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GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
| 19 |
icml
| 7 | 3 |
2023-06-17 04:13:07.817000
|
https://github.com/IdanAchituve/GP-Tree
| 27 |
Gp-tree: A gaussian process classifier for few-shot incremental learning
|
https://scholar.google.com/scholar?cluster=3252666331118779321&hl=en&as_sdt=0,5
| 1 | 2,021 |
Towards Rigorous Interpretations: a Formalisation of Feature Attribution
| 9 |
icml
| 1 | 0 |
2023-06-17 04:13:08.019000
|
https://github.com/DariusAf/functional_attribution
| 6 |
Towards rigorous interpretations: a formalisation of feature attribution
|
https://scholar.google.com/scholar?cluster=6443235161573305083&hl=en&as_sdt=0,5
| 3 | 2,021 |
Sparse Bayesian Learning via Stepwise Regression
| 3 |
icml
| 1 | 1 |
2023-06-17 04:13:08.222000
|
https://github.com/SebastianAment/CompressedSensing.jl
| 21 |
Sparse bayesian learning via stepwise regression
|
https://scholar.google.com/scholar?cluster=14029385398750356286&hl=en&as_sdt=0,14
| 2 | 2,021 |
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
| 9 |
icml
| 3 | 0 |
2023-06-17 04:13:08.424000
|
https://github.com/h-aboutalebi/SparseBaseline
| 2 |
Locally persistent exploration in continuous control tasks with sparse rewards
|
https://scholar.google.com/scholar?cluster=15739830429970028692&hl=en&as_sdt=0,41
| 3 | 2,021 |
Preferential Temporal Difference Learning
| 1 |
icml
| 3 | 0 |
2023-06-17 04:13:08.627000
|
https://github.com/NishanthVAnand/Preferential-Temporal-Difference-Learning
| 4 |
Preferential temporal difference learning
|
https://scholar.google.com/scholar?cluster=17314820173846745739&hl=en&as_sdt=0,45
| 0 | 2,021 |
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
| 7 |
icml
| 1 | 0 |
2023-06-17 04:13:08.829000
|
https://github.com/ranaa-b/OOCS
| 3 |
On-off center-surround receptive fields for accurate and robust image classification
|
https://scholar.google.com/scholar?cluster=14788977888396220864&hl=en&as_sdt=0,10
| 1 | 2,021 |
Stabilizing Equilibrium Models by Jacobian Regularization
| 36 |
icml
| 75 | 5 |
2023-06-17 04:13:09.032000
|
https://github.com/locuslab/deq
| 650 |
Stabilizing equilibrium models by jacobian regularization
|
https://scholar.google.com/scholar?cluster=7648841566854588035&hl=en&as_sdt=0,21
| 20 | 2,021 |
Principled Exploration via Optimistic Bootstrapping and Backward Induction
| 25 |
icml
| 1 | 0 |
2023-06-17 04:13:09.235000
|
https://github.com/Baichenjia/OB2I
| 7 |
Principled exploration via optimistic bootstrapping and backward induction
|
https://scholar.google.com/scholar?cluster=732043823350828929&hl=en&as_sdt=0,33
| 2 | 2,021 |
Breaking the Limits of Message Passing Graph Neural Networks
| 69 |
icml
| 3 | 0 |
2023-06-17 04:13:09.436000
|
https://github.com/balcilar/gnn-matlang
| 30 |
Breaking the limits of message passing graph neural networks
|
https://scholar.google.com/scholar?cluster=7981688691402609281&hl=en&as_sdt=0,33
| 3 | 2,021 |
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
| 12 |
icml
| 1 | 0 |
2023-06-17 04:13:09.638000
|
https://github.com/YujiaBao/Predict-then-Interpolate
| 16 |
Predict then interpolate: A simple algorithm to learn stable classifiers
|
https://scholar.google.com/scholar?cluster=2357278583556296891&hl=en&as_sdt=0,5
| 1 | 2,021 |
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
| 7 |
icml
| 2 | 0 |
2023-06-17 04:13:09.841000
|
https://github.com/baofff/VaGES
| 5 |
Variational (gradient) estimate of the score function in energy-based latent variable models
|
https://scholar.google.com/scholar?cluster=17355652803431034105&hl=en&as_sdt=0,21
| 1 | 2,021 |
Compositional Video Synthesis with Action Graphs
| 22 |
icml
| 3 | 5 |
2023-06-17 04:13:10.043000
|
https://github.com/roeiherz/AG2Video
| 28 |
Compositional video synthesis with action graphs
|
https://scholar.google.com/scholar?cluster=835836297893492143&hl=en&as_sdt=0,5
| 6 | 2,021 |
Optimal Thompson Sampling strategies for support-aware CVaR bandits
| 26 |
icml
| 1 | 0 |
2023-06-17 04:13:10.246000
|
https://github.com/rgautron/DssatBanditEnv
| 5 |
Optimal thompson sampling strategies for support-aware cvar bandits
|
https://scholar.google.com/scholar?cluster=13964455175632716086&hl=en&as_sdt=0,10
| 1 | 2,021 |
On Limited-Memory Subsampling Strategies for Bandits
| 8 |
icml
| 2 | 0 |
2023-06-17 04:13:10.448000
|
https://github.com/YRussac/LB-SDA
| 3 |
On Limited-Memory Subsampling Strategies for Bandits
|
https://scholar.google.com/scholar?cluster=4227884458802378115&hl=en&as_sdt=0,34
| 2 | 2,021 |
Directional Graph Networks
| 104 |
icml
| 13 | 3 |
2023-06-17 04:13:10.650000
|
https://github.com/Saro00/DGN
| 109 |
Directional graph networks
|
https://scholar.google.com/scholar?cluster=6256455976929564913&hl=en&as_sdt=0,6
| 3 | 2,021 |
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
| 32 |
icml
| 9 | 3 |
2023-06-17 04:13:10.852000
|
https://github.com/g-benton/loss-surface-simplexes
| 96 |
Loss surface simplexes for mode connecting volumes and fast ensembling
|
https://scholar.google.com/scholar?cluster=11311661921259603537&hl=en&as_sdt=0,5
| 5 | 2,021 |
Is Space-Time Attention All You Need for Video Understanding?
| 959 |
icml
| 187 | 61 |
2023-06-17 04:13:11.054000
|
https://github.com/facebookresearch/TimeSformer
| 1,187 |
Is space-time attention all you need for video understanding?
|
https://scholar.google.com/scholar?cluster=6828425192739736056&hl=en&as_sdt=0,5
| 22 | 2,021 |
Size-Invariant Graph Representations for Graph Classification Extrapolations
| 51 |
icml
| 1 | 0 |
2023-06-17 04:13:11.257000
|
https://github.com/PurdueMINDS/size-invariant-GNNs
| 18 |
Size-invariant graph representations for graph classification extrapolations
|
https://scholar.google.com/scholar?cluster=18387285677592946358&hl=en&as_sdt=0,10
| 4 | 2,021 |
Principal Bit Analysis: Autoencoding with Schur-Concave Loss
| 0 |
icml
| 0 | 0 |
2023-06-17 04:13:11.459000
|
https://github.com/SourbhBh/PBA
| 0 |
Principal Bit Analysis: Autoencoding with Schur-Concave Loss
|
https://scholar.google.com/scholar?cluster=11365886742546689505&hl=en&as_sdt=0,5
| 1 | 2,021 |
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
| 5 |
icml
| 1 | 0 |
2023-06-17 04:13:11.661000
|
https://github.com/arjunbhagoji/log-loss-lower-bounds
| 4 |
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
|
https://scholar.google.com/scholar?cluster=9078439186014463953&hl=en&as_sdt=0,36
| 2 | 2,021 |
TempoRL: Learning When to Act
| 14 |
icml
| 4 | 0 |
2023-06-17 04:13:11.863000
|
https://github.com/automl/TempoRL
| 14 |
TempoRL: Learning when to act
|
https://scholar.google.com/scholar?cluster=16276824665719650733&hl=en&as_sdt=0,5
| 8 | 2,021 |
Neural Symbolic Regression that scales
| 59 |
icml
| 7 | 2 |
2023-06-17 04:13:12.073000
|
https://github.com/SymposiumOrganization/NeuralSymbolicRegressionThatScales
| 44 |
Neural symbolic regression that scales
|
https://scholar.google.com/scholar?cluster=13426541991949181353&hl=en&as_sdt=0,26
| 2 | 2,021 |
Multiplying Matrices Without Multiplying
| 23 |
icml
| 171 | 19 |
2023-06-17 04:13:12.275000
|
https://github.com/dblalock/bolt
| 2,397 |
Multiplying matrices without multiplying
|
https://scholar.google.com/scholar?cluster=16672894839769153249&hl=en&as_sdt=0,41
| 47 | 2,021 |
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
| 18 |
icml
| 0 | 0 |
2023-06-17 04:13:12.483000
|
https://github.com/rlphilli/Collaborative-Incentives
| 5 |
One for one, or all for all: Equilibria and optimality of collaboration in federated learning
|
https://scholar.google.com/scholar?cluster=3850848411825917524&hl=en&as_sdt=0,33
| 2 | 2,021 |
Black-box density function estimation using recursive partitioning
| 4 |
icml
| 1 | 0 |
2023-06-17 04:13:12.697000
|
https://github.com/bodin-e/defer
| 4 |
Black-box density function estimation using recursive partitioning
|
https://scholar.google.com/scholar?cluster=17001427494872038467&hl=en&as_sdt=0,5
| 1 | 2,021 |
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
| 128 |
icml
| 20 | 0 |
2023-06-17 04:13:12.899000
|
https://github.com/twitter-research/cwn
| 124 |
Weisfeiler and lehman go topological: Message passing simplicial networks
|
https://scholar.google.com/scholar?cluster=8275189776192061574&hl=en&as_sdt=0,5
| 7 | 2,021 |
Offline Contextual Bandits with Overparameterized Models
| 6 |
icml
| 0 | 0 |
2023-06-17 04:13:13.110000
|
https://github.com/davidbrandfonbrener/deep-offline-bandits
| 1 |
Offline contextual bandits with overparameterized models
|
https://scholar.google.com/scholar?cluster=11852183431002924037&hl=en&as_sdt=0,5
| 2 | 2,021 |
Value Alignment Verification
| 19 |
icml
| 0 | 0 |
2023-06-17 04:13:13.346000
|
https://github.com/dsbrown1331/vav-icml
| 1 |
Value alignment verification
|
https://scholar.google.com/scholar?cluster=5318002618951129429&hl=en&as_sdt=0,46
| 3 | 2,021 |
Lenient Regret and Good-Action Identification in Gaussian Process Bandits
| 2 |
icml
| 0 | 0 |
2023-06-17 04:13:13.552000
|
https://github.com/caitree/GoodAction
| 1 |
Lenient regret and good-action identification in Gaussian process bandits
|
https://scholar.google.com/scholar?cluster=13998414945788250067&hl=en&as_sdt=0,33
| 1 | 2,021 |
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization
| 13 |
icml
| 1 | 0 |
2023-06-17 04:13:13.761000
|
https://github.com/YuchenLou/ZO-BCD
| 4 |
A zeroth-order block coordinate descent algorithm for huge-scale black-box optimization
|
https://scholar.google.com/scholar?cluster=10394095959262689530&hl=en&as_sdt=0,33
| 2 | 2,021 |
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections
| 11 |
icml
| 0 | 0 |
2023-06-17 04:13:13.964000
|
https://github.com/alexander-camuto/asym-heavy-tails-bias-GNI
| 1 |
Asymmetric heavy tails and implicit bias in gaussian noise injections
|
https://scholar.google.com/scholar?cluster=6154175937826979347&hl=en&as_sdt=0,5
| 1 | 2,021 |
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
| 18 |
icml
| 8 | 5 |
2023-06-17 04:13:14.166000
|
https://github.com/IBM/fold2seq
| 46 |
Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design
|
https://scholar.google.com/scholar?cluster=9442126458531954169&hl=en&as_sdt=0,5
| 4 | 2,021 |
Optimizing persistent homology based functions
| 29 |
icml
| 2 | 0 |
2023-06-17 04:13:14.368000
|
https://github.com/MathieuCarriere/difftda
| 15 |
Optimizing persistent homology based functions
|
https://scholar.google.com/scholar?cluster=1795374418354954800&hl=en&as_sdt=0,5
| 4 | 2,021 |
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
| 56 |
icml
| 8 | 1 |
2023-06-17 04:13:14.570000
|
https://github.com/JohanSamir/revisiting_rainbow
| 72 |
Revisiting rainbow: Promoting more insightful and inclusive deep reinforcement learning research
|
https://scholar.google.com/scholar?cluster=12882829322787597157&hl=en&as_sdt=0,33
| 1 | 2,021 |
GRAND: Graph Neural Diffusion
| 115 |
icml
| 42 | 4 |
2023-06-17 04:13:14.773000
|
https://github.com/twitter-research/graph-neural-pde
| 254 |
Grand: Graph neural diffusion
|
https://scholar.google.com/scholar?cluster=6075394870168508131&hl=en&as_sdt=0,5
| 12 | 2,021 |
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
| 22 |
icml
| 2 | 1 |
2023-06-17 04:13:14.975000
|
https://github.com/NVlabs/RIO
| 17 |
Image-level or object-level? a tale of two resampling strategies for long-tailed detection
|
https://scholar.google.com/scholar?cluster=121160204477537085&hl=en&as_sdt=0,14
| 6 | 2,021 |
DeepWalking Backwards: From Embeddings Back to Graphs
| 5 |
icml
| 3 | 0 |
2023-06-17 04:13:15.178000
|
https://github.com/konsotirop/Invert_Embeddings
| 6 |
Deepwalking backwards: from embeddings back to graphs
|
https://scholar.google.com/scholar?cluster=367308941848540342&hl=en&as_sdt=0,5
| 1 | 2,021 |
Unsupervised Learning of Visual 3D Keypoints for Control
| 22 |
icml
| 7 | 0 |
2023-06-17 04:13:15.380000
|
https://github.com/buoyancy99/unsup-3d-keypoints
| 37 |
Unsupervised learning of visual 3d keypoints for control
|
https://scholar.google.com/scholar?cluster=7013737531012764740&hl=en&as_sdt=0,5
| 5 | 2,021 |
Integer Programming for Causal Structure Learning in the Presence of Latent Variables
| 3 |
icml
| 1 | 0 |
2023-06-17 04:13:15.582000
|
https://github.com/rchen234/IP4AncADMG
| 1 |
Integer programming for causal structure learning in the presence of latent variables
|
https://scholar.google.com/scholar?cluster=14082497365746391672&hl=en&as_sdt=0,5
| 1 | 2,021 |
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
| 33 |
icml
| 1 | 0 |
2023-06-17 04:13:15.784000
|
https://github.com/sbyebss/scalable-wasserstein-barycenter
| 8 |
Scalable computations of wasserstein barycenter via input convex neural networks
|
https://scholar.google.com/scholar?cluster=7480420834678810462&hl=en&as_sdt=0,44
| 2 | 2,021 |
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
| 20 |
icml
| 2 | 1 |
2023-06-17 04:13:15.987000
|
https://github.com/chenshixiang/Decentralized_Riemannian_gradient_descent_on_Stiefel_manifold
| 7 |
Decentralized riemannian gradient descent on the stiefel manifold
|
https://scholar.google.com/scholar?cluster=10235515881899160189&hl=en&as_sdt=0,5
| 2 | 2,021 |
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation
| 7 |
icml
| 1 | 0 |
2023-06-17 04:13:16.189000
|
https://github.com/cchao0116/CTSMA-ICML21
| 10 |
Learning self-modulating attention in continuous time space with applications to sequential recommendation
|
https://scholar.google.com/scholar?cluster=16476778005591065966&hl=en&as_sdt=0,22
| 1 | 2,021 |
Mandoline: Model Evaluation under Distribution Shift
| 25 |
icml
| 4 | 0 |
2023-06-17 04:13:16.390000
|
https://github.com/HazyResearch/mandoline
| 30 |
Mandoline: Model evaluation under distribution shift
|
https://scholar.google.com/scholar?cluster=3421066091815040064&hl=en&as_sdt=0,5
| 18 | 2,021 |
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
| 16 |
icml
| 6 | 1 |
2023-06-17 04:13:16.593000
|
https://github.com/tufts-ml/graph-generation-vi
| 20 |
Order matters: Probabilistic modeling of node sequence for graph generation
|
https://scholar.google.com/scholar?cluster=10391803537150156085&hl=en&as_sdt=0,5
| 9 | 2,021 |
CARTL: Cooperative Adversarially-Robust Transfer Learning
| 5 |
icml
| 1 | 1 |
2023-06-17 04:13:16.795000
|
https://github.com/NISP-official/CARTL
| 5 |
CARTL: Cooperative Adversarially-Robust Transfer Learning
|
https://scholar.google.com/scholar?cluster=16986605262499697725&hl=en&as_sdt=0,5
| 1 | 2,021 |
SpreadsheetCoder: Formula Prediction from Semi-structured Context
| 15 |
icml
| 7,322 | 1,026 |
2023-06-17 04:13:16.997000
|
https://github.com/google-research/google-research
| 29,791 |
Spreadsheetcoder: Formula prediction from semi-structured context
|
https://scholar.google.com/scholar?cluster=422033345602932532&hl=en&as_sdt=0,25
| 727 | 2,021 |
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
| 38 |
icml
| 8 | 2 |
2023-06-17 04:13:17.200000
|
https://github.com/Z-GCNETs/Z-GCNETs
| 29 |
Z-GCNETs: Time zigzags at graph convolutional networks for time series forecasting
|
https://scholar.google.com/scholar?cluster=7480163184753342890&hl=en&as_sdt=0,5
| 2 | 2,021 |
A Unified Lottery Ticket Hypothesis for Graph Neural Networks
| 82 |
icml
| 13 | 4 |
2023-06-17 04:13:17.402000
|
https://github.com/VITA-Group/Unified-LTH-GNN
| 45 |
A unified lottery ticket hypothesis for graph neural networks
|
https://scholar.google.com/scholar?cluster=14150091349849211712&hl=en&as_sdt=0,33
| 10 | 2,021 |
Analysis of stochastic Lanczos quadrature for spectrum approximation
| 11 |
icml
| 0 | 0 |
2023-06-17 04:13:17.604000
|
https://github.com/chentyl/SLQ_analysis
| 0 |
Analysis of stochastic Lanczos quadrature for spectrum approximation
|
https://scholar.google.com/scholar?cluster=3718766219336547017&hl=en&as_sdt=0,19
| 1 | 2,021 |
Cyclically Equivariant Neural Decoders for Cyclic Codes
| 11 |
icml
| 4 | 0 |
2023-06-17 04:13:17.807000
|
https://github.com/cyclicallyneuraldecoder/CyclicallyEquivariantNeuralDecoders
| 7 |
Cyclically equivariant neural decoders for cyclic codes
|
https://scholar.google.com/scholar?cluster=14253987085025630344&hl=en&as_sdt=0,5
| 2 | 2,021 |
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
| 36 |
icml
| 29 | 9 |
2023-06-17 04:13:18.009000
|
https://github.com/ucbrise/actnn
| 186 |
Actnn: Reducing training memory footprint via 2-bit activation compressed training
|
https://scholar.google.com/scholar?cluster=3861965596155884920&hl=en&as_sdt=0,37
| 6 | 2,021 |
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
| 3 |
icml
| 2 | 2 |
2023-06-17 04:13:18.211000
|
https://github.com/Feng-Research/SPADE
| 6 |
Spade: A spectral method for black-box adversarial robustness evaluation
|
https://scholar.google.com/scholar?cluster=174985207826748384&hl=en&as_sdt=0,5
| 0 | 2,021 |
Exact Optimization of Conformal Predictors via Incremental and Decremental Learning
| 9 |
icml
| 2 | 0 |
2023-06-17 04:13:18.413000
|
https://github.com/gchers/exact-cp-optimization
| 6 |
Exact optimization of conformal predictors via incremental and decremental learning
|
https://scholar.google.com/scholar?cluster=9789883793705911412&hl=en&as_sdt=0,5
| 2 | 2,021 |
Understanding and Mitigating Accuracy Disparity in Regression
| 12 |
icml
| 1 | 0 |
2023-06-17 04:13:18.615000
|
https://github.com/JFChi/Understanding-and-Mitigating-Accuracy-Disparity-in-Regression
| 3 |
Understanding and mitigating accuracy disparity in regression
|
https://scholar.google.com/scholar?cluster=9962646376890451048&hl=en&as_sdt=0,5
| 2 | 2,021 |
Robust Learning-Augmented Caching: An Experimental Study
| 9 |
icml
| 1 | 0 |
2023-06-17 04:13:18.818000
|
https://github.com/chledowski/Robust-Learning-Augmented-Caching-An-Experimental-Study-Datasets
| 0 |
Robust learning-augmented caching: An experimental study
|
https://scholar.google.com/scholar?cluster=7732162850430458310&hl=en&as_sdt=0,14
| 2 | 2,021 |
Unifying Vision-and-Language Tasks via Text Generation
| 262 |
icml
| 55 | 14 |
2023-06-17 04:13:19.020000
|
https://github.com/j-min/VL-T5
| 317 |
Unifying vision-and-language tasks via text generation
|
https://scholar.google.com/scholar?cluster=17951690001214387773&hl=en&as_sdt=0,5
| 9 | 2,021 |
Label-Only Membership Inference Attacks
| 225 |
icml
| 6 | 6 |
2023-06-17 04:13:19.223000
|
https://github.com/cchoquette/membership-inference
| 48 |
Label-only membership inference attacks
|
https://scholar.google.com/scholar?cluster=18421653793757811360&hl=en&as_sdt=0,5
| 4 | 2,021 |
Modeling Hierarchical Structures with Continuous Recursive Neural Networks
| 4 |
icml
| 1 | 0 |
2023-06-17 04:13:19.424000
|
https://github.com/JRC1995/Continuous-RvNN
| 10 |
Modeling hierarchical structures with continuous recursive neural networks
|
https://scholar.google.com/scholar?cluster=12633108093638083396&hl=en&as_sdt=0,5
| 3 | 2,021 |
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
| 45 |
icml
| 1 | 0 |
2023-06-17 04:13:19.626000
|
https://github.com/uoe-agents/seps
| 11 |
Scaling multi-agent reinforcement learning with selective parameter sharing
|
https://scholar.google.com/scholar?cluster=5803292243518473578&hl=en&as_sdt=0,5
| 1 | 2,021 |
Phasic Policy Gradient
| 90 |
icml
| 51 | 5 |
2023-06-17 04:13:19.830000
|
https://github.com/openai/phasic-policy-gradient
| 224 |
Phasic policy gradient
|
https://scholar.google.com/scholar?cluster=10786895332065637304&hl=en&as_sdt=0,5
| 7 | 2,021 |
Riemannian Convex Potential Maps
| 11 |
icml
| 4 | 1 |
2023-06-17 04:13:20.032000
|
https://github.com/facebookresearch/rcpm
| 64 |
Riemannian convex potential maps
|
https://scholar.google.com/scholar?cluster=8877178841663842639&hl=en&as_sdt=0,5
| 7 | 2,021 |
Scaling Properties of Deep Residual Networks
| 14 |
icml
| 0 | 0 |
2023-06-17 04:13:20.234000
|
https://github.com/instadeepai/scaling-resnets
| 5 |
Scaling properties of deep residual networks
|
https://scholar.google.com/scholar?cluster=8302805439596916242&hl=en&as_sdt=0,33
| 3 | 2,021 |
Exploiting Shared Representations for Personalized Federated Learning
| 214 |
icml
| 25 | 4 |
2023-06-17 04:13:20.437000
|
https://github.com/lgcollins/FedRep
| 98 |
Exploiting shared representations for personalized federated learning
|
https://scholar.google.com/scholar?cluster=15594469304978697146&hl=en&as_sdt=0,44
| 1 | 2,021 |
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
| 47 |
icml
| 3 | 1 |
2023-06-17 04:13:20.638000
|
https://github.com/JTT94/filterflow
| 33 |
Differentiable particle filtering via entropy-regularized optimal transport
|
https://scholar.google.com/scholar?cluster=6170897491109878876&hl=en&as_sdt=0,21
| 4 | 2,021 |
Explaining Time Series Predictions with Dynamic Masks
| 28 |
icml
| 15 | 5 |
2023-06-17 04:13:20.840000
|
https://github.com/JonathanCrabbe/Dynamask
| 55 |
Explaining time series predictions with dynamic masks
|
https://scholar.google.com/scholar?cluster=3877310140943578440&hl=en&as_sdt=0,14
| 2 | 2,021 |
Environment Inference for Invariant Learning
| 184 |
icml
| 8 | 0 |
2023-06-17 04:13:21.042000
|
https://github.com/ecreager/eiil
| 44 |
Environment inference for invariant learning
|
https://scholar.google.com/scholar?cluster=7012730739761324020&hl=en&as_sdt=0,5
| 3 | 2,021 |
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
| 6 |
icml
| 0 | 0 |
2023-06-17 04:13:21.246000
|
https://github.com/modestyachts/stochastic-rec-reachability
| 5 |
Quantifying availability and discovery in recommender systems via stochastic reachability
|
https://scholar.google.com/scholar?cluster=6680880425324910585&hl=en&as_sdt=0,44
| 4 | 2,021 |
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
| 435 |
icml
| 52 | 4 |
2023-06-17 04:13:21.449000
|
https://github.com/facebookresearch/convit
| 440 |
Convit: Improving vision transformers with soft convolutional inductive biases
|
https://scholar.google.com/scholar?cluster=817698272872287436&hl=en&as_sdt=0,41
| 17 | 2,021 |
Sliced Iterative Normalizing Flows
| 20 |
icml
| 10 | 3 |
2023-06-17 04:13:21.661000
|
https://github.com/biweidai/SIG
| 32 |
Sliced iterative normalizing flows
|
https://scholar.google.com/scholar?cluster=2467748158069488227&hl=en&as_sdt=0,31
| 4 | 2,021 |
Re-understanding Finite-State Representations of Recurrent Policy Networks
| 12 |
icml
| 0 | 2 |
2023-06-17 04:13:21.869000
|
https://github.com/modanesh/Differential_IG
| 10 |
Re-understanding finite-state representations of recurrent policy networks
|
https://scholar.google.com/scholar?cluster=2835459084556077542&hl=en&as_sdt=0,5
| 2 | 2,021 |
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
| 54 |
icml
| 11 | 2 |
2023-06-17 04:13:22.073000
|
https://github.com/giannisdaras/ilo
| 115 |
Intermediate layer optimization for inverse problems using deep generative models
|
https://scholar.google.com/scholar?cluster=10888680252420581266&hl=en&as_sdt=0,3
| 5 | 2,021 |
Measuring Robustness in Deep Learning Based Compressive Sensing
| 50 |
icml
| 4 | 0 |
2023-06-17 04:13:22.275000
|
https://github.com/MLI-lab/Robustness-CS
| 25 |
Measuring robustness in deep learning based compressive sensing
|
https://scholar.google.com/scholar?cluster=15924992003782305417&hl=en&as_sdt=0,23
| 2 | 2,021 |
Lipschitz normalization for self-attention layers with application to graph neural networks
| 13 |
icml
| 0 | 1 |
2023-06-17 04:13:22.478000
|
https://github.com/gdasoulas/lipschitznorm
| 9 |
Lipschitz normalization for self-attention layers with application to graph neural networks
|
https://scholar.google.com/scholar?cluster=11996902541195607773&hl=en&as_sdt=0,5
| 2 | 2,021 |
Bayesian Deep Learning via Subnetwork Inference
| 55 |
icml
| 45 | 45 |
2023-06-17 04:13:22.713000
|
https://github.com/AlexImmer/Laplace
| 327 |
Bayesian deep learning via subnetwork inference
|
https://scholar.google.com/scholar?cluster=4967391317568444060&hl=en&as_sdt=0,5
| 9 | 2,021 |
Adversarial Robustness Guarantees for Random Deep Neural Networks
| 4 |
icml
| 0 | 0 |
2023-06-17 04:13:22.915000
|
https://github.com/bkiani/Adversarial-robustness-guarantees-for-random-deep-neural-networks
| 1 |
Adversarial robustness guarantees for random deep neural networks
|
https://scholar.google.com/scholar?cluster=2504173380091047222&hl=en&as_sdt=0,5
| 1 | 2,021 |
Kernel Continual Learning
| 20 |
icml
| 0 | 1 |
2023-06-17 04:13:23.118000
|
https://github.com/mmderakhshani/KCL
| 7 |
Kernel continual learning
|
https://scholar.google.com/scholar?cluster=16309190237334513251&hl=en&as_sdt=0,33
| 2 | 2,021 |
Bayesian Optimization over Hybrid Spaces
| 24 |
icml
| 5 | 0 |
2023-06-17 04:13:23.321000
|
https://github.com/aryandeshwal/HyBO
| 18 |
Bayesian optimization over hybrid spaces
|
https://scholar.google.com/scholar?cluster=10724416920548508977&hl=en&as_sdt=0,3
| 1 | 2,021 |
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
| 15 |
icml
| 2 | 0 |
2023-06-17 04:13:23.525000
|
https://github.com/microsoft/NTT
| 11 |
Navigation turing test (NTT): Learning to evaluate human-like navigation
|
https://scholar.google.com/scholar?cluster=1633562910551633122&hl=en&as_sdt=0,39
| 3 | 2,021 |
Versatile Verification of Tree Ensembles
| 9 |
icml
| 3 | 1 |
2023-06-17 04:13:23.754000
|
https://github.com/laudv/veritas
| 12 |
Versatile verification of tree ensembles
|
https://scholar.google.com/scholar?cluster=16419931013195180348&hl=en&as_sdt=0,47
| 3 | 2,021 |
A Wasserstein Minimax Framework for Mixed Linear Regression
| 4 |
icml
| 0 | 0 |
2023-06-17 04:13:23.957000
|
https://github.com/tjdiamandis/WMLR
| 0 |
A Wasserstein minimax framework for mixed linear regression
|
https://scholar.google.com/scholar?cluster=3546795848288703283&hl=en&as_sdt=0,14
| 1 | 2,021 |
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
| 7 |
icml
| 1 | 0 |
2023-06-17 04:13:24.162000
|
https://github.com/alekdimi/arms
| 3 |
ARMS: Antithetic-REINFORCE-Multi-Sample gradient for binary variables
|
https://scholar.google.com/scholar?cluster=546385727654075781&hl=en&as_sdt=0,5
| 1 | 2,021 |
On Energy-Based Models with Overparametrized Shallow Neural Networks
| 7 |
icml
| 0 | 0 |
2023-06-17 04:13:24.366000
|
https://github.com/CDEnrich/ebms_shallow_nn
| 4 |
On energy-based models with overparametrized shallow neural networks
|
https://scholar.google.com/scholar?cluster=2626488009584096909&hl=en&as_sdt=0,31
| 2 | 2,021 |
Kernel-Based Reinforcement Learning: A Finite-Time Analysis
| 17 |
icml
| 1 | 1 |
2023-06-17 04:13:24.571000
|
https://github.com/omardrwch/kernel_ucbvi_experiments
| 3 |
Kernel-based reinforcement learning: A finite-time analysis
|
https://scholar.google.com/scholar?cluster=1350124438767928735&hl=en&as_sdt=0,34
| 2 | 2,021 |
Attention is not all you need: pure attention loses rank doubly exponentially with depth
| 158 |
icml
| 10 | 0 |
2023-06-17 04:13:24.775000
|
https://github.com/twistedcubic/attention-rank-collapse
| 138 |
Attention is not all you need: Pure attention loses rank doubly exponentially with depth
|
https://scholar.google.com/scholar?cluster=6882435683900456661&hl=en&as_sdt=0,5
| 7 | 2,021 |
How rotational invariance of common kernels prevents generalization in high dimensions
| 10 |
icml
| 0 | 0 |
2023-06-17 04:13:24.977000
|
https://github.com/DonhauserK/High-dim-kernel-paper
| 2 |
How rotational invariance of common kernels prevents generalization in high dimensions
|
https://scholar.google.com/scholar?cluster=15941159767452882886&hl=en&as_sdt=0,5
| 1 | 2,021 |
Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation
| 53 |
icml
| 3 | 1 |
2023-06-17 04:13:25.180000
|
https://github.com/tencent-ailab/ICML21_OAXE
| 21 |
Order-agnostic cross entropy for non-autoregressive machine translation
|
https://scholar.google.com/scholar?cluster=10622606881880564341&hl=en&as_sdt=0,23
| 6 | 2,021 |
Learning Diverse-Structured Networks for Adversarial Robustness
| 15 |
icml
| 2 | 0 |
2023-06-17 04:13:25.384000
|
https://github.com/d12306/dsnet
| 6 |
Learning diverse-structured networks for adversarial robustness
|
https://scholar.google.com/scholar?cluster=4158996356819287139&hl=en&as_sdt=0,39
| 1 | 2,021 |
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
| 19 |
icml
| 3 | 0 |
2023-06-17 04:13:25.592000
|
https://github.com/BoChenGroup/SawETM
| 6 |
Sawtooth factorial topic embeddings guided gamma belief network
|
https://scholar.google.com/scholar?cluster=14933868730567582356&hl=en&as_sdt=0,5
| 3 | 2,021 |
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
| 4 |
icml
| 1 | 0 |
2023-06-17 04:13:25.798000
|
https://github.com/lanl-ansi/learning-ising-dynamics
| 2 |
Exponential reduction in sample complexity with learning of ising model dynamics
|
https://scholar.google.com/scholar?cluster=14788105086389586758&hl=en&as_sdt=0,50
| 4 | 2,021 |
Reinforcement Learning Under Moral Uncertainty
| 21 |
icml
| 2 | 0 |
2023-06-17 04:13:26.002000
|
https://github.com/uber-research/normative-uncertainty
| 15 |
Reinforcement learning under moral uncertainty
|
https://scholar.google.com/scholar?cluster=2905901650161533369&hl=en&as_sdt=0,44
| 2 | 2,021 |
Self-Paced Context Evaluation for Contextual Reinforcement Learning
| 14 |
icml
| 1 | 0 |
2023-06-17 04:13:26.207000
|
https://github.com/automl/SPaCE
| 2 |
Self-paced context evaluation for contextual reinforcement learning
|
https://scholar.google.com/scholar?cluster=18295369493204614247&hl=en&as_sdt=0,36
| 8 | 2,021 |
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations
| 31 |
icml
| 3 | 0 |
2023-06-17 04:13:26.410000
|
https://github.com/pemami4911/EfficientMORL
| 22 |
Efficient iterative amortized inference for learning symmetric and disentangled multi-object representations
|
https://scholar.google.com/scholar?cluster=7263217510523036363&hl=en&as_sdt=0,14
| 3 | 2,021 |
Whitening for Self-Supervised Representation Learning
| 177 |
icml
| 28 | 0 |
2023-06-17 04:13:26.614000
|
https://github.com/htdt/self-supervised
| 112 |
Whitening for self-supervised representation learning
|
https://scholar.google.com/scholar?cluster=14222215050873553089&hl=en&as_sdt=0,5
| 3 | 2,021 |
Graph Mixture Density Networks
| 11 |
icml
| 2 | 0 |
2023-06-17 04:13:26.815000
|
https://github.com/diningphil/graph-mixture-density-networks
| 20 |
Graph mixture density networks
|
https://scholar.google.com/scholar?cluster=13606441826263868149&hl=en&as_sdt=0,5
| 2 | 2,021 |
Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data
| 27 |
icml
| 2 | 1 |
2023-06-17 04:13:27.018000
|
https://github.com/yasesf93/CrossGradientAggregation
| 6 |
Cross-gradient aggregation for decentralized learning from non-iid data
|
https://scholar.google.com/scholar?cluster=13501782840884499288&hl=en&as_sdt=0,5
| 2 | 2,021 |
Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
| 11 |
icml
| 1 | 1 |
2023-06-17 04:13:27.220000
|
https://github.com/yingfan-bot/mbpsrl
| 5 |
Model-based reinforcement learning for continuous control with posterior sampling
|
https://scholar.google.com/scholar?cluster=9782112597540480270&hl=en&as_sdt=0,15
| 1 | 2,021 |
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
| 35 |
icml
| 6 | 0 |
2023-06-17 04:13:27.423000
|
https://github.com/LinxiFan/SECANT
| 37 |
Secant: Self-expert cloning for zero-shot generalization of visual policies
|
https://scholar.google.com/scholar?cluster=16889342839830358284&hl=en&as_sdt=0,6
| 4 | 2,021 |
Learning Bounds for Open-Set Learning
| 34 |
icml
| 5 | 0 |
2023-06-17 04:13:27.626000
|
https://github.com/Anjin-Liu/Openset_Learning_AOSR
| 33 |
Learning bounds for open-set learning
|
https://scholar.google.com/scholar?cluster=5726822076204238537&hl=en&as_sdt=0,5
| 1 | 2,021 |
Provably Correct Optimization and Exploration with Non-linear Policies
| 11 |
icml
| 0 | 0 |
2023-06-17 04:13:27.833000
|
https://github.com/FlorenceFeng/ENIAC
| 2 |
Provably correct optimization and exploration with non-linear policies
|
https://scholar.google.com/scholar?cluster=5246454033177283474&hl=en&as_sdt=0,5
| 2 | 2,021 |
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
| 52 |
icml
| 10 | 0 |
2023-06-17 04:13:28.035000
|
https://github.com/FengHZ/KD3A
| 98 |
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation.
|
https://scholar.google.com/scholar?cluster=14984342689086286396&hl=en&as_sdt=0,10
| 3 | 2,021 |
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