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|---|---|---|---|---|---|---|---|---|---|---|---|
A New Distribution on the Simplex with Auto-Encoding Applications
| 3
|
neurips
| 2
| 6
|
2023-06-15 23:43:10.915000
|
https://github.com/astirn/MV-Kumaraswamy
| 9
|
A new distribution on the simplex with auto-encoding applications
|
https://scholar.google.com/scholar?cluster=3624843939474502459&hl=en&as_sdt=0,5
| 1
| 2,019
|
Model Selection for Contextual Bandits
| 75
|
neurips
| 11
| 1
|
2023-06-15 23:43:11.097000
|
https://github.com/akshaykr/oracle_cb
| 28
|
Model selection for contextual bandits
|
https://scholar.google.com/scholar?cluster=604693572400865214&hl=en&as_sdt=0,5
| 6
| 2,019
|
FreeAnchor: Learning to Match Anchors for Visual Object Detection
| 306
|
neurips
| 113
| 15
|
2023-06-15 23:43:11.280000
|
https://github.com/zhangxiaosong18/FreeAnchor
| 670
|
Freeanchor: Learning to match anchors for visual object detection
|
https://scholar.google.com/scholar?cluster=8989326398890700545&hl=en&as_sdt=0,5
| 21
| 2,019
|
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
| 1,343
|
neurips
| 286
| 72
|
2023-06-15 23:43:11.462000
|
https://github.com/nyu-mll/jiant
| 1,526
|
Superglue: A stickier benchmark for general-purpose language understanding systems
|
https://scholar.google.com/scholar?cluster=12169300718787849246&hl=en&as_sdt=0,5
| 47
| 2,019
|
Glyce: Glyph-vectors for Chinese Character Representations
| 155
|
neurips
| 73
| 31
|
2023-06-15 23:43:11.646000
|
https://github.com/ShannonAI/glyce
| 400
|
Glyce: Glyph-vectors for chinese character representations
|
https://scholar.google.com/scholar?cluster=12813244310394658475&hl=en&as_sdt=0,50
| 12
| 2,019
|
General E(2)-Equivariant Steerable CNNs
| 319
|
neurips
| 69
| 6
|
2023-06-15 23:43:11.828000
|
https://github.com/QUVA-Lab/e2cnn
| 511
|
General e (2)-equivariant steerable cnns
|
https://scholar.google.com/scholar?cluster=11235150486117594383&hl=en&as_sdt=0,14
| 18
| 2,019
|
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
| 67
|
neurips
| 0
| 0
|
2023-06-15 23:43:12.010000
|
https://github.com/RohithKuditipudi/mode-connectivity
| 0
|
Explaining landscape connectivity of low-cost solutions for multilayer nets
|
https://scholar.google.com/scholar?cluster=11853008675817475458&hl=en&as_sdt=0,33
| 1
| 2,019
|
Limitations of the empirical Fisher approximation for natural gradient descent
| 139
|
neurips
| 5
| 0
|
2023-06-15 23:43:12.192000
|
https://github.com/fkunstner/limitations-empirical-fisher
| 42
|
Limitations of the empirical Fisher approximation for natural gradient descent
|
https://scholar.google.com/scholar?cluster=7342864390936584496&hl=en&as_sdt=0,33
| 5
| 2,019
|
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression
| 31
|
neurips
| 1
| 0
|
2023-06-15 23:43:12.374000
|
https://github.com/utoronto-theory/pIRLS
| 8
|
Fast, provably convergent irls algorithm for p-norm linear regression
|
https://scholar.google.com/scholar?cluster=4351185537881682779&hl=en&as_sdt=0,36
| 4
| 2,019
|
A Model to Search for Synthesizable Molecules
| 84
|
neurips
| 23
| 11
|
2023-06-15 23:43:12.556000
|
https://github.com/john-bradshaw/molecule-chef
| 73
|
A model to search for synthesizable molecules
|
https://scholar.google.com/scholar?cluster=11917452358715261450&hl=en&as_sdt=0,33
| 5
| 2,019
|
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
| 23
|
neurips
| 3
| 0
|
2023-06-15 23:43:12.738000
|
https://github.com/xiaozhanguva/Measure-Concentration
| 7
|
Empirically measuring concentration: Fundamental limits on intrinsic robustness
|
https://scholar.google.com/scholar?cluster=2460203345511372640&hl=en&as_sdt=0,5
| 2
| 2,019
|
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
| 24
|
neurips
| 3
| 0
|
2023-06-15 23:43:12.921000
|
https://github.com/uvavision/DrillDown
| 11
|
Drill-down: Interactive retrieval of complex scenes using natural language queries
|
https://scholar.google.com/scholar?cluster=15992977486578029861&hl=en&as_sdt=0,33
| 7
| 2,019
|
Fast and Accurate Least-Mean-Squares Solvers
| 62
|
neurips
| 11
| 0
|
2023-06-15 23:43:13.103000
|
https://github.com/ibramjub/Fast-and-Accurate-Least-Mean-Squares-Solvers
| 72
|
Fast and accurate least-mean-squares solvers
|
https://scholar.google.com/scholar?cluster=11022765373503234984&hl=en&as_sdt=0,44
| 4
| 2,019
|
Graph Agreement Models for Semi-Supervised Learning
| 55
|
neurips
| 193
| 1
|
2023-06-15 23:43:13.285000
|
https://github.com/tensorflow/neural-structured-learning
| 967
|
Graph agreement models for semi-supervised learning
|
https://scholar.google.com/scholar?cluster=17001131817438418296&hl=en&as_sdt=0,5
| 48
| 2,019
|
A Kernel Loss for Solving the Bellman Equation
| 59
|
neurips
| 0
| 1
|
2023-06-15 23:43:13.467000
|
https://github.com/lewisKit/Kernel-Bellman-Loss
| 0
|
A kernel loss for solving the bellman equation
|
https://scholar.google.com/scholar?cluster=2203690645569443989&hl=en&as_sdt=0,25
| 2
| 2,019
|
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
| 12
|
neurips
| 1
| 0
|
2023-06-15 23:43:13.657000
|
https://github.com/gbc16/AGEM
| 3
|
Agem: Solving linear inverse problems via deep priors and sampling
|
https://scholar.google.com/scholar?cluster=5796954409607252223&hl=en&as_sdt=0,5
| 1
| 2,019
|
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
| 3
|
neurips
| 0
| 0
|
2023-06-15 23:43:13.839000
|
https://github.com/hci-unihd/Probabilistic_Watershed
| 8
|
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
|
https://scholar.google.com/scholar?cluster=9919550398432186214&hl=en&as_sdt=0,5
| 2
| 2,019
|
Learning Robust Options by Conditional Value at Risk Optimization
| 19
|
neurips
| 1
| 0
|
2023-06-15 23:43:14.030000
|
https://github.com/TakuyaHiraoka/Learning-Robust-Options-by-Conditional-Value-at-Risk-Optimization
| 9
|
Learning robust options by conditional value at risk optimization
|
https://scholar.google.com/scholar?cluster=14168282705388373415&hl=en&as_sdt=0,45
| 3
| 2,019
|
A Generic Acceleration Framework for Stochastic Composite Optimization
| 38
|
neurips
| 1
| 0
|
2023-06-15 23:43:14.212000
|
https://github.com/KuluAndrej/NIPS-2019-code
| 1
|
A generic acceleration framework for stochastic composite optimization
|
https://scholar.google.com/scholar?cluster=10947919871280582095&hl=en&as_sdt=0,5
| 2
| 2,019
|
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
| 141
|
neurips
| 47
| 8
|
2023-06-15 23:43:14.395000
|
https://github.com/RunzheYang/MORL
| 189
|
A generalized algorithm for multi-objective reinforcement learning and policy adaptation
|
https://scholar.google.com/scholar?cluster=7721047641895252765&hl=en&as_sdt=0,33
| 8
| 2,019
|
Communication trade-offs for Local-SGD with large step size
| 40
|
neurips
| 1
| 0
|
2023-06-15 23:43:14.577000
|
https://github.com/kishinmh/Local-SGD
| 2
|
Communication trade-offs for local-sgd with large step size
|
https://scholar.google.com/scholar?cluster=16743369759814373109&hl=en&as_sdt=0,5
| 1
| 2,019
|
Towards modular and programmable architecture search
| 28
|
neurips
| 15
| 4
|
2023-06-15 23:43:14.759000
|
https://github.com/negrinho/deep_architect
| 121
|
Towards modular and programmable architecture search
|
https://scholar.google.com/scholar?cluster=6733031206160413504&hl=en&as_sdt=0,5
| 12
| 2,019
|
Large-scale optimal transport map estimation using projection pursuit
| 35
|
neurips
| 4
| 0
|
2023-06-15 23:43:14.942000
|
https://github.com/ChengzijunAixiaoli/PPMM
| 13
|
Large-scale optimal transport map estimation using projection pursuit
|
https://scholar.google.com/scholar?cluster=5340124406367691762&hl=en&as_sdt=0,18
| 1
| 2,019
|
Understanding Attention and Generalization in Graph Neural Networks
| 205
|
neurips
| 49
| 1
|
2023-06-15 23:43:15.124000
|
https://github.com/bknyaz/graph_attention_pool
| 263
|
Understanding attention and generalization in graph neural networks
|
https://scholar.google.com/scholar?cluster=9139711807100164053&hl=en&as_sdt=0,39
| 8
| 2,019
|
Twin Auxilary Classifiers GAN
| 64
|
neurips
| 13
| 3
|
2023-06-15 23:43:15.307000
|
https://github.com/batmanlab/twin_ac
| 47
|
Twin auxilary classifiers gan
|
https://scholar.google.com/scholar?cluster=6377027598993488889&hl=en&as_sdt=0,23
| 1
| 2,019
|
Online Prediction of Switching Graph Labelings with Cluster Specialists
| 3
|
neurips
| 0
| 0
|
2023-06-15 23:43:15.489000
|
https://github.com/jamesro/cluster-specialists
| 0
|
Online prediction of switching graph labelings with cluster specialists
|
https://scholar.google.com/scholar?cluster=7730779833279774550&hl=en&as_sdt=0,47
| 2
| 2,019
|
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters
| 127
|
neurips
| 0
| 1
|
2023-06-15 23:43:15.670000
|
https://github.com/xxshdw/auto_prune
| 6
|
Autoprune: Automatic network pruning by regularizing auxiliary parameters
|
https://scholar.google.com/scholar?cluster=11406488290397197193&hl=en&as_sdt=0,5
| 0
| 2,019
|
Understanding the Role of Momentum in Stochastic Gradient Methods
| 72
|
neurips
| 3
| 0
|
2023-06-15 23:43:15.852000
|
https://github.com/Kipok/understanding-momentum
| 14
|
Understanding the role of momentum in stochastic gradient methods
|
https://scholar.google.com/scholar?cluster=10334362605827292159&hl=en&as_sdt=0,5
| 2
| 2,019
|
DAC: The Double Actor-Critic Architecture for Learning Options
| 47
|
neurips
| 658
| 6
|
2023-06-15 23:43:16.035000
|
https://github.com/ShangtongZhang/DeepRL
| 2,943
|
DAC: The double actor-critic architecture for learning options
|
https://scholar.google.com/scholar?cluster=6317609422653411407&hl=en&as_sdt=0,43
| 93
| 2,019
|
Learning from Label Proportions with Generative Adversarial Networks
| 26
|
neurips
| 1
| 1
|
2023-06-15 23:43:16.217000
|
https://github.com/liujiabin008/LLP-GAN
| 8
|
Learning from label proportions with generative adversarial networks
|
https://scholar.google.com/scholar?cluster=12276305081354929369&hl=en&as_sdt=0,5
| 3
| 2,019
|
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
| 22
|
neurips
| 1
| 0
|
2023-06-15 23:43:16.400000
|
https://github.com/TURuibo/Neuropathic-Pain-Diagnosis-Simulator
| 7
|
Neuropathic pain diagnosis simulator for causal discovery algorithm evaluation
|
https://scholar.google.com/scholar?cluster=3595858583853803295&hl=en&as_sdt=0,5
| 3
| 2,019
|
Budgeted Reinforcement Learning in Continuous State Space
| 18
|
neurips
| 127
| 29
|
2023-06-15 23:43:16.581000
|
https://github.com/eleurent/rl-agents
| 455
|
Budgeted reinforcement learning in continuous state space
|
https://scholar.google.com/scholar?cluster=1156851409573476480&hl=en&as_sdt=0,33
| 20
| 2,019
|
Parameter elimination in particle Gibbs sampling
| 11
|
neurips
| 1
| 0
|
2023-06-15 23:43:16.764000
|
https://github.com/uu-sml/neurips2019-parameter-elimination
| 5
|
Parameter elimination in particle Gibbs sampling
|
https://scholar.google.com/scholar?cluster=11832975274278617749&hl=en&as_sdt=0,50
| 7
| 2,019
|
Understanding Sparse JL for Feature Hashing
| 22
|
neurips
| 0
| 0
|
2023-06-15 23:43:16.946000
|
https://github.com/mjagadeesan/sparsejl-featurehashing
| 4
|
Understanding sparse JL for feature hashing
|
https://scholar.google.com/scholar?cluster=13523913285751839530&hl=en&as_sdt=0,10
| 1
| 2,019
|
Planning in entropy-regularized Markov decision processes and games
| 18
|
neurips
| 1
| 0
|
2023-06-15 23:43:17.128000
|
https://github.com/omardrwch/smoothcruiser-check
| 1
|
Planning in entropy-regularized Markov decision processes and games
|
https://scholar.google.com/scholar?cluster=18118594423877089336&hl=en&as_sdt=0,19
| 2
| 2,019
|
Dynamic Local Regret for Non-convex Online Forecasting
| 10
|
neurips
| 2
| 0
|
2023-06-15 23:43:17.310000
|
https://github.com/Timbasa/Dynamic_Local_Regret_for_Non-convex_Online_Forecasting_NeurIPS2019
| 8
|
Dynamic local regret for non-convex online forecasting
|
https://scholar.google.com/scholar?cluster=6302409167507525072&hl=en&as_sdt=0,5
| 4
| 2,019
|
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
| 93
|
neurips
| 10
| 3
|
2023-06-15 23:43:17.492000
|
https://github.com/felixykliu/NAOMI
| 46
|
Naomi: Non-autoregressive multiresolution sequence imputation
|
https://scholar.google.com/scholar?cluster=5654873960381975776&hl=en&as_sdt=0,5
| 2
| 2,019
|
Conformalized Quantile Regression
| 299
|
neurips
| 38
| 4
|
2023-06-15 23:43:17.674000
|
https://github.com/yromano/cqr
| 170
|
Conformalized quantile regression
|
https://scholar.google.com/scholar?cluster=5581207407270823451&hl=en&as_sdt=0,5
| 8
| 2,019
|
MarginGAN: Adversarial Training in Semi-Supervised Learning
| 36
|
neurips
| 2
| 0
|
2023-06-15 23:43:17.857000
|
https://github.com/xdu-DJhao/MarginGAN
| 9
|
MarginGAN: adversarial training in semi-supervised learning
|
https://scholar.google.com/scholar?cluster=6031857058045286818&hl=en&as_sdt=0,5
| 1
| 2,019
|
Cold Case: The Lost MNIST Digits
| 101
|
neurips
| 32
| 0
|
2023-06-15 23:43:18.040000
|
https://github.com/facebookresearch/qmnist
| 231
|
Cold case: The lost mnist digits
|
https://scholar.google.com/scholar?cluster=9918380668226002925&hl=en&as_sdt=0,5
| 12
| 2,019
|
RUBi: Reducing Unimodal Biases for Visual Question Answering
| 267
|
neurips
| 15
| 3
|
2023-06-15 23:43:18.222000
|
https://github.com/cdancette/rubi.bootstrap.pytorch
| 57
|
Rubi: Reducing unimodal biases for visual question answering
|
https://scholar.google.com/scholar?cluster=3200511868750352559&hl=en&as_sdt=0,49
| 5
| 2,019
|
Learning Sample-Specific Models with Low-Rank Personalized Regression
| 12
|
neurips
| 2
| 1
|
2023-06-15 23:43:18.404000
|
https://github.com/blengerich/personalized_regression
| 15
|
Learning sample-specific models with low-rank personalized regression
|
https://scholar.google.com/scholar?cluster=9544461235321687427&hl=en&as_sdt=0,43
| 6
| 2,019
|
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
| 23
|
neurips
| 0
| 1
|
2023-06-15 23:43:18.586000
|
https://github.com/drgrhm/alg_config
| 1
|
Procrastinating with confidence: Near-optimal, anytime, adaptive algorithm configuration
|
https://scholar.google.com/scholar?cluster=12402924190582219171&hl=en&as_sdt=0,5
| 1
| 2,019
|
Unsupervised Scalable Representation Learning for Multivariate Time Series
| 236
|
neurips
| 84
| 0
|
2023-06-15 23:43:18.769000
|
https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries
| 340
|
Unsupervised scalable representation learning for multivariate time series
|
https://scholar.google.com/scholar?cluster=1013253939456705166&hl=en&as_sdt=0,31
| 17
| 2,019
|
Total Least Squares Regression in Input Sparsity Time
| 8
|
neurips
| 6
| 0
|
2023-06-15 23:43:18.951000
|
https://github.com/yangxinuw/total_least_squares_code
| 4
|
Total least squares regression in input sparsity time
|
https://scholar.google.com/scholar?cluster=976445259821829575&hl=en&as_sdt=0,5
| 1
| 2,019
|
Bayesian Learning of Sum-Product Networks
| 34
|
neurips
| 6
| 3
|
2023-06-15 23:43:19.134000
|
https://github.com/trappmartin/BayesianSumProductNetworks
| 12
|
Bayesian learning of sum-product networks
|
https://scholar.google.com/scholar?cluster=10871336632487264585&hl=en&as_sdt=0,5
| 3
| 2,019
|
Discriminative Topic Modeling with Logistic LDA
| 18
|
neurips
| 5
| 0
|
2023-06-15 23:43:19.316000
|
https://github.com/lucastheis/logistic_lda
| 18
|
Discriminative topic modeling with logistic LDA
|
https://scholar.google.com/scholar?cluster=8692276849254224947&hl=en&as_sdt=0,33
| 2
| 2,019
|
Disentangling Influence: Using disentangled representations to audit model predictions
| 20
|
neurips
| 1
| 0
|
2023-06-15 23:43:19.498000
|
https://github.com/charliemarx/disentangling-influence
| 4
|
Disentangling influence: Using disentangled representations to audit model predictions
|
https://scholar.google.com/scholar?cluster=800319645349031007&hl=en&as_sdt=0,5
| 1
| 2,019
|
Deep Structured Prediction for Facial Landmark Detection
| 24
|
neurips
| 5
| 0
|
2023-06-15 23:43:19.680000
|
https://github.com/lisha-chen/Deep-structured-facial-landmark-detection
| 18
|
Deep structured prediction for facial landmark detection
|
https://scholar.google.com/scholar?cluster=18147202694366911205&hl=en&as_sdt=0,32
| 3
| 2,019
|
Mutually Regressive Point Processes
| 16
|
neurips
| 0
| 0
|
2023-06-15 23:43:19.862000
|
https://github.com/ifiaposto/Mutually-Regressive-Point-Processes
| 4
|
Mutually regressive point processes
|
https://scholar.google.com/scholar?cluster=9562149540635904941&hl=en&as_sdt=0,5
| 2
| 2,019
|
Demystifying Black-box Models with Symbolic Metamodels
| 73
|
neurips
| 22
| 1
|
2023-06-15 23:43:20.045000
|
https://github.com/ahmedmalaa/Symbolic-Metamodeling
| 43
|
Demystifying black-box models with symbolic metamodels
|
https://scholar.google.com/scholar?cluster=4982738822209753358&hl=en&as_sdt=0,33
| 5
| 2,019
|
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data
| 76
|
neurips
| 6
| 2
|
2023-06-15 23:43:20.227000
|
https://github.com/qianlou/SHE
| 22
|
She: A fast and accurate deep neural network for encrypted data
|
https://scholar.google.com/scholar?cluster=13256787420791403235&hl=en&as_sdt=0,5
| 1
| 2,019
|
Competitive Gradient Descent
| 95
|
neurips
| 2
| 0
|
2023-06-15 23:43:20.409000
|
https://github.com/f-t-s/CGD
| 22
|
Competitive gradient descent
|
https://scholar.google.com/scholar?cluster=16079761912267834651&hl=en&as_sdt=0,5
| 4
| 2,019
|
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration
| 25
|
neurips
| 3
| 1
|
2023-06-15 23:43:20.610000
|
https://github.com/nyummvc/Arbicon-Net
| 15
|
Arbicon-net: Arbitrary continuous geometric transformation networks for image registration
|
https://scholar.google.com/scholar?cluster=7779525469156132489&hl=en&as_sdt=0,33
| 4
| 2,019
|
Point-Voxel CNN for Efficient 3D Deep Learning
| 456
|
neurips
| 126
| 0
|
2023-06-15 23:43:20.799000
|
https://github.com/mit-han-lab/pvcnn
| 556
|
Point-voxel cnn for efficient 3d deep learning
|
https://scholar.google.com/scholar?cluster=10002989291325329256&hl=en&as_sdt=0,48
| 24
| 2,019
|
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
| 68
|
neurips
| 7
| 0
|
2023-06-15 23:43:20.982000
|
https://github.com/KaidiXu/ZO-AdaMM
| 22
|
Zo-adamm: Zeroth-order adaptive momentum method for black-box optimization
|
https://scholar.google.com/scholar?cluster=410761263442584539&hl=en&as_sdt=0,33
| 2
| 2,019
|
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
| 162
|
neurips
| 54
| 3
|
2023-06-15 23:43:21.164000
|
https://github.com/perslev/U-Time
| 201
|
U-time: A fully convolutional network for time series segmentation applied to sleep staging
|
https://scholar.google.com/scholar?cluster=8255933860376596525&hl=en&as_sdt=0,21
| 8
| 2,019
|
Meta-Curvature
| 106
|
neurips
| 1
| 2
|
2023-06-15 23:43:21.346000
|
https://github.com/silverbottlep/meta_curvature
| 9
|
Meta-curvature
|
https://scholar.google.com/scholar?cluster=8144207372117342162&hl=en&as_sdt=0,5
| 4
| 2,019
|
Exploration via Hindsight Goal Generation
| 57
|
neurips
| 8
| 0
|
2023-06-15 23:43:21.533000
|
https://github.com/Stilwell-Git/Hindsight-Goal-Generation
| 24
|
Exploration via hindsight goal generation
|
https://scholar.google.com/scholar?cluster=15515347371168435712&hl=en&as_sdt=0,33
| 3
| 2,019
|
VIREL: A Variational Inference Framework for Reinforcement Learning
| 40
|
neurips
| 5
| 1
|
2023-06-15 23:43:21.724000
|
https://github.com/AnujMahajanOxf/VIREL
| 15
|
Virel: A variational inference framework for reinforcement learning
|
https://scholar.google.com/scholar?cluster=3837224869714850766&hl=en&as_sdt=0,33
| 2
| 2,019
|
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
| 31
|
neurips
| 3
| 0
|
2023-06-15 23:43:21.907000
|
https://github.com/jliang993/A3DMM
| 8
|
Trajectory of alternating direction method of multipliers and adaptive acceleration
|
https://scholar.google.com/scholar?cluster=8586222791731543519&hl=en&as_sdt=0,26
| 2
| 2,019
|
Focused Quantization for Sparse CNNs
| 25
|
neurips
| 20
| 4
|
2023-06-15 23:43:22.095000
|
https://github.com/deep-fry/mayo
| 109
|
Focused quantization for sparse CNNs
|
https://scholar.google.com/scholar?cluster=5764391117070924493&hl=en&as_sdt=0,10
| 11
| 2,019
|
Knowledge Extraction with No Observable Data
| 75
|
neurips
| 10
| 0
|
2023-06-15 23:43:22.277000
|
https://github.com/snudatalab/KegNet
| 37
|
Knowledge extraction with no observable data
|
https://scholar.google.com/scholar?cluster=3775105952512776839&hl=en&as_sdt=0,5
| 4
| 2,019
|
Global Guarantees for Blind Demodulation with Generative Priors
| 28
|
neurips
| 0
| 0
|
2023-06-15 23:43:22.460000
|
https://github.com/babhrujoshi/Blind_demod_gen_prior
| 2
|
Global guarantees for blind demodulation with generative priors
|
https://scholar.google.com/scholar?cluster=49698119456763508&hl=en&as_sdt=0,32
| 1
| 2,019
|
Neural Jump Stochastic Differential Equations
| 182
|
neurips
| 17
| 0
|
2023-06-15 23:43:22.642000
|
https://github.com/000Justin000/torchdiffeq
| 45
|
Neural jump stochastic differential equations
|
https://scholar.google.com/scholar?cluster=14697126289882105767&hl=en&as_sdt=0,33
| 4
| 2,019
|
Learning about an exponential amount of conditional distributions
| 26
|
neurips
| 0
| 0
|
2023-06-15 23:43:22.824000
|
https://github.com/IshmaelBelghazi/learning_an_exponential_amount_of_conditional_distributions
| 0
|
Learning about an exponential amount of conditional distributions
|
https://scholar.google.com/scholar?cluster=15393166666132601264&hl=en&as_sdt=0,48
| 2
| 2,019
|
Multi-mapping Image-to-Image Translation via Learning Disentanglement
| 92
|
neurips
| 15
| 1
|
2023-06-15 23:43:23.007000
|
https://github.com/Xiaoming-Yu/DMIT
| 110
|
Multi-mapping image-to-image translation via learning disentanglement
|
https://scholar.google.com/scholar?cluster=18213035425048385822&hl=en&as_sdt=0,5
| 15
| 2,019
|
Explicitly disentangling image content from translation and rotation with spatial-VAE
| 71
|
neurips
| 18
| 2
|
2023-06-15 23:43:23.189000
|
https://github.com/tbepler/spatial-VAE
| 55
|
Explicitly disentangling image content from translation and rotation with spatial-VAE
|
https://scholar.google.com/scholar?cluster=6574810273867158367&hl=en&as_sdt=0,14
| 7
| 2,019
|
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
| 149
|
neurips
| 1
| 0
|
2023-06-15 23:43:23.372000
|
https://github.com/ykasten/Convergence-Rate-NN-Different-Frequencies
| 6
|
The convergence rate of neural networks for learned functions of different frequencies
|
https://scholar.google.com/scholar?cluster=12179223750271364799&hl=en&as_sdt=0,23
| 5
| 2,019
|
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
| 125
|
neurips
| 1
| 0
|
2023-06-15 23:43:23.554000
|
https://github.com/gomena/statistical_bounds_entropic_OT
| 1
|
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
|
https://scholar.google.com/scholar?cluster=6105913006833342284&hl=en&as_sdt=0,44
| 1
| 2,019
|
A Game Theoretic Approach to Class-wise Selective Rationalization
| 47
|
neurips
| 3
| 5
|
2023-06-15 23:43:23.736000
|
https://github.com/code-terminator/classwise_rationale
| 12
|
A game theoretic approach to class-wise selective rationalization
|
https://scholar.google.com/scholar?cluster=8292388829309690879&hl=en&as_sdt=0,21
| 4
| 2,019
|
Variational Bayesian Decision-making for Continuous Utilities
| 18
|
neurips
| 1
| 0
|
2023-06-15 23:43:23.920000
|
https://github.com/tkusmierczyk/lcvi
| 4
|
Variational Bayesian decision-making for continuous utilities
|
https://scholar.google.com/scholar?cluster=2997507484784259646&hl=en&as_sdt=0,5
| 2
| 2,019
|
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
| 204
|
neurips
| 7,320
| 1,025
|
2023-06-15 23:43:24.103000
|
https://github.com/google-research/google-research
| 29,776
|
Search on the replay buffer: Bridging planning and reinforcement learning
|
https://scholar.google.com/scholar?cluster=17777579381680460522&hl=en&as_sdt=0,5
| 727
| 2,019
|
Transductive Zero-Shot Learning with Visual Structure Constraint
| 72
|
neurips
| 9
| 1
|
2023-06-15 23:43:24.285000
|
https://github.com/raywzy/VSC
| 42
|
Transductive zero-shot learning with visual structure constraint
|
https://scholar.google.com/scholar?cluster=14823968865961413196&hl=en&as_sdt=0,10
| 2
| 2,019
|
Implicit Regularization for Optimal Sparse Recovery
| 63
|
neurips
| 1
| 0
|
2023-06-15 23:43:24.467000
|
https://github.com/TomasVaskevicius/implicit_sparsity_neurips2019
| 3
|
Implicit regularization for optimal sparse recovery
|
https://scholar.google.com/scholar?cluster=6600835910528488334&hl=en&as_sdt=0,5
| 3
| 2,019
|
Residual Flows for Invertible Generative Modeling
| 279
|
neurips
| 44
| 3
|
2023-06-15 23:43:24.649000
|
https://github.com/rtqichen/residual-flows
| 251
|
Residual flows for invertible generative modeling
|
https://scholar.google.com/scholar?cluster=13099094504334344711&hl=en&as_sdt=0,5
| 12
| 2,019
|
Adversarial Training and Robustness for Multiple Perturbations
| 309
|
neurips
| 8
| 16
|
2023-06-15 23:43:24.831000
|
https://github.com/ftramer/MultiRobustness
| 46
|
Adversarial training and robustness for multiple perturbations
|
https://scholar.google.com/scholar?cluster=6630235695392252264&hl=en&as_sdt=0,22
| 2
| 2,019
|
Stein Variational Gradient Descent With Matrix-Valued Kernels
| 54
|
neurips
| 1
| 0
|
2023-06-15 23:43:25.013000
|
https://github.com/dilinwang820/matrix_svgd
| 8
|
Stein variational gradient descent with matrix-valued kernels
|
https://scholar.google.com/scholar?cluster=6300168546020464188&hl=en&as_sdt=0,10
| 3
| 2,019
|
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
| 140
|
neurips
| 22
| 1
|
2023-06-15 23:43:25.195000
|
https://github.com/thegregyang/GP4A
| 220
|
Wide feedforward or recurrent neural networks of any architecture are gaussian processes
|
https://scholar.google.com/scholar?cluster=13759507907397409226&hl=en&as_sdt=0,5
| 9
| 2,019
|
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums
| 37
|
neurips
| 2
| 0
|
2023-06-15 23:43:25.378000
|
https://github.com/HadrienHx/ADFS_NeurIPS
| 1
|
An accelerated decentralized stochastic proximal algorithm for finite sums
|
https://scholar.google.com/scholar?cluster=748231720425736301&hl=en&as_sdt=0,5
| 1
| 2,019
|
Data Cleansing for Models Trained with SGD
| 51
|
neurips
| 6
| 1
|
2023-06-15 23:43:25.560000
|
https://github.com/sato9hara/sgd-influence
| 48
|
Data cleansing for models trained with SGD
|
https://scholar.google.com/scholar?cluster=11335556309506749393&hl=en&as_sdt=0,5
| 2
| 2,019
|
Generating Diverse High-Fidelity Images with VQ-VAE-2
| 1,024
|
neurips
| 1,360
| 34
|
2023-06-15 23:43:25.742000
|
https://github.com/deepmind/sonnet
| 9,571
|
Generating diverse high-fidelity images with vq-vae-2
|
https://scholar.google.com/scholar?cluster=7339215229612384474&hl=en&as_sdt=0,5
| 425
| 2,019
|
When to Trust Your Model: Model-Based Policy Optimization
| 611
|
neurips
| 77
| 18
|
2023-06-15 23:43:25.924000
|
https://github.com/JannerM/mbpo
| 416
|
When to trust your model: Model-based policy optimization
|
https://scholar.google.com/scholar?cluster=4248859125840907707&hl=en&as_sdt=0,39
| 10
| 2,019
|
On Making Stochastic Classifiers Deterministic
| 23
|
neurips
| 7,320
| 1,025
|
2023-06-15 23:43:26.106000
|
https://github.com/google-research/google-research
| 29,776
|
On making stochastic classifiers deterministic
|
https://scholar.google.com/scholar?cluster=9514965586959557733&hl=en&as_sdt=0,39
| 727
| 2,019
|
Blind Super-Resolution Kernel Estimation using an Internal-GAN
| 323
|
neurips
| 73
| 38
|
2023-06-15 23:43:26.288000
|
https://github.com/sefibk/KernelGAN
| 312
|
Blind super-resolution kernel estimation using an internal-gan
|
https://scholar.google.com/scholar?cluster=248352425941813595&hl=en&as_sdt=0,5
| 8
| 2,019
|
Learning to Learn By Self-Critique
| 66
|
neurips
| 7
| 3
|
2023-06-15 23:43:26.471000
|
https://github.com/AntreasAntoniou/Learning_to_Learn_via_Self-Critique
| 44
|
Learning to learn by self-critique
|
https://scholar.google.com/scholar?cluster=1091119097992623438&hl=en&as_sdt=0,33
| 6
| 2,019
|
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
| 33
|
neurips
| 0
| 0
|
2023-06-15 23:43:26.652000
|
https://github.com/umarteau/Newton-Method-for-GSC-losses-
| 3
|
Globally convergent newton methods for ill-conditioned generalized self-concordant losses
|
https://scholar.google.com/scholar?cluster=4570728101284672632&hl=en&as_sdt=0,3
| 3
| 2,019
|
Is Deeper Better only when Shallow is Good?
| 37
|
neurips
| 0
| 0
|
2023-06-15 23:43:26.835000
|
https://github.com/emalach/IsDeeperBetter
| 0
|
Is deeper better only when shallow is good?
|
https://scholar.google.com/scholar?cluster=8541069837961005267&hl=en&as_sdt=0,44
| 1
| 2,019
|
The Thermodynamic Variational Objective
| 36
|
neurips
| 0
| 0
|
2023-06-15 23:43:27.017000
|
https://github.com/vmasrani/tvo
| 0
|
The thermodynamic variational objective
|
https://scholar.google.com/scholar?cluster=8303803537398982071&hl=en&as_sdt=0,5
| 0
| 2,019
|
Sampling Sketches for Concave Sublinear Functions of Frequencies
| 6
|
neurips
| 0
| 0
|
2023-06-15 23:43:27.199000
|
https://github.com/ofirgeri/concave-sublinear-sampling
| 0
|
Sampling sketches for concave sublinear functions of frequencies
|
https://scholar.google.com/scholar?cluster=5473104206629301806&hl=en&as_sdt=0,4
| 1
| 2,019
|
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
| 962
|
neurips
| 111
| 12
|
2023-06-15 23:43:27.381000
|
https://github.com/kaidic/LDAM-DRW
| 569
|
Learning imbalanced datasets with label-distribution-aware margin loss
|
https://scholar.google.com/scholar?cluster=14488921758498385858&hl=en&as_sdt=0,5
| 15
| 2,019
|
Multivariate Triangular Quantile Maps for Novelty Detection
| 18
|
neurips
| 3
| 5
|
2023-06-15 23:43:27.564000
|
https://github.com/GinGinWang/MTQ
| 7
|
Multivariate triangular quantile maps for novelty detection
|
https://scholar.google.com/scholar?cluster=7987123893251995250&hl=en&as_sdt=0,5
| 3
| 2,019
|
Gradient-based Adaptive Markov Chain Monte Carlo
| 23
|
neurips
| 5
| 0
|
2023-06-15 23:43:27.746000
|
https://github.com/mtitsias/gadMCMC
| 21
|
Gradient-based adaptive markov chain monte carlo
|
https://scholar.google.com/scholar?cluster=13990086497515936909&hl=en&as_sdt=0,18
| 3
| 2,019
|
Online Forecasting of Total-Variation-bounded Sequences
| 34
|
neurips
| 0
| 0
|
2023-06-15 23:43:27.928000
|
https://github.com/yuxiangw/tv_online
| 3
|
Online forecasting of total-variation-bounded sequences
|
https://scholar.google.com/scholar?cluster=1207130136020942361&hl=en&as_sdt=0,33
| 2
| 2,019
|
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
| 386
|
neurips
| 52
| 15
|
2023-06-15 23:43:28.111000
|
https://github.com/JiawangBian/sc_depth_pl
| 293
|
Unsupervised scale-consistent depth and ego-motion learning from monocular video
|
https://scholar.google.com/scholar?cluster=1362055635586007597&hl=en&as_sdt=0,5
| 10
| 2,019
|
Variational Denoising Network: Toward Blind Noise Modeling and Removal
| 242
|
neurips
| 45
| 3
|
2023-06-15 23:43:28.292000
|
https://github.com/zsyOAOA/VDNet
| 194
|
Variational denoising network: Toward blind noise modeling and removal
|
https://scholar.google.com/scholar?cluster=18313022457936123811&hl=en&as_sdt=0,5
| 3
| 2,019
|
Multi-task Learning for Aggregated Data using Gaussian Processes
| 25
|
neurips
| 1
| 0
|
2023-06-15 23:43:28.475000
|
https://github.com/frb-yousefi/aggregated-multitask-gp
| 10
|
Multi-task learning for aggregated data using Gaussian processes
|
https://scholar.google.com/scholar?cluster=9068915187307088687&hl=en&as_sdt=0,33
| 2
| 2,019
|
Efficient characterization of electrically evoked responses for neural interfaces
| 5
|
neurips
| 0
| 0
|
2023-06-15 23:43:28.657000
|
https://github.com/Chichilnisky-Lab/shah-neurips-2019
| 3
|
Efficient characterization of electrically evoked responses for neural interfaces
|
https://scholar.google.com/scholar?cluster=18237433847770575510&hl=en&as_sdt=0,5
| 8
| 2,019
|
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
| 197
|
neurips
| 17
| 1
|
2023-06-15 23:43:28.839000
|
https://github.com/KangchengHou/gntk
| 94
|
Graph neural tangent kernel: Fusing graph neural networks with graph kernels
|
https://scholar.google.com/scholar?cluster=7700085274406978551&hl=en&as_sdt=0,23
| 6
| 2,019
|
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces
| 40
|
neurips
| 3
| 0
|
2023-06-15 23:43:29.021000
|
https://github.com/wangbx66/differentially-private-q-learning
| 10
|
Privacy-preserving q-learning with functional noise in continuous spaces
|
https://scholar.google.com/scholar?cluster=253585098814477836&hl=en&as_sdt=0,5
| 2
| 2,019
|
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