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TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning | 221 | icml | 12 | 4 | 2023-06-17 03:10:45.844000 | https://github.com/istarjun/TapNet | 52 | Tapnet: Neural network augmented with task-adaptive projection for few-shot learning | https://scholar.google.com/scholar?cluster=12575801957058912486&hl=en&as_sdt=0,5 | 3 | 2,019 |
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation | 110 | icml | 10 | 2 | 2023-06-17 03:10:46.060000 | https://github.com/thuml/Deep-Embedded-Validation | 58 | Towards accurate model selection in deep unsupervised domain adaptation | https://scholar.google.com/scholar?cluster=2565642679287912484&hl=en&as_sdt=0,39 | 3 | 2,019 |
Position-aware Graph Neural Networks | 386 | icml | 75 | 11 | 2023-06-17 03:10:46.276000 | https://github.com/JiaxuanYou/P-GNN | 367 | Position-aware graph neural networks | https://scholar.google.com/scholar?cluster=2886623965746954945&hl=en&as_sdt=0,5 | 15 | 2,019 |
DAG-GNN: DAG Structure Learning with Graph Neural Networks | 277 | icml | 55 | 21 | 2023-06-17 03:10:46.492000 | https://github.com/fishmoon1234/DAG-GNN | 233 | DAG-GNN: DAG structure learning with graph neural networks | https://scholar.google.com/scholar?cluster=12962909633357312064&hl=en&as_sdt=0,34 | 8 | 2,019 |
Multi-Agent Adversarial Inverse Reinforcement Learning | 90 | icml | 26 | 6 | 2023-06-17 03:10:46.723000 | https://github.com/ermongroup/MA-AIRL | 153 | Multi-agent adversarial inverse reinforcement learning | https://scholar.google.com/scholar?cluster=13913946030309510400&hl=en&as_sdt=0,5 | 16 | 2,019 |
Online Adaptive Principal Component Analysis and Its extensions | 4 | icml | 1 | 0 | 2023-06-17 03:10:46.938000 | https://github.com/yuanx270/online-adaptive-PCA | 7 | Online adaptive principal component analysis and its extensions | https://scholar.google.com/scholar?cluster=11284462216308687300&hl=en&as_sdt=0,34 | 2 | 2,019 |
Bayesian Nonparametric Federated Learning of Neural Networks | 394 | icml | 30 | 2 | 2023-06-17 03:10:47.156000 | https://github.com/IBM/probabilistic-federated-neural-matching | 120 | Bayesian nonparametric federated learning of neural networks | https://scholar.google.com/scholar?cluster=14489502397862024393&hl=en&as_sdt=0,21 | 15 | 2,019 |
Dirichlet Simplex Nest and Geometric Inference | 5 | icml | 1 | 0 | 2023-06-17 03:10:47.371000 | https://github.com/moonfolk/VLAD | 3 | Dirichlet simplex nest and geometric inference | https://scholar.google.com/scholar?cluster=3107204927758089702&hl=en&as_sdt=0,36 | 1 | 2,019 |
Making Convolutional Networks Shift-Invariant Again | 669 | icml | 206 | 14 | 2023-06-17 03:10:47.587000 | https://github.com/adobe/antialiased-cnns | 1,613 | Making convolutional networks shift-invariant again | https://scholar.google.com/scholar?cluster=6405795848737680233&hl=en&as_sdt=0,5 | 39 | 2,019 |
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback | 27 | icml | 2 | 0 | 2023-06-17 03:10:47.802000 | https://github.com/zcc1307/warmcb_scripts | 4 | Warm-starting contextual bandits: Robustly combining supervised and bandit feedback | https://scholar.google.com/scholar?cluster=13381714542277312288&hl=en&as_sdt=0,5 | 2 | 2,019 |
Self-Attention Generative Adversarial Networks | 3,723 | icml | 173 | 17 | 2023-06-17 03:10:48.018000 | https://github.com/brain-research/self-attention-gan | 967 | Self-attention generative adversarial networks | https://scholar.google.com/scholar?cluster=7330853420568873733&hl=en&as_sdt=0,31 | 38 | 2,019 |
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition | 78 | icml | 14 | 2 | 2023-06-17 03:10:48.233000 | https://github.com/latentgnn/LatentGNN-V1-PyTorch | 74 | Latentgnn: Learning efficient non-local relations for visual recognition | https://scholar.google.com/scholar?cluster=7578360606999759452&hl=en&as_sdt=0,5 | 6 | 2,019 |
Bridging Theory and Algorithm for Domain Adaptation | 530 | icml | 27 | 5 | 2023-06-17 03:10:48.449000 | https://github.com/thuml/MDD | 121 | Bridging theory and algorithm for domain adaptation | https://scholar.google.com/scholar?cluster=12036658661059863941&hl=en&as_sdt=0,5 | 5 | 2,019 |
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning | 234 | icml | 18 | 5 | 2023-06-17 03:10:48.665000 | https://github.com/sharadmv/parasol | 67 | Solar: Deep structured representations for model-based reinforcement learning | https://scholar.google.com/scholar?cluster=3160286257401504607&hl=en&as_sdt=0,33 | 3 | 2,019 |
Theoretically Principled Trade-off between Robustness and Accuracy | 1,779 | icml | 119 | 3 | 2023-06-17 03:10:48.880000 | https://github.com/yaodongyu/TRADES | 474 | Theoretically principled trade-off between robustness and accuracy | https://scholar.google.com/scholar?cluster=3311622924435738798&hl=en&as_sdt=0,5 | 10 | 2,019 |
Interpreting Adversarially Trained Convolutional Neural Networks | 120 | icml | 9 | 0 | 2023-06-17 03:10:49.095000 | https://github.com/PKUAI26/AT-CNN | 62 | Interpreting adversarially trained convolutional neural networks | https://scholar.google.com/scholar?cluster=6664229559742953811&hl=en&as_sdt=0,5 | 7 | 2,019 |
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits | 15 | icml | 3 | 0 | 2023-06-17 03:10:49.311000 | https://github.com/martinjzhang/AMT | 4 | Adaptive monte carlo multiple testing via multi-armed bandits | https://scholar.google.com/scholar?cluster=17419761528871683302&hl=en&as_sdt=0,44 | 0 | 2,019 |
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning | 73 | icml | 6 | 1 | 2023-06-17 03:10:49.527000 | https://github.com/ruizhaogit/mep | 21 | Maximum entropy-regularized multi-goal reinforcement learning | https://scholar.google.com/scholar?cluster=12004531622883216435&hl=en&as_sdt=0,5 | 3 | 2,019 |
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization | 9 | icml | 3 | 0 | 2023-06-17 03:10:49.742000 | https://github.com/baojianzhou/graph-sto-iht | 3 | Stochastic iterative hard thresholding for graph-structured sparsity optimization | https://scholar.google.com/scholar?cluster=4121937272467164287&hl=en&as_sdt=0,26 | 2 | 2,019 |
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets | 227 | icml | 10 | 4 | 2023-06-17 03:10:49.964000 | https://github.com/zhuchen03/ConvexPolytopePosioning | 28 | Transferable clean-label poisoning attacks on deep neural nets | https://scholar.google.com/scholar?cluster=457598797512585014&hl=en&as_sdt=0,5 | 3 | 2,019 |
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects | 197 | icml | 0 | 0 | 2023-06-17 03:10:50.205000 | https://github.com/uuujf/SGDNoise | 11 | The anisotropic noise in stochastic gradient descent: Its behavior of escaping from sharp minima and regularization effects | https://scholar.google.com/scholar?cluster=8530319537943237114&hl=en&as_sdt=0,5 | 2 | 2,019 |
Latent Normalizing Flows for Discrete Sequences | 103 | icml | 15 | 4 | 2023-06-17 03:10:50.420000 | https://github.com/harvardnlp/TextFlow | 113 | Latent normalizing flows for discrete sequences | https://scholar.google.com/scholar?cluster=14468956623112090674&hl=en&as_sdt=0,36 | 11 | 2,019 |
Fast Context Adaptation via Meta-Learning | 342 | icml | 39 | 1 | 2023-06-17 03:10:50.635000 | https://github.com/lmzintgraf/cavia | 126 | Fast context adaptation via meta-learning | https://scholar.google.com/scholar?cluster=731845317332872337&hl=en&as_sdt=0,36 | 6 | 2,019 |
A distributional view on multi-objective policy optimization | 51 | icml | 613 | 69 | 2023-06-17 03:56:43.582000 | https://github.com/deepmind/dm_control | 3,200 | A distributional view on multi-objective policy optimization | https://scholar.google.com/scholar?cluster=8438162900583355554&hl=en&as_sdt=0,11 | 127 | 2,020 |
An Optimistic Perspective on Offline Reinforcement Learning | 366 | icml | 72 | 9 | 2023-06-17 03:56:43.785000 | https://github.com/google-research/batch_rl | 441 | An optimistic perspective on offline reinforcement learning | https://scholar.google.com/scholar?cluster=199235154784983919&hl=en&as_sdt=0,37 | 12 | 2,020 |
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments | 8 | icml | 0 | 0 | 2023-06-17 03:56:43.987000 | https://github.com/teshnizi/LazyIter | 7 | Lazyiter: a fast algorithm for counting Markov equivalent DAGs and designing experiments | https://scholar.google.com/scholar?cluster=11588857558630683059&hl=en&as_sdt=0,5 | 1 | 2,020 |
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay | 16 | icml | 0 | 0 | 2023-06-17 03:56:44.190000 | https://github.com/Ralami1859/Restarted-BOCPD | 2 | Restarted Bayesian online change-point detector achieves optimal detection delay | https://scholar.google.com/scholar?cluster=12357062813763301915&hl=en&as_sdt=0,5 | 2 | 2,020 |
Structural Language Models of Code | 82 | icml | 7 | 6 | 2023-06-17 03:56:44.392000 | https://github.com/tech-srl/slm-code-generation | 75 | Structural language models of code | https://scholar.google.com/scholar?cluster=12400277411486589122&hl=en&as_sdt=0,44 | 11 | 2,020 |
LowFER: Low-rank Bilinear Pooling for Link Prediction | 31 | icml | 5 | 0 | 2023-06-17 03:56:44.595000 | https://github.com/suamin/LowFER | 12 | LowFER: Low-rank bilinear pooling for link prediction | https://scholar.google.com/scholar?cluster=6369643568974944132&hl=en&as_sdt=0,5 | 0 | 2,020 |
Discount Factor as a Regularizer in Reinforcement Learning | 42 | icml | 2 | 1 | 2023-06-17 03:56:44.797000 | https://github.com/ron-amit/Discount_as_Regularizer | 5 | Discount factor as a regularizer in reinforcement learning | https://scholar.google.com/scholar?cluster=4222677586854479535&hl=en&as_sdt=0,33 | 2 | 2,020 |
The Differentiable Cross-Entropy Method | 45 | icml | 10 | 0 | 2023-06-17 03:56:44.998000 | https://github.com/facebookresearch/dcem | 118 | The differentiable cross-entropy method | https://scholar.google.com/scholar?cluster=5207717261153832790&hl=en&as_sdt=0,5 | 9 | 2,020 |
Fairwashing explanations with off-manifold detergent | 73 | icml | 3 | 0 | 2023-06-17 03:56:45.201000 | https://github.com/fairwashing/fairwashing | 10 | Fairwashing explanations with off-manifold detergent | https://scholar.google.com/scholar?cluster=869145400827496969&hl=en&as_sdt=0,14 | 2 | 2,020 |
Online metric algorithms with untrusted predictions | 96 | icml | 1 | 0 | 2023-06-17 03:56:45.403000 | https://github.com/adampolak/mts-with-predictions | 1 | Online metric algorithms with untrusted predictions | https://scholar.google.com/scholar?cluster=8779637967313325541&hl=en&as_sdt=0,24 | 4 | 2,020 |
Invertible generative models for inverse problems: mitigating representation error and dataset bias | 113 | icml | 14 | 3 | 2023-06-17 03:56:45.605000 | https://github.com/CACTuS-AI/GlowIP | 17 | Invertible generative models for inverse problems: mitigating representation error and dataset bias | https://scholar.google.com/scholar?cluster=18360186920065669378&hl=en&as_sdt=0,5 | 5 | 2,020 |
Forecasting Sequential Data Using Consistent Koopman Autoencoders | 78 | icml | 17 | 2 | 2023-06-17 03:56:45.807000 | https://github.com/erichson/koopmanAE | 45 | Forecasting sequential data using consistent koopman autoencoders | https://scholar.google.com/scholar?cluster=604581388291751037&hl=en&as_sdt=0,5 | 4 | 2,020 |
Learning De-biased Representations with Biased Representations | 178 | icml | 28 | 0 | 2023-06-17 03:56:46.008000 | https://github.com/clovaai/rebias | 152 | Learning de-biased representations with biased representations | https://scholar.google.com/scholar?cluster=2454950202861832490&hl=en&as_sdt=0,33 | 7 | 2,020 |
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training | 281 | icml | 1,868 | 365 | 2023-06-17 03:56:46.210000 | https://github.com/microsoft/unilm | 12,786 | Unilmv2: Pseudo-masked language models for unified language model pre-training | https://scholar.google.com/scholar?cluster=17252701423323416900&hl=en&as_sdt=0,5 | 260 | 2,020 |
Option Discovery in the Absence of Rewards with Manifold Analysis | 5 | icml | 0 | 0 | 2023-06-17 03:56:46.412000 | https://github.com/amitaybar/Diffusion-options | 0 | Option discovery in the absence of rewards with manifold analysis | https://scholar.google.com/scholar?cluster=5097986500723178583&hl=en&as_sdt=0,33 | 1 | 2,020 |
Decoupled Greedy Learning of CNNs | 74 | icml | 4 | 1 | 2023-06-17 03:56:46.614000 | https://github.com/eugenium/DGL | 24 | Decoupled greedy learning of cnns | https://scholar.google.com/scholar?cluster=984410843298404679&hl=en&as_sdt=0,41 | 7 | 2,020 |
Efficient Policy Learning from Surrogate-Loss Classification Reductions | 16 | icml | 1 | 0 | 2023-06-17 03:56:46.816000 | https://github.com/CausalML/ESPRM | 2 | Efficient policy learning from surrogate-loss classification reductions | https://scholar.google.com/scholar?cluster=17482295204063069180&hl=en&as_sdt=0,33 | 3 | 2,020 |
Training Neural Networks for and by Interpolation | 35 | icml | 5 | 0 | 2023-06-17 03:56:47.018000 | https://github.com/oval-group/ali-g | 22 | Training neural networks for and by interpolation | https://scholar.google.com/scholar?cluster=12646838748171851359&hl=en&as_sdt=0,26 | 3 | 2,020 |
Implicit differentiation of Lasso-type models for hyperparameter optimization | 50 | icml | 14 | 20 | 2023-06-17 03:56:47.220000 | https://github.com/QB3/sparse-ho | 37 | Implicit differentiation of lasso-type models for hyperparameter optimization | https://scholar.google.com/scholar?cluster=9364706080727749786&hl=en&as_sdt=0,5 | 6 | 2,020 |
The Boomerang Sampler | 32 | icml | 0 | 0 | 2023-06-17 03:56:47.421000 | https://github.com/jbierkens/ICML-boomerang | 7 | The boomerang sampler | https://scholar.google.com/scholar?cluster=8538965772361697464&hl=en&as_sdt=0,5 | 4 | 2,020 |
Fast Differentiable Sorting and Ranking | 132 | icml | 40 | 11 | 2023-06-17 03:56:47.623000 | https://github.com/google-research/fast-soft-sort | 483 | Fast differentiable sorting and ranking | https://scholar.google.com/scholar?cluster=1601300606865471199&hl=en&as_sdt=0,10 | 15 | 2,020 |
Beyond Signal Propagation: Is Feature Diversity Necessary in Deep Neural Network Initialization? | 12 | icml | 0 | 0 | 2023-06-17 03:56:47.826000 | https://github.com/yanivbl6/BeyondSigProp | 2 | Beyond signal propagation: is feature diversity necessary in deep neural network initialization? | https://scholar.google.com/scholar?cluster=12443428565734084047&hl=en&as_sdt=0,5 | 1 | 2,020 |
Deep Coordination Graphs | 130 | icml | 20 | 9 | 2023-06-17 03:56:48.027000 | https://github.com/wendelinboehmer/dcg | 65 | Deep coordination graphs | https://scholar.google.com/scholar?cluster=8113641514627174064&hl=en&as_sdt=0,23 | 5 | 2,020 |
Lorentz Group Equivariant Neural Network for Particle Physics | 97 | icml | 6 | 0 | 2023-06-17 03:56:48.229000 | https://github.com/fizisist/LorentzGroupNetwork | 40 | Lorentz group equivariant neural network for particle physics | https://scholar.google.com/scholar?cluster=354482020847877812&hl=en&as_sdt=0,33 | 5 | 2,020 |
Proper Network Interpretability Helps Adversarial Robustness in Classification | 49 | icml | 3 | 0 | 2023-06-17 03:56:48.431000 | https://github.com/AkhilanB/Proper-Interpretability | 11 | Proper network interpretability helps adversarial robustness in classification | https://scholar.google.com/scholar?cluster=9035074662025671292&hl=en&as_sdt=0,15 | 4 | 2,020 |
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks | 106 | icml | 3 | 5 | 2023-06-17 03:56:48.632000 | https://github.com/Pehlevan-Group/NTK_Learning_Curves | 3 | Spectrum dependent learning curves in kernel regression and wide neural networks | https://scholar.google.com/scholar?cluster=3712020461682803664&hl=en&as_sdt=0,5 | 4 | 2,020 |
Latent Variable Modelling with Hyperbolic Normalizing Flows | 40 | icml | 7 | 23 | 2023-06-17 03:56:48.834000 | https://github.com/joeybose/HyperbolicNF | 52 | Latent variable modelling with hyperbolic normalizing flows | https://scholar.google.com/scholar?cluster=16943766719750515886&hl=en&as_sdt=0,15 | 3 | 2,020 |
Preference Modeling with Context-Dependent Salient Features | 9 | icml | 0 | 0 | 2023-06-17 03:56:49.052000 | https://github.com/Amandarg/salient_features | 1 | Preference modeling with context-dependent salient features | https://scholar.google.com/scholar?cluster=14377795205947287878&hl=en&as_sdt=0,14 | 2 | 2,020 |
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference | 12 | icml | 0 | 0 | 2023-06-17 03:56:49.254000 | https://github.com/vmasrani/tvo_all_in | 0 | All in the exponential family: Bregman duality in thermodynamic variational inference | https://scholar.google.com/scholar?cluster=6653952944869299139&hl=en&as_sdt=0,47 | 0 | 2,020 |
Estimating the Number and Effect Sizes of Non-null Hypotheses | 8 | icml | 0 | 0 | 2023-06-17 03:56:49.455000 | https://github.com/jenniferbrennan/CountingDiscoveries | 1 | Estimating the number and effect sizes of non-null hypotheses | https://scholar.google.com/scholar?cluster=13761193891605574377&hl=en&as_sdt=0,44 | 1 | 2,020 |
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation | 83 | icml | 229 | 10 | 2023-06-17 03:56:49.658000 | https://github.com/microsoft/tf-gnn-samples | 877 | Gnn-film: Graph neural networks with feature-wise linear modulation | https://scholar.google.com/scholar?cluster=17006226546313472447&hl=en&as_sdt=0,1 | 35 | 2,020 |
TaskNorm: Rethinking Batch Normalization for Meta-Learning | 82 | icml | 22 | 1 | 2023-06-17 03:56:49.860000 | https://github.com/cambridge-mlg/cnaps | 152 | Tasknorm: Rethinking batch normalization for meta-learning | https://scholar.google.com/scholar?cluster=5780176448524951533&hl=en&as_sdt=0,5 | 11 | 2,020 |
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences | 78 | icml | 4 | 0 | 2023-06-17 03:56:50.083000 | https://github.com/dsbrown1331/bayesianrex | 11 | Safe imitation learning via fast bayesian reward inference from preferences | https://scholar.google.com/scholar?cluster=7057495303121096550&hl=en&as_sdt=0,5 | 3 | 2,020 |
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models | 21 | icml | 1 | 0 | 2023-06-17 03:56:50.284000 | https://github.com/clinicalml/overparam | 6 | Empirical study of the benefits of overparameterization in learning latent variable models | https://scholar.google.com/scholar?cluster=18021082651755132236&hl=en&as_sdt=0,5 | 2 | 2,020 |
DeBayes: a Bayesian Method for Debiasing Network Embeddings | 54 | icml | 1 | 1 | 2023-06-17 03:56:50.486000 | https://github.com/aida-ugent/DeBayes | 7 | Debayes: a bayesian method for debiasing network embeddings | https://scholar.google.com/scholar?cluster=12507703931590961178&hl=en&as_sdt=0,47 | 2 | 2,020 |
Online Learned Continual Compression with Adaptive Quantization Modules | 55 | icml | 5 | 0 | 2023-06-17 03:56:50.691000 | https://github.com/pclucas14/adaptive-quantization-modules | 26 | Online learned continual compression with adaptive quantization modules | https://scholar.google.com/scholar?cluster=4962059148023200241&hl=en&as_sdt=0,5 | 4 | 2,020 |
Near-linear time Gaussian process optimization with adaptive batching and resparsification | 19 | icml | 1 | 0 | 2023-06-17 03:56:50.893000 | https://github.com/luigicarratino/batch-bkb | 11 | Near-linear time Gaussian process optimization with adaptive batching and resparsification | https://scholar.google.com/scholar?cluster=9965392032053007731&hl=en&as_sdt=0,44 | 3 | 2,020 |
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates | 55 | icml | 19 | 0 | 2023-06-17 03:56:51.095000 | https://github.com/jwcalder/GraphLearning | 62 | Poisson learning: Graph based semi-supervised learning at very low label rates | https://scholar.google.com/scholar?cluster=11788739359346189749&hl=en&as_sdt=0,43 | 3 | 2,020 |
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills | 85 | icml | 0 | 0 | 2023-06-17 03:56:51.297000 | https://github.com/imatge-upc/edl | 3 | Explore, discover and learn: Unsupervised discovery of state-covering skills | https://scholar.google.com/scholar?cluster=6344383621952136699&hl=en&as_sdt=0,5 | 2 | 2,020 |
Data preprocessing to mitigate bias: A maximum entropy based approach | 29 | icml | 1 | 2 | 2023-06-17 03:56:51.499000 | https://github.com/vijaykeswani/Fair-Max-Entropy-Distributions | 8 | Data preprocessing to mitigate bias: A maximum entropy based approach | https://scholar.google.com/scholar?cluster=1389448522545210547&hl=en&as_sdt=0,10 | 3 | 2,020 |
Concise Explanations of Neural Networks using Adversarial Training | 40 | icml | 1 | 17 | 2023-06-17 03:56:51.701000 | https://github.com/jfc43/advex | 5 | Concise explanations of neural networks using adversarial training | https://scholar.google.com/scholar?cluster=13018632630820208929&hl=en&as_sdt=0,10 | 3 | 2,020 |
Optimizing for the Future in Non-Stationary MDPs | 48 | icml | 0 | 3 | 2023-06-17 03:56:51.903000 | https://github.com/yashchandak/OptFuture_NSMDP | 7 | Optimizing for the future in non-stationary mdps | https://scholar.google.com/scholar?cluster=2732891290707774950&hl=en&as_sdt=0,33 | 2 | 2,020 |
Learning to Simulate and Design for Structural Engineering | 27 | icml | 0 | 0 | 2023-06-17 03:56:52.105000 | https://github.com/AutodeskAILab/LSDSE-Dataset | 7 | Learning to simulate and design for structural engineering | https://scholar.google.com/scholar?cluster=3089482596592308925&hl=en&as_sdt=0,33 | 3 | 2,020 |
Invariant Rationalization | 119 | icml | 4 | 11 | 2023-06-17 03:56:52.306000 | https://github.com/code-terminator/invariant_rationalization | 43 | Invariant rationalization | https://scholar.google.com/scholar?cluster=2718521387879023599&hl=en&as_sdt=0,10 | 4 | 2,020 |
Explainable and Discourse Topic-aware Neural Language Understanding | 5 | icml | 2 | 4 | 2023-06-17 03:56:52.508000 | https://github.com/YatinChaudhary/NCLM | 9 | Explainable and discourse topic-aware neural language understanding | https://scholar.google.com/scholar?cluster=159060864795495099&hl=en&as_sdt=0,31 | 2 | 2,020 |
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training | 61 | icml | 13 | 0 | 2023-06-17 03:56:52.710000 | https://github.com/TAMU-VITA/Self-PU | 51 | Self-pu: Self boosted and calibrated positive-unlabeled training | https://scholar.google.com/scholar?cluster=10514971696768538295&hl=en&as_sdt=0,5 | 15 | 2,020 |
Graph Optimal Transport for Cross-Domain Alignment | 106 | icml | 21 | 3 | 2023-06-17 03:56:52.912000 | https://github.com/LiqunChen0606/Graph-Optimal-Transport | 131 | Graph optimal transport for cross-domain alignment | https://scholar.google.com/scholar?cluster=13506984443465445309&hl=en&as_sdt=0,5 | 6 | 2,020 |
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization | 141 | icml | 12 | 1 | 2023-06-17 03:56:53.114000 | https://github.com/xiangning-chen/SmoothDARTS | 70 | Stabilizing differentiable architecture search via perturbation-based regularization | https://scholar.google.com/scholar?cluster=16658085005261012709&hl=en&as_sdt=0,34 | 3 | 2,020 |
Convolutional Kernel Networks for Graph-Structured Data | 47 | icml | 9 | 2 | 2023-06-17 03:56:53.316000 | https://github.com/claying/GCKN | 47 | Convolutional kernel networks for graph-structured data | https://scholar.google.com/scholar?cluster=6544343038344215140&hl=en&as_sdt=0,14 | 5 | 2,020 |
A Simple Framework for Contrastive Learning of Visual Representations | 10,491 | icml | 570 | 69 | 2023-06-17 03:56:53.522000 | https://github.com/google-research/simclr | 3,562 | A simple framework for contrastive learning of visual representations | https://scholar.google.com/scholar?cluster=13219652991368821610&hl=en&as_sdt=0,23 | 46 | 2,020 |
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search | 66 | icml | 20 | 11 | 2023-06-17 03:56:53.723000 | https://github.com/binghong-ml/retro_star | 101 | Retro*: learning retrosynthetic planning with neural guided A* search | https://scholar.google.com/scholar?cluster=6946559653071134529&hl=en&as_sdt=0,5 | 4 | 2,020 |
Differentiable Product Quantization for End-to-End Embedding Compression | 37 | icml | 10 | 3 | 2023-06-17 03:56:53.924000 | https://github.com/chentingpc/dpq_embedding_compression | 52 | Differentiable product quantization for end-to-end embedding compression | https://scholar.google.com/scholar?cluster=15237200124504416658&hl=en&as_sdt=0,34 | 4 | 2,020 |
VFlow: More Expressive Generative Flows with Variational Data Augmentation | 46 | icml | 3 | 0 | 2023-06-17 03:56:54.126000 | https://github.com/thu-ml/vflow | 34 | Vflow: More expressive generative flows with variational data augmentation | https://scholar.google.com/scholar?cluster=3780987304943068813&hl=en&as_sdt=0,26 | 10 | 2,020 |
Generative Pretraining From Pixels | 1,046 | icml | 362 | 13 | 2023-06-17 03:56:54.329000 | https://github.com/openai/image-gpt | 1,909 | Generative pretraining from pixels | https://scholar.google.com/scholar?cluster=7981583694904172555&hl=en&as_sdt=0,5 | 81 | 2,020 |
Simple and Deep Graph Convolutional Networks | 828 | icml | 64 | 12 | 2023-06-17 03:56:54.531000 | https://github.com/chennnM/GCNII | 270 | Simple and deep graph convolutional networks | https://scholar.google.com/scholar?cluster=16283804483876681464&hl=en&as_sdt=0,18 | 6 | 2,020 |
On Breaking Deep Generative Model-based Defenses and Beyond | 5 | icml | 2 | 0 | 2023-06-17 03:56:54.734000 | https://github.com/cyz-ai/attack_DGM | 7 | On breaking deep generative model-based defenses and beyond | https://scholar.google.com/scholar?cluster=13887603208363837628&hl=en&as_sdt=0,39 | 2 | 2,020 |
Automated Synthetic-to-Real Generalization | 63 | icml | 4 | 3 | 2023-06-17 03:56:54.935000 | https://github.com/NVlabs/ASG | 30 | Automated synthetic-to-real generalization | https://scholar.google.com/scholar?cluster=14261788417891163581&hl=en&as_sdt=0,3 | 16 | 2,020 |
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information | 147 | icml | 35 | 7 | 2023-06-17 03:56:55.137000 | https://github.com/Linear95/CLUB | 226 | Club: A contrastive log-ratio upper bound of mutual information | https://scholar.google.com/scholar?cluster=384230567728582843&hl=en&as_sdt=0,43 | 7 | 2,020 |
Streaming Coresets for Symmetric Tensor Factorization | 9 | icml | 0 | 0 | 2023-06-17 03:56:55.339000 | https://github.com/supratim05/Streaming-Coresets-for-Symmetric-Tensor-Factorization | 0 | Streaming coresets for symmetric tensor factorization | https://scholar.google.com/scholar?cluster=17659645573901290217&hl=en&as_sdt=0,18 | 2 | 2,020 |
Fair Generative Modeling via Weak Supervision | 69 | icml | 7 | 6 | 2023-06-17 03:56:55.540000 | https://github.com/ermongroup/fairgen | 15 | Fair generative modeling via weak supervision | https://scholar.google.com/scholar?cluster=17083056249871731008&hl=en&as_sdt=0,36 | 4 | 2,020 |
Distance Metric Learning with Joint Representation Diversification | 7 | icml | 0 | 0 | 2023-06-17 03:56:55.742000 | https://github.com/YangLin122/JRD | 1 | Distance metric learning with joint representation diversification | https://scholar.google.com/scholar?cluster=1557397264873578069&hl=en&as_sdt=0,33 | 1 | 2,020 |
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations | 32 | icml | 2 | 0 | 2023-06-17 03:56:55.944000 | https://github.com/chingyaoc/estimating-generalization | 21 | Estimating generalization under distribution shifts via domain-invariant representations | https://scholar.google.com/scholar?cluster=2002502648003109319&hl=en&as_sdt=0,10 | 3 | 2,020 |
Boosting Frank-Wolfe by Chasing Gradients | 24 | icml | 2 | 0 | 2023-06-17 03:56:56.145000 | https://github.com/cyrillewcombettes/boostfw | 3 | Boosting Frank-Wolfe by chasing gradients | https://scholar.google.com/scholar?cluster=3076591881269921139&hl=en&as_sdt=0,5 | 2 | 2,020 |
Learnable Group Transform For Time-Series | 14 | icml | 4 | 0 | 2023-06-17 03:56:56.347000 | https://github.com/Koldh/LearnableGroupTransform-TimeSeries | 7 | Learnable group transform for time-series | https://scholar.google.com/scholar?cluster=11923042673090742544&hl=en&as_sdt=0,5 | 3 | 2,020 |
Causal Modeling for Fairness In Dynamical Systems | 42 | icml | 4 | 5 | 2023-06-17 03:56:56.549000 | https://github.com/ecreager/causal-dyna-fair | 8 | Causal modeling for fairness in dynamical systems | https://scholar.google.com/scholar?cluster=12839359629093476958&hl=en&as_sdt=0,44 | 4 | 2,020 |
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack | 307 | icml | 8 | 0 | 2023-06-17 03:56:56.751000 | https://github.com/fra31/fab-attack | 32 | Minimally distorted adversarial examples with a fast adaptive boundary attack | https://scholar.google.com/scholar?cluster=11433432412885384423&hl=en&as_sdt=0,25 | 2 | 2,020 |
Scalable Deep Generative Modeling for Sparse Graphs | 40 | icml | 7,322 | 1,026 | 2023-06-17 03:56:56.953000 | https://github.com/google-research/google-research | 29,791 | Scalable deep generative modeling for sparse graphs | https://scholar.google.com/scholar?cluster=13017453490963979295&hl=en&as_sdt=0,34 | 727 | 2,020 |
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting | 16 | icml | 1 | 2 | 2023-06-17 03:56:57.155000 | https://github.com/Mr8ND/ACORE-LFI | 9 | Confidence sets and hypothesis testing in a likelihood-free inference setting | https://scholar.google.com/scholar?cluster=14385524652709102879&hl=en&as_sdt=0,5 | 2 | 2,020 |
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models | 20 | icml | 11 | 7 | 2023-06-17 03:56:57.356000 | https://github.com/eth-sri/probabilistic-forecasts-attacks | 29 | Adversarial attacks on probabilistic autoregressive forecasting models | https://scholar.google.com/scholar?cluster=1773916962694787403&hl=en&as_sdt=0,5 | 8 | 2,020 |
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction | 116 | icml | 31 | 5 | 2023-06-17 03:56:57.558000 | https://github.com/locuslab/cfd-gcn | 91 | Combining differentiable PDE solvers and graph neural networks for fluid flow prediction | https://scholar.google.com/scholar?cluster=5822388869556870864&hl=en&as_sdt=0,23 | 9 | 2,020 |
Randomly Projected Additive Gaussian Processes for Regression | 24 | icml | 3 | 1 | 2023-06-17 03:56:57.759000 | https://github.com/idelbrid/Randomly-Projected-Additive-GPs | 24 | Randomly projected additive Gaussian processes for regression | https://scholar.google.com/scholar?cluster=11838391975313028153&hl=en&as_sdt=0,5 | 4 | 2,020 |
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC | 27 | icml | 4 | 0 | 2023-06-17 03:56:57.961000 | https://github.com/gaoliyao/Replica_Exchange_Stochastic_Gradient_MCMC | 22 | Non-convex learning via replica exchange stochastic gradient mcmc | https://scholar.google.com/scholar?cluster=6979152849103979749&hl=en&as_sdt=0,5 | 4 | 2,020 |
A Swiss Army Knife for Minimax Optimal Transport | 15 | icml | 1 | 0 | 2023-06-17 03:56:58.163000 | https://github.com/sofiendhouib/minimax_OT | 6 | A swiss army knife for minimax optimal transport | https://scholar.google.com/scholar?cluster=2500404421772704612&hl=en&as_sdt=0,5 | 2 | 2,020 |
Margin-aware Adversarial Domain Adaptation with Optimal Transport | 22 | icml | 3 | 0 | 2023-06-17 03:56:58.365000 | https://github.com/sofiendhouib/MADAOT | 14 | Margin-aware adversarial domain adaptation with optimal transport | https://scholar.google.com/scholar?cluster=5511163225310216545&hl=en&as_sdt=0,1 | 1 | 2,020 |
Growing Adaptive Multi-hyperplane Machines | 1 | icml | 1 | 0 | 2023-06-17 03:56:58.567000 | https://github.com/djurikom/BudgetedSVM | 6 | Growing adaptive multi-hyperplane machines | https://scholar.google.com/scholar?cluster=8685157416945290118&hl=en&as_sdt=0,43 | 1 | 2,020 |
Towards Adaptive Residual Network Training: A Neural-ODE Perspective | 22 | icml | 0 | 0 | 2023-06-17 03:56:58.769000 | https://github.com/shwinshaker/LipGrow | 14 | Towards adaptive residual network training: A neural-ode perspective | https://scholar.google.com/scholar?cluster=790808977072857265&hl=en&as_sdt=0,5 | 4 | 2,020 |
On the Expressivity of Neural Networks for Deep Reinforcement Learning | 21 | icml | 5 | 0 | 2023-06-17 03:56:58.971000 | https://github.com/roosephu/boots | 11 | On the expressivity of neural networks for deep reinforcement learning | https://scholar.google.com/scholar?cluster=10031650459091105952&hl=en&as_sdt=0,10 | 4 | 2,020 |
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