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Manifold Identification for Ultimately Communication-Efficient Distributed Optimization
| 4 |
icml
| 0 | 0 |
2023-06-17 03:57:19.438000
|
https://github.com/leepei/madpqn
| 0 |
Manifold identification for ultimately communication-efficient distributed optimization
|
https://scholar.google.com/scholar?cluster=7891580359300327237&hl=en&as_sdt=0,47
| 4 | 2,020 |
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
| 12 |
icml
| 4 | 4 |
2023-06-17 03:57:19.640000
|
https://github.com/timlee0212/PENNI
| 7 |
Penni: Pruned kernel sharing for efficient CNN inference
|
https://scholar.google.com/scholar?cluster=15394571534654834943&hl=en&as_sdt=0,33
| 3 | 2,020 |
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
| 54 |
icml
| 5 | 1 |
2023-06-17 03:57:19.841000
|
https://github.com/liqing-ustc/NGS
| 50 |
Closed loop neural-symbolic learning via integrating neural perception, grammar parsing, and symbolic reasoning
|
https://scholar.google.com/scholar?cluster=9257372000778020812&hl=en&as_sdt=0,47
| 3 | 2,020 |
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
| 30 |
icml
| 3 | 2 |
2023-06-17 03:57:20.043000
|
https://github.com/lissomx/MSP
| 10 |
Latent space factorisation and manipulation via matrix subspace projection
|
https://scholar.google.com/scholar?cluster=9592355331559392684&hl=en&as_sdt=0,45
| 2 | 2,020 |
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective
| 40 |
icml
| 11 | 1 |
2023-06-17 03:57:20.246000
|
https://github.com/steveli/partial-encoder-decoder
| 34 |
Learning from irregularly-sampled time series: A missing data perspective
|
https://scholar.google.com/scholar?cluster=9259999612636522766&hl=en&as_sdt=0,5
| 2 | 2,020 |
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
| 618 |
icml
| 77 | 2 |
2023-06-17 03:57:20.448000
|
https://github.com/tim-learn/SHOT
| 340 |
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
|
https://scholar.google.com/scholar?cluster=2414062070271265691&hl=en&as_sdt=0,31
| 7 | 2,020 |
Variable Skipping for Autoregressive Range Density Estimation
| 7 |
icml
| 3 | 0 |
2023-06-17 03:57:20.651000
|
https://github.com/var-skip/var-skip
| 6 |
Variable skipping for autoregressive range density estimation
|
https://scholar.google.com/scholar?cluster=16617388741966363068&hl=en&as_sdt=0,5
| 2 | 2,020 |
Handling the Positive-Definite Constraint in the Bayesian Learning Rule
| 19 |
icml
| 1 | 1 |
2023-06-17 03:57:20.854000
|
https://github.com/yorkerlin/iBayesLRule
| 4 |
Handling the positive-definite constraint in the Bayesian learning rule
|
https://scholar.google.com/scholar?cluster=14519338791070687660&hl=en&as_sdt=0,26
| 4 | 2,020 |
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
| 62 |
icml
| 8 | 0 |
2023-06-17 03:57:21.073000
|
https://github.com/fjxmlzn/InfoGAN-CR
| 40 |
Infogan-cr and modelcentrality: Self-supervised model training and selection for disentangling gans
|
https://scholar.google.com/scholar?cluster=4410576608706121212&hl=en&as_sdt=0,5
| 6 | 2,020 |
Generalized and Scalable Optimal Sparse Decision Trees
| 93 |
icml
| 29 | 11 |
2023-06-17 03:57:21.275000
|
https://github.com/Jimmy-Lin/GeneralizedOptimalSparseDecisionTrees
| 48 |
Generalized and scalable optimal sparse decision trees
|
https://scholar.google.com/scholar?cluster=15979140727083888111&hl=en&as_sdt=0,14
| 5 | 2,020 |
Time-aware Large Kernel Convolutions
| 23 |
icml
| 6 | 0 |
2023-06-17 03:57:21.478000
|
https://github.com/lioutasb/TaLKConvolutions
| 28 |
Time-aware large kernel convolutions
|
https://scholar.google.com/scholar?cluster=2978340010054806540&hl=en&as_sdt=0,5
| 4 | 2,020 |
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
| 19 |
icml
| 2 | 0 |
2023-06-17 03:57:21.680000
|
https://github.com/selwyn96/Quant_CS
| 1 |
Sample complexity bounds for 1-bit compressive sensing and binary stable embeddings with generative priors
|
https://scholar.google.com/scholar?cluster=14332918764703179344&hl=en&as_sdt=0,5
| 2 | 2,020 |
An Imitation Learning Approach for Cache Replacement
| 53 |
icml
| 7,322 | 1,026 |
2023-06-17 03:57:21.882000
|
https://github.com/google-research/google-research
| 29,791 |
An imitation learning approach for cache replacement
|
https://scholar.google.com/scholar?cluster=14524866221937250156&hl=en&as_sdt=0,5
| 727 | 2,020 |
Hallucinative Topological Memory for Zero-Shot Visual Planning
| 33 |
icml
| 6 | 0 |
2023-06-17 03:57:22.084000
|
https://github.com/thanard/hallucinative-topological-memory
| 12 |
Hallucinative topological memory for zero-shot visual planning
|
https://scholar.google.com/scholar?cluster=2366589002127869836&hl=en&as_sdt=0,5
| 2 | 2,020 |
Learning Deep Kernels for Non-Parametric Two-Sample Tests
| 125 |
icml
| 9 | 0 |
2023-06-17 03:57:22.286000
|
https://github.com/fengliu90/DK-for-TST
| 38 |
Learning deep kernels for non-parametric two-sample tests
|
https://scholar.google.com/scholar?cluster=11419051350787047758&hl=en&as_sdt=0,10
| 5 | 2,020 |
Finding trainable sparse networks through Neural Tangent Transfer
| 21 |
icml
| 8 | 1 |
2023-06-17 03:57:22.487000
|
https://github.com/fmi-basel/neural-tangent-transfer
| 14 |
Finding trainable sparse networks through neural tangent transfer
|
https://scholar.google.com/scholar?cluster=4513428362784750127&hl=en&as_sdt=0,5
| 4 | 2,020 |
Weakly-Supervised Disentanglement Without Compromises
| 212 |
icml
| 199 | 20 |
2023-06-17 03:57:22.689000
|
https://github.com/google-research/disentanglement_lib
| 1,301 |
Weakly-supervised disentanglement without compromises
|
https://scholar.google.com/scholar?cluster=17730117604231114120&hl=en&as_sdt=0,11
| 35 | 2,020 |
Too Relaxed to Be Fair
| 45 |
icml
| 3 | 0 |
2023-06-17 03:57:22.892000
|
https://github.com/mlohaus/SearchFair
| 9 |
Too relaxed to be fair
|
https://scholar.google.com/scholar?cluster=8729544437248973666&hl=en&as_sdt=0,34
| 2 | 2,020 |
Differentiating through the Fréchet Mean
| 52 |
icml
| 2 | 4 |
2023-06-17 03:57:23.095000
|
https://github.com/CUAI/Differentiable-Frechet-Mean
| 50 |
Differentiating through the fréchet mean
|
https://scholar.google.com/scholar?cluster=1425573169014829533&hl=en&as_sdt=0,5
| 7 | 2,020 |
Progressive Graph Learning for Open-Set Domain Adaptation
| 73 |
icml
| 5 | 2 |
2023-06-17 03:57:23.296000
|
https://github.com/BUserName/PGL
| 28 |
Progressive graph learning for open-set domain adaptation
|
https://scholar.google.com/scholar?cluster=2624735787669105317&hl=en&as_sdt=0,5
| 4 | 2,020 |
Learning Algebraic Multigrid Using Graph Neural Networks
| 43 |
icml
| 3 | 0 |
2023-06-17 03:57:23.497000
|
https://github.com/ilayluz/learning-amg
| 12 |
Learning algebraic multigrid using graph neural networks
|
https://scholar.google.com/scholar?cluster=9215058872113912967&hl=en&as_sdt=0,5
| 4 | 2,020 |
Progressive Identification of True Labels for Partial-Label Learning
| 99 |
icml
| 5 | 0 |
2023-06-17 03:57:23.700000
|
https://github.com/Lvcrezia77/PRODEN
| 41 |
Progressive identification of true labels for partial-label learning
|
https://scholar.google.com/scholar?cluster=17946181753810073887&hl=en&as_sdt=0,5
| 1 | 2,020 |
Efficient Continuous Pareto Exploration in Multi-Task Learning
| 54 |
icml
| 27 | 1 |
2023-06-17 03:57:23.901000
|
https://github.com/mit-gfx/ContinuousParetoMTL
| 117 |
Efficient continuous pareto exploration in multi-task learning
|
https://scholar.google.com/scholar?cluster=14510629090081206490&hl=en&as_sdt=0,5
| 20 | 2,020 |
Normalized Loss Functions for Deep Learning with Noisy Labels
| 239 |
icml
| 25 | 1 |
2023-06-17 03:57:24.103000
|
https://github.com/HanxunH/Active-Passive-Losses
| 106 |
Normalized loss functions for deep learning with noisy labels
|
https://scholar.google.com/scholar?cluster=15594415410821742634&hl=en&as_sdt=0,5
| 4 | 2,020 |
Adversarial Neural Pruning with Latent Vulnerability Suppression
| 37 |
icml
| 1 | 0 |
2023-06-17 03:57:24.305000
|
https://github.com/divyam3897/ANP_VS
| 14 |
Adversarial neural pruning with latent vulnerability suppression
|
https://scholar.google.com/scholar?cluster=14781666760584022356&hl=en&as_sdt=0,5
| 3 | 2,020 |
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization
| 75 |
icml
| 11 | 3 |
2023-06-17 03:57:24.507000
|
https://github.com/dbmptr/EPOSearch
| 41 |
Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization
|
https://scholar.google.com/scholar?cluster=14380308074302940199&hl=en&as_sdt=0,5
| 1 | 2,020 |
Adversarial Robustness Against the Union of Multiple Perturbation Models
| 124 |
icml
| 3 | 1 |
2023-06-17 03:57:24.709000
|
https://github.com/locuslab/robust_union
| 23 |
Adversarial robustness against the union of multiple perturbation models
|
https://scholar.google.com/scholar?cluster=7466169251019166105&hl=en&as_sdt=0,14
| 7 | 2,020 |
Adaptive Gradient Descent without Descent
| 51 |
icml
| 5 | 0 |
2023-06-17 03:57:24.911000
|
https://github.com/ymalitsky/adaptive_gd
| 39 |
Adaptive gradient descent without descent
|
https://scholar.google.com/scholar?cluster=9121623366075061608&hl=en&as_sdt=0,5
| 5 | 2,020 |
Emergence of Separable Manifolds in Deep Language Representations
| 31 |
icml
| 3 | 0 |
2023-06-17 03:57:25.114000
|
https://github.com/schung039/contextual-repr-manifolds
| 5 |
Emergence of separable manifolds in deep language representations
|
https://scholar.google.com/scholar?cluster=5179476739222728970&hl=en&as_sdt=0,5
| 2 | 2,020 |
Minimax Pareto Fairness: A Multi Objective Perspective
| 126 |
icml
| 5 | 0 |
2023-06-17 03:57:25.316000
|
https://github.com/natalialmg/MMPF
| 21 |
Minimax pareto fairness: A multi objective perspective
|
https://scholar.google.com/scholar?cluster=7690434188548585535&hl=en&as_sdt=0,31
| 3 | 2,020 |
Predictive Multiplicity in Classification
| 75 |
icml
| 2 | 2 |
2023-06-17 03:57:25.519000
|
https://github.com/charliemarx/pmtools
| 9 |
Predictive multiplicity in classification
|
https://scholar.google.com/scholar?cluster=12971902900115271261&hl=en&as_sdt=0,5
| 3 | 2,020 |
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
| 17 |
icml
| 5 | 1 |
2023-06-17 03:57:25.720000
|
https://github.com/HMEIatJHU/neural-datalog-through-time
| 30 |
Neural Datalog through time: Informed temporal modeling via logical specification
|
https://scholar.google.com/scholar?cluster=13196809524951928440&hl=en&as_sdt=0,5
| 1 | 2,020 |
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
| 8 |
icml
| 1 | 1 |
2023-06-17 03:57:25.923000
|
https://github.com/sisl/CEEM
| 8 |
Scalable identification of partially observed systems with certainty-equivalent EM
|
https://scholar.google.com/scholar?cluster=12141244862224511768&hl=en&as_sdt=0,32
| 17 | 2,020 |
Training Binary Neural Networks using the Bayesian Learning Rule
| 32 |
icml
| 5 | 1 |
2023-06-17 03:57:26.124000
|
https://github.com/team-approx-bayes/BayesBiNN
| 33 |
Training binary neural networks using the bayesian learning rule
|
https://scholar.google.com/scholar?cluster=8866131573979767036&hl=en&as_sdt=0,33
| 7 | 2,020 |
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
| 23 |
icml
| 1 | 0 |
2023-06-17 03:57:26.327000
|
https://github.com/albertometelli/pfqi
| 3 |
Control frequency adaptation via action persistence in batch reinforcement learning
|
https://scholar.google.com/scholar?cluster=6884047998353070413&hl=en&as_sdt=0,33
| 3 | 2,020 |
Projective Preferential Bayesian Optimization
| 7 |
icml
| 0 | 1 |
2023-06-17 03:57:26.530000
|
https://github.com/AaltoPML/PPBO
| 10 |
Projective preferential bayesian optimization
|
https://scholar.google.com/scholar?cluster=16344312867654899507&hl=en&as_sdt=0,5
| 8 | 2,020 |
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing
| 0 |
icml
| 0 | 0 |
2023-06-17 03:57:26.732000
|
https://github.com/srph25/videoonenet
| 0 |
VideoOneNet: bidirectional convolutional recurrent onenet with trainable data steps for video processing
|
https://scholar.google.com/scholar?cluster=1084769805460535145&hl=en&as_sdt=0,5
| 2 | 2,020 |
Learning Reasoning Strategies in End-to-End Differentiable Proving
| 63 |
icml
| 17 | 3 |
2023-06-17 03:57:26.935000
|
https://github.com/uclnlp/ctp
| 47 |
Learning reasoning strategies in end-to-end differentiable proving
|
https://scholar.google.com/scholar?cluster=16334802341623350418&hl=en&as_sdt=0,10
| 2 | 2,020 |
Coresets for Data-efficient Training of Machine Learning Models
| 137 |
icml
| 18 | 4 |
2023-06-17 03:57:27.137000
|
https://github.com/baharanm/craig
| 47 |
Coresets for data-efficient training of machine learning models
|
https://scholar.google.com/scholar?cluster=15062918067238617199&hl=en&as_sdt=0,14
| 1 | 2,020 |
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
| 53 |
icml
| 4 | 0 |
2023-06-17 03:57:27.339000
|
https://github.com/sarthmit/BRIMs
| 27 |
Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules
|
https://scholar.google.com/scholar?cluster=15085852194314811643&hl=en&as_sdt=0,37
| 3 | 2,020 |
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time
| 8 |
icml
| 0 | 0 |
2023-06-17 03:57:27.539000
|
https://github.com/DurstewitzLab/contPLRNN
| 0 |
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time
|
https://scholar.google.com/scholar?cluster=8416515873686618077&hl=en&as_sdt=0,44
| 1 | 2,020 |
An end-to-end approach for the verification problem: learning the right distance
| 12 |
icml
| 1 | 0 |
2023-06-17 03:57:27.742000
|
https://github.com/joaomonteirof/e2e_verification
| 6 |
An end-to-end approach for the verification problem: learning the right distance
|
https://scholar.google.com/scholar?cluster=18311458565256398722&hl=en&as_sdt=0,5
| 4 | 2,020 |
Confidence-Aware Learning for Deep Neural Networks
| 89 |
icml
| 11 | 2 |
2023-06-17 03:57:27.943000
|
https://github.com/daintlab/confidence-aware-learning
| 62 |
Confidence-aware learning for deep neural networks
|
https://scholar.google.com/scholar?cluster=7136169408479402844&hl=en&as_sdt=0,36
| 6 | 2,020 |
Topological Autoencoders
| 111 |
icml
| 26 | 0 |
2023-06-17 03:57:28.145000
|
https://github.com/BorgwardtLab/topological-autoencoders
| 105 |
Topological autoencoders
|
https://scholar.google.com/scholar?cluster=11510547932502602061&hl=en&as_sdt=0,10
| 7 | 2,020 |
Fair Learning with Private Demographic Data
| 48 |
icml
| 1 | 0 |
2023-06-17 03:57:28.347000
|
https://github.com/husseinmozannar/fairlearn_private_data
| 4 |
Fair learning with private demographic data
|
https://scholar.google.com/scholar?cluster=16497841133836187682&hl=en&as_sdt=0,5
| 2 | 2,020 |
Consistent Estimators for Learning to Defer to an Expert
| 101 |
icml
| 7 | 15 |
2023-06-17 03:57:28.548000
|
https://github.com/clinicalml/learn-to-defer
| 9 |
Consistent estimators for learning to defer to an expert
|
https://scholar.google.com/scholar?cluster=3621001929696373512&hl=en&as_sdt=0,5
| 3 | 2,020 |
Missing Data Imputation using Optimal Transport
| 62 |
icml
| 11 | 1 |
2023-06-17 03:57:28.750000
|
https://github.com/BorisMuzellec/MissingDataOT
| 73 |
Missing data imputation using optimal transport
|
https://scholar.google.com/scholar?cluster=1517478488560941748&hl=en&as_sdt=0,5
| 4 | 2,020 |
Voice Separation with an Unknown Number of Multiple Speakers
| 145 |
icml
| 159 | 27 |
2023-06-17 03:57:28.952000
|
https://github.com/facebookresearch/svoice
| 1,030 |
Voice separation with an unknown number of multiple speakers
|
https://scholar.google.com/scholar?cluster=8245320586171214224&hl=en&as_sdt=0,21
| 24 | 2,020 |
Reliable Fidelity and Diversity Metrics for Generative Models
| 147 |
icml
| 28 | 7 |
2023-06-17 03:57:29.153000
|
https://github.com/clovaai/generative-evaluation-prdc
| 207 |
Reliable fidelity and diversity metrics for generative models
|
https://scholar.google.com/scholar?cluster=6046067727543252873&hl=en&as_sdt=0,5
| 9 | 2,020 |
Bayesian Sparsification of Deep C-valued Networks
| 10 |
icml
| 25 | 7 |
2023-06-17 03:57:29.357000
|
https://github.com/ivannz/cplxmodule
| 119 |
Bayesian sparsification of deep c-valued networks
|
https://scholar.google.com/scholar?cluster=17209924131548214610&hl=en&as_sdt=0,33
| 11 | 2,020 |
Oracle Efficient Private Non-Convex Optimization
| 7 |
icml
| 1 | 0 |
2023-06-17 03:57:29.559000
|
https://github.com/giusevtr/private_objective_perturbation
| 3 |
Oracle efficient private non-convex optimization
|
https://scholar.google.com/scholar?cluster=7786612400665657488&hl=en&as_sdt=0,5
| 0 | 2,020 |
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
| 25 |
icml
| 35 | 22 |
2023-06-17 03:57:29.761000
|
https://github.com/openopt/copt
| 125 |
Stochastic Frank-Wolfe for constrained finite-sum minimization
|
https://scholar.google.com/scholar?cluster=611899428047262705&hl=en&as_sdt=0,14
| 12 | 2,020 |
Aggregation of Multiple Knockoffs
| 15 |
icml
| 7 | 5 |
2023-06-17 03:57:29.963000
|
https://github.com/ja-che/hidimstat
| 20 |
Aggregation of multiple knockoffs
|
https://scholar.google.com/scholar?cluster=656849439593762318&hl=en&as_sdt=0,5
| 7 | 2,020 |
Knowing The What But Not The Where in Bayesian Optimization
| 34 |
icml
| 3 | 0 |
2023-06-17 03:57:30.166000
|
https://github.com/ntienvu/KnownOptimum_BO
| 13 |
Knowing the what but not the where in Bayesian optimization
|
https://scholar.google.com/scholar?cluster=16424117469518186156&hl=en&as_sdt=0,33
| 1 | 2,020 |
Robust Bayesian Classification Using An Optimistic Score Ratio
| 11 |
icml
| 0 | 0 |
2023-06-17 03:57:30.368000
|
https://github.com/nian-si/bsc
| 0 |
Robust bayesian classification using an optimistic score ratio
|
https://scholar.google.com/scholar?cluster=7833733923868334694&hl=en&as_sdt=0,33
| 1 | 2,020 |
LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
| 13 |
icml
| 7 | 2 |
2023-06-17 03:57:30.572000
|
https://github.com/deep-spin/lp-sparsemap
| 39 |
Lp-sparsemap: Differentiable relaxed optimization for sparse structured prediction
|
https://scholar.google.com/scholar?cluster=13952332112683207065&hl=en&as_sdt=0,36
| 7 | 2,020 |
Consistent Structured Prediction with Max-Min Margin Markov Networks
| 12 |
icml
| 5 | 1 |
2023-06-17 03:57:30.777000
|
https://github.com/alexnowakvila/maxminloss
| 7 |
Consistent structured prediction with max-min margin markov networks
|
https://scholar.google.com/scholar?cluster=10738021504710900469&hl=en&as_sdt=0,10
| 2 | 2,020 |
T-Basis: a Compact Representation for Neural Networks
| 22 |
icml
| 1 | 0 |
2023-06-17 03:57:30.992000
|
https://github.com/toshas/tbasis
| 8 |
T-basis: a compact representation for neural networks
|
https://scholar.google.com/scholar?cluster=12293196328367856783&hl=en&as_sdt=0,5
| 1 | 2,020 |
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
| 6 |
icml
| 1 | 0 |
2023-06-17 03:57:31.205000
|
https://github.com/edouardoyallon/interferometric-graph-transform
| 9 |
Interferometric graph transform: a deep unsupervised graph representation
|
https://scholar.google.com/scholar?cluster=7788892344484265680&hl=en&as_sdt=0,5
| 2 | 2,020 |
Learning to Score Behaviors for Guided Policy Optimization
| 26 |
icml
| 7 | 0 |
2023-06-17 03:57:31.409000
|
https://github.com/behaviorguidedRL/BGRL
| 23 |
Learning to score behaviors for guided policy optimization
|
https://scholar.google.com/scholar?cluster=7653224630549423499&hl=en&as_sdt=0,5
| 4 | 2,020 |
Adversarial Mutual Information for Text Generation
| 4 |
icml
| 1 | 2 |
2023-06-17 03:57:31.657000
|
https://github.com/ZJULearning/AMI
| 7 |
Adversarial mutual information for text generation
|
https://scholar.google.com/scholar?cluster=5510716302378812620&hl=en&as_sdt=0,32
| 3 | 2,020 |
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
| 14 |
icml
| 9 | 0 |
2023-06-17 03:57:31.877000
|
https://github.com/Rose-STL-Lab/mrtl
| 11 |
Multiresolution tensor learning for efficient and interpretable spatial analysis
|
https://scholar.google.com/scholar?cluster=15097484700920257271&hl=en&as_sdt=0,10
| 4 | 2,020 |
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
| 46 |
icml
| 80 | 9 |
2023-06-17 03:57:32.105000
|
https://github.com/alex-petrenko/sample-factory
| 593 |
Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning
|
https://scholar.google.com/scholar?cluster=7436378038868807375&hl=en&as_sdt=0,5
| 17 | 2,020 |
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
| 34 |
icml
| 1 | 0 |
2023-06-17 03:57:32.315000
|
https://github.com/haiphanNJIT/StoBatch
| 6 |
Scalable differential privacy with certified robustness in adversarial learning
|
https://scholar.google.com/scholar?cluster=11508415782067363031&hl=en&as_sdt=0,48
| 3 | 2,020 |
WaveFlow: A Compact Flow-based Model for Raw Audio
| 95 |
icml
| 82 | 0 |
2023-06-17 03:57:32.518000
|
https://github.com/PaddlePaddle/Parakeet
| 584 |
Waveflow: A compact flow-based model for raw audio
|
https://scholar.google.com/scholar?cluster=15645705670677592172&hl=en&as_sdt=0,39
| 29 | 2,020 |
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
| 129 |
icml
| 7 | 0 |
2023-06-17 03:57:32.721000
|
https://github.com/vihari/csd
| 43 |
Efficient domain generalization via common-specific low-rank decomposition
|
https://scholar.google.com/scholar?cluster=11307656152978308596&hl=en&as_sdt=0,47
| 3 | 2,020 |
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
| 82 |
icml
| 21 | 8 |
2023-06-17 03:57:32.923000
|
https://github.com/spitis/mrl
| 95 |
Maximum entropy gain exploration for long horizon multi-goal reinforcement learning
|
https://scholar.google.com/scholar?cluster=11035896371402538645&hl=en&as_sdt=0,47
| 5 | 2,020 |
Explaining Groups of Points in Low-Dimensional Representations
| 16 |
icml
| 2 | 1 |
2023-06-17 03:57:33.151000
|
https://github.com/GDPlumb/ELDR
| 7 |
Explaining groups of points in low-dimensional representations
|
https://scholar.google.com/scholar?cluster=2769965454437760669&hl=en&as_sdt=0,24
| 4 | 2,020 |
SoftSort: A Continuous Relaxation for the argsort Operator
| 30 |
icml
| 5 | 4 |
2023-06-17 03:57:33.353000
|
https://github.com/sprillo/softsort
| 31 |
Softsort: A continuous relaxation for the argsort operator
|
https://scholar.google.com/scholar?cluster=16358906798054657773&hl=en&as_sdt=0,5
| 5 | 2,020 |
Graph-based Nearest Neighbor Search: From Practice to Theory
| 34 |
icml
| 2 | 0 |
2023-06-17 03:57:33.554000
|
https://github.com/Shekhale/gbnns_theory
| 15 |
Graph-based nearest neighbor search: From practice to theory
|
https://scholar.google.com/scholar?cluster=13724716068024753657&hl=en&as_sdt=0,5
| 0 | 2,020 |
Deep Isometric Learning for Visual Recognition
| 42 |
icml
| 21 | 0 |
2023-06-17 03:57:33.757000
|
https://github.com/HaozhiQi/ISONet
| 143 |
Deep isometric learning for visual recognition
|
https://scholar.google.com/scholar?cluster=11095100806384225671&hl=en&as_sdt=0,14
| 9 | 2,020 |
Unsupervised Speech Decomposition via Triple Information Bottleneck
| 131 |
icml
| 93 | 27 |
2023-06-17 03:57:33.960000
|
https://github.com/auspicious3000/SpeechSplit
| 529 |
Unsupervised speech decomposition via triple information bottleneck
|
https://scholar.google.com/scholar?cluster=6104818093122244998&hl=en&as_sdt=0,44
| 23 | 2,020 |
DeepCoDA: personalized interpretability for compositional health data
| 8 |
icml
| 1 | 0 |
2023-06-17 03:57:34.162000
|
https://github.com/nphdang/DeepCoDA
| 6 |
Deepcoda: personalized interpretability for compositional health data
|
https://scholar.google.com/scholar?cluster=1822616617548782028&hl=en&as_sdt=0,5
| 3 | 2,020 |
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
| 82 |
icml
| 2 | 0 |
2023-06-17 03:57:34.364000
|
https://github.com/adishs/icml2020_rl-policy-teaching_code
| 8 |
Policy teaching via environment poisoning: Training-time adversarial attacks against reinforcement learning
|
https://scholar.google.com/scholar?cluster=2440833771930412039&hl=en&as_sdt=0,47
| 1 | 2,020 |
The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons
| 10 |
icml
| 0 | 0 |
2023-06-17 03:57:34.564000
|
https://github.com/WenboRen/Topk-Ranking-from-Pairwise-Comparisons
| 1 |
The Sample Complexity of Best- Items Selection from Pairwise Comparisons
|
https://scholar.google.com/scholar?cluster=5765760591952820635&hl=en&as_sdt=0,5
| 1 | 2,020 |
Overfitting in adversarially robust deep learning
| 555 |
icml
| 30 | 2 |
2023-06-17 03:57:34.771000
|
https://github.com/locuslab/robust_overfitting
| 145 |
Overfitting in adversarially robust deep learning
|
https://scholar.google.com/scholar?cluster=3283552716843896977&hl=en&as_sdt=0,34
| 8 | 2,020 |
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge
| 147 |
icml
| 13 | 1 |
2023-06-17 03:57:34.973000
|
https://github.com/laura-rieger/deep-explanation-penalization
| 120 |
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
|
https://scholar.google.com/scholar?cluster=15865202666417121360&hl=en&as_sdt=0,33
| 8 | 2,020 |
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
| 53 |
icml
| 4 | 0 |
2023-06-17 03:57:35.176000
|
https://github.com/yuji-roh/fr-train
| 12 |
Fr-train: A mutual information-based approach to fair and robust training
|
https://scholar.google.com/scholar?cluster=13680487688009337153&hl=en&as_sdt=0,33
| 3 | 2,020 |
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
| 20 |
icml
| 1 | 0 |
2023-06-17 03:57:35.378000
|
https://github.com/estherrolf/multi-objective-impact
| 5 |
Balancing competing objectives with noisy data: Score-based classifiers for welfare-aware machine learning
|
https://scholar.google.com/scholar?cluster=13495786516885485801&hl=en&as_sdt=0,33
| 8 | 2,020 |
Attentive Group Equivariant Convolutional Networks
| 61 |
icml
| 3 | 0 |
2023-06-17 03:57:35.579000
|
https://github.com/dwromero/att_gconvs
| 46 |
Attentive group equivariant convolutional networks
|
https://scholar.google.com/scholar?cluster=7532982364611268025&hl=en&as_sdt=0,5
| 3 | 2,020 |
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
| 63 |
icml
| 5 | 2 |
2023-06-17 03:57:35.782000
|
https://github.com/rubinxin/CoCaBO_code
| 38 |
Bayesian optimisation over multiple continuous and categorical inputs
|
https://scholar.google.com/scholar?cluster=6939944017464158601&hl=en&as_sdt=0,5
| 3 | 2,020 |
Bounding the fairness and accuracy of classifiers from population statistics
| 12 |
icml
| 1 | 0 |
2023-06-17 03:57:35.985000
|
https://github.com/sivansabato/bfa
| 0 |
Bounding the fairness and accuracy of classifiers from population statistics
|
https://scholar.google.com/scholar?cluster=2023767612415868273&hl=en&as_sdt=0,15
| 2 | 2,020 |
Radioactive data: tracing through training
| 47 |
icml
| 9 | 3 |
2023-06-17 03:57:36.186000
|
https://github.com/facebookresearch/radioactive_data
| 37 |
Radioactive data: tracing through training
|
https://scholar.google.com/scholar?cluster=10544737846821362051&hl=en&as_sdt=0,48
| 7 | 2,020 |
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
| 51 |
icml
| 0 | 0 |
2023-06-17 03:57:36.389000
|
https://github.com/saharaja/ICML2020-fairness
| 0 |
Measuring non-expert comprehension of machine learning fairness metrics
|
https://scholar.google.com/scholar?cluster=9761297825118487455&hl=en&as_sdt=0,44
| 2 | 2,020 |
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
| 22 |
icml
| 6 | 2 |
2023-06-17 03:57:36.590000
|
https://github.com/usaito/counterfactual-cv
| 29 |
Counterfactual cross-validation: Stable model selection procedure for causal inference models
|
https://scholar.google.com/scholar?cluster=10053699039608727761&hl=en&as_sdt=0,39
| 2 | 2,020 |
Learning to Simulate Complex Physics with Graph Networks
| 658 |
icml
| 2,436 | 170 |
2023-06-17 03:57:36.792000
|
https://github.com/deepmind/deepmind-research
| 11,905 |
Learning to simulate complex physics with graph networks
|
https://scholar.google.com/scholar?cluster=7841761417368333272&hl=en&as_sdt=0,5
| 336 | 2,020 |
Discriminative Adversarial Search for Abstractive Summarization
| 24 |
icml
| 1,868 | 365 |
2023-06-17 03:57:36.994000
|
https://github.com/microsoft/unilm
| 12,786 |
Discriminative adversarial search for abstractive summarization
|
https://scholar.google.com/scholar?cluster=2830447746758496884&hl=en&as_sdt=0,5
| 260 | 2,020 |
Planning to Explore via Self-Supervised World Models
| 237 |
icml
| 26 | 12 |
2023-06-17 03:57:37.196000
|
https://github.com/ramanans1/plan2explore
| 201 |
Planning to explore via self-supervised world models
|
https://scholar.google.com/scholar?cluster=804828726250878727&hl=en&as_sdt=0,33
| 14 | 2,020 |
Lookahead-Bounded Q-learning
| 6 |
icml
| 1 | 0 |
2023-06-17 03:57:37.398000
|
https://github.com/ibrahim-elshar/LBQL_ICML2020
| 4 |
Lookahead-bounded q-learning
|
https://scholar.google.com/scholar?cluster=15722192187033607775&hl=en&as_sdt=0,39
| 1 | 2,020 |
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
| 27 |
icml
| 5 | 2 |
2023-06-17 03:57:37.600000
|
https://github.com/shenzy08/PDO-eConvs
| 13 |
Pdo-econvs: Partial differential operator based equivariant convolutions
|
https://scholar.google.com/scholar?cluster=8875071450506377272&hl=en&as_sdt=0,14
| 1 | 2,020 |
Educating Text Autoencoders: Latent Representation Guidance via Denoising
| 44 |
icml
| 39 | 3 |
2023-06-17 03:57:37.801000
|
https://github.com/shentianxiao/text-autoencoders
| 185 |
Educating text autoencoders: Latent representation guidance via denoising
|
https://scholar.google.com/scholar?cluster=3322516432269705271&hl=en&as_sdt=0,31
| 9 | 2,020 |
PowerNorm: Rethinking Batch Normalization in Transformers
| 55 |
icml
| 16 | 2 |
2023-06-17 03:57:38.004000
|
https://github.com/sIncerass/powernorm
| 107 |
Powernorm: Rethinking batch normalization in transformers
|
https://scholar.google.com/scholar?cluster=11876493237600488243&hl=en&as_sdt=0,5
| 8 | 2,020 |
Incremental Sampling Without Replacement for Sequence Models
| 14 |
icml
| 3 | 0 |
2023-06-17 03:57:38.206000
|
https://github.com/google-research/unique-randomizer
| 6 |
Incremental sampling without replacement for sequence models
|
https://scholar.google.com/scholar?cluster=570267648910120463&hl=en&as_sdt=0,5
| 6 | 2,020 |
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
| 74 |
icml
| 6 | 24 |
2023-06-17 03:57:38.407000
|
https://github.com/bfshi/InfoDrop
| 121 |
Informative dropout for robust representation learning: A shape-bias perspective
|
https://scholar.google.com/scholar?cluster=14939290265495016487&hl=en&as_sdt=0,11
| 10 | 2,020 |
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
| 18 |
icml
| 2 | 0 |
2023-06-17 03:57:38.609000
|
https://github.com/wenxianxian/demvae
| 25 |
Dispersed exponential family mixture vaes for interpretable text generation
|
https://scholar.google.com/scholar?cluster=8941211277689628269&hl=en&as_sdt=0,5
| 3 | 2,020 |
Predictive Coding for Locally-Linear Control
| 11 |
icml
| 3 | 0 |
2023-06-17 03:57:38.810000
|
https://github.com/VinAIResearch/PC3-pytorch
| 16 |
Predictive coding for locally-linear control
|
https://scholar.google.com/scholar?cluster=8037643226796861111&hl=en&as_sdt=0,5
| 3 | 2,020 |
A Generative Model for Molecular Distance Geometry
| 68 |
icml
| 13 | 5 |
2023-06-17 03:57:39.013000
|
https://github.com/gncs/graphdg
| 32 |
A generative model for molecular distance geometry
|
https://scholar.google.com/scholar?cluster=11522427677669311015&hl=en&as_sdt=0,5
| 2 | 2,020 |
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
| 82 |
icml
| 22 | 7 |
2023-06-17 03:57:39.218000
|
https://github.com/gncs/molgym
| 94 |
Reinforcement learning for molecular design guided by quantum mechanics
|
https://scholar.google.com/scholar?cluster=2647402113412769429&hl=en&as_sdt=0,7
| 5 | 2,020 |
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
| 40 |
icml
| 0 | 0 |
2023-06-17 03:57:39.421000
|
https://github.com/umutsimsekli/fuld
| 0 |
Fractional underdamped langevin dynamics: Retargeting sgd with momentum under heavy-tailed gradient noise
|
https://scholar.google.com/scholar?cluster=12546091337586051753&hl=en&as_sdt=0,5
| 1 | 2,020 |
FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis
| 22 |
icml
| 2 | 0 |
2023-06-17 03:57:39.627000
|
https://github.com/travelbureau/f0_icml_code
| 5 |
FormulaZero: Distributionally robust online adaptation via offline population synthesis
|
https://scholar.google.com/scholar?cluster=4155022533808347163&hl=en&as_sdt=0,47
| 4 | 2,020 |
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