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|---|---|---|---|---|---|---|---|---|---|---|---|
Fast Estimation of Causal Interactions using Wold Processes
| 12
|
neurips
| 4
| 2
|
2023-06-15 17:55:11.886000
|
https://github.com/flaviovdf/granger-busca
| 6
|
Fast estimation of causal interactions using wold processes
|
https://scholar.google.com/scholar?cluster=3436970798067835046&hl=en&as_sdt=0,44
| 3
| 2,018
|
Reparameterization Gradient for Non-differentiable Models
| 25
|
neurips
| 1
| 0
|
2023-06-15 17:55:12.077000
|
https://github.com/wonyeol/reparam-nondiff
| 5
|
Reparameterization gradient for non-differentiable models
|
https://scholar.google.com/scholar?cluster=15564293157719874680&hl=en&as_sdt=0,31
| 3
| 2,018
|
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
| 352
|
neurips
| 298
| 20
|
2023-06-15 17:55:12.267000
|
https://github.com/uber-research/deep-neuroevolution
| 1,597
|
Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents
|
https://scholar.google.com/scholar?cluster=9461747331584701646&hl=en&as_sdt=0,11
| 82
| 2,018
|
Generalizing Tree Probability Estimation via Bayesian Networks
| 23
|
neurips
| 6
| 0
|
2023-06-15 17:55:12.458000
|
https://github.com/zcrabbit/sbn
| 8
|
Generalizing tree probability estimation via Bayesian networks
|
https://scholar.google.com/scholar?cluster=17096075908350325992&hl=en&as_sdt=0,5
| 1
| 2,018
|
SimplE Embedding for Link Prediction in Knowledge Graphs
| 661
|
neurips
| 36
| 1
|
2023-06-15 17:55:12.648000
|
https://github.com/Mehran-k/SimplE
| 134
|
Simple embedding for link prediction in knowledge graphs
|
https://scholar.google.com/scholar?cluster=1390081697322675650&hl=en&as_sdt=0,5
| 9
| 2,018
|
Statistical mechanics of low-rank tensor decomposition
| 16
|
neurips
| 0
| 0
|
2023-06-15 17:55:12.839000
|
https://github.com/ganguli-lab/tensorAMP
| 4
|
Statistical mechanics of low-rank tensor decomposition
|
https://scholar.google.com/scholar?cluster=9594213569092054865&hl=en&as_sdt=0,1
| 4
| 2,018
|
A Structured Prediction Approach for Label Ranking
| 30
|
neurips
| 2
| 0
|
2023-06-15 17:55:13.030000
|
https://github.com/akorba/Structured_Approach_Label_Ranking
| 6
|
A structured prediction approach for label ranking
|
https://scholar.google.com/scholar?cluster=7075820179073932212&hl=en&as_sdt=0,41
| 3
| 2,018
|
Sparsified SGD with Memory
| 594
|
neurips
| 11
| 1
|
2023-06-15 17:55:13.221000
|
https://github.com/epfml/sparsifiedSGD
| 50
|
Sparsified SGD with memory
|
https://scholar.google.com/scholar?cluster=6832257024596167334&hl=en&as_sdt=0,36
| 9
| 2,018
|
Model Agnostic Supervised Local Explanations
| 167
|
neurips
| 8
| 0
|
2023-06-15 17:55:13.411000
|
https://github.com/GDPlumb/MAPLE
| 26
|
Model agnostic supervised local explanations
|
https://scholar.google.com/scholar?cluster=3090118674779699868&hl=en&as_sdt=0,23
| 3
| 2,018
|
Probabilistic Matrix Factorization for Automated Machine Learning
| 126
|
neurips
| 13
| 4
|
2023-06-15 17:55:13.601000
|
https://github.com/rsheth80/pmf-automl
| 41
|
Probabilistic matrix factorization for automated machine learning
|
https://scholar.google.com/scholar?cluster=6902330776298089199&hl=en&as_sdt=0,21
| 4
| 2,018
|
Norm-Ranging LSH for Maximum Inner Product Search
| 47
|
neurips
| 10
| 0
|
2023-06-15 17:55:13.792000
|
https://github.com/xinyandai/similarity-search
| 18
|
Norm-ranging lsh for maximum inner product search
|
https://scholar.google.com/scholar?cluster=4956999863940081632&hl=en&as_sdt=0,47
| 10
| 2,018
|
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
| 85
|
neurips
| 2
| 0
|
2023-06-15 17:55:13.983000
|
https://github.com/BorisMuzellec/EllipticalEmbeddings
| 9
|
Generalizing point embeddings using the wasserstein space of elliptical distributions
|
https://scholar.google.com/scholar?cluster=3601826070675882278&hl=en&as_sdt=0,23
| 4
| 2,018
|
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
| 60
|
neurips
| 2
| 0
|
2023-06-15 17:55:14.173000
|
https://github.com/dmilios/dirichletGPC
| 13
|
Dirichlet-based gaussian processes for large-scale calibrated classification
|
https://scholar.google.com/scholar?cluster=7488422957804807823&hl=en&as_sdt=0,36
| 2
| 2,018
|
Latent Alignment and Variational Attention
| 138
|
neurips
| 60
| 2
|
2023-06-15 17:55:14.363000
|
https://github.com/harvardnlp/var-attn
| 324
|
Latent alignment and variational attention
|
https://scholar.google.com/scholar?cluster=6335407498429393003&hl=en&as_sdt=0,37
| 23
| 2,018
|
Infinite-Horizon Gaussian Processes
| 29
|
neurips
| 7
| 3
|
2023-06-15 17:55:14.554000
|
https://github.com/AaltoML/IHGP
| 28
|
Infinite-horizon Gaussian processes
|
https://scholar.google.com/scholar?cluster=13722784833220822191&hl=en&as_sdt=0,5
| 6
| 2,018
|
Constrained Graph Variational Autoencoders for Molecule Design
| 405
|
neurips
| 54
| 4
|
2023-06-15 17:55:14.744000
|
https://github.com/Microsoft/constrained-graph-variational-autoencoder
| 202
|
Constrained graph variational autoencoders for molecule design
|
https://scholar.google.com/scholar?cluster=2838800553083041205&hl=en&as_sdt=0,23
| 11
| 2,018
|
Hardware Conditioned Policies for Multi-Robot Transfer Learning
| 65
|
neurips
| 7
| 0
|
2023-06-15 17:55:14.935000
|
https://github.com/taochenshh/hcp
| 17
|
Hardware conditioned policies for multi-robot transfer learning
|
https://scholar.google.com/scholar?cluster=11432360308578824406&hl=en&as_sdt=0,33
| 4
| 2,018
|
Learning Disentangled Joint Continuous and Discrete Representations
| 203
|
neurips
| 65
| 1
|
2023-06-15 17:55:15.125000
|
https://github.com/Schlumberger/joint-vae
| 449
|
Learning disentangled joint continuous and discrete representations
|
https://scholar.google.com/scholar?cluster=14996308996785863098&hl=en&as_sdt=0,10
| 21
| 2,018
|
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
| 158
|
neurips
| 6
| 1
|
2023-06-15 17:55:15.316000
|
https://github.com/AmIAttribute/AmI
| 29
|
Attacks meet interpretability: Attribute-steered detection of adversarial samples
|
https://scholar.google.com/scholar?cluster=2985314933504776828&hl=en&as_sdt=0,5
| 1
| 2,018
|
Differentiable MPC for End-to-end Planning and Control
| 286
|
neurips
| 42
| 4
|
2023-06-15 17:55:15.506000
|
https://github.com/locuslab/differentiable-mpc
| 157
|
Differentiable mpc for end-to-end planning and control
|
https://scholar.google.com/scholar?cluster=14843462917652881335&hl=en&as_sdt=0,43
| 10
| 2,018
|
Binary Classification from Positive-Confidence Data
| 58
|
neurips
| 6
| 0
|
2023-06-15 17:55:15.697000
|
https://github.com/takashiishida/pconf
| 50
|
Binary classification from positive-confidence data
|
https://scholar.google.com/scholar?cluster=10725870998628923240&hl=en&as_sdt=0,33
| 7
| 2,018
|
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
| 492
|
neurips
| 39
| 1
|
2023-06-15 17:55:15.887000
|
https://github.com/timgaripov/dnn-mode-connectivity
| 217
|
Loss surfaces, mode connectivity, and fast ensembling of dnns
|
https://scholar.google.com/scholar?cluster=7857512178594187445&hl=en&as_sdt=0,1
| 12
| 2,018
|
A Unified View of Piecewise Linear Neural Network Verification
| 294
|
neurips
| 8
| 0
|
2023-06-15 17:55:16.078000
|
https://github.com/oval-group/PLNN-verification
| 33
|
A unified view of piecewise linear neural network verification
|
https://scholar.google.com/scholar?cluster=5109084814333031747&hl=en&as_sdt=0,22
| 9
| 2,018
|
Can We Gain More from Orthogonality Regularizations in Training Deep Networks?
| 284
|
neurips
| 28
| 0
|
2023-06-15 17:55:16.268000
|
https://github.com/nbansal90/Can-we-Gain-More-from-Orthogonality
| 113
|
Can we gain more from orthogonality regularizations in training deep networks?
|
https://scholar.google.com/scholar?cluster=16253012284749788151&hl=en&as_sdt=0,33
| 9
| 2,018
|
Training deep learning based denoisers without ground truth data
| 114
|
neurips
| 11
| 0
|
2023-06-15 17:55:16.459000
|
https://github.com/Shakarim94/Net-SURE
| 43
|
Training deep learning based denoisers without ground truth data
|
https://scholar.google.com/scholar?cluster=10949844547317882495&hl=en&as_sdt=0,33
| 2
| 2,018
|
Structural Causal Bandits: Where to Intervene?
| 74
|
neurips
| 10
| 0
|
2023-06-15 17:55:16.649000
|
https://github.com/sanghack81/SCMMAB-NIPS2018
| 16
|
Structural causal bandits: Where to intervene?
|
https://scholar.google.com/scholar?cluster=4413359648093381122&hl=en&as_sdt=0,5
| 1
| 2,018
|
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
| 964
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neurips
| 98
| 8
|
2023-06-15 17:55:16.840000
|
https://github.com/brain-research/realistic-ssl-evaluation
| 448
|
Realistic evaluation of deep semi-supervised learning algorithms
|
https://scholar.google.com/scholar?cluster=15456844754123849487&hl=en&as_sdt=0,19
| 43
| 2,018
|
Revisiting Decomposable Submodular Function Minimization with Incidence Relations
| 22
|
neurips
| 1
| 0
|
2023-06-15 17:55:17.031000
|
https://github.com/lipan00123/DSFM-with-incidence-relations
| 0
|
Revisiting decomposable submodular function minimization with incidence relations
|
https://scholar.google.com/scholar?cluster=11168625649110015445&hl=en&as_sdt=0,25
| 1
| 2,018
|
Scaling Gaussian Process Regression with Derivatives
| 79
|
neurips
| 8
| 4
|
2023-06-15 17:55:17.221000
|
https://github.com/ericlee0803/GP_Derivatives
| 31
|
Scaling Gaussian process regression with derivatives
|
https://scholar.google.com/scholar?cluster=12933093226685125068&hl=en&as_sdt=0,33
| 11
| 2,018
|
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
| 327
|
neurips
| 80
| 10
|
2023-06-15 17:55:17.411000
|
https://github.com/yxgeee/FD-GAN
| 275
|
Fd-gan: Pose-guided feature distilling gan for robust person re-identification
|
https://scholar.google.com/scholar?cluster=8848217033553196180&hl=en&as_sdt=0,1
| 8
| 2,018
|
Graphical Generative Adversarial Networks
| 41
|
neurips
| 15
| 4
|
2023-06-15 17:55:17.602000
|
https://github.com/zhenxuan00/graphical-gan
| 71
|
Graphical generative adversarial networks
|
https://scholar.google.com/scholar?cluster=13094733406106291079&hl=en&as_sdt=0,29
| 14
| 2,018
|
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
| 488
|
neurips
| 15
| 0
|
2023-06-15 17:55:17.792000
|
https://github.com/IBM/Contrastive-Explanation-Method
| 51
|
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
|
https://scholar.google.com/scholar?cluster=14566322531022731329&hl=en&as_sdt=0,39
| 13
| 2,018
|
Context-aware Synthesis and Placement of Object Instances
| 94
|
neurips
| 10
| 6
|
2023-06-15 17:55:17.983000
|
https://github.com/NVlabs/Instance_Insertion
| 84
|
Context-aware synthesis and placement of object instances
|
https://scholar.google.com/scholar?cluster=16175327312247199712&hl=en&as_sdt=0,31
| 17
| 2,018
|
Group Equivariant Capsule Networks
| 119
|
neurips
| 9
| 5
|
2023-06-15 17:55:18.174000
|
https://github.com/mrjel/group_equivariant_capsules_pytorch
| 29
|
Group equivariant capsule networks
|
https://scholar.google.com/scholar?cluster=11608023930229611825&hl=en&as_sdt=0,10
| 2
| 2,018
|
MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval
| 5
|
neurips
| 2
| 0
|
2023-06-15 17:55:18.364000
|
https://github.com/epfl-lts2/mulan
| 1
|
Mulan: A blind and off-grid method for multichannel echo retrieval
|
https://scholar.google.com/scholar?cluster=88608764706264858&hl=en&as_sdt=0,5
| 9
| 2,018
|
Breaking the Activation Function Bottleneck through Adaptive Parameterization
| 12
|
neurips
| 5
| 1
|
2023-06-15 17:55:18.554000
|
https://github.com/flennerhag/alstm
| 25
|
Breaking the activation function bottleneck through adaptive parameterization
|
https://scholar.google.com/scholar?cluster=707894120541881868&hl=en&as_sdt=0,5
| 2
| 2,018
|
Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements
| 11
|
neurips
| 0
| 0
|
2023-06-15 17:55:18.745000
|
https://github.com/ankushmandal/topkapi
| 11
|
Topkapi: parallel and fast sketches for finding top-k frequent elements
|
https://scholar.google.com/scholar?cluster=17308935081714564523&hl=en&as_sdt=0,26
| 2
| 2,018
|
The Price of Fair PCA: One Extra dimension
| 118
|
neurips
| 15
| 1
|
2023-06-15 17:55:18.935000
|
https://github.com/samirasamadi/Fair-PCA
| 23
|
The price of fair pca: One extra dimension
|
https://scholar.google.com/scholar?cluster=6814300972813312615&hl=en&as_sdt=0,30
| 4
| 2,018
|
Orthogonally Decoupled Variational Gaussian Processes
| 43
|
neurips
| 1
| 0
|
2023-06-15 17:55:19.125000
|
https://github.com/hughsalimbeni/orth_decoupled_var_gps
| 12
|
Orthogonally decoupled variational Gaussian processes
|
https://scholar.google.com/scholar?cluster=13926573353559028690&hl=en&as_sdt=0,47
| 4
| 2,018
|
Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
| 22
|
neurips
| 0
| 0
|
2023-06-15 17:55:19.316000
|
https://github.com/shivapratap/AlgorithmicAssurance_NIPS2018
| 3
|
Algorithmic assurance: An active approach to algorithmic testing using bayesian optimisation
|
https://scholar.google.com/scholar?cluster=6517267723562437007&hl=en&as_sdt=0,15
| 1
| 2,018
|
Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
| 204
|
neurips
| 23
| 2
|
2023-06-15 17:55:19.508000
|
https://github.com/xchen-tamu/linear-lista-cpss
| 48
|
Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds
|
https://scholar.google.com/scholar?cluster=8395828592719058096&hl=en&as_sdt=0,5
| 5
| 2,018
|
Efficient Neural Network Robustness Certification with General Activation Functions
| 580
|
neurips
| 6
| 0
|
2023-06-15 17:55:19.699000
|
https://github.com/huanzhang12/CROWN-Robustness-Certification
| 13
|
Efficient neural network robustness certification with general activation functions
|
https://scholar.google.com/scholar?cluster=6606953928208344058&hl=en&as_sdt=0,44
| 4
| 2,018
|
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning
| 86
|
neurips
| 9
| 4
|
2023-06-15 17:55:19.889000
|
https://github.com/tylersco/adapted_deep_embeddings
| 26
|
Adapted deep embeddings: A synthesis of methods for k-shot inductive transfer learning
|
https://scholar.google.com/scholar?cluster=11224359097846918125&hl=en&as_sdt=0,14
| 4
| 2,018
|
KONG: Kernels for ordered-neighborhood graphs
| 3
|
neurips
| 2
| 0
|
2023-06-15 17:55:20.080000
|
https://github.com/kokiche/KONG
| 8
|
KONG: Kernels for ordered-neighborhood graphs
|
https://scholar.google.com/scholar?cluster=7783420986460591653&hl=en&as_sdt=0,6
| 2
| 2,018
|
Glow: Generative Flow with Invertible 1x1 Convolutions
| 2,412
|
neurips
| 509
| 64
|
2023-06-15 17:55:20.270000
|
https://github.com/openai/glow
| 3,016
|
Glow: Generative flow with invertible 1x1 convolutions
|
https://scholar.google.com/scholar?cluster=5834689841973227263&hl=en&as_sdt=0,5
| 212
| 2,018
|
Efficient Projection onto the Perfect Phylogeny Model
| 4
|
neurips
| 1
| 0
|
2023-06-15 17:55:20.461000
|
https://github.com/bentoayr/Efficient-Projection-onto-the-Perfect-Phylogeny-Model
| 2
|
Efficient projection onto the perfect phylogeny model
|
https://scholar.google.com/scholar?cluster=5821955687711188887&hl=en&as_sdt=0,5
| 2
| 2,018
|
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
| 54
|
neurips
| 2
| 0
|
2023-06-15 17:55:20.651000
|
https://github.com/aaronpmishkin/SLANG
| 8
|
Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient
|
https://scholar.google.com/scholar?cluster=16145055537497825367&hl=en&as_sdt=0,47
| 4
| 2,018
|
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
| 29
|
neurips
| 2
| 0
|
2023-06-15 17:55:20.841000
|
https://github.com/boschresearch/PAC_GP
| 9
|
Learning gaussian processes by minimizing pac-bayesian generalization bounds
|
https://scholar.google.com/scholar?cluster=10486427122061554310&hl=en&as_sdt=0,44
| 8
| 2,018
|
Lipschitz regularity of deep neural networks: analysis and efficient estimation
| 369
|
neurips
| 14
| 3
|
2023-06-15 17:55:21.032000
|
https://github.com/avirmaux/lipEstimation
| 49
|
Lipschitz regularity of deep neural networks: analysis and efficient estimation
|
https://scholar.google.com/scholar?cluster=16196721810320018514&hl=en&as_sdt=0,36
| 1
| 2,018
|
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
| 792
|
neurips
| 102
| 9
|
2023-06-15 17:55:21.222000
|
https://github.com/bowenliu16/rl_graph_generation
| 310
|
Graph convolutional policy network for goal-directed molecular graph generation
|
https://scholar.google.com/scholar?cluster=15276529180320001334&hl=en&as_sdt=0,39
| 19
| 2,018
|
Video-to-Video Synthesis
| 927
|
neurips
| 1,195
| 104
|
2023-06-15 17:55:21.413000
|
https://github.com/NVIDIA/vid2vid
| 8,266
|
Video-to-video synthesis
|
https://scholar.google.com/scholar?cluster=3120460092236365926&hl=en&as_sdt=0,23
| 250
| 2,018
|
Bandit Learning with Implicit Feedback
| 22
|
neurips
| 4
| 0
|
2023-06-15 17:55:21.604000
|
https://github.com/qy7171/ec_bandit
| 7
|
Bandit learning with implicit feedback
|
https://scholar.google.com/scholar?cluster=11670456531413289871&hl=en&as_sdt=0,6
| 1
| 2,018
|
Adversarial Regularizers in Inverse Problems
| 202
|
neurips
| 6
| 1
|
2023-06-15 17:55:21.794000
|
https://github.com/lunz-s/DeepAdverserialRegulariser
| 13
|
Adversarial regularizers in inverse problems
|
https://scholar.google.com/scholar?cluster=3594915696133260277&hl=en&as_sdt=0,34
| 2
| 2,018
|
Hyperbolic Neural Networks
| 411
|
neurips
| 26
| 3
|
2023-06-15 17:55:21.985000
|
https://github.com/dalab/hyperbolic_nn
| 162
|
Hyperbolic neural networks
|
https://scholar.google.com/scholar?cluster=12122146629122312177&hl=en&as_sdt=0,31
| 14
| 2,018
|
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
| 492
|
neurips
| 31
| 9
|
2023-06-15 17:55:22.176000
|
https://github.com/hujie-frank/GENet
| 227
|
Gather-excite: Exploiting feature context in convolutional neural networks
|
https://scholar.google.com/scholar?cluster=9719951211536151216&hl=en&as_sdt=0,5
| 19
| 2,018
|
Active Learning for Non-Parametric Regression Using Purely Random Trees
| 21
|
neurips
| 3
| 0
|
2023-06-15 17:55:22.366000
|
https://github.com/jackrgoetz/Mondrian_Tree_AL
| 3
|
Active learning for non-parametric regression using purely random trees
|
https://scholar.google.com/scholar?cluster=7681049792975239576&hl=en&as_sdt=0,44
| 4
| 2,018
|
Image-to-image translation for cross-domain disentanglement
| 265
|
neurips
| 19
| 5
|
2023-06-15 17:55:22.557000
|
https://github.com/agonzgarc/cross-domain-disen
| 88
|
Image-to-image translation for cross-domain disentanglement
|
https://scholar.google.com/scholar?cluster=7146735712017629088&hl=en&as_sdt=0,48
| 3
| 2,018
|
Practical Methods for Graph Two-Sample Testing
| 36
|
neurips
| 2
| 0
|
2023-06-15 17:55:22.747000
|
https://github.com/gdebarghya/Network-TwoSampleTesting
| 5
|
Practical methods for graph two-sample testing
|
https://scholar.google.com/scholar?cluster=3213877141900838189&hl=en&as_sdt=0,6
| 1
| 2,018
|
Learning to Navigate in Cities Without a Map
| 279
|
neurips
| 56
| 4
|
2023-06-15 17:55:22.938000
|
https://github.com/deepmind/streetlearn
| 268
|
Learning to navigate in cities without a map
|
https://scholar.google.com/scholar?cluster=9758707731169438744&hl=en&as_sdt=0,39
| 12
| 2,018
|
Invertibility of Convolutional Generative Networks from Partial Measurements
| 79
|
neurips
| 2
| 1
|
2023-06-15 17:55:23.129000
|
https://github.com/fangchangma/invert-generative-networks
| 19
|
Invertibility of convolutional generative networks from partial measurements
|
https://scholar.google.com/scholar?cluster=13691072756611951369&hl=en&as_sdt=0,19
| 4
| 2,018
|
Towards Robust Detection of Adversarial Examples
| 184
|
neurips
| 11
| 0
|
2023-06-15 17:55:23.320000
|
https://github.com/P2333/Reverse-Cross-Entropy
| 41
|
Towards robust detection of adversarial examples
|
https://scholar.google.com/scholar?cluster=12795339654045612460&hl=en&as_sdt=0,18
| 4
| 2,018
|
Direct Estimation of Differences in Causal Graphs
| 24
|
neurips
| 0
| 0
|
2023-06-15 17:55:23.510000
|
https://github.com/csquires/dci
| 8
|
Direct estimation of differences in causal graphs
|
https://scholar.google.com/scholar?cluster=6891353891081698977&hl=en&as_sdt=0,26
| 5
| 2,018
|
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
| 145
|
neurips
| 820
| 36
|
2023-06-15 17:55:23.701000
|
https://github.com/deepmind/open_spiel
| 3,694
|
Actor-critic policy optimization in partially observable multiagent environments
|
https://scholar.google.com/scholar?cluster=8096003745039146783&hl=en&as_sdt=0,34
| 106
| 2,018
|
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
| 305
|
neurips
| 428
| 52
|
2023-06-15 17:55:23.891000
|
https://github.com/deepmodeling/deepmd-kit
| 1,144
|
End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
|
https://scholar.google.com/scholar?cluster=4009423108945551834&hl=en&as_sdt=0,41
| 49
| 2,018
|
DAGs with NO TEARS: Continuous Optimization for Structure Learning
| 501
|
neurips
| 111
| 5
|
2023-06-15 17:55:24.082000
|
https://github.com/xunzheng/notears
| 482
|
Dags with no tears: Continuous optimization for structure learning
|
https://scholar.google.com/scholar?cluster=7128195536288105484&hl=en&as_sdt=0,36
| 21
| 2,018
|
Connectionist Temporal Classification with Maximum Entropy Regularization
| 49
|
neurips
| 41
| 8
|
2023-06-15 17:55:24.273000
|
https://github.com/liuhu-bigeye/enctc.crnn
| 137
|
Connectionist temporal classification with maximum entropy regularization
|
https://scholar.google.com/scholar?cluster=16455105685023612483&hl=en&as_sdt=0,5
| 10
| 2,018
|
Are GANs Created Equal? A Large-Scale Study
| 994
|
neurips
| 322
| 16
|
2023-06-15 17:55:24.464000
|
https://github.com/google/compare_gan
| 1,814
|
Are gans created equal? a large-scale study
|
https://scholar.google.com/scholar?cluster=3229217754457345915&hl=en&as_sdt=0,5
| 52
| 2,018
|
FRAGE: Frequency-Agnostic Word Representation
| 149
|
neurips
| 21
| 6
|
2023-06-15 17:55:24.655000
|
https://github.com/ChengyueGongR/FrequencyAgnostic
| 117
|
Frage: Frequency-agnostic word representation
|
https://scholar.google.com/scholar?cluster=899516517229807927&hl=en&as_sdt=0,31
| 6
| 2,018
|
Variational Memory Encoder-Decoder
| 37
|
neurips
| 5
| 0
|
2023-06-15 17:55:24.845000
|
https://github.com/thaihungle/VMED
| 18
|
Variational memory encoder-decoder
|
https://scholar.google.com/scholar?cluster=16470131384989674730&hl=en&as_sdt=0,10
| 4
| 2,018
|
Data-Efficient Hierarchical Reinforcement Learning
| 690
|
neurips
| 46,276
| 1,206
|
2023-06-15 17:55:25.036000
|
https://github.com/tensorflow/models
| 75,922
|
Data-efficient hierarchical reinforcement learning
|
https://scholar.google.com/scholar?cluster=8228365515476642671&hl=en&as_sdt=0,11
| 2,774
| 2,018
|
Removing the Feature Correlation Effect of Multiplicative Noise
| 8
|
neurips
| 1
| 0
|
2023-06-15 17:55:25.226000
|
https://github.com/zj10/NCMN
| 3
|
Removing the feature correlation effect of multiplicative noise
|
https://scholar.google.com/scholar?cluster=17402472050771179089&hl=en&as_sdt=0,5
| 1
| 2,018
|
Efficient Loss-Based Decoding on Graphs for Extreme Classification
| 12
|
neurips
| 4
| 0
|
2023-06-15 17:55:25.417000
|
https://github.com/ievron/wltls
| 4
|
Efficient loss-based decoding on graphs for extreme classification
|
https://scholar.google.com/scholar?cluster=17119928599826946784&hl=en&as_sdt=0,41
| 2
| 2,018
|
Scalable methods for 8-bit training of neural networks
| 284
|
neurips
| 56
| 10
|
2023-06-15 17:55:25.607000
|
https://github.com/eladhoffer/quantized.pytorch
| 210
|
Scalable methods for 8-bit training of neural networks
|
https://scholar.google.com/scholar?cluster=6261172322646700444&hl=en&as_sdt=0,10
| 13
| 2,018
|
Step Size Matters in Deep Learning
| 26
|
neurips
| 1
| 0
|
2023-06-15 17:55:25.798000
|
https://github.com/nar-k/NIPS-2018
| 3
|
Step size matters in deep learning
|
https://scholar.google.com/scholar?cluster=5460214845816514152&hl=en&as_sdt=0,47
| 1
| 2,018
|
Dirichlet belief networks for topic structure learning
| 29
|
neurips
| 4
| 2
|
2023-06-15 17:55:25.989000
|
https://github.com/ethanhezhao/DirBN
| 7
|
Dirichlet belief networks for topic structure learning
|
https://scholar.google.com/scholar?cluster=13908644537239897303&hl=en&as_sdt=0,47
| 2
| 2,018
|
HOUDINI: Lifelong Learning as Program Synthesis
| 68
|
neurips
| 5
| 0
|
2023-06-15 17:55:26.180000
|
https://github.com/capergroup/houdini
| 45
|
Houdini: Lifelong learning as program synthesis
|
https://scholar.google.com/scholar?cluster=10841457222027435818&hl=en&as_sdt=0,33
| 6
| 2,018
|
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
| 39
|
neurips
| 4
| 1
|
2023-06-15 17:55:26.371000
|
https://github.com/flatironinstitute/mantis
| 10
|
Manifold-tiling localized receptive fields are optimal in similarity-preserving neural networks
|
https://scholar.google.com/scholar?cluster=1758414387739465296&hl=en&as_sdt=0,47
| 3
| 2,018
|
Embedding Logical Queries on Knowledge Graphs
| 228
|
neurips
| 39
| 9
|
2023-06-15 17:55:26.562000
|
https://github.com/williamleif/graphqembed
| 116
|
Embedding logical queries on knowledge graphs
|
https://scholar.google.com/scholar?cluster=9948805019620970484&hl=en&as_sdt=0,5
| 8
| 2,018
|
Parsimonious Bayesian deep networks
| 7
|
neurips
| 2
| 0
|
2023-06-15 17:55:26.752000
|
https://github.com/mingyuanzhou/PBDN
| 3
|
Parsimonious Bayesian deep networks
|
https://scholar.google.com/scholar?cluster=14376157659087127451&hl=en&as_sdt=0,5
| 5
| 2,018
|
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
| 289
|
neurips
| 46,276
| 1,206
|
2023-06-15 17:55:26.943000
|
https://github.com/tensorflow/models
| 75,922
|
Sample-efficient reinforcement learning with stochastic ensemble value expansion
|
https://scholar.google.com/scholar?cluster=12106658410656872341&hl=en&as_sdt=0,5
| 2,774
| 2,018
|
Neural Nearest Neighbors Networks
| 292
|
neurips
| 44
| 17
|
2023-06-15 17:55:27.134000
|
https://github.com/visinf/n3net
| 276
|
Neural nearest neighbors networks
|
https://scholar.google.com/scholar?cluster=11963067599142958734&hl=en&as_sdt=0,10
| 15
| 2,018
|
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
| 546
|
neurips
| 27
| 5
|
2023-06-15 17:55:27.325000
|
https://github.com/kirthevasank/nasbot
| 128
|
Neural architecture search with bayesian optimisation and optimal transport
|
https://scholar.google.com/scholar?cluster=7308576573219301832&hl=en&as_sdt=0,11
| 12
| 2,018
|
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
| 68
|
neurips
| 10
| 0
|
2023-06-15 17:55:27.526000
|
https://github.com/maciejzieba/binGAN
| 36
|
Bingan: Learning compact binary descriptors with a regularized gan
|
https://scholar.google.com/scholar?cluster=7540991992898429437&hl=en&as_sdt=0,23
| 7
| 2,018
|
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
| 124
|
neurips
| 71
| 4
|
2023-06-15 17:55:27.717000
|
https://github.com/crazydonkey200/neural-symbolic-machines
| 371
|
Memory augmented policy optimization for program synthesis and semantic parsing
|
https://scholar.google.com/scholar?cluster=4398387474099067788&hl=en&as_sdt=0,5
| 26
| 2,018
|
LF-Net: Learning Local Features from Images
| 445
|
neurips
| 67
| 13
|
2023-06-15 17:55:27.908000
|
https://github.com/vcg-uvic/lf-net-release
| 300
|
LF-Net: Learning local features from images
|
https://scholar.google.com/scholar?cluster=8243342192916977654&hl=en&as_sdt=0,5
| 19
| 2,018
|
PointCNN: Convolution On X-Transformed Points
| 2,077
|
neurips
| 359
| 59
|
2023-06-15 17:55:28.099000
|
https://github.com/yangyanli/PointCNN
| 1,305
|
Pointcnn: Convolution on x-transformed points
|
https://scholar.google.com/scholar?cluster=9461711858418183791&hl=en&as_sdt=0,47
| 56
| 2,018
|
Assessing Generative Models via Precision and Recall
| 373
|
neurips
| 10
| 5
|
2023-06-15 17:55:28.289000
|
https://github.com/msmsajjadi/precision-recall-distributions
| 89
|
Assessing generative models via precision and recall
|
https://scholar.google.com/scholar?cluster=651893942780229&hl=en&as_sdt=0,3
| 2
| 2,018
|
Improved Network Robustness with Adversary Critic
| 13
|
neurips
| 0
| 0
|
2023-06-15 17:55:28.479000
|
https://github.com/aam-at/adversary_critic
| 13
|
Improved network robustness with adversary critic
|
https://scholar.google.com/scholar?cluster=4193325299886417643&hl=en&as_sdt=0,47
| 4
| 2,018
|
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
| 25
|
neurips
| 1
| 0
|
2023-06-15 17:55:28.670000
|
https://github.com/keisuke198619/metricNLDS
| 1
|
Metric on nonlinear dynamical systems with perron-frobenius operators
|
https://scholar.google.com/scholar?cluster=9736849801126744369&hl=en&as_sdt=0,24
| 2
| 2,018
|
Non-Local Recurrent Network for Image Restoration
| 536
|
neurips
| 39
| 0
|
2023-06-15 17:55:28.861000
|
https://github.com/Ding-Liu/NLRN
| 169
|
Non-local recurrent network for image restoration
|
https://scholar.google.com/scholar?cluster=17713021931965385894&hl=en&as_sdt=0,11
| 14
| 2,018
|
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
| 11
|
neurips
| 1
| 1
|
2023-06-15 17:55:29.051000
|
https://github.com/hsvgbkhgbv/TACTHMC
| 7
|
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
|
https://scholar.google.com/scholar?cluster=1359920802371030920&hl=en&as_sdt=0,22
| 3
| 2,018
|
A Stein variational Newton method
| 114
|
neurips
| 3
| 0
|
2023-06-15 17:55:29.242000
|
https://github.com/gianlucadetommaso/Stein-variational-samplers
| 21
|
A Stein variational Newton method
|
https://scholar.google.com/scholar?cluster=2381223671647654052&hl=en&as_sdt=0,5
| 4
| 2,018
|
Compositional Plan Vectors
| 12
|
neurips
| 0
| 14
|
2023-06-15 23:42:32.928000
|
https://github.com/cdevin/cpv
| 8
|
Compositional plan vectors
|
https://scholar.google.com/scholar?cluster=15635463865993301870&hl=en&as_sdt=0,5
| 4
| 2,019
|
Learning to Propagate for Graph Meta-Learning
| 90
|
neurips
| 3
| 2
|
2023-06-15 23:42:33.114000
|
https://github.com/liulu112601/Gated-Propagation-Net
| 36
|
Learning to propagate for graph meta-learning
|
https://scholar.google.com/scholar?cluster=3473165000863905721&hl=en&as_sdt=0,5
| 2
| 2,019
|
Multi-resolution Multi-task Gaussian Processes
| 33
|
neurips
| 3
| 0
|
2023-06-15 23:42:33.297000
|
https://github.com/ohamelijnck/multi_res_gps
| 6
|
Multi-resolution multi-task Gaussian processes
|
https://scholar.google.com/scholar?cluster=5029064741200470600&hl=en&as_sdt=0,26
| 1
| 2,019
|
Deep Equilibrium Models
| 452
|
neurips
| 75
| 5
|
2023-06-15 23:42:33.479000
|
https://github.com/locuslab/deq
| 650
|
Deep equilibrium models
|
https://scholar.google.com/scholar?cluster=659851965041196662&hl=en&as_sdt=0,5
| 20
| 2,019
|
Exact Gaussian Processes on a Million Data Points
| 205
|
neurips
| 501
| 318
|
2023-06-15 23:42:33.662000
|
https://github.com/cornellius-gp/gpytorch
| 3,140
|
Exact Gaussian processes on a million data points
|
https://scholar.google.com/scholar?cluster=4013716764327710087&hl=en&as_sdt=0,29
| 55
| 2,019
|
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
| 14
|
neurips
| 1
| 2
|
2023-06-15 23:42:33.844000
|
https://github.com/sorooshafiee/Optimistic_Likelihoods
| 3
|
Calculating optimistic likelihoods using (geodesically) convex optimization
|
https://scholar.google.com/scholar?cluster=5806305643748445691&hl=en&as_sdt=0,14
| 1
| 2,019
|
Improved Precision and Recall Metric for Assessing Generative Models
| 355
|
neurips
| 15
| 0
|
2023-06-15 23:42:34.026000
|
https://github.com/kynkaat/improved-precision-and-recall-metric
| 126
|
Improved precision and recall metric for assessing generative models
|
https://scholar.google.com/scholar?cluster=16244569923752023320&hl=en&as_sdt=0,33
| 4
| 2,019
|
Zero-Shot Semantic Segmentation
| 166
|
neurips
| 23
| 6
|
2023-06-15 23:42:34.208000
|
https://github.com/valeoai/ZS3
| 170
|
Zero-shot semantic segmentation
|
https://scholar.google.com/scholar?cluster=9122033339368914969&hl=en&as_sdt=0,49
| 14
| 2,019
|
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