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.