title
stringlengths 8
155
| citations_google_scholar
int64 0
28.9k
| conference
stringclasses 5
values | forks
int64 0
46.3k
| issues
int64 0
12.2k
| lastModified
stringlengths 19
26
| repo_url
stringlengths 26
130
| stars
int64 0
75.9k
| title_google_scholar
stringlengths 8
155
| url_google_scholar
stringlengths 75
206
| watchers
int64 0
2.77k
| year
int64 2.02k
2.02k
|
|---|---|---|---|---|---|---|---|---|---|---|---|
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group | 155 | icml | 19 | 1 | 2023-06-17 03:10:24.214000 | https://github.com/Lezcano/expRNN | 116 | Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group | https://scholar.google.com/scholar?cluster=17536814525953471769&hl=en&as_sdt=0,5 | 6 | 2,019 |
Are Generative Classifiers More Robust to Adversarial Attacks? | 85 | icml | 9 | 0 | 2023-06-17 03:10:24.429000 | https://github.com/deepgenerativeclassifier/DeepBayes | 22 | Are generative classifiers more robust to adversarial attacks? | https://scholar.google.com/scholar?cluster=10770378244624939531&hl=en&as_sdt=0,45 | 2 | 2,019 |
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning | 97 | icml | 24 | 3 | 2023-06-17 03:10:24.645000 | https://github.com/likesiwell/LGM-Net | 84 | LGM-Net: Learning to generate matching networks for few-shot learning | https://scholar.google.com/scholar?cluster=17373853660485197406&hl=en&as_sdt=0,5 | 4 | 2,019 |
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks | 217 | icml | 14 | 0 | 2023-06-17 03:10:24.859000 | https://github.com/Cold-Winter/Nattack | 46 | Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks | https://scholar.google.com/scholar?cluster=1133340624710172210&hl=en&as_sdt=0,5 | 5 | 2,019 |
Bayesian Joint Spike-and-Slab Graphical Lasso | 20 | icml | 2 | 0 | 2023-06-17 03:10:25.075000 | https://github.com/richardli/SSJGL | 7 | Bayesian joint spike-and-slab graphical lasso | https://scholar.google.com/scholar?cluster=11980207298770096957&hl=en&as_sdt=0,48 | 4 | 2,019 |
Adversarial camera stickers: A physical camera-based attack on deep learning systems | 115 | icml | 2 | 0 | 2023-06-17 03:10:25.290000 | https://github.com/yoheikikuta/adversarial-camera-stickers | 8 | Adversarial camera stickers: A physical camera-based attack on deep learning systems | https://scholar.google.com/scholar?cluster=8454184380086098103&hl=en&as_sdt=0,33 | 3 | 2,019 |
Feature-Critic Networks for Heterogeneous Domain Generalization | 200 | icml | 10 | 9 | 2023-06-17 03:10:25.505000 | https://github.com/liyiying/Feature_Critic | 42 | Feature-critic networks for heterogeneous domain generalization | https://scholar.google.com/scholar?cluster=15160705294700481017&hl=en&as_sdt=0,33 | 5 | 2,019 |
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations | 45 | icml | 6 | 0 | 2023-06-17 03:10:25.720000 | https://github.com/yorkerlin/VB-MixEF | 15 | Fast and simple natural-gradient variational inference with mixture of exponential-family approximations | https://scholar.google.com/scholar?cluster=9800018690635650774&hl=en&as_sdt=0,5 | 4 | 2,019 |
Acceleration of SVRG and Katyusha X by Inexact Preconditioning | 7 | icml | 3 | 0 | 2023-06-17 03:10:25.934000 | https://github.com/uclaopt/IPSVRG | 8 | Acceleration of svrg and katyusha x by inexact preconditioning | https://scholar.google.com/scholar?cluster=13059368819279986289&hl=en&as_sdt=0,45 | 5 | 2,019 |
Rao-Blackwellized Stochastic Gradients for Discrete Distributions | 33 | icml | 3 | 0 | 2023-06-17 03:10:26.161000 | https://github.com/Runjing-Liu120/RaoBlackwellizedSGD | 22 | Rao-Blackwellized stochastic gradients for discrete distributions | https://scholar.google.com/scholar?cluster=12116217648667930393&hl=en&as_sdt=0,23 | 2 | 2,019 |
Understanding and Accelerating Particle-Based Variational Inference | 75 | icml | 5 | 0 | 2023-06-17 03:10:26.375000 | https://github.com/chang-ml-thu/AWGF | 17 | Understanding and accelerating particle-based variational inference | https://scholar.google.com/scholar?cluster=7410249710967287826&hl=en&as_sdt=0,11 | 2 | 2,019 |
Understanding MCMC Dynamics as Flows on the Wasserstein Space | 21 | icml | 4 | 0 | 2023-06-17 03:10:26.592000 | https://github.com/chang-ml-thu/FGH-flow | 11 | Understanding mcmc dynamics as flows on the wasserstein space | https://scholar.google.com/scholar?cluster=16148000850438563191&hl=en&as_sdt=0,14 | 3 | 2,019 |
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions | 98 | icml | 5 | 1 | 2023-06-17 03:10:26.808000 | https://github.com/aliutkus/swf | 14 | Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions | https://scholar.google.com/scholar?cluster=7685202431169756099&hl=en&as_sdt=0,5 | 6 | 2,019 |
CoT: Cooperative Training for Generative Modeling of Discrete Data | 24 | icml | 28 | 1 | 2023-06-17 03:10:27.023000 | https://github.com/desire2020/Cooperative-Training | 75 | Cot: Cooperative training for generative modeling of discrete data | https://scholar.google.com/scholar?cluster=4231322493080735140&hl=en&as_sdt=0,10 | 11 | 2,019 |
High-Fidelity Image Generation With Fewer Labels | 145 | icml | 322 | 16 | 2023-06-17 03:10:27.237000 | https://github.com/google/compare_gan | 1,814 | High-fidelity image generation with fewer labels | https://scholar.google.com/scholar?cluster=13622749687496052538&hl=en&as_sdt=0,11 | 52 | 2,019 |
Variational Implicit Processes | 57 | icml | 3 | 0 | 2023-06-17 03:10:27.452000 | https://github.com/LaurantChao/VIP | 8 | Variational implicit processes | https://scholar.google.com/scholar?cluster=11479270094313825180&hl=en&as_sdt=0,6 | 1 | 2,019 |
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE | 110 | icml | 15 | 0 | 2023-06-17 03:10:27.667000 | https://github.com/microsoft/EDDI | 37 | Eddi: Efficient dynamic discovery of high-value information with partial vae | https://scholar.google.com/scholar?cluster=7932877212524867960&hl=en&as_sdt=0,5 | 8 | 2,019 |
Guided evolutionary strategies: augmenting random search with surrogate gradients | 79 | icml | 25 | 2 | 2023-06-17 03:10:27.882000 | https://github.com/brain-research/guided-evolutionary-strategies | 262 | Guided evolutionary strategies: Augmenting random search with surrogate gradients | https://scholar.google.com/scholar?cluster=13097058951649931158&hl=en&as_sdt=0,5 | 15 | 2,019 |
Adversarial Generation of Time-Frequency Features with application in audio synthesis | 70 | icml | 13 | 5 | 2023-06-17 03:10:28.096000 | https://github.com/tifgan/stftGAN | 105 | Adversarial generation of time-frequency features with application in audio synthesis | https://scholar.google.com/scholar?cluster=7293234438017145749&hl=en&as_sdt=0,5 | 7 | 2,019 |
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models | 12 | icml | 4 | 0 | 2023-06-17 03:10:28.311000 | https://github.com/kasparmartens/c-GPLVM | 24 | Decomposing feature-level variation with covariate Gaussian process latent variable models | https://scholar.google.com/scholar?cluster=3291712378520398367&hl=en&as_sdt=0,15 | 2 | 2,019 |
Disentangling Disentanglement in Variational Autoencoders | 232 | icml | 13 | 0 | 2023-06-17 03:10:28.525000 | https://github.com/iffsid/disentangling-disentanglement | 86 | Disentangling disentanglement in variational autoencoders | https://scholar.google.com/scholar?cluster=4865252587822770331&hl=en&as_sdt=0,5 | 15 | 2,019 |
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems | 18 | icml | 18 | 0 | 2023-06-17 03:10:28.739000 | https://github.com/Microsoft/vi-hds | 45 | Efficient amortised Bayesian inference for hierarchical and nonlinear dynamical systems | https://scholar.google.com/scholar?cluster=1247732699292692241&hl=en&as_sdt=0,44 | 8 | 2,019 |
Toward Controlling Discrimination in Online Ad Auctions | 48 | icml | 4 | 0 | 2023-06-17 03:10:28.956000 | https://github.com/AnayMehrotra/Fair-Online-Advertising | 2 | Toward controlling discrimination in online ad auctions | https://scholar.google.com/scholar?cluster=3881350113786532991&hl=en&as_sdt=0,43 | 2 | 2,019 |
Imputing Missing Events in Continuous-Time Event Streams | 31 | icml | 17 | 2 | 2023-06-17 03:10:29.192000 | https://github.com/HMEIatJHU/neural-hawkes-particle-smoothing | 41 | Imputing missing events in continuous-time event streams | https://scholar.google.com/scholar?cluster=8012453208848277577&hl=en&as_sdt=0,14 | 4 | 2,019 |
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks | 125 | icml | 28 | 12 | 2023-06-17 03:10:29.407000 | https://github.com/psg-mit/Ithemal | 139 | Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks | https://scholar.google.com/scholar?cluster=6452183013544894818&hl=en&as_sdt=0,33 | 14 | 2,019 |
On Dropout and Nuclear Norm Regularization | 19 | icml | 2 | 0 | 2023-06-17 03:10:29.621000 | https://github.com/r3831/dln_dropout | 3 | On dropout and nuclear norm regularization | https://scholar.google.com/scholar?cluster=2540515501706995243&hl=en&as_sdt=0,40 | 2 | 2,019 |
Flat Metric Minimization with Applications in Generative Modeling | 3 | icml | 4 | 0 | 2023-06-17 03:10:29.836000 | https://github.com/moellenh/flatgan | 18 | Flat metric minimization with applications in generative modeling | https://scholar.google.com/scholar?cluster=16621113036066180234&hl=en&as_sdt=0,33 | 2 | 2,019 |
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization | 113 | icml | 14 | 0 | 2023-06-17 03:10:30.050000 | https://github.com/snu-mllab/parsimonious-blackbox-attack | 35 | Parsimonious black-box adversarial attacks via efficient combinatorial optimization | https://scholar.google.com/scholar?cluster=16009538798728740698&hl=en&as_sdt=0,5 | 6 | 2,019 |
Relational Pooling for Graph Representations | 185 | icml | 7 | 0 | 2023-06-17 03:10:30.265000 | https://github.com/PurdueMINDS/RelationalPooling | 34 | Relational pooling for graph representations | https://scholar.google.com/scholar?cluster=6145744994249893945&hl=en&as_sdt=0,31 | 6 | 2,019 |
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning | 81 | icml | 6 | 0 | 2023-06-17 03:10:30.481000 | https://github.com/pfnet-research/hyperbolic_wrapped_distribution | 28 | A wrapped normal distribution on hyperbolic space for gradient-based learning | https://scholar.google.com/scholar?cluster=11277639546038701066&hl=en&as_sdt=0,47 | 22 | 2,019 |
Dropout as a Structured Shrinkage Prior | 39 | icml | 5 | 0 | 2023-06-17 03:10:30.697000 | https://github.com/enalisnick/dropout_icml2019 | 8 | Dropout as a structured shrinkage prior | https://scholar.google.com/scholar?cluster=16208195687877220296&hl=en&as_sdt=0,44 | 1 | 2,019 |
Zero-Shot Knowledge Distillation in Deep Networks | 182 | icml | 7 | 0 | 2023-06-17 03:10:30.910000 | https://github.com/vcl-iisc/ZSKD | 59 | Zero-shot knowledge distillation in deep networks | https://scholar.google.com/scholar?cluster=6513271489867205724&hl=en&as_sdt=0,23 | 7 | 2,019 |
Safe Grid Search with Optimal Complexity | 41 | icml | 3 | 0 | 2023-06-17 03:10:31.127000 | https://github.com/EugeneNdiaye/safe_grid_search | 7 | Safe grid search with optimal complexity | https://scholar.google.com/scholar?cluster=1378644094816844028&hl=en&as_sdt=0,5 | 5 | 2,019 |
Rotation Invariant Householder Parameterization for Bayesian PCA | 9 | icml | 3 | 10 | 2023-06-17 03:10:31.343000 | https://github.com/RSNirwan/HouseholderBPCA | 13 | Rotation invariant householder parameterization for Bayesian PCA | https://scholar.google.com/scholar?cluster=6089302904183911614&hl=en&as_sdt=0,41 | 5 | 2,019 |
Training Neural Networks with Local Error Signals | 169 | icml | 34 | 3 | 2023-06-17 03:10:31.558000 | https://github.com/anokland/local-loss | 150 | Training neural networks with local error signals | https://scholar.google.com/scholar?cluster=11332176056919584070&hl=en&as_sdt=0,5 | 10 | 2,019 |
Remember and Forget for Experience Replay | 80 | icml | 44 | 6 | 2023-06-17 03:10:31.777000 | https://github.com/cselab/smarties | 103 | Remember and forget for experience replay | https://scholar.google.com/scholar?cluster=13050806613216384530&hl=en&as_sdt=0,5 | 11 | 2,019 |
Learning to Infer Program Sketches | 92 | icml | 10 | 6 | 2023-06-17 03:10:31.992000 | https://github.com/mtensor/neural_sketch | 21 | Learning to infer program sketches | https://scholar.google.com/scholar?cluster=17303764643585588375&hl=en&as_sdt=0,23 | 8 | 2,019 |
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models | 118 | icml | 10 | 0 | 2023-06-17 03:10:32.207000 | https://github.com/clinicalml/gumbel-max-scm | 39 | Counterfactual off-policy evaluation with gumbel-max structural causal models | https://scholar.google.com/scholar?cluster=3302653893277553179&hl=en&as_sdt=0,26 | 19 | 2,019 |
Orthogonal Random Forest for Causal Inference | 79 | icml | 614 | 301 | 2023-06-17 03:10:32.422000 | https://github.com/Microsoft/EconML | 3,004 | Orthogonal random forest for causal inference | https://scholar.google.com/scholar?cluster=1871181716543524277&hl=en&as_sdt=0,5 | 70 | 2,019 |
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding | 8 | icml | 2 | 0 | 2023-06-17 03:10:32.638000 | https://github.com/Muhammad-Osama/Inferring-Heterogeneous-Causal-Effects-in-Presence-of-Spatial-Confounding | 1 | Inferring heterogeneous causal effects in presence of spatial confounding | https://scholar.google.com/scholar?cluster=14041258610340953811&hl=en&as_sdt=0,5 | 0 | 2,019 |
Improving Adversarial Robustness via Promoting Ensemble Diversity | 348 | icml | 13 | 2 | 2023-06-17 03:10:32.853000 | https://github.com/P2333/Adaptive-Diversity-Promoting | 60 | Improving adversarial robustness via promoting ensemble diversity | https://scholar.google.com/scholar?cluster=16568032932303177237&hl=en&as_sdt=0,33 | 3 | 2,019 |
Nonparametric Bayesian Deep Networks with Local Competition | 30 | icml | 1 | 0 | 2023-06-17 03:10:33.067000 | https://github.com/konpanousis/SB-LWTA | 6 | Nonparametric Bayesian deep networks with local competition | https://scholar.google.com/scholar?cluster=6949349876007421452&hl=en&as_sdt=0,5 | 1 | 2,019 |
Deep Residual Output Layers for Neural Language Generation | 7 | icml | 3 | 0 | 2023-06-17 03:10:33.282000 | https://github.com/idiap/drill | 10 | Deep residual output layers for neural language generation | https://scholar.google.com/scholar?cluster=6336276005436023906&hl=en&as_sdt=0,1 | 8 | 2,019 |
Self-Supervised Exploration via Disagreement | 272 | icml | 23 | 3 | 2023-06-17 03:10:33.496000 | https://github.com/pathak22/exploration-by-disagreement | 120 | Self-supervised exploration via disagreement | https://scholar.google.com/scholar?cluster=13780996231531586358&hl=en&as_sdt=0,47 | 4 | 2,019 |
Domain Agnostic Learning with Disentangled Representations | 194 | icml | 28 | 3 | 2023-06-17 03:10:33.710000 | https://github.com/VisionLearningGroup/DAL | 133 | Domain agnostic learning with disentangled representations | https://scholar.google.com/scholar?cluster=10085135045247935679&hl=en&as_sdt=0,33 | 8 | 2,019 |
Temporal Gaussian Mixture Layer for Videos | 77 | icml | 14 | 5 | 2023-06-17 03:10:33.924000 | https://github.com/piergiaj/tgm-icml19 | 99 | Temporal gaussian mixture layer for videos | https://scholar.google.com/scholar?cluster=7515216755463628280&hl=en&as_sdt=0,47 | 5 | 2,019 |
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss | 343 | icml | 95 | 8 | 2023-06-17 03:10:34.139000 | https://github.com/liusongxiang/StarGAN-Voice-Conversion | 460 | Autovc: Zero-shot voice style transfer with only autoencoder loss | https://scholar.google.com/scholar?cluster=16861313448156905141&hl=en&as_sdt=0,33 | 20 | 2,019 |
On the Spectral Bias of Neural Networks | 653 | icml | 18 | 0 | 2023-06-17 03:10:34.355000 | https://github.com/nasimrahaman/SpectralBias | 87 | On the spectral bias of neural networks | https://scholar.google.com/scholar?cluster=6023723620228240592&hl=en&as_sdt=0,5 | 5 | 2,019 |
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables | 487 | icml | 116 | 10 | 2023-06-17 03:10:34.571000 | https://github.com/katerakelly/oyster | 422 | Efficient off-policy meta-reinforcement learning via probabilistic context variables | https://scholar.google.com/scholar?cluster=15379570585451726919&hl=en&as_sdt=0,31 | 22 | 2,019 |
Topological Data Analysis of Decision Boundaries with Application to Model Selection | 45 | icml | 5 | 1 | 2023-06-17 03:10:34.786000 | https://github.com/nrkarthikeyan/topology-decision-boundaries | 25 | Topological data analysis of decision boundaries with application to model selection | https://scholar.google.com/scholar?cluster=16310684424372533537&hl=en&as_sdt=0,47 | 3 | 2,019 |
Do ImageNet Classifiers Generalize to ImageNet? | 1,011 | icml | 19 | 3 | 2023-06-17 03:10:35.002000 | https://github.com/modestyachts/ImageNetV2 | 200 | Do imagenet classifiers generalize to imagenet? | https://scholar.google.com/scholar?cluster=9642974458829870490&hl=en&as_sdt=0,5 | 9 | 2,019 |
Separating value functions across time-scales | 20 | icml | 8 | 1 | 2023-06-17 03:10:35.217000 | https://github.com/facebookresearch/td-delta | 17 | Separating value functions across time-scales | https://scholar.google.com/scholar?cluster=4770640199000017982&hl=en&as_sdt=0,23 | 5 | 2,019 |
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples | 162 | icml | 12 | 4 | 2023-06-17 03:10:35.432000 | https://github.com/yk/icml19_public | 21 | The odds are odd: A statistical test for detecting adversarial examples | https://scholar.google.com/scholar?cluster=6673355422445965167&hl=en&as_sdt=0,10 | 2 | 2,019 |
A Contrastive Divergence for Combining Variational Inference and MCMC | 69 | icml | 7 | 0 | 2023-06-17 03:10:35.647000 | https://github.com/franrruiz/vcd_divergence | 27 | A contrastive divergence for combining variational inference and mcmc | https://scholar.google.com/scholar?cluster=10765853948406678619&hl=en&as_sdt=0,43 | 5 | 2,019 |
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers | 243 | icml | 18 | 4 | 2023-06-17 03:10:35.863000 | https://github.com/uclaopt/Provable_Plug_and_Play | 60 | Plug-and-play methods provably converge with properly trained denoisers | https://scholar.google.com/scholar?cluster=11121192984446474149&hl=en&as_sdt=0,5 | 6 | 2,019 |
Deep Gaussian Processes with Importance-Weighted Variational Inference | 43 | icml | 4 | 1 | 2023-06-17 03:10:36.078000 | https://github.com/hughsalimbeni/DGPs_with_IWVI | 36 | Deep Gaussian processes with importance-weighted variational inference | https://scholar.google.com/scholar?cluster=17591045211502754804&hl=en&as_sdt=0,11 | 4 | 2,019 |
Exploration Conscious Reinforcement Learning Revisited | 11 | icml | 4 | 0 | 2023-06-17 03:10:36.293000 | https://github.com/shanlior/ExplorationConsciousRL | 6 | Exploration conscious reinforcement learning revisited | https://scholar.google.com/scholar?cluster=2069086734091208368&hl=en&as_sdt=0,5 | 2 | 2,019 |
Mixture Models for Diverse Machine Translation: Tricks of the Trade | 101 | icml | 5,878 | 1,031 | 2023-06-17 03:10:36.509000 | https://github.com/pytorch/fairseq | 26,482 | Mixture models for diverse machine translation: Tricks of the trade | https://scholar.google.com/scholar?cluster=10713606322116851955&hl=en&as_sdt=0,50 | 411 | 2,019 |
Replica Conditional Sequential Monte Carlo | 2 | icml | 1 | 0 | 2023-06-17 03:10:36.723000 | https://github.com/ayshestopaloff/replicacsmc | 2 | Replica Conditional Sequential Monte Carlo | https://scholar.google.com/scholar?cluster=8937563905514647283&hl=en&as_sdt=0,36 | 0 | 2,019 |
Scalable Training of Inference Networks for Gaussian-Process Models | 19 | icml | 4 | 1 | 2023-06-17 03:10:36.938000 | https://github.com/thjashin/gp-infer-net | 41 | Scalable training of inference networks for gaussian-process models | https://scholar.google.com/scholar?cluster=18315311533765480343&hl=en&as_sdt=0,5 | 4 | 2,019 |
Model-Based Active Exploration | 157 | icml | 16 | 1 | 2023-06-17 03:10:37.167000 | https://github.com/nnaisense/max | 72 | Model-based active exploration | https://scholar.google.com/scholar?cluster=4949040749673510686&hl=en&as_sdt=0,5 | 5 | 2,019 |
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension | 127 | icml | 6 | 0 | 2023-06-17 03:10:37.382000 | https://github.com/facebookresearch/AdversarialAndDimensionality | 16 | First-order adversarial vulnerability of neural networks and input dimension | https://scholar.google.com/scholar?cluster=577929050796401765&hl=en&as_sdt=0,36 | 4 | 2,019 |
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation | 42 | icml | 3 | 0 | 2023-06-17 03:10:37.597000 | https://github.com/singlasahil14/CASO | 13 | Understanding impacts of high-order loss approximations and features in deep learning interpretation | https://scholar.google.com/scholar?cluster=17624808507201697872&hl=en&as_sdt=0,22 | 3 | 2,019 |
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects | 90 | icml | 12 | 1 | 2023-06-17 03:10:37.814000 | https://github.com/EdwardSmith1884/GEOMetrics | 117 | Geometrics: Exploiting geometric structure for graph-encoded objects | https://scholar.google.com/scholar?cluster=15300382945837912303&hl=en&as_sdt=0,5 | 9 | 2,019 |
The Evolved Transformer | 420 | icml | 3,290 | 589 | 2023-06-17 03:10:38.029000 | https://github.com/tensorflow/tensor2tensor | 13,764 | The evolved transformer | https://scholar.google.com/scholar?cluster=12069106626021161148&hl=en&as_sdt=0,38 | 461 | 2,019 |
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning | 540 | icml | 15 | 3 | 2023-06-17 03:10:38.244000 | https://github.com/Sonkyunghwan/QTRAN | 64 | Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning | https://scholar.google.com/scholar?cluster=8081563128599106489&hl=en&as_sdt=0,44 | 1 | 2,019 |
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective | 46 | icml | 3 | 0 | 2023-06-17 03:10:38.461000 | https://github.com/zhao-song/Softmax-DQN | 6 | Revisiting the softmax bellman operator: New benefits and new perspective | https://scholar.google.com/scholar?cluster=12009633864988483522&hl=en&as_sdt=0,39 | 1 | 2,019 |
MASS: Masked Sequence to Sequence Pre-training for Language Generation | 910 | icml | 209 | 67 | 2023-06-17 03:10:38.676000 | https://github.com/microsoft/MASS | 1,103 | Mass: Masked sequence to sequence pre-training for language generation | https://scholar.google.com/scholar?cluster=9265562426073523323&hl=en&as_sdt=0,26 | 37 | 2,019 |
Compressing Gradient Optimizers via Count-Sketches | 29 | icml | 13 | 0 | 2023-06-17 03:10:38.891000 | https://github.com/rdspring1/Count-Sketch-Optimizers | 26 | Compressing gradient optimizers via count-sketches | https://scholar.google.com/scholar?cluster=1104222702149426557&hl=en&as_sdt=0,5 | 4 | 2,019 |
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning | 192 | icml | 25 | 1 | 2023-06-17 03:10:39.107000 | https://github.com/AsaCooperStickland/Bert-n-Pals | 74 | Bert and pals: Projected attention layers for efficient adaptation in multi-task learning | https://scholar.google.com/scholar?cluster=3136454913064441910&hl=en&as_sdt=0,38 | 3 | 2,019 |
Provably Efficient Imitation Learning from Observation Alone | 82 | icml | 5 | 0 | 2023-06-17 03:10:39.322000 | https://github.com/wensun/Imitation-Learning-from-Observation | 20 | Provably efficient imitation learning from observation alone | https://scholar.google.com/scholar?cluster=12068954688266237988&hl=en&as_sdt=0,5 | 3 | 2,019 |
Hyperbolic Disk Embeddings for Directed Acyclic Graphs | 41 | icml | 5 | 0 | 2023-06-17 03:10:39.537000 | https://github.com/lapras-inc/disk-embedding | 17 | Hyperbolic disk embeddings for directed acyclic graphs | https://scholar.google.com/scholar?cluster=15999788633415414766&hl=en&as_sdt=0,34 | 18 | 2,019 |
Equivariant Transformer Networks | 67 | icml | 7 | 1 | 2023-06-17 03:10:39.760000 | https://github.com/stanford-futuredata/equivariant-transformers | 82 | Equivariant transformer networks | https://scholar.google.com/scholar?cluster=740882376854558881&hl=en&as_sdt=0,36 | 10 | 2,019 |
Correlated Variational Auto-Encoders | 19 | icml | 4 | 0 | 2023-06-17 03:10:39.976000 | https://github.com/datang1992/Correlated-VAEs | 14 | Correlated variational auto-encoders | https://scholar.google.com/scholar?cluster=14520356175099829641&hl=en&as_sdt=0,33 | 5 | 2,019 |
The Variational Predictive Natural Gradient | 3 | icml | 1 | 0 | 2023-06-17 03:10:40.191000 | https://github.com/datang1992/VPNG | 8 | The variational predictive natural gradient | https://scholar.google.com/scholar?cluster=6073859204913275725&hl=en&as_sdt=0,47 | 2 | 2,019 |
Adaptive Neural Trees | 151 | icml | 22 | 2 | 2023-06-17 03:10:40.406000 | https://github.com/rtanno21609/AdaptiveNeuralTrees | 140 | Adaptive neural trees | https://scholar.google.com/scholar?cluster=10252139245277017232&hl=en&as_sdt=0,20 | 8 | 2,019 |
Combating Label Noise in Deep Learning using Abstention | 146 | icml | 9 | 6 | 2023-06-17 03:10:40.621000 | https://github.com/thulas/dac-label-noise | 56 | Combating label noise in deep learning using abstention | https://scholar.google.com/scholar?cluster=13352196764325122860&hl=en&as_sdt=0,5 | 5 | 2,019 |
ELF OpenGo: an analysis and open reimplementation of AlphaZero | 101 | icml | 577 | 44 | 2023-06-17 03:10:40.836000 | https://github.com/pytorch/ELF | 3,316 | Elf opengo: An analysis and open reimplementation of alphazero | https://scholar.google.com/scholar?cluster=9736512126040760893&hl=en&as_sdt=0,5 | 191 | 2,019 |
Metropolis-Hastings Generative Adversarial Networks | 85 | icml | 24 | 4 | 2023-06-17 03:10:41.051000 | https://github.com/uber-research/metropolis-hastings-gans | 112 | Metropolis-hastings generative adversarial networks | https://scholar.google.com/scholar?cluster=18080915212804537296&hl=en&as_sdt=0,26 | 7 | 2,019 |
Model Comparison for Semantic Grouping | 1 | icml | 4 | 0 | 2023-06-17 03:10:41.266000 | https://github.com/Babylonpartners/MCSG | 8 | Model comparison for semantic grouping | https://scholar.google.com/scholar?cluster=18345833118099808380&hl=en&as_sdt=0,5 | 10 | 2,019 |
Manifold Mixup: Better Representations by Interpolating Hidden States | 889 | icml | 65 | 8 | 2023-06-17 03:10:41.482000 | https://github.com/vikasverma1077/manifold_mixup | 457 | Manifold mixup: Better representations by interpolating hidden states | https://scholar.google.com/scholar?cluster=5005853392111011711&hl=en&as_sdt=0,15 | 12 | 2,019 |
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation | 60 | icml | 6 | 2 | 2023-06-17 03:10:41.697000 | https://github.com/RuohanW/RED | 28 | Random expert distillation: Imitation learning via expert policy support estimation | https://scholar.google.com/scholar?cluster=2838461363780817206&hl=en&as_sdt=0,44 | 2 | 2,019 |
Improving Neural Language Modeling via Adversarial Training | 93 | icml | 3 | 3 | 2023-06-17 03:10:41.913000 | https://github.com/ChengyueGongR/advsoft | 40 | Improving neural language modeling via adversarial training | https://scholar.google.com/scholar?cluster=13673209609848344447&hl=en&as_sdt=0,39 | 3 | 2,019 |
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis | 84 | icml | 18 | 1 | 2023-06-17 03:10:42.127000 | https://github.com/alecwangcq/EigenDamage-Pytorch | 108 | Eigendamage: Structured pruning in the kronecker-factored eigenbasis | https://scholar.google.com/scholar?cluster=15048467937573583684&hl=en&as_sdt=0,48 | 5 | 2,019 |
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions | 66 | icml | 2 | 1 | 2023-06-17 03:10:42.342000 | https://github.com/ustunb/ctfdist | 10 | Repairing without retraining: Avoiding disparate impact with counterfactual distributions | https://scholar.google.com/scholar?cluster=16561986856093629430&hl=en&as_sdt=0,5 | 5 | 2,019 |
Non-Monotonic Sequential Text Generation | 105 | icml | 11 | 2 | 2023-06-17 03:10:42.557000 | https://github.com/wellecks/nonmonotonic_text | 73 | Non-monotonic sequential text generation | https://scholar.google.com/scholar?cluster=16018486661840997659&hl=en&as_sdt=0,5 | 7 | 2,019 |
Learning deep kernels for exponential family densities | 70 | icml | 2 | 0 | 2023-06-17 03:10:42.791000 | https://github.com/kevin-w-li/deep-kexpfam | 22 | Learning deep kernels for exponential family densities | https://scholar.google.com/scholar?cluster=18438114656627425154&hl=en&as_sdt=0,43 | 3 | 2,019 |
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation | 28 | icml | 1 | 0 | 2023-06-17 03:10:43.006000 | https://github.com/SamuelWiqvist/PENs-and-ABC | 5 | Partially exchangeable networks and architectures for learning summary statistics in approximate Bayesian computation | https://scholar.google.com/scholar?cluster=16942332521272083058&hl=en&as_sdt=0,44 | 5 | 2,019 |
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations | 197 | icml | 13 | 1 | 2023-06-17 03:10:43.223000 | https://github.com/locuslab/projected_sinkhorn | 86 | Wasserstein adversarial examples via projected sinkhorn iterations | https://scholar.google.com/scholar?cluster=4087808921541648707&hl=en&as_sdt=0,33 | 7 | 2,019 |
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling | 55 | icml | 5 | 1 | 2023-06-17 03:10:43.439000 | https://github.com/wushanshan/L1AE | 18 | Learning a compressed sensing measurement matrix via gradient unrolling | https://scholar.google.com/scholar?cluster=7047806265254435189&hl=en&as_sdt=0,5 | 4 | 2,019 |
Simplifying Graph Convolutional Networks | 2,063 | icml | 146 | 1 | 2023-06-17 03:10:43.654000 | https://github.com/Tiiiger/SGC | 766 | Simplifying graph convolutional networks | https://scholar.google.com/scholar?cluster=17348071344751182786&hl=en&as_sdt=0,23 | 19 | 2,019 |
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance | 158 | icml | 5 | 0 | 2023-06-17 03:10:43.870000 | https://github.com/xcgoner/icml2019_zeno | 13 | Zeno: Distributed stochastic gradient descent with suspicion-based fault-tolerance | https://scholar.google.com/scholar?cluster=10331500453771682409&hl=en&as_sdt=0,14 | 2 | 2,019 |
Differentiable Linearized ADMM | 53 | icml | 9 | 0 | 2023-06-17 03:10:44.085000 | https://github.com/zzs1994/D-LADMM | 27 | Differentiable linearized ADMM | https://scholar.google.com/scholar?cluster=7429496083508800871&hl=en&as_sdt=0,41 | 4 | 2,019 |
Gromov-Wasserstein Learning for Graph Matching and Node Embedding | 181 | icml | 17 | 0 | 2023-06-17 03:10:44.301000 | https://github.com/HongtengXu/gwl | 63 | Gromov-wasserstein learning for graph matching and node embedding | https://scholar.google.com/scholar?cluster=17323824579705471287&hl=en&as_sdt=0,10 | 5 | 2,019 |
Supervised Hierarchical Clustering with Exponential Linkage | 27 | icml | 6 | 0 | 2023-06-17 03:10:44.517000 | https://github.com/iesl/expLinkage | 9 | Supervised hierarchical clustering with exponential linkage | https://scholar.google.com/scholar?cluster=14591272843062718088&hl=en&as_sdt=0,5 | 12 | 2,019 |
Learning to Prove Theorems via Interacting with Proof Assistants | 79 | icml | 46 | 0 | 2023-06-17 03:10:44.731000 | https://github.com/princeton-vl/CoqGym | 319 | Learning to prove theorems via interacting with proof assistants | https://scholar.google.com/scholar?cluster=14925207938076962028&hl=en&as_sdt=0,4 | 17 | 2,019 |
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation | 145 | icml | 10 | 0 | 2023-06-17 03:10:44.946000 | https://github.com/YyzHarry/ME-Net | 51 | Me-net: Towards effective adversarial robustness with matrix estimation | https://scholar.google.com/scholar?cluster=15543482510654180189&hl=en&as_sdt=0,34 | 3 | 2,019 |
Hierarchically Structured Meta-learning | 197 | icml | 13 | 1 | 2023-06-17 03:10:45.197000 | https://github.com/huaxiuyao/HSML | 48 | Hierarchically structured meta-learning | https://scholar.google.com/scholar?cluster=3487980416117206371&hl=en&as_sdt=0,31 | 5 | 2,019 |
Rademacher Complexity for Adversarially Robust Generalization | 234 | icml | 1 | 0 | 2023-06-17 03:10:45.412000 | https://github.com/dongyin92/adversarially-robust-generalization | 9 | Rademacher complexity for adversarially robust generalization | https://scholar.google.com/scholar?cluster=3771850404643054723&hl=en&as_sdt=0,5 | 1 | 2,019 |
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables | 24 | icml | 10 | 0 | 2023-06-17 03:10:45.628000 | https://github.com/ARM-gradient/ARSM | 18 | ARSM: Augment-REINFORCE-swap-merge estimator for gradient backpropagation through categorical variables | https://scholar.google.com/scholar?cluster=18117321206953712314&hl=en&as_sdt=0,5 | 1 | 2,019 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.