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High-Throughput Synchronous Deep RL | 14 | neurips | 3 | 2 | 2023-06-16 15:11:50.803000 | https://github.com/IouJenLiu/HTS-RL | 18 | High-throughput synchronous deep rl | https://scholar.google.com/scholar?cluster=4006743594128174439&hl=en&as_sdt=0,21 | 4 | 2,020 |
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables | 13 | neurips | 2 | 0 | 2023-06-16 15:11:50.997000 | https://github.com/StannisZhou/mixed_hmc | 11 | Mixed Hamiltonian Monte Carlo for mixed discrete and continuous variables | https://scholar.google.com/scholar?cluster=2223840957645999633&hl=en&as_sdt=0,10 | 2 | 2,020 |
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration | 62 | neurips | 5 | 0 | 2023-06-16 15:11:51.190000 | https://github.com/XLearning-SCU/2020-NeurIPS-CLEARER | 16 | Clearer: Multi-scale neural architecture search for image restoration | https://scholar.google.com/scholar?cluster=3207659434560988619&hl=en&as_sdt=0,36 | 0 | 2,020 |
Compositional Explanations of Neurons | 89 | neurips | 10 | 1 | 2023-06-16 15:11:51.383000 | https://github.com/jayelm/compexp | 23 | Compositional explanations of neurons | https://scholar.google.com/scholar?cluster=15725346730266402738&hl=en&as_sdt=0,22 | 5 | 2,020 |
Functional Regularization for Representation Learning: A Unified Theoretical Perspective | 13 | neurips | 0 | 0 | 2023-06-16 15:11:51.576000 | https://github.com/sid7954/functional-regularization | 4 | Functional regularization for representation learning: A unified theoretical perspective | https://scholar.google.com/scholar?cluster=565293895434429828&hl=en&as_sdt=0,5 | 2 | 2,020 |
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits | 51 | neurips | 7 | 0 | 2023-06-16 15:11:51.768000 | https://github.com/jparkerholder/PB2 | 21 | Provably efficient online hyperparameter optimization with population-based bandits | https://scholar.google.com/scholar?cluster=14437140412856434698&hl=en&as_sdt=0,10 | 1 | 2,020 |
Understanding Global Feature Contributions With Additive Importance Measures | 159 | neurips | 35 | 6 | 2023-06-16 15:11:51.962000 | https://github.com/iancovert/sage | 178 | Understanding global feature contributions with additive importance measures | https://scholar.google.com/scholar?cluster=15444878093984821600&hl=en&as_sdt=0,34 | 6 | 2,020 |
Co-Tuning for Transfer Learning | 53 | neurips | 4 | 0 | 2023-06-16 15:11:52.155000 | https://github.com/thuml/CoTuning | 37 | Co-tuning for transfer learning | https://scholar.google.com/scholar?cluster=14838654300858225214&hl=en&as_sdt=0,36 | 7 | 2,020 |
Succinct and Robust Multi-Agent Communication With Temporal Message Control | 30 | neurips | 9 | 2 | 2023-06-16 15:11:52.349000 | https://github.com/saizhang0218/TMC | 21 | Succinct and robust multi-agent communication with temporal message control | https://scholar.google.com/scholar?cluster=5673533236420067969&hl=en&as_sdt=0,31 | 2 | 2,020 |
Big Bird: Transformers for Longer Sequences | 1,132 | neurips | 95 | 26 | 2023-06-16 15:11:52.542000 | https://github.com/google-research/bigbird | 510 | Big bird: Transformers for longer sequences | https://scholar.google.com/scholar?cluster=11654897857579035055&hl=en&as_sdt=0,5 | 12 | 2,020 |
Neural Execution Engines: Learning to Execute Subroutines | 32 | neurips | 1 | 0 | 2023-06-16 15:11:52.736000 | https://github.com/Yujun-Yan/Neural-Execution-Engines | 13 | Neural execution engines: Learning to execute subroutines | https://scholar.google.com/scholar?cluster=14967734265100608215&hl=en&as_sdt=0,39 | 3 | 2,020 |
Random Reshuffling: Simple Analysis with Vast Improvements | 76 | neurips | 3 | 0 | 2023-06-16 15:11:52.928000 | https://github.com/konstmish/random_reshuffling | 3 | Random reshuffling: Simple analysis with vast improvements | https://scholar.google.com/scholar?cluster=10792079397833408832&hl=en&as_sdt=0,5 | 2 | 2,020 |
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors | 46 | neurips | 7 | 7 | 2023-06-16 15:11:53.124000 | https://github.com/orybkin/video-gcp | 41 | Long-horizon visual planning with goal-conditioned hierarchical predictors | https://scholar.google.com/scholar?cluster=10633756524513419826&hl=en&as_sdt=0,5 | 5 | 2,020 |
Dual-Resolution Correspondence Networks | 75 | neurips | 8 | 2 | 2023-06-16 15:11:53.338000 | https://github.com/ActiveVisionLab/DualRC-Net | 51 | Dual-resolution correspondence networks | https://scholar.google.com/scholar?cluster=3029115928365838099&hl=en&as_sdt=0,5 | 6 | 2,020 |
The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification | 7 | neurips | 0 | 0 | 2023-06-16 15:11:53.536000 | https://github.com/LvWilliam/EWTH_Loss | 10 | The dilemma of trihard loss and an element-weighted trihard loss for person re-identification | https://scholar.google.com/scholar?cluster=8305704582517734688&hl=en&as_sdt=0,14 | 2 | 2,020 |
Towards Neural Programming Interfaces | 4 | neurips | 6 | 2 | 2023-06-16 15:11:53.729000 | https://github.com/DRAGNLabs/towards-neural-programming-interfaces | 13 | Towards neural programming interfaces | https://scholar.google.com/scholar?cluster=12937220013331905850&hl=en&as_sdt=0,21 | 3 | 2,020 |
Continuous Meta-Learning without Tasks | 71 | neurips | 4 | 13 | 2023-06-16 15:11:53.922000 | https://github.com/StanfordASL/moca | 27 | Continuous meta-learning without tasks | https://scholar.google.com/scholar?cluster=3924794146291307550&hl=en&as_sdt=0,5 | 10 | 2,020 |
Pruning Filter in Filter | 67 | neurips | 32 | 1 | 2023-06-16 15:11:54.115000 | https://github.com/fxmeng/Pruning-Filter-in-Filter | 166 | Pruning filter in filter | https://scholar.google.com/scholar?cluster=8643629430951886343&hl=en&as_sdt=0,5 | 3 | 2,020 |
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods | 27 | neurips | 1 | 1 | 2023-06-16 15:11:54.319000 | https://github.com/zwfightzw/Meta-Critic | 9 | Online meta-critic learning for off-policy actor-critic methods | https://scholar.google.com/scholar?cluster=15413829867352499622&hl=en&as_sdt=0,26 | 2 | 2,020 |
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations | 37 | neurips | 18 | 2 | 2023-06-16 15:11:54.521000 | https://github.com/yunshengtian/DGEMO | 73 | Diversity-guided multi-objective bayesian optimization with batch evaluations | https://scholar.google.com/scholar?cluster=3042580278447313182&hl=en&as_sdt=0,5 | 5 | 2,020 |
SOLOv2: Dynamic and Fast Instance Segmentation | 492 | neurips | 299 | 122 | 2023-06-16 15:11:54.715000 | https://github.com/WXinlong/SOLO | 1,594 | Solov2: Dynamic and fast instance segmentation | https://scholar.google.com/scholar?cluster=4993232610053036190&hl=en&as_sdt=0,22 | 33 | 2,020 |
Continuous Regularized Wasserstein Barycenters | 30 | neurips | 0 | 2 | 2023-06-16 15:11:54.909000 | https://github.com/lingxiaoli94/CWB | 10 | Continuous regularized wasserstein barycenters | https://scholar.google.com/scholar?cluster=7488197485560112624&hl=en&as_sdt=0,5 | 1 | 2,020 |
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting | 190 | neurips | 96 | 20 | 2023-06-16 15:11:55.103000 | https://github.com/microsoft/StemGNN | 360 | Spectral temporal graph neural network for multivariate time-series forecasting | https://scholar.google.com/scholar?cluster=8609729441168460418&hl=en&as_sdt=0,33 | 9 | 2,020 |
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies | 42 | neurips | 8 | 1 | 2023-06-16 15:11:55.296000 | https://github.com/YuehuaZhu/ProxyGML | 59 | Fewer is more: A deep graph metric learning perspective using fewer proxies | https://scholar.google.com/scholar?cluster=13172519934941641323&hl=en&as_sdt=0,5 | 2 | 2,020 |
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting | 432 | neurips | 71 | 4 | 2023-06-16 15:11:55.489000 | https://github.com/LeiBAI/AGCRN | 211 | Adaptive graph convolutional recurrent network for traffic forecasting | https://scholar.google.com/scholar?cluster=531500407384902218&hl=en&as_sdt=0,5 | 5 | 2,020 |
Learning outside the Black-Box: The pursuit of interpretable models | 17 | neurips | 7 | 1 | 2023-06-16 15:11:55.683000 | https://github.com/JonathanCrabbe/Symbolic-Pursuit | 14 | Learning outside the black-box: The pursuit of interpretable models | https://scholar.google.com/scholar?cluster=829655441463875439&hl=en&as_sdt=0,33 | 4 | 2,020 |
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach | 53 | neurips | 11 | 0 | 2023-06-16 15:11:55.881000 | https://github.com/goldblum/AdversarialQuerying | 46 | Adversarially robust few-shot learning: A meta-learning approach | https://scholar.google.com/scholar?cluster=15509526791894083783&hl=en&as_sdt=0,5 | 3 | 2,020 |
Neural Anisotropy Directions | 14 | neurips | 4 | 0 | 2023-06-16 15:11:56.082000 | https://github.com/LTS4/neural-anisotropy-directions | 16 | Neural anisotropy directions | https://scholar.google.com/scholar?cluster=13055612320165183651&hl=en&as_sdt=0,33 | 8 | 2,020 |
Digraph Inception Convolutional Networks | 50 | neurips | 7 | 3 | 2023-06-16 15:11:56.277000 | https://github.com/flyingtango/DiGCN | 35 | Digraph inception convolutional networks | https://scholar.google.com/scholar?cluster=3901637816715670823&hl=en&as_sdt=0,5 | 2 | 2,020 |
Stochastic Stein Discrepancies | 31 | neurips | 0 | 2 | 2023-06-16 15:11:56.469000 | https://github.com/jgorham/stochastic_stein_discrepancy | 0 | Stochastic stein discrepancies | https://scholar.google.com/scholar?cluster=9711426818450432498&hl=en&as_sdt=0,31 | 2 | 2,020 |
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs | 86 | neurips | 4 | 0 | 2023-06-16 15:11:56.663000 | https://github.com/ignavierng/golem | 25 | On the role of sparsity and dag constraints for learning linear dags | https://scholar.google.com/scholar?cluster=1555649342103707426&hl=en&as_sdt=0,39 | 1 | 2,020 |
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search | 53 | neurips | 168 | 24 | 2023-06-16 15:11:56.856000 | https://github.com/microsoft/cream | 1,078 | Cream of the crop: Distilling prioritized paths for one-shot neural architecture search | https://scholar.google.com/scholar?cluster=11578986430039663904&hl=en&as_sdt=0,5 | 25 | 2,020 |
Fair Multiple Decision Making Through Soft Interventions | 9 | neurips | 3 | 0 | 2023-06-16 15:11:57.049000 | https://github.com/yaoweihu/Fair-Multiple-Decision-Making | 0 | Fair multiple decision making through soft interventions | https://scholar.google.com/scholar?cluster=11596139614836314222&hl=en&as_sdt=0,39 | 1 | 2,020 |
Learning to Play No-Press Diplomacy with Best Response Policy Iteration | 33 | neurips | 7 | 0 | 2023-06-16 15:11:57.244000 | https://github.com/deepmind/diplomacy | 31 | Learning to play no-press diplomacy with best response policy iteration | https://scholar.google.com/scholar?cluster=17288570672333951438&hl=en&as_sdt=0,21 | 4 | 2,020 |
Inverse Learning of Symmetries | 6 | neurips | 1 | 0 | 2023-06-16 15:11:57.436000 | https://github.com/bmda-unibas/InverseLearningOfSymmetries | 1 | Inverse learning of symmetries | https://scholar.google.com/scholar?cluster=11141520143943539280&hl=en&as_sdt=0,5 | 1 | 2,020 |
Effective Diversity in Population Based Reinforcement Learning | 108 | neurips | 8 | 1 | 2023-06-16 15:11:57.628000 | https://github.com/jparkerholder/DvD_ES | 39 | Effective diversity in population based reinforcement learning | https://scholar.google.com/scholar?cluster=13580562811176408122&hl=en&as_sdt=0,15 | 1 | 2,020 |
Hybrid Models for Learning to Branch | 70 | neurips | 10 | 3 | 2023-06-16 15:11:57.822000 | https://github.com/pg2455/Hybrid-learn2branch | 38 | Hybrid models for learning to branch | https://scholar.google.com/scholar?cluster=15951000887589486103&hl=en&as_sdt=0,5 | 3 | 2,020 |
WoodFisher: Efficient Second-Order Approximation for Neural Network Compression | 90 | neurips | 4 | 0 | 2023-06-16 15:11:58.016000 | https://github.com/IST-DASLab/WoodFisher | 40 | Woodfisher: Efficient second-order approximation for neural network compression | https://scholar.google.com/scholar?cluster=10333842317237774040&hl=en&as_sdt=0,5 | 8 | 2,020 |
Bi-level Score Matching for Learning Energy-based Latent Variable Models | 12 | neurips | 2 | 0 | 2023-06-16 15:11:58.209000 | https://github.com/baofff/BiSM | 11 | Bi-level score matching for learning energy-based latent variable models | https://scholar.google.com/scholar?cluster=17042861642132917683&hl=en&as_sdt=0,15 | 1 | 2,020 |
Decision trees as partitioning machines to characterize their generalization properties | 10 | neurips | 0 | 0 | 2023-06-16 15:11:58.402000 | https://github.com/jsleb333/paper-decision-trees-as-partitioning-machines | 2 | Decision trees as partitioning machines to characterize their generalization properties | https://scholar.google.com/scholar?cluster=8941851754954952752&hl=en&as_sdt=0,5 | 2 | 2,020 |
Learning to Prove Theorems by Learning to Generate Theorems | 19 | neurips | 2 | 4 | 2023-06-16 15:11:58.595000 | https://github.com/princeton-vl/MetaGen | 22 | Learning to prove theorems by learning to generate theorems | https://scholar.google.com/scholar?cluster=6712350260601158611&hl=en&as_sdt=0,1 | 4 | 2,020 |
3D Self-Supervised Methods for Medical Imaging | 129 | neurips | 38 | 1 | 2023-06-16 15:11:58.788000 | https://github.com/HealthML/self-supervised-3d-tasks | 175 | 3d self-supervised methods for medical imaging | https://scholar.google.com/scholar?cluster=9530893768928591494&hl=en&as_sdt=0,5 | 14 | 2,020 |
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods | 15 | neurips | 0 | 0 | 2023-06-16 15:11:58.981000 | https://github.com/LaurenceA/adabayes | 1 | Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods | https://scholar.google.com/scholar?cluster=1727499068879761795&hl=en&as_sdt=0,5 | 2 | 2,020 |
Worst-Case Analysis for Randomly Collected Data | 3 | neurips | 2 | 0 | 2023-06-16 15:11:59.174000 | https://github.com/justc2/worst-case-randomly-collected | 3 | Worst-case analysis for randomly collected data | https://scholar.google.com/scholar?cluster=5223589641836641973&hl=en&as_sdt=0,32 | 1 | 2,020 |
Byzantine Resilient Distributed Multi-Task Learning | 7 | neurips | 3 | 0 | 2023-06-16 15:11:59.367000 | https://github.com/JianiLi/resilientDistributedMTL | 8 | Byzantine resilient distributed multi-task learning | https://scholar.google.com/scholar?cluster=2493973977655145797&hl=en&as_sdt=0,33 | 2 | 2,020 |
Improving model calibration with accuracy versus uncertainty optimization | 90 | neurips | 10 | 0 | 2023-06-16 15:11:59.559000 | https://github.com/IntelLabs/AVUC | 42 | Improving model calibration with accuracy versus uncertainty optimization | https://scholar.google.com/scholar?cluster=6764629857380442008&hl=en&as_sdt=0,5 | 10 | 2,020 |
The Convolution Exponential and Generalized Sylvester Flows | 25 | neurips | 3 | 0 | 2023-06-16 15:11:59.751000 | https://github.com/ehoogeboom/convolution_exponential_and_sylvester | 29 | The convolution exponential and generalized sylvester flows | https://scholar.google.com/scholar?cluster=17016423652429713457&hl=en&as_sdt=0,26 | 3 | 2,020 |
The MAGICAL Benchmark for Robust Imitation | 34 | neurips | 9 | 1 | 2023-06-16 15:11:59.945000 | https://github.com/qxcv/magical | 65 | The magical benchmark for robust imitation | https://scholar.google.com/scholar?cluster=1590548379851528188&hl=en&as_sdt=0,31 | 6 | 2,020 |
X-CAL: Explicit Calibration for Survival Analysis | 21 | neurips | 2 | 1 | 2023-06-16 15:12:00.138000 | https://github.com/rajesh-lab/X-CAL | 10 | X-cal: Explicit calibration for survival analysis | https://scholar.google.com/scholar?cluster=2990043349435495022&hl=en&as_sdt=0,5 | 4 | 2,020 |
BERT Loses Patience: Fast and Robust Inference with Early Exit | 153 | neurips | 6 | 3 | 2023-06-16 15:12:00.333000 | https://github.com/JetRunner/PABEE | 57 | Bert loses patience: Fast and robust inference with early exit | https://scholar.google.com/scholar?cluster=4686936952101505814&hl=en&as_sdt=0,33 | 5 | 2,020 |
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning | 65 | neurips | 5 | 1 | 2023-06-16 15:12:00.525000 | https://github.com/lanyavik/BAIL | 15 | BAIL: Best-action imitation learning for batch deep reinforcement learning | https://scholar.google.com/scholar?cluster=11856041909374113565&hl=en&as_sdt=0,5 | 2 | 2,020 |
What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes | 19 | neurips | 0 | 0 | 2023-06-16 15:12:00.718000 | https://github.com/hmhyau/rl-intention | 5 | What did you think would happen? explaining agent behaviour through intended outcomes | https://scholar.google.com/scholar?cluster=11580344209780119679&hl=en&as_sdt=0,46 | 2 | 2,020 |
What if Neural Networks had SVDs? | 4 | neurips | 9 | 1 | 2023-06-16 15:12:00.911000 | https://github.com/AlexanderMath/fasth | 65 | What if neural networks had SVDs? | https://scholar.google.com/scholar?cluster=721216332172545219&hl=en&as_sdt=0,15 | 4 | 2,020 |
CoMIR: Contrastive Multimodal Image Representation for Registration | 44 | neurips | 10 | 4 | 2023-06-16 15:12:01.103000 | https://github.com/MIDA-group/CoMIR | 61 | CoMIR: Contrastive multimodal image representation for registration | https://scholar.google.com/scholar?cluster=5281972989603667847&hl=en&as_sdt=0,19 | 8 | 2,020 |
How do fair decisions fare in long-term qualification? | 46 | neurips | 1 | 0 | 2023-06-16 15:12:01.295000 | https://github.com/TURuibo/long-term-impact-of-fairness-constraints | 4 | How do fair decisions fare in long-term qualification? | https://scholar.google.com/scholar?cluster=6407521976837665673&hl=en&as_sdt=0,37 | 3 | 2,020 |
Measuring Robustness to Natural Distribution Shifts in Image Classification | 327 | neurips | 5 | 1 | 2023-06-16 15:12:01.488000 | https://github.com/modestyachts/imagenet-testbed | 92 | Measuring robustness to natural distribution shifts in image classification | https://scholar.google.com/scholar?cluster=3019171535172049328&hl=en&as_sdt=0,5 | 9 | 2,020 |
Learning Optimal Representations with the Decodable Information Bottleneck | 27 | neurips | 2 | 0 | 2023-06-16 15:12:01.681000 | https://github.com/YannDubs/Mini_Decodable_Information_Bottleneck | 8 | Learning optimal representations with the decodable information bottleneck | https://scholar.google.com/scholar?cluster=17923868091696998967&hl=en&as_sdt=0,47 | 2 | 2,020 |
Neural Non-Rigid Tracking | 30 | neurips | 35 | 3 | 2023-06-16 15:12:01.873000 | https://github.com/DeformableFriends/NeuralTracking | 172 | Neural non-rigid tracking | https://scholar.google.com/scholar?cluster=15233540047338923816&hl=en&as_sdt=0,5 | 6 | 2,020 |
ICNet: Intra-saliency Correlation Network for Co-Saliency Detection | 43 | neurips | 3 | 1 | 2023-06-16 15:12:02.066000 | https://github.com/blanclist/ICNet | 27 | Icnet: Intra-saliency correlation network for co-saliency detection | https://scholar.google.com/scholar?cluster=7463846021499911806&hl=en&as_sdt=0,23 | 4 | 2,020 |
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows | 13 | neurips | 5 | 0 | 2023-06-16 15:12:02.259000 | https://github.com/zcrabbit/vbpi-nf | 5 | Improved variational Bayesian phylogenetic inference with normalizing flows | https://scholar.google.com/scholar?cluster=5113994271918913106&hl=en&as_sdt=0,15 | 1 | 2,020 |
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients | 349 | neurips | 109 | 6 | 2023-06-16 15:12:02.451000 | https://github.com/juntang-zhuang/Adabelief-Optimizer | 1,021 | Adabelief optimizer: Adapting stepsizes by the belief in observed gradients | https://scholar.google.com/scholar?cluster=794903835077311857&hl=en&as_sdt=0,23 | 21 | 2,020 |
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding | 44 | neurips | 0 | 1 | 2023-06-16 15:12:02.645000 | https://github.com/StanfordAI4HI/off_policy_confounding | 3 | Off-policy policy evaluation for sequential decisions under unobserved confounding | https://scholar.google.com/scholar?cluster=7361110146120594119&hl=en&as_sdt=0,33 | 4 | 2,020 |
Modern Hopfield Networks and Attention for Immune Repertoire Classification | 68 | neurips | 20 | 2 | 2023-06-16 15:12:02.838000 | https://github.com/ml-jku/DeepRC | 93 | Modern hopfield networks and attention for immune repertoire classification | https://scholar.google.com/scholar?cluster=10816753582099343978&hl=en&as_sdt=0,33 | 10 | 2,020 |
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers | 29 | neurips | 14 | 0 | 2023-06-16 15:12:03.031000 | https://github.com/MIT-SPARK/CertifiablyRobustPerception | 91 | One ring to rule them all: Certifiably robust geometric perception with outliers | https://scholar.google.com/scholar?cluster=4069822237780378965&hl=en&as_sdt=0,33 | 9 | 2,020 |
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks | 13 | neurips | 4 | 0 | 2023-06-16 15:12:03.224000 | https://github.com/delta2323/GB-GNN | 12 | Optimization and generalization analysis of transduction through gradient boosting and application to multi-scale graph neural networks | https://scholar.google.com/scholar?cluster=4267488543735531510&hl=en&as_sdt=0,47 | 3 | 2,020 |
Experimental design for MRI by greedy policy search | 26 | neurips | 5 | 0 | 2023-06-16 15:12:03.417000 | https://github.com/Timsey/pg_mri | 20 | Experimental design for MRI by greedy policy search | https://scholar.google.com/scholar?cluster=15235565020311490673&hl=en&as_sdt=0,34 | 4 | 2,020 |
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation | 17 | neurips | 8 | 1 | 2023-06-16 15:12:03.611000 | https://github.com/asonabend/ESRL | 7 | Expert-supervised reinforcement learning for offline policy learning and evaluation | https://scholar.google.com/scholar?cluster=16131210561518100341&hl=en&as_sdt=0,5 | 4 | 2,020 |
Time-Reversal Symmetric ODE Network | 18 | neurips | 1 | 0 | 2023-06-16 15:12:03.804000 | https://github.com/inhuh/trs-oden | 6 | Time-reversal symmetric ode network | https://scholar.google.com/scholar?cluster=4037950341179248560&hl=en&as_sdt=0,5 | 2 | 2,020 |
Fast Unbalanced Optimal Transport on a Tree | 21 | neurips | 0 | 0 | 2023-06-16 15:12:03.998000 | https://github.com/joisino/treegkr | 11 | Fast unbalanced optimal transport on a tree | https://scholar.google.com/scholar?cluster=14959154682905354615&hl=en&as_sdt=0,5 | 3 | 2,020 |
Handling Missing Data with Graph Representation Learning | 90 | neurips | 27 | 7 | 2023-06-16 15:12:04.193000 | https://github.com/maxiaoba/GRAPE | 109 | Handling missing data with graph representation learning | https://scholar.google.com/scholar?cluster=3645976030445533910&hl=en&as_sdt=0,5 | 2 | 2,020 |
Improving Auto-Augment via Augmentation-Wise Weight Sharing | 28 | neurips | 10 | 1 | 2023-06-16 15:12:04.385000 | https://github.com/Awesome-AutoAug-Algorithms/AWS-OHL-AutoAug | 46 | Improving auto-augment via augmentation-wise weight sharing | https://scholar.google.com/scholar?cluster=7360656205039027560&hl=en&as_sdt=0,47 | 6 | 2,020 |
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles | 9 | neurips | 2 | 0 | 2023-06-16 15:12:04.577000 | https://github.com/wznpub/MMA_Regularization | 10 | MMA regularization: Decorrelating weights of neural networks by maximizing the minimal angles | https://scholar.google.com/scholar?cluster=5540242986881962415&hl=en&as_sdt=0,5 | 1 | 2,020 |
HRN: A Holistic Approach to One Class Learning | 29 | neurips | 4 | 1 | 2023-06-16 15:12:04.769000 | https://github.com/morning-dews/HRN | 15 | Hrn: A holistic approach to one class learning | https://scholar.google.com/scholar?cluster=6301247389765291961&hl=en&as_sdt=0,31 | 1 | 2,020 |
Modeling Shared responses in Neuroimaging Studies through MultiView ICA | 18 | neurips | 3 | 1 | 2023-06-16 15:12:04.962000 | https://github.com/hugorichard/multiviewica | 24 | Modeling shared responses in neuroimaging studies through multiview ica | https://scholar.google.com/scholar?cluster=367202636846206154&hl=en&as_sdt=0,5 | 3 | 2,020 |
Efficient Learning of Generative Models via Finite-Difference Score Matching | 30 | neurips | 3 | 6 | 2023-06-16 15:12:05.154000 | https://github.com/taufikxu/FD-ScoreMatching | 11 | Efficient learning of generative models via finite-difference score matching | https://scholar.google.com/scholar?cluster=378107545503683177&hl=en&as_sdt=0,22 | 3 | 2,020 |
BayReL: Bayesian Relational Learning for Multi-omics Data Integration | 6 | neurips | 2 | 0 | 2023-06-16 15:12:05.347000 | https://github.com/ehsanhajiramezanali/BayReL | 5 | BayReL: Bayesian relational learning for multi-omics data integration | https://scholar.google.com/scholar?cluster=8576961726337855853&hl=en&as_sdt=0,33 | 1 | 2,020 |
Weakly Supervised Deep Functional Maps for Shape Matching | 38 | neurips | 4 | 2 | 2023-06-16 15:12:05.539000 | https://github.com/Not-IITian/Weakly-supervised-Functional-map | 23 | Weakly supervised deep functional maps for shape matching | https://scholar.google.com/scholar?cluster=10860093597681931185&hl=en&as_sdt=0,44 | 4 | 2,020 |
Rethinking the Value of Labels for Improving Class-Imbalanced Learning | 258 | neurips | 113 | 4 | 2023-06-16 15:12:05.731000 | https://github.com/YyzHarry/imbalanced-semi-self | 690 | Rethinking the value of labels for improving class-imbalanced learning | https://scholar.google.com/scholar?cluster=272061710147272859&hl=en&as_sdt=0,5 | 14 | 2,020 |
Provably Robust Metric Learning | 4 | neurips | 1 | 0 | 2023-06-16 15:12:05.924000 | https://github.com/wangwllu/provably_robust_metric_learning | 9 | Provably robust metric learning | https://scholar.google.com/scholar?cluster=13877432189650792111&hl=en&as_sdt=0,3 | 1 | 2,020 |
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings | 218 | neurips | 28 | 2 | 2023-06-16 15:12:06.117000 | https://github.com/hugochan/IDGL | 189 | Iterative deep graph learning for graph neural networks: Better and robust node embeddings | https://scholar.google.com/scholar?cluster=9442254169180194337&hl=en&as_sdt=0,39 | 8 | 2,020 |
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems | 111 | neurips | 16 | 2 | 2023-06-16 15:12:06.310000 | https://github.com/HazyResearch/hidden-stratification | 52 | No subclass left behind: Fine-grained robustness in coarse-grained classification problems | https://scholar.google.com/scholar?cluster=10068670017880921815&hl=en&as_sdt=0,41 | 18 | 2,020 |
Self-Adaptive Training: beyond Empirical Risk Minimization | 134 | neurips | 23 | 0 | 2023-06-16 15:12:06.526000 | https://github.com/LayneH/self-adaptive-training | 122 | Self-adaptive training: beyond empirical risk minimization | https://scholar.google.com/scholar?cluster=8932486507160067341&hl=en&as_sdt=0,5 | 4 | 2,020 |
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement | 96 | neurips | 44 | 4 | 2023-06-16 15:12:06.719000 | https://github.com/xin71/MTTS-CAN | 129 | Multi-task temporal shift attention networks for on-device contactless vitals measurement | https://scholar.google.com/scholar?cluster=9152870442516577713&hl=en&as_sdt=0,14 | 7 | 2,020 |
TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation | 3 | neurips | 3 | 2 | 2023-06-16 15:12:06.911000 | https://github.com/MiuLab/TaylorGAN | 31 | TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation | https://scholar.google.com/scholar?cluster=13902671358077823170&hl=en&as_sdt=0,34 | 9 | 2,020 |
Dual-Free Stochastic Decentralized Optimization with Variance Reduction | 26 | neurips | 0 | 0 | 2023-06-16 15:12:07.104000 | https://github.com/HadrienHx/DVR_NeurIPS | 1 | Dual-free stochastic decentralized optimization with variance reduction | https://scholar.google.com/scholar?cluster=10047292317729943616&hl=en&as_sdt=0,14 | 1 | 2,020 |
Throughput-Optimal Topology Design for Cross-Silo Federated Learning | 53 | neurips | 7 | 2 | 2023-06-16 15:12:07.298000 | https://github.com/omarfoq/communication-in-cross-silo-fl | 25 | Throughput-optimal topology design for cross-silo federated learning | https://scholar.google.com/scholar?cluster=8109752902275871461&hl=en&as_sdt=0,26 | 0 | 2,020 |
Quantized Variational Inference | 1 | neurips | 0 | 0 | 2023-06-16 15:12:07.492000 | https://github.com/amirdib/quantized-variational-inference | 1 | Quantized variational inference | https://scholar.google.com/scholar?cluster=8568625166316224952&hl=en&as_sdt=0,14 | 2 | 2,020 |
Asymptotically Optimal Exact Minibatch Metropolis-Hastings | 15 | neurips | 0 | 0 | 2023-06-16 15:12:07.685000 | https://github.com/ruqizhang/tunamh | 2 | Asymptotically optimal exact minibatch metropolis-hastings | https://scholar.google.com/scholar?cluster=3007609299912938607&hl=en&as_sdt=0,44 | 2 | 2,020 |
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search | 69 | neurips | 66 | 6 | 2023-06-16 15:12:07.886000 | https://github.com/facebookresearch/LaMCTS | 409 | Learning search space partition for black-box optimization using monte carlo tree search | https://scholar.google.com/scholar?cluster=9187963788424431133&hl=en&as_sdt=0,34 | 18 | 2,020 |
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks | 33 | neurips | 3 | 1 | 2023-06-16 15:12:08.096000 | https://github.com/KyungsuKim42/ANTLR | 15 | Unifying activation-and timing-based learning rules for spiking neural networks | https://scholar.google.com/scholar?cluster=10358457347608277973&hl=en&as_sdt=0,14 | 2 | 2,020 |
Space-Time Correspondence as a Contrastive Random Walk | 169 | neurips | 36 | 10 | 2023-06-16 15:12:08.288000 | https://github.com/ajabri/videowalk | 254 | Space-time correspondence as a contrastive random walk | https://scholar.google.com/scholar?cluster=9614996608688836578&hl=en&as_sdt=0,32 | 20 | 2,020 |
An Efficient Framework for Clustered Federated Learning | 368 | neurips | 22 | 1 | 2023-06-16 15:12:08.483000 | https://github.com/jichan3751/ifca | 77 | An efficient framework for clustered federated learning | https://scholar.google.com/scholar?cluster=351619806118785755&hl=en&as_sdt=0,34 | 2 | 2,020 |
Autoencoders that don't overfit towards the Identity | 40 | neurips | 4 | 1 | 2023-06-16 15:12:08.676000 | https://github.com/hasteck/EDLAE_NeurIPS2020 | 11 | Autoencoders that don't overfit towards the identity | https://scholar.google.com/scholar?cluster=14138077155025649539&hl=en&as_sdt=0,35 | 1 | 2,020 |
Parameterized Explainer for Graph Neural Network | 258 | neurips | 13 | 2 | 2023-06-16 15:12:08.869000 | https://github.com/flyingdoog/PGExplainer | 102 | Parameterized explainer for graph neural network | https://scholar.google.com/scholar?cluster=17322495705735423565&hl=en&as_sdt=0,22 | 5 | 2,020 |
Flexible mean field variational inference using mixtures of non-overlapping exponential families | 3 | neurips | 0 | 0 | 2023-06-16 15:12:09.062000 | https://github.com/jeffspence/non_overlapping_mixtures | 1 | Flexible mean field variational inference using mixtures of non-overlapping exponential families | https://scholar.google.com/scholar?cluster=3380676252436682174&hl=en&as_sdt=0,33 | 1 | 2,020 |
HYDRA: Pruning Adversarially Robust Neural Networks | 142 | neurips | 19 | 2 | 2023-06-16 15:12:09.254000 | https://github.com/inspire-group/compactness-robustness | 85 | Hydra: Pruning adversarially robust neural networks | https://scholar.google.com/scholar?cluster=11257797302923322781&hl=en&as_sdt=0,5 | 6 | 2,020 |
NVAE: A Deep Hierarchical Variational Autoencoder | 524 | neurips | 148 | 27 | 2023-06-16 15:12:09.447000 | https://github.com/NVlabs/NVAE | 882 | NVAE: A deep hierarchical variational autoencoder | https://scholar.google.com/scholar?cluster=9419654938449434940&hl=en&as_sdt=0,41 | 17 | 2,020 |
Learning Disentangled Representations and Group Structure of Dynamical Environments | 30 | neurips | 3 | 4 | 2023-06-16 15:12:09.640000 | https://github.com/IndustAI/learning-group-structure | 13 | Learning disentangled representations and group structure of dynamical environments | https://scholar.google.com/scholar?cluster=1554847643319320473&hl=en&as_sdt=0,5 | 3 | 2,020 |
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models | 4 | neurips | 1 | 0 | 2023-06-16 15:12:09.836000 | https://github.com/christa60/dynens | 3 | Wisdom of the ensemble: Improving consistency of deep learning models | https://scholar.google.com/scholar?cluster=5672422435437063522&hl=en&as_sdt=0,34 | 1 | 2,020 |
Universal Function Approximation on Graphs | 5 | neurips | 1 | 0 | 2023-06-16 15:12:10.029000 | https://github.com/bruel-gabrielsson/universal-function-approximation-on-graphs | 10 | Universal function approximation on graphs | https://scholar.google.com/scholar?cluster=10884321580328108356&hl=en&as_sdt=0,21 | 1 | 2,020 |
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