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
|
|---|---|---|---|---|---|---|---|---|---|---|---|
ANODEV2: A Coupled Neural ODE Framework
| 74
|
neurips
| 19
| 4
|
2023-06-15 23:42:52.656000
|
https://github.com/amirgholami/anode
| 99
|
ANODEV2: A coupled neural ODE framework
|
https://scholar.google.com/scholar?cluster=18212332066465500294&hl=en&as_sdt=0,5
| 7
| 2,019
|
Learning Neural Networks with Adaptive Regularization
| 16
|
neurips
| 14
| 0
|
2023-06-15 23:42:52.839000
|
https://github.com/yaohungt/Adaptive-Regularization-Neural-Network
| 67
|
Learning neural networks with adaptive regularization
|
https://scholar.google.com/scholar?cluster=5481205132880543162&hl=en&as_sdt=0,14
| 5
| 2,019
|
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
| 112
|
neurips
| 44
| 0
|
2023-06-15 23:42:53.027000
|
https://github.com/yihanjiang/turboae
| 68
|
Turbo autoencoder: Deep learning based channel codes for point-to-point communication channels
|
https://scholar.google.com/scholar?cluster=17000412546845490197&hl=en&as_sdt=0,30
| 9
| 2,019
|
DetNAS: Backbone Search for Object Detection
| 217
|
neurips
| 49
| 1
|
2023-06-15 23:42:53.209000
|
https://github.com/megvii-model/DetNAS
| 288
|
Detnas: Backbone search for object detection
|
https://scholar.google.com/scholar?cluster=17156640731829045371&hl=en&as_sdt=0,3
| 15
| 2,019
|
Diffusion Improves Graph Learning
| 426
|
neurips
| 35
| 0
|
2023-06-15 23:42:53.391000
|
https://github.com/klicperajo/gdc
| 212
|
Diffusion improves graph learning
|
https://scholar.google.com/scholar?cluster=17335287554708427599&hl=en&as_sdt=0,5
| 3
| 2,019
|
Inverting Deep Generative models, One layer at a time
| 49
|
neurips
| 3
| 0
|
2023-06-15 23:42:53.574000
|
https://github.com/cecilialeiqi/InvertGAN_LP
| 6
|
Inverting deep generative models, one layer at a time
|
https://scholar.google.com/scholar?cluster=11354932647596357536&hl=en&as_sdt=0,33
| 2
| 2,019
|
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
| 196
|
neurips
| 5
| 0
|
2023-06-15 23:42:53.756000
|
https://github.com/Hadisalman/robust-verify-benchmark
| 39
|
A convex relaxation barrier to tight robustness verification of neural networks
|
https://scholar.google.com/scholar?cluster=6023655920144066290&hl=en&as_sdt=0,5
| 3
| 2,019
|
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
| 272
|
neurips
| 0
| 0
|
2023-06-15 23:42:53.938000
|
https://github.com/optimization-for-data-driven-science/FairFashionMNIST
| 3
|
Solving a class of non-convex min-max games using iterative first order methods
|
https://scholar.google.com/scholar?cluster=17358134548745942568&hl=en&as_sdt=0,5
| 3
| 2,019
|
Modeling Tabular data using Conditional GAN
| 593
|
neurips
| 236
| 41
|
2023-06-15 23:42:54.120000
|
https://github.com/DAI-Lab/CTGAN
| 902
|
Modeling tabular data using conditional gan
|
https://scholar.google.com/scholar?cluster=3578506996923518478&hl=en&as_sdt=0,5
| 22
| 2,019
|
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
| 155
|
neurips
| 22
| 7
|
2023-06-15 23:42:54.303000
|
https://github.com/IssamLaradji/sls
| 113
|
Painless stochastic gradient: Interpolation, line-search, and convergence rates
|
https://scholar.google.com/scholar?cluster=14034515731155354848&hl=en&as_sdt=0,5
| 8
| 2,019
|
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
| 67
|
neurips
| 2
| 0
|
2023-06-15 23:42:54.491000
|
https://github.com/NMerlis/TabulaRL
| 2
|
Tight regret bounds for model-based reinforcement learning with greedy policies
|
https://scholar.google.com/scholar?cluster=10045062126055715763&hl=en&as_sdt=0,5
| 0
| 2,019
|
Neural Lyapunov Control
| 204
|
neurips
| 24
| 4
|
2023-06-15 23:42:54.672000
|
https://github.com/YaChienChang/Neural-Lyapunov-Control
| 93
|
Neural lyapunov control
|
https://scholar.google.com/scholar?cluster=8520646851972056742&hl=en&as_sdt=0,5
| 4
| 2,019
|
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
| 11
|
neurips
| 1
| 0
|
2023-06-15 23:42:54.855000
|
https://github.com/adidevraj/SVRPDA
| 1
|
Stochastic variance reduced primal dual algorithms for empirical composition optimization
|
https://scholar.google.com/scholar?cluster=14019914477826286322&hl=en&as_sdt=0,7
| 1
| 2,019
|
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis
| 25
|
neurips
| 0
| 0
|
2023-06-15 23:42:55.037000
|
https://github.com/yos1up/data-dependence-of-plateau
| 2
|
Data-dependence of plateau phenomenon in learning with neural network---Statistical mechanical analysis
|
https://scholar.google.com/scholar?cluster=9048066171797706784&hl=en&as_sdt=0,33
| 3
| 2,019
|
Differentiable Cloth Simulation for Inverse Problems
| 119
|
neurips
| 15
| 7
|
2023-06-15 23:42:55.219000
|
https://github.com/williamljb/DifferentiableCloth
| 62
|
Differentiable cloth simulation for inverse problems
|
https://scholar.google.com/scholar?cluster=6530342369806505197&hl=en&as_sdt=0,21
| 4
| 2,019
|
Region-specific Diffeomorphic Metric Mapping
| 38
|
neurips
| 29
| 1
|
2023-06-15 23:42:55.402000
|
https://github.com/uncbiag/registration
| 245
|
Region-specific diffeomorphic metric mapping
|
https://scholar.google.com/scholar?cluster=4638584861181072263&hl=en&as_sdt=0,47
| 16
| 2,019
|
Domain Generalization via Model-Agnostic Learning of Semantic Features
| 506
|
neurips
| 19
| 5
|
2023-06-15 23:42:55.584000
|
https://github.com/biomedia-mira/masf
| 138
|
Domain generalization via model-agnostic learning of semantic features
|
https://scholar.google.com/scholar?cluster=3778888251228243033&hl=en&as_sdt=0,36
| 7
| 2,019
|
Unconstrained Monotonic Neural Networks
| 145
|
neurips
| 14
| 1
|
2023-06-15 23:42:55.766000
|
https://github.com/AWehenkel/UMNN
| 90
|
Unconstrained monotonic neural networks
|
https://scholar.google.com/scholar?cluster=199577294502605803&hl=en&as_sdt=0,15
| 3
| 2,019
|
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
| 9
|
neurips
| 0
| 0
|
2023-06-15 23:42:55.949000
|
https://github.com/dkumor/instrumental-cutsets
| 0
|
Efficient identification in linear structural causal models with instrumental cutsets
|
https://scholar.google.com/scholar?cluster=3388344391383563829&hl=en&as_sdt=0,33
| 2
| 2,019
|
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
| 39
|
neurips
| 195
| 7
|
2023-06-15 23:42:56.131000
|
https://github.com/kuleshov/audio-super-res
| 937
|
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
|
https://scholar.google.com/scholar?cluster=329745740006359011&hl=en&as_sdt=0,44
| 23
| 2,019
|
Inducing brain-relevant bias in natural language processing models
| 63
|
neurips
| 6
| 0
|
2023-06-15 23:42:56.314000
|
https://github.com/danrsc/bert_brain_neurips_2019
| 13
|
Inducing brain-relevant bias in natural language processing models
|
https://scholar.google.com/scholar?cluster=8126421380617072393&hl=en&as_sdt=0,5
| 3
| 2,019
|
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies
| 27
|
neurips
| 11
| 3
|
2023-06-15 23:42:56.496000
|
https://github.com/KamyarGh/rl_swiss
| 55
|
Smile: Scalable meta inverse reinforcement learning through context-conditional policies
|
https://scholar.google.com/scholar?cluster=9166968138900222&hl=en&as_sdt=0,34
| 2
| 2,019
|
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
| 49
|
neurips
| 4
| 1
|
2023-06-15 23:42:56.678000
|
https://github.com/pfnet-research/einconv
| 36
|
Exploring unexplored tensor network decompositions for convolutional neural networks
|
https://scholar.google.com/scholar?cluster=7698176316630164925&hl=en&as_sdt=0,5
| 18
| 2,019
|
Interval timing in deep reinforcement learning agents
| 15
|
neurips
| 1,383
| 59
|
2023-06-15 23:42:56.860000
|
https://github.com/deepmind/lab
| 6,878
|
Interval timing in deep reinforcement learning agents
|
https://scholar.google.com/scholar?cluster=7474977642715586787&hl=en&as_sdt=0,47
| 471
| 2,019
|
Uncertainty-based Continual Learning with Adaptive Regularization
| 119
|
neurips
| 8
| 1
|
2023-06-15 23:42:57.041000
|
https://github.com/csm9493/UCL
| 30
|
Uncertainty-based continual learning with adaptive regularization
|
https://scholar.google.com/scholar?cluster=12251011644241284133&hl=en&as_sdt=0,8
| 3
| 2,019
|
Implicit Posterior Variational Inference for Deep Gaussian Processes
| 37
|
neurips
| 2
| 0
|
2023-06-15 23:42:57.223000
|
https://github.com/HeroKillerEver/ipvi-dgp
| 4
|
Implicit posterior variational inference for deep Gaussian processes
|
https://scholar.google.com/scholar?cluster=9226734796788465308&hl=en&as_sdt=0,5
| 2
| 2,019
|
Are Sixteen Heads Really Better than One?
| 654
|
neurips
| 13
| 3
|
2023-06-15 23:42:57.406000
|
https://github.com/pmichel31415/are-16-heads-really-better-than-1
| 151
|
Are sixteen heads really better than one?
|
https://scholar.google.com/scholar?cluster=10123248687041820762&hl=en&as_sdt=0,33
| 6
| 2,019
|
Model Compression with Adversarial Robustness: A Unified Optimization Framework
| 117
|
neurips
| 10
| 2
|
2023-06-15 23:42:57.587000
|
https://github.com/shupenggui/ATMC
| 45
|
Model compression with adversarial robustness: A unified optimization framework
|
https://scholar.google.com/scholar?cluster=13117140860952320078&hl=en&as_sdt=0,23
| 5
| 2,019
|
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
| 78
|
neurips
| 2
| 0
|
2023-06-15 23:42:57.769000
|
https://github.com/ZiangYan/subspace-attack.pytorch
| 9
|
Subspace attack: Exploiting promising subspaces for query-efficient black-box attacks
|
https://scholar.google.com/scholar?cluster=15048956358112658396&hl=en&as_sdt=0,41
| 4
| 2,019
|
Combinatorial Bayesian Optimization using the Graph Cartesian Product
| 68
|
neurips
| 18
| 8
|
2023-06-15 23:42:57.951000
|
https://github.com/QUVA-Lab/COMBO
| 39
|
Combinatorial bayesian optimization using the graph cartesian product
|
https://scholar.google.com/scholar?cluster=17490775000583948305&hl=en&as_sdt=0,5
| 8
| 2,019
|
Sample Adaptive MCMC
| 6
|
neurips
| 0
| 0
|
2023-06-15 23:42:58.134000
|
https://github.com/michaelhzhu/SampleAdaptiveMCMC
| 0
|
Sample adaptive mcmc
|
https://scholar.google.com/scholar?cluster=2679459716559547614&hl=en&as_sdt=0,33
| 3
| 2,019
|
Tree-Sliced Variants of Wasserstein Distances
| 64
|
neurips
| 2
| 2
|
2023-06-15 23:42:58.316000
|
https://github.com/lttam/TreeWasserstein
| 12
|
Tree-sliced variants of Wasserstein distances
|
https://scholar.google.com/scholar?cluster=11585923409514731345&hl=en&as_sdt=0,36
| 3
| 2,019
|
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
| 3
|
neurips
| 1
| 0
|
2023-06-15 23:42:58.498000
|
https://github.com/kaushalpaneri/ode2scm
| 3
|
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
|
https://scholar.google.com/scholar?cluster=3048623518283550163&hl=en&as_sdt=0,5
| 1
| 2,019
|
Topology-Preserving Deep Image Segmentation
| 166
|
neurips
| 18
| 10
|
2023-06-15 23:42:58.680000
|
https://github.com/HuXiaoling/TopoLoss
| 104
|
Topology-preserving deep image segmentation
|
https://scholar.google.com/scholar?cluster=16336319447146727941&hl=en&as_sdt=0,5
| 5
| 2,019
|
Progressive Augmentation of GANs
| 18
|
neurips
| 1
| 0
|
2023-06-15 23:42:58.862000
|
https://github.com/boschresearch/PA-GAN
| 6
|
Progressive augmentation of gans
|
https://scholar.google.com/scholar?cluster=202132054535931802&hl=en&as_sdt=0,31
| 4
| 2,019
|
Online sampling from log-concave distributions
| 6
|
neurips
| 2
| 0
|
2023-06-15 23:42:59.044000
|
https://github.com/holdenlee/Online_Sampling
| 0
|
Online sampling from log-concave distributions
|
https://scholar.google.com/scholar?cluster=1144827139395736431&hl=en&as_sdt=0,5
| 4
| 2,019
|
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
| 3
|
neurips
| 0
| 0
|
2023-06-15 23:42:59.226000
|
https://github.com/joshuaas/GBDSP-NeurIPS19
| 5
|
Generalized block-diagonal structure pursuit: Learning soft latent task assignment against negative transfer
|
https://scholar.google.com/scholar?cluster=3170413548219724478&hl=en&as_sdt=0,33
| 2
| 2,019
|
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems
| 26
|
neurips
| 1
| 0
|
2023-06-15 23:42:59.408000
|
https://github.com/yhjung88/ThompsonSamplinginRestlessBandits
| 4
|
Regret bounds for thompson sampling in episodic restless bandit problems
|
https://scholar.google.com/scholar?cluster=2292837516141377796&hl=en&as_sdt=0,5
| 1
| 2,019
|
Adaptive Sequence Submodularity
| 27
|
neurips
| 0
| 0
|
2023-06-15 23:42:59.590000
|
https://github.com/ehsankazemi/adaptiveSubseq
| 5
|
Adaptive sequence submodularity
|
https://scholar.google.com/scholar?cluster=11662805676922738881&hl=en&as_sdt=0,5
| 1
| 2,019
|
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
| 117
|
neurips
| 8
| 0
|
2023-06-15 23:42:59.772000
|
https://github.com/chao1224/n_gram_graph
| 30
|
N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
|
https://scholar.google.com/scholar?cluster=10555688337090524490&hl=en&as_sdt=0,37
| 3
| 2,019
|
The spiked matrix model with generative priors
| 44
|
neurips
| 1
| 0
|
2023-06-15 23:42:59.954000
|
https://github.com/sphinxteam/StructuredPrior_demo
| 3
|
The spiked matrix model with generative priors
|
https://scholar.google.com/scholar?cluster=598500019720272007&hl=en&as_sdt=0,33
| 5
| 2,019
|
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares
| 111
|
neurips
| 116
| 0
|
2023-06-15 23:43:00.138000
|
https://github.com/D-X-Y/ResNeXt-DenseNet
| 608
|
The step decay schedule: A near optimal, geometrically decaying learning rate procedure for least squares
|
https://scholar.google.com/scholar?cluster=18119082696067324871&hl=en&as_sdt=0,5
| 19
| 2,019
|
Understanding and Improving Layer Normalization
| 171
|
neurips
| 0
| 3
|
2023-06-15 23:43:00.320000
|
https://github.com/lancopku/AdaNorm
| 39
|
Understanding and improving layer normalization
|
https://scholar.google.com/scholar?cluster=12686324462743591705&hl=en&as_sdt=0,5
| 7
| 2,019
|
Generative Modeling by Estimating Gradients of the Data Distribution
| 1,107
|
neurips
| 76
| 5
|
2023-06-15 23:43:00.503000
|
https://github.com/ermongroup/ncsn
| 514
|
Generative modeling by estimating gradients of the data distribution
|
https://scholar.google.com/scholar?cluster=7819543055117584506&hl=en&as_sdt=0,5
| 9
| 2,019
|
Balancing Efficiency and Fairness in On-Demand Ridesourcing
| 47
|
neurips
| 3
| 0
|
2023-06-15 23:43:00.685000
|
https://github.com/zxok365/On-Demand-Ridesourcing-Project
| 4
|
Balancing efficiency and fairness in on-demand ridesourcing
|
https://scholar.google.com/scholar?cluster=7775414618361693698&hl=en&as_sdt=0,5
| 2
| 2,019
|
A coupled autoencoder approach for multi-modal analysis of cell types
| 26
|
neurips
| 1
| 0
|
2023-06-15 23:43:00.867000
|
https://github.com/AllenInstitute/coupledAE
| 6
|
A coupled autoencoder approach for multi-modal analysis of cell types
|
https://scholar.google.com/scholar?cluster=4156171046829362168&hl=en&as_sdt=0,10
| 6
| 2,019
|
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
| 55
|
neurips
| 8
| 6
|
2023-06-15 23:43:01.049000
|
https://github.com/ermongroup/MetaIRL
| 60
|
Meta-inverse reinforcement learning with probabilistic context variables
|
https://scholar.google.com/scholar?cluster=5700441467138799438&hl=en&as_sdt=0,44
| 10
| 2,019
|
Practical and Consistent Estimation of f-Divergences
| 37
|
neurips
| 7,320
| 1,025
|
2023-06-15 23:43:01.231000
|
https://github.com/google-research/google-research
| 29,776
|
Practical and consistent estimation of f-divergences
|
https://scholar.google.com/scholar?cluster=11789682867268248535&hl=en&as_sdt=0,36
| 727
| 2,019
|
Policy Poisoning in Batch Reinforcement Learning and Control
| 83
|
neurips
| 3
| 0
|
2023-06-15 23:43:01.415000
|
https://github.com/myzwisc/PPRL_NeurIPS19
| 5
|
Policy poisoning in batch reinforcement learning and control
|
https://scholar.google.com/scholar?cluster=7958681038301936389&hl=en&as_sdt=0,5
| 1
| 2,019
|
R2D2: Reliable and Repeatable Detector and Descriptor
| 145
|
neurips
| 78
| 15
|
2023-06-15 23:43:01.600000
|
https://github.com/naver/r2d2
| 399
|
R2d2: Reliable and repeatable detector and descriptor
|
https://scholar.google.com/scholar?cluster=3698474168660752568&hl=en&as_sdt=0,11
| 25
| 2,019
|
First Order Motion Model for Image Animation
| 544
|
neurips
| 3,084
| 287
|
2023-06-15 23:43:01.782000
|
https://github.com/AliaksandrSiarohin/first-order-model
| 13,547
|
First order motion model for image animation
|
https://scholar.google.com/scholar?cluster=8970624957269493610&hl=en&as_sdt=0,5
| 352
| 2,019
|
Scalable inference of topic evolution via models for latent geometric structures
| 12
|
neurips
| 0
| 0
|
2023-06-15 23:43:01.964000
|
https://github.com/moonfolk/SDDM
| 3
|
Scalable inference of topic evolution via models for latent geometric structures
|
https://scholar.google.com/scholar?cluster=14180440036747609592&hl=en&as_sdt=0,5
| 2
| 2,019
|
Anti-efficient encoding in emergent communication
| 73
|
neurips
| 98
| 7
|
2023-06-15 23:43:02.147000
|
https://github.com/facebookresearch/EGG
| 261
|
Anti-efficient encoding in emergent communication
|
https://scholar.google.com/scholar?cluster=434185138707911239&hl=en&as_sdt=0,41
| 16
| 2,019
|
Improving Black-box Adversarial Attacks with a Transfer-based Prior
| 209
|
neurips
| 10
| 4
|
2023-06-15 23:43:02.346000
|
https://github.com/thu-ml/Prior-Guided-RGF
| 35
|
Improving black-box adversarial attacks with a transfer-based prior
|
https://scholar.google.com/scholar?cluster=327803698641685395&hl=en&as_sdt=0,38
| 7
| 2,019
|
REM: From Structural Entropy to Community Structure Deception
| 38
|
neurips
| 0
| 1
|
2023-06-15 23:43:02.528000
|
https://github.com/CommunityDeception/CommunityDeceptor
| 0
|
REM: From structural entropy to community structure deception
|
https://scholar.google.com/scholar?cluster=9942215555170717160&hl=en&as_sdt=0,10
| 1
| 2,019
|
Unsupervised Object Segmentation by Redrawing
| 122
|
neurips
| 40
| 1
|
2023-06-15 23:43:02.711000
|
https://github.com/mickaelChen/ReDO
| 175
|
Unsupervised object segmentation by redrawing
|
https://scholar.google.com/scholar?cluster=3034099820799167647&hl=en&as_sdt=0,5
| 9
| 2,019
|
The Implicit Bias of AdaGrad on Separable Data
| 10
|
neurips
| 0
| 0
|
2023-06-15 23:43:02.894000
|
https://github.com/qianqian513/Implicit-bias-Adagrad
| 0
|
The implicit bias of adagrad on separable data
|
https://scholar.google.com/scholar?cluster=8719652805953776322&hl=en&as_sdt=0,5
| 1
| 2,019
|
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
| 2
|
neurips
| 1
| 0
|
2023-06-15 23:43:03.076000
|
https://github.com/qianqianxu010/NeurIPS2019-iSplitLBI
| 1
|
iSplit LBI: Individualized partial ranking with ties via split LBI
|
https://scholar.google.com/scholar?cluster=2046333522679278867&hl=en&as_sdt=0,21
| 1
| 2,019
|
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
| 136
|
neurips
| 24
| 5
|
2023-06-15 23:43:03.258000
|
https://github.com/canqin001/PointDAN
| 125
|
Pointdan: A multi-scale 3d domain adaption network for point cloud representation
|
https://scholar.google.com/scholar?cluster=4237979119463438115&hl=en&as_sdt=0,44
| 14
| 2,019
|
Certified Adversarial Robustness with Additive Noise
| 264
|
neurips
| 4
| 1
|
2023-06-15 23:43:03.440000
|
https://github.com/Bai-Li/STN-Code
| 20
|
Certified adversarial robustness with additive noise
|
https://scholar.google.com/scholar?cluster=15944556675714796056&hl=en&as_sdt=0,33
| 2
| 2,019
|
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer
| 17
|
neurips
| 0
| 0
|
2023-06-15 23:43:03.622000
|
https://github.com/theonlybars/neurips-2019-rppa
| 0
|
Optimal pricing in repeated posted-price auctions with different patience of the seller and the buyer
|
https://scholar.google.com/scholar?cluster=4438568951333221100&hl=en&as_sdt=0,37
| 1
| 2,019
|
Stand-Alone Self-Attention in Vision Models
| 897
|
neurips
| 7,320
| 1,025
|
2023-06-15 23:43:03.804000
|
https://github.com/google-research/google-research
| 29,776
|
Stand-alone self-attention in vision models
|
https://scholar.google.com/scholar?cluster=16072663067784939588&hl=en&as_sdt=0,5
| 727
| 2,019
|
Debiased Bayesian inference for average treatment effects
| 12
|
neurips
| 2
| 0
|
2023-06-15 23:43:03.986000
|
https://github.com/kolyanray/Bayesian-Causal-Inference
| 1
|
Debiased Bayesian inference for average treatment effects
|
https://scholar.google.com/scholar?cluster=3807772267363050118&hl=en&as_sdt=0,5
| 1
| 2,019
|
Explicit Disentanglement of Appearance and Perspective in Generative Models
| 39
|
neurips
| 5
| 1
|
2023-06-15 23:43:04.168000
|
https://github.com/SkafteNicki/unsuper
| 7
|
Explicit disentanglement of appearance and perspective in generative models
|
https://scholar.google.com/scholar?cluster=10895888132618213021&hl=en&as_sdt=0,10
| 0
| 2,019
|
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
| 42
|
neurips
| 171
| 1
|
2023-06-15 23:43:04.351000
|
https://github.com/snorkel-team/snorkel-tutorials
| 352
|
Slice-based learning: A programming model for residual learning in critical data slices
|
https://scholar.google.com/scholar?cluster=1884557173665882878&hl=en&as_sdt=0,14
| 22
| 2,019
|
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
| 11
|
neurips
| 0
| 0
|
2023-06-15 23:43:04.533000
|
https://github.com/ruqizhang/poisson-gibbs
| 0
|
Poisson-minibatching for gibbs sampling with convergence rate guarantees
|
https://scholar.google.com/scholar?cluster=8342800199415035207&hl=en&as_sdt=0,44
| 3
| 2,019
|
Thompson Sampling for Multinomial Logit Contextual Bandits
| 36
|
neurips
| 0
| 0
|
2023-06-15 23:43:04.715000
|
https://github.com/minhwanoh/Thompson-sampling-for-MNL-contextual-bandits
| 0
|
Thompson sampling for multinomial logit contextual bandits
|
https://scholar.google.com/scholar?cluster=3730407973811497775&hl=en&as_sdt=0,47
| 1
| 2,019
|
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
| 52
|
neurips
| 11
| 0
|
2023-06-15 23:43:04.897000
|
https://github.com/Caselles/NeurIPS19-SBDRL
| 35
|
Symmetry-based disentangled representation learning requires interaction with environments
|
https://scholar.google.com/scholar?cluster=742614888975626574&hl=en&as_sdt=0,38
| 5
| 2,019
|
Mining GOLD Samples for Conditional GANs
| 12
|
neurips
| 5
| 0
|
2023-06-15 23:43:05.079000
|
https://github.com/sangwoomo/gold
| 16
|
Mining GOLD samples for conditional GANs
|
https://scholar.google.com/scholar?cluster=13194436655250832310&hl=en&as_sdt=0,43
| 2
| 2,019
|
Implicit Generation and Modeling with Energy Based Models
| 226
|
neurips
| 61
| 2
|
2023-06-15 23:43:05.261000
|
https://github.com/openai/ebm_code_release
| 311
|
Implicit generation and modeling with energy based models
|
https://scholar.google.com/scholar?cluster=4613962658885230569&hl=en&as_sdt=0,39
| 7
| 2,019
|
Evaluating Protein Transfer Learning with TAPE
| 516
|
neurips
| 134
| 26
|
2023-06-15 23:43:05.444000
|
https://github.com/songlab-cal/tape
| 559
|
Evaluating protein transfer learning with TAPE
|
https://scholar.google.com/scholar?cluster=2465375203234748072&hl=en&as_sdt=0,47
| 22
| 2,019
|
Recurrent Space-time Graph Neural Networks
| 32
|
neurips
| 5
| 0
|
2023-06-15 23:43:05.626000
|
https://github.com/IuliaDuta/RSTG
| 39
|
Recurrent space-time graph neural networks
|
https://scholar.google.com/scholar?cluster=8909911889342573482&hl=en&as_sdt=0,21
| 6
| 2,019
|
Policy Continuation with Hindsight Inverse Dynamics
| 27
|
neurips
| 0
| 0
|
2023-06-15 23:43:05.808000
|
https://github.com/2Groza/PCHID_code
| 14
|
Policy continuation with hindsight inverse dynamics
|
https://scholar.google.com/scholar?cluster=18153731156196581430&hl=en&as_sdt=0,5
| 2
| 2,019
|
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
| 12
|
neurips
| 1
| 0
|
2023-06-15 23:43:05.990000
|
https://github.com/yanivbl6/quantized_meanfield
| 13
|
A mean field theory of quantized deep networks: The quantization-depth trade-off
|
https://scholar.google.com/scholar?cluster=9411987115140184550&hl=en&as_sdt=0,33
| 2
| 2,019
|
Function-Space Distributions over Kernels
| 33
|
neurips
| 7
| 0
|
2023-06-15 23:43:06.172000
|
https://github.com/wjmaddox/spectralgp
| 29
|
Function-space distributions over kernels
|
https://scholar.google.com/scholar?cluster=12057901025111797760&hl=en&as_sdt=0,10
| 4
| 2,019
|
Fully Neural Network based Model for General Temporal Point Processes
| 106
|
neurips
| 16
| 1
|
2023-06-15 23:43:06.354000
|
https://github.com/omitakahiro/NeuralNetworkPointProcess
| 51
|
Fully neural network based model for general temporal point processes
|
https://scholar.google.com/scholar?cluster=2876413970836324639&hl=en&as_sdt=0,32
| 6
| 2,019
|
Improving Textual Network Learning with Variational Homophilic Embeddings
| 13
|
neurips
| 0
| 1
|
2023-06-15 23:43:06.537000
|
https://github.com/Wenlin-Wang/VHE19
| 2
|
Improving textual network learning with variational homophilic embeddings
|
https://scholar.google.com/scholar?cluster=11511162412153376997&hl=en&as_sdt=0,47
| 2
| 2,019
|
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
| 247
|
neurips
| 44
| 5
|
2023-06-15 23:43:06.719000
|
https://github.com/rajatsen91/deepglo
| 160
|
Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
|
https://scholar.google.com/scholar?cluster=13798952634467747016&hl=en&as_sdt=0,28
| 10
| 2,019
|
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
| 17
|
neurips
| 3
| 5
|
2023-06-15 23:43:06.903000
|
https://github.com/QB3/CLaR
| 9
|
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
|
https://scholar.google.com/scholar?cluster=4147865524251608502&hl=en&as_sdt=0,5
| 5
| 2,019
|
PAC-Bayes under potentially heavy tails
| 25
|
neurips
| 0
| 0
|
2023-06-15 23:43:07.085000
|
https://github.com/feedbackward/1dim
| 1
|
PAC-Bayes under potentially heavy tails
|
https://scholar.google.com/scholar?cluster=8266455462422665081&hl=en&as_sdt=0,33
| 2
| 2,019
|
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
| 404
|
neurips
| 36
| 0
|
2023-06-15 23:43:07.267000
|
https://github.com/Hadisalman/smoothing-adversarial
| 211
|
Provably robust deep learning via adversarially trained smoothed classifiers
|
https://scholar.google.com/scholar?cluster=9920393851690535434&hl=en&as_sdt=0,48
| 9
| 2,019
|
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
| 146
|
neurips
| 31
| 3
|
2023-06-15 23:43:07.450000
|
https://github.com/d909b/cxplain
| 113
|
Cxplain: Causal explanations for model interpretation under uncertainty
|
https://scholar.google.com/scholar?cluster=1657473688091727017&hl=en&as_sdt=0,5
| 8
| 2,019
|
Compacting, Picking and Growing for Unforgetting Continual Learning
| 180
|
neurips
| 22
| 6
|
2023-06-15 23:43:07.632000
|
https://github.com/ivclab/CPG
| 115
|
Compacting, picking and growing for unforgetting continual learning
|
https://scholar.google.com/scholar?cluster=4980143563579080366&hl=en&as_sdt=0,18
| 9
| 2,019
|
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
| 36
|
neurips
| 614
| 301
|
2023-06-15 23:43:07.814000
|
https://github.com/Microsoft/EconML
| 3,002
|
Machine learning estimation of heterogeneous treatment effects with instruments
|
https://scholar.google.com/scholar?cluster=4151014229440412539&hl=en&as_sdt=0,19
| 70
| 2,019
|
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
| 25
|
neurips
| 128
| 12
|
2023-06-15 23:43:07.996000
|
https://github.com/TorchCraft/TorchCraftAI
| 640
|
A structured prediction approach for generalization in cooperative multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=7420014982047754701&hl=en&as_sdt=0,5
| 49
| 2,019
|
On Fenchel Mini-Max Learning
| 21
|
neurips
| 1
| 0
|
2023-06-15 23:43:08.178000
|
https://github.com/chenyang-tao/FML
| 3
|
On fenchel mini-max learning
|
https://scholar.google.com/scholar?cluster=17698432686807766794&hl=en&as_sdt=0,5
| 2
| 2,019
|
Optimizing Generalized Rate Metrics with Three Players
| 22
|
neurips
| 7,320
| 1,025
|
2023-06-15 23:43:08.360000
|
https://github.com/google-research/google-research
| 29,776
|
Optimizing generalized rate metrics with three players
|
https://scholar.google.com/scholar?cluster=5386000896654989772&hl=en&as_sdt=0,5
| 727
| 2,019
|
Stability of Graph Scattering Transforms
| 62
|
neurips
| 4
| 0
|
2023-06-15 23:43:08.543000
|
https://github.com/alelab-upenn/graph-scattering-transforms
| 27
|
Stability of graph scattering transforms
|
https://scholar.google.com/scholar?cluster=1026238758085282246&hl=en&as_sdt=0,32
| 2
| 2,019
|
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation
| 110
|
neurips
| 16
| 3
|
2023-06-15 23:43:08.726000
|
https://github.com/IBM/bLVNet-TAM
| 53
|
More is less: Learning efficient video representations by big-little network and depthwise temporal aggregation
|
https://scholar.google.com/scholar?cluster=955029637361553625&hl=en&as_sdt=0,5
| 9
| 2,019
|
PAC-Bayes Un-Expected Bernstein Inequality
| 32
|
neurips
| 0
| 0
|
2023-06-15 23:43:08.909000
|
https://github.com/bguedj/PAC-Bayesian-Un-Expected-Bernstein-Inequality
| 6
|
PAC-Bayes un-expected Bernstein inequality
|
https://scholar.google.com/scholar?cluster=7074764130481002753&hl=en&as_sdt=0,14
| 5
| 2,019
|
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
| 16
|
neurips
| 1
| 0
|
2023-06-15 23:43:09.092000
|
https://github.com/arunv3rma/NeurIPS-2019
| 2
|
Censored semi-bandits: A framework for resource allocation with censored feedback
|
https://scholar.google.com/scholar?cluster=15760111358296803544&hl=en&as_sdt=0,5
| 1
| 2,019
|
Defending Against Neural Fake News
| 688
|
neurips
| 218
| 39
|
2023-06-15 23:43:09.274000
|
https://github.com/rowanz/grover
| 879
|
Defending against neural fake news
|
https://scholar.google.com/scholar?cluster=5656807327286323509&hl=en&as_sdt=0,5
| 36
| 2,019
|
Faster Boosting with Smaller Memory
| 7
|
neurips
| 4
| 2
|
2023-06-15 23:43:09.457000
|
https://github.com/arapat/sparrow
| 21
|
Faster boosting with smaller memory
|
https://scholar.google.com/scholar?cluster=10204358402782261121&hl=en&as_sdt=0,5
| 3
| 2,019
|
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
| 91
|
neurips
| 2
| 0
|
2023-06-15 23:43:09.639000
|
https://github.com/hwang595/DETOX
| 15
|
DETOX: A redundancy-based framework for faster and more robust gradient aggregation
|
https://scholar.google.com/scholar?cluster=6276765982452512417&hl=en&as_sdt=0,5
| 3
| 2,019
|
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
| 37
|
neurips
| 4
| 0
|
2023-06-15 23:43:09.822000
|
https://github.com/apple2373/MetaIRNet
| 28
|
Meta-reinforced synthetic data for one-shot fine-grained visual recognition
|
https://scholar.google.com/scholar?cluster=4113151338341724063&hl=en&as_sdt=0,5
| 2
| 2,019
|
PHYRE: A New Benchmark for Physical Reasoning
| 95
|
neurips
| 62
| 22
|
2023-06-15 23:43:10.004000
|
https://github.com/facebookresearch/phyre
| 421
|
Phyre: A new benchmark for physical reasoning
|
https://scholar.google.com/scholar?cluster=9555658528231205655&hl=en&as_sdt=0,5
| 19
| 2,019
|
Provably robust boosted decision stumps and trees against adversarial attacks
| 55
|
neurips
| 11
| 0
|
2023-06-15 23:43:10.186000
|
https://github.com/max-andr/provably-robust-boosting
| 47
|
Provably robust boosted decision stumps and trees against adversarial attacks
|
https://scholar.google.com/scholar?cluster=6608146364863001507&hl=en&as_sdt=0,5
| 5
| 2,019
|
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response
| 9
|
neurips
| 1
| 2
|
2023-06-15 23:43:10.368000
|
https://github.com/mlzxzhou/keras-gnm
| 2
|
Graph-based semi-supervised learning with non-ignorable non-response
|
https://scholar.google.com/scholar?cluster=6776605979147432576&hl=en&as_sdt=0,22
| 3
| 2,019
|
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
| 579
|
neurips
| 119
| 6
|
2023-06-15 23:43:10.551000
|
https://github.com/YuliaRubanova/latent_ode
| 429
|
Latent ordinary differential equations for irregularly-sampled time series
|
https://scholar.google.com/scholar?cluster=4522947842501588842&hl=en&as_sdt=0,5
| 20
| 2,019
|
On the Correctness and Sample Complexity of Inverse Reinforcement Learning
| 13
|
neurips
| 1
| 0
|
2023-06-15 23:43:10.733000
|
https://github.com/akomandu/L1SVMIRL
| 2
|
On the correctness and sample complexity of inverse reinforcement learning
|
https://scholar.google.com/scholar?cluster=5503249221034094355&hl=en&as_sdt=0,5
| 2
| 2,019
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.