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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 | 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 | iclr | 5 | 1 | 2023-06-18 08:51:07.297000 | 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 | iclr | 6 | 1 | 2023-06-18 08:51:07.498000 | 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 | 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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