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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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