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NGBoost: Natural Gradient Boosting for Probabilistic Prediction | 197 | icml | 207 | 47 | 2023-06-17 03:56:59.173000 | https://github.com/stanfordmlgroup/ngboost | 1,440 | Ngboost: Natural gradient boosting for probabilistic prediction | https://scholar.google.com/scholar?cluster=4894543059596757711&hl=en&as_sdt=0,33 | 45 | 2,020 |
Familywise Error Rate Control by Interactive Unmasking | 9 | icml | 0 | 0 | 2023-06-17 03:56:59.375000 | https://github.com/duanby/i-FWER | 0 | Familywise error rate control by interactive unmasking | https://scholar.google.com/scholar?cluster=4720846503563749113&hl=en&as_sdt=0,41 | 1 | 2,020 |
On Contrastive Learning for Likelihood-free Inference | 82 | icml | 10 | 0 | 2023-06-17 03:56:59.577000 | https://github.com/conormdurkan/lfi | 36 | On contrastive learning for likelihood-free inference | https://scholar.google.com/scholar?cluster=2331371123661181745&hl=en&as_sdt=0,10 | 4 | 2,020 |
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors | 155 | icml | 79 | 73 | 2023-06-17 03:56:59.782000 | https://github.com/google/edward2 | 644 | Efficient and scalable bayesian neural nets with rank-1 factors | https://scholar.google.com/scholar?cluster=14999664725860521004&hl=en&as_sdt=0,5 | 20 | 2,020 |
Self-Concordant Analysis of Frank-Wolfe Algorithms | 19 | icml | 1 | 0 | 2023-06-17 03:56:59.984000 | https://github.com/kamil-safin/SCFW | 3 | Self-concordant analysis of Frank-Wolfe algorithms | https://scholar.google.com/scholar?cluster=10274753710668333699&hl=en&as_sdt=0,5 | 2 | 2,020 |
Decision Trees for Decision-Making under the Predict-then-Optimize Framework | 88 | icml | 16 | 0 | 2023-06-17 03:57:00.187000 | https://github.com/rtm2130/SPOTree | 21 | Decision trees for decision-making under the predict-then-optimize framework | https://scholar.google.com/scholar?cluster=2000494760504517215&hl=en&as_sdt=0,5 | 2 | 2,020 |
Identifying Statistical Bias in Dataset Replication | 47 | icml | 5 | 0 | 2023-06-17 03:57:00.390000 | https://github.com/MadryLab/dataset-replication-analysis | 25 | Identifying statistical bias in dataset replication | https://scholar.google.com/scholar?cluster=16322569355368565071&hl=en&as_sdt=0,5 | 9 | 2,020 |
Latent Bernoulli Autoencoder | 5 | icml | 2 | 2 | 2023-06-17 03:57:00.591000 | https://github.com/ok1zjf/lbae | 18 | Latent bernoulli autoencoder | https://scholar.google.com/scholar?cluster=8997104581865575542&hl=en&as_sdt=0,5 | 4 | 2,020 |
Growing Action Spaces | 26 | icml | 128 | 12 | 2023-06-17 03:57:00.795000 | https://github.com/TorchCraft/TorchCraftAI | 640 | Growing action spaces | https://scholar.google.com/scholar?cluster=2822509827640565136&hl=en&as_sdt=0,5 | 49 | 2,020 |
Why Are Learned Indexes So Effective? | 25 | icml | 4 | 0 | 2023-06-17 03:57:00.997000 | https://github.com/gvinciguerra/Learned-indexes-effectiveness | 15 | Why are learned indexes so effective? | https://scholar.google.com/scholar?cluster=10615073257658129787&hl=en&as_sdt=0,33 | 4 | 2,020 |
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? | 125 | icml | 34 | 10 | 2023-06-17 03:57:01.200000 | https://github.com/OATML/oatomobile | 176 | Can autonomous vehicles identify, recover from, and adapt to distribution shifts? | https://scholar.google.com/scholar?cluster=12116616826636126634&hl=en&as_sdt=0,5 | 12 | 2,020 |
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data | 199 | icml | 28 | 2 | 2023-06-17 03:57:01.402000 | https://github.com/mfinzi/LieConv | 239 | Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data | https://scholar.google.com/scholar?cluster=5464981001229463744&hl=en&as_sdt=0,5 | 10 | 2,020 |
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains | 24 | icml | 4 | 2 | 2023-06-17 03:57:01.604000 | https://github.com/johannes-fischer/icml2020_ipft | 9 | Information particle filter tree: An online algorithm for pomdps with belief-based rewards on continuous domains | https://scholar.google.com/scholar?cluster=12906174048753061788&hl=en&as_sdt=0,21 | 2 | 2,020 |
p-Norm Flow Diffusion for Local Graph Clustering | 12 | icml | 1 | 0 | 2023-06-17 03:57:01.806000 | https://github.com/s-h-yang/pNormFlowDiffusion | 1 | p-Norm flow diffusion for local graph clustering | https://scholar.google.com/scholar?cluster=13045214522176891757&hl=en&as_sdt=0,44 | 1 | 2,020 |
Stochastic Latent Residual Video Prediction | 110 | icml | 16 | 0 | 2023-06-17 03:57:02.010000 | https://github.com/edouardelasalles/srvp | 75 | Stochastic latent residual video prediction | https://scholar.google.com/scholar?cluster=13364014516718772272&hl=en&as_sdt=0,34 | 3 | 2,020 |
Leveraging Frequency Analysis for Deep Fake Image Recognition | 247 | icml | 21 | 9 | 2023-06-17 03:57:02.213000 | https://github.com/RUB-SysSec/GANDCTAnalysis | 141 | Leveraging frequency analysis for deep fake image recognition | https://scholar.google.com/scholar?cluster=15424504685179897985&hl=en&as_sdt=0,33 | 8 | 2,020 |
No-Regret and Incentive-Compatible Online Learning | 9 | icml | 0 | 0 | 2023-06-17 03:57:02.422000 | https://github.com/charapod/noregr-and-ic | 3 | No-regret and incentive-compatible online learning | https://scholar.google.com/scholar?cluster=10101414388050703329&hl=en&as_sdt=0,3 | 3 | 2,020 |
Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods | 79 | icml | 21 | 2 | 2023-06-17 03:57:02.630000 | https://github.com/HazyResearch/flyingsquid | 302 | Fast and three-rious: Speeding up weak supervision with triplet methods | https://scholar.google.com/scholar?cluster=13381739478195543351&hl=en&as_sdt=0,21 | 26 | 2,020 |
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks | 78 | icml | 19 | 0 | 2023-06-17 03:57:02.832000 | https://github.com/TAMU-VITA/AGD | 101 | Autogan-distiller: Searching to compress generative adversarial networks | https://scholar.google.com/scholar?cluster=1452214065033971023&hl=en&as_sdt=0,5 | 16 | 2,020 |
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths | 4 | icml | 5 | 0 | 2023-06-17 03:57:03.034000 | https://github.com/DessiLBI2020/DessiLBI | 35 | Dessilbi: Exploring structural sparsity of deep networks via differential inclusion paths | https://scholar.google.com/scholar?cluster=10194996533073442340&hl=en&as_sdt=0,5 | 1 | 2,020 |
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs | 14 | icml | 0 | 0 | 2023-06-17 03:57:03.237000 | https://github.com/syanga/dglearn | 6 | Characterizing distribution equivalence and structure learning for cyclic and acyclic directed graphs | https://scholar.google.com/scholar?cluster=4341241833833873634&hl=en&as_sdt=0,10 | 2 | 2,020 |
Gradient Temporal-Difference Learning with Regularized Corrections | 35 | icml | 9 | 1 | 2023-06-17 03:57:03.439000 | https://github.com/rlai-lab/Regularized-GradientTD | 32 | Gradient temporal-difference learning with regularized corrections | https://scholar.google.com/scholar?cluster=8254675597355502028&hl=en&as_sdt=0,44 | 10 | 2,020 |
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks | 62 | icml | 5 | 0 | 2023-06-17 03:57:03.641000 | https://github.com/goldblum/FeatureClustering | 12 | Unraveling meta-learning: Understanding feature representations for few-shot tasks | https://scholar.google.com/scholar?cluster=17583362370632834127&hl=en&as_sdt=0,5 | 2 | 2,020 |
Towards a General Theory of Infinite-Width Limits of Neural Classifiers | 9 | icml | 0 | 0 | 2023-06-17 03:57:03.843000 | https://github.com/deepmipt/infinite-width_nets | 3 | Towards a general theory of infinite-width limits of neural classifiers | https://scholar.google.com/scholar?cluster=6182164811378755380&hl=en&as_sdt=0,5 | 4 | 2,020 |
Differentially Private Set Union | 26 | icml | 1 | 0 | 2023-06-17 03:57:04.072000 | https://github.com/heyyjudes/differentially-private-set-union | 6 | Differentially private set union | https://scholar.google.com/scholar?cluster=2482149851439545745&hl=en&as_sdt=0,41 | 3 | 2,020 |
The continuous categorical: a novel simplex-valued exponential family | 15 | icml | 7 | 0 | 2023-06-17 03:57:04.273000 | https://github.com/cunningham-lab/cb_and_cc | 31 | The continuous categorical: a novel simplex-valued exponential family | https://scholar.google.com/scholar?cluster=17174456964236691188&hl=en&as_sdt=0,44 | 4 | 2,020 |
Automatic Reparameterisation of Probabilistic Programs | 21 | icml | 3 | 1 | 2023-06-17 03:57:04.475000 | https://github.com/mgorinova/autoreparam | 33 | Automatic reparameterisation of probabilistic programs | https://scholar.google.com/scholar?cluster=1767777764184099722&hl=en&as_sdt=0,5 | 6 | 2,020 |
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning | 82 | icml | 10 | 1 | 2023-06-17 03:57:04.677000 | https://github.com/99andBeyond/Apollo1060 | 62 | Learning to navigate the synthetically accessible chemical space using reinforcement learning | https://scholar.google.com/scholar?cluster=12254018210357831699&hl=en&as_sdt=0,5 | 7 | 2,020 |
Ordinal Non-negative Matrix Factorization for Recommendation | 14 | icml | 5 | 0 | 2023-06-17 03:57:04.879000 | https://github.com/Oligou/OrdNMF | 9 | Ordinal non-negative matrix factorization for recommendation | https://scholar.google.com/scholar?cluster=3251477194660112209&hl=en&as_sdt=0,33 | 3 | 2,020 |
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination | 33 | icml | 13 | 3 | 2023-06-17 03:57:05.080000 | https://github.com/IBM/PoWER-BERT | 53 | PoWER-BERT: Accelerating BERT inference via progressive word-vector elimination | https://scholar.google.com/scholar?cluster=306627104113108298&hl=en&as_sdt=0,33 | 7 | 2,020 |
PackIt: A Virtual Environment for Geometric Planning | 7 | icml | 4 | 4 | 2023-06-17 03:57:05.282000 | https://github.com/princeton-vl/PackIt | 45 | Packit: A virtual environment for geometric planning | https://scholar.google.com/scholar?cluster=5535871935242220151&hl=en&as_sdt=0,33 | 6 | 2,020 |
DROCC: Deep Robust One-Class Classification | 121 | icml | 369 | 28 | 2023-06-17 03:57:05.484000 | https://github.com/Microsoft/EdgeML | 1,453 | DROCC: Deep robust one-class classification | https://scholar.google.com/scholar?cluster=3986505951359290998&hl=en&as_sdt=0,29 | 87 | 2,020 |
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling | 63 | icml | 5 | 1 | 2023-06-17 03:57:05.686000 | https://github.com/wgrathwohl/LSD | 43 | Learning the stein discrepancy for training and evaluating energy-based models without sampling | https://scholar.google.com/scholar?cluster=12824935271809632059&hl=en&as_sdt=0,5 | 2 | 2,020 |
On the Iteration Complexity of Hypergradient Computation | 116 | icml | 17 | 1 | 2023-06-17 03:57:05.887000 | https://github.com/prolearner/hypertorch | 112 | On the iteration complexity of hypergradient computation | https://scholar.google.com/scholar?cluster=3451320004072265708&hl=en&as_sdt=0,36 | 6 | 2,020 |
Robust Learning with the Hilbert-Schmidt Independence Criterion | 33 | icml | 6 | 0 | 2023-06-17 03:57:06.088000 | https://github.com/danielgreenfeld3/XIC | 34 | Robust learning with the hilbert-schmidt independence criterion | https://scholar.google.com/scholar?cluster=13054295788524587578&hl=en&as_sdt=0,29 | 2 | 2,020 |
Implicit Geometric Regularization for Learning Shapes | 408 | icml | 35 | 5 | 2023-06-17 03:57:06.291000 | https://github.com/amosgropp/IGR | 331 | Implicit geometric regularization for learning shapes | https://scholar.google.com/scholar?cluster=18082545558132742834&hl=en&as_sdt=0,33 | 7 | 2,020 |
Recurrent Hierarchical Topic-Guided RNN for Language Generation | 22 | icml | 2 | 5 | 2023-06-17 03:57:06.493000 | https://github.com/Dan123dan/rGBN-RNN | 6 | Recurrent hierarchical topic-guided RNN for language generation | https://scholar.google.com/scholar?cluster=11674844584780363467&hl=en&as_sdt=0,4 | 1 | 2,020 |
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search | 52 | icml | 7 | 1 | 2023-06-17 03:57:06.695000 | https://github.com/guoyongcs/CNAS | 17 | Breaking the curse of space explosion: Towards efficient nas with curriculum search | https://scholar.google.com/scholar?cluster=5489996847363496431&hl=en&as_sdt=0,10 | 4 | 2,020 |
Certified Data Removal from Machine Learning Models | 163 | icml | 8 | 0 | 2023-06-17 03:57:06.897000 | https://github.com/facebookresearch/certified-removal | 39 | Certified data removal from machine learning models | https://scholar.google.com/scholar?cluster=5421394926787368463&hl=en&as_sdt=0,33 | 8 | 2,020 |
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks | 28 | icml | 0 | 0 | 2023-06-17 03:57:07.099000 | https://github.com/ZhishuaiGuo/DistributedAUC | 2 | Communication-efficient distributed stochastic auc maximization with deep neural networks | https://scholar.google.com/scholar?cluster=992924762353556583&hl=en&as_sdt=0,48 | 1 | 2,020 |
Neural Topic Modeling with Continual Lifelong Learning | 29 | icml | 3 | 1 | 2023-06-17 03:57:07.301000 | https://github.com/pgcool/Lifelong-Neural-Topic-Modeling | 23 | Neural topic modeling with continual lifelong learning | https://scholar.google.com/scholar?cluster=5694355012238035603&hl=en&as_sdt=0,5 | 2 | 2,020 |
Optimal approximation for unconstrained non-submodular minimization | 22 | icml | 0 | 0 | 2023-06-17 03:57:07.503000 | https://github.com/marwash25/non-sub-min | 0 | Optimal approximation for unconstrained non-submodular minimization | https://scholar.google.com/scholar?cluster=16541151635330995478&hl=en&as_sdt=0,31 | 2 | 2,020 |
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix | 7 | icml | 0 | 1 | 2023-06-17 03:57:07.705000 | https://github.com/insuhan/polytensorsketch | 2 | Polynomial tensor sketch for element-wise function of low-rank matrix | https://scholar.google.com/scholar?cluster=15937632034353153696&hl=en&as_sdt=0,48 | 1 | 2,020 |
Improving generalization by controlling label-noise information in neural network weights | 39 | icml | 8 | 0 | 2023-06-17 03:57:07.906000 | https://github.com/hrayrhar/limit-label-memorization | 37 | Improving generalization by controlling label-noise information in neural network weights | https://scholar.google.com/scholar?cluster=8186840532226802329&hl=en&as_sdt=0,11 | 5 | 2,020 |
Contrastive Multi-View Representation Learning on Graphs | 683 | icml | 46 | 11 | 2023-06-17 03:57:08.108000 | https://github.com/kavehhassani/mvgrl | 225 | Contrastive multi-view representation learning on graphs | https://scholar.google.com/scholar?cluster=11131425815493661687&hl=en&as_sdt=0,47 | 9 | 2,020 |
Nested Subspace Arrangement for Representation of Relational Data | 3 | icml | 0 | 0 | 2023-06-17 03:57:08.311000 | https://github.com/KyushuUniversityMathematics/DANCAR | 2 | Nested subspace arrangement for representation of relational data | https://scholar.google.com/scholar?cluster=5195931229921461485&hl=en&as_sdt=0,5 | 4 | 2,020 |
The Tree Ensemble Layer: Differentiability meets Conditional Computation | 48 | icml | 7,322 | 1,026 | 2023-06-17 03:57:08.513000 | https://github.com/google-research/google-research | 29,791 | The tree ensemble layer: Differentiability meets conditional computation | https://scholar.google.com/scholar?cluster=4646704514802719017&hl=en&as_sdt=0,22 | 727 | 2,020 |
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation | 59 | icml | 2 | 0 | 2023-06-17 03:57:08.715000 | https://github.com/MLI-lab/cs_deep_decoder | 15 | Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation | https://scholar.google.com/scholar?cluster=17885213602830705754&hl=en&as_sdt=0,5 | 2 | 2,020 |
Hierarchically Decoupled Imitation For Morphological Transfer | 21 | icml | 5 | 3 | 2023-06-17 03:57:08.917000 | https://github.com/jhejna/hierarchical_morphology_transfer | 16 | Hierarchically decoupled imitation for morphological transfer | https://scholar.google.com/scholar?cluster=7821488667980467803&hl=en&as_sdt=0,5 | 3 | 2,020 |
Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD) | 23 | icml | 2 | 1 | 2023-06-17 03:57:09.128000 | https://github.com/FabianHinder/DAWIDD | 7 | Towards non-parametric drift detection via dynamic adapting window independence drift detection (DAWIDD) | https://scholar.google.com/scholar?cluster=4763047039028062564&hl=en&as_sdt=0,7 | 2 | 2,020 |
Topologically Densified Distributions | 13 | icml | 1 | 0 | 2023-06-17 03:57:09.331000 | https://github.com/c-hofer/topologically_densified_distributions | 2 | Topologically densified distributions | https://scholar.google.com/scholar?cluster=18143439633922765637&hl=en&as_sdt=0,10 | 2 | 2,020 |
Graph Filtration Learning | 63 | icml | 8 | 0 | 2023-06-17 03:57:09.534000 | https://github.com/c-hofer/graph_filtration_learning | 14 | Graph filtration learning | https://scholar.google.com/scholar?cluster=16680082495324217816&hl=en&as_sdt=0,44 | 3 | 2,020 |
Set Functions for Time Series | 73 | icml | 26 | 2 | 2023-06-17 03:57:09.736000 | https://github.com/BorgwardtLab/Set_Functions_for_Time_Series | 104 | Set functions for time series | https://scholar.google.com/scholar?cluster=11653676919176974096&hl=en&as_sdt=0,45 | 7 | 2,020 |
Lifted Disjoint Paths with Application in Multiple Object Tracking | 112 | icml | 8 | 2 | 2023-06-17 03:57:09.938000 | https://github.com/AndreaHor/LifT_Solver | 51 | Lifted disjoint paths with application in multiple object tracking | https://scholar.google.com/scholar?cluster=7450982728927056524&hl=en&as_sdt=0,5 | 7 | 2,020 |
Infinite attention: NNGP and NTK for deep attention networks | 71 | icml | 227 | 58 | 2023-06-17 03:57:10.141000 | https://github.com/google/neural-tangents | 2,024 | Infinite attention: NNGP and NTK for deep attention networks | https://scholar.google.com/scholar?cluster=8612471018033907356&hl=en&as_sdt=0,5 | 64 | 2,020 |
The Non-IID Data Quagmire of Decentralized Machine Learning | 373 | icml | 8 | 0 | 2023-06-17 03:57:10.342000 | https://github.com/kevinhsieh/non_iid_dml | 26 | The non-iid data quagmire of decentralized machine learning | https://scholar.google.com/scholar?cluster=6995419568802932569&hl=en&as_sdt=0,5 | 1 | 2,020 |
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation | 633 | icml | 108 | 27 | 2023-06-17 03:57:10.544000 | https://github.com/google-research/xtreme | 583 | Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation | https://scholar.google.com/scholar?cluster=3128313942238375094&hl=en&as_sdt=0,10 | 21 | 2,020 |
Momentum-Based Policy Gradient Methods | 29 | icml | 2 | 0 | 2023-06-17 03:57:10.746000 | https://github.com/gaosh/MBPG | 6 | Momentum-based policy gradient methods | https://scholar.google.com/scholar?cluster=12318216464045418856&hl=en&as_sdt=0,47 | 2 | 2,020 |
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control | 97 | icml | 28 | 12 | 2023-06-17 03:57:10.948000 | https://github.com/huangwl18/modular-rl | 194 | One policy to control them all: Shared modular policies for agent-agnostic control | https://scholar.google.com/scholar?cluster=14540777310694580207&hl=en&as_sdt=0,33 | 11 | 2,020 |
Generating Programmatic Referring Expressions via Program Synthesis | 8 | icml | 3 | 0 | 2023-06-17 03:57:11.150000 | https://github.com/moqingyan/object_reference_synthesis | 5 | Generating programmatic referring expressions via program synthesis | https://scholar.google.com/scholar?cluster=6959334433424014581&hl=en&as_sdt=0,33 | 2 | 2,020 |
Accelerated Stochastic Gradient-free and Projection-free Methods | 16 | icml | 0 | 0 | 2023-06-17 03:57:11.352000 | https://github.com/TLMichael/Acc-SZOFW | 3 | Accelerated stochastic gradient-free and projection-free methods | https://scholar.google.com/scholar?cluster=9296344013020465952&hl=en&as_sdt=0,6 | 2 | 2,020 |
Multigrid Neural Memory | 5 | icml | 1 | 0 | 2023-06-17 03:57:11.554000 | https://github.com/trihuynh88/multigrid_mem | 7 | Multigrid neural memory | https://scholar.google.com/scholar?cluster=15687545930604068210&hl=en&as_sdt=0,34 | 4 | 2,020 |
Meta-Learning with Shared Amortized Variational Inference | 15 | icml | 0 | 2 | 2023-06-17 03:57:11.756000 | https://github.com/katafeya/samovar | 3 | Meta-learning with shared amortized variational inference | https://scholar.google.com/scholar?cluster=5105160592289559562&hl=en&as_sdt=0,21 | 6 | 2,020 |
Do We Need Zero Training Loss After Achieving Zero Training Error? | 90 | icml | 6 | 0 | 2023-06-17 03:57:11.959000 | https://github.com/takashiishida/flooding | 82 | Do we need zero training loss after achieving zero training error? | https://scholar.google.com/scholar?cluster=6131533147705685027&hl=en&as_sdt=0,33 | 5 | 2,020 |
Semi-Supervised Learning with Normalizing Flows | 77 | icml | 12 | 1 | 2023-06-17 03:57:12.160000 | https://github.com/izmailovpavel/flowgmm | 129 | Semi-supervised learning with normalizing flows | https://scholar.google.com/scholar?cluster=9421035999149534110&hl=en&as_sdt=0,5 | 10 | 2,020 |
Source Separation with Deep Generative Priors | 28 | icml | 5 | 3 | 2023-06-17 03:57:12.362000 | https://github.com/jthickstun/basis-separation | 33 | Source separation with deep generative priors | https://scholar.google.com/scholar?cluster=17132907753659598254&hl=en&as_sdt=0,5 | 7 | 2,020 |
T-GD: Transferable GAN-generated Images Detection Framework | 31 | icml | 4 | 0 | 2023-06-17 03:57:12.564000 | https://github.com/cutz-j/T-GD | 16 | T-gd: Transferable gan-generated images detection framework | https://scholar.google.com/scholar?cluster=17021668985815827092&hl=en&as_sdt=0,5 | 2 | 2,020 |
Information-Theoretic Local Minima Characterization and Regularization | 9 | icml | 0 | 0 | 2023-06-17 03:57:12.766000 | https://github.com/SeanJia/InfoMCR | 5 | Information-theoretic local minima characterization and regularization | https://scholar.google.com/scholar?cluster=16854698489852164998&hl=en&as_sdt=0,11 | 2 | 2,020 |
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation | 89 | icml | 9 | 3 | 2023-06-17 03:57:12.968000 | https://github.com/xiangdal/implicit_alignment | 87 | Implicit class-conditioned domain alignment for unsupervised domain adaptation | https://scholar.google.com/scholar?cluster=17175487218857833755&hl=en&as_sdt=0,5 | 6 | 2,020 |
Multi-Objective Molecule Generation using Interpretable Substructures | 124 | icml | 42 | 8 | 2023-06-17 03:57:13.171000 | https://github.com/wengong-jin/multiobj-rationale | 119 | Multi-objective molecule generation using interpretable substructures | https://scholar.google.com/scholar?cluster=7786133206388752764&hl=en&as_sdt=0,44 | 3 | 2,020 |
On Relativistic f-Divergences | 18 | icml | 17 | 0 | 2023-06-17 03:57:13.373000 | https://github.com/AlexiaJM/relativistic-f-divergences | 85 | On relativistic f-divergences | https://scholar.google.com/scholar?cluster=17068494214467307697&hl=en&as_sdt=0,5 | 8 | 2,020 |
Being Bayesian about Categorical Probability | 44 | icml | 4 | 2 | 2023-06-17 03:57:13.575000 | https://github.com/tjoo512/belief-matching-framework | 33 | Being bayesian about categorical probability | https://scholar.google.com/scholar?cluster=6426225307727814668&hl=en&as_sdt=0,5 | 5 | 2,020 |
Evaluating the Performance of Reinforcement Learning Algorithms | 46 | icml | 0 | 2 | 2023-06-17 03:57:13.777000 | https://github.com/ScottJordan/EvaluationOfRLAlgs | 26 | Evaluating the performance of reinforcement learning algorithms | https://scholar.google.com/scholar?cluster=4785496300749883115&hl=en&as_sdt=0,5 | 2 | 2,020 |
Stochastic Differential Equations with Variational Wishart Diffusions | 7 | icml | 1 | 0 | 2023-06-17 03:57:13.979000 | https://github.com/JorgensenMart/Wishart-priored-SDE | 8 | Stochastic differential equations with variational wishart diffusions | https://scholar.google.com/scholar?cluster=8080141843979887658&hl=en&as_sdt=0,5 | 1 | 2,020 |
Partial Trace Regression and Low-Rank Kraus Decomposition | 6 | icml | 1 | 0 | 2023-06-17 03:57:14.182000 | https://github.com/Stef-hub/partial_trace_kraus | 0 | Partial trace regression and low-rank kraus decomposition | https://scholar.google.com/scholar?cluster=794742801432087247&hl=en&as_sdt=0,47 | 3 | 2,020 |
Operation-Aware Soft Channel Pruning using Differentiable Masks | 96 | icml | 0 | 0 | 2023-06-17 03:57:14.384000 | https://github.com/kminsoo/SCP | 6 | Operation-aware soft channel pruning using differentiable masks | https://scholar.google.com/scholar?cluster=6963448174836272281&hl=en&as_sdt=0,31 | 1 | 2,020 |
Non-autoregressive Machine Translation with Disentangled Context Transformer | 71 | icml | 9 | 3 | 2023-06-17 03:57:14.586000 | https://github.com/facebookresearch/DisCo | 78 | Non-autoregressive machine translation with disentangled context transformer | https://scholar.google.com/scholar?cluster=8958608366652830932&hl=en&as_sdt=0,25 | 11 | 2,020 |
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention | 705 | icml | 162 | 28 | 2023-06-17 03:57:14.788000 | https://github.com/idiap/fast-transformers | 1,434 | Transformers are rnns: Fast autoregressive transformers with linear attention | https://scholar.google.com/scholar?cluster=15303739914785429862&hl=en&as_sdt=0,6 | 27 | 2,020 |
Entropy Minimization In Emergent Languages | 20 | icml | 98 | 7 | 2023-06-17 03:57:14.991000 | https://github.com/facebookresearch/EGG | 261 | Entropy minimization in emergent languages | https://scholar.google.com/scholar?cluster=9085278772671430646&hl=en&as_sdt=0,6 | 16 | 2,020 |
What can I do here? A Theory of Affordances in Reinforcement Learning | 49 | icml | 1 | 0 | 2023-06-17 03:57:15.193000 | https://github.com/deepmind/affordances_option_models | 21 | What can I do here? A Theory of Affordances in Reinforcement Learning | https://scholar.google.com/scholar?cluster=2336774470554893443&hl=en&as_sdt=0,9 | 4 | 2,020 |
FACT: A Diagnostic for Group Fairness Trade-offs | 28 | icml | 0 | 1 | 2023-06-17 03:57:15.395000 | https://github.com/wnstlr/FACT | 4 | FACT: A diagnostic for group fairness trade-offs | https://scholar.google.com/scholar?cluster=17087884984751620008&hl=en&as_sdt=0,34 | 4 | 2,020 |
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup | 208 | icml | 17 | 0 | 2023-06-17 03:57:15.598000 | https://github.com/snu-mllab/PuzzleMix | 144 | Puzzle mix: Exploiting saliency and local statistics for optimal mixup | https://scholar.google.com/scholar?cluster=58056101510275173&hl=en&as_sdt=0,47 | 10 | 2,020 |
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation | 52 | icml | 2 | 0 | 2023-06-17 03:57:15.799000 | https://github.com/stevenkleinegesse/minebed | 7 | Bayesian experimental design for implicit models by mutual information neural estimation | https://scholar.google.com/scholar?cluster=18098257663902816323&hl=en&as_sdt=0,5 | 1 | 2,020 |
Learning Similarity Metrics for Numerical Simulations | 14 | icml | 4 | 0 | 2023-06-17 03:57:16.001000 | https://github.com/tum-pbs/LSIM | 28 | Learning similarity metrics for numerical simulations | https://scholar.google.com/scholar?cluster=16424748636461420663&hl=en&as_sdt=0,31 | 6 | 2,020 |
Online Learning for Active Cache Synchronization | 4 | icml | 15 | 0 | 2023-06-17 03:57:16.204000 | https://github.com/microsoft/Optimal-Freshness-Crawl-Scheduling | 34 | Online learning for active cache synchronization | https://scholar.google.com/scholar?cluster=17855139660047402604&hl=en&as_sdt=0,10 | 12 | 2,020 |
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates | 72 | icml | 17 | 3 | 2023-06-17 03:57:16.406000 | https://github.com/Lingkai-Kong/SDE-Net | 86 | Sde-net: Equipping deep neural networks with uncertainty estimates | https://scholar.google.com/scholar?cluster=8672163591192600750&hl=en&as_sdt=0,5 | 5 | 2,020 |
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks | 171 | icml | 13 | 1 | 2023-06-17 03:57:16.608000 | https://github.com/wiseodd/last_layer_laplace | 68 | Being bayesian, even just a bit, fixes overconfidence in relu networks | https://scholar.google.com/scholar?cluster=12071417821093265788&hl=en&as_sdt=0,10 | 3 | 2,020 |
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness | 77 | icml | 2 | 0 | 2023-06-17 03:57:16.810000 | https://github.com/alevine0/smoothingGenGaussian | 3 | Curse of dimensionality on randomized smoothing for certifiable robustness | https://scholar.google.com/scholar?cluster=3011754801302314262&hl=en&as_sdt=0,22 | 3 | 2,020 |
Two Routes to Scalable Credit Assignment without Weight Symmetry | 27 | icml | 3 | 0 | 2023-06-17 03:57:17.012000 | https://github.com/neuroailab/Neural-Alignment | 22 | Two routes to scalable credit assignment without weight symmetry | https://scholar.google.com/scholar?cluster=5596776573115388882&hl=en&as_sdt=0,21 | 6 | 2,020 |
Soft Threshold Weight Reparameterization for Learnable Sparsity | 134 | icml | 9 | 5 | 2023-06-17 03:57:17.214000 | https://github.com/RAIVNLab/STR | 78 | Soft threshold weight reparameterization for learnable sparsity | https://scholar.google.com/scholar?cluster=6875882562671228073&hl=en&as_sdt=0,21 | 6 | 2,020 |
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics | 89 | icml | 10 | 1 | 2023-06-17 03:57:17.416000 | https://github.com/bayesgroup/tqc_pytorch | 63 | Controlling overestimation bias with truncated mixture of continuous distributional quantile critics | https://scholar.google.com/scholar?cluster=17490032530609728383&hl=en&as_sdt=0,5 | 10 | 2,020 |
Principled learning method for Wasserstein distributionally robust optimization with local perturbations | 6 | icml | 2 | 2 | 2023-06-17 03:57:17.618000 | https://github.com/ykwon0407/wdro_local_perturbation | 19 | Principled learning method for Wasserstein distributionally robust optimization with local perturbations | https://scholar.google.com/scholar?cluster=2114088593646438168&hl=en&as_sdt=0,22 | 2 | 2,020 |
Bidirectional Model-based Policy Optimization | 35 | icml | 0 | 1 | 2023-06-17 03:57:17.820000 | https://github.com/hanglai/bmpo | 21 | Bidirectional model-based policy optimization | https://scholar.google.com/scholar?cluster=8899413271083643198&hl=en&as_sdt=0,5 | 3 | 2,020 |
CURL: Contrastive Unsupervised Representations for Reinforcement Learning | 724 | icml | 84 | 12 | 2023-06-17 03:57:18.023000 | https://github.com/MishaLaskin/curl | 519 | Curl: Contrastive unsupervised representations for reinforcement learning | https://scholar.google.com/scholar?cluster=10576608792458329488&hl=en&as_sdt=0,5 | 11 | 2,020 |
Self-Attentive Associative Memory | 43 | icml | 7 | 0 | 2023-06-17 03:57:18.226000 | https://github.com/thaihungle/SAM | 39 | Self-attentive associative memory | https://scholar.google.com/scholar?cluster=10962782688418035731&hl=en&as_sdt=0,5 | 4 | 2,020 |
Self-supervised Label Augmentation via Input Transformations | 110 | icml | 14 | 0 | 2023-06-17 03:57:18.428000 | https://github.com/hankook/SLA | 100 | Self-supervised label augmentation via input transformations | https://scholar.google.com/scholar?cluster=8322322680850611064&hl=en&as_sdt=0,33 | 5 | 2,020 |
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning | 73 | icml | 6 | 1 | 2023-06-17 03:57:18.630000 | https://github.com/younggyoseo/CaDM | 51 | Context-aware dynamics model for generalization in model-based reinforcement learning | https://scholar.google.com/scholar?cluster=4703047670466451533&hl=en&as_sdt=0,33 | 6 | 2,020 |
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression | 37 | icml | 16 | 3 | 2023-06-17 03:57:18.832000 | https://github.com/chl8856/AC_TPC | 40 | Temporal phenotyping using deep predictive clustering of disease progression | https://scholar.google.com/scholar?cluster=529698419891828395&hl=en&as_sdt=0,43 | 2 | 2,020 |
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks | 18 | icml | 2 | 0 | 2023-06-17 03:57:19.033000 | https://github.com/Karbo123/AnalyticMesh | 51 | Analytic marching: An analytic meshing solution from deep implicit surface networks | https://scholar.google.com/scholar?cluster=13457623400866526866&hl=en&as_sdt=0,5 | 5 | 2,020 |
ACFlow: Flow Models for Arbitrary Conditional Likelihoods | 17 | icml | 0 | 4 | 2023-06-17 03:57:19.235000 | https://github.com/lupalab/ACFlow | 11 | ACFlow: Flow models for arbitrary conditional likelihoods | https://scholar.google.com/scholar?cluster=4436891943483900806&hl=en&as_sdt=0,9 | 3 | 2,020 |
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