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Online Variance Reduction with Mixtures | 14 | icml | 1 | 0 | 2023-06-17 03:10:02.470000 | https://github.com/zalanborsos/variance-reduction-mixtures | 3 | Online variance reduction with mixtures | https://scholar.google.com/scholar?cluster=14403425847063612414&hl=en&as_sdt=0,10 | 2 | 2,019 |
Compositional Fairness Constraints for Graph Embeddings | 197 | icml | 18 | 2 | 2023-06-17 03:10:02.685000 | https://github.com/joeybose/Flexible-Fairness-Constraints | 44 | Compositional fairness constraints for graph embeddings | https://scholar.google.com/scholar?cluster=2983154672519525426&hl=en&as_sdt=0,5 | 4 | 2,019 |
Active Manifolds: A non-linear analogue to Active Subspaces | 21 | icml | 2 | 0 | 2023-06-17 03:10:02.900000 | https://github.com/bridgesra/active-manifold-icml2019-code | 4 | Active Manifolds: A non-linear analogue to Active Subspaces | https://scholar.google.com/scholar?cluster=12766453925065005709&hl=en&as_sdt=0,31 | 2 | 2,019 |
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations | 240 | icml | 21 | 3 | 2023-06-17 03:10:03.116000 | https://github.com/hiwonjoon/ICML2019-TREX | 71 | Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations | https://scholar.google.com/scholar?cluster=14944046691955331663&hl=en&as_sdt=0,10 | 6 | 2,019 |
Understanding the Origins of Bias in Word Embeddings | 168 | icml | 12 | 3 | 2023-06-17 03:10:03.331000 | https://github.com/mebrunet/understanding-bias | 21 | Understanding the origins of bias in word embeddings | https://scholar.google.com/scholar?cluster=18061585171680402541&hl=en&as_sdt=0,5 | 3 | 2,019 |
Low Latency Privacy Preserving Inference | 161 | icml | 68 | 0 | 2023-06-17 03:10:03.547000 | https://github.com/microsoft/CryptoNets | 242 | Low latency privacy preserving inference | https://scholar.google.com/scholar?cluster=86142108232916247&hl=en&as_sdt=0,5 | 13 | 2,019 |
Active Embedding Search via Noisy Paired Comparisons | 16 | icml | 3 | 0 | 2023-06-17 03:10:03.764000 | https://github.com/siplab-gt/pairsearch | 9 | Active embedding search via noisy paired comparisons | https://scholar.google.com/scholar?cluster=10123441327203003064&hl=en&as_sdt=0,23 | 3 | 2,019 |
Dynamic Measurement Scheduling for Event Forecasting using Deep RL | 12 | icml | 6 | 1 | 2023-06-17 03:10:03.978000 | https://github.com/zzzace2000/autodiagnosis | 9 | Dynamic measurement scheduling for event forecasting using deep RL | https://scholar.google.com/scholar?cluster=16682086403586827063&hl=en&as_sdt=0,5 | 3 | 2,019 |
Stein Point Markov Chain Monte Carlo | 55 | icml | 4 | 1 | 2023-06-17 03:10:04.193000 | https://github.com/wilson-ye-chen/sp-mcmc | 12 | Stein point markov chain monte carlo | https://scholar.google.com/scholar?cluster=6889028915730960186&hl=en&as_sdt=0,33 | 0 | 2,019 |
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System | 165 | icml | 30 | 4 | 2023-06-17 03:10:04.408000 | https://github.com/xinshi-chen/GenerativeAdversarialUserModel | 124 | Generative adversarial user model for reinforcement learning based recommendation system | https://scholar.google.com/scholar?cluster=18416272509453441398&hl=en&as_sdt=0,5 | 4 | 2,019 |
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels | 291 | icml | 23 | 2 | 2023-06-17 03:10:04.622000 | https://github.com/chenpf1025/noisy_label_understanding_utilizing | 82 | Understanding and utilizing deep neural networks trained with noisy labels | https://scholar.google.com/scholar?cluster=1459914703144318986&hl=en&as_sdt=0,33 | 6 | 2,019 |
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation | 358 | icml | 17 | 3 | 2023-06-17 03:10:04.837000 | https://github.com/thuml/Batch-Spectral-Penalization | 78 | Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation | https://scholar.google.com/scholar?cluster=8590630247063758749&hl=en&as_sdt=0,5 | 5 | 2,019 |
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications | 24 | icml | 6 | 0 | 2023-06-17 03:10:05.054000 | https://github.com/pinyuchen/FINGER | 6 | Fast incremental von neumann graph entropy computation: Theory, algorithm, and applications | https://scholar.google.com/scholar?cluster=15943782295657868941&hl=en&as_sdt=0,5 | 2 | 2,019 |
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching | 7 | icml | 1 | 0 | 2023-06-17 03:10:05.269000 | https://github.com/MintYiqingchen/MMI-ALI | 5 | Multivariate-information adversarial ensemble for scalable joint distribution matching | https://scholar.google.com/scholar?cluster=2407036909986043494&hl=en&as_sdt=0,5 | 0 | 2,019 |
Robust Decision Trees Against Adversarial Examples | 105 | icml | 11 | 3 | 2023-06-17 03:10:05.483000 | https://github.com/chenhongge/RobustTrees | 64 | Robust decision trees against adversarial examples | https://scholar.google.com/scholar?cluster=18298482644739407816&hl=en&as_sdt=0,31 | 8 | 2,019 |
RaFM: Rank-Aware Factorization Machines | 12 | icml | 6 | 1 | 2023-06-17 03:10:05.698000 | https://github.com/cxsmarkchan/RaFM | 12 | RaFM: rank-aware factorization machines | https://scholar.google.com/scholar?cluster=9961787920931572726&hl=en&as_sdt=0,11 | 4 | 2,019 |
Control Regularization for Reduced Variance Reinforcement Learning | 67 | icml | 5 | 0 | 2023-06-17 03:10:05.913000 | https://github.com/rcheng805/CORE-RL | 30 | Control regularization for reduced variance reinforcement learning | https://scholar.google.com/scholar?cluster=4210711157444974813&hl=en&as_sdt=0,5 | 1 | 2,019 |
Predictor-Corrector Policy Optimization | 21 | icml | 3 | 1 | 2023-06-17 03:10:06.127000 | https://github.com/gtrll/rlfamily | 3 | Predictor-corrector policy optimization | https://scholar.google.com/scholar?cluster=13913575152899689436&hl=en&as_sdt=0,31 | 4 | 2,019 |
Neural Joint Source-Channel Coding | 96 | icml | 13 | 2 | 2023-06-17 03:10:06.342000 | https://github.com/ermongroup/necst | 38 | Neural joint source-channel coding | https://scholar.google.com/scholar?cluster=13260217163651536800&hl=en&as_sdt=0,5 | 5 | 2,019 |
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables | 54 | icml | 14 | 0 | 2023-06-17 03:10:06.556000 | https://github.com/IBM/online-alt-min | 22 | Beyond backprop: Online alternating minimization with auxiliary variables | https://scholar.google.com/scholar?cluster=13143560607415133217&hl=en&as_sdt=0,5 | 9 | 2,019 |
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization | 161 | icml | 52 | 16 | 2023-06-17 03:10:06.771000 | https://github.com/sosuperic/MeanSum | 112 | Meansum: A neural model for unsupervised multi-document abstractive summarization | https://scholar.google.com/scholar?cluster=11126017598001925179&hl=en&as_sdt=0,33 | 10 | 2,019 |
Quantifying Generalization in Reinforcement Learning | 532 | icml | 84 | 3 | 2023-06-17 03:10:06.985000 | https://github.com/openai/coinrun | 361 | Quantifying generalization in reinforcement learning | https://scholar.google.com/scholar?cluster=9870113474300692969&hl=en&as_sdt=0,26 | 134 | 2,019 |
Certified Adversarial Robustness via Randomized Smoothing | 1,382 | icml | 71 | 3 | 2023-06-17 03:10:07.200000 | https://github.com/locuslab/smoothing | 318 | Certified adversarial robustness via randomized smoothing | https://scholar.google.com/scholar?cluster=7039519782328477041&hl=en&as_sdt=0,14 | 11 | 2,019 |
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning | 174 | icml | 6 | 1 | 2023-06-17 03:10:07.415000 | https://github.com/flowersteam/curious | 26 | Curious: intrinsically motivated modular multi-goal reinforcement learning | https://scholar.google.com/scholar?cluster=329489517258350795&hl=en&as_sdt=0,48 | 12 | 2,019 |
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets | 17 | icml | 1 | 0 | 2023-06-17 03:10:07.629000 | https://github.com/pjcv/smh | 3 | Scalable Metropolis-Hastings for exact Bayesian inference with large datasets | https://scholar.google.com/scholar?cluster=10400262915897387298&hl=en&as_sdt=0,33 | 1 | 2,019 |
Minimal Achievable Sufficient Statistic Learning | 12 | icml | 4 | 0 | 2023-06-17 03:10:07.844000 | https://github.com/mwcvitkovic/MASS-Learning | 8 | Minimal achievable sufficient statistic learning | https://scholar.google.com/scholar?cluster=16216829176165913924&hl=en&as_sdt=0,33 | 3 | 2,019 |
Open Vocabulary Learning on Source Code with a Graph-Structured Cache | 42 | icml | 10 | 0 | 2023-06-17 03:10:08.060000 | https://github.com/mwcvitkovic/Deep_Learning_On_Code_With_A_Graph_Vocabulary--Code_Preprocessor | 21 | Open vocabulary learning on source code with a graph-structured cache | https://scholar.google.com/scholar?cluster=1145489630896909786&hl=en&as_sdt=0,41 | 2 | 2,019 |
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations | 76 | icml | 27 | 16 | 2023-06-17 03:10:08.277000 | https://github.com/HazyResearch/butterfly | 118 | Learning fast algorithms for linear transforms using butterfly factorizations | https://scholar.google.com/scholar?cluster=8670371133727236715&hl=en&as_sdt=0,10 | 20 | 2,019 |
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization | 99 | icml | 1 | 0 | 2023-06-17 03:10:08.492000 | https://github.com/prolearner/onlineLTL | 3 | Learning-to-learn stochastic gradient descent with biased regularization | https://scholar.google.com/scholar?cluster=5491276692157599761&hl=en&as_sdt=0,5 | 4 | 2,019 |
Sever: A Robust Meta-Algorithm for Stochastic Optimization | 251 | icml | 6 | 0 | 2023-06-17 03:10:08.709000 | https://github.com/hoonose/sever | 26 | Sever: A robust meta-algorithm for stochastic optimization | https://scholar.google.com/scholar?cluster=1735563344640957243&hl=en&as_sdt=0,32 | 4 | 2,019 |
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization | 104 | icml | 10 | 2 | 2023-06-17 03:10:08.929000 | https://github.com/ShawnDing1994/AOFP | 30 | Approximated oracle filter pruning for destructive cnn width optimization | https://scholar.google.com/scholar?cluster=979238780615518812&hl=en&as_sdt=0,5 | 3 | 2,019 |
Trajectory-Based Off-Policy Deep Reinforcement Learning | 5 | icml | 2 | 0 | 2023-06-17 03:10:09.157000 | https://github.com/boschresearch/DD_OPG | 11 | Trajectory-based off-policy deep reinforcement learning | https://scholar.google.com/scholar?cluster=3089333550231775288&hl=en&as_sdt=0,10 | 3 | 2,019 |
Provably efficient RL with Rich Observations via Latent State Decoding | 180 | icml | 14 | 1 | 2023-06-17 03:10:09.372000 | https://github.com/Microsoft/StateDecoding | 28 | Provably efficient rl with rich observations via latent state decoding | https://scholar.google.com/scholar?cluster=17139201255005810211&hl=en&as_sdt=0,14 | 5 | 2,019 |
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning | 43 | icml | 2 | 0 | 2023-06-17 03:10:09.587000 | https://github.com/yilundu/task_agnostic_dynamics_prior | 13 | Task-agnostic dynamics priors for deep reinforcement learning | https://scholar.google.com/scholar?cluster=2869858217562916387&hl=en&as_sdt=0,5 | 1 | 2,019 |
Autoregressive Energy Machines | 52 | icml | 12 | 1 | 2023-06-17 03:10:09.802000 | https://github.com/conormdurkan/autoregressive-energy-machines | 79 | Autoregressive energy machines | https://scholar.google.com/scholar?cluster=6729811760374247021&hl=en&as_sdt=0,5 | 10 | 2,019 |
Imitating Latent Policies from Observation | 113 | icml | 21 | 1 | 2023-06-17 03:10:10.017000 | https://github.com/ashedwards/ILPO | 71 | Imitating latent policies from observation | https://scholar.google.com/scholar?cluster=16539609081927748607&hl=en&as_sdt=0,5 | 9 | 2,019 |
On the Connection Between Adversarial Robustness and Saliency Map Interpretability | 128 | icml | 1 | 0 | 2023-06-17 03:10:10.233000 | https://github.com/cetmann/robustness-interpretability | 15 | On the connection between adversarial robustness and saliency map interpretability | https://scholar.google.com/scholar?cluster=9006157315043198858&hl=en&as_sdt=0,47 | 1 | 2,019 |
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap | 31 | icml | 2 | 0 | 2023-06-17 03:10:10.450000 | https://github.com/edfong/npl | 8 | Scalable nonparametric sampling from multimodal posteriors with the posterior bootstrap | https://scholar.google.com/scholar?cluster=14627195645565170893&hl=en&as_sdt=0,5 | 2 | 2,019 |
Approximating Orthogonal Matrices with Effective Givens Factorization | 15 | icml | 3 | 0 | 2023-06-17 03:10:10.665000 | https://github.com/tfrerix/givens-factorization | 7 | Approximating orthogonal matrices with effective Givens factorization | https://scholar.google.com/scholar?cluster=16649468225264145943&hl=en&as_sdt=0,5 | 0 | 2,019 |
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement | 227 | icml | 30 | 12 | 2023-06-17 03:10:10.881000 | https://github.com/JasonSWFu/MetricGAN | 119 | Metricgan: Generative adversarial networks based black-box metric scores optimization for speech enhancement | https://scholar.google.com/scholar?cluster=10740262477107408585&hl=en&as_sdt=0,39 | 3 | 2,019 |
Off-Policy Deep Reinforcement Learning without Exploration | 944 | icml | 127 | 4 | 2023-06-17 03:10:11.105000 | https://github.com/sfujim/BCQ | 508 | Off-policy deep reinforcement learning without exploration | https://scholar.google.com/scholar?cluster=13735420516544008547&hl=en&as_sdt=0,33 | 6 | 2,019 |
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation | 100 | icml | 14 | 3 | 2023-06-17 03:10:11.320000 | https://github.com/ShaniGam/RL-GAN | 44 | Transfer learning for related reinforcement learning tasks via image-to-image translation | https://scholar.google.com/scholar?cluster=9611056051873190205&hl=en&as_sdt=0,5 | 4 | 2,019 |
Graph U-Nets | 839 | icml | 91 | 8 | 2023-06-17 03:10:11.537000 | https://github.com/HongyangGao/gunet | 445 | Graph u-nets | https://scholar.google.com/scholar?cluster=2250116536319373587&hl=en&as_sdt=0,5 | 11 | 2,019 |
Deep Generative Learning via Variational Gradient Flow | 21 | icml | 7 | 2 | 2023-06-17 03:10:11.756000 | https://github.com/xjtuygao/VGrow | 12 | Deep generative learning via variational gradient flow | https://scholar.google.com/scholar?cluster=13167225334345346820&hl=en&as_sdt=0,5 | 1 | 2,019 |
Optimal Mini-Batch and Step Sizes for SAGA | 35 | icml | 9 | 0 | 2023-06-17 03:10:11.975000 | https://github.com/gowerrobert/StochOpt.jl | 15 | Optimal mini-batch and step sizes for SAGA | https://scholar.google.com/scholar?cluster=14147185624190732996&hl=en&as_sdt=0,11 | 2 | 2,019 |
SelectiveNet: A Deep Neural Network with an Integrated Reject Option | 228 | icml | 13 | 4 | 2023-06-17 03:10:12.228000 | https://github.com/geifmany/SelectiveNet | 44 | Selectivenet: A deep neural network with an integrated reject option | https://scholar.google.com/scholar?cluster=3455752188101558663&hl=en&as_sdt=0,23 | 6 | 2,019 |
Data Shapley: Equitable Valuation of Data for Machine Learning | 441 | icml | 60 | 7 | 2023-06-17 03:10:12.445000 | https://github.com/amiratag/DataShapley | 212 | Data shapley: Equitable valuation of data for machine learning | https://scholar.google.com/scholar?cluster=7645060584356925514&hl=en&as_sdt=0,5 | 11 | 2,019 |
Amortized Monte Carlo Integration | 6 | icml | 1 | 0 | 2023-06-17 03:10:12.660000 | https://github.com/talesa/amci | 15 | Amortized monte carlo integration | https://scholar.google.com/scholar?cluster=7430114062861179606&hl=en&as_sdt=0,14 | 3 | 2,019 |
A Statistical Investigation of Long Memory in Language and Music | 19 | icml | 2 | 0 | 2023-06-17 03:10:12.877000 | https://github.com/alecgt/RNN_long_memory | 8 | A statistical investigation of long memory in language and music | https://scholar.google.com/scholar?cluster=3204260135600784159&hl=en&as_sdt=0,44 | 4 | 2,019 |
Automatic Posterior Transformation for Likelihood-Free Inference | 180 | icml | 29 | 0 | 2023-06-17 03:10:13.092000 | https://github.com/mackelab/delfi | 71 | Automatic posterior transformation for likelihood-free inference | https://scholar.google.com/scholar?cluster=9520658637115522401&hl=en&as_sdt=0,10 | 14 | 2,019 |
Multi-Object Representation Learning with Iterative Variational Inference | 385 | icml | 2,436 | 170 | 2023-06-17 03:10:13.314000 | https://github.com/deepmind/deepmind-research | 11,905 | Multi-object representation learning with iterative variational inference | https://scholar.google.com/scholar?cluster=213712144958725221&hl=en&as_sdt=0,11 | 336 | 2,019 |
An Investigation of Model-Free Planning | 79 | icml | 15 | 1 | 2023-06-17 03:10:13.533000 | https://github.com/deepmind/boxoban-levels | 54 | An investigation of model-free planning | https://scholar.google.com/scholar?cluster=7566080617462830679&hl=en&as_sdt=0,9 | 9 | 2,019 |
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops | 10 | icml | 2 | 0 | 2023-06-17 03:10:13.764000 | https://github.com/limorigu/Cockamamie-Gobbledegook | 6 | Humor in word embeddings: Cockamamie gobbledegook for nincompoops | https://scholar.google.com/scholar?cluster=13364498492064893478&hl=en&as_sdt=0,33 | 2 | 2,019 |
Simple Black-box Adversarial Attacks | 377 | icml | 54 | 0 | 2023-06-17 03:10:13.995000 | https://github.com/cg563/simple-blackbox-attack | 172 | Simple black-box adversarial attacks | https://scholar.google.com/scholar?cluster=14524309362525785070&hl=en&as_sdt=0,5 | 5 | 2,019 |
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs | 217 | icml | 17 | 0 | 2023-06-17 03:10:14.210000 | https://github.com/nju-websoft/RSN | 97 | Learning to exploit long-term relational dependencies in knowledge graphs | https://scholar.google.com/scholar?cluster=13843373750336430796&hl=en&as_sdt=0,5 | 8 | 2,019 |
On The Power of Curriculum Learning in Training Deep Networks | 330 | icml | 24 | 6 | 2023-06-17 03:10:14.426000 | https://github.com/GuyHacohen/curriculum_learning | 88 | On the power of curriculum learning in training deep networks | https://scholar.google.com/scholar?cluster=13645945393876441822&hl=en&as_sdt=0,39 | 3 | 2,019 |
Learning Latent Dynamics for Planning from Pixels | 1,075 | icml | 203 | 4 | 2023-06-17 03:10:14.641000 | https://github.com/google-research/planet | 1,130 | Learning latent dynamics for planning from pixels | https://scholar.google.com/scholar?cluster=17717536865000191198&hl=en&as_sdt=0,44 | 47 | 2,019 |
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning | 19 | icml | 3 | 0 | 2023-06-17 03:10:14.855000 | https://github.com/seungyulhan/disc | 8 | Dimension-wise importance sampling weight clipping for sample-efficient reinforcement learning | https://scholar.google.com/scholar?cluster=17087407211234698411&hl=en&as_sdt=0,33 | 1 | 2,019 |
Importance Sampling Policy Evaluation with an Estimated Behavior Policy | 57 | icml | 5 | 1 | 2023-06-17 03:10:15.071000 | https://github.com/LARG/regression-importance-sampling | 8 | Importance sampling policy evaluation with an estimated behavior policy | https://scholar.google.com/scholar?cluster=11718610357007396139&hl=en&as_sdt=0,23 | 4 | 2,019 |
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications | 79 | icml | 2 | 0 | 2023-06-17 03:10:15.312000 | https://github.com/crharshaw/submodular-minus-linear | 5 | Submodular maximization beyond non-negativity: Guarantees, fast algorithms, and applications | https://scholar.google.com/scholar?cluster=4032047436455480189&hl=en&as_sdt=0,5 | 2 | 2,019 |
Provably Efficient Maximum Entropy Exploration | 203 | icml | 0 | 0 | 2023-06-17 03:10:15.527000 | https://github.com/abbyvansoest/maxent_ant | 8 | Provably efficient maximum entropy exploration | https://scholar.google.com/scholar?cluster=7107307515820944527&hl=en&as_sdt=0,5 | 3 | 2,019 |
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning | 51 | icml | 2 | 0 | 2023-06-17 03:10:15.742000 | https://github.com/nvedant07/effort_reward_fairness | 3 | On the long-term impact of algorithmic decision policies: Effort unfairness and feature segregation through social learning | https://scholar.google.com/scholar?cluster=17715435590222166097&hl=en&as_sdt=0,5 | 2 | 2,019 |
Using Pre-Training Can Improve Model Robustness and Uncertainty | 563 | icml | 15 | 3 | 2023-06-17 03:10:15.958000 | https://github.com/hendrycks/pre-training | 92 | Using pre-training can improve model robustness and uncertainty | https://scholar.google.com/scholar?cluster=12052219296634461852&hl=en&as_sdt=0,39 | 6 | 2,019 |
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules | 353 | icml | 86 | 10 | 2023-06-17 03:10:16.194000 | https://github.com/arcelien/pba | 502 | Population based augmentation: Efficient learning of augmentation policy schedules | https://scholar.google.com/scholar?cluster=9297667920061606267&hl=en&as_sdt=0,34 | 20 | 2,019 |
Connectivity-Optimized Representation Learning via Persistent Homology | 65 | icml | 5 | 0 | 2023-06-17 03:10:16.408000 | https://github.com/c-hofer/COREL_icml2019 | 10 | Connectivity-optimized representation learning via persistent homology | https://scholar.google.com/scholar?cluster=6723358631694302455&hl=en&as_sdt=0,33 | 4 | 2,019 |
Emerging Convolutions for Generative Normalizing Flows | 90 | icml | 4 | 2 | 2023-06-17 03:10:16.622000 | https://github.com/ehoogeboom/emerging | 39 | Emerging convolutions for generative normalizing flows | https://scholar.google.com/scholar?cluster=17212015756232898698&hl=en&as_sdt=0,11 | 4 | 2,019 |
Parameter-Efficient Transfer Learning for NLP | 1,330 | icml | 39 | 7 | 2023-06-17 03:10:16.837000 | https://github.com/google-research/adapter-bert | 399 | Parameter-efficient transfer learning for NLP | https://scholar.google.com/scholar?cluster=18111543891993452201&hl=en&as_sdt=0,33 | 9 | 2,019 |
Unsupervised Deep Learning by Neighbourhood Discovery | 138 | icml | 19 | 1 | 2023-06-17 03:10:17.052000 | https://github.com/raymond-sci/AND | 148 | Unsupervised deep learning by neighbourhood discovery | https://scholar.google.com/scholar?cluster=2594287551241248539&hl=en&as_sdt=0,47 | 6 | 2,019 |
Stable and Fair Classification | 59 | icml | 0 | 3 | 2023-06-17 03:10:17.267000 | https://github.com/huanglx12/Stable-Fair-Classification | 1 | Stable and fair classification | https://scholar.google.com/scholar?cluster=6209492851752994222&hl=en&as_sdt=0,33 | 2 | 2,019 |
HexaGAN: Generative Adversarial Nets for Real World Classification | 43 | icml | 4 | 3 | 2023-06-17 03:10:17.483000 | https://github.com/shinyflight/HexaGAN | 20 | Hexagan: Generative adversarial nets for real world classification | https://scholar.google.com/scholar?cluster=9625100105337863533&hl=en&as_sdt=0,39 | 4 | 2,019 |
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models | 26 | icml | 4 | 0 | 2023-06-17 03:10:17.697000 | https://github.com/ialong/GPt | 19 | Overcoming mean-field approximations in recurrent Gaussian process models | https://scholar.google.com/scholar?cluster=13109450737746036374&hl=en&as_sdt=0,5 | 5 | 2,019 |
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! | 5 | icml | 1 | 0 | 2023-06-17 03:10:17.912000 | https://github.com/nbip/ppca_ICML2019 | 1 | Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! | https://scholar.google.com/scholar?cluster=809292088427670370&hl=en&as_sdt=0,5 | 0 | 2,019 |
Actor-Attention-Critic for Multi-Agent Reinforcement Learning | 577 | icml | 152 | 10 | 2023-06-17 03:10:18.127000 | https://github.com/shariqiqbal2810/MAAC | 531 | Actor-attention-critic for multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=241844530313281803&hl=en&as_sdt=0,14 | 7 | 2,019 |
Complementary-Label Learning for Arbitrary Losses and Models | 72 | icml | 16 | 0 | 2023-06-17 03:10:18.341000 | https://github.com/takashiishida/comp | 41 | Complementary-label learning for arbitrary losses and models | https://scholar.google.com/scholar?cluster=4663196775584030091&hl=en&as_sdt=0,5 | 1 | 2,019 |
Learning What and Where to Transfer | 118 | icml | 48 | 2 | 2023-06-17 03:10:18.558000 | https://github.com/alinlab/L2T-ww | 246 | Learning what and where to transfer | https://scholar.google.com/scholar?cluster=12979255639867638665&hl=en&as_sdt=0,5 | 8 | 2,019 |
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning | 337 | icml | 121 | 35 | 2023-06-17 03:10:18.775000 | https://github.com/eugenevinitsky/sequential_social_dilemma_games | 332 | Social influence as intrinsic motivation for multi-agent deep reinforcement learning | https://scholar.google.com/scholar?cluster=13693459800833279358&hl=en&as_sdt=0,44 | 13 | 2,019 |
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement | 28 | icml | 9 | 1 | 2023-06-17 03:10:18.992000 | https://github.com/snu-mllab/DisentanglementICML19 | 22 | Learning discrete and continuous factors of data via alternating disentanglement | https://scholar.google.com/scholar?cluster=14742637203782847188&hl=en&as_sdt=0,33 | 4 | 2,019 |
Neural Logic Reinforcement Learning | 50 | icml | 27 | 1 | 2023-06-17 03:10:19.206000 | https://github.com/ZhengyaoJiang/NLRL | 71 | Neural logic reinforcement learning | https://scholar.google.com/scholar?cluster=18074632043038701502&hl=en&as_sdt=0,41 | 4 | 2,019 |
Kernel Mean Matching for Content Addressability of GANs | 8 | icml | 4 | 0 | 2023-06-17 03:10:19.434000 | https://github.com/wittawatj/cadgan | 22 | Kernel mean matching for content addressability of GANs | https://scholar.google.com/scholar?cluster=235365843120524307&hl=en&as_sdt=0,5 | 7 | 2,019 |
Error Feedback Fixes SignSGD and other Gradient Compression Schemes | 381 | icml | 9 | 2 | 2023-06-17 03:10:19.663000 | https://github.com/epfml/error-feedback-SGD | 24 | Error feedback fixes signsgd and other gradient compression schemes | https://scholar.google.com/scholar?cluster=15067189376913629578&hl=en&as_sdt=0,36 | 6 | 2,019 |
Riemannian adaptive stochastic gradient algorithms on matrix manifolds | 44 | icml | 19 | 0 | 2023-06-17 03:10:19.878000 | https://github.com/hiroyuki-kasai/RSOpt | 55 | Riemannian adaptive stochastic gradient algorithms on matrix manifolds | https://scholar.google.com/scholar?cluster=11814345447980112497&hl=en&as_sdt=0,39 | 5 | 2,019 |
Neural Inverse Knitting: From Images to Manufacturing Instructions | 27 | icml | 7 | 6 | 2023-06-17 03:10:20.093000 | https://github.com/xionluhnis/neural_inverse_knitting | 39 | Neural inverse knitting: from images to manufacturing instructions | https://scholar.google.com/scholar?cluster=15939506219703518176&hl=en&as_sdt=0,5 | 7 | 2,019 |
Processing Megapixel Images with Deep Attention-Sampling Models | 54 | icml | 18 | 15 | 2023-06-17 03:10:20.309000 | https://github.com/idiap/attention-sampling | 91 | Processing megapixel images with deep attention-sampling models | https://scholar.google.com/scholar?cluster=16495958235848738135&hl=en&as_sdt=0,5 | 9 | 2,019 |
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking | 183 | icml | 8 | 1 | 2023-06-17 03:10:20.524000 | https://github.com/yigitcankaya/Shallow-Deep-Networks | 33 | Shallow-deep networks: Understanding and mitigating network overthinking | https://scholar.google.com/scholar?cluster=6970216830123198900&hl=en&as_sdt=0,3 | 1 | 2,019 |
Collaborative Evolutionary Reinforcement Learning | 105 | icml | 23 | 3 | 2023-06-17 03:10:20.740000 | https://github.com/intelai/cerl | 71 | Collaborative evolutionary reinforcement learning | https://scholar.google.com/scholar?cluster=17431562445096471732&hl=en&as_sdt=0,43 | 12 | 2,019 |
EMI: Exploration with Mutual Information | 83 | icml | 11 | 0 | 2023-06-17 03:10:20.958000 | https://github.com/snu-mllab/EMI | 32 | Emi: Exploration with mutual information | https://scholar.google.com/scholar?cluster=13544760374723251277&hl=en&as_sdt=0,19 | 5 | 2,019 |
FloWaveNet : A Generative Flow for Raw Audio | 174 | icml | 113 | 4 | 2023-06-17 03:10:21.202000 | https://github.com/ksw0306/FloWaveNet | 494 | FloWaveNet: A generative flow for raw audio | https://scholar.google.com/scholar?cluster=6708907651291228140&hl=en&as_sdt=0,34 | 43 | 2,019 |
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables | 69 | icml | 40 | 4 | 2023-06-17 03:10:21.418000 | https://github.com/fhkingma/bitswap | 239 | Bit-swap: Recursive bits-back coding for lossless compression with hierarchical latent variables | https://scholar.google.com/scholar?cluster=12443881008782599419&hl=en&as_sdt=0,5 | 9 | 2,019 |
CompILE: Compositional Imitation Learning and Execution | 88 | icml | 33 | 0 | 2023-06-17 03:10:21.633000 | https://github.com/tkipf/compile | 104 | Compile: Compositional imitation learning and execution | https://scholar.google.com/scholar?cluster=12302759254570528216&hl=en&as_sdt=0,22 | 5 | 2,019 |
Fair k-Center Clustering for Data Summarization | 138 | icml | 4 | 0 | 2023-06-17 03:10:21.849000 | https://github.com/matthklein/fair_k_center_clustering | 10 | Fair k-center clustering for data summarization | https://scholar.google.com/scholar?cluster=10384783714256817355&hl=en&as_sdt=0,5 | 3 | 2,019 |
Guarantees for Spectral Clustering with Fairness Constraints | 122 | icml | 1 | 0 | 2023-06-17 03:10:22.065000 | https://github.com/matthklein/fair_spectral_clustering | 7 | Guarantees for spectral clustering with fairness constraints | https://scholar.google.com/scholar?cluster=10455657164331034065&hl=en&as_sdt=0,5 | 2 | 2,019 |
POPQORN: Quantifying Robustness of Recurrent Neural Networks | 86 | icml | 11 | 0 | 2023-06-17 03:10:22.280000 | https://github.com/ZhaoyangLyu/POPQORN | 45 | POPQORN: Quantifying robustness of recurrent neural networks | https://scholar.google.com/scholar?cluster=2942353004594500868&hl=en&as_sdt=0,5 | 5 | 2,019 |
Robust Learning from Untrusted Sources | 61 | icml | 4 | 0 | 2023-06-17 03:10:22.495000 | https://github.com/NikolaKon1994/Robust-Learning-from-Untrusted-Sources | 15 | Robust learning from untrusted sources | https://scholar.google.com/scholar?cluster=4366540847036601471&hl=en&as_sdt=0,5 | 2 | 2,019 |
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement | 128 | icml | 6 | 0 | 2023-06-17 03:10:22.711000 | https://github.com/wouterkool/stochastic-beam-search | 90 | Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement | https://scholar.google.com/scholar?cluster=13121847178128779153&hl=en&as_sdt=0,33 | 7 | 2,019 |
Loss Landscapes of Regularized Linear Autoencoders | 70 | icml | 12 | 2 | 2023-06-17 03:10:22.926000 | https://github.com/danielkunin/Regularized-Linear-Autoencoders | 139 | Loss landscapes of regularized linear autoencoders | https://scholar.google.com/scholar?cluster=15048938764743692524&hl=en&as_sdt=0,47 | 8 | 2,019 |
A Large-Scale Study on Regularization and Normalization in GANs | 174 | icml | 322 | 16 | 2023-06-17 03:10:23.141000 | https://github.com/google/compare_gan | 1,814 | A large-scale study on regularization and normalization in GANs | https://scholar.google.com/scholar?cluster=2102263768032678612&hl=en&as_sdt=0,5 | 52 | 2,019 |
Characterizing Well-Behaved vs. Pathological Deep Neural Networks | 14 | icml | 1 | 2 | 2023-06-17 03:10:23.356000 | https://github.com/alabatie/moments-dnns | 5 | Characterizing well-behaved vs. pathological deep neural networks | https://scholar.google.com/scholar?cluster=3271469999043438586&hl=en&as_sdt=0,40 | 3 | 2,019 |
Self-Attention Graph Pooling | 846 | icml | 78 | 11 | 2023-06-17 03:10:23.570000 | https://github.com/inyeoplee77/SAGPool | 324 | Self-attention graph pooling | https://scholar.google.com/scholar?cluster=8950252210828065007&hl=en&as_sdt=0,10 | 8 | 2,019 |
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks | 777 | icml | 85 | 9 | 2023-06-17 03:10:23.785000 | https://github.com/juho-lee/set_transformer | 449 | Set transformer: A framework for attention-based permutation-invariant neural networks | https://scholar.google.com/scholar?cluster=564620061424738263&hl=en&as_sdt=0,34 | 13 | 2,019 |
Robust Inference via Generative Classifiers for Handling Noisy Labels | 93 | icml | 5 | 1 | 2023-06-17 03:10:23.999000 | https://github.com/pokaxpoka/RoGNoisyLabel | 29 | Robust inference via generative classifiers for handling noisy labels | https://scholar.google.com/scholar?cluster=14567604075585438767&hl=en&as_sdt=0,43 | 3 | 2,019 |
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