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|---|---|---|---|---|---|---|---|---|---|---|---|
Proximal Backpropagation
| 139
|
iclr
| 6
| 0
|
2023-06-18 08:50:58.437000
|
https://github.com/tfrerix/proxprop
| 41
|
Proximal backpropagation
|
https://scholar.google.com/scholar?cluster=13919472914722495778&hl=en&as_sdt=0,3
| 15
| 2,018
|
The Implicit Bias of Gradient Descent on Separable Data
| 739
|
iclr
| 1
| 0
|
2023-06-18 08:50:58.638000
|
https://github.com/paper-submissions/MaxMargin
| 3
|
The implicit bias of gradient descent on separable data
|
https://scholar.google.com/scholar?cluster=8363232294125339657&hl=en&as_sdt=0,5
| 2
| 2,018
|
Regularizing and Optimizing LSTM Language Models
| 1,144
|
iclr
| 502
| 63
|
2023-06-18 08:50:58.839000
|
https://github.com/salesforce/awd-lstm-lm
| 1,912
|
Regularizing and optimizing LSTM language models
|
https://scholar.google.com/scholar?cluster=10613038919449342432&hl=en&as_sdt=0,39
| 70
| 2,018
|
Word translation without parallel data
| 1,567
|
iclr
| 544
| 79
|
2023-06-18 08:50:59.040000
|
https://github.com/facebookresearch/MUSE
| 3,099
|
Word translation without parallel data
|
https://scholar.google.com/scholar?cluster=10646845124593498896&hl=en&as_sdt=0,5
| 99
| 2,018
|
Natural Language Inference over Interaction Space
| 291
|
iclr
| 58
| 11
|
2023-06-18 08:50:59.241000
|
https://github.com/YichenGong/Densely-Interactive-Inference-Network
| 243
|
Natural language inference over interaction space
|
https://scholar.google.com/scholar?cluster=3763530184208671433&hl=en&as_sdt=0,5
| 8
| 2,018
|
Multi-Task Learning for Document Ranking and Query Suggestion
| 57
|
iclr
| 31
| 0
|
2023-06-18 08:50:59.442000
|
https://github.com/wasiahmad/mnsrf_ranking_suggestion
| 110
|
Multi-task learning for document ranking and query suggestion
|
https://scholar.google.com/scholar?cluster=14352356705152132006&hl=en&as_sdt=0,3
| 9
| 2,018
|
Cascade Adversarial Machine Learning Regularized with a Unified Embedding
| 107
|
iclr
| 3
| 1
|
2023-06-18 08:50:59.644000
|
https://github.com/taesikna/cascade_adv_training
| 5
|
Cascade adversarial machine learning regularized with a unified embedding
|
https://scholar.google.com/scholar?cluster=11749941240097246023&hl=en&as_sdt=0,33
| 2
| 2,018
|
Mitigating Adversarial Effects Through Randomization
| 948
|
iclr
| 19
| 5
|
2023-06-18 08:50:59.845000
|
https://github.com/cihangxie/NIPS2017_adv_challenge_defense
| 109
|
Mitigating adversarial effects through randomization
|
https://scholar.google.com/scholar?cluster=1119418123159333221&hl=en&as_sdt=0,5
| 6
| 2,018
|
Decision Boundary Analysis of Adversarial Examples
| 126
|
iclr
| 6
| 1
|
2023-06-18 08:51:00.046000
|
https://github.com/sunblaze-ucb/decision-boundaries
| 24
|
Decision boundary analysis of adversarial examples
|
https://scholar.google.com/scholar?cluster=14822232947259136601&hl=en&as_sdt=0,47
| 10
| 2,018
|
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
| 198
|
iclr
| 23
| 5
|
2023-06-18 08:51:00.248000
|
https://github.com/mkocaoglu/CausalGAN
| 122
|
Causalgan: Learning causal implicit generative models with adversarial training
|
https://scholar.google.com/scholar?cluster=16773515662718074217&hl=en&as_sdt=0,25
| 9
| 2,018
|
Activation Maximization Generative Adversarial Nets
| 96
|
iclr
| 1
| 1
|
2023-06-18 08:51:00.448000
|
https://github.com/ZhimingZhou/AM-GAN
| 15
|
Activation maximization generative adversarial nets
|
https://scholar.google.com/scholar?cluster=5158804099762139876&hl=en&as_sdt=0,15
| 2
| 2,018
|
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
| 78
|
iclr
| 13
| 0
|
2023-06-18 08:51:00.650000
|
https://github.com/bioinf-jku/coulomb_gan
| 62
|
Coulomb gans: Provably optimal nash equilibria via potential fields
|
https://scholar.google.com/scholar?cluster=14788505867309328713&hl=en&as_sdt=0,24
| 12
| 2,018
|
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
| 260
|
iclr
| 15
| 4
|
2023-06-18 08:51:00.850000
|
https://github.com/biuyq/CT-GAN
| 47
|
Improving the improved training of wasserstein gans: A consistency term and its dual effect
|
https://scholar.google.com/scholar?cluster=3155067773578991569&hl=en&as_sdt=0,5
| 3
| 2,018
|
FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension
| 196
|
iclr
| 39
| 5
|
2023-06-18 08:51:01.051000
|
https://github.com/momohuang/FusionNet-NLI
| 134
|
Fusionnet: Fusing via fully-aware attention with application to machine comprehension
|
https://scholar.google.com/scholar?cluster=17073455781225282077&hl=en&as_sdt=0,5
| 10
| 2,018
|
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
| 453
|
iclr
| 83
| 8
|
2023-06-18 08:51:01.252000
|
https://github.com/shehzaadzd/MINERVA
| 287
|
Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning
|
https://scholar.google.com/scholar?cluster=4820794446342808007&hl=en&as_sdt=0,5
| 11
| 2,018
|
Compositional Attention Networks for Machine Reasoning
| 510
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iclr
| 124
| 15
|
2023-06-18 08:51:01.454000
|
https://github.com/stanfordnlp/mac-network
| 483
|
Compositional attention networks for machine reasoning
|
https://scholar.google.com/scholar?cluster=6263143180991689473&hl=en&as_sdt=0,47
| 32
| 2,018
|
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs
| 36
|
iclr
| 8
| 2
|
2023-06-18 08:51:01.655000
|
https://github.com/ForoughA/neuralMath
| 31
|
Combining symbolic expressions and black-box function evaluations in neural programs
|
https://scholar.google.com/scholar?cluster=12704807079952611027&hl=en&as_sdt=0,5
| 4
| 2,018
|
Active Learning for Convolutional Neural Networks: A Core-Set Approach
| 1,218
|
iclr
| 43
| 0
|
2023-06-18 08:51:01.857000
|
https://github.com/ozansener/active_learning_coreset
| 218
|
Active learning for convolutional neural networks: A core-set approach
|
https://scholar.google.com/scholar?cluster=11951024346317000591&hl=en&as_sdt=0,5
| 4
| 2,018
|
Loss-aware Weight Quantization of Deep Networks
| 135
|
iclr
| 6
| 0
|
2023-06-18 08:51:02.060000
|
https://github.com/houlu369/Loss-aware-weight-quantization
| 24
|
Loss-aware weight quantization of deep networks
|
https://scholar.google.com/scholar?cluster=17603219917891692242&hl=en&as_sdt=0,3
| 3
| 2,018
|
SpectralNet: Spectral Clustering using Deep Neural Networks
| 269
|
iclr
| 104
| 15
|
2023-06-18 08:51:02.261000
|
https://github.com/kstant0725/SpectralNet
| 299
|
Spectralnet: Spectral clustering using deep neural networks
|
https://scholar.google.com/scholar?cluster=4554119900285680620&hl=en&as_sdt=0,5
| 13
| 2,018
|
Not-So-Random Features
| 22
|
iclr
| 0
| 1
|
2023-06-18 08:51:02.462000
|
https://github.com/yz-ignescent/Not-So-Random-Features
| 3
|
Not-so-random features
|
https://scholar.google.com/scholar?cluster=16622124799980351573&hl=en&as_sdt=0,5
| 1
| 2,018
|
Generating Natural Adversarial Examples
| 560
|
iclr
| 43
| 3
|
2023-06-18 08:51:02.664000
|
https://github.com/zhengliz/natural-adversary
| 138
|
Generating natural adversarial examples
|
https://scholar.google.com/scholar?cluster=6487263081764376046&hl=en&as_sdt=0,15
| 5
| 2,018
|
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
| 265
|
iclr
| 29
| 3
|
2023-06-18 08:51:02.865000
|
https://github.com/duvenaud/relax
| 156
|
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
|
https://scholar.google.com/scholar?cluster=14404204871710653077&hl=en&as_sdt=0,3
| 21
| 2,018
|
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference
| 47
|
iclr
| 8
| 0
|
2023-06-18 08:51:03.066000
|
https://github.com/Microsoft/jackknife-variational-inference
| 21
|
Debiasing evidence approximations: On importance-weighted autoencoders and jackknife variational inference
|
https://scholar.google.com/scholar?cluster=9069832931054868249&hl=en&as_sdt=0,5
| 5
| 2,018
|
Learning a Generative Model for Validity in Complex Discrete Structures
| 21
|
iclr
| 1
| 2
|
2023-06-18 08:51:03.267000
|
https://github.com/DavidJanz/molecule_grammar_rnn
| 2
|
Learning a generative model for validity in complex discrete structures
|
https://scholar.google.com/scholar?cluster=5246820158519363051&hl=en&as_sdt=0,33
| 2
| 2,018
|
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
| 111
|
iclr
| 7
| 1
|
2023-06-18 08:51:03.469000
|
https://github.com/renmengye/meta-optim-public
| 37
|
Understanding short-horizon bias in stochastic meta-optimization
|
https://scholar.google.com/scholar?cluster=10519066902248713180&hl=en&as_sdt=0,5
| 3
| 2,018
|
Self-ensembling for visual domain adaptation
| 492
|
iclr
| 36
| 6
|
2023-06-18 08:51:03.670000
|
https://github.com/Britefury/self-ensemble-visual-domain-adapt
| 187
|
Self-ensembling for visual domain adaptation
|
https://scholar.google.com/scholar?cluster=9203351470159334271&hl=en&as_sdt=0,1
| 5
| 2,018
|
Gradient Estimators for Implicit Models
| 85
|
iclr
| 4
| 0
|
2023-06-18 08:51:03.872000
|
https://github.com/YingzhenLi/SteinGrad
| 19
|
Gradient estimators for implicit models
|
https://scholar.google.com/scholar?cluster=29993418784277680&hl=en&as_sdt=0,5
| 2
| 2,018
|
An image representation based convolutional network for DNA classification
| 30
|
iclr
| 7
| 5
|
2023-06-18 08:51:04.073000
|
https://github.com/Bojian/Hilbert-CNN
| 21
|
An image representation based convolutional network for DNA classification
|
https://scholar.google.com/scholar?cluster=4721638019752473074&hl=en&as_sdt=0,5
| 0
| 2,018
|
SMASH: One-Shot Model Architecture Search through HyperNetworks
| 697
|
iclr
| 59
| 4
|
2023-06-18 08:51:04.275000
|
https://github.com/ajbrock/SMASH
| 481
|
Smash: one-shot model architecture search through hypernetworks
|
https://scholar.google.com/scholar?cluster=10456857144668119976&hl=en&as_sdt=0,5
| 20
| 2,018
|
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
| 223
|
iclr
| 8
| 0
|
2023-06-18 08:51:04.477000
|
https://github.com/manuelmolano/Spike-GAN
| 20
|
Synthesizing realistic neural population activity patterns using generative adversarial networks
|
https://scholar.google.com/scholar?cluster=3292717005509087968&hl=en&as_sdt=0,3
| 2
| 2,018
|
PixelNN: Example-based Image Synthesis
| 109
|
iclr
| 0
| 0
|
2023-06-18 08:51:04.680000
|
https://github.com/aayushbansal/PixelNN-Code
| 3
|
Pixelnn: Example-based image synthesis
|
https://scholar.google.com/scholar?cluster=16832087782645647806&hl=en&as_sdt=0,5
| 2
| 2,018
|
Non-Autoregressive Neural Machine Translation
| 640
|
iclr
| 49
| 3
|
2023-06-18 08:51:04.881000
|
https://github.com/salesforce/nonauto-nmt
| 263
|
Non-autoregressive neural machine translation
|
https://scholar.google.com/scholar?cluster=3482831974828539059&hl=en&as_sdt=0,5
| 18
| 2,018
|
mixup: Beyond Empirical Risk Minimization
| 6,796
|
iclr
| 217
| 15
|
2023-06-18 08:51:05.082000
|
https://github.com/facebookresearch/mixup-cifar10
| 1,077
|
mixup: Beyond empirical risk minimization
|
https://scholar.google.com/scholar?cluster=12669856454801555406&hl=en&as_sdt=0,31
| 22
| 2,018
|
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
| 19
|
iclr
| 6
| 0
|
2023-06-18 08:51:05.283000
|
https://github.com/lmb-freiburg/td-or-not-td
| 12
|
TD or not TD: Analyzing the role of temporal differencing in deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=17309732018163861252&hl=en&as_sdt=0,33
| 12
| 2,018
|
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
| 56
|
iclr
| 2
| 0
|
2023-06-18 08:51:05.485000
|
https://github.com/borgr/DORA
| 6
|
Dora the explorer: Directed outreaching reinforcement action-selection
|
https://scholar.google.com/scholar?cluster=10658112327839471119&hl=en&as_sdt=0,5
| 4
| 2,018
|
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
| 130
|
iclr
| 17
| 2
|
2023-06-18 08:51:05.687000
|
https://github.com/oxwhirl/treeqn
| 86
|
Treeqn and atreec: Differentiable tree-structured models for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=10647768083329764430&hl=en&as_sdt=0,18
| 10
| 2,018
|
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback
| 5
|
iclr
| 2
| 0
|
2023-06-18 08:51:05.888000
|
https://github.com/hal3/reslope
| 4
|
Residual loss prediction: Reinforcement learning with no incremental feedback
|
https://scholar.google.com/scholar?cluster=11251280234880641754&hl=en&as_sdt=0,44
| 2
| 2,018
|
Guide Actor-Critic for Continuous Control
| 24
|
iclr
| 5
| 0
|
2023-06-18 08:51:06.089000
|
https://github.com/voot-t/guide-actor-critic
| 10
|
Guide actor-critic for continuous control
|
https://scholar.google.com/scholar?cluster=6316181617581438246&hl=en&as_sdt=0,47
| 1
| 2,018
|
Online Learning Rate Adaptation with Hypergradient Descent
| 202
|
iclr
| 17
| 7
|
2023-06-18 08:51:06.291000
|
https://github.com/gbaydin/hypergradient-descent
| 121
|
Online learning rate adaptation with hypergradient descent
|
https://scholar.google.com/scholar?cluster=2792585694661059835&hl=en&as_sdt=0,36
| 10
| 2,018
|
On the regularization of Wasserstein GANs
| 244
|
iclr
| 5
| 0
|
2023-06-18 08:51:06.492000
|
https://github.com/lukovnikov/improved_wgan_training
| 6
|
On the regularization of wasserstein gans
|
https://scholar.google.com/scholar?cluster=16449463251581049938&hl=en&as_sdt=0,5
| 2
| 2,018
|
Divide-and-Conquer Reinforcement Learning
| 111
|
iclr
| 12
| 1
|
2023-06-18 08:51:06.694000
|
https://github.com/dibyaghosh/dnc
| 56
|
Divide-and-conquer reinforcement learning
|
https://scholar.google.com/scholar?cluster=8527540948926777430&hl=en&as_sdt=0,51
| 4
| 2,018
|
A New Method of Region Embedding for Text Classification
| 58
|
iclr
| 13
| 0
|
2023-06-18 08:51:06.895000
|
https://github.com/text-representation/local-context-unit
| 56
|
A New Method of Region Embedding for Text Classification.
|
https://scholar.google.com/scholar?cluster=4730426859617818868&hl=en&as_sdt=0,3
| 7
| 2,018
|
Fix your classifier: the marginal value of training the last weight layer
| 90
|
iclr
| 7
| 1
|
2023-06-18 08:51:07.096000
|
https://github.com/eladhoffer/fix_your_classifier
| 34
|
Fix your classifier: the marginal value of training the last weight layer
|
https://scholar.google.com/scholar?cluster=10161515370917941482&hl=en&as_sdt=0,5
| 3
| 2,018
|
Temporally Efficient Deep Learning with Spikes
| 18
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iclr
| 5
| 1
|
2023-06-18 08:51:07.297000
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https://github.com/petered/pdnn
| 17
|
Temporally efficient deep learning with spikes
|
https://scholar.google.com/scholar?cluster=10962726962539033469&hl=en&as_sdt=0,32
| 4
| 2,018
|
Training GANs with Optimism
| 452
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iclr
| 6
| 1
|
2023-06-18 08:51:07.498000
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https://github.com/vsyrgkanis/optimistic_GAN_training
| 42
|
Training gans with optimism
|
https://scholar.google.com/scholar?cluster=721555332302459217&hl=en&as_sdt=0,14
| 5
| 2,018
|
Learning From Noisy Singly-labeled Data
| 151
|
iclr
| 5
| 3
|
2023-06-18 08:51:07.699000
|
https://github.com/khetan2/MBEM
| 20
|
Learning from noisy singly-labeled data
|
https://scholar.google.com/scholar?cluster=1761205373572122420&hl=en&as_sdt=0,33
| 4
| 2,018
|
Gaussian Process Behaviour in Wide Deep Neural Networks
| 365
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iclr
| 10
| 1
|
2023-06-18 08:51:07.900000
|
https://github.com/widedeepnetworks/widedeepnetworks
| 47
|
Gaussian process behaviour in wide deep neural networks
|
https://scholar.google.com/scholar?cluster=14179398766282481068&hl=en&as_sdt=0,5
| 5
| 2,018
|
On the Information Bottleneck Theory of Deep Learning
| 469
|
iclr
| 44
| 0
|
2023-06-18 08:51:08.102000
|
https://github.com/artemyk/ibsgd
| 127
|
On the information bottleneck theory of deep learning
|
https://scholar.google.com/scholar?cluster=12271240925674881982&hl=en&as_sdt=0,22
| 9
| 2,018
|
Deterministic Variational Inference for Robust Bayesian Neural Networks
| 174
|
iclr
| 21
| 0
|
2023-06-18 08:57:43.942000
|
https://github.com/Microsoft/deterministic-variational-inference
| 94
|
Deterministic variational inference for robust bayesian neural networks
|
https://scholar.google.com/scholar?cluster=180186411545863756&hl=en&as_sdt=0,44
| 7
| 2,019
|
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
| 326
|
iclr
| 101
| 8
|
2023-06-18 08:57:44.143000
|
https://github.com/yikangshen/Ordered-Neurons
| 572
|
Ordered neurons: Integrating tree structures into recurrent neural networks
|
https://scholar.google.com/scholar?cluster=18012332994072296158&hl=en&as_sdt=0,3
| 15
| 2,019
|
Learning deep representations by mutual information estimation and maximization
| 2,178
|
iclr
| 103
| 18
|
2023-06-18 08:57:44.346000
|
https://github.com/rdevon/DIM
| 774
|
Learning deep representations by mutual information estimation and maximization
|
https://scholar.google.com/scholar?cluster=9102831258285751412&hl=en&as_sdt=0,36
| 21
| 2,019
|
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
| 2,124
|
iclr
| 63
| 1
|
2023-06-18 08:57:44.546000
|
https://github.com/rgeirhos/Stylized-ImageNet
| 469
|
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
|
https://scholar.google.com/scholar?cluster=14190455085351957023&hl=en&as_sdt=0,5
| 13
| 2,019
|
Meta-Learning Update Rules for Unsupervised Representation Learning
| 104
|
iclr
| 46,278
| 1,207
|
2023-06-18 08:57:44.748000
|
https://github.com/tensorflow/models
| 75,928
|
Meta-learning update rules for unsupervised representation learning
|
https://scholar.google.com/scholar?cluster=5989711063339819997&hl=en&as_sdt=0,24
| 2,774
| 2,019
|
Transferring Knowledge across Learning Processes
| 58
|
iclr
| 60
| 6
|
2023-06-18 08:57:44.950000
|
https://github.com/amzn/xfer
| 250
|
Transferring knowledge across learning processes
|
https://scholar.google.com/scholar?cluster=12789436144351549005&hl=en&as_sdt=0,21
| 19
| 2,019
|
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
| 75
|
iclr
| 13
| 0
|
2023-06-18 08:57:45.154000
|
https://github.com/ganguli-lab/RetinalResources
| 47
|
A unified theory of early visual representations from retina to cortex through anatomically constrained deep CNNs
|
https://scholar.google.com/scholar?cluster=2073469512347644047&hl=en&as_sdt=0,5
| 15
| 2,019
|
Pay Less Attention with Lightweight and Dynamic Convolutions
| 538
|
iclr
| 5,883
| 1,031
|
2023-06-18 08:57:45.356000
|
https://github.com/pytorch/fairseq
| 26,500
|
Pay less attention with lightweight and dynamic convolutions
|
https://scholar.google.com/scholar?cluster=3358231780148394025&hl=en&as_sdt=0,3
| 411
| 2,019
|
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
| 317
|
iclr
| 40
| 6
|
2023-06-18 08:57:45.558000
|
https://github.com/ftramer/slalom
| 147
|
Slalom: Fast, verifiable and private execution of neural networks in trusted hardware
|
https://scholar.google.com/scholar?cluster=7461531422951047390&hl=en&as_sdt=0,5
| 10
| 2,019
|
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
| 577
|
iclr
| 91
| 7
|
2023-06-18 08:57:45.759000
|
https://github.com/vacancy/NSCL-PyTorch-Release
| 383
|
The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
|
https://scholar.google.com/scholar?cluster=8837128214653317831&hl=en&as_sdt=0,18
| 20
| 2,019
|
How Powerful are Graph Neural Networks?
| 4,871
|
iclr
| 211
| 17
|
2023-06-18 08:57:45.959000
|
https://github.com/weihua916/powerful-gnns
| 1,038
|
How powerful are graph neural networks?
|
https://scholar.google.com/scholar?cluster=9955904491400591671&hl=en&as_sdt=0,5
| 25
| 2,019
|
Variance Networks: When Expectation Does Not Meet Your Expectations
| 26
|
iclr
| 3
| 1
|
2023-06-18 08:57:46.161000
|
https://github.com/da-molchanov/variance-networks
| 39
|
Variance networks: When expectation does not meet your expectations
|
https://scholar.google.com/scholar?cluster=3938870273847182783&hl=en&as_sdt=0,5
| 2
| 2,019
|
Explaining Image Classifiers by Counterfactual Generation
| 214
|
iclr
| 1
| 0
|
2023-06-18 08:57:46.361000
|
https://github.com/zzzace2000/FIDO-saliency
| 27
|
Explaining image classifiers by counterfactual generation
|
https://scholar.google.com/scholar?cluster=6313449476805696850&hl=en&as_sdt=0,33
| 4
| 2,019
|
Snip: single-Shot Network Pruning based on Connection sensitivity
| 792
|
iclr
| 18
| 1
|
2023-06-18 08:57:46.562000
|
https://github.com/namhoonlee/snip-public
| 97
|
Snip: Single-shot network pruning based on connection sensitivity
|
https://scholar.google.com/scholar?cluster=9820036975414969048&hl=en&as_sdt=0,11
| 8
| 2,019
|
Diagnosing and Enhancing VAE Models
| 328
|
iclr
| 33
| 11
|
2023-06-18 08:57:46.765000
|
https://github.com/daib13/TwoStageVAE
| 223
|
Diagnosing and enhancing VAE models
|
https://scholar.google.com/scholar?cluster=15377413262741867924&hl=en&as_sdt=0,47
| 13
| 2,019
|
Automatically Composing Representation Transformations as a Means for Generalization
| 76
|
iclr
| 5
| 1
|
2023-06-18 08:57:46.966000
|
https://github.com/mbchang/crl
| 22
|
Automatically composing representation transformations as a means for generalization
|
https://scholar.google.com/scholar?cluster=2301953604663446405&hl=en&as_sdt=0,44
| 6
| 2,019
|
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
| 534
|
iclr
| 33
| 2
|
2023-06-18 08:57:47.168000
|
https://github.com/mattriemer/mer
| 136
|
Learning to learn without forgetting by maximizing transfer and minimizing interference
|
https://scholar.google.com/scholar?cluster=1577299111936747730&hl=en&as_sdt=0,10
| 5
| 2,019
|
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
| 73
|
iclr
| 4
| 1
|
2023-06-18 08:57:47.368000
|
https://github.com/lunanbit/UUlearning
| 22
|
On the minimal supervision for training any binary classifier from only unlabeled data
|
https://scholar.google.com/scholar?cluster=12632779449090033610&hl=en&as_sdt=0,1
| 1
| 2,019
|
Neural Speed Reading with Structural-Jump-LSTM
| 30
|
iclr
| 5
| 0
|
2023-06-18 08:57:47.569000
|
https://github.com/Varyn/Neural-Speed-Reading-with-Structural-Jump-LSTM
| 25
|
Neural speed reading with structural-jump-lstm
|
https://scholar.google.com/scholar?cluster=10699754124824317847&hl=en&as_sdt=0,33
| 5
| 2,019
|
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
| 172
|
iclr
| 7
| 4
|
2023-06-18 08:57:47.771000
|
https://github.com/roosephu/slbo
| 53
|
Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees
|
https://scholar.google.com/scholar?cluster=3175696566467828309&hl=en&as_sdt=0,43
| 6
| 2,019
|
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
| 182
|
iclr
| 5
| 6
|
2023-06-18 08:57:47.972000
|
https://github.com/MadryLab/relu_stable
| 26
|
Training for faster adversarial robustness verification via inducing relu stability
|
https://scholar.google.com/scholar?cluster=11696009804149879522&hl=en&as_sdt=0,5
| 5
| 2,019
|
Unsupervised Adversarial Image Reconstruction
| 30
|
iclr
| 3
| 4
|
2023-06-18 08:57:48.173000
|
https://github.com/UNIR-Anonymous/UNIR
| 15
|
Unsupervised adversarial image reconstruction
|
https://scholar.google.com/scholar?cluster=552778780795437052&hl=en&as_sdt=0,39
| 0
| 2,019
|
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
| 34
|
iclr
| 1
| 0
|
2023-06-18 08:57:48.375000
|
https://github.com/Newbeeer/Max-MIG
| 23
|
Max-mig: an information theoretic approach for joint learning from crowds
|
https://scholar.google.com/scholar?cluster=14993809510724823282&hl=en&as_sdt=0,5
| 3
| 2,019
|
Meta-Learning with Latent Embedding Optimization
| 1,251
|
iclr
| 57
| 1
|
2023-06-18 08:57:48.576000
|
https://github.com/deepmind/leo
| 292
|
Meta-learning with latent embedding optimization
|
https://scholar.google.com/scholar?cluster=11552536411545683614&hl=en&as_sdt=0,22
| 14
| 2,019
|
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
| 155
|
iclr
| 4
| 0
|
2023-06-18 08:57:48.779000
|
https://github.com/wendazhou/nnet-compression-generalization
| 25
|
Non-vacuous generalization bounds at the imagenet scale: a PAC-bayesian compression approach
|
https://scholar.google.com/scholar?cluster=12180551458196751211&hl=en&as_sdt=0,33
| 4
| 2,019
|
Learning to Represent Edits
| 98
|
iclr
| 10
| 0
|
2023-06-18 08:57:48.980000
|
https://github.com/Microsoft/msrc-dpu-learning-to-represent-edits
| 27
|
Learning to represent edits
|
https://scholar.google.com/scholar?cluster=15643648406405720624&hl=en&as_sdt=0,3
| 9
| 2,019
|
An Empirical Study of Example Forgetting during Deep Neural Network Learning
| 348
|
iclr
| 26
| 3
|
2023-06-18 08:57:49.182000
|
https://github.com/mtoneva/example_forgetting
| 151
|
An empirical study of example forgetting during deep neural network learning
|
https://scholar.google.com/scholar?cluster=14912040563601232331&hl=en&as_sdt=0,33
| 6
| 2,019
|
RNNs implicitly implement tensor-product representations
| 46
|
iclr
| 3
| 0
|
2023-06-18 08:57:49.384000
|
https://github.com/tommccoy1/tpdn
| 18
|
RNNs implicitly implement tensor product representations
|
https://scholar.google.com/scholar?cluster=8578120166770522666&hl=en&as_sdt=0,33
| 7
| 2,019
|
Dynamic Channel Pruning: Feature Boosting and Suppression
| 290
|
iclr
| 20
| 4
|
2023-06-18 08:57:49.585000
|
https://github.com/deep-fry/mayo
| 109
|
Dynamic channel pruning: Feature boosting and suppression
|
https://scholar.google.com/scholar?cluster=1895104173020407133&hl=en&as_sdt=0,50
| 11
| 2,019
|
Towards Metamerism via Foveated Style Transfer
| 33
|
iclr
| 0
| 0
|
2023-06-18 08:57:49.787000
|
https://github.com/ArturoDeza/NeuroFovea
| 18
|
Towards metamerism via foveated style transfer
|
https://scholar.google.com/scholar?cluster=17935865817929282522&hl=en&as_sdt=0,44
| 3
| 2,019
|
Generative Code Modeling with Graphs
| 154
|
iclr
| 37
| 4
|
2023-06-18 08:57:49.988000
|
https://github.com/Microsoft/graph-based-code-modelling
| 157
|
Generative code modeling with graphs
|
https://scholar.google.com/scholar?cluster=2376600485661149991&hl=en&as_sdt=0,34
| 13
| 2,019
|
CEM-RL: Combining evolutionary and gradient-based methods for policy search
| 130
|
iclr
| 17
| 1
|
2023-06-18 08:57:50.190000
|
https://github.com/apourchot/CEM-RL
| 88
|
CEM-RL: Combining evolutionary and gradient-based methods for policy search
|
https://scholar.google.com/scholar?cluster=11981496156929972562&hl=en&as_sdt=0,5
| 4
| 2,019
|
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
| 221
|
iclr
| 64
| 4
|
2023-06-18 08:57:50.393000
|
https://github.com/lrjconan/LanczosNetwork
| 307
|
Lanczosnet: Multi-scale deep graph convolutional networks
|
https://scholar.google.com/scholar?cluster=4668385491596284189&hl=en&as_sdt=0,5
| 8
| 2,019
|
No Training Required: Exploring Random Encoders for Sentence Classification
| 111
|
iclr
| 28
| 1
|
2023-06-18 08:57:50.594000
|
https://github.com/facebookresearch/randsent
| 183
|
No training required: Exploring random encoders for sentence classification
|
https://scholar.google.com/scholar?cluster=12787240152315433650&hl=en&as_sdt=0,5
| 12
| 2,019
|
Neural Graph Evolution: Towards Efficient Automatic Robot Design
| 46
|
iclr
| 12
| 5
|
2023-06-18 08:57:50.794000
|
https://github.com/WilsonWangTHU/neural_graph_evolution
| 43
|
Neural graph evolution: Towards efficient automatic robot design
|
https://scholar.google.com/scholar?cluster=2252025967426248193&hl=en&as_sdt=0,5
| 2
| 2,019
|
Function Space Particle Optimization for Bayesian Neural Networks
| 52
|
iclr
| 7
| 2
|
2023-06-18 08:57:50.995000
|
https://github.com/thu-ml/fpovi
| 16
|
Function space particle optimization for bayesian neural networks
|
https://scholar.google.com/scholar?cluster=3265058804151062573&hl=en&as_sdt=0,3
| 8
| 2,019
|
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
| 160
|
iclr
| 7
| 1
|
2023-06-18 08:57:51.196000
|
https://github.com/KaidiXu/StrAttack
| 30
|
Structured adversarial attack: Towards general implementation and better interpretability
|
https://scholar.google.com/scholar?cluster=2416957312060244972&hl=en&as_sdt=0,5
| 4
| 2,019
|
Spherical CNNs on Unstructured Grids
| 154
|
iclr
| 24
| 6
|
2023-06-18 08:57:51.398000
|
https://github.com/maxjiang93/ugscnn
| 157
|
Spherical CNNs on unstructured grids
|
https://scholar.google.com/scholar?cluster=8988090417232263617&hl=en&as_sdt=0,5
| 15
| 2,019
|
Selfless Sequential Learning
| 109
|
iclr
| 5
| 0
|
2023-06-18 08:57:51.600000
|
https://github.com/rahafaljundi/Selfless-Sequential-Learning
| 23
|
Selfless sequential learning
|
https://scholar.google.com/scholar?cluster=11518728044683719539&hl=en&as_sdt=0,5
| 4
| 2,019
|
The Deep Weight Prior
| 37
|
iclr
| 8
| 0
|
2023-06-18 08:57:51.803000
|
https://github.com/bayesgroup/deep-weight-prior
| 44
|
The deep weight prior
|
https://scholar.google.com/scholar?cluster=15422497541572460475&hl=en&as_sdt=0,44
| 11
| 2,019
|
Adversarial Audio Synthesis
| 579
|
iclr
| 269
| 50
|
2023-06-18 08:57:52.008000
|
https://github.com/chrisdonahue/wavegan
| 1,225
|
Adversarial audio synthesis
|
https://scholar.google.com/scholar?cluster=5918610073287101746&hl=en&as_sdt=0,11
| 49
| 2,019
|
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
| 80
|
iclr
| 13
| 0
|
2023-06-18 08:57:52.210000
|
https://github.com/cogentlabs/apl
| 46
|
Adaptive posterior learning: few-shot learning with a surprise-based memory module
|
https://scholar.google.com/scholar?cluster=3877086335539241291&hl=en&as_sdt=0,33
| 5
| 2,019
|
DHER: Hindsight Experience Replay for Dynamic Goals
| 72
|
iclr
| 6
| 0
|
2023-06-18 08:57:52.427000
|
https://github.com/mengf1/DHER
| 63
|
DHER: Hindsight experience replay for dynamic goals
|
https://scholar.google.com/scholar?cluster=810824099491823319&hl=en&as_sdt=0,5
| 4
| 2,019
|
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
| 115
|
iclr
| 58
| 19
|
2023-06-18 08:57:52.674000
|
https://github.com/momohuang/FlowQA
| 198
|
Flowqa: Grasping flow in history for conversational machine comprehension
|
https://scholar.google.com/scholar?cluster=13021094548556076955&hl=en&as_sdt=0,36
| 10
| 2,019
|
Learning to Design RNA
| 55
|
iclr
| 14
| 1
|
2023-06-18 08:57:52.889000
|
https://github.com/automl/learna
| 50
|
Learning to design RNA
|
https://scholar.google.com/scholar?cluster=17240520904353756155&hl=en&as_sdt=0,3
| 12
| 2,019
|
Robust Conditional Generative Adversarial Networks
| 128
|
iclr
| 2
| 1
|
2023-06-18 08:57:53.090000
|
https://github.com/grigorisg9gr/rocgan
| 15
|
Robust conditional generative adversarial networks
|
https://scholar.google.com/scholar?cluster=15862016331433813666&hl=en&as_sdt=0,3
| 3
| 2,019
|
Cost-Sensitive Robustness against Adversarial Examples
| 21
|
iclr
| 2
| 0
|
2023-06-18 08:57:53.290000
|
https://github.com/xiaozhanguva/Cost-Sensitive-Robustness
| 20
|
Cost-sensitive robustness against adversarial examples
|
https://scholar.google.com/scholar?cluster=16169861265468560490&hl=en&as_sdt=0,50
| 4
| 2,019
|
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
| 40
|
iclr
| 5
| 0
|
2023-06-18 08:57:53.491000
|
https://github.com/hyang1990/model_based_energy_constrained_compression
| 17
|
Energy-constrained compression for deep neural networks via weighted sparse projection and layer input masking
|
https://scholar.google.com/scholar?cluster=6237094978821638350&hl=en&as_sdt=0,37
| 3
| 2,019
|
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure
| 10
|
iclr
| 1
| 0
|
2023-06-18 08:57:53.691000
|
https://github.com/StanfordAI4HI/ICLR2019_evaluating_discrete_temporal_structure
| 3
|
Learning procedural abstractions and evaluating discrete latent temporal structure
|
https://scholar.google.com/scholar?cluster=11760620653931209024&hl=en&as_sdt=0,5
| 6
| 2,019
|
Adversarial Attacks on Graph Neural Networks via Meta Learning
| 81
|
iclr
| 25
| 0
|
2023-06-18 08:57:53.893000
|
https://github.com/danielzuegner/gnn-meta-attack
| 125
|
Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach
|
https://scholar.google.com/scholar?cluster=15469142668663053021&hl=en&as_sdt=0,5
| 5
| 2,019
|
Information-Directed Exploration for Deep Reinforcement Learning
| 72
|
iclr
| 23
| 0
|
2023-06-18 08:57:54.093000
|
https://github.com/nikonikolov/rltf
| 80
|
Information-directed exploration for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=12419979613667846761&hl=en&as_sdt=0,5
| 13
| 2,019
|
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