2023-06-18 09:09:59.384000,https://github.com/wyjung0625/p3s,19,Population-guided parallel policy search for reinforcement learning,"https://scholar.google.com/scholar?cluster=13101828686651859537&hl=en&as_sdt=0,44",1,2020
Compositional languages emerge in a neural iterated learning model,55,iclr,2,4,2023-06-18 09:09:59.586000,https://github.com/Joshua-Ren/Neural_Iterated_Learning,11,Compositional languages emerge in a neural iterated learning model,"https://scholar.google.com/scholar?cluster=12260597755376568294&hl=en&as_sdt=0,5",3,2020
Black-Box Adversarial Attack with Transferable Model-based Embedding,85,iclr,16,2,2023-06-18 09:09:59.788000,https://github.com/TransEmbedBA/TREMBA,52,Black-box adversarial attack with transferable model-based embedding,"https://scholar.google.com/scholar?cluster=2817331092772484407&hl=en&as_sdt=0,5",4,2020
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively,18,iclr,3,0,2023-06-18 09:09:59.991000,https://github.com/TAMU-VITA/MAD,19,I am going MAD: Maximum discrepancy competition for comparing classifiers adaptively,"https://scholar.google.com/scholar?cluster=7857243656260190793&hl=en&as_sdt=0,5",12,2020
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models,122,iclr,6,0,2023-06-18 09:10:00.194000,https://github.com/bloodwass/mixout,70,Mixout: Effective regularization to finetune large-scale pretrained language models,"https://scholar.google.com/scholar?cluster=476449558403052711&hl=en&as_sdt=0,33",3,2020
Deep Network Classification by Scattering and Homotopy Dictionary Learning,37,iclr,7,1,2023-06-18 09:10:00.397000,https://github.com/j-zarka/SparseScatNet,22,Deep network classification by scattering and homotopy dictionary learning,"https://scholar.google.com/scholar?cluster=8953532076769179699&hl=en&as_sdt=0,5",2,2020
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems,27,iclr,7,8,2023-06-18 09:10:00.599000,https://github.com/MAS-anony/ASN,26,Action semantics network: Considering the effects of actions in multiagent systems,"https://scholar.google.com/scholar?cluster=12922359203743384074&hl=en&as_sdt=0,47",2,2020
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing,66,iclr,2,0,2023-06-18 09:10:00.802000,https://github.com/jjy1994/Certify_Topk,9,Certified robustness for top-k predictions against adversarial perturbations via randomized smoothing,"https://scholar.google.com/scholar?cluster=12562520033309681005&hl=en&as_sdt=0,1",2,2020
Optimistic Exploration even with a Pessimistic Initialisation,39,iclr,2,0,2023-06-18 09:10:01.008000,https://github.com/oxwhirl/opiq,13,Optimistic exploration even with a pessimistic initialisation,"https://scholar.google.com/scholar?cluster=3638632110441158229&hl=en&as_sdt=0,10",4,2020
VL-BERT: Pre-training of Generic Visual-Linguistic Representations,1249,iclr,108,20,2023-06-18 09:10:01.210000,https://github.com/jackroos/VL-BERT,715,Vl-bert: Pre-training of generic visual-linguistic representations,"https://scholar.google.com/scholar?cluster=7768062511032572067&hl=en&as_sdt=0,43",14,2020
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality,16,iclr,3,2,2023-06-18 09:10:01.413000,https://github.com/spitis/deepnorms,10,An inductive bias for distances: Neural nets that respect the triangle inequality,"https://scholar.google.com/scholar?cluster=17260770554780214553&hl=en&as_sdt=0,23",5,2020
NAS evaluation is frustratingly hard,164,iclr,23,1,2023-06-18 09:10:01.617000,https://github.com/antoyang/NAS-Benchmark,146,NAS evaluation is frustratingly hard,"https://scholar.google.com/scholar?cluster=12471694483970544806&hl=en&as_sdt=0,5",4,2020
Order Learning and Its Application to Age Estimation,19,iclr,6,9,2023-06-18 09:10:01.820000,https://github.com/changsukim-ku/order-learning,18,Order learning and its application to age estimation,"https://scholar.google.com/scholar?cluster=11437601791215885877&hl=en&as_sdt=0,5",2,2020
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning,102,iclr,12,1,2023-06-18 09:10:02.022000,https://github.com/yuweihao/reclor,70,Reclor: A reading comprehension dataset requiring logical reasoning,"https://scholar.google.com/scholar?cluster=4598160843843301931&hl=en&as_sdt=0,22",2,2020
From Variational to Deterministic Autoencoders,238,iclr,14,13,2023-06-18 09:10:02.225000,https://github.com/ParthaEth/Regularized_autoencoders-RAE-,112,From variational to deterministic autoencoders,"https://scholar.google.com/scholar?cluster=10583740506297544895&hl=en&as_sdt=0,33",4,2020
A Fair Comparison of Graph Neural Networks for Graph Classification,336,iclr,48,6,2023-06-18 09:10:02.428000,https://github.com/diningphil/gnn-comparison,323,A fair comparison of graph neural networks for graph classification,"https://scholar.google.com/scholar?cluster=3840429300245249800&hl=en&as_sdt=0,46",9,2020
SAdam: A Variant of Adam for Strongly Convex Functions,37,iclr,1,0,2023-06-18 09:10:02.632000,https://github.com/SAdam-ICLR2020/codes,1,Sadam: A variant of adam for strongly convex functions,"https://scholar.google.com/scholar?cluster=4099818587284366739&hl=en&as_sdt=0,47",1,2020
Few-Shot Learning on graphs via super-Classes based on Graph spectral Measures,51,iclr,6,5,2023-06-18 09:10:02.835000,https://github.com/chauhanjatin10/GraphsFewShot,27,Few-shot learning on graphs via super-classes based on graph spectral measures,"https://scholar.google.com/scholar?cluster=14327533105664935166&hl=en&as_sdt=0,38",2,2020
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning,43,iclr,0,1,2023-06-18 09:10:03.038000,https://github.com/shrezaei/Target-Agnostic-Attack,8,A target-agnostic attack on deep models: Exploiting security vulnerabilities of transfer learning,"https://scholar.google.com/scholar?cluster=15887045580387501339&hl=en&as_sdt=0,5",1,2020
Option Discovery using Deep Skill Chaining,71,iclr,9,2,2023-06-18 09:10:03.240000,https://github.com/deep-skill-chaining/deep-skill-chaining,26,Option discovery using deep skill chaining,"https://scholar.google.com/scholar?cluster=3599079453056617566&hl=en&as_sdt=0,5",2,2020
Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations,1,iclr,30,4,2023-06-18 09:10:03.443000,https://github.com/pkorus/neural-imaging,139,Quantifying the cost of reliable photo authentication via high-performance learned lossy representations,"https://scholar.google.com/scholar?cluster=1043795359610865764&hl=en&as_sdt=0,5",11,2020
On the Variance of the Adaptive Learning Rate and Beyond,1557,iclr,340,12,2023-06-18 09:10:03.646000,https://github.com/LiyuanLucasLiu/RAdam,2494,On the variance of the adaptive learning rate and beyond,"https://scholar.google.com/scholar?cluster=2176563085556003509&hl=en&as_sdt=0,14",58,2020
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection,48,iclr,11,7,2023-06-18 09:10:03.849000,https://github.com/mtsang/interaction_interpretability,34,Feature interaction interpretability: A case for explaining ad-recommendation systems via neural interaction detection,"https://scholar.google.com/scholar?cluster=3857662297580644261&hl=en&as_sdt=0,48",5,2020
Understanding the Limitations of Variational Mutual Information Estimators,138,iclr,7,0,2023-06-18 09:10:04.054000,https://github.com/ermongroup/smile-mi-estimator,58,Understanding the limitations of variational mutual information estimators,"https://scholar.google.com/scholar?cluster=4523141934967854838&hl=en&as_sdt=0,25",5,2020
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations,210,iclr,18,2,2023-06-18 09:10:04.273000,https://github.com/applied-ai-lab/genesis,87,Genesis: Generative scene inference and sampling with object-centric latent representations,"https://scholar.google.com/scholar?cluster=12595023313997791876&hl=en&as_sdt=0,5",4,2020
Language GANs Falling Short,185,iclr,11,0,2023-06-18 09:10:04.475000,https://github.com/pclucas14/GansFallingShort,57,Language gans falling short,"https://scholar.google.com/scholar?cluster=5625942263097164405&hl=en&as_sdt=0,5",6,2020
Reinforced active learning for image segmentation,72,iclr,19,1,2023-06-18 09:10:04.678000,https://github.com/ArantxaCasanova/ralis,83,Reinforced active learning for image segmentation,"https://scholar.google.com/scholar?cluster=1054013285080220526&hl=en&as_sdt=0,5",4,2020
Sign Bits Are All You Need for Black-Box Attacks,42,iclr,4,0,2023-06-18 09:10:04.882000,https://github.com/ash-aldujaili/blackbox-adv-examples-signhunter,19,Sign bits are all you need for black-box attacks,"https://scholar.google.com/scholar?cluster=7597354738321523797&hl=en&as_sdt=0,5",4,2020
Deep Semi-Supervised Anomaly Detection,407,iclr,88,17,2023-06-18 09:10:05.084000,https://github.com/lukasruff/Deep-SAD-PyTorch,303,Deep semi-supervised anomaly detection,"https://scholar.google.com/scholar?cluster=5100822312770479848&hl=en&as_sdt=0,5",9,2020
Minimizing FLOPs to Learn Efficient Sparse Representations,30,iclr,2,0,2023-06-18 09:10:05.288000,https://github.com/biswajitsc/sparse-embed,19,Minimizing flops to learn efficient sparse representations,"https://scholar.google.com/scholar?cluster=16391107852895136725&hl=en&as_sdt=0,31",4,2020
Imitation Learning via Off-Policy Distribution Matching,111,iclr,7332,1026,2023-06-18 09:10:05.491000,https://github.com/google-research/google-research,29803,Imitation learning via off-policy distribution matching,"https://scholar.google.com/scholar?cluster=17232131883135762020&hl=en&as_sdt=0,32",728,2020
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space,104,iclr,33,0,2023-06-18 09:10:05.693000,https://github.com/aspuru-guzik-group/GA,80,Augmenting genetic algorithms with deep neural networks for exploring the chemical space,"https://scholar.google.com/scholar?cluster=4690781735136459726&hl=en&as_sdt=0,5",6,2020
Neural Text Generation With Unlikelihood Training,334,iclr,43,7,2023-06-18 09:10:05.897000,https://github.com/facebookresearch/unlikelihood_training,293,Neural text generation with unlikelihood training,"https://scholar.google.com/scholar?cluster=16638535268657480159&hl=en&as_sdt=0,5",16,2020
Dynamic Time Lag Regression: Predicting What & When,9,iclr,1,7,2023-06-18 09:10:06.100000,https://github.com/transcendent-ai-labs/PlasmaML,16,Dynamic Time Lag Regression: Predicting What and When,"https://scholar.google.com/scholar?cluster=5170552035479326246&hl=en&as_sdt=0,37",7,2020
Unpaired Point Cloud Completion on Real Scans using Adversarial Training,96,iclr,11,1,2023-06-18 09:10:06.302000,https://github.com/xuelin-chen/pcl2pcl-gan-pub,81,Unpaired point cloud completion on real scans using adversarial training,"https://scholar.google.com/scholar?cluster=6319477762897752803&hl=en&as_sdt=0,5",7,2020
Selection via Proxy: Efficient Data Selection for Deep Learning,133,iclr,19,1,2023-06-18 09:10:06.506000,https://github.com/stanford-futuredata/selection-via-proxy,78,Selection via proxy: Efficient data selection for deep learning,"https://scholar.google.com/scholar?cluster=10606664093807319412&hl=en&as_sdt=0,32",8,2020
Global Relational Models of Source Code,194,iclr,20,3,2023-06-18 09:10:06.708000,https://github.com/VHellendoorn/ICLR20-Great,79,Global relational models of source code,"https://scholar.google.com/scholar?cluster=5949441341653621917&hl=en&as_sdt=0,5",4,2020
Adversarially robust transfer learning,103,iclr,2,1,2023-06-18 09:10:06.912000,https://github.com/ashafahi/RobustTransferLWF,16,Adversarially robust transfer learning,"https://scholar.google.com/scholar?cluster=247907928453605112&hl=en&as_sdt=0,47",4,2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness,119,iclr,6,1,2023-06-18 09:10:07.115000,https://github.com/IBM/model-sanitization,22,Bridging mode connectivity in loss landscapes and adversarial robustness,"https://scholar.google.com/scholar?cluster=14988732432147772285&hl=en&as_sdt=0,33",7,2020
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples,491,iclr,136,44,2023-06-18 09:10:07.317000,https://github.com/google-research/meta-dataset,698,Meta-dataset: A dataset of datasets for learning to learn from few examples,"https://scholar.google.com/scholar?cluster=14266702502378757393&hl=en&as_sdt=0,32",24,2020
"Deep Imitative Models for Flexible Inference, Planning, and Control",124,iclr,14,19,2023-06-18 09:10:07.521000,https://github.com/nrhine1/deep_imitative_models,68,"Deep imitative models for flexible inference, planning, and control","https://scholar.google.com/scholar?cluster=599185864570432210&hl=en&as_sdt=0,45",3,2020
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning,75,iclr,10,0,2023-06-18 09:10:07.723000,https://github.com/011235813/cm3,47,Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=11188676090053014781&hl=en&as_sdt=0,3",3,2020
Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks,84,iclr,9,3,2023-06-18 09:10:07.925000,https://github.com/LabForComputationalVision/bias_free_denoising,36,Robust and interpretable blind image denoising via bias-free convolutional neural networks,"https://scholar.google.com/scholar?cluster=11707547899272178627&hl=en&as_sdt=0,36",5,2020
DeepV2D: Video to Depth with Differentiable Structure from Motion,146,iclr,89,28,2023-06-18 09:10:08.128000,https://github.com/princeton-vl/DeepV2D,598,Deepv2d: Video to depth with differentiable structure from motion,"https://scholar.google.com/scholar?cluster=564045569449021652&hl=en&as_sdt=0,33",20,2020
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack,142,iclr,27,10,2023-06-18 09:10:08.331000,https://github.com/cmhcbb/attackbox,50,Sign-opt: A query-efficient hard-label adversarial attack,"https://scholar.google.com/scholar?cluster=4337120578340154737&hl=en&as_sdt=0,5",5,2020
Fast is better than free: Revisiting adversarial training,869,iclr,92,2,2023-06-18 09:10:08.534000,https://github.com/locuslab/fast_adversarial,385,Fast is better than free: Revisiting adversarial training,"https://scholar.google.com/scholar?cluster=227717459026762223&hl=en&as_sdt=0,6",12,2020
DBA: Distributed Backdoor Attacks against Federated Learning,377,iclr,37,4,2023-06-18 09:10:08.737000,https://github.com/AI-secure/DBA,134,Dba: Distributed backdoor attacks against federated learning,"https://scholar.google.com/scholar?cluster=12314378493827075057&hl=en&as_sdt=0,1",2,2020
DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling,19,iclr,50,7,2023-06-18 09:10:08.941000,https://github.com/sacmehta/delight,443,Define: Deep factorized input token embeddings for neural sequence modeling,"https://scholar.google.com/scholar?cluster=1535018014104631427&hl=en&as_sdt=0,29",14,2020
Learning to solve the credit assignment problem,53,iclr,0,0,2023-06-18 09:10:09.143000,https://github.com/benlansdell/synthfeedback,3,Learning to solve the credit assignment problem,"https://scholar.google.com/scholar?cluster=1954938718512669715&hl=en&as_sdt=0,37",5,2020
Four Things Everyone Should Know to Improve Batch Normalization,48,iclr,1,1,2023-06-18 09:10:09.347000,https://github.com/ceciliaresearch/four_things_batch_norm,20,Four things everyone should know to improve batch normalization,"https://scholar.google.com/scholar?cluster=8831824515210942226&hl=en&as_sdt=0,5",1,2020
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving,312,iclr,116,18,2023-06-18 09:10:09.551000,https://github.com/mileyan/Pseudo_Lidar_V2,539,Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving,"https://scholar.google.com/scholar?cluster=10904480408184954283&hl=en&as_sdt=0,10",40,2020
Learning to Learn by Zeroth-Order Oracle,14,iclr,5,0,2023-06-18 09:10:09.753000,https://github.com/RYoungJ/ZO-L2L,13,Learning to learn by zeroth-order oracle,"https://scholar.google.com/scholar?cluster=8954748594282159172&hl=en&as_sdt=0,31",2,2020
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames,273,iclr,378,170,2023-06-18 09:10:09.955000,https://github.com/facebookresearch/habitat-api,1109,Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames,"https://scholar.google.com/scholar?cluster=4884965845219755657&hl=en&as_sdt=0,6",43,2020
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction,38,iclr,1,0,2023-06-18 09:10:10.159000,https://github.com/sangdon/PAC-confidence-set,5,PAC confidence sets for deep neural networks via calibrated prediction,"https://scholar.google.com/scholar?cluster=13464804698510313899&hl=en&as_sdt=0,5",2,2020
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations,19,iclr,12,3,2023-06-18 09:10:10.362000,https://github.com/cornell-zhang/dnn-gating,69,Precision gating: Improving neural network efficiency with dynamic dual-precision activations,"https://scholar.google.com/scholar?cluster=5604094105865350488&hl=en&as_sdt=0,39",9,2020
Oblique Decision Trees from Derivatives of ReLU Networks,12,iclr,7,1,2023-06-18 09:10:10.564000,https://github.com/guanghelee/iclr20-lcn,20,Oblique decision trees from derivatives of relu networks,"https://scholar.google.com/scholar?cluster=15458108821420666095&hl=en&as_sdt=0,31",4,2020
Learn to Explain Efficiently via Neural Logic Inductive Learning,58,iclr,17,3,2023-06-18 09:10:10.768000,https://github.com/gblackout/NLIL,38,Learn to explain efficiently via neural logic inductive learning,"https://scholar.google.com/scholar?cluster=4550874980727321525&hl=en&as_sdt=0,15",4,2020
Improved memory in recurrent neural networks with sequential non-normal dynamics,12,iclr,2,0,2023-06-18 09:10:10.971000,https://github.com/eminorhan/nonnormal-init,3,Improved memory in recurrent neural networks with sequential non-normal dynamics,"https://scholar.google.com/scholar?cluster=2472327505855554396&hl=en&as_sdt=0,26",3,2020
Neural Module Networks for Reasoning over Text,121,iclr,14,3,2023-06-18 09:10:11.174000,https://github.com/nitishgupta/nmn-drop,120,Neural module networks for reasoning over text,"https://scholar.google.com/scholar?cluster=2046532742306416986&hl=en&as_sdt=0,5",11,2020
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling,2,iclr,1,1,2023-06-18 09:10:11.377000,https://github.com/BoChenGroup/VHE-GAN,9,Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling,"https://scholar.google.com/scholar?cluster=6283375856940214417&hl=en&as_sdt=0,5",2,2020
Towards Fast Adaptation of Neural Architectures with Meta Learning,70,iclr,7,2,2023-06-18 09:10:11.580000,https://github.com/dongzelian/T-NAS,27,Towards fast adaptation of neural architectures with meta learning,"https://scholar.google.com/scholar?cluster=2375275580093901945&hl=en&as_sdt=0,5",3,2020
Graph Constrained Reinforcement Learning for Natural Language Action Spaces,83,iclr,13,1,2023-06-18 09:10:11.783000,https://github.com/rajammanabrolu/KG-A2C,54,Graph constrained reinforcement learning for natural language action spaces,"https://scholar.google.com/scholar?cluster=15066208654437399788&hl=en&as_sdt=0,5",2,2020
BERTScore: Evaluating Text Generation with BERT,2078,iclr,186,12,2023-06-18 09:10:11.986000,https://github.com/Tiiiger/bert_score,1161,Bertscore: Evaluating text generation with bert,"https://scholar.google.com/scholar?cluster=5304773001741994283&hl=en&as_sdt=0,5",22,2020
Composition-based Multi-Relational Graph Convolutional Networks,533,iclr,102,13,2023-06-18 09:10:12.190000,https://github.com/malllabiisc/CompGCN,545,Composition-based multi-relational graph convolutional networks,"https://scholar.google.com/scholar?cluster=4927480689371858635&hl=en&as_sdt=0,5",17,2020
Gradient-Based Neural DAG Learning,150,iclr,19,2,2023-06-18 09:10:12.393000,https://github.com/kurowasan/GraN-DAG,78,Gradient-based neural dag learning,"https://scholar.google.com/scholar?cluster=10487378596908501013&hl=en&as_sdt=0,10",6,2020
The Local Elasticity of Neural Networks,29,iclr,2,1,2023-06-18 09:10:12.596000,https://github.com/HornHehhf/LocalElasticity,6,The local elasticity of neural networks,"https://scholar.google.com/scholar?cluster=2497659647078092985&hl=en&as_sdt=0,38",3,2020
Convergence of Gradient Methods on Bilinear Zero-Sum Games,33,iclr,1,0,2023-06-18 09:10:12.799000,https://github.com/Gordon-Guojun-Zhang/ICLR-2020,1,Convergence of gradient methods on bilinear zero-sum games,"https://scholar.google.com/scholar?cluster=18092221422699658079&hl=en&as_sdt=0,31",2,2020
Learning from Explanations with Neural Execution Tree,33,iclr,4,0,2023-06-18 09:10:13.002000,https://github.com/INK-USC/NExT,18,Learning from explanations with neural execution tree,"https://scholar.google.com/scholar?cluster=7878469874238216625&hl=en&as_sdt=0,5",6,2020
Jelly Bean World: A Testbed for Never-Ending Learning,19,iclr,14,2,2023-06-18 09:10:13.205000,https://github.com/eaplatanios/jelly-bean-world,68,Jelly bean world: A testbed for never-ending learning,"https://scholar.google.com/scholar?cluster=13920710483001851413&hl=en&as_sdt=0,5",6,2020
Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality,32,iclr,3,0,2023-06-18 09:10:13.408000,https://github.com/sakhanna/SRU_for_GCI,21,Economy statistical recurrent units for inferring nonlinear granger causality,"https://scholar.google.com/scholar?cluster=9739971127623592335&hl=en&as_sdt=0,14",2,2020
Bayesian Meta Sampling for Fast Uncertainty Adaptation,17,iclr,3,0,2023-06-18 09:10:13.611000,https://github.com/zheshiyige/meta-sampling,8,Bayesian meta sampling for fast uncertainty adaptation,"https://scholar.google.com/scholar?cluster=15645160927746258341&hl=en&as_sdt=0,5",1,2020
Non-Autoregressive Dialog State Tracking,49,iclr,3,2,2023-06-18 09:10:13.814000,https://github.com/henryhungle/NADST,45,Non-autoregressive dialog state tracking,"https://scholar.google.com/scholar?cluster=13522465904465807685&hl=en&as_sdt=0,5",5,2020
RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?,40,iclr,1,0,2023-06-18 09:10:14.018000,https://github.com/anilkagak2/TARNN,6,Rnns incrementally evolving on an equilibrium manifold: A panacea for vanishing and exploding gradients?,"https://scholar.google.com/scholar?cluster=14548762609337726303&hl=en&as_sdt=0,5",3,2020
The Early Phase of Neural Network Training,128,iclr,106,15,2023-06-18 09:10:14.220000,https://github.com/facebookresearch/open_lth,590,The early phase of neural network training,"https://scholar.google.com/scholar?cluster=15707294236176535435&hl=en&as_sdt=0,5",57,2020
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization,37,iclr,25,0,2023-06-18 09:10:14.424000,https://github.com/megvii-model/MABN,182,Towards stabilizing batch statistics in backward propagation of batch normalization,"https://scholar.google.com/scholar?cluster=2467606863922912536&hl=en&as_sdt=0,5",8,2020
Single Episode Policy Transfer in Reinforcement Learning,27,iclr,3,0,2023-06-18 09:10:14.627000,https://github.com/011235813/SEPT,16,Single episode policy transfer in reinforcement learning,"https://scholar.google.com/scholar?cluster=2255040216539653326&hl=en&as_sdt=0,14",5,2020
Generalization through Memorization: Nearest Neighbor Language Models,360,iclr,41,4,2023-06-18 09:10:14.830000,https://github.com/urvashik/knnlm,253,Generalization through memorization: Nearest neighbor language models,"https://scholar.google.com/scholar?cluster=17433739628027955410&hl=en&as_sdt=0,5",7,2020
Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention,98,iclr,15,1,2023-06-18 09:10:15.034000,https://github.com/microsoft/Transformer-XH,67,Transformer-xh: Multi-evidence reasoning with extra hop attention,"https://scholar.google.com/scholar?cluster=1330946954324829338&hl=en&as_sdt=0,5",8,2020
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks,56,iclr,12,0,2023-06-18 09:10:15.236000,https://github.com/facebookresearch/GAN-optimization-landscape,31,A closer look at the optimization landscapes of generative adversarial networks,"https://scholar.google.com/scholar?cluster=8697338348379515621&hl=en&as_sdt=0,3",6,2020
Revisiting Self-Training for Neural Sequence Generation,191,iclr,8,2,2023-06-18 09:10:15.440000,https://github.com/jxhe/self-training-text-generation,45,Revisiting self-training for neural sequence generation,"https://scholar.google.com/scholar?cluster=7004703497998979134&hl=en&as_sdt=0,47",2,2020
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators,58,iclr,4,0,2023-06-18 09:10:15.643000,https://github.com/MLI-lab/overparameterized_convolutional_generators,14,Denoising and regularization via exploiting the structural bias of convolutional generators,"https://scholar.google.com/scholar?cluster=11773092557321050875&hl=en&as_sdt=0,5",4,2020
LambdaNet: Probabilistic Type Inference using Graph Neural Networks,88,iclr,12,0,2023-06-18 09:10:15.846000,https://github.com/MrVPlusOne/LambdaNet,42,Lambdanet: Probabilistic type inference using graph neural networks,"https://scholar.google.com/scholar?cluster=14484091760382594314&hl=en&as_sdt=0,5",9,2020
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping,22,iclr,4,0,2023-06-18 09:10:16.050000,https://github.com/aharley/neural_3d_mapping,31,Learning from unlabelled videos using contrastive predictive neural 3d mapping,"https://scholar.google.com/scholar?cluster=7365572649342061474&hl=en&as_sdt=0,33",8,2020
Decoupling Representation and Classifier for Long-Tailed Recognition,786,iclr,117,13,2023-06-18 09:10:16.279000,https://github.com/facebookresearch/classifier-balancing,873,Decoupling representation and classifier for long-tailed recognition,"https://scholar.google.com/scholar?cluster=2236026226436038230&hl=en&as_sdt=0,41",21,2020
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework,61,iclr,10,1,2023-06-18 09:10:16.482000,https://github.com/thespectrewithin/joint-align,51,Cross-lingual alignment vs joint training: A comparative study and a simple unified framework,"https://scholar.google.com/scholar?cluster=17808816563200033029&hl=en&as_sdt=0,33",4,2020
Uncertainty-guided Continual Learning with Bayesian Neural Networks,165,iclr,11,8,2023-06-18 09:10:16.686000,https://github.com/SaynaEbrahimi/UCB,66,Uncertainty-guided continual learning with bayesian neural networks,"https://scholar.google.com/scholar?cluster=10082473234430355613&hl=en&as_sdt=0,39",4,2020
Picking Winning Tickets Before Training by Preserving Gradient Flow,378,iclr,11,1,2023-06-18 09:10:16.889000,https://github.com/alecwangcq/GraSP,91,Picking winning tickets before training by preserving gradient flow,"https://scholar.google.com/scholar?cluster=9466463567127487961&hl=en&as_sdt=0,10",2,2020
Inductive representation learning on temporal graphs,299,iclr,53,12,2023-06-18 09:10:17.092000,https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs,222,Inductive representation learning on temporal graphs,"https://scholar.google.com/scholar?cluster=6732351798905235278&hl=en&as_sdt=0,36",3,2020
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning,287,iclr,79,73,2023-06-18 09:10:17.295000,https://github.com/google/edward2,645,Batchensemble: an alternative approach to efficient ensemble and lifelong learning,"https://scholar.google.com/scholar?cluster=2684475579133602&hl=en&as_sdt=0,21",20,2020
Towards neural networks that provably know when they don't know,121,iclr,1,1,2023-06-18 09:10:17.498000,https://github.com/AlexMeinke/certified-certain-uncertainty,34,Towards neural networks that provably know when they don't know,"https://scholar.google.com/scholar?cluster=3907037768613550224&hl=en&as_sdt=0,5",5,2020
Learning representations for binary-classification without backpropagation,7,iclr,2,0,2023-06-18 09:10:17.702000,https://github.com/mlech26l/iclr_paper_mdfa,2,Learning representations for binary-classification without backpropagation,"https://scholar.google.com/scholar?cluster=6618144182532521283&hl=en&as_sdt=0,34",2,2020
HiLLoC: lossless image compression with hierarchical latent variable models,51,iclr,7,1,2023-06-18 09:10:17.915000,https://github.com/hilloc-submission/hilloc,34,Hilloc: Lossless image compression with hierarchical latent variable models,"https://scholar.google.com/scholar?cluster=8743808448385898182&hl=en&as_sdt=0,36",7,2020
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation,3,iclr,3,0,2023-06-18 09:10:18.119000,https://github.com/xinjiefan/ACMC_ICLR,4,Adaptive correlated Monte Carlo for contextual categorical sequence generation,"https://scholar.google.com/scholar?cluster=3786399280246105812&hl=en&as_sdt=0,15",4,2020
PairNorm: Tackling Oversmoothing in GNNs,371,iclr,11,4,2023-06-18 09:10:18.321000,https://github.com/LingxiaoShawn/PairNorm,68,Pairnorm: Tackling oversmoothing in gnns,"https://scholar.google.com/scholar?cluster=244277682967965047&hl=en&as_sdt=0,5",2,2020
Controlling generative models with continuous factors of variations,104,iclr,4,8,2023-06-18 09:10:18.524000,https://github.com/AntoinePlumerault/Controlling-generative-models-with-continuous-factors-of-variations,20,Controlling generative models with continuous factors of variations,"https://scholar.google.com/scholar?cluster=9062279682169095695&hl=en&as_sdt=0,5",2,2020
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control,211,iclr,12,0,2023-06-18 09:10:18.727000,https://github.com/Physics-aware-AI/Symplectic-ODENet,34,Symplectic ode-net: Learning hamiltonian dynamics with control,"https://scholar.google.com/scholar?cluster=16212087481734650197&hl=en&as_sdt=0,33",5,2020
Quantum Algorithms for Deep Convolutional Neural Networks,103,iclr,15,1,2023-06-18 09:10:18.929000,https://github.com/JonasLandman/QCNN,84,Quantum algorithms for deep convolutional neural networks,"https://scholar.google.com/scholar?cluster=6858802029383173289&hl=en&as_sdt=0,10",1,2020
Deep Graph Matching Consensus,175,iclr,45,4,2023-06-18 09:10:19.132000,https://github.com/rusty1s/deep-graph-matching-consensus,238,Deep graph matching consensus,"https://scholar.google.com/scholar?cluster=13831077548402480322&hl=en&as_sdt=0,33",9,2020
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers,77,iclr,5,1,2023-06-18 09:10:19.336000,https://github.com/junjieliu2910/DynamicSaprseTraining,27,Dynamic sparse training: Find efficient sparse network from scratch with trainable masked layers,"https://scholar.google.com/scholar?cluster=2417069645139449524&hl=en&as_sdt=0,5",3,2020
"Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference",72,iclr,7,1,2023-06-18 09:10:19.538000,https://github.com/TAMU-VITA/triple-wins,22,"Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference","https://scholar.google.com/scholar?cluster=16965650260059633977&hl=en&as_sdt=0,33",12,2020
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation,250,iclr,4,1,2023-06-18 09:10:19.741000,https://github.com/DeepGraphLearning/GraphAF,44,Graphaf: a flow-based autoregressive model for molecular graph generation,"https://scholar.google.com/scholar?cluster=2901334410635777038&hl=en&as_sdt=0,19",8,2020
The Curious Case of Neural Text Degeneration,1564,iclr,13,2,2023-06-18 09:10:19.943000,https://github.com/ari-holtzman/degen,131,The curious case of neural text degeneration,"https://scholar.google.com/scholar?cluster=13091440005032798110&hl=en&as_sdt=0,33",5,2020
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning,85,iclr,1,1,2023-06-18 09:10:20.146000,https://github.com/KDL-umass/saliency_maps,9,Exploratory not explanatory: Counterfactual analysis of saliency maps for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=6988064126122361563&hl=en&as_sdt=0,5",6,2020
Guiding Program Synthesis by Learning to Generate Examples,12,iclr,3,1,2023-06-18 09:10:20.349000,https://github.com/eth-sri/guiding-synthesizers,12,Guiding program synthesis by learning to generate examples,"https://scholar.google.com/scholar?cluster=5759998545534932408&hl=en&as_sdt=0,14",9,2020
Once-for-All: Train One Network and Specialize it for Efficient Deployment,930,iclr,309,55,2023-06-18 09:10:20.553000,https://github.com/mit-han-lab/once-for-all,1676,Once-for-all: Train one network and specialize it for efficient deployment,"https://scholar.google.com/scholar?cluster=5004054402916064925&hl=en&as_sdt=0,47",53,2020
Multi-Agent Interactions Modeling with Correlated Policies,14,iclr,1,0,2023-06-18 09:10:20.755000,https://github.com/apexrl/CoDAIL,19,Multi-agent interactions modeling with correlated policies,"https://scholar.google.com/scholar?cluster=1707555896923900607&hl=en&as_sdt=0,11",4,2020
PCMC-Net: Feature-based Pairwise Choice Markov Chains,4,iclr,2,0,2023-06-18 09:10:20.958000,https://github.com/alherit/PCMC-Net,0,PCMC-Net: Feature-based pairwise choice Markov chains,"https://scholar.google.com/scholar?cluster=6364308783173808929&hl=en&as_sdt=0,5",2,2020
Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings,192,iclr,25,4,2023-06-18 09:10:21.161000,https://github.com/hyren/query2box,185,Query2box: Reasoning over knowledge graphs in vector space using box embeddings,"https://scholar.google.com/scholar?cluster=12162114509339906104&hl=en&as_sdt=0,23",5,2020
Rethinking the Hyperparameters for Fine-tuning,91,iclr,35,8,2023-06-18 09:10:21.364000,https://github.com/richardaecn/cvpr18-inaturalist-transfer,189,Rethinking the hyperparameters for fine-tuning,"https://scholar.google.com/scholar?cluster=14029720773108023404&hl=en&as_sdt=0,44",9,2020
Plug and Play Language Models: A Simple Approach to Controlled Text Generation,532,iclr,187,26,2023-06-18 09:10:21.567000,https://github.com/uber-research/PPLM,1061,Plug and play language models: A simple approach to controlled text generation,"https://scholar.google.com/scholar?cluster=9850887597524341216&hl=en&as_sdt=0,5",29,2020
Jacobian Adversarially Regularized Networks for Robustness,59,iclr,0,2,2023-06-18 09:10:21.769000,https://github.com/alvinchangw/JARN_ICLR2020,20,Jacobian adversarially regularized networks for robustness,"https://scholar.google.com/scholar?cluster=8296271536774350168&hl=en&as_sdt=0,5",3,2020
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning,69,iclr,24,15,2023-06-18 09:10:21.972000,https://github.com/qian18long/epciclr2020,103,Evolutionary population curriculum for scaling multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=13227492821855003720&hl=en&as_sdt=0,5",6,2020
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators,2536,iclr,339,58,2023-06-18 09:10:22.175000,https://github.com/google-research/electra,2195,Electra: Pre-training text encoders as discriminators rather than generators,"https://scholar.google.com/scholar?cluster=18273102803868155691&hl=en&as_sdt=0,22",61,2020
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering,245,iclr,66,2,2023-06-18 09:10:22.382000,https://github.com/AkariAsai/learning_to_retrieve_reasoning_paths,409,Learning to retrieve reasoning paths over wikipedia graph for question answering,"https://scholar.google.com/scholar?cluster=9983656712986759365&hl=en&as_sdt=0,5",18,2020
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks,51,iclr,7,2,2023-06-18 09:10:22.585000,https://github.com/ml-research/pau,53,Pad\'e Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks,"https://scholar.google.com/scholar?cluster=10060434819073628670&hl=en&as_sdt=0,5",6,2020
Contrastive Representation Distillation,731,iclr,352,34,2023-06-18 09:10:22.788000,https://github.com/HobbitLong/RepDistiller,1829,Contrastive representation distillation,"https://scholar.google.com/scholar?cluster=11598873002614112751&hl=en&as_sdt=0,33",17,2020
Certified Defenses for Adversarial Patches,120,iclr,3,0,2023-06-18 09:10:22.992000,https://github.com/Ping-C/certifiedpatchdefense,30,Certified defenses for adversarial patches,"https://scholar.google.com/scholar?cluster=2964763599882748614&hl=en&as_sdt=0,5",2,2020
Deep Symbolic Superoptimization Without Human Knowledge,4,iclr,1,2,2023-06-18 09:10:23.195000,https://github.com/shihui2010/symbolic_simplifier,14,Deep symbolic superoptimization without human knowledge,"https://scholar.google.com/scholar?cluster=1299108471437991049&hl=en&as_sdt=0,33",4,2020
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution,56,iclr,1,4,2023-06-18 09:10:23.399000,https://github.com/rl-interpretation/understandingRL,4,Explain your move: Understanding agent actions using specific and relevant feature attribution,"https://scholar.google.com/scholar?cluster=5830219427979176885&hl=en&as_sdt=0,5",0,2020
Universal Approximation with Certified Networks,19,iclr,0,0,2023-06-18 09:10:23.601000,https://github.com/eth-sri/UniversalCertificationTheory,10,Universal approximation with certified networks,"https://scholar.google.com/scholar?cluster=8301791316229019028&hl=en&as_sdt=0,21",8,2020
Measuring and Improving the Use of Graph Information in Graph Neural Networks,101,iclr,10,0,2023-06-18 09:10:23.806000,https://github.com/yifan-h/CS-GNN,77,Measuring and improving the use of graph information in graph neural networks,"https://scholar.google.com/scholar?cluster=6471418699996704565&hl=en&as_sdt=0,10",5,2020
State-only Imitation with Transition Dynamics Mismatch,38,iclr,3,1,2023-06-18 09:10:24.010000,https://github.com/tgangwani/RL-Indirect-imitation,20,State-only imitation with transition dynamics mismatch,"https://scholar.google.com/scholar?cluster=14672237104350314112&hl=en&as_sdt=0,39",4,2020
Meta Dropout: Learning to Perturb Latent Features for Generalization,51,iclr,4,1,2023-06-18 09:10:24.213000,https://github.com/haebeom-lee/metadrop,26,Meta dropout: Learning to perturb latent features for generalization,"https://scholar.google.com/scholar?cluster=14333755794039765777&hl=en&as_sdt=0,11",3,2020
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget,25,iclr,4,1,2023-06-18 09:10:24.415000,https://github.com/BayesWatch/pytorch-blockswap,32,Blockswap: Fisher-guided block substitution for network compression on a budget,"https://scholar.google.com/scholar?cluster=2671023600912683387&hl=en&as_sdt=0,10",8,2020
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks,257,iclr,18,1,2023-06-18 09:10:24.618000,https://github.com/JHL-HUST/SI-NI-FGSM,53,Nesterov accelerated gradient and scale invariance for adversarial attacks,"https://scholar.google.com/scholar?cluster=10642064480465270866&hl=en&as_sdt=0,5",4,2020
Robustness Verification for Transformers,84,iclr,1,0,2023-06-18 09:10:24.820000,https://github.com/shizhouxing/Robustness-Verification-for-Transformers,25,Robustness verification for transformers,"https://scholar.google.com/scholar?cluster=2702221835826609078&hl=en&as_sdt=0,38",2,2020
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning,142,iclr,9,2,2023-06-18 09:10:25.024000,https://github.com/pokaxpoka/netrand,53,Network randomization: A simple technique for generalization in deep reinforcement learning,"https://scholar.google.com/scholar?cluster=6049043144348184316&hl=en&as_sdt=0,5",10,2020
Tensor Decompositions for Temporal Knowledge Base Completion,147,iclr,19,2,2023-06-18 09:10:25.227000,https://github.com/facebookresearch/tkbc,65,Tensor decompositions for temporal knowledge base completion,"https://scholar.google.com/scholar?cluster=18234698389055794905&hl=en&as_sdt=0,10",9,2020
On Universal Equivariant Set Networks,46,iclr,0,1,2023-06-18 09:10:25.430000,https://github.com/NimrodSegol/On-Universal-Equivariant-Set-Networks,10,On universal equivariant set networks,"https://scholar.google.com/scholar?cluster=17434444729278914575&hl=en&as_sdt=0,11",1,2020
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$,3,iclr,2,0,2023-06-18 09:10:25.633000,https://github.com/fra31/mmr-universal,6,Provable robustness against all adversarial -perturbations for,"https://scholar.google.com/scholar?cluster=14050453960562252546&hl=en&as_sdt=0,33",2,2020
"Don't Use Large Mini-batches, Use Local SGD",369,iclr,6,0,2023-06-18 09:10:25.836000,https://github.com/epfml/LocalSGD-Code,39,"Don't use large mini-batches, use local sgd","https://scholar.google.com/scholar?cluster=3406394348267726989&hl=en&as_sdt=0,15",10,2020
Distributionally Robust Neural Networks,852,iclr,39,1,2023-06-18 09:10:26.040000,https://github.com/kohpangwei/group_DRO,184,Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization,"https://scholar.google.com/scholar?cluster=11052704904492332793&hl=en&as_sdt=0,14",7,2020
A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning,148,iclr,15,0,2023-06-18 09:10:26.243000,https://github.com/soochan-lee/CN-DPM,91,A neural dirichlet process mixture model for task-free continual learning,"https://scholar.google.com/scholar?cluster=14278617304843676910&hl=en&as_sdt=0,21",7,2020
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs,128,iclr,19,0,2023-06-18 09:10:26.446000,https://github.com/ma-compbio/Hyper-SAGNN,68,Hyper-SAGNN: a self-attention based graph neural network for hypergraphs,"https://scholar.google.com/scholar?cluster=10735269367403451355&hl=en&as_sdt=0,36",4,2020
Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks,28,iclr,0,1,2023-06-18 09:10:26.655000,https://github.com/JoonyoungYi/sparsity-normalization,6,Why not to use zero imputation? correcting sparsity bias in training neural networks,"https://scholar.google.com/scholar?cluster=363482687084089467&hl=en&as_sdt=0,47",4,2020
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification,916,iclr,73,13,2023-06-18 09:10:26.862000,https://github.com/DropEdge/DropEdge,434,Dropedge: Towards deep graph convolutional networks on node classification,"https://scholar.google.com/scholar?cluster=16127626475319244243&hl=en&as_sdt=0,36",11,2020
Masked Based Unsupervised Content Transfer,52,iclr,9,0,2023-06-18 09:10:27.066000,https://github.com/rmokady/mbu-content-tansfer,42,A hierarchical reinforced sequence operation method for unsupervised text style transfer,"https://scholar.google.com/scholar?cluster=10160450979699237379&hl=en&as_sdt=0,33",6,2020
Learning Robust Representations via Multi-View Information Bottleneck,144,iclr,16,1,2023-06-18 09:10:27.332000,https://github.com/mfederici/Multi-View-Information-Bottleneck,99,Learning robust representations via multi-view information bottleneck,"https://scholar.google.com/scholar?cluster=11405202326075018962&hl=en&as_sdt=0,33",2,2020
Deep probabilistic subsampling for task-adaptive compressed sensing,29,iclr,3,0,2023-06-18 09:10:27.536000,https://github.com/IamHuijben/Deep-Probabilistic-Subsampling,18,Deep probabilistic subsampling for task-adaptive compressed sensing,"https://scholar.google.com/scholar?cluster=10812881230787929312&hl=en&as_sdt=0,5",2,2020
Learning to Guide Random Search,15,iclr,6,1,2023-06-18 09:10:27.773000,https://github.com/intel-isl/LMRS,40,Learning to guide random search,"https://scholar.google.com/scholar?cluster=10046802470639742746&hl=en&as_sdt=0,5",11,2020
Lagrangian Fluid Simulation with Continuous Convolutions,124,iclr,251,9,2023-06-18 09:10:27.976000,https://github.com/InteractiveComputerGraphics/SPlisHSPlasH,1287,Lagrangian fluid simulation with continuous convolutions,"https://scholar.google.com/scholar?cluster=1663443529429747125&hl=en&as_sdt=0,33",68,2020
Learning To Explore Using Active Neural SLAM,349,iclr,130,5,2023-06-18 09:10:28.179000,https://github.com/devendrachaplot/Neural-SLAM,633,Learning to explore using active neural slam,"https://scholar.google.com/scholar?cluster=11696547235753024845&hl=en&as_sdt=0,10",23,2020
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks,55,iclr,6,0,2023-06-18 09:10:28.383000,https://github.com/sancharisen/EMPIR,3,Empir: Ensembles of mixed precision deep networks for increased robustness against adversarial attacks,"https://scholar.google.com/scholar?cluster=16573248157245653901&hl=en&as_sdt=0,5",4,2020
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel,45,iclr,2,0,2023-06-18 09:10:28.585000,https://github.com/leaf-ai/rio-paper,6,Quantifying point-prediction uncertainty in neural networks via residual estimation with an i/o kernel,"https://scholar.google.com/scholar?cluster=10327919157136760182&hl=en&as_sdt=0,33",11,2020
B-Spline CNNs on Lie groups,104,iclr,3,0,2023-06-18 09:10:28.788000,https://github.com/ebekkers/gsplinets,46,B-spline cnns on lie groups,"https://scholar.google.com/scholar?cluster=14711713420421113660&hl=en&as_sdt=0,5",6,2020
Neural Outlier Rejection for Self-Supervised Keypoint Learning,21,iclr,34,10,2023-06-18 09:10:28.992000,https://github.com/TRI-ML/KP2D,164,Neural outlier rejection for self-supervised keypoint learning,"https://scholar.google.com/scholar?cluster=823859441730123149&hl=en&as_sdt=0,5",17,2020
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments,132,iclr,22,6,2023-06-18 09:10:29.195000,https://github.com/facebookresearch/impact-driven-exploration,119,Ride: Rewarding impact-driven exploration for procedurally-generated environments,"https://scholar.google.com/scholar?cluster=220681399532996329&hl=en&as_sdt=0,34",9,2020
Low-dimensional statistical manifold embedding of directed graphs,5,iclr,0,0,2023-06-18 09:10:29.399000,https://github.com/funket/dinet_public,2,Low-dimensional statistical manifold embedding of directed graphs,"https://scholar.google.com/scholar?cluster=9660939784062408067&hl=en&as_sdt=0,14",2,2020
Efficient Probabilistic Logic Reasoning with Graph Neural Networks,100,iclr,23,5,2023-06-18 09:10:29.601000,https://github.com/expressGNN/ExpressGNN,91,Efficient probabilistic logic reasoning with graph neural networks,"https://scholar.google.com/scholar?cluster=12549090467067040217&hl=en&as_sdt=0,33",2,2020
GraphSAINT: Graph Sampling Based Inductive Learning Method,666,iclr,79,4,2023-06-18 09:10:29.805000,https://github.com/GraphSAINT/GraphSAINT,407,Graphsaint: Graph sampling based inductive learning method,"https://scholar.google.com/scholar?cluster=4707766140408831355&hl=en&as_sdt=0,26",8,2020
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators,2,iclr,3,5,2023-06-18 09:10:30.007000,https://github.com/f90/FactorGAN,32,Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators,"https://scholar.google.com/scholar?cluster=8246776617616848513&hl=en&as_sdt=0,10",4,2020
Decentralized Deep Learning with Arbitrary Communication Compression,175,iclr,17,0,2023-06-18 09:10:30.211000,https://github.com/epfml/ChocoSGD,49,Decentralized deep learning with arbitrary communication compression,"https://scholar.google.com/scholar?cluster=11705017815367904988&hl=en&as_sdt=0,48",7,2020
On the Relationship between Self-Attention and Convolutional Layers,438,iclr,130,6,2023-06-18 09:10:30.415000,https://github.com/epfml/attention-cnn,1013,On the relationship between self-attention and convolutional layers,"https://scholar.google.com/scholar?cluster=11977726124453844540&hl=en&as_sdt=0,33",27,2020
Structured Object-Aware Physics Prediction for Video Modeling and Planning,50,iclr,8,2,2023-06-18 09:10:30.618000,https://github.com/jlko/STOVE,31,Structured object-aware physics prediction for video modeling and planning,"https://scholar.google.com/scholar?cluster=9673300822333166750&hl=en&as_sdt=0,41",5,2020
Incorporating BERT into Neural Machine Translation,354,iclr,99,24,2023-06-18 09:10:30.821000,https://github.com/bert-nmt/bert-nmt,336,Incorporating bert into neural machine translation,"https://scholar.google.com/scholar?cluster=2826043205996388394&hl=en&as_sdt=0,6",10,2020
MMA Training: Direct Input Space Margin Maximization through Adversarial Training,224,iclr,10,0,2023-06-18 09:10:31.025000,https://github.com/BorealisAI/mma_training,33,Mma training: Direct input space margin maximization through adversarial training,"https://scholar.google.com/scholar?cluster=2454066962339603131&hl=en&as_sdt=0,19",9,2020
Meta-learning curiosity algorithms,47,iclr,18,2,2023-06-18 09:10:31.228000,https://github.com/mfranzs/meta-learning-curiosity-algorithms,78,Meta-learning curiosity algorithms,"https://scholar.google.com/scholar?cluster=957030808144457280&hl=en&as_sdt=0,5",5,2020
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning,168,iclr,26,1,2023-06-18 09:10:31.443000,https://github.com/lmzintgraf/varibad,146,Varibad: A very good method for bayes-adaptive deep rl via meta-learning,"https://scholar.google.com/scholar?cluster=4911534686383009186&hl=en&as_sdt=0,33",7,2020
Lookahead: A Far-sighted Alternative of Magnitude-based Pruning,69,iclr,7,3,2023-06-18 09:10:31.647000,https://github.com/alinlab/lookahead_pruning,32,Lookahead: A far-sighted alternative of magnitude-based pruning,"https://scholar.google.com/scholar?cluster=2120869474011210882&hl=en&as_sdt=0,5",4,2020
Demystifying Inter-Class Disentanglement,45,iclr,1,1,2023-06-18 09:10:31.850000,https://github.com/avivga/lord,6,Demystifying inter-class disentanglement,"https://scholar.google.com/scholar?cluster=4997623727964047990&hl=en&as_sdt=0,33",2,2020
Mixed-curvature Variational Autoencoders,24,iclr,13,2,2023-06-18 09:10:32.053000,https://github.com/oskopek/mvae,57,Mixed-curvature variational autoencoders,"https://scholar.google.com/scholar?cluster=4577288345206475501&hl=en&as_sdt=0,33",5,2020
BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations,41,iclr,1,1,2023-06-18 09:10:32.257000,https://github.com/Hyungjun-K1m/BinaryDuo,8,Binaryduo: Reducing gradient mismatch in binary activation network by coupling binary activations,"https://scholar.google.com/scholar?cluster=14477900274189098502&hl=en&as_sdt=0,39",2,2020
BayesOpt Adversarial Attack,67,iclr,3,2,2023-06-18 09:10:32.462000,https://github.com/rubinxin/BayesOpt_Attack,32,Bayesopt adversarial attack,"https://scholar.google.com/scholar?cluster=45917786652802815&hl=en&as_sdt=0,33",2,2020
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies,35,iclr,2,0,2023-06-18 09:10:32.665000,https://github.com/srsohn/msgi,16,Meta reinforcement learning with autonomous inference of subtask dependencies,"https://scholar.google.com/scholar?cluster=15507319353031290390&hl=en&as_sdt=0,33",5,2020
Dynamics-Aware Embeddings,42,iclr,4,0,2023-06-18 09:10:32.869000,https://github.com/dyne-submission/dynamics-aware-embeddings,14,Dynamics-aware embeddings,"https://scholar.google.com/scholar?cluster=8354834388426273229&hl=en&as_sdt=0,33",3,2020
AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing,11,iclr,2,0,2023-06-18 09:10:33.077000,https://github.com/DIUDIUDIUDIUDIU/AdvectiveNet-An-Eulerian-Lagrangian-Fluidic-Reservoir-for-Point-Cloud-Processing,7,Advectivenet: An eulerian-lagrangian fluidic reservoir for point cloud processing,"https://scholar.google.com/scholar?cluster=16984583145926125597&hl=en&as_sdt=0,5",2,2020
Fair Resource Allocation in Federated Learning,542,iclr,56,2,2023-06-18 09:10:33.291000,https://github.com/litian96/fair_flearn,209,Fair resource allocation in federated learning,"https://scholar.google.com/scholar?cluster=15902848371437893934&hl=en&as_sdt=0,43",6,2020
Training binary neural networks with real-to-binary convolutions,168,iclr,2,3,2023-06-18 09:10:33.494000,https://github.com/brais-martinez/real2binary,35,Training binary neural networks with real-to-binary convolutions,"https://scholar.google.com/scholar?cluster=6977393399937358089&hl=en&as_sdt=0,11",8,2020
Permutation Equivariant Models for Compositional Generalization in Language,75,iclr,8,0,2023-06-18 09:10:33.698000,https://github.com/facebookresearch/Permutation-Equivariant-Seq2Seq,26,Permutation equivariant models for compositional generalization in language,"https://scholar.google.com/scholar?cluster=5726550999314038954&hl=en&as_sdt=0,7",8,2020
Continual learning with hypernetworks,247,iclr,15,0,2023-06-18 09:10:33.901000,https://github.com/chrhenning/hypercl,140,Continual learning with hypernetworks,"https://scholar.google.com/scholar?cluster=12864438704892139972&hl=en&as_sdt=0,33",6,2020
Variational Template Machine for Data-to-Text Generation,41,iclr,8,1,2023-06-18 09:10:34.104000,https://github.com/ReneeYe/VariationalTemplateMachine,29,Variational template machine for data-to-text generation,"https://scholar.google.com/scholar?cluster=7425104340562846421&hl=en&as_sdt=0,33",3,2020
Memory-Based Graph Networks,63,iclr,21,5,2023-06-18 09:10:34.307000,https://github.com/amirkhas/GraphMemoryNet,100,Memory-based graph networks,"https://scholar.google.com/scholar?cluster=8513021522669466053&hl=en&as_sdt=0,33",5,2020
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty,828,iclr,159,3,2023-06-18 09:10:34.510000,https://github.com/google-research/augmix,915,Augmix: A simple data processing method to improve robustness and uncertainty,"https://scholar.google.com/scholar?cluster=10820297852320096780&hl=en&as_sdt=0,33",30,2020
AtomNAS: Fine-Grained End-to-End Neural Architecture Search,102,iclr,21,3,2023-06-18 09:10:34.714000,https://github.com/meijieru/AtomNAS,224,Atomnas: Fine-grained end-to-end neural architecture search,"https://scholar.google.com/scholar?cluster=16282779625023333674&hl=en&as_sdt=0,33",7,2020
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation,10,iclr,2,0,2023-06-18 09:10:34.917000,https://github.com/pbecker93/ExpectedInformationMaximization,6,Expected information maximization: Using the i-projection for mixture density estimation,"https://scholar.google.com/scholar?cluster=10322383053162964662&hl=en&as_sdt=0,34",3,2020
On the interaction between supervision and self-play in emergent communication,48,iclr,2,1,2023-06-18 09:10:35.119000,https://github.com/backpropper/s2p,15,On the interaction between supervision and self-play in emergent communication,"https://scholar.google.com/scholar?cluster=3074457436364179887&hl=en&as_sdt=0,47",3,2020
Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings,28,iclr,12,2,2023-06-18 09:10:35.323000,https://github.com/visinf/lnfmm,32,Latent normalizing flows for many-to-many cross-domain mappings,"https://scholar.google.com/scholar?cluster=3579800435067088843&hl=en&as_sdt=0,44",3,2020
Lite Transformer with Long-Short Range Attention,213,iclr,77,9,2023-06-18 09:10:35.526000,https://github.com/mit-han-lab/lite-transformer,574,Lite transformer with long-short range attention,"https://scholar.google.com/scholar?cluster=417738905489358302&hl=en&as_sdt=0,33",22,2020
Compositional Language Continual Learning,24,iclr,5,0,2023-06-18 09:10:35.728000,https://github.com/yli1/CLCL,17,Compositional language continual learning,"https://scholar.google.com/scholar?cluster=7117391709673102792&hl=en&as_sdt=0,5",1,2020
End to End Trainable Active Contours via Differentiable Rendering,28,iclr,10,4,2023-06-18 09:10:35.932000,https://github.com/shirgur/ACDRNet,81,End to end trainable active contours via differentiable rendering,"https://scholar.google.com/scholar?cluster=4625537332937451422&hl=en&as_sdt=0,43",6,2020
Provable Filter Pruning for Efficient Neural Networks,127,iclr,22,9,2023-06-18 09:10:36.135000,https://github.com/lucaslie/provable_pruning,146,Provable filter pruning for efficient neural networks,"https://scholar.google.com/scholar?cluster=9217069157983955160&hl=en&as_sdt=0,5",5,2020
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories,34,iclr,4,0,2023-06-18 09:10:36.338000,https://github.com/tiangeluo/Learning-to-Group,35,Learning to group: A bottom-up framework for 3d part discovery in unseen categories,"https://scholar.google.com/scholar?cluster=11555751649018705803&hl=en&as_sdt=0,11",6,2020
Discriminative Particle Filter Reinforcement Learning for Complex Partial observations,30,iclr,1,2,2023-06-18 09:10:36.541000,https://github.com/Yusufma03/DPFRL,24,Discriminative particle filter reinforcement learning for complex partial observations,"https://scholar.google.com/scholar?cluster=1615417312084406584&hl=en&as_sdt=0,33",5,2020
Learning to Move with Affordance Maps,21,iclr,2,0,2023-06-18 09:10:36.745000,https://github.com/wqi/A2L,32,Learning to move with affordance maps,"https://scholar.google.com/scholar?cluster=10625760242588523450&hl=en&as_sdt=0,11",3,2020
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning,240,iclr,22,3,2023-06-18 09:10:36.948000,https://github.com/bayesgroup/pytorch-ensembles,219,Pitfalls of in-domain uncertainty estimation and ensembling in deep learning,"https://scholar.google.com/scholar?cluster=6945290947528515507&hl=en&as_sdt=0,33",15,2020
Deep Orientation Uncertainty Learning based on a Bingham Loss,50,iclr,8,0,2023-06-18 09:10:37.151000,https://github.com/igilitschenski/deep_bingham,28,Deep orientation uncertainty learning based on a bingham loss,"https://scholar.google.com/scholar?cluster=2663295630618004041&hl=en&as_sdt=0,31",3,2020
Mixed Precision DNNs: All you need is a good parametrization,124,iclr,57,11,2023-06-18 09:10:37.355000,https://github.com/sony/ai-research-code,315,Mixed precision dnns: All you need is a good parametrization,"https://scholar.google.com/scholar?cluster=4816865987143977033&hl=en&as_sdt=0,41",32,2020
Extreme Classification via Adversarial Softmax Approximation,22,iclr,4,1,2023-06-18 09:10:37.558000,https://github.com/mandt-lab/adversarial-negative-sampling,14,Extreme classification via adversarial softmax approximation,"https://scholar.google.com/scholar?cluster=14613263140871789751&hl=en&as_sdt=0,33",4,2020
Learning Nearly Decomposable Value Functions Via Communication Minimization,81,iclr,14,8,2023-06-18 09:10:37.761000,https://github.com/TonghanWang/NDQ,73,Learning nearly decomposable value functions via communication minimization,"https://scholar.google.com/scholar?cluster=9765925761850787056&hl=en&as_sdt=0,43",5,2020
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection,53,iclr,5,1,2023-06-18 09:10:37.965000,https://github.com/dmzou/RSRAE,36,Robust subspace recovery layer for unsupervised anomaly detection,"https://scholar.google.com/scholar?cluster=11513209509503726282&hl=en&as_sdt=0,39",1,2020
Learning to Coordinate Manipulation Skills via Skill Behavior Diversification,46,iclr,10,2,2023-06-18 09:10:38.169000,https://github.com/clvrai/coordination,39,Learning to coordinate manipulation skills via skill behavior diversification,"https://scholar.google.com/scholar?cluster=5168095143260669466&hl=en&as_sdt=0,11",9,2020
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search,147,iclr,14,3,2023-06-18 09:10:38.374000,https://github.com/automl/nasbench-1shot1,66,Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search,"https://scholar.google.com/scholar?cluster=14286994733629357547&hl=en&as_sdt=0,19",9,2020
How to 0wn the NAS in Your Spare Time,29,iclr,0,0,2023-06-18 09:10:38.578000,https://github.com/Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time,1,How to 0wn nas in your spare time,"https://scholar.google.com/scholar?cluster=6624307467439583182&hl=en&as_sdt=0,5",3,2020
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation,194,iclr,23,13,2023-06-18 09:10:38.783000,https://github.com/nitin-rathi/hybrid-snn-conversion,78,Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation,"https://scholar.google.com/scholar?cluster=2336999671459388564&hl=en&as_sdt=0,33",7,2020
Breaking Certified Defenses: Semantic Adversarial Examples with Spoofed robustness Certificates,53,iclr,4,4,2023-06-18 09:10:38.986000,https://github.com/AminJun/BreakingCertifiableDefenses,16,Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates,"https://scholar.google.com/scholar?cluster=15252610687731481790&hl=en&as_sdt=0,33",5,2020
Query-efficient Meta Attack to Deep Neural Networks,62,iclr,6,21,2023-06-18 09:10:39.189000,https://github.com/dydjw9/MetaAttack_ICLR2020,41,Query-efficient meta attack to deep neural networks,"https://scholar.google.com/scholar?cluster=13046330660709295854&hl=en&as_sdt=0,33",1,2020
Massively Multilingual Sparse Word Representations,1,iclr,1,0,2023-06-18 09:10:39.391000,https://github.com/begab/mamus,13,Massively multilingual sparse word representations,"https://scholar.google.com/scholar?cluster=9628937347076669673&hl=en&as_sdt=0,5",4,2020
Monotonic Multihead Attention,101,iclr,5883,1031,2023-06-18 09:10:39.595000,https://github.com/pytorch/fairseq,26500,Monotonic multihead attention,"https://scholar.google.com/scholar?cluster=15976847532322302730&hl=en&as_sdt=0,19",411,2020
Sparse Coding with Gated Learned ISTA,40,iclr,7,0,2023-06-18 09:10:39.798000,https://github.com/wukailun/GLISTA,23,Sparse coding with gated learned ISTA,"https://scholar.google.com/scholar?cluster=17364655028001424684&hl=en&as_sdt=0,33",4,2020
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification,424,iclr,5,0,2023-06-18 09:10:40.001000,https://github.com/delta2323/gnn-asymptotics,30,Graph neural networks exponentially lose expressive power for node classification,"https://scholar.google.com/scholar?cluster=15290010211141332792&hl=en&as_sdt=0,33",4,2020
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells,61,iclr,18,6,2023-06-18 09:10:40.205000,https://github.com/gengchenmai/space2vec,91,Multi-scale representation learning for spatial feature distributions using grid cells,"https://scholar.google.com/scholar?cluster=5890605928845244555&hl=en&as_sdt=0,11",6,2020
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization,574,iclr,42,3,2023-06-18 09:10:40.408000,https://github.com/fanyun-sun/InfoGraph,267,Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization,"https://scholar.google.com/scholar?cluster=16670911678056840041&hl=en&as_sdt=0,26",7,2020
On Robustness of Neural Ordinary Differential Equations,114,iclr,4,0,2023-06-18 09:10:40.612000,https://github.com/HanshuYAN/TisODE,7,On robustness of neural ordinary differential equations,"https://scholar.google.com/scholar?cluster=12991236712487678100&hl=en&as_sdt=0,39",1,2020
Defending Against Physically Realizable Attacks on Image Classification,86,iclr,9,0,2023-06-18 09:10:40.815000,https://github.com/tongwu2020/phattacks,32,Defending against physically realizable attacks on image classification,"https://scholar.google.com/scholar?cluster=1916491151191652203&hl=en&as_sdt=0,32",2,2020
Estimating Gradients for Discrete Random Variables by Sampling without Replacement,48,iclr,6,0,2023-06-18 09:10:41.018000,https://github.com/wouterkool/estimating-gradients-without-replacement,36,Estimating gradients for discrete random variables by sampling without replacement,"https://scholar.google.com/scholar?cluster=8729691714489659626&hl=en&as_sdt=0,33",5,2020
Learning to Control PDEs with Differentiable Physics,126,iclr,135,2,2023-06-18 09:10:41.221000,https://github.com/tum-pbs/PhiFlow,893,Learning to control pdes with differentiable physics,"https://scholar.google.com/scholar?cluster=7687371584395325411&hl=en&as_sdt=0,18",22,2020
Intensity-Free Learning of Temporal Point Processes,100,iclr,26,3,2023-06-18 09:10:41.424000,https://github.com/shchur/ifl-tpp,63,Intensity-free learning of temporal point processes,"https://scholar.google.com/scholar?cluster=6068412872697213311&hl=en&as_sdt=0,33",5,2020
A Signal Propagation Perspective for Pruning Neural Networks at Initialization,125,iclr,3,2,2023-06-18 09:10:41.627000,https://github.com/namhoonlee/spp-public,14,A signal propagation perspective for pruning neural networks at initialization,"https://scholar.google.com/scholar?cluster=17910397385067453379&hl=en&as_sdt=0,11",5,2020
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets,209,iclr,9,1,2023-06-18 09:10:41.830000,https://github.com/csdongxian/skip-connections-matter,66,Skip connections matter: On the transferability of adversarial examples generated with resnets,"https://scholar.google.com/scholar?cluster=6211233010132912229&hl=en&as_sdt=0,44",4,2020
White Noise Analysis of Neural Networks,1124,iclr,1,0,2023-06-18 09:10:42.033000,https://github.com/aliborji/WhiteNoiseAnalysis,13,A simple white noise analysis of neuronal light responses,"https://scholar.google.com/scholar?cluster=14064393613524789097&hl=en&as_sdt=0,33",2,2020
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search,603,iclr,109,14,2023-06-18 09:10:42.237000,https://github.com/yuhuixu1993/PC-DARTS,419,Pc-darts: Partial channel connections for memory-efficient architecture search,"https://scholar.google.com/scholar?cluster=1268458894093697275&hl=en&as_sdt=0,7",10,2020
Enhancing Adversarial Defense by k-Winners-Take-All,89,iclr,16,0,2023-06-18 09:10:42.440000,https://github.com/a554b554/kWTA-Activation,43,Enhancing adversarial defense by k-winners-take-all,"https://scholar.google.com/scholar?cluster=11915603925298453431&hl=en&as_sdt=0,33",3,2020
Encoding word order in complex embeddings,76,iclr,13,1,2023-06-18 09:10:42.643000,https://github.com/iclr-complex-order/complex-order,78,Encoding word order in complex embeddings,"https://scholar.google.com/scholar?cluster=4348415605145944586&hl=en&as_sdt=0,33",3,2020
DDSP: Differentiable Digital Signal Processing,306,iclr,301,39,2023-06-18 09:10:42.846000,https://github.com/magenta/ddsp,2538,DDSP: Differentiable digital signal processing,"https://scholar.google.com/scholar?cluster=494865138250348922&hl=en&as_sdt=0,33",64,2020
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation,304,iclr,62,30,2023-06-18 09:10:43.050000,https://github.com/hytseng0509/CrossDomainFewShot,294,Cross-domain few-shot classification via learned feature-wise transformation,"https://scholar.google.com/scholar?cluster=7014117950265754591&hl=en&as_sdt=0,31",8,2020
"Ridge Regression: Structure, Cross-Validation, and Sketching",45,iclr,1,0,2023-06-18 09:10:43.253000,https://github.com/liusf15/RidgeRegression,5,"Ridge regression: Structure, cross-validation, and sketching","https://scholar.google.com/scholar?cluster=16996813941555291674&hl=en&as_sdt=0,5",4,2020
Influence-Based Multi-Agent Exploration,82,iclr,5,3,2023-06-18 09:10:43.456000,https://github.com/TonghanWang/EITI-EDTI,25,Influence-based multi-agent exploration,"https://scholar.google.com/scholar?cluster=3107558689865611591&hl=en&as_sdt=0,18",3,2020
Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs,169,iclr,14,10,2023-06-18 09:10:43.659000,https://github.com/AI-nstein/hoppity,54,Hoppity: Learning graph transformations to detect and fix bugs in programs,"https://scholar.google.com/scholar?cluster=3537740923229776123&hl=en&as_sdt=0,10",6,2020
Inductive Matrix Completion Based on Graph Neural Networks,193,iclr,80,5,2023-06-18 09:10:43.862000,https://github.com/muhanzhang/IGMC,330,Inductive matrix completion based on graph neural networks,"https://scholar.google.com/scholar?cluster=16467785209736673104&hl=en&as_sdt=0,5",13,2020
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations,4946,iclr,559,101,2023-06-18 09:10:44.065000,https://github.com/google-research/ALBERT,3115,Albert: A lite bert for self-supervised learning of language representations,"https://scholar.google.com/scholar?cluster=6606720413006378435&hl=en&as_sdt=0,10",75,2020
Symplectic Recurrent Neural Networks,173,iclr,8,1,2023-06-18 09:10:44.273000,https://github.com/zhengdao-chen/SRNN,25,Symplectic recurrent neural networks,"https://scholar.google.com/scholar?cluster=16381042632484621201&hl=en&as_sdt=0,33",4,2020
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning,71,iclr,37,12,2023-06-18 09:10:44.478000,https://github.com/facebookresearch/Hanabi_SAD,90,Simplified action decoder for deep multi-agent reinforcement learning,"https://scholar.google.com/scholar?cluster=17934059469747464722&hl=en&as_sdt=0,33",11,2020
"Real or Not Real, that is the Question",34,iclr,39,6,2023-06-18 09:10:44.682000,https://github.com/kam1107/RealnessGAN,285,"Real or not real, that is the question","https://scholar.google.com/scholar?cluster=1314869860103528088&hl=en&as_sdt=0,33",7,2020
Dream to Control: Learning Behaviors by Latent Imagination,768,iclr,100,4,2023-06-18 09:10:44.885000,https://github.com/danijar/dreamer,446,Dream to control: Learning behaviors by latent imagination,"https://scholar.google.com/scholar?cluster=14974700822970491825&hl=en&as_sdt=0,33",11,2020
A Probabilistic Formulation of Unsupervised Text Style Transfer,102,iclr,25,5,2023-06-18 09:10:45.098000,https://github.com/cindyxinyiwang/deep-latent-sequence-model,160,A probabilistic formulation of unsupervised text style transfer,"https://scholar.google.com/scholar?cluster=12354733292674478284&hl=en&as_sdt=0,34",7,2020
Emergent Tool Use From Multi-Agent Autocurricula,597,iclr,290,26,2023-06-18 09:10:45.312000,https://github.com/openai/multi-agent-emergence-environments,1471,Emergent tool use from multi-agent autocurricula,"https://scholar.google.com/scholar?cluster=428666358348789864&hl=en&as_sdt=0,33",167,2020
Behaviour Suite for Reinforcement Learning,130,iclr,179,16,2023-06-18 09:10:45.514000,https://github.com/deepmind/bsuite,1400,Behaviour suite for reinforcement learning,"https://scholar.google.com/scholar?cluster=10471200174222163517&hl=en&as_sdt=0,5",62,2020
FreeLB: Enhanced Adversarial Training for Natural Language Understanding,332,iclr,38,4,2023-06-18 09:10:45.717000,https://github.com/zhuchen03/FreeLB,242,Freelb: Enhanced adversarial training for natural language understanding,"https://scholar.google.com/scholar?cluster=18174532754984286160&hl=en&as_sdt=0,21",9,2020
Kernelized Wasserstein Natural Gradient,16,iclr,2,1,2023-06-18 09:10:45.921000,https://github.com/MichaelArbel/KWNG,12,Kernelized wasserstein natural gradient,"https://scholar.google.com/scholar?cluster=4819202851249905644&hl=en&as_sdt=0,1",2,2020
And the Bit Goes Down: Revisiting the Quantization of Neural Networks,131,iclr,128,0,2023-06-18 09:10:46.123000,https://github.com/facebookresearch/kill-the-bits,628,And the bit goes down: Revisiting the quantization of neural networks,"https://scholar.google.com/scholar?cluster=9220174723943814446&hl=en&as_sdt=0,46",25,2020
A Latent Morphology Model for Open-Vocabulary Neural Machine Translation,19,iclr,1,0,2023-06-18 09:10:46.354000,https://github.com/d-ataman/lmm,8,A latent morphology model for open-vocabulary neural machine translation,"https://scholar.google.com/scholar?cluster=9869395538651177404&hl=en&as_sdt=0,18",2,2020
Disagreement-Regularized Imitation Learning,77,iclr,11,0,2023-06-18 09:10:46.557000,https://github.com/xkianteb/dril,27,Disagreement-regularized imitation learning,"https://scholar.google.com/scholar?cluster=11799935294964766757&hl=en&as_sdt=0,5",3,2020
Measuring the Reliability of Reinforcement Learning Algorithms,59,iclr,20,0,2023-06-18 09:10:46.760000,https://github.com/google-research/rl-reliability-metrics,143,Measuring the reliability of reinforcement learning algorithms,"https://scholar.google.com/scholar?cluster=921553679446510240&hl=en&as_sdt=0,5",11,2020
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue,120,iclr,11,3,2023-06-18 09:10:46.963000,https://github.com/bckim92/sequential-knowledge-transformer,130,Sequential latent knowledge selection for knowledge-grounded dialogue,"https://scholar.google.com/scholar?cluster=7577905586548983330&hl=en&as_sdt=0,47",5,2020
Self-labelling via simultaneous clustering and representation learning,545,iclr,49,3,2023-06-18 09:10:47.165000,https://github.com/yukimasano/self-label,504,Self-labelling via simultaneous clustering and representation learning,"https://scholar.googleusercontent.com/scholar?q=cache:DDYCGAHoC_AJ:scholar.google.com/+Self-labelling+via+simultaneous+clustering+and+representation+learning&hl=en&as_sdt=0,33",12,2020
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks,139,iclr,24,0,2023-06-18 09:10:47.369000,https://github.com/LeoYu/neural-tangent-kernel-UCI,71,Harnessing the power of infinitely wide deep nets on small-data tasks,"https://scholar.google.com/scholar?cluster=5915084017375187299&hl=en&as_sdt=0,33",7,2020
Differentiation of Blackbox Combinatorial Solvers,98,iclr,35,3,2023-06-18 09:10:47.572000,https://github.com/martius-lab/blackbox-backprop,317,Differentiation of blackbox combinatorial solvers,"https://scholar.google.com/scholar?cluster=3712362332828550033&hl=en&as_sdt=0,47",15,2020
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement,25,iclr,6,0,2023-06-18 09:10:47.775000,https://github.com/panaali/word2ket,45,word2ket: Space-efficient word embeddings inspired by quantum entanglement,"https://scholar.google.com/scholar?cluster=4927881667581942745&hl=en&as_sdt=0,19",5,2020
What Can Neural Networks Reason About?,194,iclr,6,0,2023-06-18 09:10:47.978000,https://github.com/NNReasoning/What-Can-Neural-Networks-Reason-About,42,What can neural networks reason about?,"https://scholar.google.com/scholar?cluster=9843737946108249280&hl=en&as_sdt=0,47",3,2020
Training individually fair ML models with sensitive subspace robustness,91,iclr,14,0,2023-06-18 09:10:48.181000,https://github.com/IBM/sensitive-subspace-robustness,14,Training individually fair ML models with sensitive subspace robustness,"https://scholar.google.com/scholar?cluster=18102623998603329338&hl=en&as_sdt=0,33",5,2020
Learning from Rules Generalizing Labeled Exemplars,68,iclr,5,3,2023-06-18 09:10:48.384000,https://github.com/awasthiabhijeet/Learning-From-Rules,47,Learning from rules generalizing labeled exemplars,"https://scholar.google.com/scholar?cluster=18218931920464777128&hl=en&as_sdt=0,31",4,2020
Directional Message Passing for Molecular Graphs,495,iclr,51,2,2023-06-18 09:10:48.588000,https://github.com/klicperajo/dimenet,238,Directional message passing for molecular graphs,"https://scholar.google.com/scholar?cluster=18349010234285626260&hl=en&as_sdt=0,5",3,2020
Explanation by Progressive Exaggeration,82,iclr,3,4,2023-06-18 09:10:48.791000,https://github.com/batmanlab/Explanation_by_Progressive_Exaggeration,16,Explanation by progressive exaggeration,"https://scholar.google.com/scholar?cluster=14406325811451832998&hl=en&as_sdt=0,33",4,2020
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?,17,iclr,4,0,2023-06-18 09:10:48.994000,https://github.com/paper-submissions/delay_stability,7,At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?,"https://scholar.google.com/scholar?cluster=11430740455622782158&hl=en&as_sdt=0,23",4,2020
Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks,182,iclr,30,1,2023-06-18 09:10:49.197000,https://github.com/RICE-EIC/Early-Bird-Tickets,127,Drawing early-bird tickets: Towards more efficient training of deep networks,"https://scholar.google.com/scholar?cluster=6381702828996735814&hl=en&as_sdt=0,33",6,2020
Truth or backpropaganda? An empirical investigation of deep learning theory,29,iclr,0,0,2023-06-18 09:10:49.401000,https://github.com/goldblum/TruthOrBackpropaganda,16,Truth or backpropaganda? An empirical investigation of deep learning theory,"https://scholar.google.com/scholar?cluster=705485090125694801&hl=en&as_sdt=0,33",4,2020
Neural Arithmetic Units,47,iclr,14,1,2023-06-18 09:10:49.604000,https://github.com/AndreasMadsen/stable-nalu,140,Neural arithmetic units,"https://scholar.google.com/scholar?cluster=10738415609014250822&hl=en&as_sdt=0,33",9,2020
DeepSphere: a graph-based spherical CNN,67,iclr,1,0,2023-06-18 09:10:49.807000,https://github.com/deepsphere/deepsphere-tf1,12,DeepSphere: a graph-based spherical CNN,"https://scholar.google.com/scholar?cluster=17982837150918641650&hl=en&as_sdt=0,33",7,2020
Energy-based models for atomic-resolution protein conformations,41,iclr,18,1,2023-06-18 09:10:50.010000,https://github.com/facebookresearch/protein-ebm,89,Energy-based models for atomic-resolution protein conformations,"https://scholar.google.com/scholar?cluster=1721264646237527179&hl=en&as_sdt=0,4",8,2020
Progressive Learning and Disentanglement of Hierarchical Representations,31,iclr,3,0,2023-06-18 09:10:50.213000,https://github.com/Zhiyuan1991/proVLAE,27,Progressive learning and disentanglement of hierarchical representations,"https://scholar.google.com/scholar?cluster=478514677450330118&hl=en&as_sdt=0,33",3,2020
Geom-GCN: Geometric Graph Convolutional Networks,578,iclr,66,0,2023-06-18 09:10:50.416000,https://github.com/graphdml-uiuc-jlu/geom-gcn,253,Geom-gcn: Geometric graph convolutional networks,"https://scholar.google.com/scholar?cluster=10425996329335567417&hl=en&as_sdt=0,33",10,2020
On the Convergence of FedAvg on Non-IID Data,1417,iclr,63,1,2023-06-18 09:10:50.620000,https://github.com/lx10077/fedavgpy,205,On the convergence of fedavg on non-iid data,"https://scholar.google.com/scholar?cluster=3930147365387936427&hl=en&as_sdt=0,31",5,2020
Contrastive Learning of Structured World Models,222,iclr,69,10,2023-06-18 09:10:50.823000,https://github.com/tkipf/c-swm,380,Contrastive learning of structured world models,"https://scholar.google.com/scholar?cluster=11077069577434733177&hl=en&as_sdt=0,33",14,2020
Neural Network Branching for Neural Network Verification,54,iclr,2,2,2023-06-18 09:10:51.026000,https://github.com/oval-group/GNN_branching,9,Neural network branching for neural network verification,"https://scholar.google.com/scholar?cluster=3408814607972511538&hl=en&as_sdt=0,23",11,2020
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity,272,iclr,6,2,2023-06-18 09:10:51.229000,https://github.com/JingzhaoZhang/why-clipping-accelerates,41,Why gradient clipping accelerates training: A theoretical justification for adaptivity,"https://scholar.google.com/scholar?cluster=2986024522916828418&hl=en&as_sdt=0,5",3,2020
Mogrifier LSTM,104,iclr,23,5,2023-06-18 09:10:51.432000,https://github.com/deepmind/lamb,130,Mogrifier lstm,"https://scholar.google.com/scholar?cluster=5142385516232440833&hl=en&as_sdt=0,33",10,2020
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning,211,iclr,21,3,2023-06-18 09:10:51.636000,https://github.com/ruqizhang/csgmcmc,89,Cyclical stochastic gradient MCMC for Bayesian deep learning,"https://scholar.google.com/scholar?cluster=10285617544422902301&hl=en&as_sdt=0,33",7,2020
Your classifier is secretly an energy based model and you should treat it like one,379,iclr,61,8,2023-06-18 09:10:51.840000,https://github.com/wgrathwohl/JEM,387,Your classifier is secretly an energy based model and you should treat it like one,"https://scholar.google.com/scholar?cluster=13087658900756056358&hl=en&as_sdt=0,33",16,2020
Dynamics-Aware Unsupervised Discovery of Skills,276,iclr,49,6,2023-06-18 09:10:52.043000,https://github.com/google-research/dads,171,Dynamics-aware unsupervised discovery of skills,"https://scholar.google.com/scholar?cluster=17528482615651308176&hl=en&as_sdt=0,5",7,2020
Optimal Strategies Against Generative Attacks,4,iclr,0,4,2023-06-18 09:10:52.247000,https://github.com/roymor1/OptimalStrategiesAgainstGenerativeAttacks,8,Optimal strategies against generative attacks,"https://scholar.google.com/scholar?cluster=4890495554214932299&hl=en&as_sdt=0,25",3,2020
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding,96,iclr,14,5,2023-06-18 09:10:52.452000,https://github.com/cornell-zhang/GraphZoom,102,Graphzoom: A multi-level spectral approach for accurate and scalable graph embedding,"https://scholar.google.com/scholar?cluster=10802093213472366412&hl=en&as_sdt=0,33",10,2020
Harnessing Structures for Value-Based Planning and Reinforcement Learning,26,iclr,6,0,2023-06-18 09:10:52.655000,https://github.com/YyzHarry/SV-RL,33,Harnessing structures for value-based planning and reinforcement learning,"https://scholar.google.com/scholar?cluster=4756177392092487919&hl=en&as_sdt=0,21",4,2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning,289,iclr,11,2,2023-06-18 09:10:52.858000,https://github.com/lottery-ticket/rewinding-iclr20-public,66,Comparing rewinding and fine-tuning in neural network pruning,"https://scholar.google.com/scholar?cluster=15288579142798778406&hl=en&as_sdt=0,33",5,2020
Meta-Q-Learning,107,iclr,17,0,2023-06-18 09:10:53.061000,https://github.com/amazon-research/meta-q-learning,91,Meta-q-learning,"https://scholar.google.com/scholar?cluster=2865388954464396222&hl=en&as_sdt=0,33",5,2020
"Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds",430,iclr,26,9,2023-06-18 09:10:53.264000,https://github.com/JordanAsh/badge,150,"Deep batch active learning by diverse, uncertain gradient lower bounds","https://scholar.google.com/scholar?cluster=5483695014257396730&hl=en&as_sdt=0,33",5,2020
Understanding and Robustifying Differentiable Architecture Search,299,iclr,38,2,2023-06-18 09:10:53.468000,https://github.com/automl/RobustDARTS,150,Understanding and robustifying differentiable architecture search,"https://scholar.google.com/scholar?cluster=16596643818035948993&hl=en&as_sdt=0,33",11,2020
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks,93,iclr,9,0,2023-06-18 09:10:53.671000,https://github.com/haebeom-lee/l2b,96,Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks,"https://scholar.google.com/scholar?cluster=5580496720042011830&hl=en&as_sdt=0,22",4,2020
RNA Secondary Structure Prediction By Learning Unrolled Algorithms,67,iclr,18,7,2023-06-18 09:10:53.876000,https://github.com/ml4bio/e2efold,79,RNA secondary structure prediction by learning unrolled algorithms,"https://scholar.google.com/scholar?cluster=973131369176852672&hl=en&as_sdt=0,33",9,2020
Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search,24,iclr,21,3,2023-06-18 09:10:54.087000,https://github.com/liuanji/WU-UCT,90,Watch the unobserved: A simple approach to parallelizing monte carlo tree search,"https://scholar.google.com/scholar?cluster=11844597295814428948&hl=en&as_sdt=0,33",5,2020
Causal Discovery with Reinforcement Learning,166,iclr,164,18,2023-06-18 09:10:54.297000,https://github.com/huawei-noah/trustworthyAI,734,Causal discovery with reinforcement learning,"https://scholar.google.com/scholar?cluster=15746962195892177964&hl=en&as_sdt=0,44",21,2020
Building Deep Equivariant Capsule Networks,34,iclr,4,0,2023-06-18 09:10:54.499000,https://github.com/sairaamVenkatraman/SOVNET,11,"Building deep, equivariant capsule networks","https://scholar.google.com/scholar?cluster=14724285179956079&hl=en&as_sdt=0,33",1,2020
A Generalized Training Approach for Multiagent Learning,70,iclr,820,36,2023-06-18 09:10:54.703000,https://github.com/deepmind/open_spiel,3698,A generalized training approach for multiagent learning,"https://scholar.google.com/scholar?cluster=15325169882978328378&hl=en&as_sdt=0,21",106,2020
High Fidelity Speech Synthesis with Adversarial Networks,235,iclr,11,1,2023-06-18 09:10:54.906000,https://github.com/mbinkowski/DeepSpeechDistances,123,High fidelity speech synthesis with adversarial networks,"https://scholar.google.com/scholar?cluster=11783894509127365289&hl=en&as_sdt=0,21",7,2020
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference,109,iclr,144,0,2023-06-18 09:10:55.110000,https://github.com/google-research/seed_rl,770,Seed rl: Scalable and efficient deep-rl with accelerated central inference,"https://scholar.google.com/scholar?cluster=5459654094981321816&hl=en&as_sdt=0,1",47,2020
Meta-Learning with Warped Gradient Descent,193,iclr,18,4,2023-06-18 09:10:55.313000,https://github.com/flennerhag/warpgrad,87,Meta-learning with warped gradient descent,"https://scholar.google.com/scholar?cluster=11176205486602510509&hl=en&as_sdt=0,33",2,2020
Convolutional Conditional Neural Processes,101,iclr,18,2,2023-06-18 09:10:55.516000,https://github.com/cambridge-mlg/convcnp,109,Convolutional conditional neural processes,"https://scholar.google.com/scholar?cluster=12448908036618273456&hl=en&as_sdt=0,44",13,2020
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks,223,iclr,1,0,2023-06-18 09:10:55.724000,https://github.com/vfleaking/max-margin,4,Gradient descent maximizes the margin of homogeneous neural networks,"https://scholar.google.com/scholar?cluster=383487913613560767&hl=en&as_sdt=0,31",2,2020
Adversarial Training and Provable Defenses: Bridging the Gap,133,iclr,3,6,2023-06-18 09:10:55.926000,https://github.com/eth-sri/colt,28,Adversarial training and provable defenses: Bridging the gap,"https://scholar.google.com/scholar?cluster=16785232142228633680&hl=en&as_sdt=0,31",9,2020
Federated Learning with Matched Averaging,650,iclr,85,15,2023-06-18 09:10:56.129000,https://github.com/IBM/FedMA,294,Federated learning with matched averaging,"https://scholar.google.com/scholar?cluster=5955953368413772471&hl=en&as_sdt=0,7",13,2020
Learning to Reach Goals via Iterated Supervised Learning,82,iclr,9,8,2023-06-18 09:24:19.731000,https://github.com/dibyaghosh/gcsl,68,Learning to reach goals via iterated supervised learning,"https://scholar.google.com/scholar?cluster=9578485446756907663&hl=en&as_sdt=0,43",6,2021
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients,142,iclr,87,9,2023-06-18 09:24:19.935000,https://github.com/brendenpetersen/deep-symbolic-regression,374,Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients,"https://scholar.google.com/scholar?cluster=17597706484193834031&hl=en&as_sdt=0,5",12,2021
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes,14,iclr,5,6,2023-06-18 09:24:20.138000,https://github.com/cgartrel/nonsymmetric-DPP-learning,20,Scalable learning and MAP inference for nonsymmetric determinantal point processes,"https://scholar.google.com/scholar?cluster=14875734078245489785&hl=en&as_sdt=0,44",3,2021
Randomized Automatic Differentiation,15,iclr,8,0,2023-06-18 09:24:20.341000,https://github.com/PrincetonLIPS/RandomizedAutomaticDifferentiation,63,Randomized automatic differentiation,"https://scholar.google.com/scholar?cluster=6609236106251590049&hl=en&as_sdt=0,33",7,2021
Rethinking Attention with Performers,838,iclr,7332,1026,2023-06-18 09:24:20.544000,https://github.com/google-research/google-research,29803,Rethinking attention with performers,"https://scholar.google.com/scholar?cluster=8431737427115756173&hl=en&as_sdt=0,47",728,2021
When Do Curricula Work?,75,iclr,12,1,2023-06-18 09:24:20.747000,https://github.com/google-research/understanding-curricula,30,When do curricula work?,"https://scholar.google.com/scholar?cluster=3107359508568919583&hl=en&as_sdt=0,5",7,2021
Federated Learning Based on Dynamic Regularization,317,iclr,18,0,2023-06-18 09:24:20.950000,https://github.com/alpemreacar/FedDyn,46,Federated learning based on dynamic regularization,"https://scholar.google.com/scholar?cluster=10329355946947839611&hl=en&as_sdt=0,29",2,2021
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity,92,iclr,9,0,2023-06-18 09:24:21.153000,https://github.com/snu-mllab/Co-Mixup,101,Co-mixup: Saliency guided joint mixup with supermodular diversity,"https://scholar.google.com/scholar?cluster=11593453321688788497&hl=en&as_sdt=0,36",8,2021
Dataset Condensation with Gradient Matching,138,iclr,73,0,2023-06-18 09:24:21.356000,https://github.com/VICO-UoE/DatasetCondensation,331,Dataset condensation with gradient matching,"https://scholar.google.com/scholar?cluster=4284286750665251123&hl=en&as_sdt=0,34",9,2021
Rethinking Architecture Selection in Differentiable NAS,109,iclr,13,7,2023-06-18 09:24:21.558000,https://github.com/ruocwang/darts-pt,93,Rethinking architecture selection in differentiable nas,"https://scholar.google.com/scholar?cluster=803192450904020326&hl=en&as_sdt=0,33",1,2021
A Distributional Approach to Controlled Text Generation,61,iclr,21,0,2023-06-18 09:24:21.761000,https://github.com/naver/gdc,108,A distributional approach to controlled text generation,"https://scholar.google.com/scholar?cluster=15785314016898136958&hl=en&as_sdt=0,10",10,2021
Learning Invariant Representations for Reinforcement Learning without Reconstruction,281,iclr,34,10,2023-06-18 09:24:21.965000,https://github.com/facebookresearch/deep_bisim4control,129,Learning invariant representations for reinforcement learning without reconstruction,"https://scholar.google.com/scholar?cluster=12190335456477106043&hl=en&as_sdt=0,33",5,2021
Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs,78,iclr,93,33,2023-06-18 09:24:22.168000,https://github.com/XingangPan/GAN2Shape,541,Do 2d gans know 3d shape? unsupervised 3d shape reconstruction from 2d image gans,"https://scholar.google.com/scholar?cluster=8733088455639387061&hl=en&as_sdt=0,5",34,2021
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments,27,iclr,9,1,2023-06-18 09:24:22.373000,https://github.com/lushleaf/varying-coefficient-net-with-functional-tr,32,Vcnet and functional targeted regularization for learning causal effects of continuous treatments,"https://scholar.google.com/scholar?cluster=15633273494286118783&hl=en&as_sdt=0,23",1,2021
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability,29,iclr,0,0,2023-06-18 09:24:22.583000,https://github.com/idiap/rethinking-saliency,3,Rethinking the role of gradient-based attribution methods for model interpretability,"https://scholar.google.com/scholar?cluster=17373814268606866605&hl=en&as_sdt=0,33",4,2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,17087,iclr,979,108,2023-06-18 09:24:22.794000,https://github.com/google-research/vision_transformer,7392,An image is worth 16x16 words: Transformers for image recognition at scale,"https://scholar.google.com/scholar?cluster=6504906206403591467&hl=en&as_sdt=0,42",83,2021
Deformable DETR: Deformable Transformers for End-to-End Object Detection,2249,iclr,406,135,2023-06-18 09:24:22.996000,https://github.com/fundamentalvision/Deformable-DETR,2366,Deformable detr: Deformable transformers for end-to-end object detection,"https://scholar.google.com/scholar?cluster=7911999856845003856&hl=en&as_sdt=0,6",32,2021
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting,86,iclr,4,1,2023-06-18 09:24:23.199000,https://github.com/yuan-yin/aphynity,30,Augmenting physical models with deep networks for complex dynamics forecasting,"https://scholar.google.com/scholar?cluster=11618345269923974227&hl=en&as_sdt=0,33",1,2021
Complex Query Answering with Neural Link Predictors,67,iclr,9,0,2023-06-18 09:24:23.402000,https://github.com/uclnlp/cqd,85,Complex query answering with neural link predictors,"https://scholar.google.com/scholar?cluster=8823088409575332587&hl=en&as_sdt=0,39",8,2021
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding,67,iclr,9,1,2023-06-18 09:24:23.605000,https://github.com/bethgelab/slow_disentanglement,67,Towards nonlinear disentanglement in natural data with temporal sparse coding,"https://scholar.google.com/scholar?cluster=6210780149209435477&hl=en&as_sdt=0,33",13,2021
Self-training For Few-shot Transfer Across Extreme Task Differences,71,iclr,7,2,2023-06-18 09:24:23.818000,https://github.com/cpphoo/STARTUP,36,Self-training for few-shot transfer across extreme task differences,"https://scholar.google.com/scholar?cluster=13876494869867170602&hl=en&as_sdt=0,5",5,2021
Score-Based Generative Modeling through Stochastic Differential Equations,1196,iclr,146,13,2023-06-18 09:24:24.021000,https://github.com/yang-song/score_sde,997,Score-based generative modeling through stochastic differential equations,"https://scholar.google.com/scholar?cluster=14592788616550656262&hl=en&as_sdt=0,33",15,2021
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions,26,iclr,2,2,2023-06-18 09:24:24.224000,https://github.com/SuerpX/Embedded-Self-Predictions,5,Contrastive explanations for reinforcement learning via embedded self predictions,"https://scholar.google.com/scholar?cluster=13298349970407315344&hl=en&as_sdt=0,33",1,2021
Gradient Projection Memory for Continual Learning,109,iclr,16,4,2023-06-18 09:24:24.427000,https://github.com/sahagobinda/GPM,58,Gradient projection memory for continual learning,"https://scholar.google.com/scholar?cluster=17694030675794523744&hl=en&as_sdt=0,5",3,2021
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies,49,iclr,7,0,2023-06-18 09:24:24.630000,https://github.com/tk-rusch/coRNN,31,Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies,"https://scholar.google.com/scholar?cluster=12873705644376791624&hl=en&as_sdt=0,11",3,2021
Dynamic Tensor Rematerialization,46,iclr,14,6,2023-06-18 09:24:24.833000,https://github.com/uwsampl/dtr-prototype,119,Dynamic tensor rematerialization,"https://scholar.google.com/scholar?cluster=12055190010589601446&hl=en&as_sdt=0,26",12,2021
CPT: Efficient Deep Neural Network Training via Cyclic Precision,23,iclr,4,2,2023-06-18 09:24:25.037000,https://github.com/RICE-EIC/CPT,27,Cpt: Efficient deep neural network training via cyclic precision,"https://scholar.google.com/scholar?cluster=3211001313795403006&hl=en&as_sdt=0,5",4,2021
Expressive Power of Invariant and Equivariant Graph Neural Networks,94,iclr,18,0,2023-06-18 09:24:25.240000,https://github.com/mlelarge/graph_neural_net,37,Expressive power of invariant and equivariant graph neural networks,"https://scholar.google.com/scholar?cluster=50497212731966151&hl=en&as_sdt=0,44",4,2021
Model-Based Visual Planning with Self-Supervised Functional Distances,36,iclr,2,1,2023-06-18 09:24:25.448000,https://github.com/s-tian/mbold,18,Model-based visual planning with self-supervised functional distances,"https://scholar.google.com/scholar?cluster=15181192101393533301&hl=en&as_sdt=0,48",1,2021
VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models,68,iclr,5,2,2023-06-18 09:24:25.653000,https://github.com/NVlabs/VAEBM,51,Vaebm: A symbiosis between variational autoencoders and energy-based models,"https://scholar.google.com/scholar?cluster=16833810899074704050&hl=en&as_sdt=0,33",4,2021
Geometry-Aware Gradient Algorithms for Neural Architecture Search,60,iclr,8,0,2023-06-18 09:24:25.856000,https://github.com/liamcli/gaea_release,18,Geometry-aware gradient algorithms for neural architecture search,"https://scholar.google.com/scholar?cluster=1063083377324377559&hl=en&as_sdt=0,33",2,2021
Autoregressive Entity Retrieval,220,iclr,88,12,2023-06-18 09:24:26.059000,https://github.com/facebookresearch/GENRE,678,Autoregressive entity retrieval,"https://scholar.google.com/scholar?cluster=12682955665631142454&hl=en&as_sdt=0,36",19,2021
Learning with Feature-Dependent Label Noise: A Progressive Approach,84,iclr,9,2,2023-06-18 09:24:26.262000,https://github.com/pxiangwu/PLC,40,Learning with feature-dependent label noise: A progressive approach,"https://scholar.google.com/scholar?cluster=18267610289621295878&hl=en&as_sdt=0,44",3,2021
Dataset Inference: Ownership Resolution in Machine Learning,47,iclr,5,0,2023-06-18 09:24:26.466000,https://github.com/cleverhans-lab/dataset-inference,20,Dataset inference: Ownership resolution in machine learning,"https://scholar.google.com/scholar?cluster=13973590273303252355&hl=en&as_sdt=0,33",3,2021
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks,136,iclr,62,46,2023-06-18 09:24:26.669000,https://github.com/zsyzzsoft/co-mod-gan,396,Large scale image completion via co-modulated generative adversarial networks,"https://scholar.google.com/scholar?cluster=640233925925041896&hl=en&as_sdt=0,33",13,2021
Sharpness-aware Minimization for Efficiently Improving Generalization,635,iclr,62,13,2023-06-18 09:24:26.871000,https://github.com/google-research/sam,456,Sharpness-aware minimization for efficiently improving generalization,"https://scholar.google.com/scholar?cluster=10001060203038731755&hl=en&as_sdt=0,15",9,2021
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images,193,iclr,77,12,2023-06-18 09:24:27.077000,https://github.com/openai/vdvae,402,Very deep vaes generalize autoregressive models and can outperform them on images,"https://scholar.google.com/scholar?cluster=4071942168841188529&hl=en&as_sdt=0,5",109,2021
Data-Efficient Reinforcement Learning with Self-Predictive Representations,162,iclr,26,4,2023-06-18 09:24:27.282000,https://github.com/mila-iqia/spr,133,Data-efficient reinforcement learning with self-predictive representations,"https://scholar.google.com/scholar?cluster=13957280220130364108&hl=en&as_sdt=0,33",9,2021
Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration,53,iclr,15,4,2023-06-18 09:24:27.487000,https://github.com/xavierpuigf/watch_and_help,51,Watch-and-help: A challenge for social perception and human-ai collaboration,"https://scholar.google.com/scholar?cluster=16340001407726295133&hl=en&as_sdt=0,19",7,2021
A Good Image Generator Is What You Need for High-Resolution Video Synthesis,76,iclr,22,1,2023-06-18 09:24:27.692000,https://github.com/snap-research/MoCoGAN-HD,230,A good image generator is what you need for high-resolution video synthesis,"https://scholar.google.com/scholar?cluster=10838620537951090836&hl=en&as_sdt=0,5",24,2021
Improving Adversarial Robustness via Channel-wise Activation Suppressing,74,iclr,8,1,2023-06-18 09:24:27.896000,https://github.com/bymavis/CAS_ICLR2021,51,Improving adversarial robustness via channel-wise activation suppressing,"https://scholar.google.com/scholar?cluster=16315998776184141539&hl=en&as_sdt=0,11",1,2021
Unlearnable Examples: Making Personal Data Unexploitable,70,iclr,14,4,2023-06-18 09:24:28.106000,https://github.com/HanxunH/Unlearnable-Examples,119,Unlearnable examples: Making personal data unexploitable,"https://scholar.google.com/scholar?cluster=17937052451720151059&hl=en&as_sdt=0,23",3,2021
Learning Mesh-Based Simulation with Graph Networks,370,iclr,2435,170,2023-06-18 09:24:28.316000,https://github.com/deepmind/deepmind-research,11911,Learning mesh-based simulation with graph networks,"https://scholar.google.com/scholar?cluster=7248438205563105155&hl=en&as_sdt=0,48",336,2021
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking,124,iclr,9,2,2023-06-18 09:24:28.519000,https://github.com/michschli/graphmask,32,Interpreting graph neural networks for nlp with differentiable edge masking,"https://scholar.google.com/scholar?cluster=17658586374087115859&hl=en&as_sdt=0,33",3,2021
Tent: Fully Test-Time Adaptation by Entropy Minimization,384,iclr,39,11,2023-06-18 09:24:28.722000,https://github.com/DequanWang/tent,259,Tent: Fully test-time adaptation by entropy minimization,"https://scholar.google.com/scholar?cluster=2996193136579278806&hl=en&as_sdt=0,5",15,2021
Predicting Infectiousness for Proactive Contact Tracing,17,iclr,1,1,2023-06-18 09:24:28.925000,https://github.com/mila-iqia/COVI-ML,12,Predicting infectiousness for proactive contact tracing,"https://scholar.google.com/scholar?cluster=8263900041412831777&hl=en&as_sdt=0,10",15,2021
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control,47,iclr,0,0,2023-06-18 09:24:29.128000,https://github.com/anonymouscode114/iclr2021_rlreg,1,Regularization Matters in Policy Optimization--An Empirical Study on Continuous Control,"https://scholar.google.com/scholar?cluster=17313044144719497988&hl=en&as_sdt=0,44",1,2021
Towards Robustness Against Natural Language Word Substitutions,75,iclr,4,0,2023-06-18 09:24:29.331000,https://github.com/dongxinshuai/ASCC,26,Towards robustness against natural language word substitutions,"https://scholar.google.com/scholar?cluster=9278627648882779677&hl=en&as_sdt=0,10",2,2021
Structured Prediction as Translation between Augmented Natural Languages,137,iclr,24,2,2023-06-18 09:24:29.534000,https://github.com/amazon-research/tanl,112,Structured prediction as translation between augmented natural languages,"https://scholar.google.com/scholar?cluster=11540512380172595430&hl=en&as_sdt=0,47",5,2021
Emergent Symbols through Binding in External Memory,31,iclr,6,0,2023-06-18 09:24:29.737000,https://github.com/taylorwwebb/emergent_symbols,17,Emergent symbols through binding in external memory,"https://scholar.google.com/scholar?cluster=6169432592073428363&hl=en&as_sdt=0,33",1,2021
Influence Estimation for Generative Adversarial Networks,8,iclr,0,0,2023-06-18 09:24:29.939000,https://github.com/hitachi-rd-cv/influence-estimation-for-gans,2,Influence estimation for generative adversarial networks,"https://scholar.google.com/scholar?cluster=14900396530057016144&hl=en&as_sdt=0,33",1,2021
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics,58,iclr,24,4,2023-06-18 09:24:30.142000,https://github.com/hzaskywalker/PlasticineLab,107,Plasticinelab: A soft-body manipulation benchmark with differentiable physics,"https://scholar.google.com/scholar?cluster=4373640289744241183&hl=en&as_sdt=0,47",5,2021
Implicit Normalizing Flows,27,iclr,6,2,2023-06-18 09:24:30.344000,https://github.com/thu-ml/implicit-normalizing-flows,34,Implicit normalizing flows,"https://scholar.google.com/scholar?cluster=12318247723954884767&hl=en&as_sdt=0,10",9,2021
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts,191,iclr,24,0,2023-06-18 09:24:30.547000,https://github.com/frank-xwang/RIDE-LongTailRecognition,221,Long-tailed recognition by routing diverse distribution-aware experts,"https://scholar.google.com/scholar?cluster=13544394725234163867&hl=en&as_sdt=0,33",6,2021
Differentially Private Learning Needs Better Features (or Much More Data),144,iclr,15,0,2023-06-18 09:24:30.750000,https://github.com/ftramer/Handcrafted-DP,67,Differentially private learning needs better features (or much more data),"https://scholar.google.com/scholar?cluster=17298633673163365273&hl=en&as_sdt=0,33",1,2021
Unsupervised Object Keypoint Learning using Local Spatial Predictability,21,iclr,2,1,2023-06-18 09:24:30.953000,https://github.com/agopal42/permakey,10,Unsupervised object keypoint learning using local spatial predictability,"https://scholar.google.com/scholar?cluster=2846223975982040461&hl=en&as_sdt=0,43",3,2021
DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs,11,iclr,117,2,2023-06-18 09:24:31.156000,https://github.com/maximecb/gym-miniworld,610,Deepaveragers: Offline reinforcement learning by solving derived non-parametric mdps,"https://scholar.google.com/scholar?cluster=11392379415434294495&hl=en&as_sdt=0,33",18,2021
Learning from Protein Structure with Geometric Vector Perceptrons,141,iclr,35,8,2023-06-18 09:24:31.359000,https://github.com/drorlab/gvp-pytorch,167,Learning from protein structure with geometric vector perceptrons,"https://scholar.google.com/scholar?cluster=151372908751868472&hl=en&as_sdt=0,33",11,2021
Undistillable: Making A Nasty Teacher That CANNOT teach students,24,iclr,12,3,2023-06-18 09:24:31.562000,https://github.com/VITA-Group/Nasty-Teacher,77,Undistillable: Making a nasty teacher that cannot teach students,"https://scholar.google.com/scholar?cluster=3474115554286885687&hl=en&as_sdt=0,10",12,2021
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows,112,iclr,168,63,2023-06-18 09:24:31.765000,https://github.com/zalandoresearch/pytorch-ts,1006,Multivariate probabilistic time series forecasting via conditioned normalizing flows,"https://scholar.google.com/scholar?cluster=1580250645511202930&hl=en&as_sdt=0,33",24,2021
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels,484,iclr,51,2,2023-06-18 09:24:31.968000,https://github.com/denisyarats/drq,376,Image augmentation is all you need: Regularizing deep reinforcement learning from pixels,"https://scholar.google.com/scholar?cluster=11402905305811900268&hl=en&as_sdt=0,33",13,2021
A Gradient Flow Framework For Analyzing Network Pruning,28,iclr,7,0,2023-06-18 09:24:32.171000,https://github.com/EkdeepSLubana/flowandprune,21,A gradient flow framework for analyzing network pruning,"https://scholar.google.com/scholar?cluster=82764651389872637&hl=en&as_sdt=0,5",2,2021
The Intrinsic Dimension of Images and Its Impact on Learning,100,iclr,5,1,2023-06-18 09:24:32.374000,https://github.com/ppope/dimensions,49,The intrinsic dimension of images and its impact on learning,"https://scholar.google.com/scholar?cluster=4972021380000634715&hl=en&as_sdt=0,44",6,2021
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy,4,iclr,1,0,2023-06-18 09:24:32.578000,https://github.com/TaikiMiyagawa/SPRT-TANDEM,12,Sequential density ratio estimation for simultaneous optimization of speed and accuracy,"https://scholar.google.com/scholar?cluster=17595723028286278692&hl=en&as_sdt=0,10",4,2021
"A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference",33,iclr,1,0,2023-06-18 09:24:32.781000,https://github.com/sanghyun-hong/deepsloth,13,"A panda? no, it's a sloth: Slowdown attacks on adaptive multi-exit neural network inference","https://scholar.google.com/scholar?cluster=7387967890679036055&hl=en&as_sdt=0,43",2,2021
Orthogonalizing Convolutional Layers with the Cayley Transform,62,iclr,7,0,2023-06-18 09:24:32.984000,https://github.com/locuslab/orthogonal-convolutions,36,Orthogonalizing convolutional layers with the cayley transform,"https://scholar.google.com/scholar?cluster=7972253340344904687&hl=en&as_sdt=0,33",3,2021
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning,23,iclr,5,1,2023-06-18 09:24:33.187000,https://github.com/db-Lee/Meta-GMVAE,33,Meta-gmvae: Mixture of gaussian vae for unsupervised meta-learning,"https://scholar.google.com/scholar?cluster=2848780669531491814&hl=en&as_sdt=0,33",2,2021
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN,78,iclr,1,1,2023-06-18 09:24:33.391000,https://github.com/shangqing-liu/CCSD-benchmark-for-code-summarization,17,Retrieval-augmented generation for code summarization via hybrid gnn,"https://scholar.google.com/scholar?cluster=1074914140927042539&hl=en&as_sdt=0,33",2,2021
Self-supervised Visual Reinforcement Learning with Object-centric Representations,18,iclr,3,0,2023-06-18 09:24:33.596000,https://github.com/martius-lab/SMORL,19,Self-supervised visual reinforcement learning with object-centric representations,"https://scholar.google.com/scholar?cluster=14115681907548561734&hl=en&as_sdt=0,19",4,2021
Neural Topic Model via Optimal Transport,26,iclr,5,0,2023-06-18 09:24:33.807000,https://github.com/ethanhezhao/NeuralSinkhornTopicModel,14,Neural topic model via optimal transport,"https://scholar.google.com/scholar?cluster=689828574745146932&hl=en&as_sdt=0,14",1,2021
Memory Optimization for Deep Networks,14,iclr,19,1,2023-06-18 09:24:34.023000,https://github.com/utsaslab/MONeT,166,Memory optimization for deep networks,"https://scholar.google.com/scholar?cluster=6587488061913328550&hl=en&as_sdt=0,5",10,2021
Stabilized Medical Image Attacks,19,iclr,1,3,2023-06-18 09:24:34.226000,https://github.com/imogenqi/SMA,5,Stabilized medical image attacks,"https://scholar.google.com/scholar?cluster=5943786222126044204&hl=en&as_sdt=0,5",1,2021
Quantifying Differences in Reward Functions,34,iclr,5,6,2023-06-18 09:24:34.431000,https://github.com/HumanCompatibleAI/evaluating-rewards,52,Quantifying differences in reward functions,"https://scholar.google.com/scholar?cluster=3868524216566349741&hl=en&as_sdt=0,33",10,2021
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery,77,iclr,1,0,2023-06-18 09:24:34.637000,https://github.com/yutxie/mars,2,Mars: Markov molecular sampling for multi-objective drug discovery,"https://scholar.google.com/scholar?cluster=3117547435494031636&hl=en&as_sdt=0,47",1,2021
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs,81,iclr,5,2,2023-06-18 09:24:34.866000,https://github.com/qualcomm-ai-research/gauge-equivariant-mesh-cnn,56,Gauge equivariant mesh CNNs: Anisotropic convolutions on geometric graphs,"https://scholar.google.com/scholar?cluster=17703338276692634777&hl=en&as_sdt=0,5",5,2021
Revisiting Dynamic Convolution via Matrix Decomposition,34,iclr,13,4,2023-06-18 09:24:35.070000,https://github.com/liyunsheng13/dcd,116,Revisiting dynamic convolution via matrix decomposition,"https://scholar.google.com/scholar?cluster=18300094964606568091&hl=en&as_sdt=0,7",5,2021
Explainable Deep One-Class Classification,138,iclr,58,10,2023-06-18 09:24:35.273000,https://github.com/liznerski/fcdd,198,Explainable deep one-class classification,"https://scholar.google.com/scholar?cluster=1382712243609022780&hl=en&as_sdt=0,31",10,2021
Neural Pruning via Growing Regularization,71,iclr,17,0,2023-06-18 09:24:35.476000,https://github.com/mingsun-tse/regularization-pruning,70,Neural pruning via growing regularization,"https://scholar.google.com/scholar?cluster=12329421876682813123&hl=en&as_sdt=0,6",5,2021
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition,36,iclr,21,2,2023-06-18 09:24:35.680000,https://github.com/LeePleased/NegSampling-NER,130,Empirical analysis of unlabeled entity problem in named entity recognition,"https://scholar.google.com/scholar?cluster=2091894969577971912&hl=en&as_sdt=0,5",2,2021
Nearest Neighbor Machine Translation,160,iclr,41,4,2023-06-18 09:24:35.883000,https://github.com/urvashik/knnlm,253,Nearest neighbor machine translation,"https://scholar.google.com/scholar?cluster=6208883901750253359&hl=en&as_sdt=0,10",7,2021
Wandering within a world: Online contextualized few-shot learning,25,iclr,6,2,2023-06-18 09:24:36.087000,https://github.com/renmengye/oc-fewshot-public,21,Wandering within a world: Online contextualized few-shot learning,"https://scholar.google.com/scholar?cluster=17017727329271450811&hl=en&as_sdt=0,5",8,2021
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models,63,iclr,12,2,2023-06-18 09:24:36.289000,https://github.com/datake/AdaGCN,50,Adagcn: Adaboosting graph convolutional networks into deep models,"https://scholar.google.com/scholar?cluster=9537937835922263498&hl=en&as_sdt=0,3",4,2021
Meta Back-Translation,16,iclr,7332,1026,2023-06-18 09:24:36.493000,https://github.com/google-research/google-research,29803,Meta back-translation,"https://scholar.google.com/scholar?cluster=8104983143273406902&hl=en&as_sdt=0,5",728,2021
Viewmaker Networks: Learning Views for Unsupervised Representation Learning,49,iclr,11,2,2023-06-18 09:24:36.695000,https://github.com/alextamkin/viewmaker,32,Viewmaker networks: Learning views for unsupervised representation learning,"https://scholar.google.com/scholar?cluster=5109645673103206177&hl=en&as_sdt=0,3",2,2021
Negative Data Augmentation,59,iclr,4,0,2023-06-18 09:24:36.898000,https://github.com/ermongroup/NDA,22,Negative data augmentation,"https://scholar.google.com/scholar?cluster=1155111694700482040&hl=en&as_sdt=0,5",8,2021
Teaching with Commentaries,21,iclr,1,2,2023-06-18 09:24:37.101000,https://github.com/googleinterns/commentaries,5,Teaching with commentaries,"https://scholar.google.com/scholar?cluster=12512263277235607257&hl=en&as_sdt=0,5",2,2021
"On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines",243,iclr,21,3,2023-06-18 09:24:37.305000,https://github.com/uds-lsv/bert-stable-fine-tuning,128,"On the stability of fine-tuning bert: Misconceptions, explanations, and strong baselines","https://scholar.google.com/scholar?cluster=5096550339009628342&hl=en&as_sdt=0,5",12,2021
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning,34,iclr,4,1,2023-06-18 09:24:37.509000,https://github.com/rabeehk/vibert,25,Variational information bottleneck for effective low-resource fine-tuning,"https://scholar.google.com/scholar?cluster=8332334041068386059&hl=en&as_sdt=0,5",2,2021
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching,117,iclr,7,0,2023-06-18 09:24:37.712000,https://github.com/JonasGeiping/poisoning-gradient-matching,77,Witches' brew: Industrial scale data poisoning via gradient matching,"https://scholar.google.com/scholar?cluster=12446963321584021008&hl=en&as_sdt=0,5",2,2021
Deberta: decoding-Enhanced Bert with Disentangled Attention,1009,iclr,188,56,2023-06-18 09:24:37.916000,https://github.com/microsoft/DeBERTa,1587,Deberta: Decoding-enhanced bert with disentangled attention,"https://scholar.google.com/scholar?cluster=17165415294113919367&hl=en&as_sdt=0,46",44,2021
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning,13,iclr,2,0,2023-06-18 09:24:38.119000,https://github.com/isi-usc-edu/gttf,7,Graph traversal with tensor functionals: A meta-algorithm for scalable learning,"https://scholar.google.com/scholar?cluster=4421735277125867362&hl=en&as_sdt=0,5",4,2021
Diverse Video Generation using a Gaussian Process Trigger,5,iclr,6,1,2023-06-18 09:24:38.323000,https://github.com/shgaurav1/DVG,16,Diverse video generation using a Gaussian process trigger,"https://scholar.google.com/scholar?cluster=4423790628235777527&hl=en&as_sdt=0,34",4,2021
"Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU",64,iclr,26,13,2023-06-18 09:24:38.527000,https://github.com/patrick-kidger/signatory,222,"Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU","https://scholar.google.com/scholar?cluster=17137105822248313945&hl=en&as_sdt=0,32",10,2021
MoPro: Webly Supervised Learning with Momentum Prototypes,63,iclr,8,0,2023-06-18 09:24:38.743000,https://github.com/salesforce/MoPro,79,Mopro: Webly supervised learning with momentum prototypes,"https://scholar.google.com/scholar?cluster=3510417880461380553&hl=en&as_sdt=0,5",9,2021
A Universal Representation Transformer Layer for Few-Shot Image Classification,93,iclr,18,3,2023-06-18 09:24:38.964000,https://github.com/liulu112601/URT,96,A universal representation transformer layer for few-shot image classification,"https://scholar.google.com/scholar?cluster=6018140255832554871&hl=en&as_sdt=0,22",4,2021
Learning perturbation sets for robust machine learning,61,iclr,9,0,2023-06-18 09:24:39.168000,https://github.com/locuslab/perturbation_learning,63,Learning perturbation sets for robust machine learning,"https://scholar.google.com/scholar?cluster=14923687105877479161&hl=en&as_sdt=0,33",10,2021
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks,10,iclr,3,0,2023-06-18 09:24:39.370000,https://github.com/jiaqima/CopulaGNN,10,Copulagnn: Towards integrating representational and correlational roles of graphs in graph neural networks,"https://scholar.google.com/scholar?cluster=15600465450888406918&hl=en&as_sdt=0,33",4,2021
On the Critical Role of Conventions in Adaptive Human-AI Collaboration,24,iclr,4,0,2023-06-18 09:24:39.573000,https://github.com/Stanford-ILIAD/Conventions-ModularPolicy,11,On the critical role of conventions in adaptive human-AI collaboration,"https://scholar.google.com/scholar?cluster=11035601410057323120&hl=en&as_sdt=0,33",2,2021
On the Bottleneck of Graph Neural Networks and its Practical Implications,338,iclr,18,0,2023-06-18 09:24:39.776000,https://github.com/tech-srl/bottleneck,85,On the bottleneck of graph neural networks and its practical implications,"https://scholar.google.com/scholar?cluster=5884209795367025285&hl=en&as_sdt=0,33",6,2021
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability,110,iclr,13,1,2023-06-18 09:24:39.979000,https://github.com/locuslab/edge-of-stability,35,Gradient descent on neural networks typically occurs at the edge of stability,"https://scholar.google.com/scholar?cluster=1829576952258168273&hl=en&as_sdt=0,5",3,2021
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers,36,iclr,2,0,2023-06-18 09:24:40.182000,https://github.com/preetum/deep-bootstrap-code,3,The deep bootstrap framework: Good online learners are good offline generalizers,"https://scholar.google.com/scholar?cluster=6565841002314510004&hl=en&as_sdt=0,33",1,2021
What Can You Learn From Your Muscles? Learning Visual Representation from Human Interactions,1,iclr,5,0,2023-06-18 09:24:40.387000,https://github.com/ehsanik/muscleTorch,34,What can you learn from your muscles? Learning visual representation from human interactions,"https://scholar.google.com/scholar?cluster=550456704334967809&hl=en&as_sdt=0,33",4,2021
EEC: Learning to Encode and Regenerate Images for Continual Learning,33,iclr,2,1,2023-06-18 09:24:40.590000,https://github.com/aliayub7/EEC,6,Eec: Learning to encode and regenerate images for continual learning,"https://scholar.google.com/scholar?cluster=4496455065101916683&hl=en&as_sdt=0,22",1,2021
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space,37,iclr,7,0,2023-06-18 09:24:40.813000,https://github.com/jamestszhim/modals,39,Modals: Modality-agnostic automated data augmentation in the latent space,"https://scholar.google.com/scholar?cluster=500252256958905673&hl=en&as_sdt=0,39",3,2021
Learning the Pareto Front with Hypernetworks,65,iclr,10,0,2023-06-18 09:24:41.016000,https://github.com/AvivNavon/pareto-hypernetworks,83,Learning the pareto front with hypernetworks,"https://scholar.google.com/scholar?cluster=13675122104724715473&hl=en&as_sdt=0,50",3,2021
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors,28,iclr,15,2,2023-06-18 09:24:41.219000,https://github.com/asharakeh/probdet,54,Estimating and evaluating regression predictive uncertainty in deep object detectors,"https://scholar.google.com/scholar?cluster=3972283505781057189&hl=en&as_sdt=0,33",1,2021
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction,143,iclr,46,21,2023-06-18 09:24:41.423000,https://github.com/yhhhli/BRECQ,180,Brecq: Pushing the limit of post-training quantization by block reconstruction,"https://scholar.google.com/scholar?cluster=4375514065793876125&hl=en&as_sdt=0,31",6,2021
GraphCodeBERT: Pre-training Code Representations with Data Flow,358,iclr,346,41,2023-06-18 09:24:41.630000,https://github.com/microsoft/CodeBERT,1451,Graphcodebert: Pre-training code representations with data flow,"https://scholar.google.com/scholar?cluster=12215762142211425404&hl=en&as_sdt=0,33",25,2021
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors,99,iclr,6,7,2023-06-18 09:24:41.834000,https://github.com/ArchipLab-LinfengZhang/Object-Detection-Knowledge-Distillation-ICLR2021,49,Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors,"https://scholar.google.com/scholar?cluster=4883781250295766379&hl=en&as_sdt=0,33",5,2021
A Temporal Kernel Approach for Deep Learning with Continuous-time Information,2,iclr,53,12,2023-06-18 09:24:42.037000,https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs,222,A temporal kernel approach for deep learning with continuous-time information,"https://scholar.google.com/scholar?cluster=2677250892342211490&hl=en&as_sdt=0,33",3,2021
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision,159,iclr,24,2,2023-06-18 09:24:42.240000,https://github.com/dongkwan-kim/SuperGAT,135,How to find your friendly neighborhood: Graph attention design with self-supervision,"https://scholar.google.com/scholar?cluster=7594913044183235646&hl=en&as_sdt=0,33",4,2021
Interpretable Models for Granger Causality Using Self-explaining Neural Networks,11,iclr,14,0,2023-06-18 09:24:42.443000,https://github.com/i6092467/GVAR,34,Learning interaction rules from multi-animal trajectories via augmented behavioral models,"https://scholar.google.com/scholar?cluster=13190745890031985835&hl=en&as_sdt=0,47",1,2021
Meta-learning Symmetries by Reparameterization,55,iclr,6,1,2023-06-18 09:24:42.646000,https://github.com/AllanYangZhou/metalearning-symmetries,48,Meta-learning symmetries by reparameterization,"https://scholar.google.com/scholar?cluster=9023763137137918184&hl=en&as_sdt=0,33",12,2021
Removing Undesirable Feature Contributions Using Out-of-Distribution Data,17,iclr,2,0,2023-06-18 09:24:42.850000,https://github.com/Saehyung-Lee/OAT,8,Removing undesirable feature contributions using out-of-distribution data,"https://scholar.google.com/scholar?cluster=16828055548257424172&hl=en&as_sdt=0,47",1,2021
On the Universality of the Double Descent Peak in Ridgeless Regression,11,iclr,1,0,2023-06-18 09:24:43.052000,https://github.com/dholzmueller/universal_double_descent,1,On the universality of the double descent peak in ridgeless regression,"https://scholar.google.com/scholar?cluster=6446983561543714244&hl=en&as_sdt=0,36",1,2021
Fair Mixup: Fairness via Interpolation,78,iclr,4,0,2023-06-18 09:24:43.255000,https://github.com/chingyaoc/fair-mixup,54,Fair mixup: Fairness via interpolation,"https://scholar.google.com/scholar?cluster=15581530866838341454&hl=en&as_sdt=0,5",2,2021
Self-supervised Learning from a Multi-view Perspective,119,iclr,8,3,2023-06-18 09:24:43.459000,https://github.com/yaohungt/Demystifying_Self_Supervised_Learning,38,Self-supervised learning from a multi-view perspective,"https://scholar.google.com/scholar?cluster=12546454131517763029&hl=en&as_sdt=0,14",6,2021
Integrating Categorical Semantics into Unsupervised Domain Translation,3,iclr,0,0,2023-06-18 09:24:43.662000,https://github.com/lavoiems/Cats-UDT,4,Integrating categorical semantics into unsupervised domain translation,"https://scholar.google.com/scholar?cluster=16605089044349710257&hl=en&as_sdt=0,44",2,2021
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods,15,iclr,3,0,2023-06-18 09:24:43.865000,https://github.com/louity/patches,8,The unreasonable effectiveness of patches in deep convolutional kernels methods,"https://scholar.google.com/scholar?cluster=1695072614668071777&hl=en&as_sdt=0,5",4,2021
Open Question Answering over Tables and Text,99,iclr,23,3,2023-06-18 09:24:44.067000,https://github.com/wenhuchen/OTT-QA,135,Open question answering over tables and text,"https://scholar.google.com/scholar?cluster=3303883977664528561&hl=en&as_sdt=0,39",4,2021
Evaluation of Similarity-based Explanations,31,iclr,3,2,2023-06-18 09:24:44.270000,https://github.com/k-hanawa/criteria_for_instance_based_explanation,8,Evaluation of similarity-based explanations,"https://scholar.google.com/scholar?cluster=2157018204021335072&hl=en&as_sdt=0,5",3,2021
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary,88,iclr,10,1,2023-06-18 09:24:44.474000,https://github.com/huanzhang12/ATLA_robust_RL,42,Robust reinforcement learning on state observations with learned optimal adversary,"https://scholar.google.com/scholar?cluster=16441750250550804230&hl=en&as_sdt=0,14",4,2021
Hierarchical Autoregressive Modeling for Neural Video Compression,36,iclr,2,0,2023-06-18 09:24:44.678000,https://github.com/privateyoung/Youtube-NT,11,Hierarchical autoregressive modeling for neural video compression,"https://scholar.google.com/scholar?cluster=12525554845016581336&hl=en&as_sdt=0,5",2,2021
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits,38,iclr,5,1,2023-06-18 09:24:44.881000,https://github.com/jiawangbai/TA-LBF,16,Targeted attack against deep neural networks via flipping limited weight bits,"https://scholar.google.com/scholar?cluster=14009845567586991922&hl=en&as_sdt=0,5",1,2021
Generalized Multimodal ELBO,34,iclr,4,1,2023-06-18 09:24:45.085000,https://github.com/thomassutter/MoPoE,17,Generalized multimodal ELBO,"https://scholar.google.com/scholar?cluster=17699698224745360599&hl=en&as_sdt=0,39",2,2021
Auxiliary Learning by Implicit Differentiation,28,iclr,11,0,2023-06-18 09:24:45.289000,https://github.com/AvivNavon/AuxiLearn,76,Auxiliary learning by implicit differentiation,"https://scholar.google.com/scholar?cluster=5217604319390827754&hl=en&as_sdt=0,5",5,2021
Adversarially Guided Actor-Critic,54,iclr,8,0,2023-06-18 09:24:45.491000,https://github.com/yfletberliac/adversarially-guided-actor-critic,44,Adversarially guided actor-critic,"https://scholar.google.com/scholar?cluster=15451474207173582523&hl=en&as_sdt=0,44",4,2021
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators,103,iclr,10,4,2023-06-18 09:24:45.699000,https://github.com/Meituan-AutoML/DARTS-,55,Darts-: robustly stepping out of performance collapse without indicators,"https://scholar.google.com/scholar?cluster=14536849517699271582&hl=en&as_sdt=0,10",2,2021
Are wider nets better given the same number of parameters?,37,iclr,2,0,2023-06-18 09:24:45.902000,https://github.com/google-research/wide-sparse-nets,18,Are wider nets better given the same number of parameters?,"https://scholar.google.com/scholar?cluster=5708484653398941764&hl=en&as_sdt=0,15",5,2021
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks,101,iclr,5,0,2023-06-18 09:24:46.105000,https://github.com/Jackn0/snn_optimal_conversion_pipeline,27,Optimal conversion of conventional artificial neural networks to spiking neural networks,"https://scholar.google.com/scholar?cluster=1643416764815138161&hl=en&as_sdt=0,47",1,2021
Deep Equals Shallow for ReLU Networks in Kernel Regimes,51,iclr,1,0,2023-06-18 09:24:46.308000,https://github.com/albietz/deep_shallow_kernel,1,Deep equals shallow for ReLU networks in kernel regimes,"https://scholar.google.com/scholar?cluster=9990384037530599388&hl=en&as_sdt=0,5",1,2021
Early Stopping in Deep Networks: Double Descent and How to Eliminate it,32,iclr,7,7,2023-06-18 09:24:46.511000,https://github.com/MLI-lab/early_stopping_double_descent,12,Early stopping in deep networks: Double descent and how to eliminate it,"https://scholar.google.com/scholar?cluster=7207613062069404274&hl=en&as_sdt=0,5",3,2021
FairBatch: Batch Selection for Model Fairness,67,iclr,4,0,2023-06-18 09:24:46.714000,https://github.com/yuji-roh/fairbatch,16,Fairbatch: Batch selection for model fairness,"https://scholar.google.com/scholar?cluster=9329551878628232526&hl=en&as_sdt=0,19",2,2021
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction,5,iclr,3,0,2023-06-18 09:24:46.917000,https://github.com/WayneDW/Variance_Reduced_Replica_Exchange_SGMCMC,8,Accelerating convergence of replica exchange stochastic gradient MCMC via variance reduction,"https://scholar.google.com/scholar?cluster=11364151654891538000&hl=en&as_sdt=0,5",3,2021
The Importance of Pessimism in Fixed-Dataset Policy Optimization,111,iclr,0,1,2023-06-18 09:24:47.120000,https://github.com/jbuckman/tiopifdpo,6,The importance of pessimism in fixed-dataset policy optimization,"https://scholar.google.com/scholar?cluster=7642597601487950859&hl=en&as_sdt=0,33",3,2021
Hopfield Networks is All You Need,242,iclr,158,8,2023-06-18 09:24:47.324000,https://github.com/ml-jku/hopfield-layers,1488,Hopfield networks is all you need,"https://scholar.google.com/scholar?cluster=3659395221954190351&hl=en&as_sdt=0,6",42,2021
Understanding the failure modes of out-of-distribution generalization,110,iclr,5,0,2023-06-18 09:24:47.527000,https://github.com/google-research/OOD-failures,23,Understanding the failure modes of out-of-distribution generalization,"https://scholar.google.com/scholar?cluster=5584692372209891992&hl=en&as_sdt=0,36",5,2021
Emergent Road Rules In Multi-Agent Driving Environments,13,iclr,21,0,2023-06-18 09:24:47.730000,https://github.com/fidler-lab/social-driving,130,Emergent road rules in multi-agent driving environments,"https://scholar.google.com/scholar?cluster=11147585939846933269&hl=en&as_sdt=0,33",12,2021
Wasserstein-2 Generative Networks,62,iclr,4,0,2023-06-18 09:24:47.934000,https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks,45,Wasserstein-2 generative networks,"https://scholar.google.com/scholar?cluster=5186040077204830092&hl=en&as_sdt=0,23",5,2021
LEAF: A Learnable Frontend for Audio Classification,91,iclr,50,20,2023-06-18 09:24:48.138000,https://github.com/google-research/leaf-audio,444,LEAF: A learnable frontend for audio classification,"https://scholar.google.com/scholar?cluster=14147422070521797916&hl=en&as_sdt=0,33",12,2021
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms,56,iclr,12,1,2023-06-18 09:24:48.342000,https://github.com/alshedivat/fedpa,44,Federated learning via posterior averaging: A new perspective and practical algorithms,"https://scholar.google.com/scholar?cluster=2486025806014234529&hl=en&as_sdt=0,32",2,2021
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval,593,iclr,48,13,2023-06-18 09:24:48.545000,https://github.com/microsoft/ANCE,311,Approximate nearest neighbor negative contrastive learning for dense text retrieval,"https://scholar.google.com/scholar?cluster=8917790448070447494&hl=en&as_sdt=0,33",13,2021
"Auxiliary Task Update Decomposition: the Good, the Bad and the neutral",11,iclr,0,0,2023-06-18 09:24:48.748000,https://github.com/ldery/ATTITTUD,9,"Auxiliary task update decomposition: The good, the bad and the neutral","https://scholar.google.com/scholar?cluster=5872379773640363834&hl=en&as_sdt=0,33",2,2021
SSD: A Unified Framework for Self-Supervised Outlier Detection,156,iclr,27,1,2023-06-18 09:24:48.952000,https://github.com/inspire-group/SSD,121,Ssd: A unified framework for self-supervised outlier detection,"https://scholar.google.com/scholar?cluster=18087700552913806931&hl=en&as_sdt=0,47",4,2021
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning,24,iclr,1,3,2023-06-18 09:24:49.155000,https://github.com/valeriechen/ask-your-humans,9,Ask your humans: Using human instructions to improve generalization in reinforcement learning,"https://scholar.google.com/scholar?cluster=12446456886016968703&hl=en&as_sdt=0,47",1,2021
Revisiting Few-sample BERT Fine-tuning,269,iclr,14,5,2023-06-18 09:24:49.360000,https://github.com/asappresearch/revisit-bert-finetuning,182,Revisiting few-sample BERT fine-tuning,"https://scholar.google.com/scholar?cluster=4118367966283373449&hl=en&as_sdt=0,29",2,2021
Tilted Empirical Risk Minimization,78,iclr,9,1,2023-06-18 09:24:49.563000,https://github.com/litian96/TERM,47,Tilted empirical risk minimization,"https://scholar.google.com/scholar?cluster=13273330371410515607&hl=en&as_sdt=0,33",3,2021
Calibration tests beyond classification,8,iclr,1,1,2023-06-18 09:24:49.766000,https://github.com/devmotion/calibration_iclr2021,4,Calibration tests beyond classification,"https://scholar.google.com/scholar?cluster=7019919403601581708&hl=en&as_sdt=0,33",2,2021
You Only Need Adversarial Supervision for Semantic Image Synthesis,102,iclr,55,20,2023-06-18 09:24:49.970000,https://github.com/boschresearch/OASIS,296,You only need adversarial supervision for semantic image synthesis,"https://scholar.google.com/scholar?cluster=11330153460925373123&hl=en&as_sdt=0,32",14,2021
Learning to Recombine and Resample Data For Compositional Generalization,61,iclr,1,0,2023-06-18 09:24:50.173000,https://github.com/ekinakyurek/compgen,10,Learning to recombine and resample data for compositional generalization,"https://scholar.google.com/scholar?cluster=16034423626440720931&hl=en&as_sdt=0,50",2,2021
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving,25,iclr,2,9,2023-06-18 09:24:50.376000,https://github.com/albertqjiang/INT,26,Int: An inequality benchmark for evaluating generalization in theorem proving,"https://scholar.google.com/scholar?cluster=2622676809142200746&hl=en&as_sdt=0,33",5,2021
On the Dynamics of Training Attention Models,3925,iclr,0,0,2023-06-18 09:24:50.579000,https://github.com/haoyelyu/On_the_Dynamics_of_Training_Attention_Models,1,Recurrent models of visual attention,"https://scholar.google.com/scholar?cluster=4636836599580194602&hl=en&as_sdt=0,22",1,2021
Contextual Dropout: An Efficient Sample-Dependent Dropout Module,23,iclr,1,1,2023-06-18 09:24:50.782000,https://github.com/szhang42/Contextual_dropout_release,1,Contextual dropout: An efficient sample-dependent dropout module,"https://scholar.google.com/scholar?cluster=17581927588225290546&hl=en&as_sdt=0,44",1,2021
Mirostat: a Neural Text decoding Algorithm that directly controls perplexity,23,iclr,2,1,2023-06-18 09:24:50.985000,https://github.com/basusourya/mirostat,32,Mirostat: A neural text decoding algorithm that directly controls perplexity,"https://scholar.google.com/scholar?cluster=4013825852088640582&hl=en&as_sdt=0,21",2,2021
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues,17,iclr,5,1,2023-06-18 09:24:51.189000,https://github.com/rishabhjoshi/DialoGraph_ICLR21,12,Dialograph: Incorporating interpretable strategy-graph networks into negotiation dialogues,"https://scholar.google.com/scholar?cluster=13588714176146046430&hl=en&as_sdt=0,33",3,2021
Multi-Time Attention Networks for Irregularly Sampled Time Series,67,iclr,16,5,2023-06-18 09:24:51.391000,https://github.com/reml-lab/mTAN,76,Multi-time attention networks for irregularly sampled time series,"https://scholar.google.com/scholar?cluster=6069781928255471893&hl=en&as_sdt=0,33",3,2021
SEED: Self-supervised Distillation For Visual Representation,116,iclr,11,1,2023-06-18 09:24:51.595000,https://github.com/jacobswan1/SEED,32,Seed: Self-supervised distillation for visual representation,"https://scholar.google.com/scholar?cluster=8472207324878329601&hl=en&as_sdt=0,6",2,2021
Effective and Efficient Vote Attack on Capsule Networks,14,iclr,1,0,2023-06-18 09:24:51.798000,https://github.com/JindongGu/VoteAttack,8,Effective and efficient vote attack on capsule networks,"https://scholar.google.com/scholar?cluster=17735896064607887754&hl=en&as_sdt=0,5",1,2021
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization,44,iclr,1,2,2023-06-18 09:24:52.001000,https://github.com/kaidic/HAR,30,Heteroskedastic and imbalanced deep learning with adaptive regularization,"https://scholar.google.com/scholar?cluster=5140614749291211049&hl=en&as_sdt=0,39",1,2021
Neural Thompson Sampling,60,iclr,3,0,2023-06-18 09:24:52.204000,https://github.com/ZeroWeight/NeuralTS,9,Neural thompson sampling,"https://scholar.google.com/scholar?cluster=5718992412450799651&hl=en&as_sdt=0,31",1,2021
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics,42,iclr,3,1,2023-06-18 09:24:52.408000,https://github.com/danielkunin/neural-mechanics,18,Neural mechanics: Symmetry and broken conservation laws in deep learning dynamics,"https://scholar.google.com/scholar?cluster=8694895381782484369&hl=en&as_sdt=0,11",3,2021
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction,18,iclr,2,0,2023-06-18 09:24:52.618000,https://github.com/1Konny/HierarchicalVideoPrediction,21,Revisiting hierarchical approach for persistent long-term video prediction,"https://scholar.google.com/scholar?cluster=3252280345602395682&hl=en&as_sdt=0,34",3,2021
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System,31,iclr,0,2,2023-06-18 09:24:52.824000,https://github.com/mikezhang95/HDNO,18,Modelling hierarchical structure between dialogue policy and natural language generator with option framework for task-oriented dialogue system,"https://scholar.google.com/scholar?cluster=10305196769092538999&hl=en&as_sdt=0,5",3,2021
Categorical Normalizing Flows via Continuous Transformations,20,iclr,11,1,2023-06-18 09:24:53.027000,https://github.com/phlippe/CategoricalNF,51,Categorical normalizing flows via continuous transformations,"https://scholar.google.com/scholar?cluster=3325488278431925119&hl=en&as_sdt=0,18",3,2021
Learning to Represent Action Values as a Hypergraph on the Action Vertices,11,iclr,6,0,2023-06-18 09:24:53.230000,https://github.com/atavakol/action-hypergraph-networks,19,Learning to represent action values as a hypergraph on the action vertices,"https://scholar.google.com/scholar?cluster=8032720011491457722&hl=en&as_sdt=0,5",1,2021
Lifelong Learning of Compositional Structures,25,iclr,8,1,2023-06-18 09:24:53.433000,https://github.com/GRASP-ML/Mendez2020Compositional,12,Lifelong learning of compositional structures,"https://scholar.google.com/scholar?cluster=11061523929398124661&hl=en&as_sdt=0,5",5,2021
Creative Sketch Generation,42,iclr,13,0,2023-06-18 09:24:53.643000,https://github.com/facebookresearch/DoodlerGAN,101,Creative sketch generation,"https://scholar.google.com/scholar?cluster=2511600747232100268&hl=en&as_sdt=0,5",8,2021
Concept Learners for Few-Shot Learning,61,iclr,13,6,2023-06-18 09:24:53.863000,https://github.com/snap-stanford/comet,104,Concept learners for few-shot learning,"https://scholar.google.com/scholar?cluster=14029381289602972954&hl=en&as_sdt=0,5",8,2021
DeLighT: Deep and Light-weight Transformer,88,iclr,50,7,2023-06-18 09:24:54.066000,https://github.com/sacmehta/delight,443,Delight: Deep and light-weight transformer,"https://scholar.google.com/scholar?cluster=13638554196274165568&hl=en&as_sdt=0,47",14,2021
Mastering Atari with Discrete World Models,385,iclr,183,6,2023-06-18 09:24:54.269000,https://github.com/danijar/dreamerv2,767,Mastering atari with discrete world models,"https://scholar.google.com/scholar?cluster=2696098032395844049&hl=en&as_sdt=0,5",27,2021
Learning Neural Event Functions for Ordinary Differential Equations,76,iclr,848,61,2023-06-18 09:24:54.472000,https://github.com/rtqichen/torchdiffeq,4676,Learning neural event functions for ordinary differential equations,"https://scholar.google.com/scholar?cluster=15727092148990578310&hl=en&as_sdt=0,1",123,2021
Contemplating Real-World Object Classification,6,iclr,0,0,2023-06-18 09:24:54.675000,https://github.com/aliborji/ObjectNetReanalysis,15,Contemplating real-world object classification,"https://scholar.google.com/scholar?cluster=6078463704696071400&hl=en&as_sdt=0,14",3,2021
Neural Spatio-Temporal Point Processes,56,iclr,16,5,2023-06-18 09:24:54.878000,https://github.com/facebookresearch/neural_stpp,85,Neural spatio-temporal point processes,"https://scholar.google.com/scholar?cluster=6976487564397209584&hl=en&as_sdt=0,33",10,2021
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach,105,iclr,5,0,2023-06-18 09:24:55.081000,https://github.com/UCSC-REAL/cores,27,Learning with instance-dependent label noise: A sample sieve approach,"https://scholar.google.com/scholar?cluster=1816427362683189606&hl=en&as_sdt=0,22",4,2021
Unbiased Teacher for Semi-Supervised Object Detection,254,iclr,82,32,2023-06-18 09:24:55.284000,https://github.com/facebookresearch/unbiased-teacher,393,Unbiased teacher for semi-supervised object detection,"https://scholar.google.com/scholar?cluster=860392753310305868&hl=en&as_sdt=0,33",17,2021
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks,183,iclr,13,0,2023-06-18 09:24:55.487000,https://github.com/bboylyg/NAD,98,Neural attention distillation: Erasing backdoor triggers from deep neural networks,"https://scholar.google.com/scholar?cluster=11473045902984731830&hl=en&as_sdt=0,22",2,2021
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation,66,iclr,3,3,2023-06-18 09:24:55.690000,https://github.com/seanie12/CLAPS,77,Contrastive learning with adversarial perturbations for conditional text generation,"https://scholar.google.com/scholar?cluster=13654340302052439773&hl=en&as_sdt=0,5",4,2021
Text Generation by Learning from Demonstrations,34,iclr,6,0,2023-06-18 09:24:55.893000,https://github.com/yzpang/gold-off-policy-text-gen-iclr21,42,Text generation by learning from demonstrations,"https://scholar.google.com/scholar?cluster=7301017997862747001&hl=en&as_sdt=0,5",3,2021
Learning Long-term Visual Dynamics with Region Proposal Interaction Networks,40,iclr,12,0,2023-06-18 09:24:56.097000,https://github.com/HaozhiQi/RPIN,110,Learning long-term visual dynamics with region proposal interaction networks,"https://scholar.google.com/scholar?cluster=12876852900832613209&hl=en&as_sdt=0,5",5,2021
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations,21,iclr,7,5,2023-06-18 09:24:56.301000,https://github.com/transmuteAI/ChipNet,20,Chipnet: Budget-aware pruning with heaviside continuous approximations,"https://scholar.google.com/scholar?cluster=18315278844626956767&hl=en&as_sdt=0,5",4,2021
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation,17,iclr,0,1,2023-06-18 09:24:56.504000,https://github.com/INK-USC/deceive-KG-models,4,Learning to deceive knowledge graph augmented models via targeted perturbation,"https://scholar.google.com/scholar?cluster=10251964553453690301&hl=en&as_sdt=0,5",5,2021
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning,67,iclr,4,5,2023-06-18 09:24:56.708000,https://github.com/rucmlcv/IEPT_FSL,32,IEPT: Instance-level and episode-level pretext tasks for few-shot learning,"https://scholar.google.com/scholar?cluster=13782822580168472981&hl=en&as_sdt=0,33",1,2021
Training with Quantization Noise for Extreme Model Compression,163,iclr,5883,1031,2023-06-18 09:24:56.912000,https://github.com/pytorch/fairseq,26500,Training with quantization noise for extreme model compression,"https://scholar.google.com/scholar?cluster=10846655234663420432&hl=en&as_sdt=0,38",411,2021
Distilling Knowledge from Reader to Retriever for Question Answering,113,iclr,98,18,2023-06-18 09:24:57.115000,https://github.com/facebookresearch/FiD,410,Distilling knowledge from reader to retriever for question answering,"https://scholar.google.com/scholar?cluster=18188741483036284668&hl=en&as_sdt=0,24",8,2021
not-MIWAE: Deep Generative Modelling with Missing not at Random Data,35,iclr,2,1,2023-06-18 09:24:57.318000,https://github.com/nbip/notMIWAE,10,not-MIWAE: Deep generative modelling with missing not at random data,"https://scholar.google.com/scholar?cluster=6702862707745789629&hl=en&as_sdt=0,5",1,2021
Learning with AMIGo: Adversarially Motivated Intrinsic Goals,107,iclr,7,6,2023-06-18 09:24:57.522000,https://github.com/facebookresearch/adversarially-motivated-intrinsic-goals,60,Learning with amigo: Adversarially motivated intrinsic goals,"https://scholar.google.com/scholar?cluster=10840346887158319600&hl=en&as_sdt=0,5",13,2021
CaPC Learning: Confidential and Private Collaborative Learning,41,iclr,6,0,2023-06-18 09:24:57.726000,https://github.com/cleverhans-lab/capc-iclr,25,Capc learning: Confidential and private collaborative learning,"https://scholar.google.com/scholar?cluster=10267580043538476414&hl=en&as_sdt=0,33",2,2021
Self-supervised Representation Learning with Relative Predictive Coding,25,iclr,1,0,2023-06-18 09:24:57.929000,https://github.com/martinmamql/relative_predictive_coding,17,Self-supervised representation learning with relative predictive coding,"https://scholar.google.com/scholar?cluster=17809486725301186145&hl=en&as_sdt=0,33",3,2021
On the Impossibility of Global Convergence in Multi-Loss Optimization,28,iclr,0,0,2023-06-18 09:24:58.134000,https://github.com/aletcher/impossibility-global-convergence,1,On the impossibility of global convergence in multi-loss optimization,"https://scholar.google.com/scholar?cluster=12737917021502438759&hl=en&as_sdt=0,8",2,2021
Discrete Graph Structure Learning for Forecasting Multiple Time Series,90,iclr,32,10,2023-06-18 09:24:58.337000,https://github.com/chaoshangcs/GTS,138,Discrete graph structure learning for forecasting multiple time series,"https://scholar.google.com/scholar?cluster=10547313552848250901&hl=en&as_sdt=0,5",1,2021
Contrastive Learning with Hard Negative Samples,372,iclr,29,4,2023-06-18 09:24:58.541000,https://github.com/joshr17/HCL,209,Contrastive learning with hard negative samples,"https://scholar.google.com/scholar?cluster=9395538845107330163&hl=en&as_sdt=0,33",4,2021
Sliced Kernelized Stein Discrepancy,32,iclr,1,0,2023-06-18 09:24:58.754000,https://github.com/WenboGong/Sliced_Kernelized_Stein_Discrepancy,1,Sliced kernelized Stein discrepancy,"https://scholar.google.com/scholar?cluster=2996953406646769622&hl=en&as_sdt=0,33",1,2021
Denoising Diffusion Implicit Models,888,iclr,117,9,2023-06-18 09:24:58.968000,https://github.com/ermongroup/ddim,739,Denoising diffusion implicit models,"https://scholar.google.com/scholar?cluster=15692403916484267912&hl=en&as_sdt=0,5",7,2021
Hierarchical Reinforcement Learning by Discovering Intrinsic Options,39,iclr,5,0,2023-06-18 09:24:59.171000,https://github.com/jesbu1/hidio,33,Hierarchical reinforcement learning by discovering intrinsic options,"https://scholar.google.com/scholar?cluster=13774457898597661274&hl=en&as_sdt=0,31",3,2021
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval,31,iclr,20,10,2023-06-18 09:24:59.375000,https://github.com/facebookresearch/multihop_dense_retrieval,193,Answering complex open-domain questions with multi-hop dense retrieval,"https://scholar.google.com/scholar?cluster=950426100300537362&hl=en&as_sdt=0,11",10,2021
Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective,68,iclr,8,2,2023-06-18 09:24:59.579000,https://github.com/bellymonster/Weighted-Soft-Label-Distillation,51,Rethinking soft labels for knowledge distillation: A bias-variance tradeoff perspective,"https://scholar.google.com/scholar?cluster=3419265116885877699&hl=en&as_sdt=0,33",2,2021
Learning to Set Waypoints for Audio-Visual Navigation,66,iclr,50,35,2023-06-18 09:24:59.782000,https://github.com/facebookresearch/sound-spaces,265,Learning to set waypoints for audio-visual navigation,"https://scholar.google.com/scholar?cluster=3241754597982195177&hl=en&as_sdt=0,34",14,2021
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective,144,iclr,31,1,2023-06-18 09:24:59.985000,https://github.com/VITA-Group/TENAS,160,Neural architecture search on imagenet in four gpu hours: A theoretically inspired perspective,"https://scholar.google.com/scholar?cluster=8900374722066786979&hl=en&as_sdt=0,33",5,2021
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning,129,iclr,15,3,2023-06-18 09:25:00.188000,https://github.com/wyjeong/FedMatch,58,Federated semi-supervised learning with inter-client consistency & disjoint learning,"https://scholar.google.com/scholar?cluster=7065606493210904394&hl=en&as_sdt=0,44",1,2021
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components,11,iclr,2,0,2023-06-18 09:25:00.391000,https://github.com/JunwenBai/DAPC,9,Representation learning for sequence data with deep autoencoding predictive components,"https://scholar.google.com/scholar?cluster=14928540791785699734&hl=en&as_sdt=0,5",2,2021
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search,21,iclr,6,0,2023-06-18 09:25:00.595000,https://github.com/PerdonLiu/CSE-Autoloss,56,Loss function discovery for object detection via convergence-simulation driven search,"https://scholar.google.com/scholar?cluster=14213953429648652822&hl=en&as_sdt=0,5",2,2021
Effective Abstract Reasoning with Dual-Contrast Network,15,iclr,1,0,2023-06-18 09:25:00.798000,https://github.com/visiontao/dcnet,9,Effective abstract reasoning with dual-contrast network,"https://scholar.google.com/scholar?cluster=17541928737490000806&hl=en&as_sdt=0,5",1,2021
Set Prediction without Imposing Structure as Conditional Density Estimation,9,iclr,1,0,2023-06-18 09:25:01.001000,https://github.com/davzha/DESP,5,Set prediction without imposing structure as conditional density estimation,"https://scholar.google.com/scholar?cluster=3129155688534171639&hl=en&as_sdt=0,5",1,2021
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation,49,iclr,7,4,2023-06-18 09:25:01.208000,https://github.com/TTN-YKK/Clustering_friendly_representation_learning,48,Clustering-friendly representation learning via instance discrimination and feature decorrelation,"https://scholar.google.com/scholar?cluster=11187160992598838223&hl=en&as_sdt=0,5",2,2021
Language-Agnostic Representation Learning of Source Code from Structure and Context,95,iclr,29,3,2023-06-18 09:25:01.412000,https://github.com/danielzuegner/code-transformer,147,Language-agnostic representation learning of source code from structure and context,"https://scholar.google.com/scholar?cluster=4574202408084137820&hl=en&as_sdt=0,5",9,2021
Training GANs with Stronger Augmentations via Contrastive Discriminator,48,iclr,25,1,2023-06-18 09:25:01.615000,https://github.com/jh-jeong/ContraD,182,Training gans with stronger augmentations via contrastive discriminator,"https://scholar.google.com/scholar?cluster=14845144713420069894&hl=en&as_sdt=0,5",11,2021
Continual learning in recurrent neural networks,25,iclr,6,0,2023-06-18 09:25:01.834000,https://github.com/mariacer/cl_in_rnns,34,Continual learning in recurrent neural networks,"https://scholar.google.com/scholar?cluster=11490619605153761902&hl=en&as_sdt=0,15",6,2021
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention,32,iclr,12,4,2023-06-18 09:25:02.038000,https://github.com/claying/OTK,99,A trainable optimal transport embedding for feature aggregation and its relationship to attention,"https://scholar.google.com/scholar?cluster=10719309701133188267&hl=en&as_sdt=0,31",3,2021
Noise or Signal: The Role of Image Backgrounds in Object Recognition,227,iclr,15,2,2023-06-18 09:25:02.241000,https://github.com/MadryLab/backgrounds_challenge,125,Noise or signal: The role of image backgrounds in object recognition,"https://scholar.google.com/scholar?cluster=14729938011425134088&hl=en&as_sdt=0,33",8,2021
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation,56,iclr,2,7,2023-06-18 09:25:02.446000,https://github.com/KelestZ/Latent2im,43,Enjoy your editing: Controllable gans for image editing via latent space navigation,"https://scholar.google.com/scholar?cluster=15259069119096220128&hl=en&as_sdt=0,47",4,2021
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models,141,iclr,8,4,2023-06-18 09:25:02.659000,https://github.com/cassidylaidlaw/perceptual-advex,50,Perceptual adversarial robustness: Defense against unseen threat models,"https://scholar.google.com/scholar?cluster=8526799141352056555&hl=en&as_sdt=0,5",2,2021
Zero-Cost Proxies for Lightweight NAS,148,iclr,15,7,2023-06-18 09:25:02.865000,https://github.com/mohsaied/zero-cost-nas,132,Zero-cost proxies for lightweight nas,"https://scholar.google.com/scholar?cluster=9734890465405015230&hl=en&as_sdt=0,33",8,2021
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit,12,iclr,178,119,2023-06-18 09:25:03.069000,https://github.com/google/uncertainty-baselines,1244,Exploring the uncertainty properties of neural networks' implicit priors in the infinite-width limit,"https://scholar.google.com/scholar?cluster=4194918161901179822&hl=en&as_sdt=0,48",20,2021
DC3: A learning method for optimization with hard constraints,75,iclr,15,1,2023-06-18 09:25:03.272000,https://github.com/locuslab/DC3,86,DC3: A learning method for optimization with hard constraints,"https://scholar.google.com/scholar?cluster=2253295560281589124&hl=en&as_sdt=0,5",5,2021
Shape-Texture Debiased Neural Network Training,78,iclr,9,2,2023-06-18 09:25:03.477000,https://github.com/LiYingwei/ShapeTextureDebiasedTraining,104,Shape-texture debiased neural network training,"https://scholar.google.com/scholar?cluster=13815083807768708857&hl=en&as_sdt=0,5",6,2021
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation,78,iclr,4,1,2023-06-18 09:25:03.680000,https://github.com/HazyResearch/model-patching,40,Model patching: Closing the subgroup performance gap with data augmentation,"https://scholar.google.com/scholar?cluster=501938357242145399&hl=en&as_sdt=0,44",17,2021
Linear Mode Connectivity in Multitask and Continual Learning,59,iclr,1,0,2023-06-18 09:25:03.885000,https://github.com/imirzadeh/MC-SGD,9,Linear mode connectivity in multitask and continual learning,"https://scholar.google.com/scholar?cluster=10468811797723946398&hl=en&as_sdt=0,48",3,2021
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures,32,iclr,8,3,2023-06-18 09:25:04.089000,https://github.com/phermosilla/IEConv_proteins,41,Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures,"https://scholar.google.com/scholar?cluster=10539365407241907053&hl=en&as_sdt=0,5",3,2021
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights,99,iclr,53,0,2023-06-18 09:25:04.292000,https://github.com/clovaai/AdamP,397,Adamp: Slowing down the slowdown for momentum optimizers on scale-invariant weights,"https://scholar.google.com/scholar?cluster=6696661613902754889&hl=en&as_sdt=0,5",13,2021
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering,32,iclr,7,2,2023-06-18 09:25:04.495000,https://github.com/TsungWeiTsai/MiCE,44,Mice: Mixture of contrastive experts for unsupervised image clustering,"https://scholar.google.com/scholar?cluster=16325984607726397092&hl=en&as_sdt=0,5",1,2021
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?,9,iclr,1,0,2023-06-18 09:25:04.699000,https://github.com/ramp-kits/rl_simulator,12,Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?,"https://scholar.google.com/scholar?cluster=13333175726397843154&hl=en&as_sdt=0,33",10,2021
Private Image Reconstruction from System Side Channels Using Generative Models,2,iclr,1,0,2023-06-18 09:25:04.902000,https://github.com/genSCA/genSCA,3,Private image reconstruction from system side channels using generative models,"https://scholar.google.com/scholar?cluster=2761181288645087029&hl=en&as_sdt=0,33",2,2021
IOT: Instance-wise Layer Reordering for Transformer Structures,4,iclr,1,0,2023-06-18 09:25:05.106000,https://github.com/instance-wise-ordered-transformer/IOT,19,IoT: Instance-wise layer reordering for transformer structures,"https://scholar.google.com/scholar?cluster=5895076023042170097&hl=en&as_sdt=0,5",2,2021
Counterfactual Generative Networks,88,iclr,24,3,2023-06-18 09:25:05.309000,https://github.com/autonomousvision/counterfactual_generative_networks,94,Counterfactual generative networks,"https://scholar.google.com/scholar?cluster=445809661981357040&hl=en&as_sdt=0,33",8,2021
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data,54,iclr,11,1,2023-06-18 09:25:05.512000,https://github.com/CAMTL/CA-MTL,48,Conditionally adaptive multi-task learning: Improving transfer learning in nlp using fewer parameters & less data,"https://scholar.google.com/scholar?cluster=814007091936446416&hl=en&as_sdt=0,33",3,2021
Domain-Robust Visual Imitation Learning with Mutual Information Constraints,5,iclr,2,0,2023-06-18 09:25:05.715000,https://github.com/Aladoro/domain-robust-visual-il,11,Domain-robust visual imitation learning with mutual information constraints,"https://scholar.google.com/scholar?cluster=18023915525010137640&hl=en&as_sdt=0,5",1,2021
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding,82,iclr,15,8,2023-06-18 09:25:05.919000,https://github.com/sanatonek/TNC_representation_learning,86,Unsupervised representation learning for time series with temporal neighborhood coding,"https://scholar.google.com/scholar?cluster=12306257235943010010&hl=en&as_sdt=0,6",1,2021
Enforcing robust control guarantees within neural network policies,58,iclr,10,0,2023-06-18 09:25:06.123000,https://github.com/locuslab/robust-nn-control,48,Enforcing robust control guarantees within neural network policies,"https://scholar.google.com/scholar?cluster=18128961654135874405&hl=en&as_sdt=0,33",7,2021
Active Contrastive Learning of Audio-Visual Video Representations,57,iclr,5,2,2023-06-18 09:25:06.327000,https://github.com/yunyikristy/CM-ACC,18,Active contrastive learning of audio-visual video representations,"https://scholar.google.com/scholar?cluster=1763906632624707840&hl=en&as_sdt=0,26",3,2021
Efficient Wasserstein Natural Gradients for Reinforcement Learning,12,iclr,3,0,2023-06-18 09:25:06.533000,https://github.com/tedmoskovitz/WNPG,9,Efficient wasserstein natural gradients for reinforcement learning,"https://scholar.google.com/scholar?cluster=18097668228161879279&hl=en&as_sdt=0,5",1,2021
Probing BERT in Hyperbolic Spaces,19,iclr,5,0,2023-06-18 09:25:06.736000,https://github.com/FranxYao/PoincareProbe,43,Probing BERT in hyperbolic spaces,"https://scholar.google.com/scholar?cluster=17283548434643857820&hl=en&as_sdt=0,5",6,2021
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning,29,iclr,23,1,2023-06-18 09:25:06.944000,https://github.com/wangren09/MetaAdv,73,On fast adversarial robustness adaptation in model-agnostic meta-learning,"https://scholar.google.com/scholar?cluster=16465003726106659039&hl=en&as_sdt=0,33",7,2021
Trusted Multi-View Classification,83,iclr,35,0,2023-06-18 09:25:07.148000,https://github.com/hanmenghan/TMC,146,Trusted multi-view classification,"https://scholar.google.com/scholar?cluster=10693215882520722160&hl=en&as_sdt=0,43",1,2021
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning,69,iclr,7,0,2023-06-18 09:25:07.352000,https://github.com/kibok90/imix,74,i-mix: A domain-agnostic strategy for contrastive representation learning,"https://scholar.google.com/scholar?cluster=17225673141444543699&hl=en&as_sdt=0,33",3,2021
Initialization and Regularization of Factorized Neural Layers,19,iclr,5,1,2023-06-18 09:25:07.556000,https://github.com/microsoft/fnl_paper,23,Initialization and regularization of factorized neural layers,"https://scholar.google.com/scholar?cluster=15693677234095389612&hl=en&as_sdt=0,40",7,2021
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning,26,iclr,1,0,2023-06-18 09:25:07.760000,https://github.com/boschresearch/imax-calibration,9,Multi-class uncertainty calibration via mutual information maximization-based binning,"https://scholar.google.com/scholar?cluster=10820552692700202554&hl=en&as_sdt=0,49",4,2021
Neural ODE Processes,47,iclr,7,0,2023-06-18 09:25:07.964000,https://github.com/crisbodnar/ndp,56,Neural ode processes,"https://scholar.google.com/scholar?cluster=12135997685697455587&hl=en&as_sdt=0,5",6,2021
An Unsupervised Deep Learning Approach for Real-World Image Denoising,7,iclr,4,1,2023-06-18 09:25:08.168000,https://github.com/zhengdharia/Unsupervised_denoising,26,An unsupervised deep learning approach for real-world image denoising,"https://scholar.google.com/scholar?cluster=10223621556329070926&hl=en&as_sdt=0,33",1,2021
Learning Parametrised Graph Shift Operators,15,iclr,0,0,2023-06-18 09:25:08.371000,https://github.com/gdasoulas/pgso,2,Learning parametrised graph shift operators,"https://scholar.google.com/scholar?cluster=8306422072823409247&hl=en&as_sdt=0,5",2,2021
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission,25,iclr,4,0,2023-06-18 09:25:08.575000,https://github.com/ajfisch/conformal-cascades,17,Efficient conformal prediction via cascaded inference with expanded admission,"https://scholar.google.com/scholar?cluster=1762284772897717346&hl=en&as_sdt=0,36",5,2021
GANs Can Play Lottery Tickets Too,35,iclr,7,0,2023-06-18 09:25:08.778000,https://github.com/VITA-Group/GAN-LTH,24,Gans can play lottery tickets too,"https://scholar.google.com/scholar?cluster=1236790394387307114&hl=en&as_sdt=0,5",9,2021
Adaptive Universal Generalized PageRank Graph Neural Network,297,iclr,24,0,2023-06-18 09:25:08.982000,https://github.com/jianhao2016/GPRGNN,95,Adaptive universal generalized pagerank graph neural network,"https://scholar.google.com/scholar?cluster=17989054169887872189&hl=en&as_sdt=0,33",1,2021
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control,34,iclr,2,2,2023-06-18 09:25:09.185000,https://github.com/yobibyte/amorpheus,36,My body is a cage: the role of morphology in graph-based incompatible control,"https://scholar.google.com/scholar?cluster=5888918560420712353&hl=en&as_sdt=0,5",3,2021
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning,135,iclr,8,3,2023-06-18 09:25:09.388000,https://github.com/hongyouc/fedbe,28,Fedbe: Making bayesian model ensemble applicable to federated learning,"https://scholar.google.com/scholar?cluster=14031237302830163445&hl=en&as_sdt=0,5",1,2021
MALI: A memory efficient and reverse accurate integrator for Neural ODEs,38,iclr,6,1,2023-06-18 09:25:09.592000,https://github.com/juntang-zhuang/TorchDiffEqPack,39,Mali: A memory efficient and reverse accurate integrator for neural odes,"https://scholar.google.com/scholar?cluster=12010857032543034567&hl=en&as_sdt=0,5",1,2021
Contrastive Syn-to-Real Generalization,38,iclr,4,2,2023-06-18 09:25:09.796000,https://github.com/NVlabs/CSG,30,Contrastive syn-to-real generalization,"https://scholar.google.com/scholar?cluster=14950252501736329080&hl=en&as_sdt=0,5",6,2021
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting,35,iclr,3,1,2023-06-18 09:25:10,https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons,30,Remembering for the right reasons: Explanations reduce catastrophic forgetting,"https://scholar.google.com/scholar?cluster=10259250203808159056&hl=en&as_sdt=0,1",4,2021
High-Capacity Expert Binary Networks,46,iclr,3,0,2023-06-18 09:25:10.205000,https://github.com/1adrianb/expert-binary-networks,22,High-capacity expert binary networks,"https://scholar.google.com/scholar?cluster=12665350713453943927&hl=en&as_sdt=0,14",2,2021
Learning What To Do by Simulating the Past,2,iclr,7,0,2023-06-18 09:25:10.408000,https://github.com/HumanCompatibleAI/deep-rlsp,24,Learning what to do by simulating the past,"https://scholar.google.com/scholar?cluster=15331293852960399558&hl=en&as_sdt=0,50",8,2021
Progressive Skeletonization: Trimming more fat from a network at initialization,49,iclr,1,0,2023-06-18 09:25:10.612000,https://github.com/naver/force,12,Progressive skeletonization: Trimming more fat from a network at initialization,"https://scholar.google.com/scholar?cluster=5929326556429040468&hl=en&as_sdt=0,33",5,2021
Learning Manifold Patch-Based Representations of Man-Made Shapes,21,iclr,6,0,2023-06-18 09:25:10.829000,https://github.com/dmsm/LearningPatches,25,Learning manifold patch-based representations of man-made shapes,"https://scholar.google.com/scholar?cluster=9102520552228338739&hl=en&as_sdt=0,5",4,2021
Aligning AI With Shared Human Values,100,iclr,24,2,2023-06-18 09:25:11.033000,https://github.com/hendrycks/ethics,131,Aligning ai with shared human values,"https://scholar.google.com/scholar?cluster=3779881846531532351&hl=en&as_sdt=0,34",6,2021
Measuring Massive Multitask Language Understanding,161,iclr,40,5,2023-06-18 09:25:11.240000,https://github.com/hendrycks/test,335,Measuring massive multitask language understanding,"https://scholar.google.com/scholar?cluster=17727716530891149102&hl=en&as_sdt=0,5",12,2021
Towards Robust Neural Networks via Close-loop Control,13,iclr,3,0,2023-06-18 09:25:11.445000,https://github.com/zhuotongchen/Towards-Robust-Neural-Networks-via-Close-loop-Control,12,Towards robust neural networks via close-loop control,"https://scholar.google.com/scholar?cluster=3798545379660922122&hl=en&as_sdt=0,5",1,2021
Statistical inference for individual fairness,19,iclr,0,0,2023-06-18 09:25:11.648000,https://github.com/smaityumich/individual-fairness-testing,3,Statistical inference for individual fairness,"https://scholar.google.com/scholar?cluster=6638472826434379762&hl=en&as_sdt=0,5",2,2021
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning,83,iclr,20,10,2023-06-18 09:25:11.857000,https://github.com/alfworld/alfworld,99,Alfworld: Aligning text and embodied environments for interactive learning,"https://scholar.google.com/scholar?cluster=11544973336902610716&hl=en&as_sdt=0,33",6,2021
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification,64,iclr,1,1,2023-06-18 09:25:12.061000,https://github.com/utrerf/robust_transfer_learning,11,Adversarially-trained deep nets transfer better: Illustration on image classification,"https://scholar.google.com/scholar?cluster=2203642732634467996&hl=en&as_sdt=0,41",6,2021
Calibration of Neural Networks using Splines,65,iclr,1,1,2023-06-18 09:25:12.264000,https://github.com/kartikgupta-at-anu/spline-calibration,15,Calibration of neural networks using splines,"https://scholar.google.com/scholar?cluster=16036759734574308055&hl=en&as_sdt=0,5",2,2021
Rethinking Positional Encoding in Language Pre-training,152,iclr,26,11,2023-06-18 09:25:12.467000,https://github.com/guolinke/TUPE,238,Rethinking positional encoding in language pre-training,"https://scholar.google.com/scholar?cluster=13553136852407909165&hl=en&as_sdt=0,33",6,2021
Discovering Non-monotonic Autoregressive Orderings with Variational Inference,3,iclr,3,0,2023-06-18 09:25:12.671000,https://github.com/xuanlinli17/autoregressive_inference,10,Discovering non-monotonic autoregressive orderings with variational inference,"https://scholar.google.com/scholar?cluster=14307542819344534269&hl=en&as_sdt=0,5",2,2021
Differentiable Trust Region Layers for Deep Reinforcement Learning,12,iclr,3,0,2023-06-18 09:25:12.874000,https://github.com/boschresearch/trust-region-layers,9,Differentiable trust region layers for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=5230487248575578545&hl=en&as_sdt=0,33",4,2021
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization,85,iclr,11,3,2023-06-18 09:25:13.078000,https://github.com/SaliencyMix/SaliencyMix,29,Saliencymix: A saliency guided data augmentation strategy for better regularization,"https://scholar.google.com/scholar?cluster=13015633056720744259&hl=en&as_sdt=0,47",2,2021
Task-Agnostic Morphology Evolution,12,iclr,4,0,2023-06-18 09:25:13.281000,https://github.com/jhejna/morphology-opt,18,Task-agnostic morphology evolution,"https://scholar.google.com/scholar?cluster=14695430945522716780&hl=en&as_sdt=0,24",2,2021
Learning Associative Inference Using Fast Weight Memory,30,iclr,5,1,2023-06-18 09:25:13.484000,https://github.com/ischlag/Fast-Weight-Memory-public,22,Learning associative inference using fast weight memory,"https://scholar.google.com/scholar?cluster=16934053175834221248&hl=en&as_sdt=0,33",2,2021
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks,30,iclr,32,4,2023-06-18 09:25:13.688000,https://github.com/nd7141/bgnn,147,Boost then convolve: Gradient boosting meets graph neural networks,"https://scholar.google.com/scholar?cluster=10385206345451191815&hl=en&as_sdt=0,33",7,2021
Network Pruning That Matters: A Case Study on Retraining Variants,33,iclr,0,0,2023-06-18 09:25:13.892000,https://github.com/lehduong/NPTM,18,Network pruning that matters: A case study on retraining variants,"https://scholar.google.com/scholar?cluster=11116406662697084057&hl=en&as_sdt=0,22",2,2021
Differentiable Segmentation of Sequences,1,iclr,2,0,2023-06-18 09:25:14.094000,https://github.com/diozaka/diffseg,4,Differentiable Segmentation of Sequences,"https://scholar.google.com/scholar?cluster=460118456936482519&hl=en&as_sdt=0,5",1,2021
Learning Deep Features in Instrumental Variable Regression,41,iclr,5,0,2023-06-18 09:25:14.298000,https://github.com/liyuan9988/DeepFeatureIV,11,Learning deep features in instrumental variable regression,"https://scholar.google.com/scholar?cluster=17960670738858141531&hl=en&as_sdt=0,31",1,2021
Graph Information Bottleneck for Subgraph Recognition,65,iclr,4,0,2023-06-18 09:25:14.501000,https://github.com/Samyu0304/graph-information-bottleneck-for-Subgraph-Recognition,30,Graph information bottleneck for subgraph recognition,"https://scholar.google.com/scholar?cluster=12146903332537302158&hl=en&as_sdt=0,5",2,2021
In Search of Lost Domain Generalization,623,iclr,250,4,2023-06-18 09:25:14.705000,https://github.com/facebookresearch/DomainBed,1087,In search of lost domain generalization,"https://scholar.google.com/scholar?cluster=5341652609507299465&hl=en&as_sdt=0,5",34,2021
CoCon: A Self-Supervised Approach for Controlled Text Generation,56,iclr,22,5,2023-06-18 09:25:14.908000,https://github.com/alvinchangw/COCON_ICLR2021,87,Cocon: A self-supervised approach for controlled text generation,"https://scholar.google.com/scholar?cluster=5100156024568984026&hl=en&as_sdt=0,47",5,2021
CT-Net: Channel Tensorization Network for Video Classification,31,iclr,11,0,2023-06-18 09:25:15.111000,https://github.com/Andy1621/CT-Net,34,Ct-net: Channel tensorization network for video classification,"https://scholar.google.com/scholar?cluster=14793670932380609397&hl=en&as_sdt=0,5",2,2021
Symmetry-Aware Actor-Critic for 3D Molecular Design,47,iclr,22,7,2023-06-18 09:25:15.315000,https://github.com/gncs/molgym,94,Symmetry-aware actor-critic for 3d molecular design,"https://scholar.google.com/scholar?cluster=6834309222206333717&hl=en&as_sdt=0,5",5,2021
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation,174,iclr,22,9,2023-06-18 09:25:15.518000,https://github.com/googleinterns/wss,149,Pseudoseg: Designing pseudo labels for semantic segmentation,"https://scholar.google.com/scholar?cluster=11801417491488488735&hl=en&as_sdt=0,5",10,2021
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks,9,iclr,0,0,2023-06-18 09:25:15.722000,https://github.com/sgk98/CRM-Better-Mistakes,7,No cost likelihood manipulation at test time for making better mistakes in deep networks,"https://scholar.google.com/scholar?cluster=7455201941557048589&hl=en&as_sdt=0,5",6,2021
Distance-Based Regularisation of Deep Networks for Fine-Tuning,24,iclr,3,1,2023-06-18 09:25:15.926000,https://github.com/henrygouk/mars-finetuning,17,Distance-based regularisation of deep networks for fine-tuning,"https://scholar.google.com/scholar?cluster=16025867309498919322&hl=en&as_sdt=0,14",3,2021
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis,134,iclr,90,14,2023-06-18 09:25:16.129000,https://github.com/odegeasslbc/FastGAN-pytorch,501,Towards faster and stabilized gan training for high-fidelity few-shot image synthesis,"https://scholar.google.com/scholar?cluster=1230561477008611475&hl=en&as_sdt=0,5",10,2021
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network,53,iclr,8,2,2023-06-18 09:25:16.332000,https://github.com/chrundle/biprop,38,Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network,"https://scholar.google.com/scholar?cluster=10684547264347032569&hl=en&as_sdt=0,5",6,2021
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization,44,iclr,6,3,2023-06-18 09:25:16.536000,https://github.com/yanghr/BSQ,29,BSQ: Exploring bit-level sparsity for mixed-precision neural network quantization,"https://scholar.google.com/scholar?cluster=11996673667923018710&hl=en&as_sdt=0,44",2,2021
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly,11,iclr,8,1,2023-06-18 09:25:16.740000,https://github.com/YuchenJin/autolrs,38,Autolrs: Automatic learning-rate schedule by bayesian optimization on the fly,"https://scholar.google.com/scholar?cluster=8044903521552367518&hl=en&as_sdt=0,5",3,2021
BERTology Meets Biology: Interpreting Attention in Protein Language Models,181,iclr,46,3,2023-06-18 09:25:16.944000,https://github.com/salesforce/provis,273,BERTology meets biology: interpreting attention in protein language models,"https://scholar.google.com/scholar?cluster=2514418869300543698&hl=en&as_sdt=0,5",18,2021
Learning Task-General Representations with Generative Neuro-Symbolic Modeling,11,iclr,7,1,2023-06-18 09:25:17.147000,https://github.com/rfeinman/GNS-Modeling,22,Learning task-general representations with generative neuro-symbolic modeling,"https://scholar.google.com/scholar?cluster=1335404082385789329&hl=en&as_sdt=0,33",5,2021
Training independent subnetworks for robust prediction,127,iclr,178,119,2023-06-18 09:25:17.350000,https://github.com/google/uncertainty-baselines,1244,Training independent subnetworks for robust prediction,"https://scholar.google.com/scholar?cluster=9264084238315698016&hl=en&as_sdt=0,43",20,2021
Meta-Learning of Structured Task Distributions in Humans and Machines,7,iclr,2,0,2023-06-18 09:25:17.554000,https://github.com/sreejank/Compositional_MetaRL,6,Meta-learning of structured task distributions in humans and machines,"https://scholar.google.com/scholar?cluster=10148595521419901644&hl=en&as_sdt=0,34",2,2021
BiPointNet: Binary Neural Network for Point Clouds,33,iclr,12,4,2023-06-18 09:25:17.769000,https://github.com/htqin/BiPointNet,66,Bipointnet: Binary neural network for point clouds,"https://scholar.google.com/scholar?cluster=2821902497514525897&hl=en&as_sdt=0,5",5,2021
Benchmarks for Deep Off-Policy Evaluation,47,iclr,11,2,2023-06-18 09:25:17.973000,https://github.com/google-research/deep_ope,78,Benchmarks for deep off-policy evaluation,"https://scholar.google.com/scholar?cluster=4005543467911115320&hl=en&as_sdt=0,19",8,2021
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation,25,iclr,11,0,2023-06-18 09:25:18.177000,https://github.com/Angtian/NeMo,78,Nemo: Neural mesh models of contrastive features for robust 3d pose estimation,"https://scholar.google.com/scholar?cluster=10173086417139954179&hl=en&as_sdt=0,5",6,2021
On Graph Neural Networks versus Graph-Augmented MLPs,30,iclr,0,0,2023-06-18 09:25:18.381000,https://github.com/leichen2018/GNN_vs_GAMLP,5,On graph neural networks versus graph-augmented mlps,"https://scholar.google.com/scholar?cluster=13883666734002011064&hl=en&as_sdt=0,23",2,2021
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization,98,iclr,4,2,2023-06-18 09:25:18.584000,https://github.com/matsuolab/BREMEN,49,Deployment-efficient reinforcement learning via model-based offline optimization,"https://scholar.google.com/scholar?cluster=6135669671400204615&hl=en&as_sdt=0,5",14,2021
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs,81,iclr,26,15,2023-06-18 09:25:18.788000,https://github.com/DeepGraphLearning/RNNLogic,105,Rnnlogic: Learning logic rules for reasoning on knowledge graphs,"https://scholar.google.com/scholar?cluster=15092610783958587096&hl=en&as_sdt=0,33",6,2021
WaNet - Imperceptible Warping-based Backdoor Attack,199,iclr,16,2,2023-06-18 09:25:18.992000,https://github.com/VinAIResearch/Warping-based_Backdoor_Attack-release,73,Wanet--imperceptible warping-based backdoor attack,"https://scholar.google.com/scholar?cluster=704714382831762036&hl=en&as_sdt=0,33",5,2021
Prototypical Contrastive Learning of Unsupervised Representations,594,iclr,75,5,2023-06-18 09:25:19.195000,https://github.com/salesforce/PCL,476,Prototypical contrastive learning of unsupervised representations,"https://scholar.google.com/scholar?cluster=298080063887760247&hl=en&as_sdt=0,31",16,2021
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling,61,iclr,5,0,2023-06-18 09:25:19.398000,https://github.com/benbo/interactive-weak-supervision,28,Interactive weak supervision: Learning useful heuristics for data labeling,"https://scholar.google.com/scholar?cluster=15628651718896902730&hl=en&as_sdt=0,31",3,2021
"Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation",110,iclr,4,0,2023-06-18 09:25:19.602000,https://github.com/jungokasai/deep-shallow,39,"Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation","https://scholar.google.com/scholar?cluster=9322073775736159949&hl=en&as_sdt=0,7",3,2021
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences,67,iclr,11,1,2023-06-18 09:25:19.804000,https://github.com/hehefan/Point-Spatio-Temporal-Convolution,76,Pstnet: Point spatio-temporal convolution on point cloud sequences,"https://scholar.google.com/scholar?cluster=2334272316624788650&hl=en&as_sdt=0,29",2,2021
Prototypical Representation Learning for Relation Extraction,33,iclr,5,6,2023-06-18 09:25:20.007000,https://github.com/Alibaba-NLP/ProtoRE,30,Prototypical representation learning for relation extraction,"https://scholar.google.com/scholar?cluster=12544006759905435219&hl=en&as_sdt=0,5",6,2021
Layer-adaptive Sparsity for the Magnitude-based Pruning,62,iclr,5,2,2023-06-18 09:25:20.211000,https://github.com/jaeho-lee/layer-adaptive-sparsity,40,Layer-adaptive sparsity for the magnitude-based pruning,"https://scholar.google.com/scholar?cluster=16870181998029600993&hl=en&as_sdt=0,33",1,2021
Refining Deep Generative Models via Discriminator Gradient Flow,26,iclr,5,0,2023-06-18 09:25:20.414000,https://github.com/clear-nus/DGflow,15,Refining deep generative models via discriminator gradient flow,"https://scholar.google.com/scholar?cluster=6216370278663020566&hl=en&as_sdt=0,5",3,2021
Lipschitz Recurrent Neural Networks,65,iclr,6,0,2023-06-18 09:25:20.618000,https://github.com/erichson/LipschitzRNN,22,Lipschitz recurrent neural networks,"https://scholar.google.com/scholar?cluster=9494951983450732150&hl=en&as_sdt=0,5",4,2021
Learning Hyperbolic Representations of Topological Features,8,iclr,0,0,2023-06-18 09:25:20.835000,https://github.com/pkyriakis/permanifold,4,Learning hyperbolic representations of topological features,"https://scholar.google.com/scholar?cluster=6250242644104147473&hl=en&as_sdt=0,5",3,2021
Risk-Averse Offline Reinforcement Learning,52,iclr,3,1,2023-06-18 09:25:21.039000,https://github.com/nuria95/O-RAAC,31,Risk-averse offline reinforcement learning,"https://scholar.google.com/scholar?cluster=13690519039445695672&hl=en&as_sdt=0,5",2,2021
Group Equivariant Stand-Alone Self-Attention For Vision,38,iclr,4,2,2023-06-18 09:25:21.242000,https://github.com/dwromero/g_selfatt,25,Group equivariant stand-alone self-attention for vision,"https://scholar.google.com/scholar?cluster=6833601088061308138&hl=en&as_sdt=0,10",2,2021
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning,39,iclr,5,1,2023-06-18 09:25:21.446000,https://github.com/SamuelHorvath/Compressed_SGD_PyTorch,11,A better alternative to error feedback for communication-efficient distributed learning,"https://scholar.google.com/scholar?cluster=3097136742513033323&hl=en&as_sdt=0,5",3,2021
Capturing Label Characteristics in VAEs,24,iclr,4,2,2023-06-18 09:25:21.650000,https://github.com/thwjoy/ccvae,10,Capturing label characteristics in VAEs,"https://scholar.google.com/scholar?cluster=9136485523673709879&hl=en&as_sdt=0,33",3,2021
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective,71,iclr,4,0,2023-06-18 09:25:21.852000,https://github.com/AI-secure/InfoBERT,75,Infobert: Improving robustness of language models from an information theoretic perspective,"https://scholar.google.com/scholar?cluster=12094007183330442951&hl=en&as_sdt=0,14",3,2021
DrNAS: Dirichlet Neural Architecture Search,83,iclr,13,2,2023-06-18 09:25:22.056000,https://github.com/xiangning-chen/DrNAS,39,Drnas: Dirichlet neural architecture search,"https://scholar.google.com/scholar?cluster=10097373512584874749&hl=en&as_sdt=0,5",3,2021
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration,10,iclr,4,0,2023-06-18 09:25:22.260000,https://github.com/jaekyeom/drop-bottleneck,11,Drop-bottleneck: Learning discrete compressed representation for noise-robust exploration,"https://scholar.google.com/scholar?cluster=4970327572686173895&hl=en&as_sdt=0,5",1,2021
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning,25,iclr,1,1,2023-06-18 09:25:22.464000,https://github.com/SunbowLiu/SurfaceFusion,23,Understanding and improving encoder layer fusion in sequence-to-sequence learning,"https://scholar.google.com/scholar?cluster=14614453829953722728&hl=en&as_sdt=0,11",4,2021
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling,6,iclr,0,0,2023-06-18 09:25:22.667000,https://github.com/djordjemila/sdn,34,Spatial dependency networks: Neural layers for improved generative image modeling,"https://scholar.google.com/scholar?cluster=4211572628480421542&hl=en&as_sdt=0,6",4,2021
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training,45,iclr,18,3,2023-06-18 09:25:22.870000,https://github.com/blackfeather-wang/InfoPro-Pytorch,85,Revisiting locally supervised learning: an alternative to end-to-end training,"https://scholar.google.com/scholar?cluster=9055003625249096504&hl=en&as_sdt=0,14",4,2021
Gradient Origin Networks,11,iclr,18,2,2023-06-18 09:25:23.073000,https://github.com/cwkx/GON,157,Gradient origin networks,"https://scholar.google.com/scholar?cluster=861384408190875414&hl=en&as_sdt=0,5",11,2021
Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets,22,iclr,8,3,2023-06-18 09:25:23.276000,https://github.com/HayeonLee/MetaD2A,53,Rapid neural architecture search by learning to generate graphs from datasets,"https://scholar.google.com/scholar?cluster=7579199201764554515&hl=en&as_sdt=0,5",4,2021
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization,14,iclr,2,0,2023-06-18 09:25:23.481000,https://github.com/bethgelab/testing_visualizations,10,Exemplary natural images explain CNN activations better than state-of-the-art feature visualization,"https://scholar.google.com/scholar?cluster=4262811630228932097&hl=en&as_sdt=0,44",10,2021
Adversarial score matching and improved sampling for image generation,62,iclr,19,0,2023-06-18 09:25:23.683000,https://github.com/AlexiaJM/AdversarialConsistentScoreMatching,116,Adversarial score matching and improved sampling for image generation,"https://scholar.google.com/scholar?cluster=10784754295814543422&hl=en&as_sdt=0,44",6,2021
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective,97,iclr,3,0,2023-06-18 09:25:23.886000,https://github.com/balcilar/gnn-spectral-expressive-power,39,Analyzing the expressive power of graph neural networks in a spectral perspective,"https://scholar.google.com/scholar?cluster=12539425234528098281&hl=en&as_sdt=0,5",1,2021
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients,226,iclr,26,1,2023-06-18 09:25:24.089000,https://github.com/dem123456789/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients,89,HeteroFL: Computation and communication efficient federated learning for heterogeneous clients,"https://scholar.google.com/scholar?cluster=2499958009868244362&hl=en&as_sdt=0,22",2,2021
DINO: A Conditional Energy-Based GAN for Domain Translation,4,iclr,2,0,2023-06-18 09:25:24.294000,https://github.com/DinoMan/DINO,16,Dino: A conditional energy-based gan for domain translation,"https://scholar.google.com/scholar?cluster=16181191897980218531&hl=en&as_sdt=0,5",4,2021
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning,45,iclr,25,4,2023-06-18 09:25:24.497000,https://github.com/twke18/SPML,92,Universal weakly supervised segmentation by pixel-to-segment contrastive learning,"https://scholar.google.com/scholar?cluster=2575509645382870246&hl=en&as_sdt=0,43",5,2021
C-Learning: Horizon-Aware Cumulative Accessibility Estimation,1,iclr,3,0,2023-06-18 09:25:24.701000,https://github.com/layer6ai-labs/CAE,3,C-learning: Horizon-aware cumulative accessibility estimation,"https://scholar.google.com/scholar?cluster=1403006878446325518&hl=en&as_sdt=0,44",6,2021
Neurally Augmented ALISTA,11,iclr,5,0,2023-06-18 09:25:24.905000,https://github.com/feeds/na-alista,13,Neurally augmented ALISTA,"https://scholar.google.com/scholar?cluster=3734900423445750140&hl=en&as_sdt=0,36",5,2021
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization,60,iclr,16,1,2023-06-18 09:25:25.108000,https://github.com/CW-Huang/CP-Flow,73,Convex potential flows: Universal probability distributions with optimal transport and convex optimization,"https://scholar.google.com/scholar?cluster=10968638702827610347&hl=en&as_sdt=0,36",5,2021
Wasserstein Embedding for Graph Learning,46,iclr,2,1,2023-06-18 09:25:25.312000,https://github.com/navid-naderi/WEGL,25,Wasserstein embedding for graph learning,"https://scholar.google.com/scholar?cluster=318944885595116091&hl=en&as_sdt=0,5",3,2021
Grounding Language to Autonomously-Acquired Skills via Goal Generation,42,iclr,3,0,2023-06-18 09:25:25.517000,https://github.com/akakzia/decstr,15,Grounding language to autonomously-acquired skills via goal generation,"https://scholar.google.com/scholar?cluster=192435658949853668&hl=en&as_sdt=0,34",2,2021
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks,21,iclr,6,0,2023-06-18 09:25:25.721000,https://github.com/yellowshippo/isogcn-iclr2021,41,Isometric transformation invariant and equivariant graph convolutional networks,"https://scholar.google.com/scholar?cluster=8837825832802039712&hl=en&as_sdt=0,1",1,2021
R-GAP: Recursive Gradient Attack on Privacy,57,iclr,1,0,2023-06-18 09:25:25.934000,https://github.com/JunyiZhu-AI/R-GAP,28,R-gap: Recursive gradient attack on privacy,"https://scholar.google.com/scholar?cluster=15519567665502998239&hl=en&as_sdt=0,5",2,2021
Multiplicative Filter Networks,60,iclr,7,1,2023-06-18 09:25:26.138000,https://github.com/boschresearch/multiplicative-filter-networks,72,Multiplicative filter networks,"https://scholar.google.com/scholar?cluster=2058143723489535198&hl=en&as_sdt=0,47",8,2021
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks,45,iclr,8,2,2023-06-18 09:25:26.341000,https://github.com/RobertCsordas/modules,33,Are neural nets modular? inspecting functional modularity through differentiable weight masks,"https://scholar.google.com/scholar?cluster=5376725240371408845&hl=en&as_sdt=0,5",0,2021
Modeling the Second Player in Distributionally Robust Optimization,17,iclr,7,0,2023-06-18 09:25:26.546000,https://github.com/pmichel31415/P-DRO,18,Modeling the second player in distributionally robust optimization,"https://scholar.google.com/scholar?cluster=16015230267051780457&hl=en&as_sdt=0,33",2,2021
Private Post-GAN Boosting,21,iclr,4,6,2023-06-18 09:25:26.750000,https://github.com/mneunhoe/post-gan-boosting,9,Private post-GAN boosting,"https://scholar.google.com/scholar?cluster=937740189813979153&hl=en&as_sdt=0,33",4,2021
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis,126,iclr,171,73,2023-06-18 09:25:26.953000,https://github.com/NVIDIA/flowtron,839,Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis,"https://scholar.google.com/scholar?cluster=1579689582070242490&hl=en&as_sdt=0,40",31,2021
Learning Structural Edits via Incremental Tree Transformations,20,iclr,3,0,2023-06-18 09:25:27.157000,https://github.com/neulab/incremental_tree_edit,40,Learning structural edits via incremental tree transformations,"https://scholar.google.com/scholar?cluster=785545051863300366&hl=en&as_sdt=0,1",13,2021
Sample-Efficient Automated Deep Reinforcement Learning,26,iclr,6,0,2023-06-18 09:25:27.361000,https://github.com/automl/SEARL,32,Sample-efficient automated deep reinforcement learning,"https://scholar.google.com/scholar?cluster=1828733930772382760&hl=en&as_sdt=0,33",10,2021
Multiscale Score Matching for Out-of-Distribution Detection,18,iclr,0,1,2023-06-18 09:25:27.565000,https://github.com/ahsanMah/msma,6,Multiscale score matching for out-of-distribution detection,"https://scholar.google.com/scholar?cluster=3312026787172969565&hl=en&as_sdt=0,5",3,2021
Linear Last-iterate Convergence in Constrained Saddle-point Optimization,66,iclr,1,0,2023-06-18 09:25:27.768000,https://github.com/bahh723/OGDA-last-iterate,1,Linear last-iterate convergence in constrained saddle-point optimization,"https://scholar.google.com/scholar?cluster=11705572357313467666&hl=en&as_sdt=0,5",4,2021
Learning advanced mathematical computations from examples,20,iclr,12,0,2023-06-18 09:25:27.972000,https://github.com/facebookresearch/MathsFromExamples,173,Learning advanced mathematical computations from examples,"https://scholar.google.com/scholar?cluster=8069536277199398832&hl=en&as_sdt=0,5",10,2021
Generalized Energy Based Models,83,iclr,4,1,2023-06-18 09:25:28.176000,https://github.com/MichaelArbel/GeneralizedEBM,46,Generalized energy based models,"https://scholar.google.com/scholar?cluster=8950051300346719301&hl=en&as_sdt=0,5",4,2021
Beyond Categorical Label Representations for Image Classification,2,iclr,8,1,2023-06-18 09:25:28.380000,https://github.com/BoyuanChen/label_representations,24,Beyond Categorical Label Representations for Image Classification,"https://scholar.google.com/scholar?cluster=6100870767960512656&hl=en&as_sdt=0,38",3,2021
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers,53,iclr,12,4,2023-06-18 09:25:28.583000,https://github.com/salesforce/coco-dst,52,Coco: Controllable counterfactuals for evaluating dialogue state trackers,"https://scholar.google.com/scholar?cluster=2147186287214525366&hl=en&as_sdt=0,38",5,2021
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models,22,iclr,6,0,2023-06-18 09:25:28.787000,https://github.com/point0bar1/ebm-defense,17,Stochastic security: Adversarial defense using long-run dynamics of energy-based models,"https://scholar.google.com/scholar?cluster=1702716547695193492&hl=en&as_sdt=0,5",2,2021
PDE-Driven Spatiotemporal Disentanglement,18,iclr,3,0,2023-06-18 09:25:28.992000,https://github.com/JeremDona/spatiotemporal_variable_separation,25,Pde-driven spatiotemporal disentanglement,"https://scholar.google.com/scholar?cluster=11182191467887081005&hl=en&as_sdt=0,5",3,2021
Directed Acyclic Graph Neural Networks,57,iclr,20,1,2023-06-18 09:25:29.196000,https://github.com/vthost/DAGNN,80,Directed acyclic graph neural networks,"https://scholar.google.com/scholar?cluster=13529849835566425247&hl=en&as_sdt=0,33",3,2021
QPLEX: Duplex Dueling Multi-Agent Q-Learning,248,iclr,25,4,2023-06-18 09:25:29.400000,https://github.com/wjh720/QPLEX,71,Qplex: Duplex dueling multi-agent q-learning,"https://scholar.google.com/scholar?cluster=785256568815923824&hl=en&as_sdt=0,23",4,2021
Learning Energy-Based Models by Diffusion Recovery Likelihood,64,iclr,13,6,2023-06-18 09:25:29.604000,https://github.com/ruiqigao/recovery_likelihood,41,Learning energy-based models by diffusion recovery likelihood,"https://scholar.google.com/scholar?cluster=4399294843209736764&hl=en&as_sdt=0,5",4,2021
Neural Networks for Learning Counterfactual G-Invariances from Single Environments,4,iclr,0,0,2023-06-18 09:25:29.809000,https://github.com/PurdueMINDS/NN_CGInvariance,1,Neural networks for learning counterfactual g-invariances from single environments,"https://scholar.google.com/scholar?cluster=11398104939483895599&hl=en&as_sdt=0,5",5,2021
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections,71,iclr,4,0,2023-06-18 09:25:30.025000,https://github.com/brandeis-machine-learning/FairAdj,7,On dyadic fairness: Exploring and mitigating bias in graph connections,"https://scholar.google.com/scholar?cluster=9084547275542590284&hl=en&as_sdt=0,5",2,2021
Faster Binary Embeddings for Preserving Euclidean Distances,2,iclr,0,0,2023-06-18 09:25:30.229000,https://github.com/jayzhang0727/Faster-Binary-Embeddings-for-Preserving-Euclidean-Distances,3,Faster binary embeddings for preserving euclidean distances,"https://scholar.google.com/scholar?cluster=16441241350533761738&hl=en&as_sdt=0,5",1,2021
Learning and Evaluating Representations for Deep One-Class Classification,134,iclr,28,3,2023-06-18 09:25:30.434000,https://github.com/google-research/deep_representation_one_class,141,Learning and evaluating representations for deep one-class classification,"https://scholar.google.com/scholar?cluster=6458276904017990971&hl=en&as_sdt=0,14",7,2021
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning,6,iclr,0,0,2023-06-18 09:25:30.644000,https://github.com/NamyeongK/USA_UFGSM,1,Repurposing pretrained models for robust out-of-domain few-shot learning,"https://scholar.google.com/scholar?cluster=14110551403229588194&hl=en&as_sdt=0,5",0,2021
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning,300,iclr,38,5,2023-06-18 09:25:30.871000,https://github.com/nayeemrizve/ups,199,In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning,"https://scholar.google.com/scholar?cluster=18358012281479028989&hl=en&as_sdt=0,44",2,2021
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning,13,iclr,1,1,2023-06-18 09:25:31.074000,https://github.com/necla-ml/cater-h,6,Hopper: Multi-hop transformer for spatiotemporal reasoning,"https://scholar.google.com/scholar?cluster=15937741305189053323&hl=en&as_sdt=0,14",6,2021
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization,2,iclr,0,1,2023-06-18 09:25:31.279000,https://github.com/mederrata/spmf,2,Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization,"https://scholar.google.com/scholar?cluster=17630346324232626458&hl=en&as_sdt=0,5",9,2021
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology,17,iclr,5,2,2023-06-18 09:25:31.483000,https://github.com/stanfordmlgroup/disentanglement,36,Evaluating the disentanglement of deep generative models through manifold topology,"https://scholar.google.com/scholar?cluster=8056390107725360961&hl=en&as_sdt=0,47",5,2021
Decoupling Global and Local Representations via Invertible Generative Flows,12,iclr,12,3,2023-06-18 09:25:31.687000,https://github.com/XuezheMax/wolf,81,Decoupling global and local representations via invertible generative flows,"https://scholar.google.com/scholar?cluster=17803747103962637793&hl=en&as_sdt=0,34",4,2021
Pre-training Text-to-Text Transformers for Concept-centric Common Sense,45,iclr,0,2,2023-06-18 09:25:31.890000,https://github.com/INK-USC/CALM,26,Pre-training text-to-text transformers for concept-centric common sense,"https://scholar.google.com/scholar?cluster=8101587242954788676&hl=en&as_sdt=0,33",5,2021
Combining Label Propagation and Simple Models out-performs Graph Neural Networks,195,iclr,51,5,2023-06-18 09:25:32.093000,https://github.com/CUAI/CorrectAndSmooth,264,Combining label propagation and simple models out-performs graph neural networks,"https://scholar.google.com/scholar?cluster=3392954372444403130&hl=en&as_sdt=0,33",9,2021
Provably robust classification of adversarial examples with detection,21,iclr,2,0,2023-06-18 09:25:32.296000,https://github.com/boschresearch/robust_classification_with_detection,7,Provably robust classification of adversarial examples with detection,"https://scholar.google.com/scholar?cluster=2472207606878267459&hl=en&as_sdt=0,47",4,2021
Fourier Neural Operator for Parametric Partial Differential Equations,853,iclr,391,12,2023-06-18 09:25:32.500000,https://github.com/zongyi-li/fourier_neural_operator,1306,Fourier neural operator for parametric partial differential equations,"https://scholar.google.com/scholar?cluster=12451804788662635900&hl=en&as_sdt=0,10",36,2021
Class Normalization for (Continual)? Generalized Zero-Shot Learning,26,iclr,4,2,2023-06-18 09:25:32.704000,https://github.com/universome/czsl,34,Class normalization for (continual)? generalized zero-shot learning,"https://scholar.google.com/scholar?cluster=12819058346113139372&hl=en&as_sdt=0,33",4,2021
Adaptive and Generative Zero-Shot Learning,42,iclr,5,2,2023-06-18 09:25:32.907000,https://github.com/anonmous529/AGZSL,16,Adaptive and generative zero-shot learning,"https://scholar.google.com/scholar?cluster=17923480096622740507&hl=en&as_sdt=0,5",2,2021
Disentangling 3D Prototypical Networks for Few-Shot Concept Learning,10,iclr,0,0,2023-06-18 09:25:33.113000,https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets,10,Disentangling 3d prototypical networks for few-shot concept learning,"https://scholar.google.com/scholar?cluster=3118057905544966050&hl=en&as_sdt=0,5",2,2021
Anytime Sampling for Autoregressive Models via Ordered Autoencoding,11,iclr,3,1,2023-06-18 09:25:33.316000,https://github.com/Newbeeer/Anytime-Auto-Regressive-Model,22,Anytime sampling for autoregressive models via ordered autoencoding,"https://scholar.google.com/scholar?cluster=8874332666353389507&hl=en&as_sdt=0,5",2,2021
Estimating informativeness of samples with Smooth Unique Information,15,iclr,4,0,2023-06-18 09:25:33.520000,https://github.com/awslabs/aws-cv-unique-information,9,Estimating informativeness of samples with smooth unique information,"https://scholar.google.com/scholar?cluster=9537970110591918556&hl=en&as_sdt=0,33",3,2021
Accurate Learning of Graph Representations with Graph Multiset Pooling,80,iclr,19,0,2023-06-18 09:25:33.723000,https://github.com/JinheonBaek/GMT,74,Accurate learning of graph representations with graph multiset pooling,"https://scholar.google.com/scholar?cluster=8033778925255724792&hl=en&as_sdt=0,11",2,2021
Large Batch Simulation for Deep Reinforcement Learning,15,iclr,5,0,2023-06-18 09:25:33.931000,https://github.com/shacklettbp/bps-nav,25,Large batch simulation for deep reinforcement learning,"https://scholar.google.com/scholar?cluster=11450590688187242744&hl=en&as_sdt=0,10",3,2021
Personalized Federated Learning with First Order Model Optimization,153,iclr,9,2,2023-06-18 09:25:34.135000,https://github.com/NVlabs/FedFomo,27,Personalized federated learning with first order model optimization,"https://scholar.google.com/scholar?cluster=7443475779505959951&hl=en&as_sdt=0,43",6,2021
Knowledge Distillation as Semiparametric Inference,17,iclr,5,0,2023-06-18 09:25:34.347000,https://github.com/microsoft/semiparametric-distillation,9,Knowledge distillation as semiparametric inference,"https://scholar.google.com/scholar?cluster=13102643237666737869&hl=en&as_sdt=0,5",6,2021
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model,98,iclr,19,0,2023-06-18 09:25:34.563000,https://github.com/watchernyu/REDQ,114,Randomized ensembled double q-learning: Learning fast without a model,"https://scholar.google.com/scholar?cluster=14970286903447223266&hl=en&as_sdt=0,5",5,2021
Adapting to Reward Progressivity via Spectral Reinforcement Learning,1,iclr,0,0,2023-06-18 09:25:34.767000,https://github.com/mchldann/SpectralDQN,2,Adapting to Reward Progressivity via Spectral Reinforcement Learning,"https://scholar.google.com/scholar?cluster=5001746864239325821&hl=en&as_sdt=0,14",1,2021
Reset-Free Lifelong Learning with Skill-Space Planning,32,iclr,12,1,2023-06-18 09:25:34.973000,https://github.com/kzl/lifelong_rl,88,Reset-free lifelong learning with skill-space planning,"https://scholar.google.com/scholar?cluster=9940357312981411546&hl=en&as_sdt=0,5",5,2021
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time,0,iclr,0,0,2023-06-18 09:25:35.182000,https://github.com/chycharlie/robust-bn-faster,0,Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time,"https://scholar.google.com/scholar?cluster=15264124860839826525&hl=en&as_sdt=0,11",1,2021
Teaching Temporal Logics to Neural Networks,37,iclr,2,0,2023-06-18 09:25:35.386000,https://github.com/reactive-systems/deepltl,22,Teaching temporal logics to neural networks,"https://scholar.google.com/scholar?cluster=12153070486471346373&hl=en&as_sdt=0,5",8,2021
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes,48,iclr,2,2,2023-06-18 09:25:35.590000,https://github.com/jakesnell/ove-polya-gamma-gp,6,Bayesian Few-Shot Classification with One-vs-Each P\'olya-Gamma Augmented Gaussian Processes,"https://scholar.google.com/scholar?cluster=6827153911215871406&hl=en&as_sdt=0,34",2,2021
Parameter-Based Value Functions,16,iclr,0,0,2023-06-18 09:25:35.795000,https://github.com/ff93/parameter-based-value-functions,4,Parameter-based value functions,"https://scholar.google.com/scholar?cluster=12104932670063298799&hl=en&as_sdt=0,5",1,2021
Hyperbolic Neural Networks++,80,iclr,6,1,2023-06-18 09:25:35.998000,https://github.com/mil-tokyo/hyperbolic_nn_plusplus,50,Hyperbolic neural networks++,"https://scholar.google.com/scholar?cluster=13702563246653838309&hl=en&as_sdt=0,33",5,2021
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections,10,iclr,4,2,2023-06-18 09:25:36.206000,https://github.com/tgcsaba/seq2tens,26,Seq2tens: An efficient representation of sequences by low-rank tensor projections,"https://scholar.google.com/scholar?cluster=14845817599481722738&hl=en&as_sdt=0,30",5,2021
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization,28,iclr,11,3,2023-06-18 09:25:36.416000,https://github.com/FOCAL-ICLR/FOCAL-ICLR,40,Focal: Efficient fully-offline meta-reinforcement learning via distance metric learning and behavior regularization,"https://scholar.google.com/scholar?cluster=9761035246816366860&hl=en&as_sdt=0,47",2,2021
Generating Adversarial Computer Programs using Optimized Obfuscations,22,iclr,4,2,2023-06-18 09:25:36.620000,https://github.com/ALFA-group/adversarial-code-generation,19,Generating adversarial computer programs using optimized obfuscations,"https://scholar.google.com/scholar?cluster=1001230882267147217&hl=en&as_sdt=0,5",4,2021
CPR: Classifier-Projection Regularization for Continual Learning,36,iclr,5,1,2023-06-18 09:25:36.855000,https://github.com/csm9493/CPR_CL,10,CPR: classifier-projection regularization for continual learning,"https://scholar.google.com/scholar?cluster=17725325187082298099&hl=en&as_sdt=0,14",2,2021
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images,17,iclr,5,1,2023-06-18 09:25:37.058000,https://github.com/csm9493/GAN2GAN,28,GAN2GAN: Generative noise learning for blind denoising with single noisy images,"https://scholar.google.com/scholar?cluster=5021545804729568427&hl=en&as_sdt=0,44",3,2021
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis,9,iclr,0,0,2023-06-18 09:25:37.262000,https://github.com/zpbao/bowtie_networks,0,Bowtie networks: Generative modeling for joint few-shot recognition and novel-view synthesis,"https://scholar.google.com/scholar?cluster=4751463230610145393&hl=en&as_sdt=0,5",1,2021
Taming GANs with Lookahead-Minmax,19,iclr,7,0,2023-06-18 09:25:37.465000,https://github.com/Chavdarova/LAGAN-Lookahead_Minimax,14,Taming GANs with lookahead-minmax,"https://scholar.google.com/scholar?cluster=14906130844734900788&hl=en&as_sdt=0,5",4,2021
Is Attention Better Than Matrix Decomposition?,74,iclr,20,0,2023-06-18 09:25:37.668000,https://github.com/Gsunshine/Enjoy-Hamburger,292,Is attention better than matrix decomposition?,"https://scholar.google.com/scholar?cluster=14362607193647727267&hl=en&as_sdt=0,36",8,2021
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers,79,iclr,4,0,2023-06-18 09:25:37.872000,https://github.com/kaidixu/LiRPA_Verify,15,Fast and complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers,"https://scholar.google.com/scholar?cluster=7107853993141989483&hl=en&as_sdt=0,6",4,2021
A Geometric Analysis of Deep Generative Image Models and Its Applications,58,iclr,4,1,2023-06-18 09:25:38.076000,https://github.com/Animadversio/GAN-Geometry,38,The geometry of deep generative image models and its applications,"https://scholar.google.com/scholar?cluster=1386616180509191154&hl=en&as_sdt=0,5",3,2021
Solving Compositional Reinforcement Learning Problems via Task Reduction,16,iclr,1,0,2023-06-18 09:25:38.279000,https://github.com/IrisLi17/self-imitation-via-reduction,14,Solving compositional reinforcement learning problems via task reduction,"https://scholar.google.com/scholar?cluster=15628616147808752058&hl=en&as_sdt=0,23",1,2021
Acting in Delayed Environments with Non-Stationary Markov Policies,12,iclr,5,0,2023-06-18 09:25:38.483000,https://github.com/galdl/rl_delay_basic,8,Acting in delayed environments with non-stationary markov policies,"https://scholar.google.com/scholar?cluster=17360966560322895494&hl=en&as_sdt=0,33",1,2021
Learnable Embedding sizes for Recommender Systems,44,iclr,9,0,2023-06-18 09:25:38.686000,https://github.com/ssui-liu/learnable-embed-sizes-for-RecSys,54,Learnable embedding sizes for recommender systems,"https://scholar.google.com/scholar?cluster=803326739583596992&hl=en&as_sdt=0,33",2,2021
Simple Spectral Graph Convolution,158,iclr,16,15,2023-06-18 09:25:38.890000,https://github.com/allenhaozhu/SSGC,70,Simple spectral graph convolution,"https://scholar.google.com/scholar?cluster=3312425761995361615&hl=en&as_sdt=0,5",5,2021
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization,12,iclr,0,1,2023-06-18 09:43:24.233000,https://github.com/conditionWang/NTL,21,Non-transferable learning: A new approach for model ownership verification and applicability authorization,"https://scholar.google.com/scholar?cluster=9579671006829239762&hl=en&as_sdt=0,31",2,2022
Neural Structured Prediction for Inductive Node Classification,6,iclr,2,2,2023-06-18 09:43:24.437000,https://github.com/deepgraphlearning/spn,27,Neural structured prediction for inductive node classification,"https://scholar.google.com/scholar?cluster=2079533968187968682&hl=en&as_sdt=0,5",4,2022
Data-Efficient Graph Grammar Learning for Molecular Generation,16,iclr,21,4,2023-06-18 09:43:24.640000,https://github.com/gmh14/data_efficient_grammar,76,Data-efficient graph grammar learning for molecular generation,"https://scholar.google.com/scholar?cluster=3349437997127524473&hl=en&as_sdt=0,5",2,2022
Weighted Training for Cross-Task Learning,16,iclr,0,0,2023-06-18 09:43:24.843000,https://github.com/HornHehhf/TAWT,0,Weighted training for cross-task learning,"https://scholar.google.com/scholar?cluster=5570518371918150850&hl=en&as_sdt=0,22",0,2022
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling,21,iclr,16,7,2023-06-18 09:43:25.047000,https://github.com/magenta/midi-ddsp,265,MIDI-DDSP: Detailed control of musical performance via hierarchical modeling,"https://scholar.google.com/scholar?cluster=13729627625392909520&hl=en&as_sdt=0,4",11,2022
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting,93,iclr,31,23,2023-06-18 09:43:25.249000,https://github.com/alipay/Pyraformer,164,Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting,"https://scholar.google.com/scholar?cluster=7428445395903169510&hl=en&as_sdt=0,5",3,2022
StyleAlign: Analysis and Applications of Aligned StyleGAN Models,31,iclr,5,6,2023-06-18 09:43:25.452000,https://github.com/betterze/StyleAlign,145,Stylealign: Analysis and applications of aligned stylegan models,"https://scholar.google.com/scholar?cluster=11079296793136133967&hl=en&as_sdt=0,44",18,2022
Efficiently Modeling Long Sequences with Structured State Spaces,127,iclr,161,22,2023-06-18 09:43:25.655000,https://github.com/hazyresearch/state-spaces,1219,Efficiently modeling long sequences with structured state spaces,"https://scholar.google.com/scholar?cluster=8624959095392391416&hl=en&as_sdt=0,5",42,2022
Large Language Models Can Be Strong Differentially Private Learners,105,iclr,17,4,2023-06-18 09:43:25.857000,https://github.com/lxuechen/private-transformers,101,Large language models can be strong differentially private learners,"https://scholar.google.com/scholar?cluster=12835205672391916982&hl=en&as_sdt=0,5",5,2022
GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation,142,iclr,47,8,2023-06-18 09:43:26.060000,https://github.com/minkaixu/geodiff,222,Geodiff: A geometric diffusion model for molecular conformation generation,"https://scholar.google.com/scholar?cluster=4830391195637525286&hl=en&as_sdt=0,31",6,2022
Learning Strides in Convolutional Neural Networks,22,iclr,6,2,2023-06-18 09:43:26.262000,https://github.com/google-research/diffstride,121,Learning strides in convolutional neural networks,"https://scholar.google.com/scholar?cluster=1272651603956213806&hl=en&as_sdt=0,5",3,2022
Understanding over-squashing and bottlenecks on graphs via curvature,136,iclr,7,2,2023-06-18 09:43:26.465000,https://github.com/jctops/understanding-oversquashing,30,Understanding over-squashing and bottlenecks on graphs via curvature,"https://scholar.google.com/scholar?cluster=13989740203838615686&hl=en&as_sdt=0,33",3,2022
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme,23,iclr,93,15,2023-06-18 09:43:26.668000,https://github.com/huawei-noah/Speech-Backbones,396,Diffusion-based voice conversion with fast maximum likelihood sampling scheme,"https://scholar.google.com/scholar?cluster=17487782166390673105&hl=en&as_sdt=0,36",26,2022
Meta-Learning with Fewer Tasks through Task Interpolation,30,iclr,3,4,2023-06-18 09:43:26.871000,https://github.com/huaxiuyao/mlti,25,Meta-learning with fewer tasks through task interpolation,"https://scholar.google.com/scholar?cluster=17468967265592568520&hl=en&as_sdt=0,5",3,2022
Discovering and Explaining the Representation Bottleneck of DNNS,25,iclr,1,0,2023-06-18 09:43:27.074000,https://github.com/nebularaid2000/bottleneck,34,Discovering and explaining the representation bottleneck of dnns,"https://scholar.google.com/scholar?cluster=6321522337570019810&hl=en&as_sdt=0,33",1,2022
Sparse Communication via Mixed Distributions,5,iclr,1,0,2023-06-18 09:43:27.278000,https://github.com/deep-spin/sparse-communication,11,Sparse communication via mixed distributions,"https://scholar.google.com/scholar?cluster=9090566515327405784&hl=en&as_sdt=0,31",5,2022
Finetuned Language Models are Zero-Shot Learners,589,iclr,111,12,2023-06-18 09:43:27.481000,https://github.com/google-research/flan,966,Finetuned language models are zero-shot learners,"https://scholar.google.com/scholar?cluster=3582238432300098245&hl=en&as_sdt=0,5",28,2022
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization,13,iclr,13,0,2023-06-18 09:43:27.684000,https://github.com/snap-research/f8net,89,F8net: Fixed-point 8-bit only multiplication for network quantization,"https://scholar.google.com/scholar?cluster=9661231870650652462&hl=en&as_sdt=0,15",14,2022
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design,12,iclr,10,1,2023-06-18 09:43:27.886000,https://github.com/Khrylx/Transform2Act,38,Transform2act: Learning a transform-and-control policy for efficient agent design,"https://scholar.google.com/scholar?cluster=871690359216860608&hl=en&as_sdt=0,34",3,2022
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics,4,iclr,0,0,2023-06-18 09:43:28.089000,https://github.com/boreshkinai/protores,1,ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics,"https://scholar.google.com/scholar?cluster=4035812674200440521&hl=en&as_sdt=0,5",1,2022
CycleMLP: A MLP-like Architecture for Dense Prediction,130,iclr,26,2,2023-06-18 09:43:28.293000,https://github.com/ShoufaChen/CycleMLP,259,Cyclemlp: A mlp-like architecture for dense prediction,"https://scholar.google.com/scholar?cluster=1322906163224921925&hl=en&as_sdt=0,23",3,2022
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models,103,iclr,11,0,2023-06-18 09:43:28.496000,https://github.com/baofff/Analytic-DPM,138,Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models,"https://scholar.google.com/scholar?cluster=799884416375929942&hl=en&as_sdt=0,5",2,2022
The Information Geometry of Unsupervised Reinforcement Learning,17,iclr,3,1,2023-06-18 09:43:28.698000,https://github.com/ben-eysenbach/info_geometry,19,The information geometry of unsupervised reinforcement learning,"https://scholar.google.com/scholar?cluster=1840572653029125797&hl=en&as_sdt=0,33",3,2022
Language modeling via stochastic processes,15,iclr,11,4,2023-06-18 09:43:28.901000,https://github.com/rosewang2008/language_modeling_via_stochastic_processes,120,Language modeling via stochastic processes,"https://scholar.google.com/scholar?cluster=15213113550965798696&hl=en&as_sdt=0,33",7,2022
Learning to Downsample for Segmentation of Ultra-High Resolution Images,17,iclr,6,3,2023-06-18 09:43:29.106000,https://github.com/lxasqjc/Deformation-Segmentation,35,Learning to downsample for segmentation of ultra-high resolution images,"https://scholar.google.com/scholar?cluster=11044772985924964414&hl=en&as_sdt=0,7",2,2022
Variational Neural Cellular Automata,6,iclr,3,1,2023-06-18 09:43:29.308000,https://github.com/rasmusbergpalm/vnca,40,Variational neural cellular automata,"https://scholar.google.com/scholar?cluster=8036499533836302391&hl=en&as_sdt=0,33",7,2022
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?,4,iclr,3,0,2023-06-18 09:43:29.512000,https://github.com/shams-sam/fedoptim,10,Recycling model updates in federated learning: Are gradient subspaces low-rank?,"https://scholar.google.com/scholar?cluster=11357128239739448107&hl=en&as_sdt=0,5",1,2022
Sample and Computation Redistribution for Efficient Face Detection,49,iclr,4436,910,2023-06-18 09:43:29.715000,https://github.com/deepinsight/insightface,16066,Sample and computation redistribution for efficient face detection,"https://scholar.google.com/scholar?cluster=249972322094479786&hl=en&as_sdt=0,50",479,2022
Sound Adversarial Audio-Visual Navigation,12,iclr,0,1,2023-06-18 09:43:29.918000,https://github.com/yyf17/saavn,12,Sound adversarial audio-visual navigation,"https://scholar.google.com/scholar?cluster=14696002671492155830&hl=en&as_sdt=0,3",2,2022
Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations,16,iclr,1,0,2023-06-18 09:43:30.120000,https://github.com/rajesh-lab/nurd-code-public,6,Out-of-distribution generalization in the presence of nuisance-induced spurious correlations,"https://scholar.google.com/scholar?cluster=11021328735736547096&hl=en&as_sdt=0,11",2,2022
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis,24,iclr,4,0,2023-06-18 09:43:30.323000,https://github.com/junfenggo/aeva-blackbox-backdoor-detection-main,22,Aeva: Black-box backdoor detection using adversarial extreme value analysis,"https://scholar.google.com/scholar?cluster=1218468715415331882&hl=en&as_sdt=0,43",0,2022
Top-label calibration and multiclass-to-binary reductions,17,iclr,5,0,2023-06-18 09:43:30.535000,https://github.com/aigen/df-posthoc-calibration,31,Top-label calibration and multiclass-to-binary reductions,"https://scholar.google.com/scholar?cluster=5210721734640980720&hl=en&as_sdt=0,33",1,2022
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future,9,iclr,2,0,2023-06-18 09:43:30.738000,https://github.com/AdityaLab/Back2Future,7,Back2future: Leveraging backfill dynamics for improving real-time predictions in future,"https://scholar.google.com/scholar?cluster=4140733824788970279&hl=en&as_sdt=0,44",3,2022
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective,9,iclr,3,1,2023-06-18 09:43:30.940000,https://github.com/albietz/ckn_kernel,13,Approximation and learning with deep convolutional models: a kernel perspective,"https://scholar.google.com/scholar?cluster=16497248736027137488&hl=en&as_sdt=0,5",2,2022
CrossBeam: Learning to Search in Bottom-Up Program Synthesis,5,iclr,7,0,2023-06-18 09:43:31.143000,https://github.com/google-research/crossbeam,35,CrossBeam: Learning to search in bottom-up program synthesis,"https://scholar.google.com/scholar?cluster=14342383468818615250&hl=en&as_sdt=0,5",7,2022
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining,12,iclr,1,1,2023-06-18 09:43:31.355000,https://github.com/AhmedImtiazPrio/MaGNET,24,Magnet: Uniform sampling from deep generative network manifolds without retraining,"https://scholar.google.com/scholar?cluster=18438387827991567060&hl=en&as_sdt=0,5",1,2022
PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks,5,iclr,3,3,2023-06-18 09:43:31.558000,https://github.com/liusiyan/PI3NN,7,PI3NN: Out-of-distribution-aware prediction intervals from three neural networks,"https://scholar.google.com/scholar?cluster=9729426911336956537&hl=en&as_sdt=0,5",3,2022
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation,6,iclr,0,0,2023-06-18 09:43:31.761000,https://github.com/yuqingd/cusp,11,It takes four to tango: Multiagent selfplay for automatic curriculum generation,"https://scholar.google.com/scholar?cluster=12921508805700086972&hl=en&as_sdt=0,33",2,2022
CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing,24,iclr,2,1,2023-06-18 09:43:31.965000,https://github.com/ai-secure/crop,5,Crop: Certifying robust policies for reinforcement learning through functional smoothing,"https://scholar.google.com/scholar?cluster=15014236512905424649&hl=en&as_sdt=0,5",2,2022
Neural Link Prediction with Walk Pooling,25,iclr,2,1,2023-06-18 09:43:32.169000,https://github.com/dadacheng/walkpooling,44,Neural link prediction with walk pooling,"https://scholar.google.com/scholar?cluster=11799693892452603057&hl=en&as_sdt=0,5",3,2022
Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators,10,iclr,1,3,2023-06-18 09:43:32.372000,https://github.com/microsoft/amos,23,Pretraining text encoders with adversarial mixture of training signal generators,"https://scholar.google.com/scholar?cluster=9770552085778615131&hl=en&as_sdt=0,5",5,2022
Non-Parallel Text Style Transfer with Self-Parallel Supervision,2,iclr,0,4,2023-06-18 09:43:32.577000,https://github.com/dapangliu/lamer,7,Non-Parallel Text Style Transfer with Self-Parallel Supervision,"https://scholar.google.com/scholar?cluster=14757482519407793869&hl=en&as_sdt=0,33",2,2022
Can an Image Classifier Suffice For Action Recognition?,8,iclr,8,3,2023-06-18 09:43:32.780000,https://github.com/ibm/sifar-pytorch,49,Can an image classifier suffice for action recognition?,"https://scholar.google.com/scholar?cluster=13822718971656558432&hl=en&as_sdt=0,5",2,2022
Interacting Contour Stochastic Gradient Langevin Dynamics,4,iclr,1,0,2023-06-18 09:43:32.983000,https://github.com/waynedw/interacting-contour-stochastic-gradient-langevin-dynamics,6,Interacting Contour Stochastic Gradient Langevin Dynamics,"https://scholar.google.com/scholar?cluster=811536455190019406&hl=en&as_sdt=0,14",2,2022
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?,25,iclr,5,0,2023-06-18 09:43:33.186000,https://github.com/rice-eic/patch-fool,20,Patch-fool: Are vision transformers always robust against adversarial perturbations?,"https://scholar.google.com/scholar?cluster=1831846608432102028&hl=en&as_sdt=0,10",1,2022
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation,49,iclr,5,5,2023-06-18 09:43:33.388000,https://github.com/google-research/adamatch,54,Adamatch: A unified approach to semi-supervised learning and domain adaptation,"https://scholar.google.com/scholar?cluster=9221339163655588943&hl=en&as_sdt=0,5",10,2022
Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound,25,iclr,1,5,2023-06-18 09:43:33.591000,https://github.com/eth-sri/mn-bab,4,Complete verification via multi-neuron relaxation guided branch-and-bound,"https://scholar.google.com/scholar?cluster=14769723255634252083&hl=en&as_sdt=0,41",5,2022
Distribution Compression in Near-Linear Time,6,iclr,2,0,2023-06-18 09:43:33.796000,https://github.com/microsoft/goodpoints,31,Distribution compression in near-linear time,"https://scholar.google.com/scholar?cluster=10525101075406225014&hl=en&as_sdt=0,5",9,2022
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks,36,iclr,6,2,2023-06-18 09:43:33.999000,https://github.com/ZPdesu/MindTheGap,43,Mind the gap: Domain gap control for single shot domain adaptation for generative adversarial networks,"https://scholar.google.com/scholar?cluster=12451250368858284023&hl=en&as_sdt=0,31",5,2022
On Evaluation Metrics for Graph Generative Models,16,iclr,3,0,2023-06-18 09:43:34.208000,https://github.com/uoguelph-mlrg/ggm-metrics,16,On evaluation metrics for graph generative models,"https://scholar.google.com/scholar?cluster=7803067086316285274&hl=en&as_sdt=0,33",2,2022
Graph Condensation for Graph Neural Networks,43,iclr,10,2,2023-06-18 09:43:34.423000,https://github.com/chandlerbang/gcond,72,Graph condensation for graph neural networks,"https://scholar.google.com/scholar?cluster=14491892748486687067&hl=en&as_sdt=0,5",4,2022
Minimax Optimization with Smooth Algorithmic Adversaries,7,iclr,2,0,2023-06-18 09:43:34.628000,https://github.com/fiezt/minmax-opt-smooth-adversary,4,Minimax optimization with smooth algorithmic adversaries,"https://scholar.google.com/scholar?cluster=11061521782135546152&hl=en&as_sdt=0,32",1,2022
Leveraging unlabeled data to predict out-of-distribution performance,41,iclr,0,1,2023-06-18 09:43:34.831000,https://github.com/saurabhgarg1996/ATC_code,8,Leveraging unlabeled data to predict out-of-distribution performance,"https://scholar.google.com/scholar?cluster=5646390275734787221&hl=en&as_sdt=0,39",1,2022
VC dimension of partially quantized neural networks in the overparametrized regime,2,iclr,0,0,2023-06-18 09:43:35.034000,https://github.com/yutongwangumich/hann,1,Vc dimension of partially quantized neural networks in the overparametrized regime,"https://scholar.google.com/scholar?cluster=11387455269961935968&hl=en&as_sdt=0,33",2,2022
Optimal Representations for Covariate Shift,28,iclr,3,1,2023-06-18 09:43:35.237000,https://github.com/ryoungj/optdom,19,Optimal representations for covariate shift,"https://scholar.google.com/scholar?cluster=2022985710361753356&hl=en&as_sdt=0,10",2,2022
Fortuitous Forgetting in Connectionist Networks,11,iclr,4,1,2023-06-18 09:43:35.441000,https://github.com/hlml/fortuitous_forgetting,18,Fortuitous forgetting in connectionist networks,"https://scholar.google.com/scholar?cluster=603488555859414419&hl=en&as_sdt=0,37",2,2022
Contextualized Scene Imagination for Generative Commonsense Reasoning,11,iclr,2,1,2023-06-18 09:43:35.645000,https://github.com/wangpf3/imagine-and-verbalize,11,Contextualized scene imagination for generative commonsense reasoning,"https://scholar.google.com/scholar?cluster=6593478295513742090&hl=en&as_sdt=0,27",1,2022
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals,33,iclr,2,0,2023-06-18 09:43:35.848000,https://github.com/asmadotgh/dissect,11,Dissect: Disentangled simultaneous explanations via concept traversals,"https://scholar.google.com/scholar?cluster=4475466485614086287&hl=en&as_sdt=0,33",2,2022
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series,5,iclr,10,3,2023-06-18 09:43:36.051000,https://github.com/reml-lab/hetvae,24,Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series,"https://scholar.google.com/scholar?cluster=3281039634260173349&hl=en&as_sdt=0,5",3,2022
Bayesian Framework for Gradient Leakage,16,iclr,3,1,2023-06-18 09:43:36.254000,https://github.com/eth-sri/bayes-framework-leakage,8,Bayesian framework for gradient leakage,"https://scholar.google.com/scholar?cluster=14925580502725272742&hl=en&as_sdt=0,43",6,2022
Maximum n-times Coverage for Vaccine Design,4,iclr,7,1,2023-06-18 09:43:36.458000,https://github.com/gifford-lab/optivax,22,Maximum n-times coverage for vaccine design,"https://scholar.google.com/scholar?cluster=17184876342921372695&hl=en&as_sdt=0,5",16,2022
KL Guided Domain Adaptation,14,iclr,2,0,2023-06-18 09:43:36.661000,https://github.com/atuannguyen/kl,6,KL guided domain adaptation,"https://scholar.google.com/scholar?cluster=17961201142994065292&hl=en&as_sdt=0,5",2,2022
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness,42,iclr,9,1,2023-06-18 09:43:36.864000,https://github.com/gnnaskernel/gnnaskernel,51,From stars to subgraphs: Uplifting any GNN with local structure awareness,"https://scholar.google.com/scholar?cluster=4598272290624376922&hl=en&as_sdt=0,5",3,2022
Gradient Importance Learning for Incomplete Observations,6,iclr,2,0,2023-06-18 09:43:37.067000,https://github.com/gaoqitong/gradient-importance-learning,1,Gradient importance learning for incomplete observations,"https://scholar.google.com/scholar?cluster=3408438792226712835&hl=en&as_sdt=0,33",2,2022
"Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset",8,iclr,0,0,2023-06-18 09:43:37.271000,https://github.com/berleon/do_users_benefit_from_interpretable_vision,4,"Do users benefit from interpretable vision? a user study, baseline, and dataset","https://scholar.google.com/scholar?cluster=17643359548454161307&hl=en&as_sdt=0,5",3,2022
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL,25,iclr,3,1,2023-06-18 09:43:37.475000,https://github.com/umd-huang-lab/paad_adv_rl,3,Who is the strongest enemy? towards optimal and efficient evasion attacks in deep rl,"https://scholar.google.com/scholar?cluster=16507433832957753266&hl=en&as_sdt=0,5",2,2022
Chunked Autoregressive GAN for Conditional Waveform Synthesis,33,iclr,30,4,2023-06-18 09:43:37.678000,https://github.com/descriptinc/cargan,161,Chunked autoregressive gan for conditional waveform synthesis,"https://scholar.google.com/scholar?cluster=12411331012561904832&hl=en&as_sdt=0,31",23,2022
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks,7,iclr,1,0,2023-06-18 09:43:37.888000,https://github.com/ai-secure/copa,7,COPA: Certifying robust policies for offline reinforcement learning against poisoning attacks,"https://scholar.google.com/scholar?cluster=11901953356085311316&hl=en&as_sdt=0,5",2,2022
Multi-Agent MDP Homomorphic Networks,11,iclr,0,0,2023-06-18 09:43:38.091000,https://github.com/elisevanderpol/marl_homomorphic_networks,4,Multi-agent MDP homomorphic networks,"https://scholar.google.com/scholar?cluster=7742088366120766374&hl=en&as_sdt=0,20",2,2022
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields,21,iclr,6,6,2023-06-18 09:43:38.294000,https://github.com/yifita/idf,108,Geometry-consistent neural shape representation with implicit displacement fields,"https://scholar.google.com/scholar?cluster=1893838131986981154&hl=en&as_sdt=0,6",5,2022
Modeling Label Space Interactions in Multi-label Classification using Box Embeddings,17,iclr,0,0,2023-06-18 09:43:38.498000,https://github.com/iesl/box-mlc-iclr-2022,9,Modeling label space interactions in multi-label classification using box embeddings,"https://scholar.google.com/scholar?cluster=10529771024100862700&hl=en&as_sdt=0,33",17,2022
It Takes Two to Tango: Mixup for Deep Metric Learning,13,iclr,4,1,2023-06-18 09:43:38.701000,https://github.com/billpsomas/Metrix_ICLR22,25,It takes two to tango: Mixup for deep metric learning,"https://scholar.google.com/scholar?cluster=11528364689956817661&hl=en&as_sdt=0,11",6,2022
Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation,18,iclr,3,1,2023-06-18 09:43:38.905000,https://github.com/facebookresearch/otter,51,Data efficient language-supervised zero-shot recognition with optimal transport distillation,"https://scholar.google.com/scholar?cluster=16240113248211357205&hl=en&as_sdt=0,5",4,2022
Learning State Representations via Retracing in Reinforcement Learning,5,iclr,1,0,2023-06-18 09:43:39.108000,https://github.com/changmin-yu/ccwm_code,4,Learning state representations via retracing in reinforcement learning,"https://scholar.google.com/scholar?cluster=5497480692580123615&hl=en&as_sdt=0,5",1,2022
Open-World Semi-Supervised Learning,58,iclr,9,6,2023-06-18 09:43:39.311000,https://github.com/snap-stanford/orca,62,Open-world semi-supervised learning,"https://scholar.google.com/scholar?cluster=13685131570461746231&hl=en&as_sdt=0,22",4,2022
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent,23,iclr,3,3,2023-06-18 09:43:39.513000,https://github.com/v-wangg/OrthogonalPGD,17,Evading adversarial example detection defenses with orthogonal projected gradient descent,"https://scholar.google.com/scholar?cluster=6627043113889326245&hl=en&as_sdt=0,26",4,2022
Fast AdvProp,6,iclr,0,1,2023-06-18 09:43:39.716000,https://github.com/meijieru/fast_advprop,33,Fast advprop,"https://scholar.google.com/scholar?cluster=17518006235660748268&hl=en&as_sdt=0,10",5,2022
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs,29,iclr,18,0,2023-06-18 09:43:39.919000,https://github.com/migalkin/NodePiece,124,Nodepiece: Compositional and parameter-efficient representations of large knowledge graphs,"https://scholar.google.com/scholar?cluster=4956010200873018529&hl=en&as_sdt=0,5",7,2022
Pix2seq: A Language Modeling Framework for Object Detection,120,iclr,55,21,2023-06-18 09:43:40.122000,https://github.com/google-research/pix2seq,652,Pix2seq: A language modeling framework for object detection,"https://scholar.google.com/scholar?cluster=17102558257176551695&hl=en&as_sdt=0,5",17,2022
Learning Curves for SGD on Structured Features,8,iclr,0,0,2023-06-18 09:43:40.326000,https://github.com/Pehlevan-Group/sgd_structured_features,0,Learning curves for sgd on structured features,"https://scholar.google.com/scholar?cluster=16931573474353829992&hl=en&as_sdt=0,33",2,2022
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training,17,iclr,4,3,2023-06-18 09:43:40.546000,https://github.com/facebookresearch/NASViT,57,Nasvit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training,"https://scholar.google.com/scholar?cluster=12012622546749628874&hl=en&as_sdt=0,41",5,2022
Graphon based Clustering and Testing of Networks: Algorithms and Theory,3,iclr,2,0,2023-06-18 09:43:40.750000,https://github.com/maha-93/Clustering-Testing-Networks,3,Graphon based Clustering and Testing of Networks: Algorithms and Theory,"https://scholar.google.com/scholar?cluster=11291859558104381886&hl=en&as_sdt=0,33",1,2022
Augmented Sliced Wasserstein Distances,13,iclr,4,1,2023-06-18 09:43:40.953000,https://github.com/xiongjiechen/ASWD,8,Augmented sliced Wasserstein distances,"https://scholar.google.com/scholar?cluster=955715037092022915&hl=en&as_sdt=0,33",2,2022
Joint Shapley values: a measure of joint feature importance,7,iclr,0,0,2023-06-18 09:43:41.155000,https://github.com/harris-chris/joint-shapley-values,13,Joint Shapley values: a measure of joint feature importance,"https://scholar.google.com/scholar?cluster=4894614344420722159&hl=en&as_sdt=0,33",1,2022
Efficient Self-supervised Vision Transformers for Representation Learning,129,iclr,42,14,2023-06-18 09:43:41.358000,https://github.com/microsoft/esvit,378,Efficient self-supervised vision transformers for representation learning,"https://scholar.google.com/scholar?cluster=15469437604545198809&hl=en&as_sdt=0,39",12,2022
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain,29,iclr,6,0,2023-06-18 09:43:41.561000,https://github.com/bethgelab/InDomainGeneralizationBenchmark,32,Visual representation learning does not generalize strongly within the same domain,"https://scholar.google.com/scholar?cluster=827943787586075996&hl=en&as_sdt=0,33",2,2022
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions,12,iclr,1,0,2023-06-18 09:43:41.764000,https://github.com/ardasahiner/ProCoGAN,5,Hidden convexity of wasserstein gans: Interpretable generative models with closed-form solutions,"https://scholar.google.com/scholar?cluster=9526825653845388729&hl=en&as_sdt=0,29",1,2022
Memory Augmented Optimizers for Deep Learning,1,iclr,1,0,2023-06-18 09:43:41.967000,https://github.com/chandar-lab/CGOptimizer,6,Memory Augmented Optimizers for Deep Learning,"https://scholar.google.com/scholar?cluster=11073351928197752868&hl=en&as_sdt=0,5",5,2022
Orchestrated Value Mapping for Reinforcement Learning,3,iclr,4,0,2023-06-18 09:43:42.170000,https://github.com/microsoft/orchestrated-value-mapping,3,Orchestrated value mapping for reinforcement learning,"https://scholar.google.com/scholar?cluster=11063352245318082342&hl=en&as_sdt=0,5",4,2022
Learning to Generalize across Domains on Single Test Samples,15,iclr,1,1,2023-06-18 09:43:42.373000,https://github.com/zzzx1224/singlesamplegeneralization-iclr2022,22,Learning to generalize across domains on single test samples,"https://scholar.google.com/scholar?cluster=10799367073706985191&hl=en&as_sdt=0,47",4,2022
How Attentive are Graph Attention Networks?,334,iclr,29,2,2023-06-18 09:43:42.577000,https://github.com/tech-srl/how_attentive_are_gats,223,How attentive are graph attention networks?,"https://scholar.google.com/scholar?cluster=5656297883023258429&hl=en&as_sdt=0,36",11,2022
Learning Transferable Reward for Query Object Localization with Policy Adaptation,0,iclr,0,0,2023-06-18 09:43:42.780000,https://github.com/litingfeng/localization-by-ordembed,1,Learning Transferable Reward for Query Object Localization with Policy Adaptation,"https://scholar.google.com/scholar?cluster=6915912044091990536&hl=en&as_sdt=0,31",3,2022
CKConv: Continuous Kernel Convolution For Sequential Data,54,iclr,12,2,2023-06-18 09:43:42.982000,https://github.com/dwromero/ckconv,100,Ckconv: Continuous kernel convolution for sequential data,"https://scholar.google.com/scholar?cluster=13572212513025696836&hl=en&as_sdt=0,14",4,2022
Towards Empirical Sandwich Bounds on the Rate-Distortion Function,8,iclr,2,0,2023-06-18 09:43:43.185000,https://github.com/mandt-lab/RD-sandwich,9,Towards empirical sandwich bounds on the rate-distortion function,"https://scholar.google.com/scholar?cluster=3922055311859946203&hl=en&as_sdt=0,36",3,2022
Fair Normalizing Flows,6,iclr,2,0,2023-06-18 09:43:43.387000,https://github.com/eth-sri/fnf,16,Fair normalizing flows,"https://scholar.google.com/scholar?cluster=12495034483324120127&hl=en&as_sdt=0,11",6,2022
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory,38,iclr,10,0,2023-06-18 09:43:43.590000,https://github.com/ghliu/sb-fbsde,55,"Likelihood training of schr\"" odinger bridge using forward-backward sdes theory","https://scholar.google.com/scholar?cluster=17490002779543160036&hl=en&as_sdt=0,23",2,2022
Imitation Learning from Observations under Transition Model Disparity,2,iclr,0,1,2023-06-18 09:43:43.793000,https://github.com/tgangwani/ailo,1,Imitation Learning from Observations under Transition Model Disparity,"https://scholar.google.com/scholar?cluster=2183334358740050451&hl=en&as_sdt=0,48",3,2022
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks,58,iclr,0,0,2023-06-18 09:43:43.996000,https://github.com/rahimentezari/permutationinvariance,19,The role of permutation invariance in linear mode connectivity of neural networks,"https://scholar.google.com/scholar?cluster=18352541695309676918&hl=en&as_sdt=0,5",1,2022
Data Poisoning Won't Save You From Facial Recognition,28,iclr,3,0,2023-06-18 09:43:44.203000,https://github.com/ftramer/facecure,9,Data poisoning won't save you from facial recognition,"https://scholar.google.com/scholar?cluster=12334665611277654156&hl=en&as_sdt=0,31",1,2022
MetaMorph: Learning Universal Controllers with Transformers,17,iclr,9,4,2023-06-18 09:43:44.406000,https://github.com/agrimgupta92/metamorph,65,Metamorph: Learning universal controllers with transformers,"https://scholar.google.com/scholar?cluster=5095019871599200934&hl=en&as_sdt=0,44",4,2022
Illiterate DALL-E Learns to Compose,44,iclr,10,4,2023-06-18 09:43:44.610000,https://github.com/singhgautam/slate,77,Illiterate dall-e learns to compose,"https://scholar.google.com/scholar?cluster=4019676252892800886&hl=en&as_sdt=0,21",1,2022
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models,24,iclr,2,0,2023-06-18 09:43:44.812000,https://github.com/aypan17/reward-misspecification,3,The effects of reward misspecification: Mapping and mitigating misaligned models,"https://scholar.google.com/scholar?cluster=13629255034936383162&hl=en&as_sdt=0,5",1,2022
Counterfactual Plans under Distributional Ambiguity,9,iclr,0,0,2023-06-18 09:43:45.015000,https://github.com/ngocbh/copa,3,Counterfactual plans under distributional ambiguity,"https://scholar.google.com/scholar?cluster=16318179024765381236&hl=en&as_sdt=0,33",2,2022
Neural Parameter Allocation Search,9,iclr,3,0,2023-06-18 09:43:45.218000,https://github.com/bryanplummer/ssn,4,Neural parameter allocation search,"https://scholar.google.com/scholar?cluster=15625823340904525164&hl=en&as_sdt=0,5",2,2022
Collapse by Conditioning: Training Class-conditional GANs with Limited Data,16,iclr,7,4,2023-06-18 09:43:45.422000,https://github.com/mshahbazi72/transitional-cgan,35,Collapse by conditioning: Training class-conditional GANs with limited data,"https://scholar.google.com/scholar?cluster=2177449249574403992&hl=en&as_sdt=0,5",2,2022
Map Induction: Compositional spatial submap learning for efficient exploration in novel environments,2,iclr,0,0,2023-06-18 09:43:45.625000,https://github.com/s72sue/map-induction,0,Map Induction: Compositional spatial submap learning for efficient exploration in novel environments,"https://scholar.google.com/scholar?cluster=15462260189293500047&hl=en&as_sdt=0,33",2,2022
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?,49,iclr,1,1,2023-06-18 09:43:45.833000,https://github.com/inspire-group/proxy-distributions,26,Robust learning meets generative models: Can proxy distributions improve adversarial robustness?,"https://scholar.google.com/scholar?cluster=15097099690109904849&hl=en&as_sdt=0,44",2,2022
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap,53,iclr,3,0,2023-06-18 09:43:46.036000,https://github.com/zhangq327/arc,22,Chaos is a ladder: A new theoretical understanding of contrastive learning via augmentation overlap,"https://scholar.google.com/scholar?cluster=7197581293948710911&hl=en&as_sdt=0,33",2,2022
Language-biased image classification: evaluation based on semantic representations,4,iclr,1,0,2023-06-18 09:43:46.239000,https://github.com/flowersteam/picture-word-interference,4,Language-biased image classification: evaluation based on semantic representations,"https://scholar.google.com/scholar?cluster=7894245425840424018&hl=en&as_sdt=0,43",7,2022
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models,40,iclr,37,0,2023-06-18 09:43:46.443000,https://github.com/JonasGeiping/breaching,178,Robbing the fed: Directly obtaining private data in federated learning with modified models,"https://scholar.google.com/scholar?cluster=15885116748368204506&hl=en&as_sdt=0,36",3,2022
Permutation-Based SGD: Is Random Optimal?,8,iclr,0,0,2023-06-18 09:43:46.646000,https://github.com/shashankrajput/flipflop,0,Permutation-Based SGD: Is Random Optimal?,"https://scholar.google.com/scholar?cluster=9197780273484525148&hl=en&as_sdt=0,19",1,2022
Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation,57,iclr,16,1,2023-06-18 09:43:46.848000,https://github.com/snap-research/graphless-neural-networks,64,Graph-less neural networks: Teaching old mlps new tricks via distillation,"https://scholar.google.com/scholar?cluster=14166973652994088038&hl=en&as_sdt=0,33",7,2022
How many degrees of freedom do we need to train deep networks: a loss landscape perspective,7,iclr,2,0,2023-06-18 09:43:47.052000,https://github.com/ganguli-lab/degrees-of-freedom,32,How many degrees of freedom do we need to train deep networks: a loss landscape perspective,"https://scholar.google.com/scholar?cluster=11943963795167204430&hl=en&as_sdt=0,5",2,2022
Is Importance Weighting Incompatible with Interpolating Classifiers?,14,iclr,3,1,2023-06-18 09:43:47.254000,https://github.com/keawang/importance-weighting-interpolating-classifiers,4,Is importance weighting incompatible with interpolating classifiers?,"https://scholar.google.com/scholar?cluster=5476028930081234281&hl=en&as_sdt=0,14",3,2022
Mirror Descent Policy Optimization,39,iclr,3,0,2023-06-18 09:43:47.458000,https://github.com/manantomar/Mirror-Descent-Policy-Optimization,28,Mirror descent policy optimization,"https://scholar.google.com/scholar?cluster=2587999722409846316&hl=en&as_sdt=0,11",2,2022
Large-Scale Representation Learning on Graphs via Bootstrapping,64,iclr,16,3,2023-06-18 09:43:47.662000,https://github.com/nerdslab/bgrl,68,Large-scale representation learning on graphs via bootstrapping,"https://scholar.google.com/scholar?cluster=3168526433938319234&hl=en&as_sdt=0,39",3,2022
Neural Processes with Stochastic Attention: Paying more attention to the context dataset,6,iclr,1,0,2023-06-18 09:43:47.865000,https://github.com/mingyukim87/npwsa,7,Neural processes with stochastic attention: Paying more attention to the context dataset,"https://scholar.google.com/scholar?cluster=4366830755369002835&hl=en&as_sdt=0,25",1,2022
Geometric Transformers for Protein Interface Contact Prediction,13,iclr,11,2,2023-06-18 09:43:48.069000,https://github.com/bioinfomachinelearning/deepinteract,47,Geometric transformers for protein interface contact prediction,"https://scholar.google.com/scholar?cluster=11431746960941491092&hl=en&as_sdt=0,5",1,2022
IGLU: Efficient GCN Training via Lazy Updates,5,iclr,0,0,2023-06-18 09:43:48.273000,https://github.com/sdeepaknarayanan/iglu,3,IGLU: Efficient GCN Training via Lazy Updates,"https://scholar.google.com/scholar?cluster=16548699335588161367&hl=en&as_sdt=0,5",3,2022
Top-N: Equivariant Set and Graph Generation without Exchangeability,11,iclr,0,1,2023-06-18 09:43:48.492000,https://github.com/cvignac/top-n,6,Top-n: Equivariant set and graph generation without exchangeability,"https://scholar.google.com/scholar?cluster=10023385156817268910&hl=en&as_sdt=0,43",4,2022
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5,29,iclr,6,2,2023-06-18 09:43:48.700000,https://github.com/qcwthu/lifelong-fewshot-language-learning,49,LFPT5: A unified framework for lifelong few-shot language learning based on prompt tuning of t5,"https://scholar.google.com/scholar?cluster=7716940912154178619&hl=en&as_sdt=0,1",3,2022
On Non-Random Missing Labels in Semi-Supervised Learning,6,iclr,0,0,2023-06-18 09:43:48.904000,https://github.com/joyhuyy1412/cadr-fixmatch,12,On non-random missing labels in semi-supervised learning,"https://scholar.google.com/scholar?cluster=2589366795376584946&hl=en&as_sdt=0,5",3,2022
Mapping conditional distributions for domain adaptation under generalized target shift,7,iclr,0,0,2023-06-18 09:43:49.107000,https://github.com/mkirchmeyer/ostar,5,Mapping conditional distributions for domain adaptation under generalized target shift,"https://scholar.google.com/scholar?cluster=3768189705171035948&hl=en&as_sdt=0,33",1,2022
Adversarial Retriever-Ranker for Dense Text Retrieval,47,iclr,6,5,2023-06-18 09:43:49.327000,https://github.com/microsoft/ar2,56,Adversarial retriever-ranker for dense text retrieval,"https://scholar.google.com/scholar?cluster=9069461514425266804&hl=en&as_sdt=0,5",9,2022
Normalization of Language Embeddings for Cross-Lingual Alignment,4,iclr,1,0,2023-06-18 09:43:49.553000,https://github.com/poaboagye/SpecNorm,6,Normalization of Language Embeddings for Cross-Lingual Alignment,"https://scholar.google.com/scholar?cluster=10286218373304313543&hl=en&as_sdt=0,44",1,2022
Boosting the Certified Robustness of L-infinity Distance Nets,19,iclr,3,0,2023-06-18 09:43:49.755000,https://github.com/zbh2047/L_inf-dist-net-v2,16,Boosting the certified robustness of l-infinity distance nets,"https://scholar.google.com/scholar?cluster=7903222136558927992&hl=en&as_sdt=0,33",1,2022
Stochastic Training is Not Necessary for Generalization,41,iclr,5,0,2023-06-18 09:43:49.958000,https://github.com/jonasgeiping/fullbatchtraining,36,Stochastic training is not necessary for generalization,"https://scholar.google.com/scholar?cluster=16676804811575846883&hl=en&as_sdt=0,5",2,2022
GATSBI: Generative Adversarial Training for Simulation-Based Inference,6,iclr,2,1,2023-06-18 09:43:50.162000,https://github.com/mackelab/gatsbi,11,GATSBI: Generative adversarial training for simulation-based inference,"https://scholar.google.com/scholar?cluster=15349002435008264502&hl=en&as_sdt=0,5",8,2022
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients,12,iclr,2,1,2023-06-18 09:43:50.373000,https://github.com/mil-ad/prospr,26,Prospect pruning: Finding trainable weights at initialization using meta-gradients,"https://scholar.google.com/scholar?cluster=8783006285808358460&hl=en&as_sdt=0,5",2,2022
Generalized rectifier wavelet covariance models for texture synthesis,2,iclr,2,1,2023-06-18 09:43:50.577000,https://github.com/abrochar/wavelet-texture-synthesis,4,Generalized rectifier wavelet covariance models for texture synthesis,"https://scholar.google.com/scholar?cluster=1160036386380312390&hl=en&as_sdt=0,5",2,2022
Towards Evaluating the Robustness of Neural Networks Learned by Transduction,8,iclr,2,0,2023-06-18 09:43:50.781000,https://github.com/jfc43/eval-transductive-robustness,4,Towards evaluating the robustness of neural networks learned by transduction,"https://scholar.google.com/scholar?cluster=10802124604610826531&hl=en&as_sdt=0,5",1,2022
Understanding Intrinsic Robustness Using Label Uncertainty,0,iclr,0,0,2023-06-18 09:43:50.986000,https://github.com/xiaozhanguva/intrinsic_rob_lu,3,Understanding Intrinsic Robustness Using Label Uncertainty,"https://scholar.google.com/scholar?cluster=7215793248994812724&hl=en&as_sdt=0,45",2,2022
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization,16,iclr,8,0,2023-06-18 09:43:51.190000,https://github.com/illidanlab/SplitMix,22,Efficient split-mix federated learning for on-demand and in-situ customization,"https://scholar.google.com/scholar?cluster=1074497134544260795&hl=en&as_sdt=0,26",3,2022
Relational Surrogate Loss Learning,2,iclr,6,0,2023-06-18 09:43:51.395000,https://github.com/hunto/reloss,35,Relational surrogate loss learning,"https://scholar.google.com/scholar?cluster=10424444268949840679&hl=en&as_sdt=0,5",3,2022
Knowledge Infused Decoding,9,iclr,8,4,2023-06-18 09:43:51.603000,https://github.com/microsoft/kid,65,Knowledge infused decoding,"https://scholar.google.com/scholar?cluster=5121405141535448243&hl=en&as_sdt=0,6",6,2022
Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences,1,iclr,5,2,2023-06-18 09:43:51.808000,https://github.com/Multilevel-NN/torchbraid,5,Parallel training of gru networks with a multi-grid solver for long sequences,"https://scholar.google.com/scholar?cluster=546461240895153656&hl=en&as_sdt=0,10",10,2022
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models,8,iclr,0,0,2023-06-18 09:43:52.012000,https://github.com/vietvo89/SparseEvoAttack.github.io,0,Query efficient decision based sparse attacks against black-box deep learning models,"https://scholar.google.com/scholar?cluster=10824573856366104653&hl=en&as_sdt=0,39",0,2022
Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL,19,iclr,0,0,2023-06-18 09:43:52.215000,https://github.com/yangrui2015/awgcsl,21,Rethinking goal-conditioned supervised learning and its connection to offline rl,"https://scholar.google.com/scholar?cluster=889787684792010852&hl=en&as_sdt=0,43",2,2022
PF-GNN: Differentiable particle filtering based approximation of universal graph representations,5,iclr,0,0,2023-06-18 09:43:52.418000,https://github.com/pfgnn/pf-gnn,8,PF-GNN: Differentiable particle filtering based approximation of universal graph representations,"https://scholar.google.com/scholar?cluster=3626161026171680219&hl=en&as_sdt=0,5",1,2022
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning,27,iclr,2,0,2023-06-18 09:43:52.621000,https://github.com/phquang/continual-normalization,8,Continual normalization: Rethinking batch normalization for online continual learning,"https://scholar.google.com/scholar?cluster=5393032746032394321&hl=en&as_sdt=0,10",2,2022
Equivariant Graph Mechanics Networks with Constraints,22,iclr,5,0,2023-06-18 09:43:52.824000,https://github.com/hanjq17/gmn,50,Equivariant graph mechanics networks with constraints,"https://scholar.google.com/scholar?cluster=3158185965758098235&hl=en&as_sdt=0,10",1,2022
Convergent Graph Solvers,9,iclr,2,0,2023-06-18 09:43:53.027000,https://github.com/Junyoungpark/CGS,22,Convergent graph solvers,"https://scholar.google.com/scholar?cluster=16292715563047713132&hl=en&as_sdt=0,47",1,2022
Generalization Through the Lens of Leave-One-Out Error,6,iclr,0,0,2023-06-18 09:43:53.231000,https://github.com/gregorbachmann/leaveoneout,2,Generalization through the lens of leave-one-out error,"https://scholar.google.com/scholar?cluster=17232289047191270815&hl=en&as_sdt=0,14",1,2022
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks,4,iclr,0,0,2023-06-18 09:43:53.434000,https://github.com/StephanLorenzen/ExactIBAnalysisInQNNs,4,Information bottleneck: Exact analysis of (quantized) neural networks,"https://scholar.google.com/scholar?cluster=14219492799643625897&hl=en&as_sdt=0,5",1,2022
Attacking deep networks with surrogate-based adversarial black-box methods is easy,5,iclr,1,0,2023-06-18 09:43:53.636000,https://github.com/fiveai/gfcs,6,Attacking deep networks with surrogate-based adversarial black-box methods is easy,"https://scholar.google.com/scholar?cluster=9504422673038646416&hl=en&as_sdt=0,5",5,2022
Auto-scaling Vision Transformers without Training,12,iclr,4,0,2023-06-18 09:43:53.840000,https://github.com/vita-group/asvit,72,Auto-scaling vision transformers without training,"https://scholar.google.com/scholar?cluster=10616211011095299898&hl=en&as_sdt=0,51",5,2022
Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics,2,iclr,1,0,2023-06-18 09:43:54.043000,https://github.com/realcrane/fine-grained-differentiable-physics-a-yarn-level-model-for-fabrics,4,Fine-grained differentiable physics: a yarn-level model for fabrics,"https://scholar.google.com/scholar?cluster=10505737509483577526&hl=en&as_sdt=0,15",2,2022
Missingness Bias in Model Debugging,13,iclr,0,0,2023-06-18 09:43:54.247000,https://github.com/madrylab/missingness,4,Missingness bias in model debugging,"https://scholar.google.com/scholar?cluster=2038886342850944148&hl=en&as_sdt=0,34",5,2022
Conditional Object-Centric Learning from Video,89,iclr,13,13,2023-06-18 09:43:54.450000,https://github.com/google-research/slot-attention-video,117,Conditional object-centric learning from video,"https://scholar.google.com/scholar?cluster=13987153077190983503&hl=en&as_sdt=0,10",7,2022
Bayesian Neural Network Priors Revisited,80,iclr,11,1,2023-06-18 09:43:54.653000,https://github.com/ratschlab/bnn_priors,51,Bayesian neural network priors revisited,"https://scholar.google.com/scholar?cluster=4553297460189369768&hl=en&as_sdt=0,37",6,2022
Hybrid Random Features,36,iclr,0,0,2023-06-18 09:43:54.856000,https://github.com/arijitthegame/hybrid-sampling,1,Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals,"https://scholar.google.com/scholar?cluster=15768792646769641893&hl=en&as_sdt=0,33",2,2022
Salient ImageNet: How to discover spurious features in Deep Learning?,33,iclr,3,0,2023-06-18 09:43:55.059000,https://github.com/singlasahil14/salient_imagenet,30,Salient ImageNet: How to discover spurious features in Deep Learning?,"https://scholar.google.com/scholar?cluster=14829986418742964472&hl=en&as_sdt=0,5",1,2022
Differentiable DAG Sampling,12,iclr,4,1,2023-06-18 09:43:55.262000,https://github.com/sharpenb/Differentiable-DAG-Sampling,25,Differentiable DAG sampling,"https://scholar.google.com/scholar?cluster=10667986307237653289&hl=en&as_sdt=0,22",2,2022
Hierarchical Few-Shot Imitation with Skill Transition Models,18,iclr,3,0,2023-06-18 09:43:55.465000,https://github.com/kouroshhakha/fist,8,Hierarchical few-shot imitation with skill transition models,"https://scholar.google.com/scholar?cluster=11314236649785473138&hl=en&as_sdt=0,14",2,2022
GeneDisco: A Benchmark for Experimental Design in Drug Discovery,10,iclr,10,5,2023-06-18 09:43:55.668000,https://github.com/genedisco/genedisco,30,Genedisco: A benchmark for experimental design in drug discovery,"https://scholar.google.com/scholar?cluster=10686323109700882145&hl=en&as_sdt=0,5",2,2022
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting,10,iclr,1,4,2023-06-18 09:43:55.871000,https://github.com/hyunwookl/pm-memnet,21,Learning to remember patterns: Pattern matching memory networks for traffic forecasting,"https://scholar.google.com/scholar?cluster=11909851991809109995&hl=en&as_sdt=0,39",2,2022
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks,29,iclr,6,1,2023-06-18 09:43:56.074000,https://github.com/graph-com/peg,27,Equivariant and stable positional encoding for more powerful graph neural networks,"https://scholar.google.com/scholar?cluster=16446538441027140116&hl=en&as_sdt=0,33",1,2022
A Deep Variational Approach to Clustering Survival Data,19,iclr,12,0,2023-06-18 09:43:56.276000,https://github.com/i6092467/vadesc,22,A deep variational approach to clustering survival data,"https://scholar.google.com/scholar?cluster=12300997661063839145&hl=en&as_sdt=0,5",1,2022
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization,69,iclr,7332,1026,2023-06-18 09:43:56.480000,https://github.com/google-research/google-research,29803,Charformer: Fast character transformers via gradient-based subword tokenization,"https://scholar.google.com/scholar?cluster=13362289969236599063&hl=en&as_sdt=0,3",728,2022
Knowledge Removal in Sampling-based Bayesian Inference,9,iclr,1,0,2023-06-18 09:43:56.683000,https://github.com/fshp971/mcmc-unlearning,16,Knowledge removal in sampling-based bayesian inference,"https://scholar.google.com/scholar?cluster=3535045679170951379&hl=en&as_sdt=0,5",2,2022
Igeood: An Information Geometry Approach to Out-of-Distribution Detection,15,iclr,0,0,2023-06-18 09:43:56.888000,https://github.com/edadaltocg/igeood,6,Igeood: An information geometry approach to out-of-distribution detection,"https://scholar.google.com/scholar?cluster=14684067719933018833&hl=en&as_sdt=0,23",1,2022
Bag of Instances Aggregation Boosts Self-supervised Distillation,9,iclr,1,0,2023-06-18 09:43:57.090000,https://github.com/haohang96/bingo,29,Bag of instances aggregation boosts self-supervised distillation,"https://scholar.google.com/scholar?cluster=3290933725411237169&hl=en&as_sdt=0,5",7,2022
Unrolling PALM for Sparse Semi-Blind Source Separation,1,iclr,4,0,2023-06-18 09:43:57.294000,https://github.com/mfahes/lpalm,9,Unrolling PALM for sparse semi-blind source separation,"https://scholar.google.com/scholar?cluster=17855454750763330141&hl=en&as_sdt=0,47",2,2022
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift,29,iclr,7,5,2023-06-18 09:43:57.498000,https://github.com/ts-kim/RevIN,87,Reversible instance normalization for accurate time-series forecasting against distribution shift,"https://scholar.google.com/scholar?cluster=15726225809303254672&hl=en&as_sdt=0,36",6,2022
Query Embedding on Hyper-Relational Knowledge Graphs,12,iclr,4,0,2023-06-18 09:43:57.701000,https://github.com/DimitrisAlivas/StarQE,22,Query embedding on hyper-relational knowledge graphs,"https://scholar.google.com/scholar?cluster=4690980256531947393&hl=en&as_sdt=0,5",4,2022
Neural Solvers for Fast and Accurate Numerical Optimal Control,4,iclr,7,1,2023-06-18 09:43:57.904000,https://github.com/diffeqml/diffeqml-research,69,Neural solvers for fast and accurate numerical optimal control,"https://scholar.google.com/scholar?cluster=3860528662060774857&hl=en&as_sdt=0,47",4,2022
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series,8,iclr,698,357,2023-06-18 09:43:58.109000,https://github.com/awslabs/gluon-ts,3623,PSA-GAN: Progressive self attention GANs for synthetic time series,"https://scholar.google.com/scholar?cluster=18377991298418272065&hl=en&as_sdt=0,5",70,2022
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind,15,iclr,3,0,2023-06-18 09:43:58.313000,https://github.com/unrealtracking/tom2c,31,Tom2c: Target-oriented multi-agent communication and cooperation with theory of mind,"https://scholar.google.com/scholar?cluster=13700850065152438149&hl=en&as_sdt=0,41",2,2022
Better Supervisory Signals by Observing Learning Paths,9,iclr,1,0,2023-06-18 09:43:58.518000,https://github.com/joshua-ren/better_supervisory_signal,3,Better supervisory signals by observing learning paths,"https://scholar.google.com/scholar?cluster=4997668798655366002&hl=en&as_sdt=0,5",2,2022
TAda! Temporally-Adaptive Convolutions for Video Understanding,19,iclr,0,0,2023-06-18 09:43:58.722000,https://github.com/alibaba-mmai-research/pytorch-video-understanding,0,Tada! temporally-adaptive convolutions for video understanding,"https://scholar.google.com/scholar?cluster=1325383719378653431&hl=en&as_sdt=0,44",1,2022
Learning a subspace of policies for online adaptation in Reinforcement Learning,7,iclr,42,0,2023-06-18 09:43:58.924000,https://github.com/facebookresearch/salina,424,Learning a subspace of policies for online adaptation in reinforcement learning,"https://scholar.google.com/scholar?cluster=8112991031910355476&hl=en&as_sdt=0,5",12,2022
Gaussian Mixture Convolution Networks,1,iclr,0,0,2023-06-18 09:43:59.128000,https://github.com/cg-tuwien/gaussian-mixture-convolution-networks,0,Gaussian Mixture Convolution Networks,"https://scholar.google.com/scholar?cluster=3285204199081775267&hl=en&as_sdt=0,33",3,2022
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior,44,iclr,133,24,2023-06-18 09:43:59.330000,https://github.com/microsoft/NeuralSpeech,1007,Priorgrad: Improving conditional denoising diffusion models with data-driven adaptive prior,"https://scholar.google.com/scholar?cluster=15402049708647149308&hl=en&as_sdt=0,33",30,2022
UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning,78,iclr,99,4,2023-06-18 09:43:59.534000,https://github.com/sense-x/uniformer,656,Uniformer: Unified transformer for efficient spatiotemporal representation learning,"https://scholar.google.com/scholar?cluster=13061863280402646662&hl=en&as_sdt=0,18",11,2022
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations,1,iclr,0,0,2023-06-18 09:43:59.737000,https://github.com/leejaehoon2016/lord,1,LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations,"https://scholar.google.com/scholar?cluster=9583526015589000772&hl=en&as_sdt=0,43",1,2022
"Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization",9,iclr,1,0,2023-06-18 09:43:59.940000,https://github.com/thanhnguyentang/offline_neural_bandits,7,"Offline neural contextual bandits: Pessimism, optimization and generalization","https://scholar.google.com/scholar?cluster=11879917374324366970&hl=en&as_sdt=0,33",1,2022
CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability,19,iclr,2,0,2023-06-18 09:44:00.143000,https://github.com/ml-research/CLEVA-Compass,17,CLEVA-compass: A continual learning evaluation assessment compass to promote research transparency and comparability,"https://scholar.google.com/scholar?cluster=16474577021609169344&hl=en&as_sdt=0,10",3,2022
Learning to Extend Molecular Scaffolds with Structural Motifs,35,iclr,30,6,2023-06-18 09:44:00.352000,https://github.com/microsoft/molecule-generation,182,Learning to extend molecular scaffolds with structural motifs,"https://scholar.google.com/scholar?cluster=16834575414277010470&hl=en&as_sdt=0,33",11,2022
Gradient Matching for Domain Generalization,127,iclr,7,4,2023-06-18 09:44:00.583000,https://github.com/YugeTen/fish,100,Gradient matching for domain generalization,"https://scholar.google.com/scholar?cluster=2851826454893571179&hl=en&as_sdt=0,48",3,2022
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios,1,iclr,2,0,2023-06-18 09:44:00.786000,https://github.com/ALRhub/HiP-RSSM,4,Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios,"https://scholar.google.com/scholar?cluster=11250070216520072781&hl=en&as_sdt=0,5",6,2022
Graph Neural Network Guided Local Search for the Traveling Salesperson Problem,22,iclr,6,1,2023-06-18 09:44:00.989000,https://github.com/proroklab/gnngls,17,Graph neural network guided local search for the traveling salesperson problem,"https://scholar.google.com/scholar?cluster=7438825804269654854&hl=en&as_sdt=0,33",4,2022
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks,28,iclr,2,0,2023-06-18 09:44:01.192000,https://github.com/martius-lab/beta-nll,17,On the pitfalls of heteroscedastic uncertainty estimation with probabilistic neural networks,"https://scholar.google.com/scholar?cluster=12019013391257516150&hl=en&as_sdt=0,44",4,2022
Label-Efficient Semantic Segmentation with Diffusion Models,99,iclr,48,3,2023-06-18 09:44:01.394000,https://github.com/yandex-research/ddpm-segmentation,501,Label-efficient semantic segmentation with diffusion models,"https://scholar.google.com/scholar?cluster=15536080386381166237&hl=en&as_sdt=0,5",7,2022
Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization,14,iclr,4,0,2023-06-18 09:44:01.598000,https://github.com/xi-l/pmoco,28,Pareto set learning for neural multi-objective combinatorial optimization,"https://scholar.google.com/scholar?cluster=10853796196468498279&hl=en&as_sdt=0,21",1,2022
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability,20,iclr,2,0,2023-06-18 09:44:01.802000,https://github.com/lfhase/gia-hao,23,Understanding and improving graph injection attack by promoting unnoticeability,"https://scholar.google.com/scholar?cluster=11546054136768832920&hl=en&as_sdt=0,10",3,2022
Learning to Guide and to be Guided in the Architect-Builder Problem,2,iclr,0,0,2023-06-18 09:44:02.005000,https://github.com/flowersteam/architect-builder-abig,5,Learning to guide and to be guided in the architect-builder problem,"https://scholar.google.com/scholar?cluster=8756083495202115468&hl=en&as_sdt=0,43",7,2022
Phase Collapse in Neural Networks,3,iclr,0,0,2023-06-18 09:44:02.209000,https://github.com/florentinguth/phasecollapse,6,Phase Collapse in Neural Networks,"https://scholar.google.com/scholar?cluster=2536829200192569115&hl=en&as_sdt=0,5",1,2022
SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training,9,iclr,93,15,2023-06-18 09:44:02.414000,https://github.com/huawei-noah/Speech-Backbones,396,SPIRAL: Self-supervised perturbation-invariant representation learning for speech pre-training,"https://scholar.google.com/scholar?cluster=7704368190007822312&hl=en&as_sdt=0,49",26,2022
Enhancing Cross-lingual Transfer by Manifold Mixup,15,iclr,2,1,2023-06-18 09:44:02.621000,https://github.com/yhy1117/x-mixup,17,Enhancing cross-lingual transfer by manifold mixup,"https://scholar.google.com/scholar?cluster=13560869660966503554&hl=en&as_sdt=0,5",1,2022
Curvature-Guided Dynamic Scale Networks for Multi-View Stereo,7,iclr,6,0,2023-06-18 09:44:02.825000,https://github.com/truongkhang/cds-mvsnet,95,Curvature-guided dynamic scale networks for multi-view stereo,"https://scholar.google.com/scholar?cluster=4920966031938804836&hl=en&as_sdt=0,5",6,2022
Exploring extreme parameter compression for pre-trained language models,5,iclr,0,0,2023-06-18 09:44:03.029000,https://github.com/twinkle0331/xcompression,17,Exploring extreme parameter compression for pre-trained language models,"https://scholar.google.com/scholar?cluster=10120048061999340751&hl=en&as_sdt=0,7",3,2022
Scale Mixtures of Neural Network Gaussian Processes,1,iclr,1,0,2023-06-18 09:44:03.232000,https://github.com/Hyungi-Lee/Scale-Mixtures-of-Neural-Network-Gaussian-Processes,1,Scale mixtures of neural network Gaussian processes,"https://scholar.google.com/scholar?cluster=1361989022651133185&hl=en&as_sdt=0,46",1,2022
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis,25,iclr,32,4,2023-06-18 09:44:03.435000,https://github.com/tsy935/eeg-gnn-ssl,74,Self-supervised graph neural networks for improved electroencephalographic seizure analysis,"https://scholar.google.com/scholar?cluster=12685516138349084049&hl=en&as_sdt=0,5",2,2022
MonoDistill: Learning Spatial Features for Monocular 3D Object Detection,33,iclr,4,4,2023-06-18 09:44:03.639000,https://github.com/monster-ghost/monodistill,55,Monodistill: Learning spatial features for monocular 3d object detection,"https://scholar.google.com/scholar?cluster=14851758558366902605&hl=en&as_sdt=0,10",6,2022
Unsupervised Semantic Segmentation by Distilling Feature Correspondences,87,iclr,119,35,2023-06-18 09:44:03.844000,https://github.com/mhamilton723/STEGO,598,Unsupervised semantic segmentation by distilling feature correspondences,"https://scholar.google.com/scholar?cluster=8638628527714032897&hl=en&as_sdt=0,5",13,2022
Graph-Relational Domain Adaptation,12,iclr,2,0,2023-06-18 09:44:04.047000,https://github.com/wang-ml-lab/grda,35,Graph-relational domain adaptation,"https://scholar.google.com/scholar?cluster=14268209839215754091&hl=en&as_sdt=0,47",3,2022
Generalized Kernel Thinning,13,iclr,2,0,2023-06-18 09:44:04.251000,https://github.com/microsoft/goodpoints,31,Generalized kernel thinning,"https://scholar.google.com/scholar?cluster=11005160819787759649&hl=en&as_sdt=0,23",9,2022
How Much Can CLIP Benefit Vision-and-Language Tasks?,205,iclr,30,5,2023-06-18 09:44:04.454000,https://github.com/clip-vil/CLIP-ViL,344,How much can clip benefit vision-and-language tasks?,"https://scholar.google.com/scholar?cluster=6434466912782408523&hl=en&as_sdt=0,22",9,2022
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication,20,iclr,5,0,2023-06-18 09:44:04.658000,https://github.com/RICE-EIC/PipeGCN,23,PipeGCN: Efficient full-graph training of graph convolutional networks with pipelined feature communication,"https://scholar.google.com/scholar?cluster=5927794723979100407&hl=en&as_sdt=0,11",3,2022
Adversarial Unlearning of Backdoors via Implicit Hypergradient,56,iclr,11,0,2023-06-18 09:44:04.862000,https://github.com/yizeng623/i-bau_adversarial_unlearning_of-backdoors_via_implicit_hypergradient,33,Adversarial unlearning of backdoors via implicit hypergradient,"https://scholar.google.com/scholar?cluster=4522682349845084821&hl=en&as_sdt=0,5",2,2022
Graph Neural Networks with Learnable Structural and Positional Representations,89,iclr,27,1,2023-06-18 09:44:05.065000,https://github.com/vijaydwivedi75/gnn-lspe,200,Graph neural networks with learnable structural and positional representations,"https://scholar.google.com/scholar?cluster=6297596382755615056&hl=en&as_sdt=0,44",4,2022
Zero-Shot Self-Supervised Learning for MRI Reconstruction,22,iclr,3,0,2023-06-18 09:44:05.268000,https://github.com/byaman14/ZS-SSL,23,Zero-shot self-supervised learning for MRI reconstruction,"https://scholar.google.com/scholar?cluster=8560658023776593054&hl=en&as_sdt=0,44",1,2022
Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction,17,iclr,8,1,2023-06-18 09:44:05.471000,https://github.com/mtang724/nwr-gae,30,Graph auto-encoder via neighborhood wasserstein reconstruction,"https://scholar.google.com/scholar?cluster=13928177988336343335&hl=en&as_sdt=0,36",1,2022
On Redundancy and Diversity in Cell-based Neural Architecture Search,11,iclr,1,0,2023-06-18 09:44:05.675000,https://github.com/xingchenwan/cell-based-nas-analysis,4,On redundancy and diversity in cell-based neural architecture search,"https://scholar.google.com/scholar?cluster=1908527067267342822&hl=en&as_sdt=0,19",1,2022
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers,11,iclr,2,0,2023-06-18 09:44:05.881000,https://github.com/deepmind/dks,47,Deep learning without shortcuts: Shaping the kernel with tailored rectifiers,"https://scholar.google.com/scholar?cluster=3445605992837467130&hl=en&as_sdt=0,5",5,2022
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias,5,iclr,0,0,2023-06-18 09:44:06.084000,https://github.com/virajmehta/vae-training,0,Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias,"https://scholar.google.com/scholar?cluster=12373917463845389421&hl=en&as_sdt=0,10",2,2022
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models,9,iclr,2,1,2023-06-18 09:44:06.287000,https://github.com/cliang1453/sage,23,No parameters left behind: Sensitivity guided adaptive learning rate for training large transformer models,"https://scholar.google.com/scholar?cluster=17779998406940212088&hl=en&as_sdt=0,44",1,2022
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations,185,iclr,72,12,2023-06-18 09:44:06.491000,https://github.com/ermongroup/SDEdit,665,Sdedit: Guided image synthesis and editing with stochastic differential equations,"https://scholar.google.com/scholar?cluster=2574908324079451158&hl=en&as_sdt=0,5",20,2022
Generalizing Few-Shot NAS with Gradient Matching,9,iclr,1,2,2023-06-18 09:44:06.695000,https://github.com/skhu101/GM-NAS,17,Generalizing few-shot nas with gradient matching,"https://scholar.google.com/scholar?cluster=10558207332757804678&hl=en&as_sdt=0,5",2,2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training,30,iclr,9,0,2023-06-18 09:44:06.898000,https://github.com/vita-group/random_pruning,61,The unreasonable effectiveness of random pruning: Return of the most naive baseline for sparse training,"https://scholar.google.com/scholar?cluster=15333598630551716586&hl=en&as_sdt=0,42",2,2022
Training Transition Policies via Distribution Matching for Complex Tasks,1,iclr,1,0,2023-06-18 09:44:07.101000,https://github.com/shashacks/irl_transition,4,Training Transition Policies via Distribution Matching for Complex Tasks,"https://scholar.google.com/scholar?cluster=11055212331250216883&hl=en&as_sdt=0,44",1,2022
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?,1,iclr,0,0,2023-06-18 09:44:07.304000,https://github.com/msf235/group-invariant-perceptron-capacity,1,Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?,"https://scholar.google.com/scholar?cluster=14636029131782047015&hl=en&as_sdt=0,5",1,2022
Learning Weakly-supervised Contrastive Representations,8,iclr,3,1,2023-06-18 09:44:07.508000,https://github.com/crazy-jack/cl-infonce,12,Learning weakly-supervised contrastive representations,"https://scholar.google.com/scholar?cluster=16658448865785997630&hl=en&as_sdt=0,5",2,2022
Conditional Contrastive Learning with Kernel,13,iclr,0,0,2023-06-18 09:44:07.713000,https://github.com/crazy-jack/cclk-release,7,Conditional contrastive learning with kernel,"https://scholar.google.com/scholar?cluster=14273339449801655874&hl=en&as_sdt=0,5",2,2022
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations,13,iclr,15,1,2023-06-18 09:44:07.916000,https://github.com/amzn/trans-encoder,119,Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations,"https://scholar.google.com/scholar?cluster=14228123078269305039&hl=en&as_sdt=0,5",7,2022
Path Integral Sampler: A Stochastic Control Approach For Sampling,12,iclr,4,0,2023-06-18 09:44:08.120000,https://github.com/qsh-zh/pis,30,Path integral sampler: a stochastic control approach for sampling,"https://scholar.google.com/scholar?cluster=17129588743049853976&hl=en&as_sdt=0,14",2,2022
Optimizer Amalgamation,1,iclr,0,0,2023-06-18 09:44:08.323000,https://github.com/vita-group/optimizeramalgamation,4,Optimizer Amalgamation,"https://scholar.google.com/scholar?cluster=12945586216189211719&hl=en&as_sdt=0,44",8,2022
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts,11,iclr,439,67,2023-06-18 09:44:08.530000,https://github.com/makcedward/nlpaug,3994,P-adapters: Robustly extracting factual information from language models with diverse prompts,"https://scholar.google.com/scholar?cluster=1866558597598479566&hl=en&as_sdt=0,10",41,2022
Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming,15,iclr,2,0,2023-06-18 09:44:08.733000,https://github.com/core-robotics-lab/infopg,3,Iterated reasoning with mutual information in cooperative and byzantine decentralized teaming,"https://scholar.google.com/scholar?cluster=15432684675510350837&hl=en&as_sdt=0,5",1,2022
Hindsight Foresight Relabeling for Meta-Reinforcement Learning,3,iclr,1,0,2023-06-18 09:44:08.936000,https://github.com/michaelwan11/hfr,6,Hindsight foresight relabeling for meta-reinforcement learning,"https://scholar.google.com/scholar?cluster=4008449180583505870&hl=en&as_sdt=0,44",2,2022
LoRA: Low-Rank Adaptation of Large Language Models,437,iclr,260,54,2023-06-18 09:44:09.140000,https://github.com/microsoft/LoRA,4922,Lora: Low-rank adaptation of large language models,"https://scholar.google.com/scholar?cluster=12933070321040047372&hl=en&as_sdt=0,5",42,2022
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data,24,iclr,7332,1026,2023-06-18 09:44:09.344000,https://github.com/google-research/google-research,29803,Trail: Near-optimal imitation learning with suboptimal data,"https://scholar.google.com/scholar?cluster=13031874054704232682&hl=en&as_sdt=0,5",728,2022
Conditional Image Generation by Conditioning Variational Auto-Encoders,4,iclr,0,0,2023-06-18 09:44:09.558000,https://github.com/plai-group/ipa,9,Conditional image generation by conditioning variational auto-encoders,"https://scholar.google.com/scholar?cluster=2137944836024750208&hl=en&as_sdt=0,11",3,2022
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations,16,iclr,7,1,2023-06-18 09:44:09.762000,https://github.com/keiradams/chiro,42,Learning 3d representations of molecular chirality with invariance to bond rotations,"https://scholar.google.com/scholar?cluster=12423983501128658396&hl=en&as_sdt=0,10",1,2022
Neural Methods for Logical Reasoning over Knowledge Graphs,10,iclr,0,0,2023-06-18 09:44:09.967000,https://github.com/amayuelas/NNKGReasoning,8,Neural methods for logical reasoning over knowledge graphs,"https://scholar.google.com/scholar?cluster=11327310359192902619&hl=en&as_sdt=0,33",2,2022
Unified Visual Transformer Compression,34,iclr,3,4,2023-06-18 09:44:10.170000,https://github.com/VITA-Group/UVC,33,Unified visual transformer compression,"https://scholar.google.com/scholar?cluster=1947517498926990042&hl=en&as_sdt=0,31",8,2022
PAC Prediction Sets Under Covariate Shift,14,iclr,1,0,2023-06-18 09:44:10.377000,https://github.com/sangdon/pac-ps-w,3,PAC prediction sets under covariate shift,"https://scholar.google.com/scholar?cluster=15533837197233330118&hl=en&as_sdt=0,10",2,2022
One After Another: Learning Incremental Skills for a Changing World,3,iclr,0,0,2023-06-18 09:44:10.580000,https://github.com/notmahi/disk,14,One After Another: Learning Incremental Skills for a Changing World,"https://scholar.google.com/scholar?cluster=7328413134619288217&hl=en&as_sdt=0,50",1,2022
Graph-Guided Network for Irregularly Sampled Multivariate Time Series,23,iclr,26,4,2023-06-18 09:44:10.784000,https://github.com/mims-harvard/raindrop,94,Graph-guided network for irregularly sampled multivariate time series,"https://scholar.google.com/scholar?cluster=1644836195235087871&hl=en&as_sdt=0,43",6,2022
FILM: Following Instructions in Language with Modular Methods,59,iclr,25,21,2023-06-18 09:44:10.986000,https://github.com/soyeonm/film,82,Film: Following instructions in language with modular methods,"https://scholar.google.com/scholar?cluster=5571461167414719963&hl=en&as_sdt=0,33",3,2022
Monotonic Differentiable Sorting Networks,8,iclr,2,2,2023-06-18 09:44:11.189000,https://github.com/Felix-Petersen/diffsort,82,Monotonic differentiable sorting networks,"https://scholar.google.com/scholar?cluster=11509121699002053809&hl=en&as_sdt=0,5",3,2022
Model Agnostic Interpretability for Multiple Instance Learning,1,iclr,1,0,2023-06-18 09:44:11.393000,https://github.com/jaearly/milli,10,Model Agnostic Interpretability for Multiple Instance Learning,"https://scholar.google.com/scholar?cluster=4511846077135181099&hl=en&as_sdt=0,5",2,2022
"When, Why, and Which Pretrained GANs Are Useful?",9,iclr,2,2,2023-06-18 09:44:11.596000,https://github.com/yandex-research/gan-transfer,20,"When, Why, and Which Pretrained GANs Are Useful?","https://scholar.google.com/scholar?cluster=1749765519247284522&hl=en&as_sdt=0,5",0,2022
Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients,19,iclr,2,2,2023-06-18 09:44:11.799000,https://github.com/lunanbit/fedul,25,Federated learning from only unlabeled data with class-conditional-sharing clients,"https://scholar.google.com/scholar?cluster=10078372194856107683&hl=en&as_sdt=0,31",2,2022
Transformer Embeddings of Irregularly Spaced Events and Their Participants,10,iclr,7,0,2023-06-18 09:44:12.002000,https://github.com/yangalan123/anhp-andtt,35,Transformer embeddings of irregularly spaced events and their participants,"https://scholar.google.com/scholar?cluster=901539587982376122&hl=en&as_sdt=0,34",3,2022
Fast Model Editing at Scale,85,iclr,23,2,2023-06-18 09:44:12.205000,https://github.com/eric-mitchell/mend,165,Fast model editing at scale,"https://scholar.google.com/scholar?cluster=16012977472608893653&hl=en&as_sdt=0,5",6,2022
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums,1,iclr,0,0,2023-06-18 09:44:12.408000,https://github.com/opensource12345678/why_cosine_works,1,Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums,"https://scholar.google.com/scholar?cluster=11376476558105496833&hl=en&as_sdt=0,34",1,2022
On Incorporating Inductive Biases into VAEs,6,iclr,0,0,2023-06-18 09:44:12.611000,https://github.com/ningmiao/intel-vae,2,On incorporating inductive biases into VAEs,"https://scholar.google.com/scholar?cluster=15494277357139593439&hl=en&as_sdt=0,5",1,2022
On the Existence of Universal Lottery Tickets,17,iclr,0,0,2023-06-18 09:44:12.814000,https://github.com/relationalml/universallt,1,On the existence of universal lottery tickets,"https://scholar.google.com/scholar?cluster=4071511330404748656&hl=en&as_sdt=0,48",0,2022
Pre-training Molecular Graph Representation with 3D Geometry,94,iclr,16,3,2023-06-18 09:44:13.018000,https://github.com/chao1224/graphmvp,114,Pre-training molecular graph representation with 3d geometry,"https://scholar.google.com/scholar?cluster=12269574784453036678&hl=en&as_sdt=0,5",5,2022
Taming Sparsely Activated Transformer with Stochastic Experts,28,iclr,5,2,2023-06-18 09:44:13.220000,https://github.com/microsoft/stochastic-mixture-of-experts,45,Taming sparsely activated transformer with stochastic experts,"https://scholar.google.com/scholar?cluster=2351258339090586276&hl=en&as_sdt=0,44",7,2022
Hierarchical Variational Memory for Few-shot Learning Across Domains,10,iclr,0,1,2023-06-18 09:44:13.424000,https://github.com/ydu-uva/hiermemory,2,Hierarchical variational memory for few-shot learning across domains,"https://scholar.google.com/scholar?cluster=1702336741267321422&hl=en&as_sdt=0,47",1,2022
Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction,91,iclr,94,39,2023-06-18 09:44:13.627000,https://github.com/facebookresearch/av_hubert,563,Learning audio-visual speech representation by masked multimodal cluster prediction,"https://scholar.google.com/scholar?cluster=10092601406427600448&hl=en&as_sdt=0,5",14,2022
An Explanation of In-context Learning as Implicit Bayesian Inference,116,iclr,12,1,2023-06-18 09:44:13.831000,https://github.com/p-lambda/incontext-learning,56,An explanation of in-context learning as implicit bayesian inference,"https://scholar.google.com/scholar?cluster=15144987797628396832&hl=en&as_sdt=0,5",13,2022
"Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System",33,iclr,4,1,2023-06-18 09:44:14.035000,https://github.com/NeurAI-Lab/CLS-ER,25,"Learning fast, learning slow: A general continual learning method based on complementary learning system","https://scholar.google.com/scholar?cluster=2178714881439527742&hl=en&as_sdt=0,5",2,2022
What Do We Mean by Generalization in Federated Learning?,26,iclr,177,12,2023-06-18 09:44:14.238000,https://github.com/google-research/federated,555,What do we mean by generalization in federated learning?,"https://scholar.google.com/scholar?cluster=7455517891491181404&hl=en&as_sdt=0,43",26,2022
Autonomous Reinforcement Learning: Formalism and Benchmarking,14,iclr,4,0,2023-06-18 09:44:14.442000,https://github.com/architsharma97/earl_benchmark,33,Autonomous reinforcement learning: Formalism and benchmarking,"https://scholar.google.com/scholar?cluster=9771677506162307722&hl=en&as_sdt=0,5",7,2022
Label Leakage and Protection in Two-party Split Learning,67,iclr,172,72,2023-06-18 09:44:14.645000,https://github.com/bytedance/fedlearner,844,Label leakage and protection in two-party split learning,"https://scholar.google.com/scholar?cluster=4111278201202932828&hl=en&as_sdt=0,33",28,2022
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation,8,iclr,3,0,2023-06-18 09:44:14.850000,https://github.com/ppashakhanloo/CodeTrek,22,Codetrek: Flexible modeling of code using an extensible relational representation,"https://scholar.google.com/scholar?cluster=10059664661976088389&hl=en&as_sdt=0,5",2,2022
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models,97,iclr,21,8,2023-06-18 09:44:15.052000,https://github.com/yang-song/score_inverse_problems,142,Solving inverse problems in medical imaging with score-based generative models,"https://scholar.google.com/scholar?cluster=16734106149627333689&hl=en&as_sdt=0,47",5,2022
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis,34,iclr,26,1,2023-06-18 09:44:15.255000,https://github.com/tencent-ailab/bddm,188,BDDM: Bilateral denoising diffusion models for fast and high-quality speech synthesis,"https://scholar.google.com/scholar?cluster=9819196866307115344&hl=en&as_sdt=0,5",8,2022
The Uncanny Similarity of Recurrence and Depth,7,iclr,1,0,2023-06-18 09:44:15.458000,https://github.com/Arjung27/DeepThinking,8,The uncanny similarity of recurrence and depth,"https://scholar.google.com/scholar?cluster=15030809144030367999&hl=en&as_sdt=0,5",3,2022
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning,118,iclr,68,11,2023-06-18 09:44:15.662000,https://github.com/facebookresearch/drqv2,269,Mastering visual continuous control: Improved data-augmented reinforcement learning,"https://scholar.google.com/scholar?cluster=6421326850849903033&hl=en&as_sdt=0,5",9,2022
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting,33,iclr,35,5,2023-06-18 09:44:15.865000,https://github.com/salesforce/CoST,164,CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting,"https://scholar.google.com/scholar?cluster=10071706504793887642&hl=en&as_sdt=0,11",6,2022
The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs,5,iclr,0,0,2023-06-18 09:44:16.068000,https://github.com/muellerjohannes/geometry-pomdps-iclr-2022,0,The geometry of memoryless stochastic policy optimization in infinite-horizon POMDPs,"https://scholar.google.com/scholar?cluster=2565752681531472620&hl=en&as_sdt=0,5",1,2022
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks,52,iclr,4,6,2023-06-18 09:44:16.272000,https://github.com/dydjw9/efficient_sam,43,Efficient sharpness-aware minimization for improved training of neural networks,"https://scholar.google.com/scholar?cluster=15803669707023220896&hl=en&as_sdt=0,34",1,2022
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities,2,iclr,0,0,2023-06-18 09:44:16.476000,https://github.com/jianda-chen/ambs,5,Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities,"https://scholar.google.com/scholar?cluster=3185167988129804114&hl=en&as_sdt=0,5",1,2022
Effective Model Sparsification by Scheduled Grow-and-Prune Methods,13,iclr,2,1,2023-06-18 09:44:16.679000,https://github.com/boone891214/gap,8,Effective model sparsification by scheduled grow-and-prune methods,"https://scholar.google.com/scholar?cluster=14488112763252453275&hl=en&as_sdt=0,46",2,2022
Efficient Active Search for Combinatorial Optimization Problems,28,iclr,6,0,2023-06-18 09:44:16.882000,https://github.com/ahottung/EAS,30,Efficient active search for combinatorial optimization problems,"https://scholar.google.com/scholar?cluster=13404693543769371304&hl=en&as_sdt=0,47",2,2022
Training Structured Neural Networks Through Manifold Identification and Variance Reduction,2,iclr,0,0,2023-06-18 09:44:17.085000,https://github.com/zihsyuan1214/rmda,0,Training Structured Neural Networks Through Manifold Identification and Variance Reduction,"https://scholar.google.com/scholar?cluster=3809096100711986966&hl=en&as_sdt=0,5",2,2022
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization,18,iclr,4,0,2023-06-18 09:44:17.288000,https://github.com/robertcsordas/ndr,24,The neural data router: Adaptive control flow in transformers improves systematic generalization,"https://scholar.google.com/scholar?cluster=10423367816942956879&hl=en&as_sdt=0,37",1,2022
Distributionally Robust Models with Parametric Likelihood Ratios,9,iclr,7,0,2023-06-18 09:44:17.492000,https://github.com/pmichel31415/P-DRO,18,Distributionally robust models with parametric likelihood ratios,"https://scholar.google.com/scholar?cluster=2606416541563470801&hl=en&as_sdt=0,10",2,2022
Understanding approximate and unrolled dictionary learning for pattern recovery,6,iclr,0,0,2023-06-18 09:44:17.696000,https://github.com/bmalezieux/unrolled_dl,1,Understanding approximate and unrolled dictionary learning for pattern recovery,"https://scholar.google.com/scholar?cluster=14808406536807467476&hl=en&as_sdt=0,10",3,2022
Constraining Linear-chain CRFs to Regular Languages,3,iclr,0,2,2023-06-18 09:44:17.899000,https://github.com/person594/regccrf-experiments,4,Constraining linear-chain crfs to regular languages,"https://scholar.google.com/scholar?cluster=14062623964127434433&hl=en&as_sdt=0,5",3,2022
Noisy Feature Mixup,15,iclr,1,0,2023-06-18 09:44:18.103000,https://github.com/erichson/noisy_mixup,17,Noisy feature mixup,"https://scholar.google.com/scholar?cluster=6823398693894797523&hl=en&as_sdt=0,24",5,2022
Subspace Regularizers for Few-Shot Class Incremental Learning,17,iclr,1,1,2023-06-18 09:44:18.306000,https://github.com/feyzaakyurek/subspace-reg,20,Subspace regularizers for few-shot class incremental learning,"https://scholar.google.com/scholar?cluster=740038996677769193&hl=en&as_sdt=0,22",4,2022
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms,6,iclr,2,1,2023-06-18 09:44:18.510000,https://github.com/clio-dl/using-sgnn-for-depression-estimate,4,Using graph representation learning with schema encoders to measure the severity of depressive symptoms,"https://scholar.google.com/scholar?cluster=18226966908018577857&hl=en&as_sdt=0,33",1,2022
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning,441,iclr,79,2,2023-06-18 09:44:18.714000,https://github.com/facebookresearch/vicreg,430,Vicreg: Variance-invariance-covariance regularization for self-supervised learning,"https://scholar.google.com/scholar?cluster=14326519942504966909&hl=en&as_sdt=0,11",7,2022
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting,67,iclr,7,2,2023-06-18 09:44:18.917000,https://github.com/gus-lab/temporal_efficient_training,34,Temporal efficient training of spiking neural network via gradient re-weighting,"https://scholar.google.com/scholar?cluster=7413408769468810617&hl=en&as_sdt=0,5",0,2022
Reliable Adversarial Distillation with Unreliable Teachers,21,iclr,2,1,2023-06-18 09:44:19.120000,https://github.com/zfancy/iad,17,Reliable adversarial distillation with unreliable teachers,"https://scholar.google.com/scholar?cluster=14735991802555928714&hl=en&as_sdt=0,48",2,2022
Delaunay Component Analysis for Evaluation of Data Representations,7,iclr,1,1,2023-06-18 09:44:19.326000,https://github.com/petrapoklukar/dca,10,Delaunay component analysis for evaluation of data representations,"https://scholar.google.com/scholar?cluster=10833565106730763520&hl=en&as_sdt=0,36",1,2022
Imitation Learning by Reinforcement Learning,8,iclr,0,0,2023-06-18 09:44:19.529000,https://github.com/spotify-research/il-by-rl,1,Imitation learning by reinforcement learning,"https://scholar.google.com/scholar?cluster=5663632794147354936&hl=en&as_sdt=0,5",3,2022
TAPEX: Table Pre-training via Learning a Neural SQL Executor,78,iclr,32,2,2023-06-18 09:44:19.732000,https://github.com/microsoft/Table-Pretraining,214,Tapex: Table pre-training via learning a neural sql executor,"https://scholar.google.com/scholar?cluster=1887020545839431374&hl=en&as_sdt=0,33",4,2022
On Robust Prefix-Tuning for Text Classification,9,iclr,2,0,2023-06-18 09:44:19.936000,https://github.com/minicheshire/robust-prefix-tuning,19,On robust prefix-tuning for text classification,"https://scholar.google.com/scholar?cluster=5512236602536653945&hl=en&as_sdt=0,4",1,2022
Learning Graphon Mean Field Games and Approximate Nash Equilibria,15,iclr,1,0,2023-06-18 09:44:20.139000,https://github.com/tudkcui/gmfg-learning,4,Learning graphon mean field games and approximate Nash equilibria,"https://scholar.google.com/scholar?cluster=18310233350128597723&hl=en&as_sdt=0,5",1,2022
cosFormer: Rethinking Softmax In Attention,62,iclr,21,5,2023-06-18 09:44:20.344000,https://github.com/OpenNLPLab/cosFormer,148,cosformer: Rethinking softmax in attention,"https://scholar.google.com/scholar?cluster=11701536560712216954&hl=en&as_sdt=0,33",5,2022
Transferable Adversarial Attack based on Integrated Gradients,12,iclr,4,0,2023-06-18 09:44:20.557000,https://github.com/yihuang2016/TAIG,14,Transferable adversarial attack based on integrated gradients,"https://scholar.google.com/scholar?cluster=12897064558581398673&hl=en&as_sdt=0,5",2,2022
Topological Graph Neural Networks,34,iclr,11,2,2023-06-18 09:44:20.761000,https://github.com/borgwardtlab/togl,81,Topological graph neural networks,"https://scholar.google.com/scholar?cluster=18101743901347787747&hl=en&as_sdt=0,14",8,2022
The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models,7,iclr,0,0,2023-06-18 09:44:20.963000,https://github.com/cassidylaidlaw/boltzmann-policy-distribution,5,The boltzmann policy distribution: Accounting for systematic suboptimality in human models,"https://scholar.google.com/scholar?cluster=403926585745142626&hl=en&as_sdt=0,5",1,2022
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection,20,iclr,1,4,2023-06-18 09:44:21.167000,https://github.com/spengliang/weakm3d,19,Weakm3d: Towards weakly supervised monocular 3d object detection,"https://scholar.google.com/scholar?cluster=1602406100270508731&hl=en&as_sdt=0,11",2,2022
Exploring Memorization in Adversarial Training,29,iclr,1,1,2023-06-18 09:44:21.371000,https://github.com/dongyp13/memorization-AT,18,Exploring memorization in adversarial training,"https://scholar.google.com/scholar?cluster=13986529616809382017&hl=en&as_sdt=0,23",1,2022
Sound and Complete Neural Network Repair with Minimality and Locality Guarantees,8,iclr,3,7,2023-06-18 09:44:21.576000,https://github.com/bu-depend-lab/reassure,4,Sound and complete neural network repair with minimality and locality guarantees,"https://scholar.google.com/scholar?cluster=862436873685923655&hl=en&as_sdt=0,5",1,2022
Automated Self-Supervised Learning for Graphs,31,iclr,3,0,2023-06-18 09:44:21.779000,https://github.com/ChandlerBang/AutoSSL,36,Automated self-supervised learning for graphs,"https://scholar.google.com/scholar?cluster=8260281940315648872&hl=en&as_sdt=0,32",5,2022
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs,9,iclr,2,0,2023-06-18 09:44:21.982000,https://github.com/isno0907/localbasis,7,Do not escape from the manifold: Discovering the local coordinates on the latent space of GANs,"https://scholar.google.com/scholar?cluster=4704378958785987295&hl=en&as_sdt=0,43",1,2022
GradSign: Model Performance Inference with Theoretical Insights,4,iclr,0,0,2023-06-18 09:44:22.186000,https://github.com/cmu-catalyst/gradsign,5,Gradsign: Model performance inference with theoretical insights,"https://scholar.google.com/scholar?cluster=3694655977867314060&hl=en&as_sdt=0,5",1,2022
You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks,30,iclr,6,1,2023-06-18 09:44:22.421000,https://github.com/jianhao2016/AllSet,61,You are allset: A multiset function framework for hypergraph neural networks,"https://scholar.google.com/scholar?cluster=2657795859999531247&hl=en&as_sdt=0,5",2,2022
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods,26,iclr,13,8,2023-06-18 09:44:22.643000,https://github.com/amazon-research/gnn-tail-generalization,43,Cold brew: Distilling graph node representations with incomplete or missing neighborhoods,"https://scholar.google.com/scholar?cluster=6445832848440992452&hl=en&as_sdt=0,5",5,2022
How to Train Your MAML to Excel in Few-Shot Classification,17,iclr,5,4,2023-06-18 09:44:22.862000,https://github.com/han-jia/unicorn-maml,24,How to train your MAML to excel in few-shot classification,"https://scholar.google.com/scholar?cluster=3274682944038978071&hl=en&as_sdt=0,5",1,2022
"MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer",314,iclr,178,24,2023-06-18 09:44:23.066000,https://github.com/apple/ml-cvnets,1389,"Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer","https://scholar.google.com/scholar?cluster=5434557493125510443&hl=en&as_sdt=0,47",34,2022
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks,27,iclr,19,4,2023-06-18 09:44:23.269000,https://github.com/automl/nasbench301,65,Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks,"https://scholar.google.com/scholar?cluster=14512036334804223590&hl=en&as_sdt=0,26",12,2022
Crystal Diffusion Variational Autoencoder for Periodic Material Generation,57,iclr,45,27,2023-06-18 09:44:23.473000,https://github.com/txie-93/cdvae,131,Crystal diffusion variational autoencoder for periodic material generation,"https://scholar.google.com/scholar?cluster=10416305679920850993&hl=en&as_sdt=0,5",3,2022
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning,8,iclr,0,0,2023-06-18 09:44:23.676000,https://github.com/lephuoccat/TAS-few-shot,3,Task affinity with maximum bipartite matching in few-shot learning,"https://scholar.google.com/scholar?cluster=10877103114487491040&hl=en&as_sdt=0,44",2,2022
Know Thyself: Transferable Visual Control Policies Through Robot-Awareness,1,iclr,0,0,2023-06-18 09:44:23.879000,https://github.com/penn-pal-lab/robot-aware-control,4,Know thyself: Transferable visual control policies through robot-awareness,"https://scholar.google.com/scholar?cluster=12842278673686640517&hl=en&as_sdt=0,34",2,2022
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction,28,iclr,97,3,2023-06-18 09:44:24.082000,https://github.com/amzn/pecos,442,Node feature extraction by self-supervised multi-scale neighborhood prediction,"https://scholar.google.com/scholar?cluster=868307857641759607&hl=en&as_sdt=0,5",20,2022
On the Learning and Learnability of Quasimetrics,4,iclr,1,0,2023-06-18 09:44:24.285000,https://github.com/ssnl/poisson_quasimetric_embedding,28,On the learning and learnablity of quasimetrics,"https://scholar.google.com/scholar?cluster=12412189900513627559&hl=en&as_sdt=0,10",1,2022
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling,2,iclr,0,0,2023-06-18 09:44:24.489000,https://github.com/gisilvs/EmbeddedModelFlows,3,Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling,"https://scholar.google.com/scholar?cluster=13875622113438069976&hl=en&as_sdt=0,31",2,2022
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning,8,iclr,2,1,2023-06-18 09:44:24.694000,https://github.com/cr-gjx/ria,9,A relational intervention approach for unsupervised dynamics generalization in model-based reinforcement learning,"https://scholar.google.com/scholar?cluster=16171191146892627821&hl=en&as_sdt=0,33",1,2022
VOS: Learning What You Don't Know by Virtual Outlier Synthesis,80,iclr,52,1,2023-06-18 09:44:24.898000,https://github.com/deeplearning-wisc/vos,265,Vos: Learning what you don't know by virtual outlier synthesis,"https://scholar.google.com/scholar?cluster=2027738849340009189&hl=en&as_sdt=0,33",8,2022
Unsupervised Disentanglement with Tensor Product Representations on the Torus,3,iclr,0,0,2023-06-18 09:44:25.102000,https://github.com/rotmanmi/unsupervised-disentanglement-torus,2,Unsupervised disentanglement with tensor product representations on the torus,"https://scholar.google.com/scholar?cluster=12503699134919857893&hl=en&as_sdt=0,5",2,2022
FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes,39,iclr,7,0,2023-06-18 09:44:25.304000,https://github.com/rjbruin/flexconv,105,Flexconv: Continuous kernel convolutions with differentiable kernel sizes,"https://scholar.google.com/scholar?cluster=1024278192039187692&hl=en&as_sdt=0,5",2,2022
Zero Pixel Directional Boundary by Vector Transform,2,iclr,0,0,2023-06-18 09:44:25.508000,https://github.com/edomel/boundaryvt,1,Zero pixel directional boundary by vector transform,"https://scholar.google.com/scholar?cluster=4154866420989883552&hl=en&as_sdt=0,5",2,2022
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion,41,iclr,11,8,2023-06-18 09:44:25.712000,https://github.com/zhaoyanglyu/point_diffusion_refinement,87,A conditional point diffusion-refinement paradigm for 3d point cloud completion,"https://scholar.google.com/scholar?cluster=4241075093947761257&hl=en&as_sdt=0,22",4,2022
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences,5,iclr,5,3,2023-06-18 09:44:25.915000,https://github.com/lxchtan/ponet,20,PoNet: Pooling network for efficient token mixing in long sequences,"https://scholar.google.com/scholar?cluster=12721480032939252557&hl=en&as_sdt=0,47",1,2022
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios,16,iclr,0,0,2023-06-18 09:44:26.117000,https://github.com/zhenxianglance/2classbadetection,6,Post-training detection of backdoor attacks for two-class and multi-attack scenarios,"https://scholar.google.com/scholar?cluster=12429921260786315326&hl=en&as_sdt=0,27",1,2022
Dynamic Token Normalization improves Vision Transformers,8,iclr,1,0,2023-06-18 09:44:26.320000,https://github.com/wqshao126/dtn,22,Dynamic token normalization improves vision transformer,"https://scholar.google.com/scholar?cluster=8641842420029450046&hl=en&as_sdt=0,5",4,2022
Symbolic Learning to Optimize: Towards Interpretability and Scalability,11,iclr,1,0,2023-06-18 09:44:26.524000,https://github.com/vita-group/symbolic-learning-to-optimize,9,Symbolic learning to optimize: Towards interpretability and scalability,"https://scholar.google.com/scholar?cluster=9878665703631985766&hl=en&as_sdt=0,5",7,2022
Pseudo Numerical Methods for Diffusion Models on Manifolds,133,iclr,27,2,2023-06-18 09:44:26.726000,https://github.com/luping-liu/PNDM,261,Pseudo numerical methods for diffusion models on manifolds,"https://scholar.google.com/scholar?cluster=13911281549093893446&hl=en&as_sdt=0,5",7,2022
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm,177,iclr,22,16,2023-06-18 09:44:26.930000,https://github.com/sense-gvt/declip,515,Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm,"https://scholar.google.com/scholar?cluster=5003089118769672378&hl=en&as_sdt=0,5",18,2022
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training,16,iclr,2,1,2023-06-18 09:44:27.133000,https://github.com/vita-group/sparsity-win-robust-generalization,33,Sparsity winning twice: Better robust generaliztion from more efficient training,"https://scholar.google.com/scholar?cluster=6953571021872677&hl=en&as_sdt=0,34",6,2022
Active Hierarchical Exploration with Stable Subgoal Representation Learning,5,iclr,1,0,2023-06-18 09:44:27.338000,https://github.com/siyuanlee/hess,5,Active hierarchical exploration with stable subgoal representation learning,"https://scholar.google.com/scholar?cluster=16962537436246841648&hl=en&as_sdt=0,5",1,2022
Deep AutoAugment,15,iclr,4,0,2023-06-18 09:44:27.540000,https://github.com/msu-mlsys-lab/deepaa,55,Deep autoaugment,"https://scholar.google.com/scholar?cluster=4048740970183234421&hl=en&as_sdt=0,5",2,2022
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice,30,iclr,5,1,2023-06-18 09:44:27.743000,https://github.com/vita-group/vit-anti-oversmoothing,55,Anti-oversmoothing in deep vision transformers via the fourier domain analysis: From theory to practice,"https://scholar.google.com/scholar?cluster=1886992923455463917&hl=en&as_sdt=0,5",8,2022
Self-ensemble Adversarial Training for Improved Robustness,24,iclr,3,0,2023-06-18 09:44:27.947000,https://github.com/whj363636/self-ensemble-adversarial-training,12,Self-ensemble adversarial training for improved robustness,"https://scholar.google.com/scholar?cluster=5523117763790476247&hl=en&as_sdt=0,50",1,2022
Do deep networks transfer invariances across classes?,8,iclr,1,2,2023-06-18 09:44:28.150000,https://github.com/allanyangzhou/generative-invariance-transfer,25,Do Deep Networks Transfer Invariances Across Classes?,"https://scholar.google.com/scholar?cluster=8418380015111535138&hl=en&as_sdt=0,5",3,2022
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL,19,iclr,1,1,2023-06-18 09:44:28.353000,https://github.com/bmazoure/ctrl_public,6,Cross-trajectory representation learning for zero-shot generalization in rl,"https://scholar.google.com/scholar?cluster=8504220534031883718&hl=en&as_sdt=0,5",1,2022
RvS: What is Essential for Offline RL via Supervised Learning?,59,iclr,5,0,2023-06-18 09:44:28.569000,https://github.com/scottemmons/rvs,57,RvS: What is Essential for Offline RL via Supervised Learning?,"https://scholar.google.com/scholar?cluster=12909820441441824737&hl=en&as_sdt=0,5",5,2022
Learning Versatile Neural Architectures by Propagating Network Codes,8,iclr,7,0,2023-06-18 09:44:28.772000,https://github.com/dingmyu/NCP,36,Learning versatile neural architectures by propagating network codes,"https://scholar.google.com/scholar?cluster=1912446105154115158&hl=en&as_sdt=0,33",3,2022
Generative Models as a Data Source for Multiview Representation Learning,49,iclr,12,4,2023-06-18 09:44:28.976000,https://github.com/ali-design/GenRep,84,Generative models as a data source for multiview representation learning,"https://scholar.google.com/scholar?cluster=13492462163020342656&hl=en&as_sdt=0,47",4,2022
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training,12,iclr,0,0,2023-06-18 09:44:29.180000,https://github.com/tuananhbui89/unified-distributional-robustness,3,A unified Wasserstein distributional robustness framework for adversarial training,"https://scholar.google.com/scholar?cluster=2935072374086624118&hl=en&as_sdt=0,18",2,2022
miniF2F: a cross-system benchmark for formal Olympiad-level mathematics,19,iclr,35,6,2023-06-18 09:44:29.383000,https://github.com/openai/minif2f,194,Minif2f: a cross-system benchmark for formal olympiad-level mathematics,"https://scholar.google.com/scholar?cluster=11007110813493819221&hl=en&as_sdt=0,33",96,2022
Acceleration of Federated Learning with Alleviated Forgetting in Local Training,23,iclr,3,0,2023-06-18 09:44:29.610000,https://github.com/zoesgithub/fedreg,19,Acceleration of federated learning with alleviated forgetting in local training,"https://scholar.google.com/scholar?cluster=637540214191418314&hl=en&as_sdt=0,5",2,2022
Discovering Invariant Rationales for Graph Neural Networks,68,iclr,14,2,2023-06-18 09:44:29.814000,https://github.com/wuyxin/dir-gnn,84,Discovering invariant rationales for graph neural networks,"https://scholar.google.com/scholar?cluster=6763314222815951542&hl=en&as_sdt=0,23",5,2022
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings,17,iclr,1,0,2023-06-18 09:44:30.018000,https://github.com/BoChenGroup/WeTe,3,Representing mixtures of word embeddings with mixtures of topic embeddings,"https://scholar.google.com/scholar?cluster=3518295104208201525&hl=en&as_sdt=0,5",0,2022
Generative Modeling with Optimal Transport Maps,32,iclr,9,0,2023-06-18 09:44:30.221000,https://github.com/LituRout/OptimalTransportModeling,37,Generative modeling with optimal transport maps,"https://scholar.google.com/scholar?cluster=7494071659521623034&hl=en&as_sdt=0,21",2,2022
Focus on the Common Good: Group Distributional Robustness Follows,11,iclr,2,0,2023-06-18 09:44:30.425000,https://github.com/vihari/cgd,5,Focus on the common good: Group distributional robustness follows,"https://scholar.google.com/scholar?cluster=7624890232005107632&hl=en&as_sdt=0,5",1,2022
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification,28,iclr,20,2,2023-06-18 09:44:30.628000,https://github.com/Wensi-Tang/OS-CNN,102,Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification,"https://scholar.google.com/scholar?cluster=2762110290029984845&hl=en&as_sdt=0,47",3,2022
Decoupled Adaptation for Cross-Domain Object Detection,17,iclr,0,3,2023-06-18 09:44:30.831000,https://github.com/thuml/Decoupled-Adaptation-for-Cross-Domain-Object-Detection,12,Decoupled adaptation for cross-domain object detection,"https://scholar.google.com/scholar?cluster=15741647354170922060&hl=en&as_sdt=0,5",4,2022
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework,170,iclr,45,1,2023-06-18 09:44:31.034000,https://github.com/ma-xu/pointmlp-pytorch,364,Rethinking network design and local geometry in point cloud: A simple residual MLP framework,"https://scholar.google.com/scholar?cluster=10170039268493179331&hl=en&as_sdt=0,44",4,2022
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts,33,iclr,3,4,2023-06-18 09:44:31.237000,https://github.com/weixin-liang/metashift,94,Metashift: A dataset of datasets for evaluating contextual distribution shifts and training conflicts,"https://scholar.google.com/scholar?cluster=11769188169482891384&hl=en&as_sdt=0,5",2,2022
Efficient and Differentiable Conformal Prediction with General Function Classes,8,iclr,0,0,2023-06-18 09:44:31.440000,https://github.com/allenbai01/cp-gen,3,Efficient and differentiable conformal prediction with general function classes,"https://scholar.google.com/scholar?cluster=54755366591296300&hl=en&as_sdt=0,5",1,2022
"Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps",2,iclr,0,0,2023-06-18 09:44:31.643000,https://github.com/nicocourts/bundle-networks,0,"Bundle networks: Fiber bundles, local trivializations, and a generative approach to exploring many-to-one maps","https://scholar.google.com/scholar?cluster=792839043857596844&hl=en&as_sdt=0,47",3,2022
On the role of population heterogeneity in emergent communication,8,iclr,0,0,2023-06-18 09:44:31.846000,https://github.com/mathieurita/population,4,On the role of population heterogeneity in emergent communication,"https://scholar.google.com/scholar?cluster=9738620591444184168&hl=en&as_sdt=0,31",2,2022
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception,5,iclr,4,1,2023-06-18 09:44:32.050000,https://github.com/yurongyou/hindsight,34,Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception,"https://scholar.google.com/scholar?cluster=15674924686724150204&hl=en&as_sdt=0,5",6,2022
Language-driven Semantic Segmentation,137,iclr,65,4,2023-06-18 09:44:32.253000,https://github.com/isl-org/lang-seg,529,Language-driven semantic segmentation,"https://scholar.google.com/scholar?cluster=17851834070670501779&hl=en&as_sdt=0,1",18,2022
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative,12,iclr,0,1,2023-06-18 09:44:32.457000,https://github.com/ldery/tartan,8,Should we be pre-training? an argument for end-task aware training as an alternative,"https://scholar.google.com/scholar?cluster=18049548390488755873&hl=en&as_sdt=0,5",5,2022
Learning Super-Features for Image Retrieval,15,iclr,6,4,2023-06-18 09:44:32.661000,https://github.com/naver/fire,108,Learning super-features for image retrieval,"https://scholar.google.com/scholar?cluster=18354886281666747980&hl=en&as_sdt=0,5",9,2022
Few-Shot Backdoor Attacks on Visual Object Tracking,30,iclr,1,0,2023-06-18 09:44:32.864000,https://github.com/hxzhong1997/fsba,9,Few-shot backdoor attacks on visual object tracking,"https://scholar.google.com/scholar?cluster=14007756108337436&hl=en&as_sdt=0,39",1,2022
Backdoor Defense via Decoupling the Training Process,60,iclr,5,1,2023-06-18 09:44:33.067000,https://github.com/sclbd/dbd,21,Backdoor defense via decoupling the training process,"https://scholar.google.com/scholar?cluster=11519386362177505857&hl=en&as_sdt=0,5",1,2022
Reverse Engineering of Imperceptible Adversarial Image Perturbations,9,iclr,0,2,2023-06-18 09:44:33.271000,https://github.com/yifanfanfanfan/reverse-engineering-of-imperceptible-adversarial-image-perturbations,9,Reverse engineering of imperceptible adversarial image perturbations,"https://scholar.google.com/scholar?cluster=16789227603564642801&hl=en&as_sdt=0,32",1,2022
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR,170,iclr,64,17,2023-06-18 09:44:33.475000,https://github.com/slongliu/dab-detr,400,Dab-detr: Dynamic anchor boxes are better queries for detr,"https://scholar.google.com/scholar?cluster=11838073149065061192&hl=en&as_sdt=0,33",15,2022
"Signing the Supermask: Keep, Hide, Invert",5,iclr,2,0,2023-06-18 09:44:33.678000,https://github.com/kosnil/signed_supermasks,2,"Signing the supermask: Keep, hide, invert","https://scholar.google.com/scholar?cluster=10618821989752755915&hl=en&as_sdt=0,33",1,2022
Bootstrapping Semantic Segmentation with Regional Contrast,52,iclr,24,0,2023-06-18 09:44:33.882000,https://github.com/lorenmt/reco,146,Bootstrapping semantic segmentation with regional contrast,"https://scholar.google.com/scholar?cluster=12918707374441736964&hl=en&as_sdt=0,33",6,2022
Generative Principal Component Analysis,8,iclr,1,0,2023-06-18 09:44:34.085000,https://github.com/liuzq09/GenerativePCA,3,Generative principal component analysis,"https://scholar.google.com/scholar?cluster=8634676628677545132&hl=en&as_sdt=0,11",1,2022
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks,33,iclr,21,0,2023-06-18 09:44:34.289000,https://github.com/Graph-Machine-Learning-Group/grin,88,Filling the g_ap_s: Multivariate time series imputation by graph neural networks,"https://scholar.google.com/scholar?cluster=14193757514570115275&hl=en&as_sdt=0,10",3,2022
Multimeasurement Generative Models,4,iclr,0,0,2023-06-18 09:44:34.492000,https://github.com/nnaisense/mems,3,Multimeasurement Generative Models,"https://scholar.google.com/scholar?cluster=5398070140675307056&hl=en&as_sdt=0,5",3,2022
Information Gain Propagation: a New Way to Graph Active Learning with Soft Labels,0,iclr,5,2,2023-06-18 09:44:34.696000,https://github.com/zwt233/igp,4,Information Gain Propagation: a new way to Graph Active Learning with Soft Labels,"https://scholar.google.com/scholar?cluster=4290124558616540696&hl=en&as_sdt=0,5",1,2022
Stein Latent Optimization for Generative Adversarial Networks,1,iclr,0,0,2023-06-18 09:44:34.900000,https://github.com/shinyflight/SLOGAN,4,Stein latent optimization for generative adversarial networks,"https://scholar.google.com/scholar?cluster=14809143039614633477&hl=en&as_sdt=0,5",2,2022
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity,53,iclr,13,9,2023-06-18 09:44:35.103000,https://github.com/kakaobrain/sparse-detr,133,Sparse detr: Efficient end-to-end object detection with learnable sparsity,"https://scholar.google.com/scholar?cluster=18202446654995980467&hl=en&as_sdt=0,5",12,2022
How Low Can We Go: Trading Memory for Error in Low-Precision Training,3,iclr,0,0,2023-06-18 09:44:35.306000,https://github.com/barterer/lp,0,How low can we go: Trading memory for error in low-precision training,"https://scholar.google.com/scholar?cluster=652848499450213393&hl=en&as_sdt=0,5",1,2022
"In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications",5,iclr,0,0,2023-06-18 09:44:35.509000,https://github.com/bgleon/latent-goal-architectures,1,"In a nutshell, the human asked for this: Latent goals for following temporal specifications","https://scholar.google.com/scholar?cluster=14969448845199870512&hl=en&as_sdt=0,33",2,2022
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation,7,iclr,0,0,2023-06-18 09:44:35.714000,https://github.com/davzha/multiset-equivariance,11,Multiset-equivariant set prediction with approximate implicit differentiation,"https://scholar.google.com/scholar?cluster=473656227219457535&hl=en&as_sdt=0,5",3,2022
Modular Lifelong Reinforcement Learning via Neural Composition,18,iclr,3,1,2023-06-18 09:44:35.917000,https://github.com/lifelong-ml/mendez2022modularlifelongrl,12,Modular lifelong reinforcement learning via neural composition,"https://scholar.google.com/scholar?cluster=17042814609795844207&hl=en&as_sdt=0,21",2,2022
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks,43,iclr,13,4,2023-06-18 09:44:36.120000,https://github.com/putshua/SNN_conversion_QCFS,30,Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks,"https://scholar.google.com/scholar?cluster=17393160110870135225&hl=en&as_sdt=0,47",1,2022
AS-MLP: An Axial Shifted MLP Architecture for Vision,105,iclr,10,1,2023-06-18 09:44:36.324000,https://github.com/svip-lab/AS-MLP,116,As-mlp: An axial shifted mlp architecture for vision,"https://scholar.google.com/scholar?cluster=1534689713476232636&hl=en&as_sdt=0,33",5,2022
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference,12,iclr,4,0,2023-06-18 09:44:36.526000,https://github.com/naver-ai/i-blurry,39,Online continual learning on class incremental blurry task configuration with anytime inference,"https://scholar.google.com/scholar?cluster=5710319088637309523&hl=en&as_sdt=0,44",1,2022
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations,72,iclr,15,0,2023-06-18 09:44:36.730000,https://github.com/ucsc-real/cifar-10-100n,136,Learning with noisy labels revisited: A study using real-world human annotations,"https://scholar.google.com/scholar?cluster=765841518981894990&hl=en&as_sdt=0,43",5,2022
Learning to Annotate Part Segmentation with Gradient Matching,7,iclr,0,0,2023-06-18 09:44:36.934000,https://github.com/yangyu12/lagm,12,Learning to annotate part segmentation with gradient matching,"https://scholar.google.com/scholar?cluster=16141754978886952440&hl=en&as_sdt=0,5",3,2022
"Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation",98,iclr,26,2,2023-06-18 09:44:37.137000,https://github.com/ofirpress/attention_with_linear_biases,324,"Train short, test long: Attention with linear biases enables input length extrapolation","https://scholar.google.com/scholar?cluster=3347460907170213441&hl=en&as_sdt=0,39",11,2022
Learning Temporally Causal Latent Processes from General Temporal Data,23,iclr,4,0,2023-06-18 09:44:37.341000,https://github.com/weirayao/leap,23,Learning temporally causal latent processes from general temporal data,"https://scholar.google.com/scholar?cluster=14364754714073733596&hl=en&as_sdt=0,14",2,2022
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning,19,iclr,3,0,2023-06-18 09:44:37.546000,https://github.com/ucsc-real/disparate-ssl,4,The rich get richer: Disparate impact of semi-supervised learning,"https://scholar.google.com/scholar?cluster=7060479972986139346&hl=en&as_sdt=0,5",2,2022
Bregman Gradient Policy Optimization,9,iclr,1,0,2023-06-18 09:44:37.757000,https://github.com/gaosh/bgpo,3,Bregman gradient policy optimization,"https://scholar.google.com/scholar?cluster=17535380024235547901&hl=en&as_sdt=0,5",1,2022
Dropout Q-Functions for Doubly Efficient Reinforcement Learning,24,iclr,2,0,2023-06-18 09:44:37.961000,https://github.com/TakuyaHiraoka/Dropout-Q-Functions-for-Doubly-Efficient-Reinforcement-Learning,44,Dropout q-functions for doubly efficient reinforcement learning,"https://scholar.google.com/scholar?cluster=207538077714334096&hl=en&as_sdt=0,31",4,2022
Uncertainty Modeling for Out-of-Distribution Generalization,48,iclr,14,3,2023-06-18 09:44:38.165000,https://github.com/lixiaotong97/dsu,114,Uncertainty modeling for out-of-distribution generalization,"https://scholar.google.com/scholar?cluster=18401330697518830514&hl=en&as_sdt=0,5",3,2022
Online Adversarial Attacks,7,iclr,6,0,2023-06-18 09:44:38.375000,https://github.com/facebookresearch/OnlineAttacks,12,Online adversarial attacks,"https://scholar.google.com/scholar?cluster=10843150111517715745&hl=en&as_sdt=0,5",6,2022
Anytime Dense Prediction with Confidence Adaptivity,7,iclr,0,0,2023-06-18 09:44:38.588000,https://github.com/liuzhuang13/anytime,44,Anytime dense prediction with confidence adaptivity,"https://scholar.google.com/scholar?cluster=14058160425117298434&hl=en&as_sdt=0,5",3,2022
Unsupervised Discovery of Object Radiance Fields,60,iclr,26,2,2023-06-18 09:44:38.791000,https://github.com/KovenYu/uORF,158,Unsupervised discovery of object radiance fields,"https://scholar.google.com/scholar?cluster=10064360192629959715&hl=en&as_sdt=0,44",9,2022
Gradient Step Denoiser for convergent Plug-and-Play,35,iclr,4,0,2023-06-18 09:44:38.996000,https://github.com/samuro95/gspnp,14,Gradient step denoiser for convergent plug-and-play,"https://scholar.google.com/scholar?cluster=3194499334349587754&hl=en&as_sdt=0,19",4,2022
Understanding Dimensional Collapse in Contrastive Self-supervised Learning,147,iclr,6,1,2023-06-18 09:44:39.205000,https://github.com/facebookresearch/directclr,57,Understanding dimensional collapse in contrastive self-supervised learning,"https://scholar.google.com/scholar?cluster=15289790182345311933&hl=en&as_sdt=0,41",7,2022
RegionViT: Regional-to-Local Attention for Vision Transformers,95,iclr,6,2,2023-06-18 09:44:39.409000,https://github.com/IBM/RegionViT,43,Regionvit: Regional-to-local attention for vision transformers,"https://scholar.google.com/scholar?cluster=17393879915811894634&hl=en&as_sdt=0,5",7,2022
Quadtree Attention for Vision Transformers,61,iclr,27,15,2023-06-18 09:44:39.613000,https://github.com/tangshitao/quadtreeattention,273,Quadtree attention for vision transformers,"https://scholar.google.com/scholar?cluster=8134043907351506595&hl=en&as_sdt=0,32",13,2022
What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization,10,iclr,2,1,2023-06-18 09:44:39.821000,https://github.com/maxiboether/mis-benchmark-framework,27,What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization,"https://scholar.google.com/scholar?cluster=18330070821470336106&hl=en&as_sdt=0,32",3,2022
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity,17,iclr,4,0,2023-06-18 09:44:40.034000,https://github.com/naver/artemis,36,Artemis: Attention-based retrieval with text-explicit matching and implicit similarity,"https://scholar.google.com/scholar?cluster=15218636624672765176&hl=en&as_sdt=0,21",4,2022
Fast Differentiable Matrix Square Root,10,iclr,1,0,2023-06-18 09:44:40.240000,https://github.com/KingJamesSong/DifferentiableSVD,47,Fast differentiable matrix square root,"https://scholar.google.com/scholar?cluster=16011321219520846906&hl=en&as_sdt=0,1",2,2022
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation,17,iclr,7,2,2023-06-18 09:44:40.444000,https://github.com/clevercool/SQuant,146,SQuant: On-the-fly data-free quantization via diagonal hessian approximation,"https://scholar.google.com/scholar?cluster=748228209807839980&hl=en&as_sdt=0,11",3,2022
Handling Distribution Shifts on Graphs: An Invariance Perspective,48,iclr,6,0,2023-06-18 09:44:40.650000,https://github.com/qitianwu/graphood-eerm,50,Handling distribution shifts on graphs: An invariance perspective,"https://scholar.google.com/scholar?cluster=15550862662340330123&hl=en&as_sdt=0,5",3,2022
Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning,3,iclr,0,0,2023-06-18 09:44:40.856000,https://github.com/cetinsamet/closed-form-sample-probing,0,Closed-form sample probing for training generative models in zero-shot learning,"https://scholar.google.com/scholar?cluster=11977259761302754277&hl=en&as_sdt=0,5",2,2022
Steerable Partial Differential Operators for Equivariant Neural Networks,14,iclr,1,0,2023-06-18 09:44:41.063000,https://github.com/ejnnr/steerable_pdos,15,Steerable partial differential operators for equivariant neural networks,"https://scholar.google.com/scholar?cluster=18342593402456805321&hl=en&as_sdt=0,10",2,2022
Neural Spectral Marked Point Processes,6,iclr,2,0,2023-06-18 09:44:41.283000,https://github.com/meowoodie/Neural-Spectral-Marked-Point-Processes,7,Neural spectral marked point processes,"https://scholar.google.com/scholar?cluster=15895838574872798573&hl=en&as_sdt=0,7",2,2022
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners,81,iclr,15,0,2023-06-18 09:44:41.505000,https://github.com/zjunlp/DART,108,Differentiable prompt makes pre-trained language models better few-shot learners,"https://scholar.google.com/scholar?cluster=17540526705863454050&hl=en&as_sdt=0,5",6,2022
OntoProtein: Protein Pretraining With Gene Ontology Embedding,26,iclr,19,0,2023-06-18 09:44:41.710000,https://github.com/zjunlp/ontoprotein,107,Ontoprotein: Protein pretraining with gene ontology embedding,"https://scholar.google.com/scholar?cluster=17820920484975929118&hl=en&as_sdt=0,5",7,2022
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection,12,iclr,2,0,2023-06-18 09:44:41.915000,https://github.com/jiwei0921/dsu,12,Promoting saliency from depth: Deep unsupervised rgb-d saliency detection,"https://scholar.google.com/scholar?cluster=15400914580495629111&hl=en&as_sdt=0,5",3,2022
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph,6,iclr,2,0,2023-06-18 09:44:42.119000,https://github.com/xrenaa/Retriever,51,Retriever: Learning content-style representation as a token-level bipartite graph,"https://scholar.google.com/scholar?cluster=17348549797304685480&hl=en&as_sdt=0,5",18,2022
Chemical-Reaction-Aware Molecule Representation Learning,29,iclr,18,4,2023-06-18 09:44:42.325000,https://github.com/hwwang55/MolR,56,Chemical-reaction-aware molecule representation learning,"https://scholar.google.com/scholar?cluster=16867974973581425308&hl=en&as_sdt=0,5",1,2022
InfinityGAN: Towards Infinite-Pixel Image Synthesis,27,iclr,22,11,2023-06-18 09:44:42.531000,https://github.com/hubert0527/infinityGAN,299,InfinityGAN: Towards infinite-pixel image synthesis,"https://scholar.google.com/scholar?cluster=11409281345563394414&hl=en&as_sdt=0,5",32,2022
On the Importance of Difficulty Calibration in Membership Inference Attacks,25,iclr,1,0,2023-06-18 09:44:42.737000,https://github.com/facebookresearch/calibration_membership,6,On the importance of difficulty calibration in membership inference attacks,"https://scholar.google.com/scholar?cluster=2933122838404146328&hl=en&as_sdt=0,33",5,2022
Dual Lottery Ticket Hypothesis,19,iclr,7,0,2023-06-18 09:44:42.942000,https://github.com/yueb17/dlth,25,Dual lottery ticket hypothesis,"https://scholar.google.com/scholar?cluster=3069306637615595875&hl=en&as_sdt=0,33",1,2022
Neural graphical modelling in continuous-time: consistency guarantees and algorithms,12,iclr,158,4,2023-06-18 09:44:43.148000,https://github.com/vanderschaarlab/mlforhealthlabpub,347,Neural graphical modelling in continuous-time: consistency guarantees and algorithms,"https://scholar.google.com/scholar?cluster=3383946799962251947&hl=en&as_sdt=0,31",13,2022
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy,18,iclr,94,29,2023-06-18 09:44:43.356000,https://github.com/automl/NASLib,403,Nas-bench-suite: NAS evaluation is (now) surprisingly easy,"https://scholar.google.com/scholar?cluster=4023865038521320162&hl=en&as_sdt=0,5",14,2022
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation,85,iclr,38,11,2023-06-18 09:44:43.614000,https://github.com/cdtrans/cdtrans,268,Cdtrans: Cross-domain transformer for unsupervised domain adaptation,"https://scholar.google.com/scholar?cluster=9897783945226246229&hl=en&as_sdt=0,21",5,2022
GradMax: Growing Neural Networks using Gradient Information,18,iclr,5,0,2023-06-18 09:44:43.819000,https://github.com/google-research/growneuron,35,Gradmax: Growing neural networks using gradient information,"https://scholar.google.com/scholar?cluster=11971978084540378903&hl=en&as_sdt=0,34",6,2022
Random matrices in service of ML footprint: ternary random features with no performance loss,2,iclr,0,0,2023-06-18 09:44:44.025000,https://github.com/hafiztiomoko/ternaryrandomfeatures,0,Random matrices in service of ML footprint: ternary random features with no performance loss,"https://scholar.google.com/scholar?cluster=5706402488540408892&hl=en&as_sdt=0,14",1,2022
Transformers Can Do Bayesian Inference,18,iclr,11,1,2023-06-18 09:44:44.230000,https://github.com/automl/transformerscandobayesianinference,122,Transformers can do bayesian inference,"https://scholar.google.com/scholar?cluster=1831390603227994904&hl=en&as_sdt=0,5",14,2022
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies,3,iclr,2,0,2023-06-18 09:44:44.436000,https://github.com/berlinera/dsvae-nes,1,Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies,"https://scholar.google.com/scholar?cluster=14166545846763510753&hl=en&as_sdt=0,10",2,2022
Learning Features with Parameter-Free Layers,3,iclr,6,0,2023-06-18 09:44:44.644000,https://github.com/naver-ai/pflayer,83,Learning Features with Parameter-Free Layers,"https://scholar.google.com/scholar?cluster=18180829610140817876&hl=en&as_sdt=0,34",7,2022
Denoising Likelihood Score Matching for Conditional Score-based Data Generation,8,iclr,1,0,2023-06-18 09:44:44.848000,https://github.com/chen-hao-chao/dlsm,7,Denoising likelihood score matching for conditional score-based data generation,"https://scholar.google.com/scholar?cluster=10811875689229589569&hl=en&as_sdt=0,33",3,2022
Memory Replay with Data Compression for Continual Learning,29,iclr,0,0,2023-06-18 09:44:45.055000,https://github.com/lywang3081/MRDC,11,Memory replay with data compression for continual learning,"https://scholar.google.com/scholar?cluster=18195224691973743635&hl=en&as_sdt=0,10",1,2022
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning,5,iclr,2,2,2023-06-18 09:44:45.260000,https://github.com/NVlabs/RelViT,56,Relvit: Concept-guided vision transformer for visual relational reasoning,"https://scholar.google.com/scholar?cluster=10463631265009137162&hl=en&as_sdt=0,5",6,2022
ViDT: An Efficient and Effective Fully Transformer-based Object Detector,46,iclr,41,16,2023-06-18 09:44:45.468000,https://github.com/naver-ai/vidt,280,Vidt: An efficient and effective fully transformer-based object detector,"https://scholar.google.com/scholar?cluster=1253153783722573136&hl=en&as_sdt=0,5",17,2022
BiBERT: Accurate Fully Binarized BERT,35,iclr,4,2,2023-06-18 09:44:45.673000,https://github.com/htqin/bibert,69,Bibert: Accurate fully binarized bert,"https://scholar.google.com/scholar?cluster=5828841794097016283&hl=en&as_sdt=0,23",3,2022
Representation-Agnostic Shape Fields,4,iclr,1,0,2023-06-18 09:44:45.878000,https://github.com/seanywang0408/rasf,17,Representation-agnostic shape fields,"https://scholar.google.com/scholar?cluster=3096162575637292302&hl=en&as_sdt=0,5",2,2022
Learning Synthetic Environments and Reward Networks for Reinforcement Learning,1,iclr,3,16,2023-06-18 09:44:46.083000,https://github.com/automl/learning_environments,18,Learning Synthetic Environments and Reward Networks for Reinforcement Learning,"https://scholar.google.com/scholar?cluster=7208756410872374371&hl=en&as_sdt=0,7",10,2022
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View,17,iclr,9,0,2023-06-18 09:44:46.288000,https://github.com/xrenaa/DisCo,124,Learning disentangled representation by exploiting pretrained generative models: A contrastive learning view,"https://scholar.google.com/scholar?cluster=5205978200663209990&hl=en&as_sdt=0,1",11,2022
Towards Building A Group-based Unsupervised Representation Disentanglement Framework,14,iclr,1,0,2023-06-18 09:44:46.494000,https://github.com/ThomasMrY/Groupified-VAE,11,Towards building a group-based unsupervised representation disentanglement framework,"https://scholar.google.com/scholar?cluster=12379032527618028840&hl=en&as_sdt=0,5",1,2022
Learning Hierarchical Structures with Differentiable Nondeterministic Stacks,5,iclr,0,0,2023-06-18 09:44:46.698000,https://github.com/bdusell/nondeterministic-stack-rnn,14,Learning hierarchical structures with differentiable nondeterministic stacks,"https://scholar.google.com/scholar?cluster=881169137976073427&hl=en&as_sdt=0,39",2,2022
Sampling with Mirrored Stein Operators,12,iclr,2,0,2023-06-18 09:44:46.903000,https://github.com/thjashin/mirror-stein-samplers,5,Sampling with mirrored Stein operators,"https://scholar.google.com/scholar?cluster=8093287446916276740&hl=en&as_sdt=0,5",1,2022
RotoGrad: Gradient Homogenization in Multitask Learning,39,iclr,5,3,2023-06-18 09:44:47.107000,https://github.com/adrianjav/rotograd,66,Rotograd: Gradient homogenization in multitask learning,"https://scholar.google.com/scholar?cluster=17548850565658345849&hl=en&as_sdt=0,44",3,2022
On the Connection between Local Attention and Dynamic Depth-wise Convolution,44,iclr,14,4,2023-06-18 09:44:47.311000,https://github.com/atten4vis/demystifylocalvit,161,On the connection between local attention and dynamic depth-wise convolution,"https://scholar.google.com/scholar?cluster=3348693561656853754&hl=en&as_sdt=0,23",4,2022
Adversarial Support Alignment,4,iclr,2,0,2023-06-18 09:44:47.515000,https://github.com/timgaripov/asa,19,Adversarial support alignment,"https://scholar.google.com/scholar?cluster=18158530648839344635&hl=en&as_sdt=0,5",3,2022
Learning meta-features for AutoML,12,iclr,1,0,2023-06-18 09:44:47.719000,https://github.com/luxusg1/metabu,11,Learning meta-features for automl,"https://scholar.google.com/scholar?cluster=9378213080876956800&hl=en&as_sdt=0,5",2,2022
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction,17,iclr,20,4,2023-06-18 09:44:47.922000,https://github.com/roggirg/AutoBots,58,Latent variable sequential set transformers for joint multi-agent motion prediction,"https://scholar.google.com/scholar?cluster=1206042525359273292&hl=en&as_sdt=0,33",1,2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks,21,iclr,0,2,2023-06-18 09:44:48.127000,https://github.com/ngruver/decon-hnn,10,Deconstructing the inductive biases of hamiltonian neural networks,"https://scholar.google.com/scholar?cluster=301233728507989887&hl=en&as_sdt=0,44",4,2022
Memorizing Transformers,63,iclr,40,9,2023-06-18 09:44:48.330000,https://github.com/lucidrains/memorizing-transformers-pytorch,538,Memorizing transformers,"https://scholar.google.com/scholar?cluster=12149100013599717090&hl=en&as_sdt=0,47",11,2022
MT3: Multi-Task Multitrack Music Transcription,28,iclr,149,35,2023-06-18 09:44:48.535000,https://github.com/magenta/mt3,1043,Mt3: Multi-task multitrack music transcription,"https://scholar.google.com/scholar?cluster=4757063593798788847&hl=en&as_sdt=0,40",28,2022
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features,5,iclr,0,0,2023-06-18 09:44:48.738000,https://github.com/JiuhaiChen/EBBS,3,Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features,"https://scholar.google.com/scholar?cluster=12401387776343070829&hl=en&as_sdt=0,5",2,2022
Geometric and Physical Quantities improve E(3) Equivariant Message Passing,66,iclr,10,4,2023-06-18 09:44:48.942000,https://github.com/robdhess/steerable-e3-gnn,73,Geometric and physical quantities improve e (3) equivariant message passing,"https://scholar.google.com/scholar?cluster=10039670233060190176&hl=en&as_sdt=0,8",4,2022
Boosting Randomized Smoothing with Variance Reduced Classifiers,15,iclr,0,2,2023-06-18 09:44:49.147000,https://github.com/eth-sri/smoothing-ensembles,8,Boosting randomized smoothing with variance reduced classifiers,"https://scholar.google.com/scholar?cluster=14327532718877741433&hl=en&as_sdt=0,43",6,2022
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning,3,iclr,0,1,2023-06-18 09:44:49.350000,https://github.com/boschresearch/sosp,1,Sosp: Efficiently capturing global correlations by second-order structured pruning,"https://scholar.google.com/scholar?cluster=7488688580406303390&hl=en&as_sdt=0,14",3,2022
Relational Multi-Task Learning: Modeling Relations between Data and Tasks,4,iclr,168,15,2023-06-18 09:44:49.555000,https://github.com/snap-stanford/graphgym,1397,Relational multi-task learning: Modeling relations between data and tasks,"https://scholar.google.com/scholar?cluster=623605199457003104&hl=en&as_sdt=0,44",23,2022
CoBERL: Contrastive BERT for Reinforcement Learning,17,iclr,613,70,2023-06-18 09:44:49.758000,https://github.com/deepmind/dm_control,3202,Coberl: Contrastive bert for reinforcement learning,"https://scholar.google.com/scholar?cluster=3823279505832239744&hl=en&as_sdt=0,5",127,2022
On Bridging Generic and Personalized Federated Learning for Image Classification,55,iclr,0,2,2023-06-18 09:44:49.962000,https://github.com/hongyouc/fed-rod,10,On bridging generic and personalized federated learning for image classification,"https://scholar.google.com/scholar?cluster=3469194395993782827&hl=en&as_sdt=0,5",3,2022
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration,25,iclr,5,0,2023-06-18 09:44:50.165000,https://github.com/desikrengarajan/logo,17,Reinforcement learning with sparse rewards using guidance from offline demonstration,"https://scholar.google.com/scholar?cluster=6148886566095169606&hl=en&as_sdt=0,33",1,2022
Linking Emergent and Natural Languages via Corpus Transfer,4,iclr,3,0,2023-06-18 09:44:50.370000,https://github.com/ysymyth/ec-nl,23,Linking Emergent and Natural Languages via Corpus Transfer,"https://scholar.google.com/scholar?cluster=1456115068072297417&hl=en&as_sdt=0,25",3,2022
Message Passing Neural PDE Solvers,79,iclr,17,0,2023-06-18 09:44:50.600000,https://github.com/brandstetter-johannes/mp-neural-pde-solvers,59,Message passing neural PDE solvers,"https://scholar.google.com/scholar?cluster=9088135830297201356&hl=en&as_sdt=0,14",1,2022
Multi-Stage Episodic Control for Strategic Exploration in Text Games,8,iclr,2,0,2023-06-18 09:44:50.804000,https://github.com/princeton-nlp/xtx,13,Multi-stage episodic control for strategic exploration in text games,"https://scholar.google.com/scholar?cluster=10027236272852708486&hl=en&as_sdt=0,5",3,2022
"AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning",25,iclr,8,0,2023-06-18 09:44:51.008000,https://github.com/adaptive-rl/adarl-code,20,"Adarl: What, where, and how to adapt in transfer reinforcement learning","https://scholar.google.com/scholar?cluster=6560728254700453684&hl=en&as_sdt=0,22",3,2022
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking,64,iclr,47,12,2023-06-18 09:44:51.211000,https://github.com/octavian-ganea/equidock_public,182,Independent se (3)-equivariant models for end-to-end rigid protein docking,"https://scholar.google.com/scholar?cluster=4354925472865069663&hl=en&as_sdt=0,31",6,2022
Towards a Unified View of Parameter-Efficient Transfer Learning,233,iclr,37,6,2023-06-18 09:44:51.415000,https://github.com/jxhe/unify-parameter-efficient-tuning,366,Towards a unified view of parameter-efficient transfer learning,"https://scholar.google.com/scholar?cluster=5204198989920297993&hl=en&as_sdt=0,15",7,2022
GNN-LM: Language Modeling based on Global Contexts via GNN,18,iclr,5,0,2023-06-18 09:44:51.619000,https://github.com/ShannonAI/GNN-LM,39,Gnn-lm: Language modeling based on global contexts via gnn,"https://scholar.google.com/scholar?cluster=7267447337261309550&hl=en&as_sdt=0,34",3,2022
Continual Learning with Filter Atom Swapping,11,iclr,3,1,2023-06-18 09:44:51.823000,https://github.com/ZichenMiao/CL_Atom_Swapping,12,Continual learning with filter atom swapping,"https://scholar.google.com/scholar?cluster=12304346311077160974&hl=en&as_sdt=0,33",1,2022
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning,27,iclr,5,1,2023-06-18 09:44:52.026000,https://github.com/zzzace2000/nodegam,27,Node-gam: Neural generalized additive model for interpretable deep learning,"https://scholar.google.com/scholar?cluster=3759801935043653070&hl=en&as_sdt=0,5",3,2022
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100,21,iclr,6,1,2023-06-18 09:44:52.229000,https://github.com/singlasahil14/SOC,12,Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100,"https://scholar.google.com/scholar?cluster=11890020481997280688&hl=en&as_sdt=0,40",1,2022
EntQA: Entity Linking as Question Answering,23,iclr,11,3,2023-06-18 09:44:52.433000,https://github.com/wenzhengzhang/entqa,51,EntQA: Entity linking as question answering,"https://scholar.google.com/scholar?cluster=8005658916202648918&hl=en&as_sdt=0,21",2,2022
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems,25,iclr,0,0,2023-06-18 09:44:52.635000,https://github.com/lions-epfl/weak-minty-code,2,Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems,"https://scholar.google.com/scholar?cluster=14926663774187351039&hl=en&as_sdt=0,5",3,2022
Compositional Attention: Disentangling Search and Retrieval,11,iclr,6,1,2023-06-18 09:44:52.840000,https://github.com/sarthmit/compositional-attention,57,Compositional attention: Disentangling search and retrieval,"https://scholar.google.com/scholar?cluster=1630545213475914915&hl=en&as_sdt=0,10",3,2022
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks,8,iclr,6,1,2023-06-18 09:44:53.044000,https://github.com/naver-ai/c3-gan,116,Contrastive fine-grained class clustering via generative adversarial networks,"https://scholar.google.com/scholar?cluster=2883627661337586326&hl=en&as_sdt=0,5",8,2022
Learning Multimodal VAEs through Mutual Supervision,5,iclr,1,3,2023-06-18 09:44:53.247000,https://github.com/thwjoy/meme,5,Learning multimodal VAEs through mutual supervision,"https://scholar.google.com/scholar?cluster=8935371068156409953&hl=en&as_sdt=0,5",3,2022
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation,10,iclr,0,2,2023-06-18 09:44:53.451000,https://github.com/deepmind/constrained_optidice,7,Coptidice: Offline constrained reinforcement learning via stationary distribution correction estimation,"https://scholar.google.com/scholar?cluster=10582988785015243548&hl=en&as_sdt=0,10",5,2022
ViTGAN: Training GANs with Vision Transformers,100,iclr,6,7,2023-06-18 09:44:53.659000,https://github.com/mlpc-ucsd/ViTGAN,32,Vitgan: Training gans with vision transformers,"https://scholar.google.com/scholar?cluster=11425422721644021530&hl=en&as_sdt=0,44",3,2022
TRGP: Trust Region Gradient Projection for Continual Learning,20,iclr,2,1,2023-06-18 09:44:53.863000,https://github.com/LYang-666/TRGP,11,Trgp: Trust region gradient projection for continual learning,"https://scholar.google.com/scholar?cluster=6594331056177251128&hl=en&as_sdt=0,39",2,2022
Learning Long-Term Reward Redistribution via Randomized Return Decomposition,8,iclr,0,0,2023-06-18 09:44:54.068000,https://github.com/stilwell-git/randomized-return-decomposition,12,Learning long-term reward redistribution via randomized return decomposition,"https://scholar.google.com/scholar?cluster=12389513535108604835&hl=en&as_sdt=0,47",1,2022
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series,20,iclr,17,4,2023-06-18 09:44:54.274000,https://github.com/enyandai/ganf,93,Graph-augmented normalizing flows for anomaly detection of multiple time series,"https://scholar.google.com/scholar?cluster=14229520023541942069&hl=en&as_sdt=0,5",3,2022
On the Importance of Firth Bias Reduction in Few-Shot Classification,9,iclr,0,0,2023-06-18 09:44:54.478000,https://github.com/ehsansaleh/firth_bias_reduction,8,On the importance of firth bias reduction in few-shot classification,"https://scholar.google.com/scholar?cluster=9186667972571213142&hl=en&as_sdt=0,47",3,2022
Towards Understanding the Data Dependency of Mixup-style Training,8,iclr,1,0,2023-06-18 09:44:54.683000,https://github.com/2014mchidamb/mixup-data-dependency,0,Towards understanding the data dependency of mixup-style training,"https://scholar.google.com/scholar?cluster=13244705498491864959&hl=en&as_sdt=0,44",1,2022
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion,88,iclr,11,7,2023-06-18 09:44:54.886000,https://github.com/nv-tlabs/CLD-SGM,168,Score-based generative modeling with critically-damped langevin diffusion,"https://scholar.google.com/scholar?cluster=1032753694243444141&hl=en&as_sdt=0,33",27,2022
Controlling Directions Orthogonal to a Classifier,10,iclr,5,0,2023-06-18 09:44:55.090000,https://github.com/newbeeer/orthogonal_classifier,34,Controlling directions orthogonal to a classifier,"https://scholar.google.com/scholar?cluster=14918052753119850605&hl=en&as_sdt=0,5",3,2022
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning,3,iclr,2,0,2023-06-18 09:44:55.294000,https://github.com/sluxsr/r5_graph_reasoning,2,R5: Rule discovery with reinforced and recurrent relational reasoning,"https://scholar.google.com/scholar?cluster=13510369297360682676&hl=en&as_sdt=0,32",1,2022
Lossless Compression with Probabilistic Circuits,11,iclr,0,0,2023-06-18 09:44:55.498000,https://github.com/juice-jl/pressedjuice.jl,11,Lossless compression with probabilistic circuits,"https://scholar.google.com/scholar?cluster=13531638226043466967&hl=en&as_sdt=0,48",4,2022
$\mathrm{SO}(2)$-Equivariant Reinforcement Learning,1,iclr,2,0,2023-06-18 09:44:55.703000,https://github.com/pointW/equi_rl,23,-Equivariant Reinforcement Learning,"https://scholar.google.com/scholar?cluster=4984868879021481594&hl=en&as_sdt=0,5",2,2022
Responsible Disclosure of Generative Models Using Scalable Fingerprinting,24,iclr,3,0,2023-06-18 09:44:55.906000,https://github.com/ningyu1991/ScalableGANFingerprints,23,Responsible disclosure of generative models using scalable fingerprinting,"https://scholar.google.com/scholar?cluster=5724642916059035277&hl=en&as_sdt=0,44",4,2022
Possibility Before Utility: Learning And Using Hierarchical Affordances,1,iclr,1,0,2023-06-18 09:44:56.109000,https://github.com/robbycostales/hal,13,Possibility Before Utility: Learning And Using Hierarchical Affordances,"https://scholar.google.com/scholar?cluster=1637590798034368393&hl=en&as_sdt=0,5",2,2022
Half-Inverse Gradients for Physical Deep Learning,5,iclr,0,0,2023-06-18 09:44:56.312000,https://github.com/tum-pbs/half-inverse-gradients,14,Half-inverse gradients for physical deep learning,"https://scholar.google.com/scholar?cluster=1729142096110683757&hl=en&as_sdt=0,44",2,2022
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits,10,iclr,2,0,2023-06-18 09:44:56.515000,https://github.com/banyikun/ee-net-iclr-2022,11,EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits,"https://scholar.google.com/scholar?cluster=959574665974730167&hl=en&as_sdt=0,5",1,2022
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective,8,iclr,3,0,2023-06-18 09:44:56.719000,https://github.com/damon-demon/black-box-defense,18,How to robustify black-box ml models? a zeroth-order optimization perspective,"https://scholar.google.com/scholar?cluster=8309073291494301716&hl=en&as_sdt=0,5",2,2022
RelaxLoss: Defending Membership Inference Attacks without Losing Utility,9,iclr,6,0,2023-06-18 09:44:56.922000,https://github.com/DingfanChen/RelaxLoss,36,RelaxLoss: defending membership inference attacks without losing utility,"https://scholar.google.com/scholar?cluster=6734125501477574187&hl=en&as_sdt=0,5",1,2022
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design,22,iclr,16,1,2023-06-18 09:44:57.125000,https://github.com/wenhao-gao/SynNet,67,Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design,"https://scholar.google.com/scholar?cluster=15831555537133301162&hl=en&as_sdt=0,11",4,2022
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation,4,iclr,2,0,2023-06-18 09:44:57.329000,https://github.com/rmclarke/optimisingweightupdatehyperparameters,9,Scalable one-pass optimisation of high-dimensional weight-update hyperparameters by implicit differentiation,"https://scholar.google.com/scholar?cluster=13151691768844954794&hl=en&as_sdt=0,7",1,2022
Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation,13,iclr,3,1,2023-06-18 09:44:57.532000,https://github.com/montrealrobotics/iv_rl,29,Sample efficient deep reinforcement learning via uncertainty estimation,"https://scholar.google.com/scholar?cluster=8416439116779187759&hl=en&as_sdt=0,5",1,2022
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions,14,iclr,3,0,2023-06-18 09:44:57.736000,https://github.com/n-gao/pesnet,20,Ab-initio potential energy surfaces by pairing GNNs with neural wave functions,"https://scholar.google.com/scholar?cluster=16901851478491451308&hl=en&as_sdt=0,5",2,2022
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data,8,iclr,1,1,2023-06-18 09:44:57.939000,https://github.com/haoang97/medi,15,Meta discovery: Learning to discover novel classes given very limited data,"https://scholar.google.com/scholar?cluster=11348139324456569930&hl=en&as_sdt=0,33",1,2022
Constrained Policy Optimization via Bayesian World Models,15,iclr,11,0,2023-06-18 09:44:58.142000,https://github.com/yardenas/la-mbda,25,Constrained policy optimization via bayesian world models,"https://scholar.google.com/scholar?cluster=15728158487087331451&hl=en&as_sdt=0,5",2,2022
Generalized Decision Transformer for Offline Hindsight Information Matching,44,iclr,3,4,2023-06-18 09:44:58.345000,https://github.com/frt03/generalized_dt,54,Generalized decision transformer for offline hindsight information matching,"https://scholar.google.com/scholar?cluster=4011968196773384178&hl=en&as_sdt=0,5",0,2022
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting,10,iclr,7,1,2023-06-18 09:44:58.549000,https://github.com/weifantt/depts,34,DEPTS: deep expansion learning for periodic time series forecasting,"https://scholar.google.com/scholar?cluster=17674123888632220585&hl=en&as_sdt=0,5",2,2022
"Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions",20,iclr,0,0,2023-06-18 09:44:58.755000,https://github.com/borgwardtlab/ggme,12,"Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions","https://scholar.google.com/scholar?cluster=2895320049488779805&hl=en&as_sdt=0,23",5,2022
Context-Aware Sparse Deep Coordination Graphs,14,iclr,3,1,2023-06-18 09:44:58.959000,https://github.com/tonghanwang/casec-maco-benchmark,11,Context-aware sparse deep coordination graphs,"https://scholar.google.com/scholar?cluster=17498858288824989874&hl=en&as_sdt=0,33",1,2022
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models,27,iclr,17,11,2023-06-18 09:44:59.163000,https://github.com/HazyResearch/pixelfly,127,Pixelated butterfly: Simple and efficient sparse training for neural network models,"https://scholar.google.com/scholar?cluster=1108492014641938411&hl=en&as_sdt=0,5",22,2022
8-bit Optimizers via Block-wise Quantization,30,iclr,38,11,2023-06-18 09:44:59.368000,https://github.com/facebookresearch/bitsandbytes,712,8-bit optimizers via block-wise quantization,"https://scholar.google.com/scholar?cluster=5491820601242999587&hl=en&as_sdt=0,44",14,2022
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks,9,iclr,1,0,2023-06-18 09:44:59.579000,https://github.com/anneharrington/adversarially-robust-periphery,2,Finding biological plausibility for adversarially robust features via metameric tasks,"https://scholar.google.com/scholar?cluster=16970067038193259370&hl=en&as_sdt=0,11",2,2022
Omni-Dimensional Dynamic Convolution,38,iclr,22,1,2023-06-18 09:44:59.782000,https://github.com/osvai/odconv,184,Omni-dimensional dynamic convolution,"https://scholar.google.com/scholar?cluster=3010782089276051732&hl=en&as_sdt=0,5",2,2022
EViT: Expediting Vision Transformers via Token Reorganizations,94,iclr,15,13,2023-06-18 09:44:59.986000,https://github.com/youweiliang/evit,122,Not all patches are what you need: Expediting vision transformers via token reorganizations,"https://scholar.google.com/scholar?cluster=13367059770507522630&hl=en&as_sdt=0,5",3,2022
Policy improvement by planning with Gumbel,14,iclr,152,0,2023-06-18 09:45:00.188000,https://github.com/deepmind/mctx,1877,Policy improvement by planning with Gumbel,"https://scholar.google.com/scholar?cluster=7251499641538462070&hl=en&as_sdt=0,5",27,2022
Learning Optimal Conformal Classifiers,24,iclr,6,2,2023-06-18 09:45:00.392000,https://github.com/deepmind/conformal_training,69,Learning optimal conformal classifiers,"https://scholar.google.com/scholar?cluster=5366968417529245684&hl=en&as_sdt=0,23",4,2022
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations,186,iclr,979,108,2023-06-18 09:45:00.601000,https://github.com/google-research/vision_transformer,7393,When vision transformers outperform resnets without pre-training or strong data augmentations,"https://scholar.google.com/scholar?cluster=4049796223449388186&hl=en&as_sdt=0,5",83,2022
Long Expressive Memory for Sequence Modeling,16,iclr,11,0,2023-06-18 09:45:00.804000,https://github.com/tk-rusch/lem,59,Long expressive memory for sequence modeling,"https://scholar.google.com/scholar?cluster=10849000047191483143&hl=en&as_sdt=0,5",2,2022
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy,94,iclr,88,10,2023-06-18 09:45:01.008000,https://github.com/thuml/Anomaly-Transformer,361,Anomaly transformer: Time series anomaly detection with association discrepancy,"https://scholar.google.com/scholar?cluster=12471325118803603403&hl=en&as_sdt=0,47",7,2022
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning,4,iclr,0,0,2023-06-18 09:45:01.211000,https://github.com/Haichao-Zhang/generative-planning,6,Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning,"https://scholar.google.com/scholar?cluster=14730527943022398215&hl=en&as_sdt=0,5",3,2022
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning,46,iclr,3,0,2023-06-18 09:45:01.415000,https://github.com/baichenjia/pbrl,24,Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning,"https://scholar.google.com/scholar?cluster=8122293342821829012&hl=en&as_sdt=0,19",2,2022
Equivariant Subgraph Aggregation Networks,74,iclr,8,1,2023-06-18 09:45:01.619000,https://github.com/beabevi/esan,68,Equivariant subgraph aggregation networks,"https://scholar.google.com/scholar?cluster=6011099715044788714&hl=en&as_sdt=0,5",5,2022
How Do Vision Transformers Work?,191,iclr,71,5,2023-06-18 09:45:01.823000,https://github.com/xxxnell/how-do-vits-work,712,How do vision transformers work?,"https://scholar.google.com/scholar?cluster=8029612233773990665&hl=en&as_sdt=0,5",6,2022
Variational methods for simulation-based inference,18,iclr,4,0,2023-06-18 09:45:02.028000,https://github.com/mackelab/snvi_repo,3,Variational methods for simulation-based inference,"https://scholar.google.com/scholar?cluster=16337891944937937425&hl=en&as_sdt=0,33",1,2022
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs,150,iclr,57,25,2023-06-18 09:45:02.231000,https://github.com/NVlabs/denoising-diffusion-gan,548,Tackling the generative learning trilemma with denoising diffusion GANs,"https://scholar.google.com/scholar?cluster=9436697539752906895&hl=en&as_sdt=0,32",40,2022
Imbedding Deep Neural Networks,0,iclr,0,0,2023-06-18 09:45:02.435000,https://github.com/andrw3000/inimnet,2,Imbedding Deep Neural Networks,"https://scholar.google.com/scholar?cluster=10680544455244654489&hl=en&as_sdt=0,11",2,2022
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration,25,iclr,3,0,2023-06-18 09:45:02.640000,https://github.com/cianeastwood/bufr,13,Source-free adaptation to measurement shift via bottom-up feature restoration,"https://scholar.google.com/scholar?cluster=13912921237099843796&hl=en&as_sdt=0,33",2,2022
Emergent Communication at Scale,19,iclr,3,1,2023-06-18 09:45:02.844000,https://github.com/deepmind/emergent_communication_at_scale,25,Emergent communication at scale,"https://scholar.google.com/scholar?cluster=4797610842429518149&hl=en&as_sdt=0,5",4,2022
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings,4,iclr,1,0,2023-06-18 09:45:03.048000,https://github.com/KnowledgeDiscovery/SCGM,2,Superclass-conditional Gaussian mixture model for learning fine-grained embeddings,"https://scholar.google.com/scholar?cluster=16398991451441380752&hl=en&as_sdt=0,39",0,2022
IntSGD: Adaptive Floatless Compression of Stochastic Gradients,16,iclr,1,0,2023-06-18 09:45:03.251000,https://github.com/bokunwang1/intsgd,2,IntSGD: Adaptive floatless compression of stochastic gradients,"https://scholar.google.com/scholar?cluster=16969044896100418296&hl=en&as_sdt=0,5",1,2022
PAC-Bayes Information Bottleneck,9,iclr,1,0,2023-06-18 09:45:03.455000,https://github.com/ryanwangzf/pac-bayes-ib,36,PAC-bayes information bottleneck,"https://scholar.google.com/scholar?cluster=8594070314886177653&hl=en&as_sdt=0,33",3,2022
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing,37,iclr,3,0,2023-06-18 09:45:03.659000,https://github.com/epfml/byzantine-robust-noniid-optimizer,10,Byzantine-robust learning on heterogeneous datasets via bucketing,"https://scholar.google.com/scholar?cluster=10653774941778356470&hl=en&as_sdt=0,5",4,2022
Label Encoding for Regression Networks,2,iclr,2,0,2023-06-18 09:45:03.862000,https://github.com/ubc-aamodt-group/bel_regression,7,Label encoding for regression networks,"https://scholar.google.com/scholar?cluster=17134575941397611216&hl=en&as_sdt=0,5",2,2022
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks,16,iclr,5,0,2023-06-18 09:45:04.065000,https://github.com/martenlienen/finite-element-networks,55,Learning the dynamics of physical systems from sparse observations with finite element networks,"https://scholar.google.com/scholar?cluster=10753878238660840723&hl=en&as_sdt=0,1",2,2022