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Multiparameter Persistence Image for Topological Machine Learning
| 32 |
neurips
| 1 | 1 |
2023-06-16 15:12:29.986000
|
https://github.com/MathieuCarriere/multipers
| 10 |
Multiparameter persistence image for topological machine learning
|
https://scholar.google.com/scholar?cluster=13550036092847919796&hl=en&as_sdt=0,5
| 3 | 2,020 |
Matrix Inference and Estimation in Multi-Layer Models
| 7 |
neurips
| 2 | 0 |
2023-06-16 15:12:30.178000
|
https://github.com/parthe/ML-Mat-VAMP
| 0 |
Matrix inference and estimation in multi-layer models
|
https://scholar.google.com/scholar?cluster=10959272077824888298&hl=en&as_sdt=0,39
| 1 | 2,020 |
MeshSDF: Differentiable Iso-Surface Extraction
| 82 |
neurips
| 18 | 3 |
2023-06-16 15:12:30.371000
|
https://github.com/cvlab-epfl/MeshSDF
| 188 |
Meshsdf: Differentiable iso-surface extraction
|
https://scholar.google.com/scholar?cluster=13067371230627821675&hl=en&as_sdt=0,33
| 10 | 2,020 |
Variational Interaction Information Maximization for Cross-domain Disentanglement
| 22 |
neurips
| 5 | 0 |
2023-06-16 15:12:30.564000
|
https://github.com/gr8joo/IIAE
| 19 |
Variational interaction information maximization for cross-domain disentanglement
|
https://scholar.google.com/scholar?cluster=13489781620394262850&hl=en&as_sdt=0,11
| 2 | 2,020 |
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning
| 16 |
neurips
| 0 | 0 |
2023-06-16 15:12:30.757000
|
https://github.com/FlorenceFeng/StateDecoding
| 5 |
Provably efficient exploration for reinforcement learning using unsupervised learning
|
https://scholar.google.com/scholar?cluster=15174934444919444347&hl=en&as_sdt=0,10
| 1 | 2,020 |
Wasserstein Distances for Stereo Disparity Estimation
| 41 |
neurips
| 16 | 2 |
2023-06-16 15:12:30.950000
|
https://github.com/Div99/W-Stereo-Disp
| 94 |
Wasserstein distances for stereo disparity estimation
|
https://scholar.google.com/scholar?cluster=10193193234465084361&hl=en&as_sdt=0,5
| 8 | 2,020 |
Multi-agent Trajectory Prediction with Fuzzy Query Attention
| 16 |
neurips
| 7 | 0 |
2023-06-16 15:12:31.143000
|
https://github.com/nitinkamra1992/FQA
| 34 |
Multi-agent trajectory prediction with fuzzy query attention
|
https://scholar.google.com/scholar?cluster=3202936941876716183&hl=en&as_sdt=0,5
| 2 | 2,020 |
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
| 3 |
neurips
| 0 | 0 |
2023-06-16 15:12:31.352000
|
https://github.com/Shashankaubaru/He-NMFGT
| 0 |
Multilabel classification by hierarchical partitioning and data-dependent grouping
|
https://scholar.google.com/scholar?cluster=12279533609015464937&hl=en&as_sdt=0,5
| 1 | 2,020 |
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
| 21 |
neurips
| 4 | 12 |
2023-06-16 15:12:31.548000
|
https://github.com/tachukao/mgplvm-pytorch
| 21 |
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
|
https://scholar.google.com/scholar?cluster=15482374417517923029&hl=en&as_sdt=0,23
| 5 | 2,020 |
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
| 134 |
neurips
| 55 | 13 |
2023-06-16 15:12:31.750000
|
https://github.com/gingsi/coot-videotext
| 259 |
Coot: Cooperative hierarchical transformer for video-text representation learning
|
https://scholar.google.com/scholar?cluster=3723984631534803573&hl=en&as_sdt=0,5
| 8 | 2,020 |
Passport-aware Normalization for Deep Model Protection
| 44 |
neurips
| 6 | 0 |
2023-06-16 15:12:31.944000
|
https://github.com/ZJZAC/Passport-aware-Normalization
| 16 |
Passport-aware normalization for deep model protection
|
https://scholar.google.com/scholar?cluster=15269211415463725285&hl=en&as_sdt=0,5
| 1 | 2,020 |
Learning One Representation to Optimize All Rewards
| 28 |
neurips
| 3 | 0 |
2023-06-16 16:05:14.922000
|
https://github.com/ahmed-touati/controllable_agent
| 24 |
Learning one representation to optimize all rewards
|
https://scholar.google.com/scholar?cluster=9814375614256861048&hl=en&as_sdt=0,16
| 4 | 2,021 |
Matrix factorisation and the interpretation of geodesic distance
| 7 |
neurips
| 3 | 0 |
2023-06-16 16:05:15.123000
|
https://github.com/anniegray52/graphs
| 2 |
Matrix factorisation and the interpretation of geodesic distance
|
https://scholar.google.com/scholar?cluster=17304238490744462864&hl=en&as_sdt=0,14
| 1 | 2,021 |
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
| 71 |
neurips
| 8 | 0 |
2023-06-16 16:05:15.322000
|
https://github.com/hengruizhang98/CCA-SSG
| 51 |
From canonical correlation analysis to self-supervised graph neural networks
|
https://scholar.google.com/scholar?cluster=7947998668914854789&hl=en&as_sdt=0,5
| 1 | 2,021 |
BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain
| 5 |
neurips
| 0 | 1 |
2023-06-16 16:05:15.522000
|
https://github.com/ztluostat/bast
| 3 |
BAST: Bayesian additive regression spanning trees for complex constrained domain
|
https://scholar.google.com/scholar?cluster=1588870201252653971&hl=en&as_sdt=0,5
| 1 | 2,021 |
Hyperbolic Busemann Learning with Ideal Prototypes
| 8 |
neurips
| 4 | 0 |
2023-06-16 16:05:15.721000
|
https://github.com/minaghadimiatigh/hyperbolic-busemann-learning
| 20 |
Hyperbolic busemann learning with ideal prototypes
|
https://scholar.google.com/scholar?cluster=16865019425945397467&hl=en&as_sdt=0,5
| 2 | 2,021 |
ReAct: Out-of-distribution Detection With Rectified Activations
| 133 |
neurips
| 8 | 0 |
2023-06-16 16:05:15.920000
|
https://github.com/deeplearning-wisc/react
| 43 |
React: Out-of-distribution detection with rectified activations
|
https://scholar.google.com/scholar?cluster=14758995866117688581&hl=en&as_sdt=0,47
| 3 | 2,021 |
AugMax: Adversarial Composition of Random Augmentations for Robust Training
| 53 |
neurips
| 21 | 0 |
2023-06-16 16:05:16.118000
|
https://github.com/vita-group/augmax
| 118 |
Augmax: Adversarial composition of random augmentations for robust training
|
https://scholar.google.com/scholar?cluster=405640925261784405&hl=en&as_sdt=0,22
| 7 | 2,021 |
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
| 205 |
neurips
| 378 | 170 |
2023-06-16 16:05:16.317000
|
https://github.com/facebookresearch/habitat-lab
| 1,109 |
Habitat 2.0: Training home assistants to rearrange their habitat
|
https://scholar.google.com/scholar?cluster=17501231246845502994&hl=en&as_sdt=0,31
| 43 | 2,021 |
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:05:16.517000
|
https://github.com/artberryx/SAR
| 5 |
Time discretization-invariant safe action repetition for policy gradient methods
|
https://scholar.google.com/scholar?cluster=557586246467545482&hl=en&as_sdt=0,50
| 2 | 2,021 |
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection
| 12 |
neurips
| 5 | 14 |
2023-06-16 16:05:16.716000
|
https://github.com/shengtao96/centripetaltext
| 28 |
Centripetaltext: An efficient text instance representation for scene text detection
|
https://scholar.google.com/scholar?cluster=9087656668537689317&hl=en&as_sdt=0,31
| 3 | 2,021 |
DRIVE: One-bit Distributed Mean Estimation
| 18 |
neurips
| 0 | 0 |
2023-06-16 16:05:16.915000
|
https://github.com/amitport/drive-one-bit-distributed-mean-estimation
| 4 |
Drive: One-bit distributed mean estimation
|
https://scholar.google.com/scholar?cluster=1334987039142805665&hl=en&as_sdt=0,5
| 3 | 2,021 |
Local Explanation of Dialogue Response Generation
| 5 |
neurips
| 0 | 1 |
2023-06-16 16:05:17.114000
|
https://github.com/Pascalson/LERG
| 16 |
Local explanation of dialogue response generation
|
https://scholar.google.com/scholar?cluster=3462316691671296408&hl=en&as_sdt=0,5
| 2 | 2,021 |
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation
| 9 |
neurips
| 2 | 1 |
2023-06-16 16:05:17.313000
|
https://github.com/aaltoml/scalable-inference-in-sdes
| 10 |
Scalable inference in SDEs by direct matching of the Fokker–Planck–Kolmogorov equation
|
https://scholar.google.com/scholar?cluster=7639024003048883297&hl=en&as_sdt=0,15
| 2 | 2,021 |
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
| 4 |
neurips
| 0 | 0 |
2023-06-16 16:05:17.511000
|
https://github.com/gkazunii/Legendre-tucker-rank-reduction
| 3 |
Fast tucker rank reduction for non-negative tensors using mean-field approximation
|
https://scholar.google.com/scholar?cluster=3870932887094976119&hl=en&as_sdt=0,15
| 1 | 2,021 |
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
| 8 |
neurips
| 3 | 0 |
2023-06-16 16:05:17.710000
|
https://github.com/vzantedeschi/StocMV
| 6 |
Learning stochastic majority votes by minimizing a PAC-Bayes generalization bound
|
https://scholar.google.com/scholar?cluster=13539537575801656514&hl=en&as_sdt=0,33
| 1 | 2,021 |
Unique sparse decomposition of low rank matrices
| 1 |
neurips
| 1 | 0 |
2023-06-16 16:05:17.908000
|
https://github.com/Jindiande/Unique_Fac_of_Low_Rank
| 0 |
Unique sparse decomposition of low rank matrices
|
https://scholar.google.com/scholar?cluster=11396351707745211823&hl=en&as_sdt=0,36
| 2 | 2,021 |
Neighborhood Reconstructing Autoencoders
| 7 |
neurips
| 2 | 0 |
2023-06-16 16:05:18.107000
|
https://github.com/Gabe-YHLee/NRAE-public
| 25 |
Neighborhood reconstructing autoencoders
|
https://scholar.google.com/scholar?cluster=17951945066721582980&hl=en&as_sdt=0,5
| 1 | 2,021 |
TopicNet: Semantic Graph-Guided Topic Discovery
| 9 |
neurips
| 1 | 1 |
2023-06-16 16:05:18.306000
|
https://github.com/bochengroup/topicnet
| 4 |
Topicnet: Semantic graph-guided topic discovery
|
https://scholar.google.com/scholar?cluster=8671863207015234727&hl=en&as_sdt=0,23
| 4 | 2,021 |
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
| 0 |
neurips
| 2 | 0 |
2023-06-16 16:05:18.505000
|
https://github.com/shengroup/observe_then_incentivize
| 0 |
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
|
https://scholar.google.com/scholar?cluster=8823665853849835723&hl=en&as_sdt=0,5
| 1 | 2,021 |
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
| 7 |
neurips
| 1 | 0 |
2023-06-16 16:05:18.704000
|
https://github.com/neu-spiral/hbar
| 14 |
Revisiting hilbert-schmidt information bottleneck for adversarial robustness
|
https://scholar.google.com/scholar?cluster=17051533810140769652&hl=en&as_sdt=0,5
| 4 | 2,021 |
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:05:18.903000
|
https://github.com/changwoo-lee/TLOHO
| 0 |
T-LoHo: A Bayesian regularization model for structured sparsity and smoothness on graphs
|
https://scholar.google.com/scholar?cluster=38237968899205623&hl=en&as_sdt=0,36
| 1 | 2,021 |
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
| 28 |
neurips
| 0 | 0 |
2023-06-16 16:05:19.103000
|
https://github.com/CORE-Robotics-Lab/Utility-of-Explainable-AI-NeurIPS2021
| 0 |
The utility of explainable ai in ad hoc human-machine teaming
|
https://scholar.google.com/scholar?cluster=14623218223463321908&hl=en&as_sdt=0,5
| 2 | 2,021 |
Subgoal Search For Complex Reasoning Tasks
| 12 |
neurips
| 4 | 1 |
2023-06-16 16:05:19.301000
|
https://github.com/subgoal-search/subgoal-search
| 17 |
Subgoal search for complex reasoning tasks
|
https://scholar.google.com/scholar?cluster=12867531461756557618&hl=en&as_sdt=0,14
| 2 | 2,021 |
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision
| 9 |
neurips
| 1 | 0 |
2023-06-16 16:05:19.500000
|
https://github.com/hekj/landmark-rxr
| 8 |
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision
|
https://scholar.google.com/scholar?cluster=10123860778510964052&hl=en&as_sdt=0,5
| 1 | 2,021 |
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
| 108 |
neurips
| 5 | 2 |
2023-06-16 16:05:19.699000
|
https://github.com/deeplearning-wisc/gradnorm_ood
| 47 |
On the importance of gradients for detecting distributional shifts in the wild
|
https://scholar.google.com/scholar?cluster=16248002193974452072&hl=en&as_sdt=0,15
| 2 | 2,021 |
Do Different Tracking Tasks Require Different Appearance Models?
| 40 |
neurips
| 32 | 21 |
2023-06-16 16:05:19.898000
|
https://github.com/Zhongdao/UniTrack
| 315 |
Do different tracking tasks require different appearance models?
|
https://scholar.google.com/scholar?cluster=5904945497934783289&hl=en&as_sdt=0,47
| 10 | 2,021 |
Towards robust vision by multi-task learning on monkey visual cortex
| 24 |
neurips
| 1 | 0 |
2023-06-16 16:05:20.097000
|
https://github.com/sinzlab/neural_cotraining
| 11 |
Towards robust vision by multi-task learning on monkey visual cortex
|
https://scholar.google.com/scholar?cluster=41919782116802759&hl=en&as_sdt=0,5
| 6 | 2,021 |
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
| 8 |
neurips
| 9 | 0 |
2023-06-16 16:05:20.296000
|
https://github.com/facebookresearch/icp-block-mdp
| 43 |
Learning domain invariant representations in goal-conditioned block mdps
|
https://scholar.google.com/scholar?cluster=6609047029323084207&hl=en&as_sdt=0,5
| 8 | 2,021 |
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
| 14 |
neurips
| 0 | 0 |
2023-06-16 16:05:20.496000
|
https://github.com/ssethz/multi-perturbation-ed
| 5 |
Near-optimal multi-perturbation experimental design for causal structure learning
|
https://scholar.google.com/scholar?cluster=15730591700962293964&hl=en&as_sdt=0,5
| 1 | 2,021 |
Fuzzy Clustering with Similarity Queries
| 1 |
neurips
| 37 | 9 |
2023-06-16 16:05:20.696000
|
https://github.com/omadson/fuzzy-c-means
| 141 |
Fuzzy Clustering with Similarity Queries
|
https://scholar.google.com/scholar?cluster=4880637304563204630&hl=en&as_sdt=0,15
| 2 | 2,021 |
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL
| 15 |
neurips
| 2 | 0 |
2023-06-16 16:05:20.895000
|
https://github.com/khalednakhleh/NeurWIN
| 2 |
Neurwin: Neural whittle index network for restless bandits via deep rl
|
https://scholar.google.com/scholar?cluster=15114473685997353079&hl=en&as_sdt=0,28
| 1 | 2,021 |
Alias-Free Generative Adversarial Networks
| 760 |
neurips
| 939 | 151 |
2023-06-16 16:05:21.094000
|
https://github.com/NVlabs/stylegan3
| 5,233 |
Alias-free generative adversarial networks
|
https://scholar.google.com/scholar?cluster=17368705487922251039&hl=en&as_sdt=0,10
| 56 | 2,021 |
Perturb-and-max-product: Sampling and learning in discrete energy-based models
| 3 |
neurips
| 1 | 0 |
2023-06-16 16:05:21.294000
|
https://github.com/vicariousinc/perturb_and_max_product
| 2 |
Perturb-and-max-product: Sampling and learning in discrete energy-based models
|
https://scholar.google.com/scholar?cluster=9625174616528081623&hl=en&as_sdt=0,34
| 7 | 2,021 |
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
| 22 |
neurips
| 2 | 0 |
2023-06-16 16:05:21.494000
|
https://github.com/sjtu-marl/bd_rd_psro
| 12 |
Towards unifying behavioral and response diversity for open-ended learning in zero-sum games
|
https://scholar.google.com/scholar?cluster=4169431602989656565&hl=en&as_sdt=0,47
| 2 | 2,021 |
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples
| 16 |
neurips
| 1 | 1 |
2023-06-16 16:05:21.693000
|
https://github.com/sungyoon-lee/losslandscapematters
| 3 |
Towards better understanding of training certifiably robust models against adversarial examples
|
https://scholar.google.com/scholar?cluster=17516226191552628723&hl=en&as_sdt=0,28
| 2 | 2,021 |
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage
| 16 |
neurips
| 2 | 0 |
2023-06-16 16:05:21.893000
|
https://github.com/jdchang1/milo
| 13 |
Mitigating covariate shift in imitation learning via offline data with partial coverage
|
https://scholar.google.com/scholar?cluster=16326608831095542308&hl=en&as_sdt=0,33
| 1 | 2,021 |
Global Filter Networks for Image Classification
| 169 |
neurips
| 32 | 5 |
2023-06-16 16:05:22.092000
|
https://github.com/raoyongming/GFNet
| 310 |
Global filter networks for image classification
|
https://scholar.google.com/scholar?cluster=17238210229818271657&hl=en&as_sdt=0,34
| 8 | 2,021 |
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
| 60 |
neurips
| 4 | 3 |
2023-06-16 16:05:22.291000
|
https://github.com/derafael/cafe
| 17 |
CAFE: Catastrophic data leakage in vertical federated learning
|
https://scholar.google.com/scholar?cluster=8108405873195186105&hl=en&as_sdt=0,33
| 1 | 2,021 |
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
| 22 |
neurips
| 7 | 0 |
2023-06-16 16:05:22.490000
|
https://github.com/flint-xf-fan/Byzantine-Federeated-RL
| 43 |
Fault-tolerant federated reinforcement learning with theoretical guarantee
|
https://scholar.google.com/scholar?cluster=16500433966124392053&hl=en&as_sdt=0,5
| 4 | 2,021 |
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
| 122 |
neurips
| 12 | 2 |
2023-06-16 16:05:22.689000
|
https://github.com/rabeehk/compacter
| 87 |
Compacter: Efficient low-rank hypercomplex adapter layers
|
https://scholar.google.com/scholar?cluster=6044403345525594141&hl=en&as_sdt=0,33
| 6 | 2,021 |
Distilling Image Classifiers in Object Detectors
| 5 |
neurips
| 3 | 1 |
2023-06-16 16:05:22.888000
|
https://github.com/NVlabs/DICOD
| 27 |
Distilling image classifiers in object detectors
|
https://scholar.google.com/scholar?cluster=10848349490425744852&hl=en&as_sdt=0,33
| 6 | 2,021 |
Subgroup Generalization and Fairness of Graph Neural Networks
| 41 |
neurips
| 4 | 1 |
2023-06-16 16:05:23.087000
|
https://github.com/theaperdeng/gnn-generalization-fairness
| 2 |
Subgroup generalization and fairness of graph neural networks
|
https://scholar.google.com/scholar?cluster=15293693344501115614&hl=en&as_sdt=0,44
| 3 | 2,021 |
Scaling Neural Tangent Kernels via Sketching and Random Features
| 15 |
neurips
| 2 | 0 |
2023-06-16 16:05:23.286000
|
https://github.com/insuhan/ntk-sketch-rf
| 8 |
Scaling neural tangent kernels via sketching and random features
|
https://scholar.google.com/scholar?cluster=12022337721774352923&hl=en&as_sdt=0,6
| 1 | 2,021 |
Long Short-Term Transformer for Online Action Detection
| 39 |
neurips
| 13 | 5 |
2023-06-16 16:05:23.485000
|
https://github.com/amazon-research/long-short-term-transformer
| 100 |
Long short-term transformer for online action detection
|
https://scholar.google.com/scholar?cluster=3271205271757526851&hl=en&as_sdt=0,47
| 8 | 2,021 |
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
| 13 |
neurips
| 7 | 1 |
2023-06-16 16:05:23.685000
|
https://github.com/kobybibas/pnml_ood_detection
| 22 |
Single layer predictive normalized maximum likelihood for out-of-distribution detection
|
https://scholar.google.com/scholar?cluster=3648486984737742004&hl=en&as_sdt=0,31
| 2 | 2,021 |
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
| 44 |
neurips
| 50 | 7 |
2023-06-16 16:05:23.884000
|
https://github.com/SysCV/pcan
| 342 |
Prototypical cross-attention networks for multiple object tracking and segmentation
|
https://scholar.google.com/scholar?cluster=9943655597902986083&hl=en&as_sdt=0,3
| 10 | 2,021 |
Learning Optimal Predictive Checklists
| 7 |
neurips
| 2 | 0 |
2023-06-16 16:05:24.084000
|
https://github.com/MLforHealth/predictive_checklists
| 5 |
Learning optimal predictive checklists
|
https://scholar.google.com/scholar?cluster=17421241641568013154&hl=en&as_sdt=0,14
| 2 | 2,021 |
Gradient Starvation: A Learning Proclivity in Neural Networks
| 130 |
neurips
| 7 | 1 |
2023-06-16 16:05:24.283000
|
https://github.com/mohammadpz/Gradient_Starvation
| 53 |
Gradient starvation: A learning proclivity in neural networks
|
https://scholar.google.com/scholar?cluster=4980681547647500046&hl=en&as_sdt=0,33
| 5 | 2,021 |
Offline Reinforcement Learning as One Big Sequence Modeling Problem
| 271 |
neurips
| 52 | 6 |
2023-06-16 16:05:24.482000
|
https://github.com/JannerM/trajectory-transformer
| 339 |
Offline reinforcement learning as one big sequence modeling problem
|
https://scholar.google.com/scholar?cluster=4951503534992558310&hl=en&as_sdt=0,33
| 5 | 2,021 |
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices
| 8 |
neurips
| 0 | 1 |
2023-06-16 16:05:24.681000
|
https://github.com/tarodz/shapeshifter
| 1 |
Shapeshifter: a parameter-efficient transformer using factorized reshaped matrices
|
https://scholar.google.com/scholar?cluster=16541495741212848836&hl=en&as_sdt=0,10
| 1 | 2,021 |
Regularized Softmax Deep Multi-Agent Q-Learning
| 12 |
neurips
| 2 | 3 |
2023-06-16 16:05:24.880000
|
https://github.com/ling-pan/res
| 19 |
Regularized softmax deep multi-agent Q-learning
|
https://scholar.google.com/scholar?cluster=16754114336798964505&hl=en&as_sdt=0,5
| 2 | 2,021 |
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
| 3 |
neurips
| 3 | 1 |
2023-06-16 16:05:25.079000
|
https://github.com/tech-submissions/physics-aware-downsampling
| 7 |
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
|
https://scholar.google.com/scholar?cluster=18263225743039070318&hl=en&as_sdt=0,33
| 1 | 2,021 |
Systematic Generalization with Edge Transformers
| 11 |
neurips
| 3 | 0 |
2023-06-16 16:05:25.278000
|
https://github.com/bergen/edgetransformer
| 15 |
Systematic generalization with edge transformers
|
https://scholar.google.com/scholar?cluster=4782172685835509964&hl=en&as_sdt=0,5
| 1 | 2,021 |
Maximum Likelihood Training of Score-Based Diffusion Models
| 159 |
neurips
| 20 | 2 |
2023-06-16 16:05:25.478000
|
https://github.com/yang-song/score_flow
| 95 |
Maximum likelihood training of score-based diffusion models
|
https://scholar.google.com/scholar?cluster=9322848153795569908&hl=en&as_sdt=0,5
| 7 | 2,021 |
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond
| 9 |
neurips
| 2 | 3 |
2023-06-16 16:05:25.677000
|
https://github.com/netw0rkf10w/crf
| 23 |
Regularized frank-wolfe for dense crfs: Generalizing mean field and beyond
|
https://scholar.google.com/scholar?cluster=7839337390537061151&hl=en&as_sdt=0,34
| 3 | 2,021 |
Scalable Intervention Target Estimation in Linear Models
| 6 |
neurips
| 0 | 0 |
2023-06-16 16:05:25.876000
|
https://github.com/bvarici/intervention-estimation
| 0 |
Scalable intervention target estimation in linear models
|
https://scholar.google.com/scholar?cluster=7424623310042885171&hl=en&as_sdt=0,10
| 2 | 2,021 |
Play to Grade: Testing Coding Games as Classifying Markov Decision Process
| 6 |
neurips
| 4 | 0 |
2023-06-16 16:05:26.075000
|
https://github.com/windweller/play-to-grade
| 5 |
Play to grade: testing coding games as classifying Markov decision process
|
https://scholar.google.com/scholar?cluster=2851413574453679120&hl=en&as_sdt=0,5
| 2 | 2,021 |
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
| 20 |
neurips
| 3 | 0 |
2023-06-16 16:05:26.275000
|
https://github.com/Ofirlin/DUFS
| 6 |
Differentiable unsupervised feature selection based on a gated laplacian
|
https://scholar.google.com/scholar?cluster=12231819460372873074&hl=en&as_sdt=0,47
| 1 | 2,021 |
Smooth Bilevel Programming for Sparse Regularization
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:05:26.474000
|
https://github.com/gpeyre/2021-NonCvxPro
| 8 |
Smooth bilevel programming for sparse regularization
|
https://scholar.google.com/scholar?cluster=1358361892892499297&hl=en&as_sdt=0,33
| 2 | 2,021 |
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
| 19 |
neurips
| 13 | 0 |
2023-06-16 16:05:26.673000
|
https://github.com/PwnerHarry/CP
| 51 |
A consciousness-inspired planning agent for model-based reinforcement learning
|
https://scholar.google.com/scholar?cluster=1913167865279429468&hl=en&as_sdt=0,5
| 6 | 2,021 |
Beltrami Flow and Neural Diffusion on Graphs
| 32 |
neurips
| 42 | 4 |
2023-06-16 16:05:26.872000
|
https://github.com/twitter-research/graph-neural-pde
| 254 |
Beltrami flow and neural diffusion on graphs
|
https://scholar.google.com/scholar?cluster=11396329542224285473&hl=en&as_sdt=0,5
| 12 | 2,021 |
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?
| 1 |
neurips
| 0 | 0 |
2023-06-16 16:05:27.072000
|
https://github.com/gonzalojaimovitch/think-big-teach-small
| 0 |
Think Big, Teach Small: Do Language Models Distil Occam's Razor?
|
https://scholar.google.com/scholar?cluster=324406477696359661&hl=en&as_sdt=0,5
| 1 | 2,021 |
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
| 29 |
neurips
| 3 | 1 |
2023-06-16 16:05:27.271000
|
https://github.com/HHalva/snica
| 10 |
Disentangling identifiable features from noisy data with structured nonlinear ICA
|
https://scholar.google.com/scholar?cluster=16776677318937402527&hl=en&as_sdt=0,21
| 3 | 2,021 |
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems
| 15 |
neurips
| 3 | 0 |
2023-06-16 16:05:27.469000
|
https://github.com/davidxujiayang/cpnets
| 11 |
Conditionally parameterized, discretization-aware neural networks for mesh-based modeling of physical systems
|
https://scholar.google.com/scholar?cluster=10149448284676300480&hl=en&as_sdt=0,33
| 1 | 2,021 |
Adaptive Conformal Inference Under Distribution Shift
| 53 |
neurips
| 1 | 0 |
2023-06-16 16:05:27.670000
|
https://github.com/ISGibbs/AdaptiveConformal
| 1 |
Adaptive conformal inference under distribution shift
|
https://scholar.google.com/scholar?cluster=263561861099566875&hl=en&as_sdt=0,14
| 2 | 2,021 |
Periodic Activation Functions Induce Stationarity
| 13 |
neurips
| 2 | 0 |
2023-06-16 16:05:27.869000
|
https://github.com/aaltoml/periodicbnn
| 14 |
Periodic activation functions induce stationarity
|
https://scholar.google.com/scholar?cluster=4217215713078286668&hl=en&as_sdt=0,41
| 1 | 2,021 |
Revealing and Protecting Labels in Distributed Training
| 12 |
neurips
| 1 | 0 |
2023-06-16 16:05:28.068000
|
https://github.com/googleinterns/learning-bag-of-words
| 0 |
Revealing and protecting labels in distributed training
|
https://scholar.google.com/scholar?cluster=3247990079527207067&hl=en&as_sdt=0,26
| 3 | 2,021 |
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning
| 2 |
neurips
| 2 | 0 |
2023-06-16 16:05:28.267000
|
https://github.com/victordarvariu/solving-graph-pgg
| 3 |
Solving Graph-based Public Goods Games with Tree Search and Imitation Learning
|
https://scholar.google.com/scholar?cluster=18441928551030825405&hl=en&as_sdt=0,39
| 1 | 2,021 |
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
| 50 |
neurips
| 5 | 2 |
2023-06-16 16:05:28.466000
|
https://github.com/GentleZhu/EGI
| 20 |
Transfer learning of graph neural networks with ego-graph information maximization
|
https://scholar.google.com/scholar?cluster=5328682952509931138&hl=en&as_sdt=0,10
| 2 | 2,021 |
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership
| 10 |
neurips
| 1 | 0 |
2023-06-16 16:05:28.665000
|
https://github.com/vita-group/no-stealing-lth
| 8 |
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership
|
https://scholar.google.com/scholar?cluster=15882101546967524183&hl=en&as_sdt=0,43
| 6 | 2,021 |
End-to-End Weak Supervision
| 25 |
neurips
| 11 | 4 |
2023-06-16 16:05:28.864000
|
https://github.com/autonlab/weasel
| 142 |
End-to-end weak supervision
|
https://scholar.google.com/scholar?cluster=10702508004213948659&hl=en&as_sdt=0,33
| 4 | 2,021 |
Shift Invariance Can Reduce Adversarial Robustness
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:05:29.064000
|
https://github.com/SongweiGe/shift-invariance-adv-robustness
| 1 |
Shift invariance can reduce adversarial robustness
|
https://scholar.google.com/scholar?cluster=11307069539231769168&hl=en&as_sdt=0,33
| 2 | 2,021 |
Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
| 1 |
neurips
| 0 | 0 |
2023-06-16 16:05:29.264000
|
https://github.com/ischubert/l2e
| 3 |
Learning to execute: Efficient learning of universal plan-conditioned policies in robotics
|
https://scholar.google.com/scholar?cluster=16472016640815710448&hl=en&as_sdt=0,36
| 2 | 2,021 |
Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks
| 10 |
neurips
| 4 | 0 |
2023-06-16 16:05:29.464000
|
https://github.com/grayhong/self-diagnosing-gan
| 21 |
Self-diagnosing gan: Diagnosing underrepresented samples in generative adversarial networks
|
https://scholar.google.com/scholar?cluster=934777026430658759&hl=en&as_sdt=0,10
| 2 | 2,021 |
Efficient Truncated Linear Regression with Unknown Noise Variance
| 4 |
neurips
| 0 | 0 |
2023-06-16 16:05:29.668000
|
https://github.com/pstefanou12/truncated-regression-with-unknown-noise-variance-neurips-2021
| 1 |
Efficient truncated linear regression with unknown noise variance
|
https://scholar.google.com/scholar?cluster=16700113284876080282&hl=en&as_sdt=0,47
| 1 | 2,021 |
Breaking the Dilemma of Medical Image-to-image Translation
| 45 |
neurips
| 18 | 3 |
2023-06-16 16:05:29.884000
|
https://github.com/kid-liet/reg-gan
| 106 |
Breaking the dilemma of medical image-to-image translation
|
https://scholar.google.com/scholar?cluster=16465540370988661764&hl=en&as_sdt=0,33
| 3 | 2,021 |
Temporally Abstract Partial Models
| 5 |
neurips
| 1 | 0 |
2023-06-16 16:05:30.085000
|
https://github.com/deepmind/affordances_option_models
| 21 |
Temporally abstract partial models
|
https://scholar.google.com/scholar?cluster=4581996143071889142&hl=en&as_sdt=0,5
| 4 | 2,021 |
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence
| 53 |
neurips
| 7 | 1 |
2023-06-16 16:05:30.286000
|
https://github.com/ahoho/topics
| 40 |
Is automated topic model evaluation broken? the incoherence of coherence
|
https://scholar.google.com/scholar?cluster=11755535918239308515&hl=en&as_sdt=0,33
| 5 | 2,021 |
Do Input Gradients Highlight Discriminative Features?
| 30 |
neurips
| 1 | 1 |
2023-06-16 16:05:30.486000
|
https://github.com/harshays/inputgradients
| 10 |
Do input gradients highlight discriminative features?
|
https://scholar.google.com/scholar?cluster=11330786422400793960&hl=en&as_sdt=0,10
| 2 | 2,021 |
Improving Conditional Coverage via Orthogonal Quantile Regression
| 16 |
neurips
| 1 | 0 |
2023-06-16 16:05:30.687000
|
https://github.com/Shai128/oqr
| 10 |
Improving conditional coverage via orthogonal quantile regression
|
https://scholar.google.com/scholar?cluster=14048759357099673213&hl=en&as_sdt=0,44
| 1 | 2,021 |
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
| 24 |
neurips
| 2 | 2 |
2023-06-16 16:05:30.889000
|
https://github.com/sli057/Geo-TRAP
| 6 |
Adversarial attacks on black box video classifiers: Leveraging the power of geometric transformations
|
https://scholar.google.com/scholar?cluster=2786816693505158644&hl=en&as_sdt=0,5
| 2 | 2,021 |
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method
| 17 |
neurips
| 5 | 1 |
2023-06-16 16:05:31.090000
|
https://github.com/pkuzengqi/skyformer
| 48 |
Skyformer: Remodel self-attention with gaussian kernel and nystr\" om method
|
https://scholar.google.com/scholar?cluster=251222659359430658&hl=en&as_sdt=0,33
| 5 | 2,021 |
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
| 182 |
neurips
| 56 | 8 |
2023-06-16 16:05:31.290000
|
https://github.com/szc19990412/TransMIL
| 188 |
Transmil: Transformer based correlated multiple instance learning for whole slide image classification
|
https://scholar.google.com/scholar?cluster=13608733975059322575&hl=en&as_sdt=0,5
| 4 | 2,021 |
Multi-view Contrastive Graph Clustering
| 70 |
neurips
| 6 | 0 |
2023-06-16 16:05:31.493000
|
https://github.com/panern/mcgc
| 40 |
Multi-view contrastive graph clustering
|
https://scholar.google.com/scholar?cluster=14221322770534641657&hl=en&as_sdt=0,5
| 1 | 2,021 |
Inverse-Weighted Survival Games
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:05:31.694000
|
https://github.com/rajesh-lab/inverse-weighted-survival-games
| 4 |
Inverse-weighted survival games
|
https://scholar.google.com/scholar?cluster=6438123884467705284&hl=en&as_sdt=0,10
| 1 | 2,021 |
Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability
| 23 |
neurips
| 1 | 0 |
2023-06-16 16:05:31.894000
|
https://github.com/irom-lab/PAC-BUS
| 3 |
Generalization bounds for meta-learning via pac-bayes and uniform stability
|
https://scholar.google.com/scholar?cluster=9536700152943509349&hl=en&as_sdt=0,44
| 8 | 2,021 |
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
| 43 |
neurips
| 319 | 64 |
2023-06-16 16:05:32.097000
|
https://github.com/pytorch/botorch
| 2,663 |
Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement
|
https://scholar.google.com/scholar?cluster=10095790416853214075&hl=en&as_sdt=0,47
| 51 | 2,021 |
Explaining Hyperparameter Optimization via Partial Dependence Plots
| 25 |
neurips
| 0 | 1 |
2023-06-16 16:05:32.300000
|
https://github.com/slds-lmu/paper_2021_xautoml
| 2 |
Explaining hyperparameter optimization via partial dependence plots
|
https://scholar.google.com/scholar?cluster=15140821706034992592&hl=en&as_sdt=0,5
| 12 | 2,021 |
Representation Learning on Spatial Networks
| 9 |
neurips
| 2 | 1 |
2023-06-16 16:05:32.507000
|
https://github.com/rollingstonezz/sgmp_code
| 14 |
Representation learning on spatial networks
|
https://scholar.google.com/scholar?cluster=18262507146784502070&hl=en&as_sdt=0,34
| 1 | 2,021 |
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