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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 |
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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