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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 | 2,021 |
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 | 2,021 |
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 | 1,306 | Fourier neural operator for parametric partial differential equations | https://scholar.google.com/scholar?cluster=12451804788662635900&hl=en&as_sdt=0,10 | 36 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 1,219 | Efficiently modeling long sequences with structured state spaces | https://scholar.google.com/scholar?cluster=8624959095392391416&hl=en&as_sdt=0,5 | 42 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
Sample and Computation Redistribution for Efficient Face Detection | 49 | iclr | 4,436 | 910 | 2023-06-18 09:43:29.715000 | https://github.com/deepinsight/insightface | 16,066 | Sample and computation redistribution for efficient face detection | https://scholar.google.com/scholar?cluster=249972322094479786&hl=en&as_sdt=0,50 | 479 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |