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Mixed Cross Entropy Loss for Neural Machine Translation | 5 | icml | 1 | 0 | 2023-06-17 04:13:48.677000 | https://github.com/haorannlp/mix | 17 | Mixed cross entropy loss for neural machine translation | https://scholar.google.com/scholar?cluster=16791533551271975512&hl=en&as_sdt=0,5 | 1 | 2,021 |
Distributionally Robust Optimization with Markovian Data | 6 | icml | 0 | 0 | 2023-06-17 04:13:48.878000 | https://github.com/mkvdro/DRO_Markov | 2 | Distributionally robust optimization with Markovian data | https://scholar.google.com/scholar?cluster=13967502296963435329&hl=en&as_sdt=0,5 | 1 | 2,021 |
Communication-Efficient Distributed SVD via Local Power Iterations | 10 | icml | 0 | 0 | 2023-06-17 04:13:49.080000 | https://github.com/lx10077/LocalPower | 0 | Communication-efficient distributed SVD via local power iterations | https://scholar.google.com/scholar?cluster=1741371435444323515&hl=en&as_sdt=0,5 | 1 | 2,021 |
FILTRA: Rethinking Steerable CNN by Filter Transform | 2 | icml | 1 | 0 | 2023-06-17 04:13:49.284000 | https://github.com/prclibo/filtra | 7 | Filtra: Rethinking steerable CNN by filter transform | https://scholar.google.com/scholar?cluster=12773800134537729615&hl=en&as_sdt=0,5 | 1 | 2,021 |
TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models | 40 | icml | 3 | 5 | 2023-06-17 04:13:49.486000 | https://github.com/zhuohan123/terapipe | 45 | Terapipe: Token-level pipeline parallelism for training large-scale language models | https://scholar.google.com/scholar?cluster=9109745061137409325&hl=en&as_sdt=0,6 | 3 | 2,021 |
Towards Understanding and Mitigating Social Biases in Language Models | 123 | icml | 8 | 0 | 2023-06-17 04:13:49.689000 | https://github.com/pliang279/LM_bias | 48 | Towards understanding and mitigating social biases in language models | https://scholar.google.com/scholar?cluster=16764320017418997560&hl=en&as_sdt=0,5 | 4 | 2,021 |
Information Obfuscation of Graph Neural Networks | 27 | icml | 7 | 2 | 2023-06-17 04:13:49.891000 | https://github.com/liaopeiyuan/GAL | 35 | Information obfuscation of graph neural networks | https://scholar.google.com/scholar?cluster=17996715912972296815&hl=en&as_sdt=0,5 | 5 | 2,021 |
Guided Exploration with Proximal Policy Optimization using a Single Demonstration | 7 | icml | 1 | 0 | 2023-06-17 04:13:50.094000 | https://github.com/compsciencelab/ppo_D | 10 | Guided exploration with proximal policy optimization using a single demonstration | https://scholar.google.com/scholar?cluster=1058578842192260735&hl=en&as_sdt=0,26 | 2 | 2,021 |
Debiasing a First-order Heuristic for Approximate Bi-level Optimization | 3 | icml | 1 | 0 | 2023-06-17 04:13:50.297000 | https://github.com/xingyousong/ufom | 3 | Debiasing a first-order heuristic for approximate bi-level optimization | https://scholar.google.com/scholar?cluster=11037305189679806516&hl=en&as_sdt=0,50 | 2 | 2,021 |
Making transport more robust and interpretable by moving data through a small number of anchor points | 13 | icml | 3 | 1 | 2023-06-17 04:13:50.498000 | https://github.com/nerdslab/latentOT | 13 | Making transport more robust and interpretable by moving data through a small number of anchor points | https://scholar.google.com/scholar?cluster=14045713528225441550&hl=en&as_sdt=0,47 | 2 | 2,021 |
Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation | 8 | icml | 4 | 0 | 2023-06-17 04:13:50.700000 | https://github.com/shawnlimn/ScaleGrad | 13 | Straight to the gradient: Learning to use novel tokens for neural text generation | https://scholar.google.com/scholar?cluster=743520526432802506&hl=en&as_sdt=0,36 | 1 | 2,021 |
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data | 57 | icml | 3 | 1 | 2023-06-17 04:13:50.903000 | https://github.com/epfml/quasi-global-momentum | 7 | Quasi-global momentum: Accelerating decentralized deep learning on heterogeneous data | https://scholar.google.com/scholar?cluster=11090813795485624273&hl=en&as_sdt=0,5 | 5 | 2,021 |
Learning by Turning: Neural Architecture Aware Optimisation | 11 | icml | 6 | 1 | 2023-06-17 04:13:51.106000 | https://github.com/jxbz/nero | 19 | Learning by turning: Neural architecture aware optimisation | https://scholar.google.com/scholar?cluster=9218227008920600415&hl=en&as_sdt=0,15 | 3 | 2,021 |
Just Train Twice: Improving Group Robustness without Training Group Information | 213 | icml | 14 | 1 | 2023-06-17 04:13:51.308000 | https://github.com/anniesch/jtt | 58 | Just train twice: Improving group robustness without training group information | https://scholar.google.com/scholar?cluster=13173846618257909762&hl=en&as_sdt=0,5 | 1 | 2,021 |
Event Outlier Detection in Continuous Time | 7 | icml | 0 | 0 | 2023-06-17 04:13:51.520000 | https://github.com/siqil/CPPOD | 9 | Event outlier detection in continuous time | https://scholar.google.com/scholar?cluster=11315185602040849494&hl=en&as_sdt=0,7 | 1 | 2,021 |
Heterogeneous Risk Minimization | 58 | icml | 8 | 0 | 2023-06-17 04:13:51.733000 | https://github.com/ljsthu/hrm | 19 | Heterogeneous risk minimization | https://scholar.google.com/scholar?cluster=12299879840182415633&hl=en&as_sdt=0,5 | 1 | 2,021 |
Elastic Graph Neural Networks | 70 | icml | 7 | 0 | 2023-06-17 04:13:51.936000 | https://github.com/lxiaorui/ElasticGNN | 35 | Elastic graph neural networks | https://scholar.google.com/scholar?cluster=7978714464929950404&hl=en&as_sdt=0,5 | 4 | 2,021 |
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition | 22 | icml | 5 | 1 | 2023-06-17 04:13:52.138000 | https://github.com/cranial-xix/marl-copa | 13 | Coach-player multi-agent reinforcement learning for dynamic team composition | https://scholar.google.com/scholar?cluster=16222834590436839078&hl=en&as_sdt=0,47 | 3 | 2,021 |
Selfish Sparse RNN Training | 29 | icml | 3 | 3 | 2023-06-17 04:13:52.340000 | https://github.com/Shiweiliuiiiiiii/Selfish-RNN | 10 | Selfish sparse rnn training | https://scholar.google.com/scholar?cluster=14857851775115975297&hl=en&as_sdt=0,5 | 1 | 2,021 |
Leveraging Public Data for Practical Private Query Release | 36 | icml | 0 | 0 | 2023-06-17 04:13:52.542000 | https://github.com/terranceliu/pmw-pub | 3 | Leveraging public data for practical private query release | https://scholar.google.com/scholar?cluster=10819180564771632569&hl=en&as_sdt=0,22 | 2 | 2,021 |
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training | 56 | icml | 6 | 0 | 2023-06-17 04:13:52.744000 | https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization | 38 | Do we actually need dense over-parameterization? in-time over-parameterization in sparse training | https://scholar.google.com/scholar?cluster=17950677328551432354&hl=en&as_sdt=0,5 | 2 | 2,021 |
Group Fisher Pruning for Practical Network Compression | 66 | icml | 12 | 5 | 2023-06-17 04:13:52.947000 | https://github.com/jshilong/FisherPruning | 138 | Group fisher pruning for practical network compression | https://scholar.google.com/scholar?cluster=7436704720048829343&hl=en&as_sdt=0,44 | 6 | 2,021 |
Relative Positional Encoding for Transformers with Linear Complexity | 22 | icml | 7 | 3 | 2023-06-17 04:13:53.150000 | https://github.com/aliutkus/spe | 58 | Relative positional encoding for transformers with linear complexity | https://scholar.google.com/scholar?cluster=16520451235518396778&hl=en&as_sdt=0,37 | 4 | 2,021 |
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach | 8 | icml | 7 | 0 | 2023-06-17 04:13:53.352000 | https://github.com/fedelopez77/sympa | 25 | Symmetric spaces for graph embeddings: A finsler-riemannian approach | https://scholar.google.com/scholar?cluster=12337649232069613673&hl=en&as_sdt=0,33 | 2 | 2,021 |
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification | 9 | icml | 0 | 0 | 2023-06-17 04:13:53.555000 | https://github.com/leishida/Um-Classification | 5 | Binary classification from multiple unlabeled datasets via surrogate set classification | https://scholar.google.com/scholar?cluster=8249584082478727878&hl=en&as_sdt=0,7 | 1 | 2,021 |
Meta-Cal: Well-controlled Post-hoc Calibration by Ranking | 15 | icml | 2 | 0 | 2023-06-17 04:13:53.758000 | https://github.com/maxc01/metacal | 6 | Meta-cal: Well-controlled post-hoc calibration by ranking | https://scholar.google.com/scholar?cluster=4779443102063826651&hl=en&as_sdt=0,14 | 2 | 2,021 |
Local Algorithms for Finding Densely Connected Clusters | 5 | icml | 3 | 0 | 2023-06-17 04:13:53.960000 | https://github.com/pmacg/local-densely-connected-clusters | 4 | Local algorithms for finding densely connected clusters | https://scholar.google.com/scholar?cluster=2599205940153817748&hl=en&as_sdt=0,25 | 1 | 2,021 |
Learning to Generate Noise for Multi-Attack Robustness | 11 | icml | 2 | 1 | 2023-06-17 04:13:54.163000 | https://github.com/divyam3897/MNG_AC | 8 | Learning to generate noise for multi-attack robustness | https://scholar.google.com/scholar?cluster=10029031126071377800&hl=en&as_sdt=0,5 | 1 | 2,021 |
Domain Generalization using Causal Matching | 153 | icml | 30 | 11 | 2023-06-17 04:13:54.365000 | https://github.com/microsoft/robustdg | 159 | Domain generalization using causal matching | https://scholar.google.com/scholar?cluster=7680827305765663856&hl=en&as_sdt=0,5 | 10 | 2,021 |
Nonparametric Hamiltonian Monte Carlo | 5 | icml | 2 | 1 | 2023-06-17 04:13:54.567000 | https://github.com/fzaiser/nonparametric-hmc | 12 | Nonparametric Hamiltonian Monte Carlo | https://scholar.google.com/scholar?cluster=15980590487021793124&hl=en&as_sdt=0,26 | 1 | 2,021 |
KO codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning | 21 | icml | 8 | 1 | 2023-06-17 04:13:54.771000 | https://github.com/deepcomm/kocodes | 12 | Ko codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning | https://scholar.google.com/scholar?cluster=6409739785381196000&hl=en&as_sdt=0,31 | 4 | 2,021 |
Inverse Constrained Reinforcement Learning | 24 | icml | 3 | 2 | 2023-06-17 04:13:54.974000 | https://github.com/shehryar-malik/icrl | 13 | Inverse constrained reinforcement learning | https://scholar.google.com/scholar?cluster=6882447057123293006&hl=en&as_sdt=0,5 | 2 | 2,021 |
A Sampling-Based Method for Tensor Ring Decomposition | 20 | icml | 1 | 0 | 2023-06-17 04:13:55.177000 | https://github.com/OsmanMalik/tr-als-sampled | 6 | A sampling-based method for tensor ring decomposition | https://scholar.google.com/scholar?cluster=9925150278480736841&hl=en&as_sdt=0,10 | 2 | 2,021 |
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers | 18 | icml | 820 | 36 | 2023-06-17 04:13:55.380000 | https://github.com/deepmind/open_spiel | 3,698 | Multi-agent training beyond zero-sum with correlated equilibrium meta-solvers | https://scholar.google.com/scholar?cluster=13991149676180937828&hl=en&as_sdt=0,9 | 106 | 2,021 |
Neural Architecture Search without Training | 214 | icml | 58 | 7 | 2023-06-17 04:13:55.583000 | https://github.com/BayesWatch/nas-without-training | 432 | Neural architecture search without training | https://scholar.google.com/scholar?cluster=12821590639566718193&hl=en&as_sdt=0,34 | 15 | 2,021 |
UCB Momentum Q-learning: Correcting the bias without forgetting | 28 | icml | 1 | 1 | 2023-06-17 04:13:55.791000 | https://github.com/omardrwch/ucbmq_code | 2 | UCB Momentum Q-learning: Correcting the bias without forgetting | https://scholar.google.com/scholar?cluster=13418224994694979040&hl=en&as_sdt=0,5 | 2 | 2,021 |
An Integer Linear Programming Framework for Mining Constraints from Data | 4 | icml | 0 | 0 | 2023-06-17 04:13:55.994000 | https://github.com/uclanlp/ILPLearning | 2 | An Integer Linear Programming Framework for Mining Constraints from Data | https://scholar.google.com/scholar?cluster=15134580706124032020&hl=en&as_sdt=0,26 | 7 | 2,021 |
Signatured Deep Fictitious Play for Mean Field Games with Common Noise | 18 | icml | 2 | 0 | 2023-06-17 04:13:56.196000 | https://github.com/mmin0/SigDFP | 1 | Signatured deep fictitious play for mean field games with common noise | https://scholar.google.com/scholar?cluster=5737626410689821885&hl=en&as_sdt=0,5 | 2 | 2,021 |
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation | 67 | icml | 37 | 10 | 2023-06-17 04:13:56.405000 | https://github.com/KevinMIN95/StyleSpeech | 196 | Meta-stylespeech: Multi-speaker adaptive text-to-speech generation | https://scholar.google.com/scholar?cluster=9200152829644981336&hl=en&as_sdt=0,33 | 8 | 2,021 |
Offline Meta-Reinforcement Learning with Advantage Weighting | 57 | icml | 9 | 1 | 2023-06-17 04:13:56.607000 | https://github.com/eric-mitchell/macaw | 34 | Offline meta-reinforcement learning with advantage weighting | https://scholar.google.com/scholar?cluster=17977945892617234025&hl=en&as_sdt=0,47 | 2 | 2,021 |
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization | 2 | icml | 1 | 0 | 2023-06-17 04:13:56.810000 | https://github.com/TaikiMiyagawa/MSPRT-TANDEM | 3 | The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization | https://scholar.google.com/scholar?cluster=8968954885886250341&hl=en&as_sdt=0,14 | 2 | 2,021 |
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games | 19 | icml | 2 | 0 | 2023-06-17 04:13:57.013000 | https://github.com/dmorrill10/hr_edl_experiments | 3 | Efficient deviation types and learning for hindsight rationality in extensive-form games | https://scholar.google.com/scholar?cluster=2350651197115820142&hl=en&as_sdt=0,33 | 2 | 2,021 |
Connecting Interpretability and Robustness in Decision Trees through Separation | 16 | icml | 0 | 0 | 2023-06-17 04:13:57.215000 | https://github.com/yangarbiter/interpretable-robust-trees | 12 | Connecting interpretability and robustness in decision trees through separation | https://scholar.google.com/scholar?cluster=2331497214666374393&hl=en&as_sdt=0,37 | 3 | 2,021 |
Oblivious Sketching for Logistic Regression | 10 | icml | 1 | 0 | 2023-06-17 04:13:57.418000 | https://github.com/cxan96/oblivious-sketching-logreg | 3 | Oblivious sketching for logistic regression | https://scholar.google.com/scholar?cluster=16316892732322711108&hl=en&as_sdt=0,5 | 1 | 2,021 |
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search | 23 | icml | 5 | 3 | 2023-06-17 04:13:57.621000 | https://github.com/Alibaba-MIIL/HardCoReNAS | 30 | Hardcore-nas: Hard constrained differentiable neural architecture search | https://scholar.google.com/scholar?cluster=12851686551366341896&hl=en&as_sdt=0,5 | 6 | 2,021 |
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information | 17 | icml | 6 | 0 | 2023-06-17 04:13:57.824000 | https://github.com/willieneis/bayesian-algorithm-execution | 40 | Bayesian algorithm execution: Estimating computable properties of black-box functions using mutual information | https://scholar.google.com/scholar?cluster=10668214102939988393&hl=en&as_sdt=0,18 | 6 | 2,021 |
Incentivizing Compliance with Algorithmic Instruments | 2 | icml | 0 | 0 | 2023-06-17 04:13:58.026000 | https://github.com/DanielNgo207/Incentivizing-Compliance-with-Algorithmic-Instruments | 0 | Incentivizing compliance with algorithmic instruments | https://scholar.google.com/scholar?cluster=8032953671879607459&hl=en&as_sdt=0,39 | 1 | 2,021 |
Cross-model Back-translated Distillation for Unsupervised Machine Translation | 7 | icml | 3 | 1 | 2023-06-17 04:13:58.228000 | https://github.com/nxphi47/multiagent_crosstranslate | 4 | Cross-model back-translated distillation for unsupervised machine translation | https://scholar.google.com/scholar?cluster=12269896059746732525&hl=en&as_sdt=0,10 | 1 | 2,021 |
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search | 24 | icml | 3 | 0 | 2023-06-17 04:13:58.430000 | https://github.com/ntienvu/TW_NAS | 4 | Optimal transport kernels for sequential and parallel neural architecture search | https://scholar.google.com/scholar?cluster=12662732608463413645&hl=en&as_sdt=0,5 | 2 | 2,021 |
Interactive Learning from Activity Description | 20 | icml | 0 | 0 | 2023-06-17 04:13:58.633000 | https://github.com/khanhptnk/iliad | 6 | Interactive learning from activity description | https://scholar.google.com/scholar?cluster=6188595152759271430&hl=en&as_sdt=0,47 | 2 | 2,021 |
Data Augmentation for Meta-Learning | 57 | icml | 5 | 0 | 2023-06-17 04:13:58.836000 | https://github.com/RenkunNi/MetaAug | 25 | Data augmentation for meta-learning | https://scholar.google.com/scholar?cluster=2872867843367483483&hl=en&as_sdt=0,5 | 1 | 2,021 |
Improved Denoising Diffusion Probabilistic Models | 754 | icml | 332 | 68 | 2023-06-17 04:13:59.039000 | https://github.com/openai/improved-diffusion | 1,891 | Improved denoising diffusion probabilistic models | https://scholar.google.com/scholar?cluster=2227179395488568184&hl=en&as_sdt=0,5 | 99 | 2,021 |
AdaXpert: Adapting Neural Architecture for Growing Data | 8 | icml | 2 | 2 | 2023-06-17 04:13:59.242000 | https://github.com/mr-eggplant/adaxpert0 | 13 | Adaxpert: Adapting neural architecture for growing data | https://scholar.google.com/scholar?cluster=1668694704547918132&hl=en&as_sdt=0,37 | 1 | 2,021 |
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points | 2 | icml | 0 | 0 | 2023-06-17 04:13:59.445000 | https://github.com/sehyunkwon/Infinite-WGAN | 3 | Wgan with an infinitely wide generator has no spurious stationary points | https://scholar.google.com/scholar?cluster=2540862355442244934&hl=en&as_sdt=0,5 | 2 | 2,021 |
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting | 16 | icml | 678 | 58 | 2023-06-17 04:13:59.648000 | https://github.com/interpretml/interpret | 5,546 | Accuracy, interpretability, and differential privacy via explainable boosting | https://scholar.google.com/scholar?cluster=3909488782505274678&hl=en&as_sdt=0,32 | 142 | 2,021 |
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes | 35 | icml | 6 | 2 | 2023-06-17 04:13:59.850000 | https://github.com/LaurenceA/bayesfunc | 12 | Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes | https://scholar.google.com/scholar?cluster=8024621603786330099&hl=en&as_sdt=0,14 | 3 | 2,021 |
Regularizing towards Causal Invariance: Linear Models with Proxies | 18 | icml | 2 | 0 | 2023-06-17 04:14:00.054000 | https://github.com/clinicalml/proxy-anchor-regression | 9 | Regularizing towards causal invariance: Linear models with proxies | https://scholar.google.com/scholar?cluster=5547608297314715512&hl=en&as_sdt=0,33 | 9 | 2,021 |
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting | 15 | icml | 4 | 0 | 2023-06-17 04:14:00.257000 | https://github.com/networkslab/rnn_flow | 18 | RNN with particle flow for probabilistic spatio-temporal forecasting | https://scholar.google.com/scholar?cluster=16256105255072962985&hl=en&as_sdt=0,43 | 4 | 2,021 |
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification | 17 | icml | 3 | 1 | 2023-06-17 04:14:00.460000 | https://github.com/bpucla/ibebm | 8 | Latent space energy-based model of symbol-vector coupling for text generation and classification | https://scholar.google.com/scholar?cluster=18132333076288060504&hl=en&as_sdt=0,5 | 2 | 2,021 |
Unsupervised Representation Learning via Neural Activation Coding | 2 | icml | 1 | 0 | 2023-06-17 04:14:00.663000 | https://github.com/yookoon/nac | 5 | Unsupervised Representation Learning via Neural Activation Coding | https://scholar.google.com/scholar?cluster=3527585526812184622&hl=en&as_sdt=0,22 | 2 | 2,021 |
Optimal Counterfactual Explanations in Tree Ensembles | 30 | icml | 5 | 0 | 2023-06-17 04:14:00.866000 | https://github.com/vidalt/OCEAN | 16 | Optimal counterfactual explanations in tree ensembles | https://scholar.google.com/scholar?cluster=1410339152566950271&hl=en&as_sdt=0,23 | 2 | 2,021 |
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints | 32 | icml | 10 | 1 | 2023-06-17 04:14:01.083000 | https://github.com/martius-lab/CombOptNet | 67 | Comboptnet: Fit the right np-hard problem by learning integer programming constraints | https://scholar.google.com/scholar?cluster=13237034191144507355&hl=en&as_sdt=0,11 | 4 | 2,021 |
How could Neural Networks understand Programs? | 37 | icml | 14 | 3 | 2023-06-17 04:14:01.286000 | https://github.com/pdlan/OSCAR | 116 | How could neural networks understand programs? | https://scholar.google.com/scholar?cluster=16362826083131548815&hl=en&as_sdt=0,44 | 4 | 2,021 |
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length | 13 | icml | 0 | 0 | 2023-06-17 04:14:01.489000 | https://github.com/ethanjperez/rda | 33 | Rissanen data analysis: Examining dataset characteristics via description length | https://scholar.google.com/scholar?cluster=5428264289372921149&hl=en&as_sdt=0,33 | 1 | 2,021 |
Megaverse: Simulating Embodied Agents at One Million Experiences per Second | 12 | icml | 19 | 4 | 2023-06-17 04:14:01.691000 | https://github.com/alex-petrenko/megaverse | 201 | Megaverse: Simulating embodied agents at one million experiences per second | https://scholar.google.com/scholar?cluster=3066110392358323524&hl=en&as_sdt=0,3 | 8 | 2,021 |
Towards Practical Mean Bounds for Small Samples | 4 | icml | 1 | 0 | 2023-06-17 04:14:01.894000 | https://github.com/myphan9/small_sample_mean_bounds | 2 | Towards practical mean bounds for small samples | https://scholar.google.com/scholar?cluster=108164015875257038&hl=en&as_sdt=0,5 | 2 | 2,021 |
GeomCA: Geometric Evaluation of Data Representations | 8 | icml | 2 | 0 | 2023-06-17 04:14:02.098000 | https://github.com/petrapoklukar/GeomCA | 10 | Geomca: Geometric evaluation of data representations | https://scholar.google.com/scholar?cluster=1763637443737261657&hl=en&as_sdt=0,5 | 1 | 2,021 |
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech | 167 | icml | 93 | 15 | 2023-06-17 04:14:02.300000 | https://github.com/huawei-noah/Speech-Backbones | 396 | Grad-tts: A diffusion probabilistic model for text-to-speech | https://scholar.google.com/scholar?cluster=6905767521784147251&hl=en&as_sdt=0,5 | 26 | 2,021 |
Bias-Free Scalable Gaussian Processes via Randomized Truncations | 13 | icml | 0 | 0 | 2023-06-17 04:14:02.503000 | https://github.com/cunningham-lab/RTGPS | 7 | Bias-free scalable gaussian processes via randomized truncations | https://scholar.google.com/scholar?cluster=5236118263143002712&hl=en&as_sdt=0,5 | 4 | 2,021 |
Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset | 5 | icml | 0 | 0 | 2023-06-17 04:14:02.705000 | https://github.com/IlanPrice/DCTpS | 12 | Dense for the price of sparse: Improved performance of sparsely initialized networks via a subspace offset | https://scholar.google.com/scholar?cluster=17879749331929716913&hl=en&as_sdt=0,36 | 1 | 2,021 |
Neural Transformation Learning for Deep Anomaly Detection Beyond Images | 54 | icml | 11 | 0 | 2023-06-17 04:14:02.908000 | https://github.com/boschresearch/NeuTraL-AD | 35 | Neural transformation learning for deep anomaly detection beyond images | https://scholar.google.com/scholar?cluster=1292087033558963213&hl=en&as_sdt=0,5 | 4 | 2,021 |
Optimization Planning for 3D ConvNets | 8 | icml | 0 | 0 | 2023-06-17 04:14:03.111000 | https://github.com/zhaofanqiu/optimization-planning-for-3d-convnets | 2 | Optimization planning for 3d convnets | https://scholar.google.com/scholar?cluster=17965785653886460675&hl=en&as_sdt=0,15 | 2 | 2,021 |
Learning Transferable Visual Models From Natural Language Supervision | 5,987 | icml | 2,336 | 151 | 2023-06-17 04:14:03.314000 | https://github.com/openai/CLIP | 15,759 | Learning transferable visual models from natural language supervision | https://scholar.google.com/scholar?cluster=15031020161691567042&hl=en&as_sdt=0,48 | 268 | 2,021 |
A General Framework For Detecting Anomalous Inputs to DNN Classifiers | 21 | icml | 3 | 3 | 2023-06-17 04:14:03.516000 | https://github.com/jayaram-r/adversarial-detection | 16 | A general framework for detecting anomalous inputs to dnn classifiers | https://scholar.google.com/scholar?cluster=7846344670241873650&hl=en&as_sdt=0,5 | 5 | 2,021 |
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning | 27 | icml | 6 | 0 | 2023-06-17 04:14:03.730000 | https://github.com/uoe-agents/GPL | 25 | Towards open ad hoc teamwork using graph-based policy learning | https://scholar.google.com/scholar?cluster=13446293545265914898&hl=en&as_sdt=0,5 | 4 | 2,021 |
Decoupling Value and Policy for Generalization in Reinforcement Learning | 62 | icml | 13 | 0 | 2023-06-17 04:14:03.932000 | https://github.com/rraileanu/idaac | 52 | Decoupling value and policy for generalization in reinforcement learning | https://scholar.google.com/scholar?cluster=12990450966698605101&hl=en&as_sdt=0,5 | 3 | 2,021 |
Differentially Private Sliced Wasserstein Distance | 9 | icml | 3 | 2 | 2023-06-17 04:14:04.136000 | https://github.com/arakotom/dp_swd | 5 | Differentially private sliced wasserstein distance | https://scholar.google.com/scholar?cluster=11153564524741628543&hl=en&as_sdt=0,33 | 1 | 2,021 |
Zero-Shot Text-to-Image Generation | 1,797 | icml | 1,904 | 65 | 2023-06-17 04:14:04.338000 | https://github.com/openai/DALL-E | 10,322 | Zero-shot text-to-image generation | https://scholar.google.com/scholar?cluster=18428055834209091582&hl=en&as_sdt=0,5 | 230 | 2,021 |
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting | 78 | icml | 168 | 63 | 2023-06-17 04:14:04.541000 | https://github.com/zalandoresearch/pytorch-ts | 1,006 | Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting | https://scholar.google.com/scholar?cluster=11453532699552258037&hl=en&as_sdt=0,14 | 24 | 2,021 |
Implicit Regularization in Tensor Factorization | 26 | icml | 0 | 0 | 2023-06-17 04:14:04.744000 | https://github.com/noamrazin/imp_reg_in_tf | 3 | Implicit regularization in tensor factorization | https://scholar.google.com/scholar?cluster=4594323532805369080&hl=en&as_sdt=0,5 | 2 | 2,021 |
Align, then memorise: the dynamics of learning with feedback alignment | 17 | icml | 2 | 0 | 2023-06-17 04:14:04.947000 | https://github.com/sdascoli/dfa-dynamics | 8 | Align, then memorise: the dynamics of learning with feedback alignment | https://scholar.google.com/scholar?cluster=10115011183031848291&hl=en&as_sdt=0,11 | 3 | 2,021 |
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed | 42 | icml | 0 | 0 | 2023-06-17 04:14:05.150000 | https://github.com/mariaref/rfvs2lnn_GMM_online | 5 | Classifying high-dimensional gaussian mixtures: Where kernel methods fail and neural networks succeed | https://scholar.google.com/scholar?cluster=2175676811548405487&hl=en&as_sdt=0,5 | 2 | 2,021 |
Solving high-dimensional parabolic PDEs using the tensor train format | 32 | icml | 6 | 1 | 2023-06-17 04:14:05.353000 | https://github.com/lorenzrichter/PDE-backward-solver | 11 | Solving high-dimensional parabolic PDEs using the tensor train format | https://scholar.google.com/scholar?cluster=11792660313798176886&hl=en&as_sdt=0,5 | 2 | 2,021 |
Principled Simplicial Neural Networks for Trajectory Prediction | 37 | icml | 2 | 0 | 2023-06-17 04:14:05.555000 | https://github.com/nglaze00/SCoNe_GCN | 9 | Principled simplicial neural networks for trajectory prediction | https://scholar.google.com/scholar?cluster=4466528152103096087&hl=en&as_sdt=0,4 | 2 | 2,021 |
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data | 13 | icml | 1 | 0 | 2023-06-17 04:14:05.757000 | https://github.com/estherrolf/representation-matters | 3 | Representation matters: Assessing the importance of subgroup allocations in training data | https://scholar.google.com/scholar?cluster=9213574703320829677&hl=en&as_sdt=0,11 | 3 | 2,021 |
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL | 14 | icml | 4 | 2 | 2023-06-17 04:14:05.960000 | https://github.com/flowersteam/TeachMyAgent | 56 | Teachmyagent: a benchmark for automatic curriculum learning in deep rl | https://scholar.google.com/scholar?cluster=11016662361926634008&hl=en&as_sdt=0,5 | 9 | 2,021 |
Discretization Drift in Two-Player Games | 6 | icml | 2,436 | 170 | 2023-06-17 04:14:06.162000 | https://github.com/deepmind/deepmind-research | 11,905 | Discretization drift in two-player games | https://scholar.google.com/scholar?cluster=5098459478601130257&hl=en&as_sdt=0,5 | 336 | 2,021 |
Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement | 8 | icml | 1 | 1 | 2023-06-17 04:14:06.364000 | https://github.com/dtak/hierarchical-disentanglement | 5 | Benchmarks, algorithms, and metrics for hierarchical disentanglement | https://scholar.google.com/scholar?cluster=9234964175960458338&hl=en&as_sdt=0,5 | 2 | 2,021 |
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees | 80 | icml | 12 | 4 | 2023-06-17 04:14:06.566000 | https://github.com/jonasrothfuss/meta_learning_pacoh | 23 | PACOH: Bayes-optimal meta-learning with PAC-guarantees | https://scholar.google.com/scholar?cluster=12050746952935759142&hl=en&as_sdt=0,30 | 5 | 2,021 |
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding | 20 | icml | 0 | 0 | 2023-06-17 04:14:06.768000 | https://github.com/ryoungj/mcbits | 13 | Improving lossless compression rates via monte carlo bits-back coding | https://scholar.google.com/scholar?cluster=1052321349567422387&hl=en&as_sdt=0,5 | 2 | 2,021 |
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes | 1 | icml | 1 | 0 | 2023-06-17 04:14:06.971000 | https://github.com/timrudner/snr_issues_in_deep_gps | 5 | On signal-to-noise ratio issues in variational inference for deep Gaussian processes | https://scholar.google.com/scholar?cluster=16244183498083641614&hl=en&as_sdt=0,16 | 3 | 2,021 |
Tilting the playing field: Dynamical loss functions for machine learning | 13 | icml | 0 | 2 | 2023-06-17 04:14:07.174000 | https://github.com/miguel-rg/dynamical-loss-functions | 3 | Tilting the playing field: Dynamical loss functions for machine learning | https://scholar.google.com/scholar?cluster=1722474778051641263&hl=en&as_sdt=0,33 | 1 | 2,021 |
UnICORNN: A recurrent model for learning very long time dependencies | 35 | icml | 3 | 0 | 2023-06-17 04:14:07.376000 | https://github.com/tk-rusch/unicornn | 23 | UnICORNN: A recurrent model for learning very long time dependencies | https://scholar.google.com/scholar?cluster=16728515819525304575&hl=en&as_sdt=0,5 | 2 | 2,021 |
Simple and Effective VAE Training with Calibrated Decoders | 52 | icml | 1 | 0 | 2023-06-17 04:14:07.579000 | https://github.com/orybkin/sigma-vae | 25 | Simple and effective VAE training with calibrated decoders | https://scholar.google.com/scholar?cluster=16943299314546110740&hl=en&as_sdt=0,26 | 3 | 2,021 |
Model-Based Reinforcement Learning via Latent-Space Collocation | 17 | icml | 0 | 0 | 2023-06-17 04:14:07.783000 | https://github.com/zchuning/latco | 26 | Model-based reinforcement learning via latent-space collocation | https://scholar.google.com/scholar?cluster=2726935776109554696&hl=en&as_sdt=0,5 | 4 | 2,021 |
Training Data Subset Selection for Regression with Controlled Generalization Error | 8 | icml | 1 | 0 | 2023-06-17 04:14:07.985000 | https://github.com/abir-de/SELCON | 7 | Training data subset selection for regression with controlled generalization error | https://scholar.google.com/scholar?cluster=8877772987506172355&hl=en&as_sdt=0,5 | 2 | 2,021 |
Momentum Residual Neural Networks | 39 | icml | 17 | 6 | 2023-06-17 04:14:08.188000 | https://github.com/michaelsdr/momentumnet | 204 | Momentum residual neural networks | https://scholar.google.com/scholar?cluster=195539269682246494&hl=en&as_sdt=0,10 | 8 | 2,021 |
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks | 12 | icml | 2 | 2 | 2023-06-17 04:14:08.390000 | https://github.com/keynans/HypeRL | 16 | Recomposing the reinforcement learning building blocks with hypernetworks | https://scholar.google.com/scholar?cluster=11431615300192492432&hl=en&as_sdt=0,7 | 2 | 2,021 |
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning | 12 | icml | 1 | 0 | 2023-06-17 04:14:08.593000 | https://github.com/nsaunshi/meta_tr_val_split | 2 | A representation learning perspective on the importance of train-validation splitting in meta-learning | https://scholar.google.com/scholar?cluster=15485330124938854681&hl=en&as_sdt=0,14 | 2 | 2,021 |
Linear Transformers Are Secretly Fast Weight Programmers | 77 | icml | 9 | 0 | 2023-06-17 04:14:08.796000 | https://github.com/ischlag/fast-weight-transformers | 80 | Linear transformers are secretly fast weight programmers | https://scholar.google.com/scholar?cluster=7929763198773172485&hl=en&as_sdt=0,39 | 5 | 2,021 |
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