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
|
|---|---|---|---|---|---|---|---|---|---|---|---|
Variational Graph Recurrent Neural Networks
| 135 |
neurips
| 31 | 3 |
2023-06-15 23:44:05.805000
|
https://github.com/VGraphRNN/VGRNN
| 99 |
Variational graph recurrent neural networks
|
https://scholar.google.com/scholar?cluster=8245745974174027367&hl=en&as_sdt=0,23
| 1 | 2,019 |
Stochastic Bandits with Context Distributions
| 14 |
neurips
| 2 | 0 |
2023-06-15 23:44:05.988000
|
https://github.com/jkirschner42/ContextDistributions
| 0 |
Stochastic bandits with context distributions
|
https://scholar.google.com/scholar?cluster=4525359308545283091&hl=en&as_sdt=0,47
| 2 | 2,019 |
Geometry-Aware Neural Rendering
| 18 |
neurips
| 2 | 1 |
2023-06-15 23:44:06.170000
|
https://github.com/josh-tobin/egqn-datasets
| 12 |
Geometry-aware neural rendering
|
https://scholar.google.com/scholar?cluster=13975640074645602977&hl=en&as_sdt=0,14
| 6 | 2,019 |
Training Language GANs from Scratch
| 64 |
neurips
| 2,436 | 170 |
2023-06-15 23:44:06.352000
|
https://github.com/deepmind/deepmind-research
| 11,902 |
Training language gans from scratch
|
https://scholar.google.com/scholar?cluster=8355933578151916965&hl=en&as_sdt=0,33
| 336 | 2,019 |
On the (In)fidelity and Sensitivity of Explanations
| 265 |
neurips
| 5 | 1 |
2023-06-15 23:44:06.535000
|
https://github.com/chihkuanyeh/saliency_evaluation
| 18 |
On the (in) fidelity and sensitivity of explanations
|
https://scholar.google.com/scholar?cluster=14868848543196386114&hl=en&as_sdt=0,49
| 5 | 2,019 |
Foundations of Comparison-Based Hierarchical Clustering
| 27 |
neurips
| 0 | 0 |
2023-06-15 23:44:06.717000
|
https://github.com/mperrot/ComparisonHC
| 7 |
Foundations of comparison-based hierarchical clustering
|
https://scholar.google.com/scholar?cluster=13988948004234767193&hl=en&as_sdt=0,33
| 1 | 2,019 |
Neural Similarity Learning
| 22 |
neurips
| 5 | 0 |
2023-06-15 23:44:06.903000
|
https://github.com/wy1iu/NSL
| 33 |
Neural similarity learning
|
https://scholar.google.com/scholar?cluster=1329367267940574099&hl=en&as_sdt=0,21
| 11 | 2,019 |
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
| 10 |
neurips
| 0 | 0 |
2023-06-15 23:44:07.085000
|
https://github.com/weiiew28/Least-Squares-EM
| 0 |
Global convergence of least squares EM for demixing two log-concave densities
|
https://scholar.google.com/scholar?cluster=12181077309043150371&hl=en&as_sdt=0,33
| 2 | 2,019 |
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
| 39 |
neurips
| 0 | 0 |
2023-06-15 23:44:07.268000
|
https://github.com/umutsimsekli/sgd_first_exit_time
| 2 |
First exit time analysis of stochastic gradient descent under heavy-tailed gradient noise
|
https://scholar.google.com/scholar?cluster=11205216308770275792&hl=en&as_sdt=0,33
| 1 | 2,019 |
Hyper-Graph-Network Decoders for Block Codes
| 50 |
neurips
| 6 | 0 |
2023-06-15 23:44:07.450000
|
https://github.com/facebookresearch/HyperNetworkDecoder
| 18 |
Hyper-graph-network decoders for block codes
|
https://scholar.google.com/scholar?cluster=7373030351038141081&hl=en&as_sdt=0,14
| 5 | 2,019 |
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
| 51 |
neurips
| 1 | 0 |
2023-06-15 23:44:07.632000
|
https://github.com/wushanshan/GraphLearn
| 3 |
Sparse logistic regression learns all discrete pairwise graphical models
|
https://scholar.google.com/scholar?cluster=18105640724379310504&hl=en&as_sdt=0,33
| 3 | 2,019 |
Coordinated hippocampal-entorhinal replay as structural inference
| 13 |
neurips
| 0 | 0 |
2023-06-15 23:44:07.814000
|
https://github.com/talfanevans/Coordinated_replay_for_structural_inference_NeurIPS_2019
| 0 |
Coordinated hippocampal-entorhinal replay as structural inference
|
https://scholar.google.com/scholar?cluster=2193106409668155739&hl=en&as_sdt=0,44
| 1 | 2,019 |
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition
| 117 |
neurips
| 13 | 2 |
2023-06-15 23:44:07.997000
|
https://github.com/vt-vl-lab/SDN
| 81 |
Why can't i dance in the mall? learning to mitigate scene bias in action recognition
|
https://scholar.google.com/scholar?cluster=7980230470759828284&hl=en&as_sdt=0,25
| 4 | 2,019 |
Invert to Learn to Invert
| 73 |
neurips
| 12 | 0 |
2023-06-15 23:44:08.183000
|
https://github.com/pputzky/invertible_rim
| 35 |
Invert to learn to invert
|
https://scholar.google.com/scholar?cluster=5749756993506538119&hl=en&as_sdt=0,11
| 3 | 2,019 |
Metamers of neural networks reveal divergence from human perceptual systems
| 45 |
neurips
| 3 | 0 |
2023-06-15 23:44:08.377000
|
https://github.com/jenellefeather/model_metamers
| 4 |
Metamers of neural networks reveal divergence from human perceptual systems
|
https://scholar.google.com/scholar?cluster=11487383284090666509&hl=en&as_sdt=0,14
| 1 | 2,019 |
Optimal Sparse Decision Trees
| 151 |
neurips
| 9 | 5 |
2023-06-15 23:44:08.574000
|
https://github.com/xiyanghu/OSDT
| 87 |
Optimal sparse decision trees
|
https://scholar.google.com/scholar?cluster=2250336388738514433&hl=en&as_sdt=0,5
| 6 | 2,019 |
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
| 9 |
neurips
| 15 | 0 |
2023-06-15 23:44:08.757000
|
https://github.com/microsoft/Optimal-Freshness-Crawl-Scheduling
| 34 |
Staying up to date with online content changes using reinforcement learning for scheduling
|
https://scholar.google.com/scholar?cluster=817478686075207663&hl=en&as_sdt=0,33
| 12 | 2,019 |
This Looks Like That: Deep Learning for Interpretable Image Recognition
| 763 |
neurips
| 104 | 17 |
2023-06-15 23:44:08.939000
|
https://github.com/cfchen-duke/ProtoPNet
| 280 |
This looks like that: deep learning for interpretable image recognition
|
https://scholar.google.com/scholar?cluster=13319230358009390187&hl=en&as_sdt=0,5
| 9 | 2,019 |
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning
| 7 |
neurips
| 6 | 0 |
2023-06-15 23:44:09.121000
|
https://github.com/kakao/DAFT
| 32 |
Learning dynamics of attention: Human prior for interpretable machine reasoning
|
https://scholar.google.com/scholar?cluster=5091360286215129323&hl=en&as_sdt=0,3
| 10 | 2,019 |
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes
| 50 |
neurips
| 5 | 7 |
2023-06-15 23:44:09.303000
|
https://github.com/revbucket/geometric-certificates
| 40 |
Provable certificates for adversarial examples: Fitting a ball in the union of polytopes
|
https://scholar.google.com/scholar?cluster=12220982508626507839&hl=en&as_sdt=0,33
| 4 | 2,019 |
Fast Parallel Algorithms for Statistical Subset Selection Problems
| 8 |
neurips
| 0 | 0 |
2023-06-15 23:44:09.486000
|
https://github.com/robo-sq/dash
| 1 |
Fast parallel algorithms for statistical subset selection problems
|
https://scholar.google.com/scholar?cluster=1744974239462418532&hl=en&as_sdt=0,34
| 2 | 2,019 |
On Lazy Training in Differentiable Programming
| 646 |
neurips
| 1 | 0 |
2023-06-15 23:44:09.680000
|
https://github.com/edouardoyallon/lazy-training-CNN
| 11 |
On lazy training in differentiable programming
|
https://scholar.google.com/scholar?cluster=7609132224233862548&hl=en&as_sdt=0,47
| 2 | 2,019 |
Estimating Convergence of Markov chains with L-Lag Couplings
| 39 |
neurips
| 0 | 0 |
2023-06-15 23:44:09.862000
|
https://github.com/niloyb/LlagCouplings
| 8 |
Estimating convergence of Markov chains with L-lag couplings
|
https://scholar.google.com/scholar?cluster=7664057036629656695&hl=en&as_sdt=0,5
| 3 | 2,019 |
Neural Multisensory Scene Inference
| 7 |
neurips
| 0 | 0 |
2023-06-15 23:44:10.045000
|
https://github.com/lim0606/pytorch-generative-multisensory-network
| 2 |
Neural multisensory scene inference
|
https://scholar.google.com/scholar?cluster=12739272795826190598&hl=en&as_sdt=0,5
| 5 | 2,019 |
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions
| 6 |
neurips
| 3 | 0 |
2023-06-15 23:44:10.228000
|
https://github.com/MatteoT90/WibergianLearning
| 6 |
Fixing implicit derivatives: Trust-region based learning of continuous energy functions
|
https://scholar.google.com/scholar?cluster=14745296383921099164&hl=en&as_sdt=0,31
| 5 | 2,019 |
Correlation Clustering with Adaptive Similarity Queries
| 14 |
neurips
| 2 | 0 |
2023-06-15 23:44:10.411000
|
https://github.com/AP15/NeurIPS_2019
| 0 |
Correlation clustering with adaptive similarity queries
|
https://scholar.google.com/scholar?cluster=7825046887341981145&hl=en&as_sdt=0,5
| 1 | 2,019 |
Ease-of-Teaching and Language Structure from Emergent Communication
| 77 |
neurips
| 0 | 0 |
2023-06-15 23:44:10.594000
|
https://github.com/FushanLi/Ease-of-teaching-and-language-structure
| 4 |
Ease-of-teaching and language structure from emergent communication
|
https://scholar.google.com/scholar?cluster=9879290810297308236&hl=en&as_sdt=0,5
| 1 | 2,019 |
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition
| 50 |
neurips
| 0 | 0 |
2023-06-15 23:44:10.776000
|
https://github.com/rrogers386/DPComposition
| 0 |
Practical differentially private top-k selection with pay-what-you-get composition
|
https://scholar.google.com/scholar?cluster=13096075295123378083&hl=en&as_sdt=0,10
| 3 | 2,019 |
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking
| 22 |
neurips
| 25 | 0 |
2023-06-15 23:44:10.959000
|
https://github.com/yu-lab-vt/muSSP
| 92 |
muSSP: Efficient min-cost flow algorithm for multi-object tracking
|
https://scholar.google.com/scholar?cluster=5651751699578908397&hl=en&as_sdt=0,36
| 5 | 2,019 |
Invertible Convolutional Flow
| 37 |
neurips
| 0 | 1 |
2023-06-15 23:44:11.142000
|
https://github.com/Karami-m/Invertible-Convolutional-Flow
| 2 |
Invertible convolutional flow
|
https://scholar.google.com/scholar?cluster=13011222781620393889&hl=en&as_sdt=0,33
| 2 | 2,019 |
Neural Relational Inference with Fast Modular Meta-learning
| 53 |
neurips
| 11 | 2 |
2023-06-15 23:44:11.324000
|
https://github.com/FerranAlet/modular-metalearning
| 72 |
Neural relational inference with fast modular meta-learning
|
https://scholar.google.com/scholar?cluster=10911682641501224691&hl=en&as_sdt=0,5
| 5 | 2,019 |
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
| 159 |
neurips
| 16 | 5 |
2023-06-15 23:44:11.507000
|
https://github.com/xh-liu/CC-FPSE
| 120 |
Learning to predict layout-to-image conditional convolutions for semantic image synthesis
|
https://scholar.google.com/scholar?cluster=10050858944004797007&hl=en&as_sdt=0,33
| 9 | 2,019 |
Approximate Inference Turns Deep Networks into Gaussian Processes
| 80 |
neurips
| 4 | 0 |
2023-06-15 23:44:11.689000
|
https://github.com/team-approx-bayes/dnn2gp
| 45 |
Approximate inference turns deep networks into gaussian processes
|
https://scholar.google.com/scholar?cluster=7367896344754763984&hl=en&as_sdt=0,33
| 2 | 2,019 |
SGD on Neural Networks Learns Functions of Increasing Complexity
| 106 |
neurips
| 2 | 0 |
2023-06-15 23:44:11.873000
|
https://github.com/anoneurips2019/SGD-learns-functions-of-increasing-complexity
| 2 |
Sgd on neural networks learns functions of increasing complexity
|
https://scholar.google.com/scholar?cluster=7545613427429088321&hl=en&as_sdt=0,33
| 0 | 2,019 |
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
| 19 |
neurips
| 1 | 1 |
2023-06-15 23:44:12.056000
|
https://github.com/sorooshafiee/Nonparam_Likelihood
| 3 |
Optimistic distributionally robust optimization for nonparametric likelihood approximation
|
https://scholar.google.com/scholar?cluster=4678014914038724524&hl=en&as_sdt=0,39
| 1 | 2,019 |
Don't take it lightly: Phasing optical random projections with unknown operators
| 10 |
neurips
| 1 | 0 |
2023-06-15 23:44:12.239000
|
https://github.com/swing-research/opu_phase
| 5 |
Don't take it lightly: Phasing optical random projections with unknown operators
|
https://scholar.google.com/scholar?cluster=17218217643749402266&hl=en&as_sdt=0,24
| 3 | 2,019 |
Visualizing the PHATE of Neural Networks
| 22 |
neurips
| 9 | 1 |
2023-06-15 23:44:12.421000
|
https://github.com/scottgigante/m-phate
| 54 |
Visualizing the phate of neural networks
|
https://scholar.google.com/scholar?cluster=10386490094735886479&hl=en&as_sdt=0,33
| 7 | 2,019 |
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
| 201 |
neurips
| 28 | 11 |
2023-06-15 23:44:12.603000
|
https://github.com/youzhonghui/gate-decorator-pruning
| 187 |
Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks
|
https://scholar.google.com/scholar?cluster=364370970700584447&hl=en&as_sdt=0,5
| 10 | 2,019 |
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights
| 13 |
neurips
| 0 | 0 |
2023-06-15 23:44:12.785000
|
https://github.com/mariajahja/kf-sf-flu-nowcasting
| 8 |
Kalman filter, sensor fusion, and constrained regression: Equivalences and insights
|
https://scholar.google.com/scholar?cluster=15806795739922263365&hl=en&as_sdt=0,5
| 2 | 2,019 |
Practical Deep Learning with Bayesian Principles
| 202 |
neurips
| 22 | 5 |
2023-06-15 23:44:12.968000
|
https://github.com/team-approx-bayes/dl-with-bayes
| 238 |
Practical deep learning with Bayesian principles
|
https://scholar.google.com/scholar?cluster=13355160445802491048&hl=en&as_sdt=0,5
| 14 | 2,019 |
Deep Active Learning with a Neural Architecture Search
| 39 |
neurips
| 1 | 0 |
2023-06-15 23:44:13.151000
|
https://github.com/geifmany/Active-inas
| 6 |
Deep active learning with a neural architecture search
|
https://scholar.google.com/scholar?cluster=17316861516778734731&hl=en&as_sdt=0,33
| 2 | 2,019 |
Quality Aware Generative Adversarial Networks
| 28 |
neurips
| 3 | 0 |
2023-06-15 23:44:13.333000
|
https://github.com/lfovia/QAGANS
| 20 |
Quality aware generative adversarial networks
|
https://scholar.google.com/scholar?cluster=7569135651621693544&hl=en&as_sdt=0,33
| 1 | 2,019 |
Control What You Can: Intrinsically Motivated Task-Planning Agent
| 25 |
neurips
| 1 | 0 |
2023-06-15 23:44:13.516000
|
https://github.com/s-bl/cwyc
| 4 |
Control what you can: Intrinsically motivated task-planning agent
|
https://scholar.google.com/scholar?cluster=4748849991670858589&hl=en&as_sdt=0,47
| 2 | 2,019 |
Momentum-Based Variance Reduction in Non-Convex SGD
| 259 |
neurips
| 7,320 | 1,025 |
2023-06-15 23:44:13.699000
|
https://github.com/google-research/google-research
| 29,776 |
Momentum-based variance reduction in non-convex sgd
|
https://scholar.google.com/scholar?cluster=15315656138665062900&hl=en&as_sdt=0,31
| 727 | 2,019 |
Adversarial Self-Defense for Cycle-Consistent GANs
| 35 |
neurips
| 2 | 0 |
2023-06-15 23:44:13.883000
|
https://github.com/dbash/pix2pix_cyclegan_guess_noise
| 10 |
Adversarial self-defense for cycle-consistent GANs
|
https://scholar.google.com/scholar?cluster=2846733163024685583&hl=en&as_sdt=0,33
| 2 | 2,019 |
Ultrametric Fitting by Gradient Descent
| 25 |
neurips
| 3 | 0 |
2023-06-15 23:44:14.067000
|
https://github.com/PerretB/ultrametric-fitting
| 8 |
Ultrametric fitting by gradient descent
|
https://scholar.google.com/scholar?cluster=1064532168086709457&hl=en&as_sdt=0,11
| 2 | 2,019 |
Expressive power of tensor-network factorizations for probabilistic modeling
| 90 |
neurips
| 9 | 0 |
2023-06-15 23:44:14.249000
|
https://github.com/glivan/tensor_networks_for_probabilistic_modeling
| 28 |
Expressive power of tensor-network factorizations for probabilistic modeling
|
https://scholar.google.com/scholar?cluster=973997541769819292&hl=en&as_sdt=0,33
| 5 | 2,019 |
Machine Teaching of Active Sequential Learners
| 24 |
neurips
| 2 | 0 |
2023-06-15 23:44:14.432000
|
https://github.com/AaltoPML/machine-teaching-of-active-sequential-learners
| 9 |
Machine teaching of active sequential learners
|
https://scholar.google.com/scholar?cluster=16295600938122436621&hl=en&as_sdt=0,5
| 12 | 2,019 |
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
| 41 |
neurips
| 2 | 0 |
2023-06-15 23:44:14.615000
|
https://github.com/marekpetrik/craam2
| 4 |
Beyond confidence regions: Tight Bayesian ambiguity sets for robust MDPs
|
https://scholar.google.com/scholar?cluster=2675496836563141950&hl=en&as_sdt=0,5
| 1 | 2,019 |
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization
| 111 |
neurips
| 6 | 2 |
2023-06-15 23:44:14.797000
|
https://github.com/shiyujiao/SAFA
| 34 |
Spatial-aware feature aggregation for image based cross-view geo-localization
|
https://scholar.google.com/scholar?cluster=9193879788898998402&hl=en&as_sdt=0,14
| 1 | 2,019 |
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification
| 68 |
neurips
| 0 | 0 |
2023-06-15 23:44:14.980000
|
https://github.com/lucaoneto/NIPS2019_Fairness
| 0 |
Leveraging labeled and unlabeled data for consistent fair binary classification
|
https://scholar.google.com/scholar?cluster=2612431805502429071&hl=en&as_sdt=0,33
| 1 | 2,019 |
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
| 12 |
neurips
| 7,320 | 1,025 |
2023-06-15 23:44:15.162000
|
https://github.com/google-research/google-research
| 29,776 |
Tight dimensionality reduction for sketching low degree polynomial kernels
|
https://scholar.google.com/scholar?cluster=2891379264114413860&hl=en&as_sdt=0,15
| 727 | 2,019 |
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
| 26 |
neurips
| 4 | 0 |
2023-06-15 23:44:15.345000
|
https://github.com/shiwj16/raa-drl
| 9 |
Regularized Anderson acceleration for off-policy deep reinforcement learning
|
https://scholar.google.com/scholar?cluster=7454070558612114755&hl=en&as_sdt=0,33
| 2 | 2,019 |
Kernel Stein Tests for Multiple Model Comparison
| 11 |
neurips
| 2 | 0 |
2023-06-15 23:44:15.527000
|
https://github.com/jenninglim/model-comparison-test
| 5 |
Kernel stein tests for multiple model comparison
|
https://scholar.google.com/scholar?cluster=8758698782174042861&hl=en&as_sdt=0,5
| 4 | 2,019 |
Explanations can be manipulated and geometry is to blame
| 245 |
neurips
| 10 | 2 |
2023-06-15 23:44:15.710000
|
https://github.com/pankessel/explanations_can_be_manipulated
| 31 |
Explanations can be manipulated and geometry is to blame
|
https://scholar.google.com/scholar?cluster=14180570023451576122&hl=en&as_sdt=0,33
| 1 | 2,019 |
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks
| 34 |
neurips
| 3 | 1 |
2023-06-15 23:44:15.893000
|
https://github.com/ayaabdelsalam91/Input-Cell-Attention
| 12 |
Input-cell attention reduces vanishing saliency of recurrent neural networks
|
https://scholar.google.com/scholar?cluster=4632327933243562924&hl=en&as_sdt=0,33
| 3 | 2,019 |
Paradoxes in Fair Machine Learning
| 31 |
neurips
| 0 | 0 |
2023-06-15 23:44:16.075000
|
https://github.com/pgoelz/equalized
| 3 |
Paradoxes in fair machine learning
|
https://scholar.google.com/scholar?cluster=18338740097234946174&hl=en&as_sdt=0,36
| 3 | 2,019 |
Volumetric Correspondence Networks for Optical Flow
| 183 |
neurips
| 23 | 5 |
2023-06-15 23:44:16.258000
|
https://github.com/gengshan-y/VCN
| 147 |
Volumetric correspondence networks for optical flow
|
https://scholar.google.com/scholar?cluster=16527531324179353765&hl=en&as_sdt=0,10
| 6 | 2,019 |
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
| 228 |
neurips
| 3 | 0 |
2023-06-15 23:44:16.441000
|
https://github.com/cwein3/large-lr-code
| 6 |
Towards explaining the regularization effect of initial large learning rate in training neural networks
|
https://scholar.google.com/scholar?cluster=6617722188304549370&hl=en&as_sdt=0,36
| 2 | 2,019 |
Multi-marginal Wasserstein GAN
| 76 |
neurips
| 10 | 0 |
2023-06-15 23:44:16.623000
|
https://github.com/caojiezhang/MWGAN
| 51 |
Multi-marginal wasserstein gan
|
https://scholar.google.com/scholar?cluster=10067080185740979237&hl=en&as_sdt=0,33
| 1 | 2,019 |
PyTorch: An Imperative Style, High-Performance Deep Learning Library
| 28,946 |
neurips
| 18,601 | 12,172 |
2023-06-15 23:44:16.806000
|
https://github.com/pytorch/pytorch
| 67,867 |
Pytorch: An imperative style, high-performance deep learning library
|
https://scholar.google.com/scholar?cluster=3528934790668989119&hl=en&as_sdt=0,5
| 1,649 | 2,019 |
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons
| 15 |
neurips
| 0 | 0 |
2023-06-15 23:44:16.989000
|
https://github.com/WenboRen/ranking-from-noisy-comparisons
| 2 |
On sample complexity upper and lower bounds for exact ranking from noisy comparisons
|
https://scholar.google.com/scholar?cluster=17371903028090691021&hl=en&as_sdt=0,41
| 2 | 2,019 |
NAT: Neural Architecture Transformer for Accurate and Compact Architectures
| 77 |
neurips
| 12 | 0 |
2023-06-15 23:44:17.171000
|
https://github.com/guoyongcs/NAT
| 57 |
Nat: Neural architecture transformer for accurate and compact architectures
|
https://scholar.google.com/scholar?cluster=2412256637570332418&hl=en&as_sdt=0,5
| 3 | 2,019 |
Learning to Self-Train for Semi-Supervised Few-Shot Classification
| 262 |
neurips
| 11 | 9 |
2023-06-15 23:44:17.354000
|
https://github.com/xinzheli1217/learning-to-self-train
| 89 |
Learning to self-train for semi-supervised few-shot classification
|
https://scholar.google.com/scholar?cluster=7879404109068143287&hl=en&as_sdt=0,5
| 8 | 2,019 |
Stochastic Frank-Wolfe for Composite Convex Minimization
| 20 |
neurips
| 2 | 0 |
2023-06-15 23:44:17.545000
|
https://github.com/alpyurtsever/SHCGM
| 2 |
Stochastic Frank-Wolfe for composite convex minimization
|
https://scholar.google.com/scholar?cluster=9717935113633697368&hl=en&as_sdt=0,33
| 1 | 2,019 |
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
| 8 |
neurips
| 1 | 0 |
2023-06-15 23:44:17.728000
|
https://github.com/modestbayes/LFGP_NeurIPS
| 4 |
Modeling dynamic functional connectivity with latent factor Gaussian processes
|
https://scholar.google.com/scholar?cluster=14525732227397762230&hl=en&as_sdt=0,33
| 4 | 2,019 |
ETNet: Error Transition Network for Arbitrary Style Transfer
| 20 |
neurips
| 5 | 2 |
2023-06-15 23:44:17.911000
|
https://github.com/zhijieW94/ETNet
| 77 |
Etnet: Error transition network for arbitrary style transfer
|
https://scholar.google.com/scholar?cluster=11291490385512424160&hl=en&as_sdt=0,33
| 8 | 2,019 |
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model
| 35 |
neurips
| 14 | 1 |
2023-06-15 23:44:18.094000
|
https://github.com/microsoft/Icebreaker
| 42 |
Icebreaker: Element-wise efficient information acquisition with a bayesian deep latent gaussian model
|
https://scholar.google.com/scholar?cluster=1836550825324169638&hl=en&as_sdt=0,5
| 8 | 2,019 |
Post training 4-bit quantization of convolutional networks for rapid-deployment
| 427 |
neurips
| 57 | 13 |
2023-06-15 23:44:18.276000
|
https://github.com/submission2019/cnn-quantization
| 210 |
Post training 4-bit quantization of convolutional networks for rapid-deployment
|
https://scholar.google.com/scholar?cluster=4498286641114478762&hl=en&as_sdt=0,36
| 8 | 2,019 |
Implicit Regularization in Deep Matrix Factorization
| 359 |
neurips
| 12 | 1 |
2023-06-15 23:44:18.460000
|
https://github.com/roosephu/deep_matrix_factorization
| 29 |
Implicit regularization in deep matrix factorization
|
https://scholar.google.com/scholar?cluster=10227179810482169638&hl=en&as_sdt=0,47
| 3 | 2,019 |
Limitations of Lazy Training of Two-layers Neural Network
| 110 |
neurips
| 0 | 0 |
2023-06-15 23:44:18.643000
|
https://github.com/bGhorbani/Lazy-Training-Neural-Nets
| 1 |
Limitations of lazy training of two-layers neural network
|
https://scholar.google.com/scholar?cluster=6757542555979455345&hl=en&as_sdt=0,14
| 3 | 2,019 |
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning
| 39 |
neurips
| 0 | 0 |
2023-06-15 23:44:18.825000
|
https://github.com/fmaxgarcia/Meta-MDP
| 8 |
A meta-MDP approach to exploration for lifelong reinforcement learning
|
https://scholar.google.com/scholar?cluster=12587192655885814669&hl=en&as_sdt=0,5
| 3 | 2,019 |
Learning by Abstraction: The Neural State Machine
| 223 |
neurips
| 124 | 15 |
2023-06-15 23:44:19.008000
|
https://github.com/stanfordnlp/mac-network
| 482 |
Learning by abstraction: The neural state machine
|
https://scholar.google.com/scholar?cluster=7361406080192630148&hl=en&as_sdt=0,6
| 32 | 2,019 |
Unified Language Model Pre-training for Natural Language Understanding and Generation
| 1,224 |
neurips
| 1,867 | 362 |
2023-06-15 23:44:19.190000
|
https://github.com/microsoft/unilm
| 12,770 |
Unified language model pre-training for natural language understanding and generation
|
https://scholar.google.com/scholar?cluster=2361521774652423867&hl=en&as_sdt=0,5
| 260 | 2,019 |
Metric Learning for Adversarial Robustness
| 156 |
neurips
| 8 | 0 |
2023-06-15 23:44:19.372000
|
https://github.com/columbia/Metric_Learning_Adversarial_Robustness
| 48 |
Metric learning for adversarial robustness
|
https://scholar.google.com/scholar?cluster=12602705747887433697&hl=en&as_sdt=0,37
| 9 | 2,019 |
Fine-grained Optimization of Deep Neural Networks
| 2 |
neurips
| 0 | 0 |
2023-06-15 23:44:19.555000
|
https://github.com/meteozay/fg-sgd
| 1 |
Fine-grained optimization of deep neural networks
|
https://scholar.google.com/scholar?cluster=17242393399395222917&hl=en&as_sdt=0,31
| 1 | 2,019 |
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
| 86 |
neurips
| 16 | 3 |
2023-06-15 23:44:19.737000
|
https://github.com/pathak22/modular-assemblies
| 106 |
Learning to control self-assembling morphologies: a study of generalization via modularity
|
https://scholar.google.com/scholar?cluster=6230712298907925889&hl=en&as_sdt=0,39
| 7 | 2,019 |
Alleviating Label Switching with Optimal Transport
| 5 |
neurips
| 1 | 0 |
2023-06-15 23:44:19.920000
|
https://github.com/pierremon/label-switching
| 1 |
Alleviating label switching with optimal transport
|
https://scholar.google.com/scholar?cluster=1201213527784885312&hl=en&as_sdt=0,10
| 1 | 2,019 |
Fisher Efficient Inference of Intractable Models
| 11 |
neurips
| 1 | 0 |
2023-06-15 23:44:20.103000
|
https://github.com/anewgithubname/Stein-Density-Ratio-Estimation
| 9 |
Fisher efficient inference of intractable models
|
https://scholar.google.com/scholar?cluster=13168405321313545565&hl=en&as_sdt=0,44
| 3 | 2,019 |
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
| 21 |
neurips
| 1 | 0 |
2023-06-15 23:44:20.285000
|
https://github.com/knowzou/SRVR
| 6 |
Stochastic gradient Hamiltonian Monte Carlo methods with recursive variance reduction
|
https://scholar.google.com/scholar?cluster=11585981262585149330&hl=en&as_sdt=0,21
| 2 | 2,019 |
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction
| 22 |
neurips
| 2 | 2 |
2023-06-15 23:44:20.468000
|
https://github.com/ErikGartner/actor
| 11 |
Domes to drones: Self-supervised active triangulation for 3d human pose reconstruction
|
https://scholar.google.com/scholar?cluster=592377778107181309&hl=en&as_sdt=0,5
| 3 | 2,019 |
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
| 94 |
neurips
| 1 | 0 |
2023-06-15 23:44:20.651000
|
https://github.com/eboursier/sic-mmab
| 3 |
SIC-MMAB: synchronisation involves communication in multiplayer multi-armed bandits
|
https://scholar.google.com/scholar?cluster=15702315682738134157&hl=en&as_sdt=0,47
| 2 | 2,019 |
A Step Toward Quantifying Independently Reproducible Machine Learning Research
| 92 |
neurips
| 5 | 0 |
2023-06-15 23:44:20.834000
|
https://github.com/EdwardRaff/Quantifying-Independently-Reproducible-ML
| 74 |
A step toward quantifying independently reproducible machine learning research
|
https://scholar.google.com/scholar?cluster=3230939669723958133&hl=en&as_sdt=0,44
| 6 | 2,019 |
Latent distance estimation for random geometric graphs
| 21 |
neurips
| 0 | 0 |
2023-06-15 23:44:21.017000
|
https://github.com/ErnestoArayaValdivia/NeurIPS_Code
| 0 |
Latent distance estimation for random geometric graphs
|
https://scholar.google.com/scholar?cluster=2780885878815062825&hl=en&as_sdt=0,23
| 1 | 2,019 |
On the Inductive Bias of Neural Tangent Kernels
| 205 |
neurips
| 3 | 1 |
2023-06-15 23:44:21.200000
|
https://github.com/albietz/ckn_kernel
| 13 |
On the inductive bias of neural tangent kernels
|
https://scholar.google.com/scholar?cluster=4267008353441249556&hl=en&as_sdt=0,5
| 2 | 2,019 |
Rethinking Kernel Methods for Node Representation Learning on Graphs
| 20 |
neurips
| 7 | 3 |
2023-06-15 23:44:21.383000
|
https://github.com/bluer555/KernelGCN
| 32 |
Rethinking kernel methods for node representation learning on graphs
|
https://scholar.google.com/scholar?cluster=3909779312042974366&hl=en&as_sdt=0,5
| 4 | 2,019 |
Input Similarity from the Neural Network Perspective
| 40 |
neurips
| 3 | 0 |
2023-06-15 23:44:21.580000
|
https://github.com/Lydorn/netsimilarity
| 26 |
Input similarity from the neural network perspective
|
https://scholar.google.com/scholar?cluster=3029405318289332183&hl=en&as_sdt=0,5
| 3 | 2,019 |
Transfer Learning via Minimizing the Performance Gap Between Domains
| 40 |
neurips
| 2 | 0 |
2023-06-15 23:44:21.762000
|
https://github.com/bwang-ml/gapBoost
| 7 |
Transfer learning via minimizing the performance gap between domains
|
https://scholar.google.com/scholar?cluster=15708830539707170384&hl=en&as_sdt=0,44
| 3 | 2,019 |
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
| 103 |
neurips
| 3 | 0 |
2023-06-15 23:44:21.946000
|
https://github.com/thuml/Batch-Spectral-Shrinkage
| 21 |
Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning
|
https://scholar.google.com/scholar?cluster=787372724003726768&hl=en&as_sdt=0,5
| 3 | 2,019 |
Efficiently Learning Fourier Sparse Set Functions
| 14 |
neurips
| 0 | 0 |
2023-06-15 23:44:22.128000
|
https://github.com/andisheh94/Efficiently-Learning-Fourier-Sparse-Set-Functions
| 2 |
Efficiently learning Fourier sparse set functions
|
https://scholar.google.com/scholar?cluster=1775522679298532934&hl=en&as_sdt=0,34
| 1 | 2,019 |
Goal-conditioned Imitation Learning
| 151 |
neurips
| 9 | 5 |
2023-06-15 23:44:22.310000
|
https://github.com/dingyiming0427/goalgail
| 60 |
Goal-conditioned imitation learning
|
https://scholar.google.com/scholar?cluster=9705309728838214557&hl=en&as_sdt=0,5
| 3 | 2,019 |
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing
| 13 |
neurips
| 3 | 0 |
2023-06-15 23:44:22.493000
|
https://github.com/flodinl/neurips-1bCS
| 0 |
Superset technique for approximate recovery in one-bit compressed sensing
|
https://scholar.google.com/scholar?cluster=5088393971521119646&hl=en&as_sdt=0,31
| 1 | 2,019 |
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
| 51 |
neurips
| 0 | 0 |
2023-06-15 23:44:22.676000
|
https://github.com/kimiandj/min_swe
| 2 |
Asymptotic guarantees for learning generative models with the sliced-Wasserstein distance
|
https://scholar.google.com/scholar?cluster=10949056232611348982&hl=en&as_sdt=0,34
| 2 | 2,019 |
Learning Nonsymmetric Determinantal Point Processes
| 41 |
neurips
| 5 | 6 |
2023-06-15 23:44:22.859000
|
https://github.com/cgartrel/nonsymmetric-DPP-learning
| 20 |
Learning nonsymmetric determinantal point processes
|
https://scholar.google.com/scholar?cluster=958215336299287859&hl=en&as_sdt=0,5
| 3 | 2,019 |
Quantum Embedding of Knowledge for Reasoning
| 35 |
neurips
| 11 | 4 |
2023-06-15 23:44:23.042000
|
https://github.com/IBM/e2r
| 22 |
Quantum embedding of knowledge for reasoning
|
https://scholar.google.com/scholar?cluster=11321153699952712196&hl=en&as_sdt=0,33
| 10 | 2,019 |
Online Normalization for Training Neural Networks
| 40 |
neurips
| 19 | 2 |
2023-06-15 23:44:23.224000
|
https://github.com/cerebras/online-normalization
| 74 |
Online normalization for training neural networks
|
https://scholar.google.com/scholar?cluster=2495221729297962361&hl=en&as_sdt=0,5
| 7 | 2,019 |
Equitable Stable Matchings in Quadratic Time
| 8 |
neurips
| 2 | 0 |
2023-06-15 23:44:23.407000
|
https://github.com/ntzia/stable-marriage
| 1 |
Equitable stable matchings in quadratic time
|
https://scholar.google.com/scholar?cluster=5357034451332937688&hl=en&as_sdt=0,34
| 2 | 2,019 |
Making AI Forget You: Data Deletion in Machine Learning
| 209 |
neurips
| 4 | 1 |
2023-06-15 23:44:23.590000
|
https://github.com/tginart/deletion-efficient-kmeans
| 22 |
Making ai forget you: Data deletion in machine learning
|
https://scholar.google.com/scholar?cluster=11624023015366681673&hl=en&as_sdt=0,5
| 4 | 2,019 |
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
| 103 |
neurips
| 10 | 0 |
2023-06-15 23:44:23.773000
|
https://github.com/s-huu/TurningWeaknessIntoStrength
| 36 |
A new defense against adversarial images: Turning a weakness into a strength
|
https://scholar.google.com/scholar?cluster=11699672055738649895&hl=en&as_sdt=0,47
| 5 | 2,019 |
Divergence-Augmented Policy Optimization
| 9 |
neurips
| 4 | 0 |
2023-06-15 23:44:23.955000
|
https://github.com/lns/dapo
| 36 |
Divergence-augmented policy optimization
|
https://scholar.google.com/scholar?cluster=6823081176814326206&hl=en&as_sdt=0,33
| 3 | 2,019 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.