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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 | 3,623 | PSA-GAN: Progressive self attention GANs for synthetic time series | https://scholar.google.com/scholar?cluster=18377991298418272065&hl=en&as_sdt=0,5 | 70 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 1,007 | 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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 3,994 | 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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
LoRA: Low-Rank Adaptation of Large Language Models | 437 | iclr | 260 | 54 | 2023-06-18 09:44:09.140000 | https://github.com/microsoft/LoRA | 4,922 | Lora: Low-rank adaptation of large language models | https://scholar.google.com/scholar?cluster=12933070321040047372&hl=en&as_sdt=0,5 | 42 | 2,022 |
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data | 24 | iclr | 7,332 | 1,026 | 2023-06-18 09:44:09.344000 | https://github.com/google-research/google-research | 29,803 | Trail: Near-optimal imitation learning with suboptimal data | https://scholar.google.com/scholar?cluster=13031874054704232682&hl=en&as_sdt=0,5 | 728 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |
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 | 2,022 |