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Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
| 3 |
icml
| 1 | 0 |
2023-06-17 04:54:57.688000
|
https://github.com/kkalais/stochlwta-ml
| 2 |
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
|
https://scholar.google.com/scholar?cluster=12812982432289049616&hl=en&as_sdt=0,22
| 1 | 2,022 |
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
| 8 |
icml
| 1 | 0 |
2023-06-17 04:54:57.893000
|
https://github.com/causalml/doubly-robust-dropel
| 4 |
Doubly robust distributionally robust off-policy evaluation and learning
|
https://scholar.google.com/scholar?cluster=3538177620069646339&hl=en&as_sdt=0,5
| 0 | 2,022 |
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
| 3 |
icml
| 0 | 1 |
2023-06-17 04:54:58.099000
|
https://github.com/kamigaito/icml2022
| 9 |
Comprehensive analysis of negative sampling in knowledge graph representation learning
|
https://scholar.google.com/scholar?cluster=4661195844634999621&hl=en&as_sdt=0,5
| 2 | 2,022 |
Composing Partial Differential Equations with Physics-Aware Neural Networks
| 6 |
icml
| 10 | 0 |
2023-06-17 04:54:58.304000
|
https://github.com/cognitivemodeling/finn
| 25 |
Composing partial differential equations with physics-aware neural networks
|
https://scholar.google.com/scholar?cluster=5219761110162787549&hl=en&as_sdt=0,44
| 5 | 2,022 |
FOCUS: Familiar Objects in Common and Uncommon Settings
| 5 |
icml
| 0 | 0 |
2023-06-17 04:54:58.509000
|
https://github.com/priyathamkat/focus
| 4 |
Focus: Familiar objects in common and uncommon settings
|
https://scholar.google.com/scholar?cluster=2485805129814216346&hl=en&as_sdt=0,5
| 2 | 2,022 |
Training OOD Detectors in their Natural Habitats
| 18 |
icml
| 1 | 0 |
2023-06-17 04:54:58.715000
|
https://github.com/jkatzsam/woods_ood
| 13 |
Training ood detectors in their natural habitats
|
https://scholar.google.com/scholar?cluster=8582043463264170613&hl=en&as_sdt=0,5
| 1 | 2,022 |
Secure Quantized Training for Deep Learning
| 18 |
icml
| 9 | 3 |
2023-06-17 04:54:58.921000
|
https://github.com/csiro-mlai/deep-mpc
| 26 |
Secure quantized training for deep learning
|
https://scholar.google.com/scholar?cluster=15154157227965198183&hl=en&as_sdt=0,5
| 3 | 2,022 |
A Convergent and Dimension-Independent Min-Max Optimization Algorithm
| 3 |
icml
| 0 | 0 |
2023-06-17 04:54:59.126000
|
https://github.com/vijaykeswani/min-max-optimization-algorithm
| 1 |
A convergent and dimension-independent first-order algorithm for min-max optimization
|
https://scholar.google.com/scholar?cluster=1442030372277689222&hl=en&as_sdt=0,5
| 2 | 2,022 |
Multi-Level Branched Regularization for Federated Learning
| 3 |
icml
| 4 | 1 |
2023-06-17 04:54:59.332000
|
https://github.com/jinkyu032/FedMLB
| 13 |
Multi-level branched regularization for federated learning
|
https://scholar.google.com/scholar?cluster=2425993830334019201&hl=en&as_sdt=0,5
| 1 | 2,022 |
Learning fair representation with a parametric integral probability metric
| 5 |
icml
| 1 | 0 |
2023-06-17 04:54:59.545000
|
https://github.com/kwkimonline/sipm-lfr
| 3 |
Learning fair representation with a parametric integral probability metric
|
https://scholar.google.com/scholar?cluster=7724112263757302618&hl=en&as_sdt=0,47
| 1 | 2,022 |
Dataset Condensation via Efficient Synthetic-Data Parameterization
| 28 |
icml
| 12 | 1 |
2023-06-17 04:54:59.750000
|
https://github.com/snu-mllab/efficient-dataset-condensation
| 65 |
Dataset condensation via efficient synthetic-data parameterization
|
https://scholar.google.com/scholar?cluster=13062983297577274052&hl=en&as_sdt=0,5
| 2 | 2,022 |
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder
| 14 |
icml
| 4 | 4 |
2023-06-17 04:54:59.956000
|
https://github.com/jumpsnack/ViT-NeT
| 21 |
Vit-net: Interpretable vision transformers with neural tree decoder
|
https://scholar.google.com/scholar?cluster=7284110818114269396&hl=en&as_sdt=0,33
| 2 | 2,022 |
Sanity Simulations for Saliency Methods
| 10 |
icml
| 0 | 0 |
2023-06-17 04:55:00.162000
|
https://github.com/wnstlr/SMERF
| 3 |
Sanity simulations for saliency methods
|
https://scholar.google.com/scholar?cluster=7944058318921349973&hl=en&as_sdt=0,10
| 2 | 2,022 |
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
| 26 |
icml
| 6 | 0 |
2023-06-17 04:55:00.367000
|
https://github.com/Kim-Dongjun/Soft-Truncation
| 43 |
Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation
|
https://scholar.google.com/scholar?cluster=547732243097530529&hl=en&as_sdt=0,5
| 4 | 2,022 |
Rotting Infinitely Many-Armed Bandits
| 0 |
icml
| 0 | 0 |
2023-06-17 04:55:00.573000
|
https://github.com/junghunkim7786/rotting_infinite_armed_bandits
| 0 |
Rotting infinitely many-armed bandits
|
https://scholar.google.com/scholar?cluster=7431943945679360181&hl=en&as_sdt=0,23
| 1 | 2,022 |
Generalizing to New Physical Systems via Context-Informed Dynamics Model
| 10 |
icml
| 1 | 0 |
2023-06-17 04:55:00.778000
|
https://github.com/yuan-yin/coda
| 12 |
Generalizing to new physical systems via context-informed dynamics model
|
https://scholar.google.com/scholar?cluster=9987364402754968813&hl=en&as_sdt=0,31
| 2 | 2,022 |
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
| 9 |
icml
| 1 | 0 |
2023-06-17 04:55:00.983000
|
https://github.com/david-knigge/separable-group-convolutional-networks
| 9 |
Exploiting redundancy: Separable group convolutional networks on lie groups
|
https://scholar.google.com/scholar?cluster=15152080644760721791&hl=en&as_sdt=0,5
| 2 | 2,022 |
Controlling Conditional Language Models without Catastrophic Forgetting
| 8 |
icml
| 21 | 0 |
2023-06-17 04:55:01.189000
|
https://github.com/naver/gdc
| 108 |
Controlling Conditional Language Models without Catastrophic Forgetting
|
https://scholar.google.com/scholar?cluster=13215553222930646661&hl=en&as_sdt=0,11
| 10 | 2,022 |
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
| 10 |
icml
| 4 | 0 |
2023-06-17 04:55:01.394000
|
https://github.com/durstewitzlab/mmplrnn
| 1 |
Reconstructing nonlinear dynamical systems from multi-modal time series
|
https://scholar.google.com/scholar?cluster=17080536605245199937&hl=en&as_sdt=0,14
| 1 | 2,022 |
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
| 4 |
icml
| 1 | 0 |
2023-06-17 04:55:01.600000
|
https://github.com/heinerkremer/functional-gel
| 1 |
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
|
https://scholar.google.com/scholar?cluster=4926746325545340155&hl=en&as_sdt=0,34
| 2 | 2,022 |
Balancing Discriminability and Transferability for Source-Free Domain Adaptation
| 26 |
icml
| 0 | 0 |
2023-06-17 04:55:01.805000
|
https://github.com/val-iisc/MixupDA
| 6 |
Balancing discriminability and transferability for source-free domain adaptation
|
https://scholar.google.com/scholar?cluster=9320809919166954591&hl=en&as_sdt=0,5
| 11 | 2,022 |
Large Batch Experience Replay
| 8 |
icml
| 1 | 1 |
2023-06-17 04:55:02.011000
|
https://github.com/sureli/laber
| 6 |
Large batch experience replay
|
https://scholar.google.com/scholar?cluster=7195743594836265223&hl=en&as_sdt=0,36
| 1 | 2,022 |
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
| 64 |
icml
| 101 | 39 |
2023-06-17 04:55:02.233000
|
https://github.com/SymbioticLab/FedScale
| 302 |
Fedscale: Benchmarking model and system performance of federated learning at scale
|
https://scholar.google.com/scholar?cluster=9366536104914467915&hl=en&as_sdt=0,5
| 10 | 2,022 |
Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-insensitive Losses
| 4 |
icml
| 0 | 0 |
2023-06-17 04:55:02.448000
|
https://github.com/allambert/foreg
| 4 |
Functional Output Regression with Infimal Convolution: Exploring the Huber and -insensitive Losses
|
https://scholar.google.com/scholar?cluster=13118582575057878063&hl=en&as_sdt=0,31
| 2 | 2,022 |
Marginal Tail-Adaptive Normalizing Flows
| 1 |
icml
| 2 | 0 |
2023-06-17 04:55:02.655000
|
https://github.com/mikelasz/marginaltailadaptiveflow
| 0 |
Marginal tail-adaptive normalizing flows
|
https://scholar.google.com/scholar?cluster=3241792279775112520&hl=en&as_sdt=0,5
| 1 | 2,022 |
Implicit Bias of Linear Equivariant Networks
| 11 |
icml
| 0 | 0 |
2023-06-17 04:55:02.862000
|
https://github.com/kristian-georgiev/implicit-bias-of-linear-equivariant-networks
| 0 |
Implicit bias of linear equivariant networks
|
https://scholar.google.com/scholar?cluster=5414336386133292832&hl=en&as_sdt=0,7
| 1 | 2,022 |
Differentially Private Maximal Information Coefficients
| 0 |
icml
| 0 | 0 |
2023-06-17 04:55:03.069000
|
https://github.com/jlazarsfeld/dp-mic
| 4 |
Differentially Private Maximal Information Coefficients
|
https://scholar.google.com/scholar?cluster=14074773669133605205&hl=en&as_sdt=0,32
| 2 | 2,022 |
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks
| 2 |
icml
| 0 | 0 |
2023-06-17 04:55:03.275000
|
https://github.com/lthilnklover/deep-narrow-ntk
| 1 |
Neural tangent kernel analysis of deep narrow neural networks
|
https://scholar.google.com/scholar?cluster=11344426025520591295&hl=en&as_sdt=0,11
| 2 | 2,022 |
Dataset Condensation with Contrastive Signals
| 18 |
icml
| 0 | 1 |
2023-06-17 04:55:03.481000
|
https://github.com/saehyung-lee/dcc
| 12 |
Dataset condensation with contrastive signals
|
https://scholar.google.com/scholar?cluster=7694046388594127798&hl=en&as_sdt=0,11
| 1 | 2,022 |
Confidence Score for Source-Free Unsupervised Domain Adaptation
| 16 |
icml
| 1 | 0 |
2023-06-17 04:55:03.686000
|
https://github.com/jhyun17/cowa-jmds
| 15 |
Confidence score for source-free unsupervised domain adaptation
|
https://scholar.google.com/scholar?cluster=10361966623265648313&hl=en&as_sdt=0,5
| 1 | 2,022 |
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
| 8 |
icml
| 0 | 0 |
2023-06-17 04:55:03.892000
|
https://github.com/snu-mllab/discreteblockbayesattack
| 17 |
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
|
https://scholar.google.com/scholar?cluster=10043868339521505770&hl=en&as_sdt=0,10
| 1 | 2,022 |
Least Squares Estimation using Sketched Data with Heteroskedastic Errors
| 2 |
icml
| 0 | 0 |
2023-06-17 04:55:04.098000
|
https://github.com/sokbae/replication-leeng-2022-icml
| 0 |
Least Squares Estimation Using Sketched Data with Heteroskedastic Errors
|
https://scholar.google.com/scholar?cluster=2973545111138164523&hl=en&as_sdt=0,47
| 1 | 2,022 |
Generalized Strategic Classification and the Case of Aligned Incentives
| 6 |
icml
| 0 | 0 |
2023-06-17 04:55:04.304000
|
https://github.com/SagiLevanon1/GSC
| 1 |
Generalized strategic classification and the case of aligned incentives
|
https://scholar.google.com/scholar?cluster=5634368728411242394&hl=en&as_sdt=0,5
| 1 | 2,022 |
Neural Inverse Transform Sampler
| 1 |
icml
| 0 | 0 |
2023-06-17 04:55:04.510000
|
https://github.com/lihenryhfl/nits
| 1 |
Neural Inverse Transform Sampler
|
https://scholar.google.com/scholar?cluster=3014954787029992873&hl=en&as_sdt=0,5
| 2 | 2,022 |
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
| 1 |
icml
| 1 | 3 |
2023-06-17 04:55:04.715000
|
https://github.com/hugo101/platinum
| 1 |
Platinum: Semi-supervised model agnostic meta-learning using submodular mutual information
|
https://scholar.google.com/scholar?cluster=1070646536780297100&hl=en&as_sdt=0,5
| 2 | 2,022 |
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
| 483 |
icml
| 504 | 186 |
2023-06-17 04:55:04.922000
|
https://github.com/salesforce/lavis
| 5,513 |
Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
|
https://scholar.google.com/scholar?cluster=7770442917120891581&hl=en&as_sdt=0,5
| 75 | 2,022 |
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
| 9 |
icml
| 2 | 0 |
2023-06-17 04:55:05.127000
|
https://github.com/brandeis-machine-learning/influence-fairness
| 5 |
Achieving fairness at no utility cost via data reweighing with influence
|
https://scholar.google.com/scholar?cluster=1481946580804842338&hl=en&as_sdt=0,10
| 0 | 2,022 |
MetAug: Contrastive Learning via Meta Feature Augmentation
| 10 |
icml
| 2 | 1 |
2023-06-17 04:55:05.333000
|
https://github.com/lionellee9089/metaug
| 15 |
Metaug: Contrastive learning via meta feature augmentation
|
https://scholar.google.com/scholar?cluster=13342110327075124099&hl=en&as_sdt=0,33
| 1 | 2,022 |
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
| 6 |
icml
| 1 | 1 |
2023-06-17 04:55:05.539000
|
https://github.com/yeshenpy/pmic
| 7 |
PMIC: Improving multi-agent reinforcement learning with progressive mutual information collaboration
|
https://scholar.google.com/scholar?cluster=2755470732694105502&hl=en&as_sdt=0,5
| 3 | 2,022 |
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
| 30 |
icml
| 1 | 1 |
2023-06-17 04:55:05.746000
|
https://github.com/lsh0520/rgcl
| 23 |
Let invariant rationale discovery inspire graph contrastive learning
|
https://scholar.google.com/scholar?cluster=13286040992676917455&hl=en&as_sdt=0,19
| 2 | 2,022 |
Private Adaptive Optimization with Side information
| 14 |
icml
| 1 | 0 |
2023-06-17 04:55:05.953000
|
https://github.com/litian96/adadps
| 12 |
Private adaptive optimization with side information
|
https://scholar.google.com/scholar?cluster=15603924695620252408&hl=en&as_sdt=0,18
| 1 | 2,022 |
Permutation Search of Tensor Network Structures via Local Sampling
| 4 |
icml
| 1 | 0 |
2023-06-17 04:55:06.158000
|
https://github.com/chaoliatriken/tnls
| 2 |
Permutation search of tensor network structures via local sampling
|
https://scholar.google.com/scholar?cluster=14266729648210963776&hl=en&as_sdt=0,5
| 1 | 2,022 |
Double Sampling Randomized Smoothing
| 5 |
icml
| 2 | 0 |
2023-06-17 04:55:06.364000
|
https://github.com/llylly/dsrs
| 5 |
Double sampling randomized smoothing
|
https://scholar.google.com/scholar?cluster=13905428147766407509&hl=en&as_sdt=0,5
| 1 | 2,022 |
HousE: Knowledge Graph Embedding with Householder Parameterization
| 10 |
icml
| 2 | 0 |
2023-06-17 04:55:06.571000
|
https://github.com/anrep/house
| 15 |
House: Knowledge graph embedding with householder parameterization
|
https://scholar.google.com/scholar?cluster=15337285257575958816&hl=en&as_sdt=0,34
| 1 | 2,022 |
Learning Multiscale Transformer Models for Sequence Generation
| 4 |
icml
| 2 | 1 |
2023-06-17 04:55:06.778000
|
https://github.com/libeineu/umst
| 10 |
Learning multiscale transformer models for sequence generation
|
https://scholar.google.com/scholar?cluster=10490177289793431927&hl=en&as_sdt=0,5
| 1 | 2,022 |
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
| 38 |
icml
| 3 | 0 |
2023-06-17 04:55:06.984000
|
https://github.com/recklessronan/glognn
| 26 |
Finding global homophily in graph neural networks when meeting heterophily
|
https://scholar.google.com/scholar?cluster=881393506933530763&hl=en&as_sdt=0,5
| 1 | 2,022 |
Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling
| 0 |
icml
| 0 | 0 |
2023-06-17 04:55:07.191000
|
https://github.com/byronliang8/hubnessgansampling
| 8 |
Exploring and exploiting hubness priors for high-quality GAN latent sampling
|
https://scholar.google.com/scholar?cluster=12825471375795704979&hl=en&as_sdt=0,5
| 1 | 2,022 |
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
| 4 |
icml
| 1 | 0 |
2023-06-17 04:55:07.396000
|
https://github.com/indylab/meanq
| 8 |
Reducing variance in temporal-difference value estimation via ensemble of deep networks
|
https://scholar.google.com/scholar?cluster=5733035201533168571&hl=en&as_sdt=0,5
| 0 | 2,022 |
Order Constraints in Optimal Transport
| 1 |
icml
| 294 | 54 |
2023-06-17 04:55:07.603000
|
https://github.com/Trusted-AI/AIX360
| 1,340 |
Order Constraints in Optimal Transport
|
https://scholar.google.com/scholar?cluster=1063075229818760095&hl=en&as_sdt=0,5
| 51 | 2,022 |
Flow-Guided Sparse Transformer for Video Deblurring
| 23 |
icml
| 12 | 1 |
2023-06-17 04:55:07.808000
|
https://github.com/linjing7/VR-Baseline
| 122 |
Flow-guided sparse transformer for video deblurring
|
https://scholar.google.com/scholar?cluster=14219657862279161517&hl=en&as_sdt=0,5
| 13 | 2,022 |
Federated Learning with Positive and Unlabeled Data
| 8 |
icml
| 1 | 2 |
2023-06-17 04:55:08.013000
|
https://github.com/littlesunlxy/fedpu-torch
| 7 |
Federated Learning with Positive and Unlabeled Data
|
https://scholar.google.com/scholar?cluster=5808543531345013860&hl=en&as_sdt=0,29
| 1 | 2,022 |
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
| 2 |
icml
| 12 | 1 |
2023-06-17 04:55:08.223000
|
https://github.com/linjing7/VR-Baseline
| 122 |
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
|
https://scholar.google.com/scholar?cluster=11447631455312360639&hl=en&as_sdt=0,5
| 13 | 2,022 |
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
| 0 |
icml
| 0 | 0 |
2023-06-17 04:55:08.433000
|
https://github.com/linweiran/CGD
| 1 |
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
|
https://scholar.google.com/scholar?cluster=14082433359159261518&hl=en&as_sdt=0,5
| 1 | 2,022 |
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
| 8 |
icml
| 0 | 0 |
2023-06-17 04:55:08.639000
|
https://github.com/lazycal/ame
| 1 |
Measuring the effect of training data on deep learning predictions via randomized experiments
|
https://scholar.google.com/scholar?cluster=7808395865683583052&hl=en&as_sdt=0,43
| 1 | 2,022 |
Interactively Learning Preference Constraints in Linear Bandits
| 1 |
icml
| 0 | 0 |
2023-06-17 04:55:08.847000
|
https://github.com/lasgroup/adaptive-constraint-learning
| 3 |
Interactively Learning Preference Constraints in Linear Bandits
|
https://scholar.google.com/scholar?cluster=10442761554995680158&hl=en&as_sdt=0,2
| 2 | 2,022 |
CITRIS: Causal Identifiability from Temporal Intervened Sequences
| 31 |
icml
| 5 | 1 |
2023-06-17 04:55:09.055000
|
https://github.com/phlippe/citris
| 38 |
Citris: Causal identifiability from temporal intervened sequences
|
https://scholar.google.com/scholar?cluster=9740161650140858183&hl=en&as_sdt=0,36
| 6 | 2,022 |
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
| 7 |
icml
| 0 | 1 |
2023-06-17 04:55:09.261000
|
https://github.com/deepmind/streamingqa
| 35 |
Streamingqa: A benchmark for adaptation to new knowledge over time in question answering models
|
https://scholar.google.com/scholar?cluster=14847402247915330134&hl=en&as_sdt=0,5
| 3 | 2,022 |
Constrained Variational Policy Optimization for Safe Reinforcement Learning
| 19 |
icml
| 6 | 2 |
2023-06-17 04:55:09.466000
|
https://github.com/liuzuxin/cvpo-safe-rl
| 42 |
Constrained variational policy optimization for safe reinforcement learning
|
https://scholar.google.com/scholar?cluster=13833315390800713597&hl=en&as_sdt=0,48
| 3 | 2,022 |
Boosting Graph Structure Learning with Dummy Nodes
| 4 |
icml
| 3 | 0 |
2023-06-17 04:55:09.672000
|
https://github.com/hkust-knowcomp/dummynode4graphlearning
| 14 |
Boosting graph structure learning with dummy nodes
|
https://scholar.google.com/scholar?cluster=11720456442737654498&hl=en&as_sdt=0,5
| 2 | 2,022 |
Rethinking Attention-Model Explainability through Faithfulness Violation Test
| 6 |
icml
| 2 | 0 |
2023-06-17 04:55:09.878000
|
https://github.com/BierOne/Attention-Faithfulness
| 15 |
Rethinking attention-model explainability through faithfulness violation test
|
https://scholar.google.com/scholar?cluster=2225803020950336962&hl=en&as_sdt=0,10
| 1 | 2,022 |
Generating 3D Molecules for Target Protein Binding
| 33 |
icml
| 22 | 0 |
2023-06-17 04:55:10.084000
|
https://github.com/divelab/graphbp
| 82 |
Generating 3d molecules for target protein binding
|
https://scholar.google.com/scholar?cluster=5832718815392405433&hl=en&as_sdt=0,23
| 4 | 2,022 |
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
| 8 |
icml
| 1 | 0 |
2023-06-17 04:55:10.290000
|
https://github.com/xingyul/revolver
| 21 |
Revolver: Continuous evolutionary models for robot-to-robot policy transfer
|
https://scholar.google.com/scholar?cluster=4925772100401553485&hl=en&as_sdt=0,5
| 0 | 2,022 |
Local Augmentation for Graph Neural Networks
| 27 |
icml
| 10 | 0 |
2023-06-17 04:55:10.496000
|
https://github.com/songtaoliu0823/lagnn
| 49 |
Local augmentation for graph neural networks
|
https://scholar.google.com/scholar?cluster=1477899180662383839&hl=en&as_sdt=0,33
| 2 | 2,022 |
GACT: Activation Compressed Training for Generic Network Architectures
| 6 |
icml
| 7 | 0 |
2023-06-17 04:55:10.702000
|
https://github.com/LiuXiaoxuanPKU/GACT-ICML
| 25 |
GACT: Activation compressed training for generic network architectures
|
https://scholar.google.com/scholar?cluster=12961558979640169971&hl=en&as_sdt=0,11
| 1 | 2,022 |
Robust Training under Label Noise by Over-parameterization
| 32 |
icml
| 6 | 2 |
2023-06-17 04:55:10.911000
|
https://github.com/shengliu66/sop
| 45 |
Robust training under label noise by over-parameterization
|
https://scholar.google.com/scholar?cluster=7351288537652812990&hl=en&as_sdt=0,5
| 4 | 2,022 |
Bayesian Model Selection, the Marginal Likelihood, and Generalization
| 22 |
icml
| 2 | 0 |
2023-06-17 04:55:11.124000
|
https://github.com/sanaelotfi/bayesian_model_comparison
| 29 |
Bayesian model selection, the marginal likelihood, and generalization
|
https://scholar.google.com/scholar?cluster=9966221610854779885&hl=en&as_sdt=0,10
| 2 | 2,022 |
Additive Gaussian Processes Revisited
| 5 |
icml
| 3 | 2 |
2023-06-17 04:55:11.347000
|
https://github.com/amzn/orthogonal-additive-gaussian-processes
| 27 |
Additive Gaussian Processes Revisited
|
https://scholar.google.com/scholar?cluster=6171646250259596364&hl=en&as_sdt=0,7
| 1 | 2,022 |
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
| 3 |
icml
| 0 | 0 |
2023-06-17 04:55:11.555000
|
https://github.com/YupuLu/ModLaNets
| 3 |
Modlanets: Learning generalisable dynamics via modularity and physical inductive bias
|
https://scholar.google.com/scholar?cluster=13273673478017721155&hl=en&as_sdt=0,5
| 1 | 2,022 |
Model-Free Opponent Shaping
| 16 |
icml
| 3 | 0 |
2023-06-17 04:55:11.762000
|
https://github.com/luchris429/model-free-opponent-shaping
| 8 |
Model-free opponent shaping
|
https://scholar.google.com/scholar?cluster=2936183608022340062&hl=en&as_sdt=0,6
| 1 | 2,022 |
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
| 13 |
icml
| 8 | 1 |
2023-06-17 04:55:11.968000
|
https://github.com/akhilmathurs/orchestra
| 34 |
Orchestra: Unsupervised federated learning via globally consistent clustering
|
https://scholar.google.com/scholar?cluster=12370876234487104592&hl=en&as_sdt=0,5
| 3 | 2,022 |
A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributions
| 13 |
icml
| 1 | 0 |
2023-06-17 04:55:12.173000
|
https://github.com/optimization-for-data-driven-science/xai
| 0 |
A rigorous study of integrated gradients method and extensions to internal neuron attributions
|
https://scholar.google.com/scholar?cluster=2734810007243082678&hl=en&as_sdt=0,14
| 3 | 2,022 |
Channel Importance Matters in Few-Shot Image Classification
| 9 |
icml
| 6 | 0 |
2023-06-17 04:55:12.378000
|
https://github.com/Frankluox/Channel_Importance_FSL
| 41 |
Channel importance matters in few-shot image classification
|
https://scholar.google.com/scholar?cluster=11800681644277658610&hl=en&as_sdt=0,5
| 3 | 2,022 |
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
| 5 |
icml
| 2 | 0 |
2023-06-17 04:55:12.584000
|
https://github.com/jasonma2016/smodice
| 20 |
Versatile offline imitation from observations and examples via regularized state-occupancy matching
|
https://scholar.google.com/scholar?cluster=11179690746522153663&hl=en&as_sdt=0,5
| 2 | 2,022 |
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
| 0 |
icml
| 0 | 1 |
2023-06-17 04:55:12.790000
|
https://github.com/haotiansustc/deepinfo
| 3 |
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
|
https://scholar.google.com/scholar?cluster=17376169416462148944&hl=en&as_sdt=0,5
| 1 | 2,022 |
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings
| 9 |
icml
| 0 | 0 |
2023-06-17 04:55:12.995000
|
https://github.com/zib-iol/fw-rde
| 4 |
Interpretable neural networks with frank-wolfe: Sparse relevance maps and relevance orderings
|
https://scholar.google.com/scholar?cluster=1124674536822867580&hl=en&as_sdt=0,21
| 2 | 2,022 |
A Tighter Analysis of Spectral Clustering, and Beyond
| 4 |
icml
| 0 | 0 |
2023-06-17 04:55:13.201000
|
https://github.com/pmacg/spectral-clustering-meta-graphs
| 3 |
A Tighter Analysis of Spectral Clustering, and Beyond
|
https://scholar.google.com/scholar?cluster=7116468291147711017&hl=en&as_sdt=0,10
| 1 | 2,022 |
Feature selection using e-values
| 1 |
icml
| 0 | 0 |
2023-06-17 04:55:13.407000
|
https://github.com/shubhobm/e-values
| 2 |
Feature Selection using e-values
|
https://scholar.google.com/scholar?cluster=14169974284290385503&hl=en&as_sdt=0,5
| 2 | 2,022 |
Nonparametric Involutive Markov Chain Monte Carlo
| 0 |
icml
| 2 | 1 |
2023-06-17 04:55:13.612000
|
https://github.com/fzaiser/nonparametric-hmc
| 12 |
Nonparametric Involutive Markov Chain Monte Carlo
|
https://scholar.google.com/scholar?cluster=17862750245568901583&hl=en&as_sdt=0,25
| 1 | 2,022 |
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees
| 9 |
icml
| 0 | 0 |
2023-06-17 04:55:13.818000
|
https://github.com/osmanmalik/td-als-es
| 3 |
More efficient sampling for tensor decomposition with worst-case guarantees
|
https://scholar.google.com/scholar?cluster=18131307988891143062&hl=en&as_sdt=0,5
| 1 | 2,022 |
Unaligned Supervision for Automatic Music Transcription in The Wild
| 4 |
icml
| 1 | 1 |
2023-06-17 04:55:14.024000
|
https://github.com/benadar293/benadar293.github.io
| 16 |
Unaligned supervision for automatic music transcription in the wild
|
https://scholar.google.com/scholar?cluster=7612759621426730574&hl=en&as_sdt=0,43
| 1 | 2,022 |
Decision-Focused Learning: Through the Lens of Learning to Rank
| 7 |
icml
| 1 | 0 |
2023-06-17 04:55:14.230000
|
https://github.com/jayman91/ltr-predopt
| 5 |
Decision-Focused Learning: Through the Lens of Learning to Rank
|
https://scholar.google.com/scholar?cluster=68474757504279365&hl=en&as_sdt=0,5
| 1 | 2,022 |
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models
| 5 |
icml
| 0 | 0 |
2023-06-17 04:55:14.440000
|
https://github.com/tmanole/refined-mixture-rates
| 1 |
Refined convergence rates for maximum likelihood estimation under finite mixture models
|
https://scholar.google.com/scholar?cluster=15536015401615707970&hl=en&as_sdt=0,34
| 2 | 2,022 |
On the Effects of Artificial Data Modification
| 0 |
icml
| 0 | 0 |
2023-06-17 04:55:14.646000
|
https://github.com/antoniamarcu/data-modification
| 1 |
On the Effects of Artificial Data Modification
|
https://scholar.google.com/scholar?cluster=5171301994487774624&hl=en&as_sdt=0,33
| 2 | 2,022 |
Personalized Federated Learning through Local Memorization
| 15 |
icml
| 11 | 1 |
2023-06-17 04:55:14.851000
|
https://github.com/omarfoq/knn-per
| 32 |
Personalized federated learning through local memorization
|
https://scholar.google.com/scholar?cluster=1735959565667819081&hl=en&as_sdt=0,5
| 1 | 2,022 |
Closed-Form Diffeomorphic Transformations for Time Series Alignment
| 0 |
icml
| 1 | 0 |
2023-06-17 04:55:15.058000
|
https://github.com/imartinezl/difw
| 12 |
Closed-Form Diffeomorphic Transformations for Time Series Alignment
|
https://scholar.google.com/scholar?cluster=15344236423757479416&hl=en&as_sdt=0,5
| 2 | 2,022 |
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
| 17 |
icml
| 4 | 0 |
2023-06-17 04:55:15.264000
|
https://github.com/karolismart/spectre
| 18 |
Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators
|
https://scholar.google.com/scholar?cluster=12175380990160510944&hl=en&as_sdt=0,14
| 2 | 2,022 |
Continual Repeated Annealed Flow Transport Monte Carlo
| 8 |
icml
| 10 | 0 |
2023-06-17 04:55:15.470000
|
https://github.com/deepmind/annealed_flow_transport
| 35 |
Continual repeated annealed flow transport Monte Carlo
|
https://scholar.google.com/scholar?cluster=15272534120760724190&hl=en&as_sdt=0,33
| 4 | 2,022 |
How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection
| 3 |
icml
| 3 | 1 |
2023-06-17 04:55:15.675000
|
https://github.com/mmazeika/model-stealing-defenses
| 2 |
How to steer your adversary: Targeted and efficient model stealing defenses with gradient redirection
|
https://scholar.google.com/scholar?cluster=12763327756240287958&hl=en&as_sdt=0,5
| 1 | 2,022 |
Causal Transformer for Estimating Counterfactual Outcomes
| 15 |
icml
| 10 | 3 |
2023-06-17 04:55:15.882000
|
https://github.com/Valentyn1997/CausalTransformer
| 48 |
Causal transformer for estimating counterfactual outcomes
|
https://scholar.google.com/scholar?cluster=15562561940840223837&hl=en&as_sdt=0,5
| 2 | 2,022 |
Steerable 3D Spherical Neurons
| 2 |
icml
| 0 | 0 |
2023-06-17 04:55:16.088000
|
https://github.com/pavlo-melnyk/steerable-3d-neurons
| 0 |
Steerable 3D Spherical Neurons
|
https://scholar.google.com/scholar?cluster=12172638513685585373&hl=en&as_sdt=0,23
| 2 | 2,022 |
Transformers are Meta-Reinforcement Learners
| 15 |
icml
| 4 | 3 |
2023-06-17 04:55:16.294000
|
https://github.com/luckeciano/transformers-metarl
| 32 |
Transformers are meta-reinforcement learners
|
https://scholar.google.com/scholar?cluster=4334650228414799916&hl=en&as_sdt=0,33
| 4 | 2,022 |
Stochastic Rising Bandits
| 4 |
icml
| 0 | 0 |
2023-06-17 04:55:16.500000
|
https://github.com/albertometelli/stochastic-rising-bandits
| 4 |
Stochastic Rising Bandits
|
https://scholar.google.com/scholar?cluster=15697580060507911770&hl=en&as_sdt=0,5
| 1 | 2,022 |
Minimizing Control for Credit Assignment with Strong Feedback
| 4 |
icml
| 3 | 0 |
2023-06-17 04:55:16.706000
|
https://github.com/mariacer/strong_dfc
| 8 |
Minimizing control for credit assignment with strong feedback
|
https://scholar.google.com/scholar?cluster=4546119476247760219&hl=en&as_sdt=0,33
| 1 | 2,022 |
Distribution Regression with Sliced Wasserstein Kernels
| 4 |
icml
| 0 | 0 |
2023-06-17 04:55:16.912000
|
https://github.com/dimsum2k/drswk
| 4 |
Distribution Regression with Sliced Wasserstein Kernels
|
https://scholar.google.com/scholar?cluster=6056433376162861662&hl=en&as_sdt=0,33
| 1 | 2,022 |
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
| 32 |
icml
| 15 | 0 |
2023-06-17 04:55:17.118000
|
https://github.com/Graph-COM/GSAT
| 112 |
Interpretable and generalizable graph learning via stochastic attention mechanism
|
https://scholar.google.com/scholar?cluster=15869188404391034141&hl=en&as_sdt=0,5
| 2 | 2,022 |
Modeling Structure with Undirected Neural Networks
| 0 |
icml
| 0 | 0 |
2023-06-17 04:55:17.323000
|
https://github.com/deep-spin/unn
| 5 |
Modeling Structure with Undirected Neural Networks
|
https://scholar.google.com/scholar?cluster=2812799179011776020&hl=en&as_sdt=0,33
| 4 | 2,022 |
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
| 10 |
icml
| 2 | 0 |
2023-06-17 04:55:17.529000
|
https://github.com/BerenMillidge/Theory_Associative_Memory
| 12 |
Universal hopfield networks: A general framework for single-shot associative memory models
|
https://scholar.google.com/scholar?cluster=11661827262437868518&hl=en&as_sdt=0,5
| 3 | 2,022 |
Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt
| 24 |
icml
| 18 | 2 |
2023-06-17 04:55:17.735000
|
https://github.com/oatml/rho-loss
| 158 |
Prioritized training on points that are learnable, worth learning, and not yet learnt
|
https://scholar.google.com/scholar?cluster=5784378723216835078&hl=en&as_sdt=0,33
| 6 | 2,022 |
POEM: Out-of-Distribution Detection with Posterior Sampling
| 20 |
icml
| 1 | 1 |
2023-06-17 04:55:17.940000
|
https://github.com/deeplearning-wisc/poem
| 22 |
Poem: Out-of-distribution detection with posterior sampling
|
https://scholar.google.com/scholar?cluster=14373980882186283690&hl=en&as_sdt=0,33
| 4 | 2,022 |
Proximal and Federated Random Reshuffling
| 21 |
icml
| 2 | 0 |
2023-06-17 04:55:18.146000
|
https://github.com/konstmish/rr_prox_fed
| 2 |
Proximal and federated random reshuffling
|
https://scholar.google.com/scholar?cluster=4410848419822485671&hl=en&as_sdt=0,33
| 2 | 2,022 |
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