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Gaussian-Based Pooling for Convolutional Neural Networks | 12 | neurips | 6 | 3 | 2023-06-15 23:44:24.138000 | https://github.com/tk1980/GaussianPooling | 29 | Gaussian-based pooling for convolutional neural networks | https://scholar.google.com/scholar?cluster=2033748482757846351&hl=en&as_sdt=0,5 | 4 | 2,019 |
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks | 140 | neurips | 8 | 1 | 2023-06-15 23:44:24.320000 | https://github.com/PwnerHarry/Stronger_GCN | 50 | Break the ceiling: Stronger multi-scale deep graph convolutional networks | https://scholar.google.com/scholar?cluster=12919950599365272311&hl=en&as_sdt=0,7 | 8 | 2,019 |
Bayesian Optimization with Unknown Search Space | 27 | neurips | 2 | 0 | 2023-06-15 23:44:24.505000 | https://github.com/HuongHa12/BO_unknown_searchspace | 7 | Bayesian optimization with unknown search space | https://scholar.google.com/scholar?cluster=201571103459263814&hl=en&as_sdt=0,5 | 4 | 2,019 |
Towards closing the gap between the theory and practice of SVRG | 18 | neurips | 9 | 0 | 2023-06-15 23:44:24.688000 | https://github.com/gowerrobert/StochOpt.jl | 15 | Towards closing the gap between the theory and practice of SVRG | https://scholar.google.com/scholar?cluster=9351168021961753833&hl=en&as_sdt=0,5 | 2 | 2,019 |
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening | 44 | neurips | 3 | 1 | 2023-06-15 23:44:24.871000 | https://github.com/Gecia/A-Unifying-Framework-for-Spectrum-Preserving-Graph-Sparsification-and-Coarsening | 12 | A unifying framework for spectrum-preserving graph sparsification and coarsening | https://scholar.google.com/scholar?cluster=9934336085644961398&hl=en&as_sdt=0,47 | 2 | 2,019 |
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks | 67 | neurips | 4 | 1 | 2023-06-15 23:44:25.054000 | https://github.com/Gunjan108/robust-ecoc | 7 | Error correcting output codes improve probability estimation and adversarial robustness of deep neural networks | https://scholar.google.com/scholar?cluster=1032446707741176052&hl=en&as_sdt=0,23 | 4 | 2,019 |
KerGM: Kernelized Graph Matching | 35 | neurips | 2 | 2 | 2023-06-15 23:44:25.237000 | https://github.com/ZhenZhang19920330/KerGM_Code | 5 | Kergm: Kernelized graph matching | https://scholar.google.com/scholar?cluster=15872566588066107290&hl=en&as_sdt=0,33 | 1 | 2,019 |
Robustness Verification of Tree-based Models | 66 | neurips | 6 | 1 | 2023-06-15 23:44:25.419000 | https://github.com/chenhongge/treeVerification | 21 | Robustness verification of tree-based models | https://scholar.google.com/scholar?cluster=17206407102957374936&hl=en&as_sdt=0,5 | 2 | 2,019 |
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition | 73 | neurips | 18 | 9 | 2023-06-15 23:44:25.601000 | https://github.com/nxsEdson/MLCR | 62 | Multi-label co-regularization for semi-supervised facial action unit recognition | https://scholar.google.com/scholar?cluster=3209132207260506033&hl=en&as_sdt=0,33 | 4 | 2,019 |
A Primal Dual Formulation For Deep Learning With Constraints | 73 | neurips | 9 | 1 | 2023-06-15 23:44:25.784000 | https://github.com/dair-iitd/dl-with-constraints | 20 | A primal dual formulation for deep learning with constraints | https://scholar.google.com/scholar?cluster=4452120867924401058&hl=en&as_sdt=0,7 | 5 | 2,019 |
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections | 234 | neurips | 7,320 | 1,025 | 2023-06-15 23:44:25.967000 | https://github.com/google-research/google-research | 29,776 | Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections | https://scholar.google.com/scholar?cluster=6244624580827104740&hl=en&as_sdt=0,14 | 727 | 2,019 |
Intrinsic dimension of data representations in deep neural networks | 158 | neurips | 11 | 5 | 2023-06-15 23:44:26.150000 | https://github.com/ansuini/IntrinsicDimDeep | 62 | Intrinsic dimension of data representations in deep neural networks | https://scholar.google.com/scholar?cluster=16544809050270363310&hl=en&as_sdt=0,32 | 4 | 2,019 |
Program Synthesis and Semantic Parsing with Learned Code Idioms | 61 | neurips | 13 | 7 | 2023-06-15 23:44:26.333000 | https://github.com/rshin/seq2struct | 22 | Program synthesis and semantic parsing with learned code idioms | https://scholar.google.com/scholar?cluster=15175412131165659282&hl=en&as_sdt=0,5 | 7 | 2,019 |
Data-driven Estimation of Sinusoid Frequencies | 37 | neurips | 18 | 0 | 2023-06-15 23:44:26.516000 | https://github.com/sreyas-mohan/DeepFreq | 33 | Data-driven estimation of sinusoid frequencies | https://scholar.google.com/scholar?cluster=11417596009919386835&hl=en&as_sdt=0,34 | 7 | 2,019 |
Discovering Neural Wirings | 89 | neurips | 18 | 5 | 2023-06-15 23:44:26.699000 | https://github.com/allenai/dnw | 139 | Discovering neural wirings | https://scholar.google.com/scholar?cluster=10495394185221457754&hl=en&as_sdt=0,5 | 8 | 2,019 |
Fixing the train-test resolution discrepancy | 486 | neurips | 158 | 5 | 2023-06-15 23:44:26.882000 | https://github.com/facebookresearch/FixRes | 999 | Fixing the train-test resolution discrepancy | https://scholar.google.com/scholar?cluster=13066354109775166927&hl=en&as_sdt=0,34 | 25 | 2,019 |
Quadratic Video Interpolation | 147 | neurips | 8 | 1 | 2023-06-15 23:44:27.070000 | https://github.com/xuxy09/QVI | 35 | Quadratic video interpolation | https://scholar.google.com/scholar?cluster=6119382903996871763&hl=en&as_sdt=0,31 | 1 | 2,019 |
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game | 57 | neurips | 6 | 3 | 2023-06-15 23:44:27.253000 | https://github.com/tntrung/msgan | 35 | Self-supervised gan: Analysis and improvement with multi-class minimax game | https://scholar.google.com/scholar?cluster=11060271873858333561&hl=en&as_sdt=0,5 | 6 | 2,019 |
Learning step sizes for unfolded sparse coding | 49 | neurips | 2 | 0 | 2023-06-15 23:44:27.437000 | https://github.com/tomMoral/adopty | 17 | Learning step sizes for unfolded sparse coding | https://scholar.google.com/scholar?cluster=1728368642998167227&hl=en&as_sdt=0,5 | 2 | 2,019 |
Efficient Graph Generation with Graph Recurrent Attention Networks | 243 | neurips | 88 | 10 | 2023-06-15 23:44:27.620000 | https://github.com/lrjconan/GRAN | 412 | Efficient graph generation with graph recurrent attention networks | https://scholar.google.com/scholar?cluster=12112068708355431361&hl=en&as_sdt=0,48 | 10 | 2,019 |
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds | 243 | neurips | 86 | 53 | 2023-06-15 23:44:27.802000 | https://github.com/Yang7879/3D-BoNet | 364 | Learning object bounding boxes for 3D instance segmentation on point clouds | https://scholar.google.com/scholar?cluster=7864241319192905246&hl=en&as_sdt=0,5 | 11 | 2,019 |
Re-examination of the Role of Latent Variables in Sequence Modeling | 6 | neurips | 1 | 0 | 2023-06-15 23:44:27.986000 | https://github.com/zihangdai/reexamine-srnn | 11 | Re-examination of the role of latent variables in sequence modeling | https://scholar.google.com/scholar?cluster=10487801182247175380&hl=en&as_sdt=0,5 | 5 | 2,019 |
Consistency-based Semi-supervised Learning for Object detection | 280 | neurips | 2 | 0 | 2023-06-15 23:44:28.169000 | https://github.com/soo89/CSD-RFCN | 31 | Consistency-based semi-supervised learning for object detection | https://scholar.google.com/scholar?cluster=920250286184474856&hl=en&as_sdt=0,5 | 1 | 2,019 |
Exact Combinatorial Optimization with Graph Convolutional Neural Networks | 310 | neurips | 81 | 8 | 2023-06-15 23:44:28.352000 | https://github.com/ds4dm/learn2branch | 291 | Exact combinatorial optimization with graph convolutional neural networks | https://scholar.google.com/scholar?cluster=7938246232558949476&hl=en&as_sdt=0,31 | 15 | 2,019 |
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge | 90 | neurips | 0 | 3 | 2023-06-15 23:44:28.535000 | https://github.com/qiaott/LeicaGAN | 35 | Learn, imagine and create: Text-to-image generation from prior knowledge | https://scholar.google.com/scholar?cluster=9101617990451691231&hl=en&as_sdt=0,33 | 1 | 2,019 |
Compiler Auto-Vectorization with Imitation Learning | 36 | neurips | 0 | 1 | 2023-06-15 23:44:28.718000 | https://github.com/ithemal/vemal | 0 | Compiler auto-vectorization with imitation learning | https://scholar.google.com/scholar?cluster=638281280745878872&hl=en&as_sdt=0,5 | 7 | 2,019 |
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations | 233 | neurips | 4 | 0 | 2023-06-15 23:44:28.901000 | https://github.com/karakusc/horovod | 2 | Qsparse-local-SGD: Distributed SGD with quantization, sparsification and local computations | https://scholar.google.com/scholar?cluster=16825277147142982104&hl=en&as_sdt=0,11 | 1 | 2,019 |
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data | 8 | neurips | 0 | 0 | 2023-06-15 23:44:29.084000 | https://github.com/cunningham-lab/drs | 1 | Deep random splines for point process intensity estimation of neural population data | https://scholar.google.com/scholar?cluster=14731396798621089484&hl=en&as_sdt=0,5 | 4 | 2,019 |
Fast Decomposable Submodular Function Minimization using Constrained Total Variation | 2 | neurips | 0 | 0 | 2023-06-15 23:44:29.268000 | https://github.com/seshkumar/FasterSFMCode | 0 | Fast decomposable submodular function minimization using constrained total variation | https://scholar.google.com/scholar?cluster=15441163684816125674&hl=en&as_sdt=0,33 | 2 | 2,019 |
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies | 52 | neurips | 2 | 0 | 2023-06-15 23:44:29.450000 | https://github.com/BaoWangMath/EnResNet | 19 | Resnets ensemble via the feynman-kac formalism to improve natural and robust accuracies | https://scholar.google.com/scholar?cluster=12953232414825992406&hl=en&as_sdt=0,5 | 2 | 2,019 |
Learning elementary structures for 3D shape generation and matching | 148 | neurips | 17 | 8 | 2023-06-15 23:44:29.632000 | https://github.com/TheoDEPRELLE/AtlasNetV2 | 127 | Learning elementary structures for 3d shape generation and matching | https://scholar.google.com/scholar?cluster=2109791820647305873&hl=en&as_sdt=0,5 | 6 | 2,019 |
Cross-Modal Learning with Adversarial Samples | 26 | neurips | 1 | 0 | 2023-06-15 23:44:29.816000 | https://github.com/ChaoLi1991/CMLA | 8 | Cross-modal learning with adversarial samples | https://scholar.google.com/scholar?cluster=5714855065686823334&hl=en&as_sdt=0,5 | 3 | 2,019 |
Learning Disentangled Representation for Robust Person Re-identification | 67 | neurips | 20 | 1 | 2023-06-15 23:44:29.999000 | https://github.com/cvlab-yonsei/projects | 98 | Learning disentangled representation for robust person re-identification | https://scholar.google.com/scholar?cluster=7569542638892703397&hl=en&as_sdt=0,41 | 9 | 2,019 |
Learning Deterministic Weighted Automata with Queries and Counterexamples | 40 | neurips | 5 | 0 | 2023-06-15 23:44:30.182000 | https://github.com/tech-srl/weighted_lstar | 14 | Learning deterministic weighted automata with queries and counterexamples | https://scholar.google.com/scholar?cluster=4739515655099675842&hl=en&as_sdt=0,22 | 7 | 2,019 |
Making the Cut: A Bandit-based Approach to Tiered Interviewing | 9 | neurips | 1 | 0 | 2023-06-15 23:44:30.364000 | https://github.com/principledhiring/TieredHiring | 1 | Making the cut: A bandit-based approach to tiered interviewing | https://scholar.google.com/scholar?cluster=14271111588210565240&hl=en&as_sdt=0,5 | 2 | 2,019 |
Manifold-regression to predict from MEG/EEG brain signals without source modeling | 42 | neurips | 5 | 1 | 2023-06-15 23:44:30.547000 | https://github.com/DavidSabbagh/NeurIPS19_manifold-regression-meeg | 7 | Manifold-regression to predict from MEG/EEG brain signals without source modeling | https://scholar.google.com/scholar?cluster=2440641167368670167&hl=en&as_sdt=0,5 | 5 | 2,019 |
Reflection Separation using a Pair of Unpolarized and Polarized Images | 38 | neurips | 0 | 0 | 2023-06-15 23:44:30.730000 | https://github.com/YouweiLyu/reflection_separation_with_un-polarized_images | 26 | Reflection separation using a pair of unpolarized and polarized images | https://scholar.google.com/scholar?cluster=9587343905338547609&hl=en&as_sdt=0,50 | 3 | 2,019 |
Co-Generation with GANs using AIS based HMC | 3 | neurips | 1 | 0 | 2023-06-15 23:44:30.913000 | https://github.com/AilsaF/cogen_by_ais | 4 | Co-Generation with GANs using AIS based HMC | https://scholar.google.com/scholar?cluster=17978444032860345049&hl=en&as_sdt=0,5 | 5 | 2,019 |
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds | 22 | neurips | 1 | 0 | 2023-06-15 23:44:31.095000 | https://github.com/CausalML/interventions-disparate-impact-responders | 11 | Assessing disparate impact of personalized interventions: identifiability and bounds | https://scholar.google.com/scholar?cluster=11034607316544592064&hl=en&as_sdt=0,44 | 3 | 2,019 |
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution | 105 | neurips | 19 | 8 | 2023-06-15 23:44:31.278000 | https://github.com/thangvubk/Cascade-RPN | 178 | Cascade rpn: Delving into high-quality region proposal network with adaptive convolution | https://scholar.google.com/scholar?cluster=15521446702742963426&hl=en&as_sdt=0,5 | 12 | 2,019 |
Variational Bayesian Optimal Experimental Design | 87 | neurips | 4 | 4 | 2023-06-15 23:44:31.461000 | https://github.com/ae-foster/pyro | 8 | Variational Bayesian optimal experimental design | https://scholar.google.com/scholar?cluster=15336498043401775880&hl=en&as_sdt=0,33 | 6 | 2,019 |
Flexible Modeling of Diversity with Strongly Log-Concave Distributions | 11 | neurips | 0 | 0 | 2023-06-15 23:44:31.644000 | https://github.com/joshr17/slc_sampling | 2 | Flexible modeling of diversity with strongly log-concave distributions | https://scholar.google.com/scholar?cluster=6837075722751509721&hl=en&as_sdt=0,47 | 1 | 2,019 |
Neural Machine Translation with Soft Prototype | 16 | neurips | 4 | 1 | 2023-06-15 23:44:31.827000 | https://github.com/ywang07/nmt_soft_prototype | 8 | Neural machine translation with soft prototype | https://scholar.google.com/scholar?cluster=10440836540964517084&hl=en&as_sdt=0,5 | 1 | 2,019 |
Doubly-Robust Lasso Bandit | 49 | neurips | 0 | 1 | 2023-06-15 23:44:32.009000 | https://github.com/gisoo1989/Doubly-Robust-Lasso-Bandit | 6 | Doubly-robust lasso bandit | https://scholar.google.com/scholar?cluster=9511761143101036812&hl=en&as_sdt=0,5 | 2 | 2,019 |
Ask not what AI can do, but what AI should do: Towards a framework of task delegability | 46 | neurips | 2 | 0 | 2023-06-15 23:44:32.192000 | https://github.com/delegability/data | 6 | Ask not what AI can do, but what AI should do: Towards a framework of task delegability | https://scholar.google.com/scholar?cluster=7940339125070572458&hl=en&as_sdt=0,10 | 0 | 2,019 |
Offline Contextual Bandits with High Probability Fairness Guarantees | 41 | neurips | 5 | 0 | 2023-06-15 23:44:32.374000 | https://github.com/sgiguere/RobinHood-NeurIPS-2019 | 9 | Offline contextual bandits with high probability fairness guarantees | https://scholar.google.com/scholar?cluster=16399125850375524530&hl=en&as_sdt=0,5 | 1 | 2,019 |
LCA: Loss Change Allocation for Neural Network Training | 23 | neurips | 15 | 2 | 2023-06-15 23:44:32.557000 | https://github.com/uber-research/loss-change-allocation | 60 | Lca: Loss change allocation for neural network training | https://scholar.google.com/scholar?cluster=7222081194296604811&hl=en&as_sdt=0,5 | 7 | 2,019 |
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians | 21 | neurips | 5 | 0 | 2023-06-15 23:44:32.739000 | https://github.com/BBVA/UMAL | 13 | Modelling heterogeneous distributions with an uncountable mixture of asymmetric laplacians | https://scholar.google.com/scholar?cluster=15284501616973353353&hl=en&as_sdt=0,32 | 7 | 2,019 |
GNNExplainer: Generating Explanations for Graph Neural Networks | 679 | neurips | 154 | 26 | 2023-06-15 23:44:32.922000 | https://github.com/RexYing/gnn-model-explainer | 696 | Gnnexplainer: Generating explanations for graph neural networks | https://scholar.google.com/scholar?cluster=3833160255595095003&hl=en&as_sdt=0,5 | 20 | 2,019 |
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption | 48 | neurips | 1 | 0 | 2023-06-15 23:44:33.111000 | https://github.com/georgehc/mnar_mc | 11 | Missing not at random in matrix completion: The effectiveness of estimating missingness probabilities under a low nuclear norm assumption | https://scholar.google.com/scholar?cluster=17667220203657585647&hl=en&as_sdt=0,31 | 1 | 2,019 |
Unsupervised learning of object structure and dynamics from videos | 115 | neurips | 7,320 | 1,025 | 2023-06-15 23:44:33.294000 | https://github.com/google-research/google-research | 29,776 | Unsupervised learning of object structure and dynamics from videos | https://scholar.google.com/scholar?cluster=988438959666734889&hl=en&as_sdt=0,5 | 727 | 2,019 |
Cross-channel Communication Networks | 24 | neurips | 3 | 2 | 2023-06-15 23:44:33.476000 | https://github.com/jwyang/C3net | 41 | Cross-channel communication networks | https://scholar.google.com/scholar?cluster=11079957716276939436&hl=en&as_sdt=0,5 | 5 | 2,019 |
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training | 210 | neurips | 11 | 1 | 2023-06-15 23:44:33.659000 | https://github.com/Haichao-Zhang/FeatureScatter | 68 | Defense against adversarial attacks using feature scattering-based adversarial training | https://scholar.google.com/scholar?cluster=16771461599702512011&hl=en&as_sdt=0,14 | 3 | 2,019 |
Differentiable Ranking and Sorting using Optimal Transport | 120 | neurips | 7,320 | 1,025 | 2023-06-15 23:44:33.841000 | https://github.com/google-research/google-research | 29,776 | Differentiable ranking and sorting using optimal transport | https://scholar.google.com/scholar?cluster=18275340937640599555&hl=en&as_sdt=0,5 | 727 | 2,019 |
Ordered Memory | 18 | neurips | 8 | 1 | 2023-06-15 23:44:34.024000 | https://github.com/yikangshen/Ordered-Memory | 27 | Ordered memory | https://scholar.google.com/scholar?cluster=4062085826792189610&hl=en&as_sdt=0,16 | 4 | 2,019 |
Initialization of ReLUs for Dynamical Isometry | 19 | neurips | 0 | 0 | 2023-06-15 23:44:34.208000 | https://github.com/alinadubatovka/information_propagation | 0 | Initialization of relus for dynamical isometry | https://scholar.google.com/scholar?cluster=13000692432916595956&hl=en&as_sdt=0,5 | 1 | 2,019 |
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset | 99 | neurips | 2 | 2 | 2023-06-15 23:44:34.393000 | https://github.com/rr-learning/disentanglement_dataset | 68 | On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset | https://scholar.google.com/scholar?cluster=17192262335599006209&hl=en&as_sdt=0,5 | 7 | 2,019 |
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization | 200 | neurips | 25 | 0 | 2023-06-15 23:44:34.586000 | https://github.com/epfml/powersgd | 111 | PowerSGD: Practical low-rank gradient compression for distributed optimization | https://scholar.google.com/scholar?cluster=7612273195837769494&hl=en&as_sdt=0,26 | 10 | 2,019 |
CNN^{2}: Viewpoint Generalization via a Binocular Vision | 4 | neurips | 2 | 1 | 2023-06-15 23:44:34.768000 | https://github.com/wdchenxyz/CNN2 | 11 | CNN^{2}: Viewpoint Generalization via a Binocular Vision | https://scholar.google.com/scholar?cluster=2893562313504828451&hl=en&as_sdt=0,5 | 3 | 2,019 |
Unsupervised Learning of Object Keypoints for Perception and Control | 155 | neurips | 2,436 | 170 | 2023-06-15 23:44:34.951000 | https://github.com/deepmind/deepmind-research | 11,902 | Unsupervised learning of object keypoints for perception and control | https://scholar.google.com/scholar?cluster=8034421720358293108&hl=en&as_sdt=0,5 | 336 | 2,019 |
The Functional Neural Process | 436 | neurips | 9 | 1 | 2023-06-15 23:44:35.134000 | https://github.com/AMLab-Amsterdam/FNP | 44 | Neural processes | https://scholar.google.com/scholar?cluster=10874092191257343841&hl=en&as_sdt=0,5 | 5 | 2,019 |
Convergent Policy Optimization for Safe Reinforcement Learning | 83 | neurips | 3 | 0 | 2023-06-15 23:44:35.317000 | https://github.com/ming93/Safe_reinforcement_learning | 10 | Convergent policy optimization for safe reinforcement learning | https://scholar.google.com/scholar?cluster=72038626648458848&hl=en&as_sdt=0,5 | 1 | 2,019 |
Diffeomorphic Temporal Alignment Nets | 25 | neurips | 13 | 1 | 2023-06-15 23:44:35.500000 | https://github.com/BGU-CS-VIL/dtan | 63 | Diffeomorphic temporal alignment nets | https://scholar.google.com/scholar?cluster=7364466321504417673&hl=en&as_sdt=0,5 | 7 | 2,019 |
Multi-source Domain Adaptation for Semantic Segmentation | 149 | neurips | 26 | 6 | 2023-06-15 23:44:35.683000 | https://github.com/Luodian/MADAN | 159 | Multi-source domain adaptation for semantic segmentation | https://scholar.google.com/scholar?cluster=3870807745428440093&hl=en&as_sdt=0,43 | 9 | 2,019 |
Spectral Modification of Graphs for Improved Spectral Clustering | 5 | neurips | 1 | 0 | 2023-06-15 23:44:35.866000 | https://github.com/ikoutis/spectral-modification | 2 | Spectral modification of graphs for improved spectral clustering | https://scholar.google.com/scholar?cluster=16104250235411376820&hl=en&as_sdt=0,10 | 2 | 2,019 |
On Exact Computation with an Infinitely Wide Neural Net | 705 | neurips | 11 | 0 | 2023-06-15 23:44:36.050000 | https://github.com/ruosongwang/CNTK | 106 | On exact computation with an infinitely wide neural net | https://scholar.google.com/scholar?cluster=9266929152941012357&hl=en&as_sdt=0,36 | 5 | 2,019 |
Amortized Bethe Free Energy Minimization for Learning MRFs | 14 | neurips | 2 | 2 | 2023-06-15 23:44:36.233000 | https://github.com/swiseman/bethe-min | 7 | Amortized bethe free energy minimization for learning mrfs | https://scholar.google.com/scholar?cluster=6385669697520989717&hl=en&as_sdt=0,5 | 4 | 2,019 |
XLNet: Generalized Autoregressive Pretraining for Language Understanding | 7,092 | neurips | 1,181 | 189 | 2023-06-15 23:44:36.416000 | https://github.com/zihangdai/xlnet | 6,046 | Xlnet: Generalized autoregressive pretraining for language understanding | https://scholar.google.com/scholar?cluster=14487406216105917109&hl=en&as_sdt=0,5 | 172 | 2,019 |
Conditional Independence Testing using Generative Adversarial Networks | 33 | neurips | 6 | 0 | 2023-06-15 23:44:36.599000 | https://github.com/alexisbellot/GCIT | 10 | Conditional independence testing using generative adversarial networks | https://scholar.google.com/scholar?cluster=1653461036135291846&hl=en&as_sdt=0,22 | 2 | 2,019 |
A Tensorized Transformer for Language Modeling | 123 | neurips | 13 | 2 | 2023-06-15 23:44:36.781000 | https://github.com/szhangtju/The-compression-of-Transformer | 52 | A tensorized transformer for language modeling | https://scholar.google.com/scholar?cluster=10172100217073548450&hl=en&as_sdt=0,5 | 2 | 2,019 |
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components | 27 | neurips | 3 | 0 | 2023-06-15 23:44:36.963000 | https://github.com/saralajew/cbc_networks | 12 | Classification-by-components: Probabilistic modeling of reasoning over a set of components | https://scholar.google.com/scholar?cluster=12691103404451941071&hl=en&as_sdt=0,5 | 4 | 2,019 |
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs | 27 | neurips | 5 | 0 | 2023-06-15 23:44:37.146000 | https://github.com/RonanFR/UCRL | 25 | Exploration bonus for regret minimization in discrete and continuous average reward mdps | https://scholar.google.com/scholar?cluster=18182504260715903341&hl=en&as_sdt=0,11 | 5 | 2,019 |
A neurally plausible model learns successor representations in partially observable environments | 35 | neurips | 0 | 0 | 2023-06-15 23:44:37.329000 | https://github.com/evertes/distributional_SF | 7 | A neurally plausible model learns successor representations in partially observable environments | https://scholar.google.com/scholar?cluster=12108010444355822428&hl=en&as_sdt=0,5 | 1 | 2,019 |
Cost Effective Active Search | 137 | neurips | 3 | 0 | 2023-06-15 23:44:37.512000 | https://github.com/shalijiang/efficient_nonmyopic_active_search | 7 | Diagnosing the search cost effect: Waiting time and the moderating impact of prior category knowledge | https://scholar.google.com/scholar?cluster=707526541184886734&hl=en&as_sdt=0,11 | 2 | 2,019 |
Inherent Weight Normalization in Stochastic Neural Networks | 5 | neurips | 1 | 1 | 2023-06-15 23:44:37.694000 | https://github.com/nmi-lab/neural_sampling_machines | 10 | Inherent weight normalization in stochastic neural networks | https://scholar.google.com/scholar?cluster=17503775532323120531&hl=en&as_sdt=0,7 | 3 | 2,019 |
Discrete Flows: Invertible Generative Models of Discrete Data | 94 | neurips | 79 | 73 | 2023-06-15 23:44:37.878000 | https://github.com/google/edward2 | 644 | Discrete flows: Invertible generative models of discrete data | https://scholar.google.com/scholar?cluster=2184666710327025867&hl=en&as_sdt=0,14 | 20 | 2,019 |
Disentangled behavioural representations | 19 | neurips | 3 | 6 | 2023-06-15 23:44:38.060000 | https://github.com/adezfouli/rnn_hypercoder | 3 | Disentangled behavioural representations | https://scholar.google.com/scholar?cluster=17586763103868560368&hl=en&as_sdt=0,5 | 2 | 2,019 |
A Flexible Generative Framework for Graph-based Semi-supervised Learning | 59 | neurips | 6 | 0 | 2023-06-15 23:44:38.243000 | https://github.com/jiaqima/G3NN | 15 | A flexible generative framework for graph-based semi-supervised learning | https://scholar.google.com/scholar?cluster=4643725798321580132&hl=en&as_sdt=0,36 | 4 | 2,019 |
Online-Within-Online Meta-Learning | 51 | neurips | 0 | 0 | 2023-06-15 23:44:38.426000 | https://github.com/dstamos/Adversarial-LTL | 4 | Online-within-online meta-learning | https://scholar.google.com/scholar?cluster=1456705708848816283&hl=en&as_sdt=0,40 | 1 | 2,019 |
Adversarial Examples Are Not Bugs, They Are Features | 1,456 | neurips | 157 | 25 | 2023-06-15 23:44:38.608000 | https://github.com/MadryLab/robustness | 799 | Adversarial examples are not bugs, they are features | https://scholar.google.com/scholar?cluster=9505899875968209772&hl=en&as_sdt=0,5 | 17 | 2,019 |
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion | 21 | neurips | 4 | 1 | 2023-06-15 23:44:38.791000 | https://github.com/choyingw/CFCNet | 35 | Deep rgb-d canonical correlation analysis for sparse depth completion | https://scholar.google.com/scholar?cluster=3860279258380832246&hl=en&as_sdt=0,21 | 3 | 2,019 |
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck | 128 | neurips | 16 | 1 | 2023-06-15 23:44:38.974000 | https://github.com/microsoft/IBAC-SNI | 47 | Generalization in reinforcement learning with selective noise injection and information bottleneck | https://scholar.google.com/scholar?cluster=4207867939848358393&hl=en&as_sdt=0,33 | 5 | 2,019 |
Untangling in Invariant Speech Recognition | 13 | neurips | 19 | 3 | 2023-06-15 23:44:39.157000 | https://github.com/schung039/neural_manifolds_replicaMFT | 43 | Untangling in invariant speech recognition | https://scholar.google.com/scholar?cluster=17300601145385623429&hl=en&as_sdt=0,33 | 3 | 2,019 |
Certifiable Robustness to Graph Perturbations | 92 | neurips | 7 | 1 | 2023-06-15 23:44:39.340000 | https://github.com/abojchevski/graph_cert | 12 | Certifiable robustness to graph perturbations | https://scholar.google.com/scholar?cluster=1934279975175671629&hl=en&as_sdt=0,5 | 2 | 2,019 |
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks | 19 | neurips | 1 | 0 | 2023-06-15 23:44:39.522000 | https://github.com/jdlafferty/surfing | 2 | Surfing: Iterative optimization over incrementally trained deep networks | https://scholar.google.com/scholar?cluster=6290748342679623443&hl=en&as_sdt=0,5 | 1 | 2,019 |
Rates of Convergence for Large-scale Nearest Neighbor Classification | 13 | neurips | 0 | 0 | 2023-06-15 23:44:39.705000 | https://github.com/duanjiexin/bigNN | 0 | Rates of convergence for large-scale nearest neighbor classification | https://scholar.google.com/scholar?cluster=2730299591243322106&hl=en&as_sdt=0,5 | 1 | 2,019 |
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning | 81 | neurips | 0 | 0 | 2023-06-15 23:44:39.888000 | https://github.com/harsh6gpt1/adaptivetwotimeRL | 0 | Finite-time performance bounds and adaptive learning rate selection for two time-scale reinforcement learning | https://scholar.google.com/scholar?cluster=14282611510273442808&hl=en&as_sdt=0,14 | 2 | 2,019 |
Pseudo-Extended Markov chain Monte Carlo | 14 | neurips | 1 | 0 | 2023-06-15 23:44:40.071000 | https://github.com/chris-nemeth/pseudo-extended-mcmc-code | 3 | Pseudo-extended Markov chain Monte Carlo | https://scholar.google.com/scholar?cluster=13706683704662205943&hl=en&as_sdt=0,39 | 5 | 2,019 |
Hierarchical Optimal Transport for Multimodal Distribution Alignment | 50 | neurips | 2 | 2 | 2023-06-15 23:44:40.254000 | https://github.com/nerdslab/HiWA | 29 | Hierarchical optimal transport for multimodal distribution alignment | https://scholar.google.com/scholar?cluster=14796992753189496904&hl=en&as_sdt=0,5 | 5 | 2,019 |
Self-Routing Capsule Networks | 79 | neurips | 12 | 1 | 2023-06-15 23:44:40.437000 | https://github.com/coder3000/SR-CapsNet | 41 | Self-routing capsule networks | https://scholar.google.com/scholar?cluster=15113384032510098808&hl=en&as_sdt=0,34 | 3 | 2,019 |
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation | 68 | neurips | 10 | 0 | 2023-06-15 23:44:40.619000 | https://github.com/JianGuanTHU/IRecGAN | 46 | A model-based reinforcement learning with adversarial training for online recommendation | https://scholar.google.com/scholar?cluster=4137106166024493753&hl=en&as_sdt=0,5 | 3 | 2,019 |
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation | 194 | neurips | 2 | 0 | 2023-06-15 23:44:40.802000 | https://github.com/vuoristo/MMAML | 18 | Multimodal model-agnostic meta-learning via task-aware modulation | https://scholar.google.com/scholar?cluster=16689078801386417335&hl=en&as_sdt=0,5 | 5 | 2,019 |
Predicting the Politics of an Image Using Webly Supervised Data | 21 | neurips | 175 | 21 | 2023-06-15 23:44:40.984000 | https://github.com/dragnet-org/dragnet | 1,142 | Predicting the politics of an image using webly supervised data | https://scholar.google.com/scholar?cluster=861412190222908721&hl=en&as_sdt=0,5 | 133 | 2,019 |
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets | 39 | neurips | 1 | 4 | 2023-06-15 23:44:41.167000 | https://github.com/victorcampos7/weightnorm-init | 12 | How to initialize your network? robust initialization for weightnorm & resnets | https://scholar.google.com/scholar?cluster=13685873871964389208&hl=en&as_sdt=0,16 | 2 | 2,019 |
Code Generation as a Dual Task of Code Summarization | 148 | neurips | 7 | 4 | 2023-06-15 23:44:41.349000 | https://github.com/Bolin0215/CSCGDual | 17 | Code generation as a dual task of code summarization | https://scholar.google.com/scholar?cluster=14746121163037489756&hl=en&as_sdt=0,34 | 1 | 2,019 |
Gradient based sample selection for online continual learning | 474 | neurips | 12 | 14 | 2023-06-15 23:44:41.532000 | https://github.com/rahafaljundi/Gradient-based-Sample-Selection | 60 | Gradient based sample selection for online continual learning | https://scholar.google.com/scholar?cluster=14210983833434346363&hl=en&as_sdt=0,22 | 5 | 2,019 |
Conditional Structure Generation through Graph Variational Generative Adversarial Nets | 84 | neurips | 12 | 5 | 2023-06-15 23:44:41.714000 | https://github.com/KelestZ/CondGen | 48 | Conditional structure generation through graph variational generative adversarial nets | https://scholar.google.com/scholar?cluster=11602636019851588443&hl=en&as_sdt=0,5 | 4 | 2,019 |
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting | 560 | neurips | 62 | 7 | 2023-06-15 23:44:41.896000 | https://github.com/xjtushujun/meta-weight-net | 253 | Meta-weight-net: Learning an explicit mapping for sample weighting | https://scholar.google.com/scholar?cluster=17581878029469126945&hl=en&as_sdt=0,5 | 7 | 2,019 |
Thompson Sampling with Information Relaxation Penalties | 7 | neurips | 0 | 0 | 2023-06-15 23:44:42.079000 | https://github.com/mskyt88/info-relax-sampling | 0 | Thompson sampling with information relaxation penalties | https://scholar.google.com/scholar?cluster=10829250718917489138&hl=en&as_sdt=0,5 | 1 | 2,019 |
Constraint-based Causal Structure Learning with Consistent Separating Sets | 15 | neurips | 0 | 0 | 2023-06-15 23:44:42.262000 | https://github.com/honghaoli42/consistent_pcalg | 0 | Constraint-based causal structure learning with consistent separating sets | https://scholar.google.com/scholar?cluster=7060466264779924892&hl=en&as_sdt=0,39 | 2 | 2,019 |
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