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A Temporal-Difference Approach to Policy Gradient Estimation | 1 | icml | 0 | 0 | 2023-06-17 04:55:39.110000 | https://github.com/samuelepolimi/temporal-difference-gradient | 3 | A Temporal-Difference Approach to Policy Gradient Estimation | https://scholar.google.com/scholar?cluster=12213929390329707477&hl=en&as_sdt=0,40 | 2 | 2,022 |
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization | 5 | icml | 0 | 0 | 2023-06-17 04:55:39.317000 | https://github.com/htt-trangtran/nasg | 0 | Nesterov accelerated shuffling gradient method for convex optimization | https://scholar.google.com/scholar?cluster=14735125807077653853&hl=en&as_sdt=0,5 | 1 | 2,022 |
Tackling covariate shift with node-based Bayesian neural networks | 4 | icml | 0 | 0 | 2023-06-17 04:55:39.523000 | https://github.com/aaltopml/node-bnn-covariate-shift | 6 | Tackling covariate shift with node-based Bayesian neural networks | https://scholar.google.com/scholar?cluster=8088780476336589916&hl=en&as_sdt=0,33 | 7 | 2,022 |
Prototype Based Classification from Hierarchy to Fairness | 1 | icml | 0 | 0 | 2023-06-17 04:55:39.729000 | https://github.com/mycal-tucker/csn | 1 | Prototype Based Classification from Hierarchy to Fairness | https://scholar.google.com/scholar?cluster=11530419927101336822&hl=en&as_sdt=0,5 | 2 | 2,022 |
Path-Gradient Estimators for Continuous Normalizing Flows | 2 | icml | 2 | 0 | 2023-06-17 04:55:39.935000 | https://github.com/lenz3000/ffjord-path | 5 | Path-gradient estimators for continuous normalizing flows | https://scholar.google.com/scholar?cluster=102102598474391702&hl=en&as_sdt=0,33 | 0 | 2,022 |
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning | 10 | icml | 1 | 0 | 2023-06-17 04:55:40.140000 | https://github.com/amitport/eden-distributed-mean-estimation | 7 | Eden: Communication-efficient and robust distributed mean estimation for federated learning | https://scholar.google.com/scholar?cluster=3209500586717789200&hl=en&as_sdt=0,34 | 2 | 2,022 |
Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds | 6 | icml | 1 | 0 | 2023-06-17 04:55:40.346000 | https://github.com/nveldt/fastcc-via-stc | 0 | Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds | https://scholar.google.com/scholar?cluster=18023293593694212775&hl=en&as_sdt=0,23 | 1 | 2,022 |
The CLRS Algorithmic Reasoning Benchmark | 15 | icml | 48 | 4 | 2023-06-17 04:55:40.552000 | https://github.com/deepmind/clrs | 304 | The CLRS algorithmic reasoning benchmark | https://scholar.google.com/scholar?cluster=9181302241653376962&hl=en&as_sdt=0,5 | 13 | 2,022 |
Bregman Power k-Means for Clustering Exponential Family Data | 3 | icml | 1 | 0 | 2023-06-17 04:55:40.759000 | https://github.com/avellal14/bregman_power_kmeans | 3 | Bregman power k-means for clustering exponential family data | https://scholar.google.com/scholar?cluster=10416936130963333532&hl=en&as_sdt=0,33 | 2 | 2,022 |
Calibrated Learning to Defer with One-vs-All Classifiers | 8 | icml | 1 | 0 | 2023-06-17 04:55:40.965000 | https://github.com/rajevv/ova-l2d | 1 | Calibrated learning to defer with one-vs-all classifiers | https://scholar.google.com/scholar?cluster=8829480964232923072&hl=en&as_sdt=0,33 | 1 | 2,022 |
Bayesian Nonparametrics for Offline Skill Discovery | 2 | icml | 1 | 0 | 2023-06-17 04:55:41.171000 | https://github.com/layer6ai-labs/bnpo | 4 | Bayesian nonparametrics for offline skill discovery | https://scholar.google.com/scholar?cluster=5074347961003664860&hl=en&as_sdt=0,33 | 4 | 2,022 |
Hermite Polynomial Features for Private Data Generation | 5 | icml | 1 | 1 | 2023-06-17 04:55:41.376000 | https://github.com/parklabml/dp-hp | 3 | Hermite polynomial features for private data generation | https://scholar.google.com/scholar?cluster=16485118791106646859&hl=en&as_sdt=0,31 | 2 | 2,022 |
Multirate Training of Neural Networks | 3 | icml | 3 | 0 | 2023-06-17 04:55:41.583000 | https://github.com/tiffanyvlaar/multiratetrainingofnns | 3 | Multirate training of neural networks | https://scholar.google.com/scholar?cluster=14672109036130949413&hl=en&as_sdt=0,33 | 2 | 2,022 |
Provably Adversarially Robust Nearest Prototype Classifiers | 1 | icml | 0 | 0 | 2023-06-17 04:55:41.788000 | https://github.com/vvoracek/provably-adversarially-robust-nearest-prototype-classifiers | 4 | Provably Adversarially Robust Nearest Prototype Classifiers | https://scholar.google.com/scholar?cluster=12783036933914721155&hl=en&as_sdt=0,21 | 1 | 2,022 |
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods | 4 | icml | 0 | 0 | 2023-06-17 04:55:41.994000 | https://github.com/chandar-lab/LoCA2 | 2 | Towards evaluating adaptivity of model-based reinforcement learning methods | https://scholar.google.com/scholar?cluster=8278156303366460605&hl=en&as_sdt=0,50 | 3 | 2,022 |
Fast Lossless Neural Compression with Integer-Only Discrete Flows | 3 | icml | 2 | 0 | 2023-06-17 04:55:42.200000 | https://github.com/thu-ml/iodf | 15 | Fast Lossless Neural Compression with Integer-Only Discrete Flows | https://scholar.google.com/scholar?cluster=9606476142959964204&hl=en&as_sdt=0,39 | 8 | 2,022 |
Accelerating Shapley Explanation via Contributive Cooperator Selection | 3 | icml | 1 | 0 | 2023-06-17 04:55:42.406000 | https://github.com/guanchuwang/shear | 10 | Accelerating Shapley Explanation via Contributive Cooperator Selection | https://scholar.google.com/scholar?cluster=2493376524235633954&hl=en&as_sdt=0,5 | 2 | 2,022 |
Denoised MDPs: Learning World Models Better Than the World Itself | 11 | icml | 8 | 0 | 2023-06-17 04:55:42.612000 | https://github.com/facebookresearch/denoised_mdp | 118 | Denoised mdps: Learning world models better than the world itself | https://scholar.google.com/scholar?cluster=4094945741122544681&hl=en&as_sdt=0,33 | 138 | 2,022 |
Robust Models Are More Interpretable Because Attributions Look Normal | 5 | icml | 1 | 1 | 2023-06-17 04:55:42.818000 | https://github.com/zifanw/boundary | 6 | Robust models are more interpretable because attributions look normal | https://scholar.google.com/scholar?cluster=14430069598728045155&hl=en&as_sdt=0,5 | 1 | 2,022 |
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix | 13 | icml | 0 | 1 | 2023-06-17 04:55:43.024000 | https://github.com/ttengwang/vlmixer | 14 | Vlmixer: Unpaired vision-language pre-training via cross-modal cutmix | https://scholar.google.com/scholar?cluster=6137962123845990063&hl=en&as_sdt=0,5 | 6 | 2,022 |
DynaMixer: A Vision MLP Architecture with Dynamic Mixing | 14 | icml | 1 | 1 | 2023-06-17 04:55:43.229000 | https://github.com/ziyuwwang/dynamixer | 19 | Dynamixer: a vision MLP architecture with dynamic mixing | https://scholar.google.com/scholar?cluster=9756910838903336255&hl=en&as_sdt=0,5 | 1 | 2,022 |
Improving Screening Processes via Calibrated Subset Selection | 7 | icml | 2 | 0 | 2023-06-17 04:55:43.445000 | https://github.com/LequnWang/Improve-Screening-via-Calibrated-Subset-Selection | 2 | Improving screening processes via calibrated subset selection | https://scholar.google.com/scholar?cluster=9485317495432772346&hl=en&as_sdt=0,19 | 1 | 2,022 |
What Dense Graph Do You Need for Self-Attention? | 1 | icml | 3 | 0 | 2023-06-17 04:55:43.651000 | https://github.com/yxzwang/normalized-information-payload | 7 | What Dense Graph Do You Need for Self-Attention? | https://scholar.google.com/scholar?cluster=6817431716045479667&hl=en&as_sdt=0,33 | 2 | 2,022 |
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation | 21 | icml | 0 | 0 | 2023-06-17 04:55:43.858000 | https://github.com/wangwenxiao/FiniteAggregation | 5 | Improved certified defenses against data poisoning with (deterministic) finite aggregation | https://scholar.google.com/scholar?cluster=13385935402210758494&hl=en&as_sdt=0,33 | 1 | 2,022 |
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond | 8 | icml | 0 | 0 | 2023-06-17 04:55:44.063000 | https://github.com/Haoxiang-Wang/gradual-domain-adaptation | 6 | Understanding gradual domain adaptation: Improved analysis, optimal path and beyond | https://scholar.google.com/scholar?cluster=8368642919883535588&hl=en&as_sdt=0,33 | 3 | 2,022 |
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering | 2 | icml | 0 | 0 | 2023-06-17 04:55:44.268000 | https://github.com/peng8wang/icml2022-k-subspaces | 1 | Convergence and recovery guarantees of the k-subspaces method for subspace clustering | https://scholar.google.com/scholar?cluster=4190201275040810423&hl=en&as_sdt=0,15 | 1 | 2,022 |
NP-Match: When Neural Processes meet Semi-Supervised Learning | 11 | icml | 20 | 0 | 2023-06-17 04:55:44.475000 | https://github.com/jianf-wang/np-match | 126 | Np-match: When neural processes meet semi-supervised learning | https://scholar.google.com/scholar?cluster=13863868059773263765&hl=en&as_sdt=0,5 | 14 | 2,022 |
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution | 11 | icml | 0 | 0 | 2023-06-17 04:55:44.680000 | https://github.com/joey-wang123/DRO-Task-free | 10 | Improving task-free continual learning by distributionally robust memory evolution | https://scholar.google.com/scholar?cluster=14894776006626228965&hl=en&as_sdt=0,47 | 1 | 2,022 |
Provable Domain Generalization via Invariant-Feature Subspace Recovery | 10 | icml | 3 | 0 | 2023-06-17 04:55:44.887000 | https://github.com/haoxiang-wang/isr | 15 | Provable domain generalization via invariant-feature subspace recovery | https://scholar.google.com/scholar?cluster=16846223791215545357&hl=en&as_sdt=0,46 | 3 | 2,022 |
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training | 15 | icml | 5 | 1 | 2023-06-17 04:55:45.093000 | https://github.com/a514514772/progfed | 14 | ProgFed: effective, communication, and computation efficient federated learning by progressive training | https://scholar.google.com/scholar?cluster=14093452975120098193&hl=en&as_sdt=0,5 | 2 | 2,022 |
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics | 25 | icml | 0 | 0 | 2023-06-17 04:55:45.299000 | https://github.com/rose-stl-lab/approximately-equivariant-nets | 7 | Approximately equivariant networks for imperfectly symmetric dynamics | https://scholar.google.com/scholar?cluster=5872423159806810171&hl=en&as_sdt=0,10 | 1 | 2,022 |
Understanding Instance-Level Impact of Fairness Constraints | 6 | icml | 0 | 1 | 2023-06-17 04:55:45.505000 | https://github.com/ucsc-real/fairinfl | 5 | Understanding instance-level impact of fairness constraints | https://scholar.google.com/scholar?cluster=3186856282017277340&hl=en&as_sdt=0,4 | 1 | 2,022 |
Causal Dynamics Learning for Task-Independent State Abstraction | 11 | icml | 4 | 2 | 2023-06-17 04:55:45.711000 | https://github.com/wangzizhao/causaldynamicslearning | 16 | Causal dynamics learning for task-independent state abstraction | https://scholar.google.com/scholar?cluster=7092132108841275612&hl=en&as_sdt=0,33 | 1 | 2,022 |
Generative Coarse-Graining of Molecular Conformations | 14 | icml | 5 | 0 | 2023-06-17 04:55:45.918000 | https://github.com/wwang2/coarsegrainingvae | 22 | Generative coarse-graining of molecular conformations | https://scholar.google.com/scholar?cluster=6589570772523921711&hl=en&as_sdt=0,44 | 4 | 2,022 |
How Powerful are Spectral Graph Neural Networks | 34 | icml | 9 | 0 | 2023-06-17 04:55:46.123000 | https://github.com/graphpku/jacobiconv | 56 | How powerful are spectral graph neural networks | https://scholar.google.com/scholar?cluster=17960766448265380456&hl=en&as_sdt=0,33 | 1 | 2,022 |
Thompson Sampling for Robust Transfer in Multi-Task Bandits | 1 | icml | 0 | 0 | 2023-06-17 04:55:46.329000 | https://github.com/zhiwang123/eps-mpmab-ts | 0 | Thompson Sampling for Robust Transfer in Multi-Task Bandits | https://scholar.google.com/scholar?cluster=9498764153726193190&hl=en&as_sdt=0,15 | 2 | 2,022 |
Removing Batch Normalization Boosts Adversarial Training | 12 | icml | 0 | 1 | 2023-06-17 04:55:46.534000 | https://github.com/amazon-research/normalizer-free-robust-training | 17 | Removing batch normalization boosts adversarial training | https://scholar.google.com/scholar?cluster=4233277386290159249&hl=en&as_sdt=0,39 | 4 | 2,022 |
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition | 11 | icml | 5 | 5 | 2023-06-17 04:55:46.740000 | https://github.com/amazon-research/long-tailed-ood-detection | 29 | Partial and asymmetric contrastive learning for out-of-distribution detection in long-tailed recognition | https://scholar.google.com/scholar?cluster=14212057730611759763&hl=en&as_sdt=0,33 | 6 | 2,022 |
Certifying Out-of-Domain Generalization for Blackbox Functions | 7 | icml | 0 | 0 | 2023-06-17 04:55:46.946000 | https://github.com/ds3lab/certified-generalization | 2 | Certifying out-of-domain generalization for blackbox functions | https://scholar.google.com/scholar?cluster=5540253257951212310&hl=en&as_sdt=0,5 | 6 | 2,022 |
To Smooth or Not? When Label Smoothing Meets Noisy Labels | 11 | icml | 9 | 2 | 2023-06-17 04:55:47.152000 | https://github.com/ucsc-real/negative-label-smoothing | 75 | To smooth or not? when label smoothing meets noisy labels | https://scholar.google.com/scholar?cluster=18297648993704774023&hl=en&as_sdt=0,5 | 10 | 2,022 |
Mitigating Neural Network Overconfidence with Logit Normalization | 45 | icml | 12 | 3 | 2023-06-17 04:55:47.359000 | https://github.com/hongxin001/logitnorm_ood | 113 | Mitigating neural network overconfidence with logit normalization | https://scholar.google.com/scholar?cluster=3765768230173383060&hl=en&as_sdt=0,19 | 1 | 2,022 |
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification | 27 | icml | 37 | 0 | 2023-06-17 04:55:47.565000 | https://github.com/JonasGeiping/breaching | 178 | Fishing for user data in large-batch federated learning via gradient magnification | https://scholar.google.com/scholar?cluster=11388041584211331417&hl=en&as_sdt=0,34 | 3 | 2,022 |
Measure Estimation in the Barycentric Coding Model | 2 | icml | 0 | 0 | 2023-06-17 04:55:47.771000 | https://github.com/mattwerenski/bcm | 2 | Measure Estimation in the Barycentric Coding Model | https://scholar.google.com/scholar?cluster=3529680784651732155&hl=en&as_sdt=0,3 | 2 | 2,022 |
COLA: Consistent Learning with Opponent-Learning Awareness | 19 | icml | 0 | 0 | 2023-06-17 04:55:47.977000 | https://github.com/aidandos/cola | 5 | COLA: consistent learning with opponent-learning awareness | https://scholar.google.com/scholar?cluster=14450342073245803366&hl=en&as_sdt=0,33 | 2 | 2,022 |
Easy Variational Inference for Categorical Models via an Independent Binary Approximation | 0 | icml | 0 | 0 | 2023-06-17 04:55:48.184000 | https://github.com/tufts-ml/categorical-from-binary | 2 | Easy Variational Inference for Categorical Models via an Independent Binary Approximation | https://scholar.google.com/scholar?cluster=13180457782658047792&hl=en&as_sdt=0,36 | 4 | 2,022 |
Continual Learning with Guarantees via Weight Interval Constraints | 1 | icml | 0 | 0 | 2023-06-17 04:55:48.390000 | https://github.com/gmum/intercontinet | 2 | Continual Learning with Guarantees via Weight Interval Constraints | https://scholar.google.com/scholar?cluster=12644818321484154250&hl=en&as_sdt=0,33 | 5 | 2,022 |
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications | 0 | icml | 0 | 0 | 2023-06-17 04:55:48.596000 | https://github.com/lu-wo/detrtime | 12 | A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications | https://scholar.google.com/scholar?cluster=561665774245262907&hl=en&as_sdt=0,10 | 2 | 2,022 |
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time | 221 | icml | 21 | 2 | 2023-06-17 04:55:48.820000 | https://github.com/mlfoundations/model-soups | 236 | Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time | https://scholar.google.com/scholar?cluster=16922194924900565989&hl=en&as_sdt=0,5 | 10 | 2,022 |
Structural Entropy Guided Graph Hierarchical Pooling | 11 | icml | 5 | 3 | 2023-06-17 04:55:49.030000 | https://github.com/wu-junran/sep | 20 | Structural entropy guided graph hierarchical pooling | https://scholar.google.com/scholar?cluster=15391796189805731538&hl=en&as_sdt=0,26 | 1 | 2,022 |
Characterizing and Overcoming the Greedy Nature of Learning in Multi-modal Deep Neural Networks | 15 | icml | 1 | 1 | 2023-06-17 04:55:49.236000 | https://github.com/nyukat/greedy_multimodal_learning | 19 | Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks | https://scholar.google.com/scholar?cluster=12235200636315362810&hl=en&as_sdt=0,44 | 2 | 2,022 |
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum | 4 | icml | 0 | 0 | 2023-06-17 04:55:49.457000 | https://github.com/jlwu002/bcl | 4 | Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum | https://scholar.google.com/scholar?cluster=6530213985097280080&hl=en&as_sdt=0,31 | 1 | 2,022 |
Flowformer: Linearizing Transformers with Conservation Flows | 13 | icml | 27 | 0 | 2023-06-17 04:55:49.663000 | https://github.com/thuml/Flowformer | 237 | Flowformer: Linearizing transformers with conservation flows | https://scholar.google.com/scholar?cluster=13534095276250575794&hl=en&as_sdt=0,5 | 8 | 2,022 |
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning | 29 | icml | 2 | 4 | 2023-06-17 04:55:49.872000 | https://github.com/junxia97/progcl | 32 | Progcl: Rethinking hard negative mining in graph contrastive learning | https://scholar.google.com/scholar?cluster=3134502444981244972&hl=en&as_sdt=0,33 | 1 | 2,022 |
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations | 18 | icml | 0 | 0 | 2023-06-17 04:55:50.079000 | https://github.com/ryanxhr/dwbc | 25 | Discriminator-weighted offline imitation learning from suboptimal demonstrations | https://scholar.google.com/scholar?cluster=12184701455253705252&hl=en&as_sdt=0,21 | 1 | 2,022 |
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests | 1 | icml | 0 | 0 | 2023-06-17 04:55:50.285000 | https://github.com/godxuxilie/robust-tst | 3 | Adversarial Attack and Defense for Non-Parametric Two-Sample Tests | https://scholar.google.com/scholar?cluster=16006347209208499674&hl=en&as_sdt=0,5 | 2 | 2,022 |
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization | 4 | icml | 0 | 0 | 2023-06-17 04:55:50.492000 | https://github.com/windxrz/independence-driven-iw | 9 | A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization | https://scholar.google.com/scholar?cluster=14134137266916397351&hl=en&as_sdt=0,5 | 1 | 2,022 |
Langevin Monte Carlo for Contextual Bandits | 6 | icml | 3 | 0 | 2023-06-17 04:55:50.699000 | https://github.com/devzhk/lmcts | 8 | Langevin monte carlo for contextual bandits | https://scholar.google.com/scholar?cluster=17947059462373456392&hl=en&as_sdt=0,5 | 1 | 2,022 |
Diversified Adversarial Attacks based on Conjugate Gradient Method | 6 | icml | 2 | 0 | 2023-06-17 04:55:50.906000 | https://github.com/yamamura-k/ACG | 5 | Diversified Adversarial Attacks based on Conjugate Gradient Method | https://scholar.google.com/scholar?cluster=13855220363786968422&hl=en&as_sdt=0,33 | 2 | 2,022 |
Cycle Representation Learning for Inductive Relation Prediction | 4 | icml | 2 | 2 | 2023-06-17 04:55:51.112000 | https://github.com/pkuyzy/cbgnn | 4 | Cycle Representation Learning for Inductive Relation Prediction | https://scholar.google.com/scholar?cluster=2061126116449549118&hl=en&as_sdt=0,41 | 1 | 2,022 |
Optimally Controllable Perceptual Lossy Compression | 2 | icml | 2 | 1 | 2023-06-17 04:55:51.318000 | https://github.com/zeyuyan/controllable-perceptual-compression | 9 | Optimally Controllable Perceptual Lossy Compression | https://scholar.google.com/scholar?cluster=15214339197144115082&hl=en&as_sdt=0,32 | 3 | 2,022 |
Self-Organized Polynomial-Time Coordination Graphs | 3 | icml | 0 | 0 | 2023-06-17 04:55:51.524000 | https://github.com/yanQval/SOP-CG | 3 | Self-organized polynomial-time coordination graphs | https://scholar.google.com/scholar?cluster=10295867697115976866&hl=en&as_sdt=0,19 | 1 | 2,022 |
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning | 4 | icml | 0 | 0 | 2023-06-17 04:55:51.730000 | https://github.com/shentao-yang/sdm-gan_icml2022 | 2 | Regularizing a model-based policy stationary distribution to stabilize offline reinforcement learning | https://scholar.google.com/scholar?cluster=1188226225988660555&hl=en&as_sdt=0,33 | 1 | 2,022 |
Does the Data Induce Capacity Control in Deep Learning? | 13 | icml | 0 | 0 | 2023-06-17 04:55:51.935000 | https://github.com/grasp-lyrl/sloppy | 1 | Does the data induce capacity control in deep learning? | https://scholar.google.com/scholar?cluster=884919534291840762&hl=en&as_sdt=0,36 | 0 | 2,022 |
A New Perspective on the Effects of Spectrum in Graph Neural Networks | 5 | icml | 6 | 0 | 2023-06-17 04:55:52.141000 | https://github.com/qslim/gnn-spectrum | 16 | A new perspective on the effects of spectrum in graph neural networks | https://scholar.google.com/scholar?cluster=12355104145181167707&hl=en&as_sdt=0,5 | 1 | 2,022 |
A Study of Face Obfuscation in ImageNet | 90 | icml | 12 | 1 | 2023-06-17 04:55:52.349000 | https://github.com/princetonvisualai/imagenet-face-obfuscation | 40 | A study of face obfuscation in imagenet | https://scholar.google.com/scholar?cluster=18170664845630332563&hl=en&as_sdt=0,33 | 7 | 2,022 |
Improving Out-of-Distribution Robustness via Selective Augmentation | 49 | icml | 5 | 2 | 2023-06-17 04:55:52.555000 | https://github.com/huaxiuyao/LISA | 35 | Improving out-of-distribution robustness via selective augmentation | https://scholar.google.com/scholar?cluster=4894079975600009568&hl=en&as_sdt=0,31 | 1 | 2,022 |
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework | 22 | icml | 21 | 8 | 2023-06-17 04:55:52.761000 | https://github.com/yaoxingcheng/TLM | 240 | Nlp from scratch without large-scale pretraining: A simple and efficient framework | https://scholar.google.com/scholar?cluster=3254978626719045112&hl=en&as_sdt=0,5 | 5 | 2,022 |
Feature Space Particle Inference for Neural Network Ensembles | 4 | icml | 0 | 0 | 2023-06-17 04:55:52.966000 | https://github.com/densoitlab/featurepi | 4 | Feature space particle inference for neural network ensembles | https://scholar.google.com/scholar?cluster=11870961066098934714&hl=en&as_sdt=0,44 | 3 | 2,022 |
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks | 5 | icml | 1 | 1 | 2023-06-17 04:55:53.172000 | https://github.com/rice-eic/shiftaddnas | 11 | ShiftAddNAS: Hardware-inspired search for more accurate and efficient neural networks | https://scholar.google.com/scholar?cluster=17026416337828414455&hl=en&as_sdt=0,33 | 2 | 2,022 |
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks | 7 | icml | 10 | 0 | 2023-06-17 04:55:53.378000 | https://github.com/zhaoningyu1996/hm-gnn | 23 | Molecular representation learning via heterogeneous motif graph neural networks | https://scholar.google.com/scholar?cluster=16142260161361576450&hl=en&as_sdt=0,33 | 2 | 2,022 |
Understanding Robust Overfitting of Adversarial Training and Beyond | 13 | icml | 0 | 1 | 2023-06-17 04:55:53.584000 | https://github.com/chaojianyu/understanding-robust-overfitting | 10 | Understanding robust overfitting of adversarial training and beyond | https://scholar.google.com/scholar?cluster=4696544864566467358&hl=en&as_sdt=0,6 | 1 | 2,022 |
Reachability Constrained Reinforcement Learning | 10 | icml | 2 | 0 | 2023-06-17 04:55:53.791000 | https://github.com/mahaitongdae/Reachability_Constrained_RL | 13 | Reachability constrained reinforcement learning | https://scholar.google.com/scholar?cluster=2404570936990332675&hl=en&as_sdt=0,31 | 3 | 2,022 |
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning | 12 | icml | 10 | 1 | 2023-06-17 04:55:53.996000 | https://github.com/yusx-swapp/gnn-rl-model-compression | 36 | Topology-aware network pruning using multi-stage graph embedding and reinforcement learning | https://scholar.google.com/scholar?cluster=9807843131373835884&hl=en&as_sdt=0,47 | 2 | 2,022 |
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks | 14 | icml | 1 | 0 | 2023-06-17 04:55:54.202000 | https://github.com/yuxwind/cbs | 8 | The combinatorial brain surgeon: Pruning weights that cancel one another in neural networks | https://scholar.google.com/scholar?cluster=2256443788852509146&hl=en&as_sdt=0,11 | 1 | 2,022 |
GraphFM: Improving Large-Scale GNN Training via Feature Momentum | 8 | icml | 239 | 19 | 2023-06-17 04:55:54.407000 | https://github.com/divelab/DIG | 1,503 | GraphFM: Improving large-scale GNN training via feature momentum | https://scholar.google.com/scholar?cluster=14093235266162728639&hl=en&as_sdt=0,33 | 33 | 2,022 |
Predicting Out-of-Distribution Error with the Projection Norm | 9 | icml | 0 | 0 | 2023-06-17 04:55:54.613000 | https://github.com/yaodongyu/projnorm | 13 | Predicting out-of-distribution error with the projection norm | https://scholar.google.com/scholar?cluster=14580458746203726066&hl=en&as_sdt=0,14 | 2 | 2,022 |
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning | 5 | icml | 3 | 3 | 2023-06-17 04:55:54.819000 | https://github.com/pku-ai-edge/corro | 15 | Robust task representations for offline meta-reinforcement learning via contrastive learning | https://scholar.google.com/scholar?cluster=5539110127380539643&hl=en&as_sdt=0,34 | 0 | 2,022 |
Time Is MattEr: Temporal Self-supervision for Video Transformers | 3 | icml | 4 | 1 | 2023-06-17 04:55:55.024000 | https://github.com/alinlab/temporal-selfsupervision | 26 | Time is matter: Temporal self-supervision for video transformers | https://scholar.google.com/scholar?cluster=10001737047837090145&hl=en&as_sdt=0,33 | 2 | 2,022 |
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images | 5 | icml | 0 | 3 | 2023-06-17 04:55:55.231000 | https://github.com/shiranzada/pure-noise | 9 | Pure noise to the rescue of insufficient data: Improving imbalanced classification by training on random noise images | https://scholar.google.com/scholar?cluster=13535908408356605995&hl=en&as_sdt=0,5 | 2 | 2,022 |
Adaptive Conformal Predictions for Time Series | 28 | icml | 10 | 1 | 2023-06-17 04:55:55.437000 | https://github.com/mzaffran/adaptiveconformalpredictionstimeseries | 30 | Adaptive conformal predictions for time series | https://scholar.google.com/scholar?cluster=6242332424381793143&hl=en&as_sdt=0,33 | 1 | 2,022 |
Multi Resolution Analysis (MRA) for Approximate Self-Attention | 2 | icml | 2 | 0 | 2023-06-17 04:55:55.643000 | https://github.com/mlpen/mra-attention | 6 | Multi Resolution Analysis (MRA) for Approximate Self-Attention | https://scholar.google.com/scholar?cluster=184055539633336213&hl=en&as_sdt=0,44 | 1 | 2,022 |
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts | 91 | icml | 49 | 15 | 2023-06-17 04:55:55.850000 | https://github.com/zengyan-97/x-vlm | 365 | Multi-grained vision language pre-training: Aligning texts with visual concepts | https://scholar.google.com/scholar?cluster=8119995839638175849&hl=en&as_sdt=0,5 | 5 | 2,022 |
PDE-Based Optimal Strategy for Unconstrained Online Learning | 5 | icml | 0 | 0 | 2023-06-17 04:55:56.056000 | https://github.com/zhiyuzz/icml2022-pde-potential | 0 | PDE-based optimal strategy for unconstrained online learning | https://scholar.google.com/scholar?cluster=2664380085986514830&hl=en&as_sdt=0,44 | 1 | 2,022 |
Revisiting End-to-End Speech-to-Text Translation From Scratch | 11 | icml | 21 | 0 | 2023-06-17 04:55:56.269000 | https://github.com/bzhangGo/zero | 135 | Revisiting end-to-end speech-to-text translation from scratch | https://scholar.google.com/scholar?cluster=1521111115547925534&hl=en&as_sdt=0,34 | 6 | 2,022 |
GALAXY: Graph-based Active Learning at the Extreme | 5 | icml | 0 | 0 | 2023-06-17 04:55:56.476000 | https://github.com/jifanz/GALAXY | 6 | GALAXY: graph-based active learning at the extreme | https://scholar.google.com/scholar?cluster=10022632741658948627&hl=en&as_sdt=0,33 | 1 | 2,022 |
A Langevin-like Sampler for Discrete Distributions | 9 | icml | 3 | 0 | 2023-06-17 04:55:56.682000 | https://github.com/ruqizhang/discrete-langevin | 18 | A Langevin-like sampler for discrete distributions | https://scholar.google.com/scholar?cluster=3541239242626478838&hl=en&as_sdt=0,33 | 3 | 2,022 |
Rich Feature Construction for the Optimization-Generalization Dilemma | 13 | icml | 1 | 1 | 2023-06-17 04:55:56.889000 | https://github.com/tjujianyu/rfc | 8 | Rich feature construction for the optimization-generalization dilemma | https://scholar.google.com/scholar?cluster=4651591858912243934&hl=en&as_sdt=0,33 | 2 | 2,022 |
Generative Flow Networks for Discrete Probabilistic Modeling | 21 | icml | 16 | 0 | 2023-06-17 04:55:57.094000 | https://github.com/zdhnarsil/eb_gfn | 62 | Generative flow networks for discrete probabilistic modeling | https://scholar.google.com/scholar?cluster=5719959167998853445&hl=en&as_sdt=0,43 | 2 | 2,022 |
Neurotoxin: Durable Backdoors in Federated Learning | 19 | icml | 3 | 5 | 2023-06-17 04:55:57.300000 | https://github.com/jhcknzzm/federated-learning-backdoor | 39 | Neurotoxin: Durable backdoors in federated learning | https://scholar.google.com/scholar?cluster=15130248935781363426&hl=en&as_sdt=0,5 | 3 | 2,022 |
Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations | 40 | icml | 4 | 0 | 2023-06-17 04:55:57.506000 | https://github.com/HazyResearch/correct-n-contrast | 14 | Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations | https://scholar.google.com/scholar?cluster=8960959356014477531&hl=en&as_sdt=0,33 | 19 | 2,022 |
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach | 25 | icml | 1 | 0 | 2023-06-17 04:55:57.712000 | https://github.com/yudasong/briee | 11 | Efficient reinforcement learning in block mdps: A model-free representation learning approach | https://scholar.google.com/scholar?cluster=10850889224658556483&hl=en&as_sdt=0,33 | 2 | 2,022 |
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets | 0 | icml | 2 | 0 | 2023-06-17 04:55:57.918000 | https://github.com/rajesh-lab/deep_permutation_invariant | 10 | Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets | https://scholar.google.com/scholar?cluster=8359318767015654610&hl=en&as_sdt=0,31 | 2 | 2,022 |
Learning to Estimate and Refine Fluid Motion with Physical Dynamics | 6 | icml | 4 | 1 | 2023-06-17 04:55:58.125000 | https://github.com/erizmr/learn-to-estimate-fluid-motion | 11 | Learning to estimate and refine fluid motion with physical dynamics | https://scholar.google.com/scholar?cluster=7117659598027113757&hl=en&as_sdt=0,31 | 2 | 2,022 |
Low-Precision Stochastic Gradient Langevin Dynamics | 2 | icml | 1 | 0 | 2023-06-17 04:55:58.332000 | https://github.com/ruqizhang/low-precision-sgld | 5 | Low-Precision Stochastic Gradient Langevin Dynamics | https://scholar.google.com/scholar?cluster=5250731865302553140&hl=en&as_sdt=0,34 | 2 | 2,022 |
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control | 10 | icml | 3 | 0 | 2023-06-17 04:55:58.545000 | https://github.com/LiangZhang1996/Advanced_XLight | 14 | Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control | https://scholar.google.com/scholar?cluster=995321608406249380&hl=en&as_sdt=0,33 | 1 | 2,022 |
Building Robust Ensembles via Margin Boosting | 8 | icml | 0 | 1 | 2023-06-17 04:55:58.751000 | https://github.com/zdhnarsil/margin-boosting | 7 | Building robust ensembles via margin boosting | https://scholar.google.com/scholar?cluster=13608655782211931186&hl=en&as_sdt=0,47 | 2 | 2,022 |
ROCK: Causal Inference Principles for Reasoning about Commonsense Causality | 2 | icml | 1 | 1 | 2023-06-17 04:55:58.958000 | https://github.com/zjiayao/ccr_rock | 7 | ROCK: Causal Inference Principles for Reasoning about Commonsense Causality | https://scholar.google.com/scholar?cluster=4757630172142505662&hl=en&as_sdt=0,41 | 1 | 2,022 |
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance | 14 | icml | 4 | 1 | 2023-06-17 04:55:59.168000 | https://github.com/qingruzhang/platon | 21 | Platon: Pruning large transformer models with upper confidence bound of weight importance | https://scholar.google.com/scholar?cluster=17654209064614422018&hl=en&as_sdt=0,34 | 2 | 2,022 |
Learning from Counterfactual Links for Link Prediction | 31 | icml | 6 | 1 | 2023-06-17 04:55:59.378000 | https://github.com/DM2-ND/CFLP | 49 | Learning from counterfactual links for link prediction | https://scholar.google.com/scholar?cluster=12649708640262432051&hl=en&as_sdt=0,33 | 2 | 2,022 |
Certified Robustness Against Natural Language Attacks by Causal Intervention | 4 | icml | 3 | 0 | 2023-06-17 04:55:59.591000 | https://github.com/zhao-ht/convexcertify | 7 | Certified Robustness Against Natural Language Attacks by Causal Intervention | https://scholar.google.com/scholar?cluster=16167491038280669708&hl=en&as_sdt=0,10 | 1 | 2,022 |
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