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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 |
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