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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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