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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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
On the Convergence of FedAvg on Non-IID Data | 1,417 | 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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
A Generalized Training Approach for Multiagent Learning | 70 | iclr | 820 | 36 | 2023-06-18 09:10:54.703000 | https://github.com/deepmind/open_spiel | 3,698 | A generalized training approach for multiagent learning | https://scholar.google.com/scholar?cluster=15325169882978328378&hl=en&as_sdt=0,21 | 106 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,020 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
Rethinking Attention with Performers | 838 | iclr | 7,332 | 1,026 | 2023-06-18 09:24:20.544000 | https://github.com/google-research/google-research | 29,803 | Rethinking attention with performers | https://scholar.google.com/scholar?cluster=8431737427115756173&hl=en&as_sdt=0,47 | 728 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | 17,087 | iclr | 979 | 108 | 2023-06-18 09:24:22.794000 | https://github.com/google-research/vision_transformer | 7,392 | 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 | 2,021 |
Deformable DETR: Deformable Transformers for End-to-End Object Detection | 2,249 | iclr | 406 | 135 | 2023-06-18 09:24:22.996000 | https://github.com/fundamentalvision/Deformable-DETR | 2,366 | Deformable detr: Deformable transformers for end-to-end object detection | https://scholar.google.com/scholar?cluster=7911999856845003856&hl=en&as_sdt=0,6 | 32 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
Score-Based Generative Modeling through Stochastic Differential Equations | 1,196 | 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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
Learning Mesh-Based Simulation with Graph Networks | 370 | iclr | 2,435 | 170 | 2023-06-18 09:24:28.316000 | https://github.com/deepmind/deepmind-research | 11,911 | Learning mesh-based simulation with graph networks | https://scholar.google.com/scholar?cluster=7248438205563105155&hl=en&as_sdt=0,48 | 336 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |
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 | 2,021 |