title
stringlengths 8
155
| citations_google_scholar
int64 0
28.9k
| conference
stringclasses 5
values | forks
int64 0
46.3k
| issues
int64 0
12.2k
| lastModified
stringlengths 19
26
| repo_url
stringlengths 26
130
| stars
int64 0
75.9k
| title_google_scholar
stringlengths 8
155
| url_google_scholar
stringlengths 75
206
| watchers
int64 0
2.77k
| year
int64 2.02k
2.02k
|
|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.