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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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