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Approximate Knowledge Compilation by Online Collapsed Importance Sampling
| 21
|
neurips
| 0
| 0
|
2023-06-15 17:54:52.719000
|
https://github.com/UCLA-StarAI/Collapsed-Compilation
| 5
|
Approximate knowledge compilation by online collapsed importance sampling
|
https://scholar.google.com/scholar?cluster=5801857808795259088&hl=en&as_sdt=0,10
| 4
| 2,018
|
Reversible Recurrent Neural Networks
| 48
|
neurips
| 7
| 1
|
2023-06-15 17:54:52.912000
|
https://github.com/matthewjmackay/reversible-rnn
| 35
|
Reversible recurrent neural networks
|
https://scholar.google.com/scholar?cluster=2936325833713118727&hl=en&as_sdt=0,44
| 3
| 2,018
|
Regularization Learning Networks: Deep Learning for Tabular Datasets
| 79
|
neurips
| 6
| 2
|
2023-06-15 17:54:53.106000
|
https://github.com/irashavitt/regularization_learning_networks
| 31
|
Regularization learning networks: deep learning for tabular datasets
|
https://scholar.google.com/scholar?cluster=12900371387873290272&hl=en&as_sdt=0,47
| 3
| 2,018
|
On Learning Intrinsic Rewards for Policy Gradient Methods
| 160
|
neurips
| 10
| 2
|
2023-06-15 17:54:53.299000
|
https://github.com/Hwhitetooth/lirpg
| 54
|
On learning intrinsic rewards for policy gradient methods
|
https://scholar.google.com/scholar?cluster=8658005357410230302&hl=en&as_sdt=0,45
| 4
| 2,018
|
Single-Agent Policy Tree Search With Guarantees
| 27
|
neurips
| 15
| 1
|
2023-06-15 17:54:53.496000
|
https://github.com/deepmind/boxoban-levels
| 54
|
Single-agent policy tree search with guarantees
|
https://scholar.google.com/scholar?cluster=17454634556201960088&hl=en&as_sdt=0,26
| 9
| 2,018
|
Bias and Generalization in Deep Generative Models: An Empirical Study
| 99
|
neurips
| 8
| 0
|
2023-06-15 17:54:53.690000
|
https://github.com/ermongroup/BiasAndGeneralization
| 25
|
Bias and generalization in deep generative models: An empirical study
|
https://scholar.google.com/scholar?cluster=17301681294706446940&hl=en&as_sdt=0,11
| 5
| 2,018
|
Link Prediction Based on Graph Neural Networks
| 1,395
|
neurips
| 129
| 24
|
2023-06-15 17:54:53.883000
|
https://github.com/muhanzhang/SEAL
| 493
|
Link prediction based on graph neural networks
|
https://scholar.google.com/scholar?cluster=11968553220977234326&hl=en&as_sdt=0,5
| 12
| 2,018
|
A flexible model for training action localization with varying levels of supervision
| 41
|
neurips
| 6
| 1
|
2023-06-15 17:54:54.074000
|
https://github.com/jalayrac/weakactionloc
| 17
|
A flexible model for training action localization with varying levels of supervision
|
https://scholar.google.com/scholar?cluster=12745987706790622376&hl=en&as_sdt=0,5
| 4
| 2,018
|
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
| 295
|
neurips
| 31
| 7
|
2023-06-15 17:54:54.264000
|
https://github.com/podgorskiy/GPND
| 125
|
Generative probabilistic novelty detection with adversarial autoencoders
|
https://scholar.google.com/scholar?cluster=13335383760622553502&hl=en&as_sdt=0,3
| 11
| 2,018
|
Informative Features for Model Comparison
| 24
|
neurips
| 3
| 0
|
2023-06-15 17:54:54.460000
|
https://github.com/wittawatj/kernel-mod
| 17
|
Informative features for model comparison
|
https://scholar.google.com/scholar?cluster=962836959160034441&hl=en&as_sdt=0,10
| 6
| 2,018
|
Discrimination-aware Channel Pruning for Deep Neural Networks
| 615
|
neurips
| 27
| 9
|
2023-06-15 17:54:54.650000
|
https://github.com/SCUT-AILab/DCP
| 179
|
Discrimination-aware channel pruning for deep neural networks
|
https://scholar.google.com/scholar?cluster=4423411645597495&hl=en&as_sdt=0,10
| 9
| 2,018
|
On Fast Leverage Score Sampling and Optimal Learning
| 78
|
neurips
| 2
| 1
|
2023-06-15 17:54:54.841000
|
https://github.com/LCSL/bless
| 12
|
On fast leverage score sampling and optimal learning
|
https://scholar.google.com/scholar?cluster=6173645811972804817&hl=en&as_sdt=0,44
| 9
| 2,018
|
Robustness of conditional GANs to noisy labels
| 174
|
neurips
| 9
| 2
|
2023-06-15 17:54:55.031000
|
https://github.com/POLane16/Robust-Conditional-GAN
| 39
|
Robustness of conditional gans to noisy labels
|
https://scholar.google.com/scholar?cluster=4597323022745403664&hl=en&as_sdt=0,10
| 3
| 2,018
|
Legendre Decomposition for Tensors
| 14
|
neurips
| 4
| 0
|
2023-06-15 17:54:55.222000
|
https://github.com/mahito-sugiyama/Legendre-decomposition
| 12
|
Legendre decomposition for tensors
|
https://scholar.google.com/scholar?cluster=12973396671492815941&hl=en&as_sdt=0,10
| 2
| 2,018
|
SING: Symbol-to-Instrument Neural Generator
| 60
|
neurips
| 25
| 1
|
2023-06-15 17:54:55.412000
|
https://github.com/facebookresearch/SING
| 155
|
Sing: Symbol-to-instrument neural generator
|
https://scholar.google.com/scholar?cluster=9576037029701279224&hl=en&as_sdt=0,33
| 10
| 2,018
|
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
| 99
|
neurips
| 7
| 2
|
2023-06-15 17:54:55.602000
|
https://github.com/sjblim/rmsn_nips_2018
| 30
|
Forecasting treatment responses over time using recurrent marginal structural networks
|
https://scholar.google.com/scholar?cluster=9312966518414628527&hl=en&as_sdt=0,33
| 1
| 2,018
|
Quadratic Decomposable Submodular Function Minimization
| 13
|
neurips
| 1
| 0
|
2023-06-15 17:54:55.793000
|
https://github.com/lipan00123/QDSDM
| 0
|
Quadratic decomposable submodular function minimization
|
https://scholar.google.com/scholar?cluster=9668278212333240026&hl=en&as_sdt=0,33
| 1
| 2,018
|
Deep Anomaly Detection Using Geometric Transformations
| 520
|
neurips
| 35
| 2
|
2023-06-15 17:54:55.983000
|
https://github.com/izikgo/AnomalyDetectionTransformations
| 154
|
Deep anomaly detection using geometric transformations
|
https://scholar.google.com/scholar?cluster=15277146675093535725&hl=en&as_sdt=0,3
| 7
| 2,018
|
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
| 88
|
neurips
| 1
| 0
|
2023-06-15 17:54:56.174000
|
https://github.com/MeckyWu/subspace-match
| 15
|
Towards understanding learning representations: To what extent do different neural networks learn the same representation
|
https://scholar.google.com/scholar?cluster=401428033641216502&hl=en&as_sdt=0,13
| 2
| 2,018
|
An intriguing failing of convolutional neural networks and the CoordConv solution
| 730
|
neurips
| 37
| 7
|
2023-06-15 17:54:56.365000
|
https://github.com/uber-research/coordconv
| 202
|
An intriguing failing of convolutional neural networks and the coordconv solution
|
https://scholar.google.com/scholar?cluster=1725137104710452960&hl=en&as_sdt=0,18
| 6
| 2,018
|
A Smoother Way to Train Structured Prediction Models
| 18
|
neurips
| 4
| 0
|
2023-06-15 17:54:56.555000
|
https://github.com/krishnap25/casimir
| 2
|
A smoother way to train structured prediction models
|
https://scholar.google.com/scholar?cluster=9176087356126828757&hl=en&as_sdt=0,10
| 2
| 2,018
|
3D-Aware Scene Manipulation via Inverse Graphics
| 176
|
neurips
| 41
| 0
|
2023-06-15 17:54:56.746000
|
https://github.com/ysymyth/3D-SDN
| 262
|
3d-aware scene manipulation via inverse graphics
|
https://scholar.google.com/scholar?cluster=1601238761105816866&hl=en&as_sdt=0,44
| 16
| 2,018
|
Complex Gated Recurrent Neural Networks
| 51
|
neurips
| 11
| 0
|
2023-06-15 17:54:56.936000
|
https://github.com/v0lta/Complex-gated-recurrent-neural-networks
| 42
|
Complex gated recurrent neural networks
|
https://scholar.google.com/scholar?cluster=10862653902258650151&hl=en&as_sdt=0,11
| 3
| 2,018
|
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
| 191
|
neurips
| 425
| 18
|
2023-06-15 17:54:57.127000
|
https://github.com/SullyChen/Autopilot-TensorFlow
| 1,235
|
Scalable end-to-end autonomous vehicle testing via rare-event simulation
|
https://scholar.google.com/scholar?cluster=5564001038044175212&hl=en&as_sdt=0,43
| 75
| 2,018
|
Learning Loop Invariants for Program Verification
| 115
|
neurips
| 23
| 1
|
2023-06-15 17:54:57.318000
|
https://github.com/PL-ML/code2inv
| 74
|
Learning loop invariants for program verification
|
https://scholar.google.com/scholar?cluster=6954633128371638771&hl=en&as_sdt=0,5
| 9
| 2,018
|
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective
| 158
|
neurips
| 1
| 1
|
2023-06-15 17:54:57.508000
|
https://github.com/leiwu1990/sgd.stability
| 10
|
How sgd selects the global minima in over-parameterized learning: A dynamical stability perspective
|
https://scholar.google.com/scholar?cluster=1980119340021099329&hl=en&as_sdt=0,33
| 2
| 2,018
|
Neural Guided Constraint Logic Programming for Program Synthesis
| 35
|
neurips
| 9
| 0
|
2023-06-15 17:54:57.698000
|
https://github.com/xuexue/neuralkanren
| 85
|
Neural guided constraint logic programming for program synthesis
|
https://scholar.google.com/scholar?cluster=5770275785272500195&hl=en&as_sdt=0,36
| 9
| 2,018
|
Neural Ordinary Differential Equations
| 3,210
|
neurips
| 848
| 61
|
2023-06-15 17:54:57.888000
|
https://github.com/rtqichen/torchdiffeq
| 4,672
|
Neural ordinary differential equations
|
https://scholar.google.com/scholar?cluster=13748354740225969894&hl=en&as_sdt=0,33
| 123
| 2,018
|
Coupled Variational Bayes via Optimization Embedding
| 29
|
neurips
| 3
| 2
|
2023-06-15 17:54:58.079000
|
https://github.com/Hanjun-Dai/cvb
| 10
|
Coupled variational bayes via optimization embedding
|
https://scholar.google.com/scholar?cluster=9010555957492755231&hl=en&as_sdt=0,5
| 5
| 2,018
|
Policy Optimization via Importance Sampling
| 87
|
neurips
| 3
| 1
|
2023-06-15 17:54:58.271000
|
https://github.com/T3p/pois
| 12
|
Policy optimization via importance sampling
|
https://scholar.google.com/scholar?cluster=16130728419946747088&hl=en&as_sdt=0,5
| 6
| 2,018
|
Task-Driven Convolutional Recurrent Models of the Visual System
| 144
|
neurips
| 15
| 0
|
2023-06-15 17:54:58.479000
|
https://github.com/neuroailab/tnn
| 92
|
Task-driven convolutional recurrent models of the visual system
|
https://scholar.google.com/scholar?cluster=11039722383223148947&hl=en&as_sdt=0,34
| 12
| 2,018
|
Paraphrasing Complex Network: Network Compression via Factor Transfer
| 374
|
neurips
| 7
| 0
|
2023-06-15 17:54:58.670000
|
https://github.com/Jangho-Kim/Factor-Transfer-pytorch
| 14
|
Paraphrasing complex network: Network compression via factor transfer
|
https://scholar.google.com/scholar?cluster=2520473274058783123&hl=en&as_sdt=0,5
| 1
| 2,018
|
A Simple Cache Model for Image Recognition
| 20
|
neurips
| 0
| 0
|
2023-06-15 17:54:58.861000
|
https://github.com/eminorhan/simple-cache
| 2
|
A simple cache model for image recognition
|
https://scholar.google.com/scholar?cluster=3091315690960335000&hl=en&as_sdt=0,29
| 3
| 2,018
|
Learning Attractor Dynamics for Generative Memory
| 20
|
neurips
| 16
| 0
|
2023-06-15 17:54:59.051000
|
https://github.com/deepmind/dynamic-kanerva-machines
| 40
|
Learning attractor dynamics for generative memory
|
https://scholar.google.com/scholar?cluster=9940290258944118765&hl=en&as_sdt=0,22
| 11
| 2,018
|
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior
| 96
|
neurips
| 2
| 0
|
2023-06-15 17:54:59.241000
|
https://github.com/rddy/isql
| 27
|
Where do you think you're going?: Inferring beliefs about dynamics from behavior
|
https://scholar.google.com/scholar?cluster=11438620297016616954&hl=en&as_sdt=0,22
| 6
| 2,018
|
Image Inpainting via Generative Multi-column Convolutional Neural Networks
| 291
|
neurips
| 91
| 25
|
2023-06-15 17:54:59.431000
|
https://github.com/shepnerd/inpainting_gmcnn
| 400
|
Image inpainting via generative multi-column convolutional neural networks
|
https://scholar.google.com/scholar?cluster=14919715529082387957&hl=en&as_sdt=0,40
| 20
| 2,018
|
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices
| 29
|
neurips
| 3
| 0
|
2023-06-15 17:54:59.622000
|
https://github.com/zhenxingjian/SPD-SRU
| 14
|
A statistical recurrent model on the manifold of symmetric positive definite matrices
|
https://scholar.google.com/scholar?cluster=5544428600595730510&hl=en&as_sdt=0,44
| 2
| 2,018
|
Object-Oriented Dynamics Predictor
| 31
|
neurips
| 3
| 0
|
2023-06-15 17:54:59.813000
|
https://github.com/mig-zh/OODP
| 13
|
Object-oriented dynamics predictor
|
https://scholar.google.com/scholar?cluster=1811390955386289421&hl=en&as_sdt=0,11
| 3
| 2,018
|
To Trust Or Not To Trust A Classifier
| 387
|
neurips
| 46
| 2
|
2023-06-15 17:55:00.003000
|
https://github.com/google/TrustScore
| 167
|
To trust or not to trust a classifier
|
https://scholar.google.com/scholar?cluster=9292152849001694574&hl=en&as_sdt=0,10
| 14
| 2,018
|
Deep Reinforcement Learning of Marked Temporal Point Processes
| 97
|
neurips
| 18
| 1
|
2023-06-15 17:55:00.194000
|
https://github.com/Networks-Learning/tpprl
| 71
|
Deep reinforcement learning of marked temporal point processes
|
https://scholar.google.com/scholar?cluster=10991436220054749409&hl=en&as_sdt=0,30
| 7
| 2,018
|
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
| 146
|
neurips
| 10
| 0
|
2023-06-15 17:55:00.384000
|
https://github.com/bargavj/distributedMachineLearning
| 25
|
Distributed learning without distress: Privacy-preserving empirical risk minimization
|
https://scholar.google.com/scholar?cluster=10577380829443665980&hl=en&as_sdt=0,38
| 0
| 2,018
|
Hybrid Knowledge Routed Modules for Large-scale Object Detection
| 74
|
neurips
| 19
| 15
|
2023-06-15 17:55:00.574000
|
https://github.com/chanyn/HKRM
| 98
|
Hybrid knowledge routed modules for large-scale object detection
|
https://scholar.google.com/scholar?cluster=18227077982790889117&hl=en&as_sdt=0,5
| 9
| 2,018
|
BRITS: Bidirectional Recurrent Imputation for Time Series
| 394
|
neurips
| 67
| 12
|
2023-06-15 17:55:00.764000
|
https://github.com/caow13/BRITS
| 173
|
Brits: Bidirectional recurrent imputation for time series
|
https://scholar.google.com/scholar?cluster=17928129084181066672&hl=en&as_sdt=0,47
| 6
| 2,018
|
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
| 222
|
neurips
| 28
| 1
|
2023-06-15 17:55:00.955000
|
https://github.com/akosiorek/sqair
| 96
|
Sequential attend, infer, repeat: Generative modelling of moving objects
|
https://scholar.google.com/scholar?cluster=7430884807828197721&hl=en&as_sdt=0,11
| 11
| 2,018
|
Boosting Black Box Variational Inference
| 26
|
neurips
| 5
| 0
|
2023-06-15 17:55:01.145000
|
https://github.com/ratschlab/boosting-bbvi
| 7
|
Boosting black box variational inference
|
https://scholar.google.com/scholar?cluster=493456481295082921&hl=en&as_sdt=0,43
| 4
| 2,018
|
Transfer of Deep Reactive Policies for MDP Planning
| 23
|
neurips
| 1
| 0
|
2023-06-15 17:55:01.337000
|
https://github.com/dair-iitd/torpido
| 7
|
Transfer of deep reactive policies for mdp planning
|
https://scholar.google.com/scholar?cluster=4580400729732661142&hl=en&as_sdt=0,10
| 4
| 2,018
|
GILBO: One Metric to Measure Them All
| 16
|
neurips
| 322
| 16
|
2023-06-15 17:55:01.527000
|
https://github.com/google/compare_gan
| 1,814
|
GILBO: One metric to measure them all
|
https://scholar.google.com/scholar?cluster=14349686696431672115&hl=en&as_sdt=0,11
| 52
| 2,018
|
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
| 103
|
neurips
| 94
| 0
|
2023-06-15 17:55:01.718000
|
https://github.com/kevin-ssy/FishNet
| 545
|
Fishnet: A versatile backbone for image, region, and pixel level prediction
|
https://scholar.google.com/scholar?cluster=8077266557125333363&hl=en&as_sdt=0,33
| 23
| 2,018
|
Automatic differentiation in ML: Where we are and where we should be going
| 75
|
neurips
| 43
| 31
|
2023-06-15 17:55:01.909000
|
https://github.com/mila-udem/myia
| 454
|
Automatic differentiation in ML: Where we are and where we should be going
|
https://scholar.google.com/scholar?cluster=11874990560582038809&hl=en&as_sdt=0,3
| 31
| 2,018
|
Evolved Policy Gradients
| 228
|
neurips
| 56
| 7
|
2023-06-15 17:55:02.100000
|
https://github.com/openai/EPG
| 240
|
Evolved policy gradients
|
https://scholar.google.com/scholar?cluster=17605986776756195620&hl=en&as_sdt=0,36
| 14
| 2,018
|
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
| 33
|
neurips
| 0
| 0
|
2023-06-15 17:55:02.291000
|
https://github.com/r3831/SAKPCA
| 0
|
Streaming Kernel PCA with Random Features
|
https://scholar.google.com/scholar?cluster=17070435901311007360&hl=en&as_sdt=0,32
| 3
| 2,018
|
Faster Neural Networks Straight from JPEG
| 175
|
neurips
| 40
| 12
|
2023-06-15 17:55:02.491000
|
https://github.com/uber-research/jpeg2dct
| 231
|
Faster neural networks straight from jpeg
|
https://scholar.google.com/scholar?cluster=9617446820670115100&hl=en&as_sdt=0,5
| 11
| 2,018
|
Visual Reinforcement Learning with Imagined Goals
| 456
|
neurips
| 520
| 39
|
2023-06-15 17:55:02.685000
|
https://github.com/vitchyr/rlkit
| 2,161
|
Visual reinforcement learning with imagined goals
|
https://scholar.google.com/scholar?cluster=5007292417648560707&hl=en&as_sdt=0,8
| 61
| 2,018
|
Deep Generative Models for Distribution-Preserving Lossy Compression
| 103
|
neurips
| 8
| 2
|
2023-06-15 17:55:02.879000
|
https://github.com/mitscha/dplc
| 34
|
Deep generative models for distribution-preserving lossy compression
|
https://scholar.google.com/scholar?cluster=10590142637711882209&hl=en&as_sdt=0,14
| 3
| 2,018
|
With Friends Like These, Who Needs Adversaries?
| 74
|
neurips
| 0
| 0
|
2023-06-15 17:55:03.072000
|
https://github.com/torrvision/whoneedsadversaries
| 12
|
With friends like these, who needs adversaries?
|
https://scholar.google.com/scholar?cluster=5740676327222968631&hl=en&as_sdt=0,10
| 9
| 2,018
|
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
| 72
|
neurips
| 19
| 10
|
2023-06-15 17:55:03.266000
|
https://github.com/thusiyuan/cooperative_scene_parsing
| 91
|
Cooperative holistic scene understanding: Unifying 3d object, layout, and camera pose estimation
|
https://scholar.google.com/scholar?cluster=5227625249975009897&hl=en&as_sdt=0,5
| 5
| 2,018
|
Empirical Risk Minimization Under Fairness Constraints
| 383
|
neurips
| 6
| 0
|
2023-06-15 17:55:03.460000
|
https://github.com/jmikko/fair_ERM
| 36
|
Empirical risk minimization under fairness constraints
|
https://scholar.google.com/scholar?cluster=5746250113194301793&hl=en&as_sdt=0,5
| 3
| 2,018
|
A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
| 284
|
neurips
| 27
| 4
|
2023-06-15 17:55:03.654000
|
https://github.com/Alexander-H-Liu/UFDN
| 131
|
A unified feature disentangler for multi-domain image translation and manipulation
|
https://scholar.google.com/scholar?cluster=6007789913986445498&hl=en&as_sdt=0,48
| 6
| 2,018
|
The committee machine: Computational to statistical gaps in learning a two-layers neural network
| 79
|
neurips
| 0
| 0
|
2023-06-15 17:55:03.848000
|
https://github.com/benjaminaubin/TheCommitteeMachine
| 0
|
The committee machine: Computational to statistical gaps in learning a two-layers neural network
|
https://scholar.google.com/scholar?cluster=4903323524016093175&hl=en&as_sdt=0,34
| 2
| 2,018
|
Evolution-Guided Policy Gradient in Reinforcement Learning
| 183
|
neurips
| 52
| 2
|
2023-06-15 17:55:04.042000
|
https://github.com/ShawK91/erl_paper_nips18
| 172
|
Evolution-guided policy gradient in reinforcement learning
|
https://scholar.google.com/scholar?cluster=7920725821302044195&hl=en&as_sdt=0,10
| 5
| 2,018
|
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
| 61
|
neurips
| 0
| 0
|
2023-06-15 17:55:04.236000
|
https://github.com/udellgroup/causal_mf_code
| 6
|
Causal inference with noisy and missing covariates via matrix factorization
|
https://scholar.google.com/scholar?cluster=14104978633422349618&hl=en&as_sdt=0,7
| 3
| 2,018
|
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
| 28
|
neurips
| 0
| 0
|
2023-06-15 17:55:04.430000
|
https://github.com/jingnantes/hybrid-mst
| 8
|
Hybrid-MST: A hybrid active sampling strategy for pairwise preference aggregation
|
https://scholar.google.com/scholar?cluster=13558401002999071074&hl=en&as_sdt=0,10
| 2
| 2,018
|
A no-regret generalization of hierarchical softmax to extreme multi-label classification
| 81
|
neurips
| 16
| 6
|
2023-06-15 17:55:04.624000
|
https://github.com/mwydmuch/extremeText
| 147
|
A no-regret generalization of hierarchical softmax to extreme multi-label classification
|
https://scholar.google.com/scholar?cluster=14171307998042582918&hl=en&as_sdt=0,3
| 13
| 2,018
|
Rectangular Bounding Process
| 21
|
neurips
| 3
| 0
|
2023-06-15 17:55:04.818000
|
https://github.com/xuhuifan/RBP
| 3
|
Rectangular bounding process
|
https://scholar.google.com/scholar?cluster=10618275895500216203&hl=en&as_sdt=0,34
| 1
| 2,018
|
Constructing Unrestricted Adversarial Examples with Generative Models
| 240
|
neurips
| 15
| 6
|
2023-06-15 17:55:05.011000
|
https://github.com/ermongroup/generative_adversary
| 60
|
Constructing unrestricted adversarial examples with generative models
|
https://scholar.google.com/scholar?cluster=14086270849571978699&hl=en&as_sdt=0,39
| 6
| 2,018
|
Boosted Sparse and Low-Rank Tensor Regression
| 32
|
neurips
| 4
| 3
|
2023-06-15 17:55:05.208000
|
https://github.com/LifangHe/SURF
| 9
|
Boosted sparse and low-rank tensor regression
|
https://scholar.google.com/scholar?cluster=13402681948996325867&hl=en&as_sdt=0,10
| 3
| 2,018
|
Deep Neural Networks with Box Convolutions
| 15
|
neurips
| 36
| 3
|
2023-06-15 17:55:05.403000
|
https://github.com/shrubb/box-convolutions
| 513
|
Deep neural networks with box convolutions
|
https://scholar.google.com/scholar?cluster=15004510562166029998&hl=en&as_sdt=0,43
| 17
| 2,018
|
Learning Compressed Transforms with Low Displacement Rank
| 42
|
neurips
| 17
| 6
|
2023-06-15 17:55:05.594000
|
https://github.com/HazyResearch/structured-nets
| 57
|
Learning compressed transforms with low displacement rank
|
https://scholar.google.com/scholar?cluster=8419515952370992696&hl=en&as_sdt=0,50
| 17
| 2,018
|
Deep Defense: Training DNNs with Improved Adversarial Robustness
| 115
|
neurips
| 6
| 0
|
2023-06-15 17:55:05.784000
|
https://github.com/ZiangYan/deepdefense.pytorch
| 37
|
Deep defense: Training dnns with improved adversarial robustness
|
https://scholar.google.com/scholar?cluster=6643757979178770669&hl=en&as_sdt=0,1
| 4
| 2,018
|
Large-Scale Stochastic Sampling from the Probability Simplex
| 5
|
neurips
| 0
| 1
|
2023-06-15 17:55:05.975000
|
https://github.com/jbaker92/scir
| 2
|
Large-scale stochastic sampling from the probability simplex
|
https://scholar.google.com/scholar?cluster=9892795582424041794&hl=en&as_sdt=0,43
| 2
| 2,018
|
Adaptive Methods for Nonconvex Optimization
| 321
|
neurips
| 15
| 3
|
2023-06-15 17:55:06.166000
|
https://github.com/stefan-it/nmt-en-vi
| 51
|
Adaptive methods for nonconvex optimization
|
https://scholar.google.com/scholar?cluster=13576720529696525340&hl=en&as_sdt=0,33
| 6
| 2,018
|
Compact Generalized Non-local Network
| 166
|
neurips
| 41
| 0
|
2023-06-15 17:55:06.357000
|
https://github.com/KaiyuYue/cgnl-network.pytorch
| 259
|
Compact generalized non-local network
|
https://scholar.google.com/scholar?cluster=12004705320658184806&hl=en&as_sdt=0,5
| 7
| 2,018
|
Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning
| 124
|
neurips
| 4
| 2
|
2023-06-15 17:55:06.548000
|
https://github.com/ylytju/sga
| 20
|
Stacked semantics-guided attention model for fine-grained zero-shot learning
|
https://scholar.google.com/scholar?cluster=17870706793172229300&hl=en&as_sdt=0,10
| 1
| 2,018
|
Banach Wasserstein GAN
| 214
|
neurips
| 10
| 1
|
2023-06-15 17:55:06.739000
|
https://github.com/adler-j/bwgan
| 31
|
Banach wasserstein gan
|
https://scholar.google.com/scholar?cluster=10419609167162928003&hl=en&as_sdt=0,5
| 6
| 2,018
|
Visual Object Networks: Image Generation with Disentangled 3D Representations
| 203
|
neurips
| 91
| 12
|
2023-06-15 17:55:06.930000
|
https://github.com/junyanz/VON
| 530
|
Visual object networks: Image generation with disentangled 3D representations
|
https://scholar.google.com/scholar?cluster=3404291286977602499&hl=en&as_sdt=0,5
| 32
| 2,018
|
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
| 214
|
neurips
| 27
| 1
|
2023-06-15 17:55:07.120000
|
https://github.com/mp2893/mime
| 98
|
Mime: Multilevel medical embedding of electronic health records for predictive healthcare
|
https://scholar.google.com/scholar?cluster=9778014794664384350&hl=en&as_sdt=0,47
| 7
| 2,018
|
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
| 74
|
neurips
| 0
| 1
|
2023-06-15 17:55:07.311000
|
https://github.com/lttam/PersistenceFisher
| 5
|
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams
|
https://scholar.google.com/scholar?cluster=1409702383947125765&hl=en&as_sdt=0,5
| 2
| 2,018
|
Bilinear Attention Networks
| 720
|
neurips
| 102
| 2
|
2023-06-15 17:55:07.501000
|
https://github.com/jnhwkim/ban-vqa
| 515
|
Bilinear attention networks
|
https://scholar.google.com/scholar?cluster=10383181412923835294&hl=en&as_sdt=0,5
| 17
| 2,018
|
Constructing Fast Network through Deconstruction of Convolution
| 67
|
neurips
| 5
| 0
|
2023-06-15 17:55:07.692000
|
https://github.com/jyh2986/Active-Shift
| 31
|
Constructing fast network through deconstruction of convolution
|
https://scholar.google.com/scholar?cluster=15893085353567655931&hl=en&as_sdt=0,14
| 3
| 2,018
|
See and Think: Disentangling Semantic Scene Completion
| 65
|
neurips
| 10
| 9
|
2023-06-15 17:55:07.882000
|
https://github.com/ShiceLiu/SATNet
| 47
|
See and think: Disentangling semantic scene completion
|
https://scholar.google.com/scholar?cluster=3218225429355211096&hl=en&as_sdt=0,10
| 5
| 2,018
|
Unsupervised Depth Estimation, 3D Face Rotation and Replacement
| 31
|
neurips
| 31
| 6
|
2023-06-15 17:55:08.073000
|
https://github.com/joelmoniz/DepthNets
| 124
|
Unsupervised depth estimation, 3d face rotation and replacement
|
https://scholar.google.com/scholar?cluster=2371681385764042999&hl=en&as_sdt=0,44
| 8
| 2,018
|
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
| 14
|
neurips
| 7
| 2
|
2023-06-15 17:55:08.263000
|
https://github.com/google-research/autoconj
| 36
|
Autoconj: recognizing and exploiting conjugacy without a domain-specific language
|
https://scholar.google.com/scholar?cluster=10948786372244458956&hl=en&as_sdt=0,34
| 11
| 2,018
|
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
| 78
|
neurips
| 26
| 2
|
2023-06-15 17:55:08.454000
|
https://github.com/ermongroup/ssdkl
| 65
|
Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance
|
https://scholar.google.com/scholar?cluster=6491716866958005670&hl=en&as_sdt=0,35
| 8
| 2,018
|
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
| 44
|
neurips
| 8
| 0
|
2023-06-15 17:55:08.645000
|
https://github.com/sinzlab/Sinz2018_NIPS
| 3
|
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
|
https://scholar.google.com/scholar?cluster=3426947555786993703&hl=en&as_sdt=0,5
| 3
| 2,018
|
Norm matters: efficient and accurate normalization schemes in deep networks
| 166
|
neurips
| 3
| 0
|
2023-06-15 17:55:08.835000
|
https://github.com/eladhoffer/norm_matters
| 22
|
Norm matters: efficient and accurate normalization schemes in deep networks
|
https://scholar.google.com/scholar?cluster=12023191299459902610&hl=en&as_sdt=0,29
| 4
| 2,018
|
Dialog-based Interactive Image Retrieval
| 145
|
neurips
| 19
| 4
|
2023-06-15 17:55:09.026000
|
https://github.com/XiaoxiaoGuo/fashion-retrieval
| 66
|
Dialog-based interactive image retrieval
|
https://scholar.google.com/scholar?cluster=4258300372823907612&hl=en&as_sdt=0,36
| 4
| 2,018
|
Co-teaching: Robust training of deep neural networks with extremely noisy labels
| 1,450
|
neurips
| 98
| 9
|
2023-06-15 17:55:09.217000
|
https://github.com/bhanML/Co-teaching
| 447
|
Co-teaching: Robust training of deep neural networks with extremely noisy labels
|
https://scholar.google.com/scholar?cluster=1619874673011079691&hl=en&as_sdt=0,5
| 11
| 2,018
|
Learning to Reason with Third Order Tensor Products
| 65
|
neurips
| 4
| 0
|
2023-06-15 17:55:09.407000
|
https://github.com/ischlag/TPR-RNN
| 39
|
Learning to reason with third order tensor products
|
https://scholar.google.com/scholar?cluster=1859815740065749231&hl=en&as_sdt=0,43
| 4
| 2,018
|
Deep Structured Prediction with Nonlinear Output Transformations
| 22
|
neurips
| 0
| 1
|
2023-06-15 17:55:09.597000
|
https://github.com/cgraber/NLStruct
| 11
|
Deep structured prediction with nonlinear output transformations
|
https://scholar.google.com/scholar?cluster=14558697357825196777&hl=en&as_sdt=0,31
| 6
| 2,018
|
Visualizing the Loss Landscape of Neural Nets
| 1,487
|
neurips
| 345
| 23
|
2023-06-15 17:55:09.787000
|
https://github.com/tomgoldstein/loss-landscape
| 2,379
|
Visualizing the loss landscape of neural nets
|
https://scholar.google.com/scholar?cluster=11650483902238288010&hl=en&as_sdt=0,5
| 33
| 2,018
|
Representation Learning for Treatment Effect Estimation from Observational Data
| 223
|
neurips
| 8
| 3
|
2023-06-15 17:55:09.979000
|
https://github.com/Osier-Yi/SITE
| 48
|
Representation learning for treatment effect estimation from observational data
|
https://scholar.google.com/scholar?cluster=8473125110526248121&hl=en&as_sdt=0,14
| 2
| 2,018
|
Memory Replay GANs: Learning to Generate New Categories without Forgetting
| 323
|
neurips
| 17
| 6
|
2023-06-15 17:55:10.169000
|
https://github.com/WuChenshen/MeRGAN
| 57
|
Memory replay gans: Learning to generate new categories without forgetting
|
https://scholar.google.com/scholar?cluster=10386986757383440246&hl=en&as_sdt=0,5
| 2
| 2,018
|
Insights on representational similarity in neural networks with canonical correlation
| 317
|
neurips
| 145
| 7
|
2023-06-15 17:55:10.360000
|
https://github.com/google/svcca
| 596
|
Insights on representational similarity in neural networks with canonical correlation
|
https://scholar.google.com/scholar?cluster=15689105000424764079&hl=en&as_sdt=0,48
| 27
| 2,018
|
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
| 191
|
neurips
| 369
| 28
|
2023-06-15 17:55:10.552000
|
https://github.com/Microsoft/EdgeML
| 1,453
|
Fastgrnn: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network
|
https://scholar.google.com/scholar?cluster=14286601091173970187&hl=en&as_sdt=0,41
| 87
| 2,018
|
Conditional Adversarial Domain Adaptation
| 1,680
|
neurips
| 88
| 15
|
2023-06-15 17:55:10.742000
|
https://github.com/thuml/CDAN
| 374
|
Conditional adversarial domain adaptation
|
https://scholar.google.com/scholar?cluster=951003799487024572&hl=en&as_sdt=0,5
| 11
| 2,018
|
Bayesian Nonparametric Spectral Estimation
| 36
|
neurips
| 2
| 2
|
2023-06-15 17:55:10.933000
|
https://github.com/GAMES-UChile/BayesianSpectralEstimation
| 14
|
Bayesian nonparametric spectral estimation
|
https://scholar.google.com/scholar?cluster=17785517224633397163&hl=en&as_sdt=0,5
| 4
| 2,018
|
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
| 1,316
|
neurips
| 78
| 16
|
2023-06-15 17:55:11.123000
|
https://github.com/pokaxpoka/deep_Mahalanobis_detector
| 303
|
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
|
https://scholar.google.com/scholar?cluster=59561906500021733&hl=en&as_sdt=0,31
| 9
| 2,018
|
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
| 464
|
neurips
| 15
| 0
|
2023-06-15 17:55:11.314000
|
https://github.com/mmazeika/glc
| 87
|
Using trusted data to train deep networks on labels corrupted by severe noise
|
https://scholar.google.com/scholar?cluster=3616817429291706463&hl=en&as_sdt=0,44
| 4
| 2,018
|
Masking: A New Perspective of Noisy Supervision
| 208
|
neurips
| 7
| 0
|
2023-06-15 17:55:11.505000
|
https://github.com/bhanML/Masking
| 55
|
Masking: A new perspective of noisy supervision
|
https://scholar.google.com/scholar?cluster=10612946092230113975&hl=en&as_sdt=0,32
| 5
| 2,018
|
Found Graph Data and Planted Vertex Covers
| 9
|
neurips
| 1
| 0
|
2023-06-15 17:55:11.695000
|
https://github.com/arbenson/FGDnPVC
| 3
|
Found graph data and planted vertex covers
|
https://scholar.google.com/scholar?cluster=3952614015987874962&hl=en&as_sdt=0,39
| 3
| 2,018
|
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