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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 |
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