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Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
| 64 |
neurips
| 0 | 0 |
2023-06-15 23:43:47.494000
|
https://github.com/SuReLI/rats-experiments
| 3 |
Non-stationary Markov decision processes, a worst-case approach using model-based reinforcement learning
|
https://scholar.google.com/scholar?cluster=6196292218607210922&hl=en&as_sdt=0,5
| 3 | 2,019 |
Optimal Decision Tree with Noisy Outcomes
| 9 |
neurips
| 1 | 0 |
2023-06-15 23:43:47.676000
|
https://github.com/sjia1/ODT-with-noisy-outcomes
| 0 |
Optimal decision tree with noisy outcomes
|
https://scholar.google.com/scholar?cluster=13675004134696566292&hl=en&as_sdt=0,33
| 1 | 2,019 |
Continual Unsupervised Representation Learning
| 211 |
neurips
| 2,436 | 170 |
2023-06-15 23:43:47.859000
|
https://github.com/deepmind/deepmind-research
| 11,902 |
Continual unsupervised representation learning
|
https://scholar.google.com/scholar?cluster=16358329377631529922&hl=en&as_sdt=0,14
| 336 | 2,019 |
Multiple Futures Prediction
| 279 |
neurips
| 27 | 5 |
2023-06-15 23:43:48.042000
|
https://github.com/apple/ml-multiple-futures-prediction
| 115 |
Multiple futures prediction
|
https://scholar.google.com/scholar?cluster=13314964675169531830&hl=en&as_sdt=0,5
| 19 | 2,019 |
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
| 32 |
neurips
| 7 | 2 |
2023-06-15 23:43:48.224000
|
https://github.com/drsrinathsridhar/xnocs
| 35 |
Multiview aggregation for learning category-specific shape reconstruction
|
https://scholar.google.com/scholar?cluster=6464092641166867923&hl=en&as_sdt=0,5
| 6 | 2,019 |
Reinforcement Learning with Convex Constraints
| 85 |
neurips
| 8 | 0 |
2023-06-15 23:43:48.407000
|
https://github.com/xkianteb/ApproPO
| 13 |
Reinforcement learning with convex constraints
|
https://scholar.google.com/scholar?cluster=17753055761505168493&hl=en&as_sdt=0,5
| 3 | 2,019 |
Learning Hawkes Processes from a handful of events
| 29 |
neurips
| 8 | 1 |
2023-06-15 23:43:48.589000
|
https://github.com/trouleau/var-hawkes
| 7 |
Learning hawkes processes from a handful of events
|
https://scholar.google.com/scholar?cluster=4846579627142993040&hl=en&as_sdt=0,3
| 1 | 2,019 |
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation
| 68 |
neurips
| 25 | 14 |
2023-06-15 23:43:48.771000
|
https://github.com/nrgeup/controllable-text-attribute-transfer
| 130 |
Controllable unsupervised text attribute transfer via editing entangled latent representation
|
https://scholar.google.com/scholar?cluster=6509221759724074439&hl=en&as_sdt=0,19
| 7 | 2,019 |
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller
| 64 |
neurips
| 5 | 1 |
2023-06-15 23:43:48.954000
|
https://github.com/pathak22/hierarchical-imitation
| 54 |
Third-person visual imitation learning via decoupled hierarchical controller
|
https://scholar.google.com/scholar?cluster=1152601165924877882&hl=en&as_sdt=0,25
| 7 | 2,019 |
Connective Cognition Network for Directional Visual Commonsense Reasoning
| 30 |
neurips
| 7 | 3 |
2023-06-15 23:43:49.136000
|
https://github.com/AmingWu/CCN
| 15 |
Connective cognition network for directional visual commonsense reasoning
|
https://scholar.google.com/scholar?cluster=10868299947293549232&hl=en&as_sdt=0,3
| 3 | 2,019 |
Discriminator optimal transport
| 45 |
neurips
| 3 | 0 |
2023-06-15 23:43:49.319000
|
https://github.com/AkinoriTanaka-phys/DOT
| 13 |
Discriminator optimal transport
|
https://scholar.google.com/scholar?cluster=18026540846498142859&hl=en&as_sdt=0,5
| 5 | 2,019 |
Sequential Experimental Design for Transductive Linear Bandits
| 86 |
neurips
| 0 | 0 |
2023-06-15 23:43:49.501000
|
https://github.com/fiezt/Transductive-Linear-Bandit-Code
| 2 |
Sequential experimental design for transductive linear bandits
|
https://scholar.google.com/scholar?cluster=12964128858664596570&hl=en&as_sdt=0,47
| 1 | 2,019 |
End to end learning and optimization on graphs
| 68 |
neurips
| 19 | 2 |
2023-06-15 23:43:49.684000
|
https://github.com/bwilder0/clusternet
| 77 |
End to end learning and optimization on graphs
|
https://scholar.google.com/scholar?cluster=2313073977352706710&hl=en&as_sdt=0,5
| 5 | 2,019 |
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
| 224 |
neurips
| 2 | 0 |
2023-06-15 23:43:49.867000
|
https://github.com/dirichletcal/dirichletcal.github.io
| 6 |
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration
|
https://scholar.google.com/scholar?cluster=8575384251894434874&hl=en&as_sdt=0,31
| 4 | 2,019 |
Curvilinear Distance Metric Learning
| 21 |
neurips
| 0 | 0 |
2023-06-15 23:43:50.049000
|
https://github.com/functioncs/CDML
| 0 |
Curvilinear distance metric learning
|
https://scholar.google.com/scholar?cluster=11833570111330207293&hl=en&as_sdt=0,36
| 1 | 2,019 |
Sampling Networks and Aggregate Simulation for Online POMDP Planning
| 2 |
neurips
| 0 | 0 |
2023-06-15 23:43:50.232000
|
https://github.com/hcui01/SNAP
| 3 |
Sampling networks and aggregate simulation for online pomdp planning
|
https://scholar.google.com/scholar?cluster=12398643923867979827&hl=en&as_sdt=0,5
| 3 | 2,019 |
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
| 104 |
neurips
| 30 | 2 |
2023-06-15 23:43:50.414000
|
https://github.com/google/bi-tempered-loss
| 142 |
Robust bi-tempered logistic loss based on bregman divergences
|
https://scholar.google.com/scholar?cluster=4731664592680946460&hl=en&as_sdt=0,5
| 10 | 2,019 |
Noise-tolerant fair classification
| 60 |
neurips
| 1 | 0 |
2023-06-15 23:43:50.596000
|
https://github.com/AIasd/noise_fairlearn
| 5 |
Noise-tolerant fair classification
|
https://scholar.google.com/scholar?cluster=11272640623843823996&hl=en&as_sdt=0,39
| 4 | 2,019 |
Saccader: Improving Accuracy of Hard Attention Models for Vision
| 64 |
neurips
| 7,320 | 1,025 |
2023-06-15 23:43:50.789000
|
https://github.com/google-research/google-research
| 29,776 |
Saccader: Improving accuracy of hard attention models for vision
|
https://scholar.google.com/scholar?cluster=6992264138718311127&hl=en&as_sdt=0,18
| 727 | 2,019 |
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
| 30 |
neurips
| 21 | 2 |
2023-06-15 23:43:50.971000
|
https://github.com/zhou13/neurvps
| 150 |
Neurvps: Neural vanishing point scanning via conic convolution
|
https://scholar.google.com/scholar?cluster=3031823208555509253&hl=en&as_sdt=0,30
| 10 | 2,019 |
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
| 14 |
neurips
| 1 | 0 |
2023-06-15 23:43:51.157000
|
https://github.com/noc-lab/Select-Optimal-Decisions-via-DRO-KNN
| 5 |
Selecting optimal decisions via distributionally robust nearest-neighbor regression
|
https://scholar.google.com/scholar?cluster=16020986183708685814&hl=en&as_sdt=0,5
| 2 | 2,019 |
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
| 47 |
neurips
| 10 | 2 |
2023-06-15 23:43:51.340000
|
https://github.com/fly519/ELGS
| 50 |
Exploiting local and global structure for point cloud semantic segmentation with contextual point representations
|
https://scholar.google.com/scholar?cluster=17515136600424535326&hl=en&as_sdt=0,5
| 4 | 2,019 |
Heterogeneous Graph Learning for Visual Commonsense Reasoning
| 41 |
neurips
| 14 | 4 |
2023-06-15 23:43:51.522000
|
https://github.com/yuweijiang/HGL-pytorch
| 46 |
Heterogeneous graph learning for visual commonsense reasoning
|
https://scholar.google.com/scholar?cluster=1264363257779833283&hl=en&as_sdt=0,47
| 7 | 2,019 |
Memory Efficient Adaptive Optimization
| 36 |
neurips
| 7,320 | 1,025 |
2023-06-15 23:43:51.705000
|
https://github.com/google-research/google-research
| 29,776 |
Memory efficient adaptive optimization
|
https://scholar.google.com/scholar?cluster=4548335888639667869&hl=en&as_sdt=0,33
| 727 | 2,019 |
Conformal Prediction Under Covariate Shift
| 165 |
neurips
| 46 | 10 |
2023-06-15 23:43:51.888000
|
https://github.com/ryantibs/conformal
| 177 |
Conformal prediction under covariate shift
|
https://scholar.google.com/scholar?cluster=6789636313624066732&hl=en&as_sdt=0,3
| 17 | 2,019 |
Adapting Neural Networks for the Estimation of Treatment Effects
| 221 |
neurips
| 44 | 2 |
2023-06-15 23:43:52.071000
|
https://github.com/claudiashi57/dragonnet
| 190 |
Adapting neural networks for the estimation of treatment effects
|
https://scholar.google.com/scholar?cluster=3867091808295935282&hl=en&as_sdt=0,5
| 8 | 2,019 |
Optimal Sampling and Clustering in the Stochastic Block Model
| 5 |
neurips
| 0 | 0 |
2023-06-15 23:43:52.253000
|
https://github.com/fbsqkd/StochasticBlockModel
| 0 |
Optimal sampling and clustering in the stochastic block model
|
https://scholar.google.com/scholar?cluster=16411279302020087962&hl=en&as_sdt=0,1
| 1 | 2,019 |
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time
| 13 |
neurips
| 2 | 0 |
2023-06-15 23:43:52.435000
|
https://github.com/LUMII-Syslab/shuffle-exchange
| 9 |
Neural shuffle-exchange networks-sequence processing in o (n log n) time
|
https://scholar.google.com/scholar?cluster=16640163416880839372&hl=en&as_sdt=0,33
| 13 | 2,019 |
Markov Random Fields for Collaborative Filtering
| 22 |
neurips
| 2 | 1 |
2023-06-15 23:43:52.617000
|
https://github.com/hasteck/MRF_NeurIPS_2019
| 19 |
Markov random fields for collaborative filtering
|
https://scholar.google.com/scholar?cluster=17117745531500946052&hl=en&as_sdt=0,21
| 2 | 2,019 |
Structured Graph Learning Via Laplacian Spectral Constraints
| 47 |
neurips
| 0 | 0 |
2023-06-15 23:43:52.800000
|
https://github.com/dppalomar/spectralGraphTopology
| 0 |
Structured graph learning via Laplacian spectral constraints
|
https://scholar.google.com/scholar?cluster=8868297779776898800&hl=en&as_sdt=0,5
| 0 | 2,019 |
Lookahead Optimizer: k steps forward, 1 step back
| 581 |
neurips
| 27 | 0 |
2023-06-15 23:43:52.982000
|
https://github.com/michaelrzhang/lookahead
| 217 |
Lookahead optimizer: k steps forward, 1 step back
|
https://scholar.google.com/scholar?cluster=2599504418931364355&hl=en&as_sdt=0,5
| 9 | 2,019 |
Finding Friend and Foe in Multi-Agent Games
| 37 |
neurips
| 5 | 7 |
2023-06-15 23:43:53.165000
|
https://github.com/Detry322/DeepRole
| 27 |
Finding friend and foe in multi-agent games
|
https://scholar.google.com/scholar?cluster=16486277193316870849&hl=en&as_sdt=0,33
| 1 | 2,019 |
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
| 191 |
neurips
| 16 | 5 |
2023-06-15 23:43:53.348000
|
https://github.com/acbull/LADIES
| 73 |
Layer-dependent importance sampling for training deep and large graph convolutional networks
|
https://scholar.google.com/scholar?cluster=8927879978865662944&hl=en&as_sdt=0,37
| 6 | 2,019 |
Self-Supervised Generalisation with Meta Auxiliary Learning
| 125 |
neurips
| 28 | 0 |
2023-06-15 23:43:53.530000
|
https://github.com/lorenmt/maxl
| 161 |
Self-supervised generalisation with meta auxiliary learning
|
https://scholar.google.com/scholar?cluster=18242502085163121025&hl=en&as_sdt=0,44
| 7 | 2,019 |
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum
| 60 |
neurips
| 23 | 0 |
2023-06-15 23:43:53.713000
|
https://github.com/apple/ml-data-parameters
| 70 |
Data parameters: A new family of parameters for learning a differentiable curriculum
|
https://scholar.google.com/scholar?cluster=6678746522052000465&hl=en&as_sdt=0,23
| 16 | 2,019 |
One-Shot Object Detection with Co-Attention and Co-Excitation
| 144 |
neurips
| 77 | 21 |
2023-06-15 23:43:53.895000
|
https://github.com/timy90022/One-Shot-Object-Detection
| 399 |
One-shot object detection with co-attention and co-excitation
|
https://scholar.google.com/scholar?cluster=5705545859762971669&hl=en&as_sdt=0,5
| 16 | 2,019 |
Are Anchor Points Really Indispensable in Label-Noise Learning?
| 238 |
neurips
| 19 | 1 |
2023-06-15 23:43:54.077000
|
https://github.com/xiaoboxia/T-Revision
| 89 |
Are anchor points really indispensable in label-noise learning?
|
https://scholar.google.com/scholar?cluster=13091313467127090506&hl=en&as_sdt=0,43
| 6 | 2,019 |
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
| 62 |
neurips
| 6 | 2 |
2023-06-15 23:43:54.260000
|
https://github.com/ArchipLab-LinfengZhang/pytorch-scalable-neural-networks
| 23 |
Scan: A scalable neural networks framework towards compact and efficient models
|
https://scholar.google.com/scholar?cluster=5724917370843261685&hl=en&as_sdt=0,5
| 4 | 2,019 |
Smoothing Structured Decomposable Circuits
| 17 |
neurips
| 1 | 0 |
2023-06-15 23:43:54.443000
|
https://github.com/AndyShih12/SSDC
| 6 |
Smoothing structured decomposable circuits
|
https://scholar.google.com/scholar?cluster=13215158158274197353&hl=en&as_sdt=0,5
| 1 | 2,019 |
Bayesian Joint Estimation of Multiple Graphical Models
| 22 |
neurips
| 0 | 0 |
2023-06-15 23:43:54.626000
|
https://github.com/xinming104/GemBag
| 1 |
Bayesian joint estimation of multiple graphical models
|
https://scholar.google.com/scholar?cluster=7666126448972292722&hl=en&as_sdt=0,21
| 1 | 2,019 |
Maximum Mean Discrepancy Gradient Flow
| 98 |
neurips
| 3 | 1 |
2023-06-15 23:43:54.808000
|
https://github.com/MichaelArbel/MMD-gradient-flow
| 6 |
Maximum mean discrepancy gradient flow
|
https://scholar.google.com/scholar?cluster=613411100718118562&hl=en&as_sdt=0,3
| 2 | 2,019 |
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
| 154 |
neurips
| 0 | 2 |
2023-06-15 23:43:54.990000
|
https://github.com/xbpeng/mcp
| 10 |
Mcp: Learning composable hierarchical control with multiplicative compositional policies
|
https://scholar.google.com/scholar?cluster=12493399866748517630&hl=en&as_sdt=0,5
| 13 | 2,019 |
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
| 122 |
neurips
| 30 | 17 |
2023-06-15 23:43:55.173000
|
https://github.com/abr/neurips2019
| 196 |
Legendre memory units: Continuous-time representation in recurrent neural networks
|
https://scholar.google.com/scholar?cluster=12694102422873016624&hl=en&as_sdt=0,18
| 23 | 2,019 |
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
| 393 |
neurips
| 52 | 1 |
2023-06-15 23:43:55.355000
|
https://github.com/BlackHC/BatchBALD
| 206 |
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
|
https://scholar.google.com/scholar?cluster=4637860255101712227&hl=en&as_sdt=0,5
| 9 | 2,019 |
Screening Sinkhorn Algorithm for Regularized Optimal Transport
| 46 |
neurips
| 1 | 0 |
2023-06-15 23:43:55.538000
|
https://github.com/mzalaya/screenkhorn
| 10 |
Screening sinkhorn algorithm for regularized optimal transport
|
https://scholar.google.com/scholar?cluster=6847300346799995877&hl=en&as_sdt=0,33
| 4 | 2,019 |
Learning Deep Bilinear Transformation for Fine-grained Image Representation
| 127 |
neurips
| 18 | 5 |
2023-06-15 23:43:55.721000
|
https://github.com/researchmm/DBTNet
| 103 |
Learning deep bilinear transformation for fine-grained image representation
|
https://scholar.google.com/scholar?cluster=5630007169775604434&hl=en&as_sdt=0,5
| 7 | 2,019 |
Learning Compositional Neural Programs with Recursive Tree Search and Planning
| 39 |
neurips
| 15 | 5 |
2023-06-15 23:43:55.904000
|
https://github.com/instadeepai/AlphaNPI
| 75 |
Learning compositional neural programs with recursive tree search and planning
|
https://scholar.google.com/scholar?cluster=2128386923909223198&hl=en&as_sdt=0,5
| 9 | 2,019 |
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
| 5 |
neurips
| 0 | 0 |
2023-06-15 23:43:56.086000
|
https://github.com/samuela/e-stops
| 4 |
Mo'states mo'problems: Emergency stop mechanisms from observation
|
https://scholar.google.com/scholar?cluster=17441120353657662710&hl=en&as_sdt=0,18
| 4 | 2,019 |
Kernelized Bayesian Softmax for Text Generation
| 3 |
neurips
| 3 | 0 |
2023-06-15 23:43:56.268000
|
https://github.com/NingMiao/KerBS
| 16 |
Kernelized bayesian softmax for text generation
|
https://scholar.google.com/scholar?cluster=9263000748514336745&hl=en&as_sdt=0,5
| 4 | 2,019 |
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
| 45 |
neurips
| 3 | 1 |
2023-06-15 23:43:56.451000
|
https://github.com/RixonC/DINGO
| 5 |
DINGO: Distributed Newton-type method for gradient-norm optimization
|
https://scholar.google.com/scholar?cluster=9185133392864435818&hl=en&as_sdt=0,47
| 1 | 2,019 |
Object landmark discovery through unsupervised adaptation
| 14 |
neurips
| 6 | 2 |
2023-06-15 23:43:56.634000
|
https://github.com/ESanchezLozano/SAIC-Unsupervised-landmark-detection-NeurIPS2019
| 29 |
Object landmark discovery through unsupervised adaptation
|
https://scholar.google.com/scholar?cluster=9031992010757447609&hl=en&as_sdt=0,5
| 3 | 2,019 |
Block Coordinate Regularization by Denoising
| 67 |
neurips
| 7 | 0 |
2023-06-15 23:43:56.816000
|
https://github.com/wustl-cig/bcred
| 9 |
Block coordinate regularization by denoising
|
https://scholar.google.com/scholar?cluster=1292618094762945559&hl=en&as_sdt=0,22
| 5 | 2,019 |
Visual Concept-Metaconcept Learning
| 56 |
neurips
| 7 | 5 |
2023-06-15 23:43:57
|
https://github.com/Glaciohound/VCML
| 46 |
Visual concept-metaconcept learning
|
https://scholar.google.com/scholar?cluster=11769852551616538355&hl=en&as_sdt=0,44
| 3 | 2,019 |
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection
| 52 |
neurips
| 6 | 0 |
2023-06-15 23:43:57.182000
|
https://github.com/vlkniaz/MAGritte
| 22 |
The point where reality meets fantasy: Mixed adversarial generators for image splice detection
|
https://scholar.google.com/scholar?cluster=12749511868838155616&hl=en&as_sdt=0,5
| 2 | 2,019 |
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
| 34 |
neurips
| 0 | 0 |
2023-06-15 23:43:57.365000
|
https://github.com/sushrutk/robust_sparse_mean_estimation
| 1 |
Outlier-robust high-dimensional sparse estimation via iterative filtering
|
https://scholar.google.com/scholar?cluster=16618101230479779573&hl=en&as_sdt=0,44
| 2 | 2,019 |
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
| 132 |
neurips
| 25 | 0 |
2023-06-15 23:43:57.558000
|
https://github.com/cagatayyildiz/ODE2VAE
| 104 |
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
|
https://scholar.google.com/scholar?cluster=12216088615598559688&hl=en&as_sdt=0,26
| 7 | 2,019 |
Cross-Domain Transferability of Adversarial Perturbations
| 96 |
neurips
| 10 | 0 |
2023-06-15 23:43:57.740000
|
https://github.com/Muzammal-Naseer/Cross-domain-perturbations
| 46 |
Cross-domain transferability of adversarial perturbations
|
https://scholar.google.com/scholar?cluster=7007287740429606925&hl=en&as_sdt=0,5
| 2 | 2,019 |
Recovering Bandits
| 36 |
neurips
| 0 | 1 |
2023-06-15 23:43:57.923000
|
https://github.com/ciarapb/recovering_bandits
| 0 |
Recovering bandits
|
https://scholar.google.com/scholar?cluster=11910471401388332238&hl=en&as_sdt=0,5
| 1 | 2,019 |
A neurally plausible model for online recognition and postdiction in a dynamical environment
| 8 |
neurips
| 1 | 0 |
2023-06-15 23:43:58.106000
|
https://github.com/kevin-w-li/ddc_ssm
| 0 |
A neurally plausible model for online recognition and postdiction in a dynamical environment
|
https://scholar.google.com/scholar?cluster=7571256273335094735&hl=en&as_sdt=0,5
| 1 | 2,019 |
Importance Resampling for Off-policy Prediction
| 33 |
neurips
| 2 | 1 |
2023-06-15 23:43:58.288000
|
https://github.com/mkschleg/Resampling.jl
| 5 |
Importance resampling for off-policy prediction
|
https://scholar.google.com/scholar?cluster=5157617091632613396&hl=en&as_sdt=0,5
| 3 | 2,019 |
A Condition Number for Joint Optimization of Cycle-Consistent Networks
| 15 |
neurips
| 1 | 0 |
2023-06-15 23:43:58.470000
|
https://github.com/huangqx/NeurIPS19_Cycle
| 9 |
A condition number for joint optimization of cycle-consistent networks
|
https://scholar.google.com/scholar?cluster=542481175348685009&hl=en&as_sdt=0,5
| 3 | 2,019 |
A Graph Theoretic Additive Approximation of Optimal Transport
| 29 |
neurips
| 4 | 0 |
2023-06-15 23:43:58.653000
|
https://github.com/nathaniellahn/CombinatorialOptimalTransport
| 6 |
A graph theoretic additive approximation of optimal transport
|
https://scholar.google.com/scholar?cluster=18196599913919395149&hl=en&as_sdt=0,5
| 2 | 2,019 |
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
| 3 |
neurips
| 1 | 0 |
2023-06-15 23:43:58.837000
|
https://github.com/sumeetsk/maxgap_bandit
| 0 |
Maxgap bandit: Adaptive algorithms for approximate ranking
|
https://scholar.google.com/scholar?cluster=3849563562528294694&hl=en&as_sdt=0,33
| 3 | 2,019 |
Exact Rate-Distortion in Autoencoders via Echo Noise
| 15 |
neurips
| 4 | 0 |
2023-06-15 23:43:59.019000
|
https://github.com/brekelma/echo
| 17 |
Exact rate-distortion in autoencoders via echo noise
|
https://scholar.google.com/scholar?cluster=14670314259355602028&hl=en&as_sdt=0,33
| 3 | 2,019 |
Bridging Machine Learning and Logical Reasoning by Abductive Learning
| 100 |
neurips
| 22 | 1 |
2023-06-15 23:43:59.202000
|
https://github.com/AbductiveLearning/ABL-HED
| 86 |
Bridging machine learning and logical reasoning by abductive learning
|
https://scholar.google.com/scholar?cluster=1518342375288126288&hl=en&as_sdt=0,33
| 5 | 2,019 |
Input-Output Equivalence of Unitary and Contractive RNNs
| 3 |
neurips
| 0 | 0 |
2023-06-15 23:43:59.386000
|
https://github.com/melikaemami/URNN
| 0 |
Input-output equivalence of unitary and contractive rnns
|
https://scholar.google.com/scholar?cluster=4797724807389043789&hl=en&as_sdt=0,46
| 1 | 2,019 |
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization
| 98 |
neurips
| 16 | 6 |
2023-06-15 23:43:59.583000
|
https://github.com/plumerai/rethinking-bnn-optimization
| 65 |
Latent weights do not exist: Rethinking binarized neural network optimization
|
https://scholar.google.com/scholar?cluster=1826223927355185182&hl=en&as_sdt=0,5
| 10 | 2,019 |
Differentiable Convex Optimization Layers
| 402 |
neurips
| 138 | 43 |
2023-06-15 23:43:59.766000
|
https://github.com/cvxgrp/cvxpylayers
| 1,544 |
Differentiable convex optimization layers
|
https://scholar.google.com/scholar?cluster=4803367516747588003&hl=en&as_sdt=0,5
| 55 | 2,019 |
Graph Transformer Networks
| 583 |
neurips
| 148 | 13 |
2023-06-15 23:43:59.948000
|
https://github.com/seongjunyun/Graph_Transformer_Networks
| 772 |
Graph transformer networks
|
https://scholar.google.com/scholar?cluster=10432505779472613736&hl=en&as_sdt=0,41
| 12 | 2,019 |
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
| 55 |
neurips
| 4 | 2 |
2023-06-15 23:44:00.131000
|
https://github.com/nnRNN/nnRNN_release
| 22 |
Non-normal recurrent neural network (nnrnn): learning long time dependencies while improving expressivity with transient dynamics
|
https://scholar.google.com/scholar?cluster=8175788544476265366&hl=en&as_sdt=0,33
| 6 | 2,019 |
Large Memory Layers with Product Keys
| 102 |
neurips
| 474 | 127 |
2023-06-15 23:44:00.314000
|
https://github.com/facebookresearch/XLM
| 2,768 |
Large memory layers with product keys
|
https://scholar.google.com/scholar?cluster=8134570978766877507&hl=en&as_sdt=0,33
| 56 | 2,019 |
Computing Full Conformal Prediction Set with Approximate Homotopy
| 13 |
neurips
| 1 | 0 |
2023-06-15 23:44:00.497000
|
https://github.com/EugeneNdiaye/homotopy_conformal_prediction
| 4 |
Computing full conformal prediction set with approximate homotopy
|
https://scholar.google.com/scholar?cluster=6957582506372918225&hl=en&as_sdt=0,31
| 4 | 2,019 |
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
| 191 |
neurips
| 39 | 4 |
2023-06-15 23:44:00.680000
|
https://github.com/yourh/AttentionXML
| 228 |
Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification
|
https://scholar.google.com/scholar?cluster=17044546851648678548&hl=en&as_sdt=0,39
| 5 | 2,019 |
Policy Learning for Fairness in Ranking
| 174 |
neurips
| 7 | 1 |
2023-06-15 23:44:00.863000
|
https://github.com/ashudeep/Fair-PGRank
| 19 |
Policy learning for fairness in ranking
|
https://scholar.google.com/scholar?cluster=11031156669451093289&hl=en&as_sdt=0,33
| 2 | 2,019 |
Integer Discrete Flows and Lossless Compression
| 118 |
neurips
| 18 | 2 |
2023-06-15 23:44:01.045000
|
https://github.com/jornpeters/integer_discrete_flows
| 94 |
Integer discrete flows and lossless compression
|
https://scholar.google.com/scholar?cluster=4833991710159138834&hl=en&as_sdt=0,36
| 5 | 2,019 |
Reconciling λ-Returns with Experience Replay
| 31 |
neurips
| 5 | 1 |
2023-06-15 23:44:01.228000
|
https://github.com/brett-daley/dqn-lambda
| 21 |
Reconciling λ-returns with experience replay
|
https://scholar.google.com/scholar?cluster=3382445004313688129&hl=en&as_sdt=0,5
| 3 | 2,019 |
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
| 64 |
neurips
| 3 | 1 |
2023-06-15 23:44:01.410000
|
https://github.com/GiulsLu/Sinkhorn-Barycenters
| 20 |
Sinkhorn barycenters with free support via frank-wolfe algorithm
|
https://scholar.google.com/scholar?cluster=8683927727496830804&hl=en&as_sdt=0,33
| 4 | 2,019 |
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
| 105 |
neurips
| 14 | 7 |
2023-06-15 23:44:01.602000
|
https://github.com/fenglinliu98/MIA
| 63 |
Aligning visual regions and textual concepts for semantic-grounded image representations
|
https://scholar.google.com/scholar?cluster=2159125820163720413&hl=en&as_sdt=0,34
| 6 | 2,019 |
Network Pruning via Transformable Architecture Search
| 230 |
neurips
| 279 | 13 |
2023-06-15 23:44:01.785000
|
https://github.com/D-X-Y/NAS-Projects
| 1,494 |
Network pruning via transformable architecture search
|
https://scholar.google.com/scholar?cluster=10081161153623762444&hl=en&as_sdt=0,5
| 45 | 2,019 |
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives
| 27 |
neurips
| 1 | 0 |
2023-06-15 23:44:01.972000
|
https://github.com/wangchimit/mdp_q
| 0 |
Regret minimization for reinforcement learning with vectorial feedback and complex objectives
|
https://scholar.google.com/scholar?cluster=15554596298446464048&hl=en&as_sdt=0,33
| 1 | 2,019 |
Selective Sampling-based Scalable Sparse Subspace Clustering
| 40 |
neurips
| 6 | 0 |
2023-06-15 23:44:02.155000
|
https://github.com/smatsus/S5C
| 10 |
Selective sampling-based scalable sparse subspace clustering
|
https://scholar.google.com/scholar?cluster=18109014271440966557&hl=en&as_sdt=0,47
| 4 | 2,019 |
On the Expressive Power of Deep Polynomial Neural Networks
| 56 |
neurips
| 2 | 0 |
2023-06-15 23:44:02.338000
|
https://github.com/mtrager/polynomial_networks
| 7 |
On the expressive power of deep polynomial neural networks
|
https://scholar.google.com/scholar?cluster=3267335187204945062&hl=en&as_sdt=0,23
| 3 | 2,019 |
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
| 60 |
neurips
| 15 | 6 |
2023-06-15 23:44:02.520000
|
https://github.com/ebatty/behavenet
| 48 |
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
|
https://scholar.google.com/scholar?cluster=8465940907490752518&hl=en&as_sdt=0,10
| 8 | 2,019 |
Accurate Layerwise Interpretable Competence Estimation
| 4 |
neurips
| 0 | 0 |
2023-06-15 23:44:02.703000
|
https://github.com/vickraj/ALICE
| 1 |
Accurate layerwise interpretable competence estimation
|
https://scholar.google.com/scholar?cluster=6989485963144950384&hl=en&as_sdt=0,33
| 2 | 2,019 |
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
| 26 |
neurips
| 7 | 0 |
2023-06-15 23:44:02.886000
|
https://github.com/facebookresearch/gala
| 19 |
Gossip-based actor-learner architectures for deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=7339058488760519540&hl=en&as_sdt=0,33
| 6 | 2,019 |
Fast and Accurate Stochastic Gradient Estimation
| 29 |
neurips
| 4 | 1 |
2023-06-15 23:44:03.068000
|
https://github.com/keroro824/LGD
| 11 |
Fast and accurate stochastic gradient estimation
|
https://scholar.google.com/scholar?cluster=14355182698351055018&hl=en&as_sdt=0,47
| 5 | 2,019 |
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
| 8 |
neurips
| 5 | 1 |
2023-06-15 23:44:03.250000
|
https://github.com/zhangzx-sjtu/LANTERN-NeurIPS-2019
| 10 |
Learning latent process from high-dimensional event sequences via efficient sampling
|
https://scholar.google.com/scholar?cluster=1725612638929468853&hl=en&as_sdt=0,5
| 3 | 2,019 |
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
| 705 |
neurips
| 30 | 3 |
2023-06-15 23:44:03.433000
|
https://github.com/hendrycks/ss-ood
| 256 |
Using self-supervised learning can improve model robustness and uncertainty
|
https://scholar.google.com/scholar?cluster=1993204184412498694&hl=en&as_sdt=0,10
| 7 | 2,019 |
Space and Time Efficient Kernel Density Estimation in High Dimensions
| 50 |
neurips
| 1 | 0 |
2023-06-15 23:44:03.615000
|
https://github.com/talwagner/efficient_kde
| 20 |
Space and time efficient kernel density estimation in high dimensions
|
https://scholar.google.com/scholar?cluster=2039472517470504550&hl=en&as_sdt=0,48
| 2 | 2,019 |
Scalable Deep Generative Relational Model with High-Order Node Dependence
| 11 |
neurips
| 0 | 0 |
2023-06-15 23:44:03.798000
|
https://github.com/xuhuifan/SDREM
| 0 |
Scalable deep generative relational model with high-order node dependence
|
https://scholar.google.com/scholar?cluster=17019622805732134469&hl=en&as_sdt=0,22
| 1 | 2,019 |
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
| 25 |
neurips
| 2 | 0 |
2023-06-15 23:44:03.980000
|
https://github.com/woodyx218/SLOPE_AMP
| 0 |
Algorithmic analysis and statistical estimation of slope via approximate message passing
|
https://scholar.google.com/scholar?cluster=6840575355552883689&hl=en&as_sdt=0,14
| 2 | 2,019 |
Multi-objects Generation with Amortized Structural Regularization
| 19 |
neurips
| 0 | 0 |
2023-06-15 23:44:04.163000
|
https://github.com/taufikxu/MOG-ASR
| 4 |
Multi-objects generation with amortized structural regularization
|
https://scholar.google.com/scholar?cluster=2034846002804376958&hl=en&as_sdt=0,10
| 2 | 2,019 |
Learning Distributions Generated by One-Layer ReLU Networks
| 20 |
neurips
| 0 | 0 |
2023-06-15 23:44:04.346000
|
https://github.com/wushanshan/densityEstimation
| 0 |
Learning distributions generated by one-layer ReLU networks
|
https://scholar.google.com/scholar?cluster=12692430709826670328&hl=en&as_sdt=0,5
| 2 | 2,019 |
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
| 125 |
neurips
| 22 | 0 |
2023-06-15 23:44:04.528000
|
https://github.com/atomistic-machine-learning/G-SchNet
| 113 |
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
|
https://scholar.google.com/scholar?cluster=10125464243837657094&hl=en&as_sdt=0,33
| 6 | 2,019 |
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
| 85 |
neurips
| 4 | 0 |
2023-06-15 23:44:04.711000
|
https://github.com/twistedcubic/que-outlier-detection
| 25 |
Quantum entropy scoring for fast robust mean estimation and improved outlier detection
|
https://scholar.google.com/scholar?cluster=841892307545276376&hl=en&as_sdt=0,33
| 6 | 2,019 |
Distributed Low-rank Matrix Factorization With Exact Consensus
| 13 |
neurips
| 0 | 0 |
2023-06-15 23:44:04.893000
|
https://github.com/xinshuoyang/DGD-LOCAL
| 0 |
Distributed low-rank matrix factorization with exact consensus
|
https://scholar.google.com/scholar?cluster=5520219022394816483&hl=en&as_sdt=0,33
| 2 | 2,019 |
Tensor Monte Carlo: Particle Methods for the GPU era
| 7 |
neurips
| 0 | 0 |
2023-06-15 23:44:05.076000
|
https://github.com/anonymous-78913/tmc-anon
| 1 |
Tensor Monte Carlo: particle methods for the GPU era
|
https://scholar.google.com/scholar?cluster=16439696992538487592&hl=en&as_sdt=0,33
| 1 | 2,019 |
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
| 20 |
neurips
| 1 | 0 |
2023-06-15 23:44:05.258000
|
https://github.com/zhaozb08/MixPL-SPO
| 2 |
Learning mixtures of plackett-luce models from structured partial orders
|
https://scholar.google.com/scholar?cluster=878719631991386250&hl=en&as_sdt=0,31
| 2 | 2,019 |
Combining Generative and Discriminative Models for Hybrid Inference
| 43 |
neurips
| 4 | 0 |
2023-06-15 23:44:05.440000
|
https://github.com/vgsatorras/hybrid-inference
| 19 |
Combining generative and discriminative models for hybrid inference
|
https://scholar.google.com/scholar?cluster=7519572566693624028&hl=en&as_sdt=0,9
| 3 | 2,019 |
Region Mutual Information Loss for Semantic Segmentation
| 82 |
neurips
| 38 | 10 |
2023-06-15 23:44:05.623000
|
https://github.com/ZJULearning/RMI
| 257 |
Region mutual information loss for semantic segmentation
|
https://scholar.google.com/scholar?cluster=686312133608642503&hl=en&as_sdt=0,33
| 10 | 2,019 |
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