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Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency | 15 | neurips | 7 | 4 | 2023-06-16 15:11:31.303000 | https://github.com/zhaofang0627/HPBTT | 34 | Human parsing based texture transfer from single image to 3D human via cross-view consistency | https://scholar.google.com/scholar?cluster=1392260009532397096&hl=en&as_sdt=0,23 | 5 | 2,020 |
Point process models for sequence detection in high-dimensional neural spike trains | 19 | neurips | 14 | 13 | 2023-06-16 15:11:31.501000 | https://github.com/lindermanlab/PPSeq.jl | 54 | Point process models for sequence detection in high-dimensional neural spike trains | https://scholar.google.com/scholar?cluster=9563598193970283659&hl=en&as_sdt=0,5 | 5 | 2,020 |
Meta-Consolidation for Continual Learning | 41 | neurips | 6 | 2 | 2023-06-16 15:11:31.694000 | https://github.com/JosephKJ/merlin | 35 | Meta-consolidation for continual learning | https://scholar.google.com/scholar?cluster=15752256087440241118&hl=en&as_sdt=0,50 | 3 | 2,020 |
Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting | 25 | neurips | 3 | 1 | 2023-06-16 15:11:31.887000 | https://github.com/GRASP-ML/LPG-FTW | 18 | Lifelong policy gradient learning of factored policies for faster training without forgetting | https://scholar.google.com/scholar?cluster=165730710114613899&hl=en&as_sdt=0,36 | 4 | 2,020 |
Kernel Methods Through the Roof: Handling Billions of Points Efficiently | 79 | neurips | 18 | 11 | 2023-06-16 15:11:32.080000 | https://github.com/FalkonML/falkon | 144 | Kernel methods through the roof: handling billions of points efficiently | https://scholar.google.com/scholar?cluster=3529879786066434320&hl=en&as_sdt=0,44 | 5 | 2,020 |
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness | 94 | neurips | 5 | 4 | 2023-06-16 15:11:32.272000 | https://github.com/garyzhao/ME-ADA | 44 | Maximum-entropy adversarial data augmentation for improved generalization and robustness | https://scholar.google.com/scholar?cluster=7615895385729702903&hl=en&as_sdt=0,33 | 5 | 2,020 |
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler | 37 | neurips | 23 | 2 | 2023-06-16 15:11:32.465000 | https://github.com/ZhiningLiu1998/mesa | 98 | MESA: boost ensemble imbalanced learning with meta-sampler | https://scholar.google.com/scholar?cluster=7795141053937994912&hl=en&as_sdt=0,44 | 6 | 2,020 |
CoinPress: Practical Private Mean and Covariance Estimation | 64 | neurips | 4 | 1 | 2023-06-16 15:11:32.657000 | https://github.com/twistedcubic/coin-press | 26 | Coinpress: Practical private mean and covariance estimation | https://scholar.google.com/scholar?cluster=13482839562115522238&hl=en&as_sdt=0,5 | 8 | 2,020 |
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks | 76 | neurips | 3 | 0 | 2023-06-16 15:11:32.850000 | https://github.com/dms-net/scatteringGCN | 18 | Scattering gcn: Overcoming oversmoothness in graph convolutional networks | https://scholar.google.com/scholar?cluster=18035755183666892660&hl=en&as_sdt=0,5 | 3 | 2,020 |
Scalable Graph Neural Networks via Bidirectional Propagation | 74 | neurips | 3 | 3 | 2023-06-16 15:11:33.043000 | https://github.com/chennnM/GBP | 22 | Scalable graph neural networks via bidirectional propagation | https://scholar.google.com/scholar?cluster=9080075378376168855&hl=en&as_sdt=0,5 | 1 | 2,020 |
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning | 101 | neurips | 12 | 1 | 2023-06-16 15:11:33.235000 | https://github.com/bbuing9/DARP | 60 | Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning | https://scholar.google.com/scholar?cluster=8038258359188951578&hl=en&as_sdt=0,6 | 4 | 2,020 |
The Strong Screening Rule for SLOPE | 11 | neurips | 0 | 0 | 2023-06-16 15:11:33.427000 | https://github.com/jolars/slope-screening-code | 0 | The strong screening rule for SLOPE | https://scholar.google.com/scholar?cluster=12320339008639900419&hl=en&as_sdt=0,39 | 2 | 2,020 |
Efficient Generation of Structured Objects with Constrained Adversarial Networks | 17 | neurips | 0 | 0 | 2023-06-16 15:11:33.620000 | https://github.com/unitn-sml/CAN | 6 | Efficient generation of structured objects with constrained adversarial networks | https://scholar.google.com/scholar?cluster=4567145751569316371&hl=en&as_sdt=0,47 | 7 | 2,020 |
Learning Sparse Prototypes for Text Generation | 16 | neurips | 2 | 1 | 2023-06-16 15:11:33.812000 | https://github.com/jxhe/sparse-text-prototype | 19 | Learning sparse prototypes for text generation | https://scholar.google.com/scholar?cluster=7964564098048473464&hl=en&as_sdt=0,5 | 2 | 2,020 |
Implicit Rank-Minimizing Autoencoder | 28 | neurips | 9 | 0 | 2023-06-16 15:11:34.004000 | https://github.com/facebookresearch/irmae | 45 | Implicit rank-minimizing autoencoder | https://scholar.google.com/scholar?cluster=13933352693018665516&hl=en&as_sdt=0,5 | 8 | 2,020 |
Task-Oriented Feature Distillation | 25 | neurips | 8 | 8 | 2023-06-16 15:11:34.198000 | https://github.com/ArchipLab-LinfengZhang/Task-Oriented-Feature-Distillation | 37 | Task-oriented feature distillation | https://scholar.google.com/scholar?cluster=4090442245139962638&hl=en&as_sdt=0,33 | 3 | 2,020 |
When Do Neural Networks Outperform Kernel Methods? | 116 | neurips | 0 | 0 | 2023-06-16 15:11:34.391000 | https://github.com/bGhorbani/linearized_neural_networks | 1 | When do neural networks outperform kernel methods? | https://scholar.google.com/scholar?cluster=9006100228205031604&hl=en&as_sdt=0,33 | 2 | 2,020 |
A Variational Approach for Learning from Positive and Unlabeled Data | 23 | neurips | 2 | 0 | 2023-06-16 15:11:34.584000 | https://github.com/HC-Feynman/vpu | 16 | A variational approach for learning from positive and unlabeled data | https://scholar.google.com/scholar?cluster=9825864282634047944&hl=en&as_sdt=0,33 | 1 | 2,020 |
Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut | 16 | neurips | 3 | 2 | 2023-06-16 15:11:34.777000 | https://github.com/ShenfeiPei/KSUMS | 7 | Efficient Clustering Based On A Unified View Of -means And Ratio-cut | https://scholar.google.com/scholar?cluster=8591169336968317824&hl=en&as_sdt=0,33 | 2 | 2,020 |
Coresets via Bilevel Optimization for Continual Learning and Streaming | 119 | neurips | 7 | 1 | 2023-06-16 15:11:34.970000 | https://github.com/zalanborsos/bilevel_coresets | 59 | Coresets via bilevel optimization for continual learning and streaming | https://scholar.google.com/scholar?cluster=8782040357228016957&hl=en&as_sdt=0,5 | 3 | 2,020 |
Deep Evidential Regression | 208 | neurips | 86 | 14 | 2023-06-16 15:11:35.162000 | https://github.com/aamini/evidential-deep-learning | 335 | Deep evidential regression | https://scholar.google.com/scholar?cluster=1290131026867107522&hl=en&as_sdt=0,5 | 17 | 2,020 |
Bayesian Pseudocoresets | 19 | neurips | 0 | 0 | 2023-06-16 15:11:35.355000 | https://github.com/trevorcampbell/pseudocoresets-experiments | 2 | Bayesian pseudocoresets | https://scholar.google.com/scholar?cluster=3191000793035049676&hl=en&as_sdt=0,5 | 1 | 2,020 |
See, Hear, Explore: Curiosity via Audio-Visual Association | 40 | neurips | 3 | 0 | 2023-06-16 15:11:35.548000 | https://github.com/vdean/audio-curiosity | 22 | See, hear, explore: Curiosity via audio-visual association | https://scholar.google.com/scholar?cluster=3876755724987793251&hl=en&as_sdt=0,34 | 6 | 2,020 |
A Biologically Plausible Neural Network for Slow Feature Analysis | 12 | neurips | 3 | 0 | 2023-06-16 15:11:35.755000 | https://github.com/flatironinstitute/bio-sfa | 9 | A biologically plausible neural network for slow feature analysis | https://scholar.google.com/scholar?cluster=9129829239701859332&hl=en&as_sdt=0,33 | 5 | 2,020 |
Learning Feature Sparse Principal Subspace | 12 | neurips | 1 | 0 | 2023-06-16 15:11:35.947000 | https://github.com/icety3/FSPCA | 3 | Learning feature sparse principal subspace | https://scholar.google.com/scholar?cluster=14875636565166044035&hl=en&as_sdt=0,33 | 1 | 2,020 |
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping | 61 | neurips | 1 | 0 | 2023-06-16 15:11:36.141000 | https://github.com/eduardgorbunov/accelerated_clipping | 0 | Stochastic optimization with heavy-tailed noise via accelerated gradient clipping | https://scholar.google.com/scholar?cluster=13617610532050808796&hl=en&as_sdt=0,5 | 1 | 2,020 |
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering | 60 | neurips | 27 | 4 | 2023-06-16 15:11:36.335000 | https://github.com/HazyResearch/HypHC | 171 | From trees to continuous embeddings and back: Hyperbolic hierarchical clustering | https://scholar.google.com/scholar?cluster=14446389788474790006&hl=en&as_sdt=0,10 | 18 | 2,020 |
A Randomized Algorithm to Reduce the Support of Discrete Measures | 11 | neurips | 0 | 0 | 2023-06-16 15:11:36.528000 | https://github.com/FraCose/Recombination_Random_Algos | 1 | A randomized algorithm to reduce the support of discrete measures | https://scholar.google.com/scholar?cluster=4952355762729380364&hl=en&as_sdt=0,31 | 2 | 2,020 |
Distributionally Robust Federated Averaging | 81 | neurips | 32 | 4 | 2023-06-16 15:11:36.720000 | https://github.com/MLOPTPSU/FedTorch | 153 | Distributionally robust federated averaging | https://scholar.google.com/scholar?cluster=7220059045750454455&hl=en&as_sdt=0,38 | 5 | 2,020 |
Supermasks in Superposition | 161 | neurips | 19 | 8 | 2023-06-16 15:11:36.912000 | https://github.com/RAIVNLab/supsup | 105 | Supermasks in superposition | https://scholar.google.com/scholar?cluster=9249214660750910893&hl=en&as_sdt=0,47 | 9 | 2,020 |
Learning to Incentivize Other Learning Agents | 42 | neurips | 5 | 0 | 2023-06-16 15:11:37.106000 | https://github.com/011235813/lio | 21 | Learning to incentivize other learning agents | https://scholar.google.com/scholar?cluster=4917678019855172893&hl=en&as_sdt=0,10 | 3 | 2,020 |
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation | 53 | neurips | 5 | 0 | 2023-06-16 15:11:37.305000 | https://github.com/jytime/DICL-Flow | 147 | Displacement-invariant matching cost learning for accurate optical flow estimation | https://scholar.google.com/scholar?cluster=18219962578086154043&hl=en&as_sdt=0,5 | 2 | 2,020 |
Calibrating Deep Neural Networks using Focal Loss | 217 | neurips | 27 | 2 | 2023-06-16 15:11:37.502000 | https://github.com/torrvision/focal_calibration | 131 | Calibrating deep neural networks using focal loss | https://scholar.google.com/scholar?cluster=5652808911409049311&hl=en&as_sdt=0,5 | 8 | 2,020 |
Optimizing Mode Connectivity via Neuron Alignment | 27 | neurips | 0 | 1 | 2023-06-16 15:11:37.694000 | https://github.com/IBM/NeuronAlignment | 11 | Optimizing mode connectivity via neuron alignment | https://scholar.google.com/scholar?cluster=2446555805962125063&hl=en&as_sdt=0,19 | 7 | 2,020 |
First Order Constrained Optimization in Policy Space | 61 | neurips | 6 | 2 | 2023-06-16 15:11:37.886000 | https://github.com/ymzhang01/focops | 19 | First order constrained optimization in policy space | https://scholar.google.com/scholar?cluster=13576739471377341905&hl=en&as_sdt=0,10 | 1 | 2,020 |
Learning Augmented Energy Minimization via Speed Scaling | 47 | neurips | 0 | 13 | 2023-06-16 15:11:38.079000 | https://github.com/andreasr27/LAS | 1 | Learning augmented energy minimization via speed scaling | https://scholar.google.com/scholar?cluster=17308040311209580615&hl=en&as_sdt=0,33 | 1 | 2,020 |
Neural Sparse Representation for Image Restoration | 21 | neurips | 6 | 2 | 2023-06-16 15:11:38.272000 | https://github.com/ychfan/nsr | 27 | Neural sparse representation for image restoration | https://scholar.google.com/scholar?cluster=10878239147304379491&hl=en&as_sdt=0,33 | 3 | 2,020 |
Certified Monotonic Neural Networks | 51 | neurips | 4 | 0 | 2023-06-16 15:11:38.470000 | https://github.com/gnobitab/CertifiedMonotonicNetwork | 19 | Certified monotonic neural networks | https://scholar.google.com/scholar?cluster=10699232933677275869&hl=en&as_sdt=0,43 | 1 | 2,020 |
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina | 8 | neurips | 1 | 0 | 2023-06-16 15:11:38.662000 | https://github.com/berenslab/bc_network | 0 | System identification with biophysical constraints: A circuit model of the inner retina | https://scholar.google.com/scholar?cluster=5347063978169460934&hl=en&as_sdt=0,47 | 4 | 2,020 |
Efficient Algorithms for Device Placement of DNN Graph Operators | 37 | neurips | 12 | 0 | 2023-06-16 15:11:38.855000 | https://github.com/msr-fiddle/dnn-partitioning | 34 | Efficient algorithms for device placement of dnn graph operators | https://scholar.google.com/scholar?cluster=9495456628645575288&hl=en&as_sdt=0,11 | 3 | 2,020 |
BOSS: Bayesian Optimization over String Spaces | 54 | neurips | 4 | 2 | 2023-06-16 15:11:39.047000 | https://github.com/henrymoss/BOSS | 19 | Boss: Bayesian optimization over string spaces | https://scholar.google.com/scholar?cluster=5626895554294984605&hl=en&as_sdt=0,33 | 3 | 2,020 |
Improved Analysis of Clipping Algorithms for Non-convex Optimization | 27 | neurips | 2 | 0 | 2023-06-16 15:11:39.240000 | https://github.com/zbh2047/clipping-algorithms | 7 | Improved analysis of clipping algorithms for non-convex optimization | https://scholar.google.com/scholar?cluster=7794174474681522409&hl=en&as_sdt=0,5 | 1 | 2,020 |
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection | 34 | neurips | 17 | 4 | 2023-06-16 15:11:39.432000 | https://github.com/kemaloksuz/aLRPLoss | 132 | A ranking-based, balanced loss function unifying classification and localisation in object detection | https://scholar.google.com/scholar?cluster=17699742698226354325&hl=en&as_sdt=0,5 | 5 | 2,020 |
Robustness of Bayesian Neural Networks to Gradient-Based Attacks | 52 | neurips | 7 | 1 | 2023-06-16 15:11:39.625000 | https://github.com/ginevracoal/robustBNNs | 15 | Robustness of bayesian neural networks to gradient-based attacks | https://scholar.google.com/scholar?cluster=10011308363254706917&hl=en&as_sdt=0,24 | 2 | 2,020 |
Sparse Weight Activation Training | 45 | neurips | 6 | 2 | 2023-06-16 15:11:39.817000 | https://github.com/AamirRaihan/SWAT | 20 | Sparse weight activation training | https://scholar.google.com/scholar?cluster=13365043317939429653&hl=en&as_sdt=0,5 | 3 | 2,020 |
Collapsing Bandits and Their Application to Public Health Intervention | 45 | neurips | 2 | 0 | 2023-06-16 15:11:40.009000 | https://github.com/AdityaMate/collapsing_bandits | 9 | Collapsing bandits and their application to public health intervention | https://scholar.google.com/scholar?cluster=8570523626474094821&hl=en&as_sdt=0,10 | 2 | 2,020 |
Neural Sparse Voxel Fields | 577 | neurips | 89 | 30 | 2023-06-16 15:11:40.202000 | https://github.com/facebookresearch/NSVF | 710 | Neural sparse voxel fields | https://scholar.google.com/scholar?cluster=8122086353742917335&hl=en&as_sdt=0,31 | 60 | 2,020 |
The Discrete Gaussian for Differential Privacy | 145 | neurips | 15 | 1 | 2023-06-16 15:11:40.397000 | https://github.com/IBM/discrete-gaussian-differential-privacy | 52 | The discrete gaussian for differential privacy | https://scholar.google.com/scholar?cluster=15167325577394029097&hl=en&as_sdt=0,22 | 10 | 2,020 |
Learning efficient task-dependent representations with synaptic plasticity | 10 | neurips | 0 | 0 | 2023-06-16 15:11:40.591000 | https://github.com/colinbredenberg/Efficient-Plasticity-Camera-Ready | 0 | Learning efficient task-dependent representations with synaptic plasticity | https://scholar.google.com/scholar?cluster=9379444748985417987&hl=en&as_sdt=0,38 | 2 | 2,020 |
Disentangling Human Error from Ground Truth in Segmentation of Medical Images | 52 | neurips | 14 | 4 | 2023-06-16 15:11:40.784000 | https://github.com/moucheng2017/Learn_Noisy_Labels_Medical_Images | 58 | Disentangling human error from ground truth in segmentation of medical images | https://scholar.google.com/scholar?cluster=285062865281898576&hl=en&as_sdt=0,5 | 3 | 2,020 |
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences | 16 | neurips | 290 | 26 | 2023-06-16 15:11:40.978000 | https://github.com/openai/multi-agent-emergence-environments | 1,469 | Emergent reciprocity and team formation from randomized uncertain social preferences | https://scholar.google.com/scholar?cluster=15635465066667628419&hl=en&as_sdt=0,10 | 167 | 2,020 |
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity | 16 | neurips | 1 | 0 | 2023-06-16 15:11:41.171000 | https://github.com/HornHehhf/LANTK | 6 | Label-aware neural tangent kernel: Toward better generalization and local elasticity | https://scholar.google.com/scholar?cluster=8612232995248267129&hl=en&as_sdt=0,34 | 2 | 2,020 |
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows | 31 | neurips | 1 | 4 | 2023-06-16 15:11:41.367000 | https://github.com/hmdolatabadi/AdvFlow | 39 | Advflow: Inconspicuous black-box adversarial attacks using normalizing flows | https://scholar.google.com/scholar?cluster=14447439050002958501&hl=en&as_sdt=0,5 | 3 | 2,020 |
On the Expressiveness of Approximate Inference in Bayesian Neural Networks | 84 | neurips | 0 | 0 | 2023-06-16 15:11:41.572000 | https://github.com/cambridge-mlg/expressiveness-approx-bnns | 11 | On the expressiveness of approximate inference in bayesian neural networks | https://scholar.google.com/scholar?cluster=5102786395821574554&hl=en&as_sdt=0,31 | 6 | 2,020 |
Dark Experience for General Continual Learning: a Strong, Simple Baseline | 308 | neurips | 65 | 4 | 2023-06-16 15:11:41.765000 | https://github.com/aimagelab/mammoth | 346 | Dark experience for general continual learning: a strong, simple baseline | https://scholar.google.com/scholar?cluster=2597864278610919682&hl=en&as_sdt=0,5 | 10 | 2,020 |
PLLay: Efficient Topological Layer based on Persistent Landscapes | 38 | neurips | 3 | 0 | 2023-06-16 15:11:41.957000 | https://github.com/jisuk1/pllay | 15 | Pllay: Efficient topological layer based on persistent landscapes | https://scholar.google.com/scholar?cluster=11445863975926543932&hl=en&as_sdt=0,1 | 2 | 2,020 |
Inductive Quantum Embedding | 5 | neurips | 11 | 4 | 2023-06-16 15:11:42.150000 | https://github.com/IBM/e2r | 22 | Inductive quantum embedding | https://scholar.google.com/scholar?cluster=3314915984353001868&hl=en&as_sdt=0,36 | 10 | 2,020 |
Understanding and Improving Fast Adversarial Training | 186 | neurips | 11 | 0 | 2023-06-16 15:11:42.365000 | https://github.com/tml-epfl/understanding-fast-adv-training | 89 | Understanding and improving fast adversarial training | https://scholar.google.com/scholar?cluster=2088861284079495555&hl=en&as_sdt=0,5 | 5 | 2,020 |
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning | 297 | neurips | 9 | 3 | 2023-06-16 15:11:42.560000 | https://github.com/ksreenivasan/OOD_Federated_Learning | 41 | Attack of the tails: Yes, you really can backdoor federated learning | https://scholar.google.com/scholar?cluster=13414623177431802672&hl=en&as_sdt=0,36 | 2 | 2,020 |
Domain Generalization via Entropy Regularization | 144 | neurips | 8 | 4 | 2023-06-16 15:11:42.753000 | https://github.com/sshan-zhao/DG_via_ER | 52 | Domain generalization via entropy regularization | https://scholar.google.com/scholar?cluster=427952868050737540&hl=en&as_sdt=0,33 | 2 | 2,020 |
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels | 94 | neurips | 28 | 1 | 2023-06-16 15:11:42.946000 | https://github.com/BayesWatch/deep-kernel-transfer | 182 | Bayesian meta-learning for the few-shot setting via deep kernels | https://scholar.google.com/scholar?cluster=18214192068106676357&hl=en&as_sdt=0,23 | 14 | 2,020 |
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding | 21 | neurips | 2 | 0 | 2023-06-16 15:11:43.140000 | https://github.com/gergely-flamich/relative-entropy-coding | 14 | Compressing images by encoding their latent representations with relative entropy coding | https://scholar.google.com/scholar?cluster=12289205300644930057&hl=en&as_sdt=0,25 | 3 | 2,020 |
An Efficient Adversarial Attack for Tree Ensembles | 12 | neurips | 5 | 1 | 2023-06-16 15:11:43.344000 | https://github.com/chong-z/tree-ensemble-attack | 19 | An efficient adversarial attack for tree ensembles | https://scholar.google.com/scholar?cluster=609360742914199443&hl=en&as_sdt=0,44 | 0 | 2,020 |
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations | 24 | neurips | 5 | 0 | 2023-06-16 15:11:43.537000 | https://github.com/ZijieH/LG-ODE | 23 | Learning continuous system dynamics from irregularly-sampled partial observations | https://scholar.google.com/scholar?cluster=8858649239314376854&hl=en&as_sdt=0,14 | 2 | 2,020 |
Robust Pre-Training by Adversarial Contrastive Learning | 140 | neurips | 16 | 2 | 2023-06-16 15:11:43.739000 | https://github.com/VITA-Group/Adversarial-Contrastive-Learning | 99 | Robust pre-training by adversarial contrastive learning | https://scholar.google.com/scholar?cluster=16518369038810216082&hl=en&as_sdt=0,3 | 3 | 2,020 |
When Counterpoint Meets Chinese Folk Melodies | 5 | neurips | 2 | 1 | 2023-06-16 15:11:43.932000 | https://github.com/nina124/FolkDuet | 12 | When counterpoint meets chinese folk melodies | https://scholar.google.com/scholar?cluster=12963487339842203249&hl=en&as_sdt=0,3 | 2 | 2,020 |
Universal Domain Adaptation through Self Supervision | 196 | neurips | 19 | 3 | 2023-06-16 15:11:44.125000 | https://github.com/VisionLearningGroup/DANCE | 111 | Universal domain adaptation through self supervision | https://scholar.google.com/scholar?cluster=11345299015007987908&hl=en&as_sdt=0,31 | 4 | 2,020 |
Stochastic Normalization | 10 | neurips | 1 | 1 | 2023-06-16 15:11:44.332000 | https://github.com/thuml/StochNorm | 23 | Stochastic normalization | https://scholar.google.com/scholar?cluster=5318680963113509022&hl=en&as_sdt=0,33 | 6 | 2,020 |
Constrained episodic reinforcement learning in concave-convex and knapsack settings | 37 | neurips | 1 | 2 | 2023-06-16 15:11:44.545000 | https://github.com/miryoosefi/ConRL | 8 | Constrained episodic reinforcement learning in concave-convex and knapsack settings | https://scholar.google.com/scholar?cluster=14503128503676733543&hl=en&as_sdt=0,5 | 2 | 2,020 |
Cross-validation Confidence Intervals for Test Error | 23 | neurips | 0 | 0 | 2023-06-16 15:11:44.738000 | https://github.com/alexandre-bayle/cvci | 7 | Cross-validation confidence intervals for test error | https://scholar.google.com/scholar?cluster=15681064119655058632&hl=en&as_sdt=0,5 | 2 | 2,020 |
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation | 57 | neurips | 71 | 21 | 2023-06-16 15:11:44.931000 | https://github.com/alexandre01/deepsvg | 730 | Deepsvg: A hierarchical generative network for vector graphics animation | https://scholar.google.com/scholar?cluster=5374969560499371553&hl=en&as_sdt=0,5 | 21 | 2,020 |
Bayesian Attention Modules | 37 | neurips | 9 | 0 | 2023-06-16 15:11:45.123000 | https://github.com/zhougroup/BAM | 29 | Bayesian attention modules | https://scholar.google.com/scholar?cluster=5527896286369211202&hl=en&as_sdt=0,33 | 4 | 2,020 |
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough | 9 | neurips | 7 | 1 | 2023-06-16 15:11:45.316000 | https://github.com/lushleaf/Network-Pruning-Greedy-Forward-Selection | 20 | Greedy optimization provably wins the lottery: Logarithmic number of winning tickets is enough | https://scholar.google.com/scholar?cluster=8946682883342660892&hl=en&as_sdt=0,5 | 2 | 2,020 |
Path Integral Based Convolution and Pooling for Graph Neural Networks | 32 | neurips | 4 | 6 | 2023-06-16 15:11:45.520000 | https://github.com/YuGuangWang/PAN | 26 | Path integral based convolution and pooling for graph neural networks | https://scholar.google.com/scholar?cluster=14179965344392955374&hl=en&as_sdt=0,14 | 2 | 2,020 |
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks | 51 | neurips | 10 | 1 | 2023-06-16 15:11:45.713000 | https://github.com/ioanabica/SCIGAN | 19 | Estimating the effects of continuous-valued interventions using generative adversarial networks | https://scholar.google.com/scholar?cluster=6398203741512669443&hl=en&as_sdt=0,5 | 1 | 2,020 |
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings | 8 | neurips | 0 | 0 | 2023-06-16 15:11:45.905000 | https://github.com/HeejongBong/ldfa | 1 | Latent dynamic factor analysis of high-dimensional neural recordings | https://scholar.google.com/scholar?cluster=2397075989835657043&hl=en&as_sdt=0,5 | 1 | 2,020 |
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning | 34 | neurips | 9 | 3 | 2023-06-16 15:11:46.108000 | https://github.com/NVlabs/Bongard-LOGO | 47 | Bongard-logo: A new benchmark for human-level concept learning and reasoning | https://scholar.google.com/scholar?cluster=9164011458889391917&hl=en&as_sdt=0,33 | 13 | 2,020 |
GAN Memory with No Forgetting | 75 | neurips | 4 | 1 | 2023-06-16 15:11:46.307000 | https://github.com/MiaoyunZhao/GANmemory_LifelongLearning | 45 | Gan memory with no forgetting | https://scholar.google.com/scholar?cluster=13145134091678192364&hl=en&as_sdt=0,5 | 3 | 2,020 |
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games | 30 | neurips | 2 | 0 | 2023-06-16 15:11:46.532000 | https://github.com/YunqiuXu/SHA-KG | 9 | Deep reinforcement learning with stacked hierarchical attention for text-based games | https://scholar.google.com/scholar?cluster=10348481176946628089&hl=en&as_sdt=0,36 | 3 | 2,020 |
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding | 34 | neurips | 0 | 1 | 2023-06-16 15:11:46.724000 | https://github.com/llan-ml/MetaTNE | 9 | Node classification on graphs with few-shot novel labels via meta transformed network embedding | https://scholar.google.com/scholar?cluster=14402211114000422574&hl=en&as_sdt=0,33 | 1 | 2,020 |
Relative gradient optimization of the Jacobian term in unsupervised deep learning | 18 | neurips | 2 | 0 | 2023-06-16 15:11:46.916000 | https://github.com/fissoreg/relative-gradient-jacobian | 19 | Relative gradient optimization of the jacobian term in unsupervised deep learning | https://scholar.google.com/scholar?cluster=6730260623059884783&hl=en&as_sdt=0,5 | 3 | 2,020 |
Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds | 4 | neurips | 0 | 6 | 2023-06-16 15:11:47.108000 | https://github.com/vlievin/ovis | 10 | Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds | https://scholar.google.com/scholar?cluster=14600314100540480653&hl=en&as_sdt=0,44 | 4 | 2,020 |
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning | 29 | neurips | 8 | 0 | 2023-06-16 15:11:47.310000 | https://github.com/juliusberner/deep_kolmogorov | 19 | Numerically solving parametric families of high-dimensional Kolmogorov partial differential equations via deep learning | https://scholar.google.com/scholar?cluster=1531427583443268400&hl=en&as_sdt=0,37 | 4 | 2,020 |
AViD Dataset: Anonymized Videos from Diverse Countries | 33 | neurips | 3 | 5 | 2023-06-16 15:11:47.520000 | https://github.com/piergiaj/AViD | 50 | Avid dataset: Anonymized videos from diverse countries | https://scholar.google.com/scholar?cluster=9859841321029436808&hl=en&as_sdt=0,48 | 6 | 2,020 |
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning | 30 | neurips | 2 | 4 | 2023-06-16 15:11:47.714000 | https://github.com/delchiaro/RATT | 16 | Ratt: Recurrent attention to transient tasks for continual image captioning | https://scholar.google.com/scholar?cluster=16302376296510206339&hl=en&as_sdt=0,10 | 4 | 2,020 |
Decisions, Counterfactual Explanations and Strategic Behavior | 43 | neurips | 3 | 0 | 2023-06-16 15:11:47.906000 | https://github.com/Networks-Learning/strategic-decisions | 21 | Decisions, counterfactual explanations and strategic behavior | https://scholar.google.com/scholar?cluster=651816189513643853&hl=en&as_sdt=0,5 | 5 | 2,020 |
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample | 38 | neurips | 11 | 3 | 2023-06-16 15:11:48.099000 | https://github.com/shirgur/hp-vae-gan | 53 | Hierarchical patch vae-gan: Generating diverse videos from a single sample | https://scholar.google.com/scholar?cluster=1314368623752181451&hl=en&as_sdt=0,5 | 7 | 2,020 |
Reservoir Computing meets Recurrent Kernels and Structured Transforms | 18 | neurips | 3 | 0 | 2023-06-16 15:11:48.291000 | https://github.com/rubenohana/Reservoir-computing-kernels | 9 | Reservoir computing meets recurrent kernels and structured transforms | https://scholar.google.com/scholar?cluster=14374060195466396389&hl=en&as_sdt=0,44 | 3 | 2,020 |
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection | 91 | neurips | 19 | 25 | 2023-06-16 15:11:48.484000 | https://github.com/DeLightCMU/CASD | 82 | Comprehensive attention self-distillation for weakly-supervised object detection | https://scholar.google.com/scholar?cluster=10475927418265297768&hl=en&as_sdt=0,21 | 9 | 2,020 |
MPNet: Masked and Permuted Pre-training for Language Understanding | 373 | neurips | 29 | 8 | 2023-06-16 15:11:48.676000 | https://github.com/microsoft/MPNet | 258 | Mpnet: Masked and permuted pre-training for language understanding | https://scholar.google.com/scholar?cluster=4431403751836804866&hl=en&as_sdt=0,20 | 13 | 2,020 |
Lipschitz-Certifiable Training with a Tight Outer Bound | 36 | neurips | 1 | 0 | 2023-06-16 15:11:48.869000 | https://github.com/sungyoon-lee/bcp | 6 | Lipschitz-certifiable training with a tight outer bound | https://scholar.google.com/scholar?cluster=11149574436277547066&hl=en&as_sdt=0,5 | 2 | 2,020 |
Conformal Symplectic and Relativistic Optimization | 47 | neurips | 0 | 0 | 2023-06-16 15:11:49.060000 | https://github.com/guisf/rgd | 3 | Conformal symplectic and relativistic optimization | https://scholar.google.com/scholar?cluster=18020920739168612378&hl=en&as_sdt=0,21 | 1 | 2,020 |
Inverting Gradients - How easy is it to break privacy in federated learning? | 582 | neurips | 57 | 0 | 2023-06-16 15:11:49.253000 | https://github.com/JonasGeiping/invertinggradients | 194 | Inverting gradients-how easy is it to break privacy in federated learning? | https://scholar.google.com/scholar?cluster=18261025537787576960&hl=en&as_sdt=0,11 | 2 | 2,020 |
Dynamic allocation of limited memory resources in reinforcement learning | 3 | neurips | 1 | 0 | 2023-06-16 15:11:49.452000 | https://github.com/nisheetpatel/DynamicResourceAllocator | 5 | Dynamic allocation of limited memory resources in reinforcement learning | https://scholar.google.com/scholar?cluster=4741311554113692472&hl=en&as_sdt=0,21 | 2 | 2,020 |
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation | 13 | neurips | 6 | 1 | 2023-06-16 15:11:49.645000 | https://github.com/IdeasLabUT/CHIP-Network-Model | 7 | CHIP: a Hawkes process model for continuous-time networks with scalable and consistent estimation | https://scholar.google.com/scholar?cluster=11549730124527673623&hl=en&as_sdt=0,5 | 7 | 2,020 |
Design Space for Graph Neural Networks | 198 | neurips | 167 | 15 | 2023-06-16 15:11:49.837000 | https://github.com/snap-stanford/graphgym | 1,396 | Design space for graph neural networks | https://scholar.google.com/scholar?cluster=11786181132461670181&hl=en&as_sdt=0,5 | 23 | 2,020 |
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | 790 | neurips | 391 | 78 | 2023-06-16 15:11:50.030000 | https://github.com/jik876/hifi-gan | 1,325 | Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis | https://scholar.google.com/scholar?cluster=6605967141544813805&hl=en&as_sdt=0,33 | 31 | 2,020 |
Unbalanced Sobolev Descent | 7 | neurips | 3 | 1 | 2023-06-16 15:11:50.223000 | https://github.com/IBM/USD | 6 | Unbalanced sobolev descent | https://scholar.google.com/scholar?cluster=14494122772083038319&hl=en&as_sdt=0,36 | 7 | 2,020 |
Identifying Mislabeled Data using the Area Under the Margin Ranking | 145 | neurips | 16 | 9 | 2023-06-16 15:11:50.416000 | https://github.com/asappresearch/aum | 68 | Identifying mislabeled data using the area under the margin ranking | https://scholar.google.com/scholar?cluster=935651973392109362&hl=en&as_sdt=0,19 | 2 | 2,020 |
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games | 93 | neurips | 99 | 5 | 2023-06-16 15:11:50.610000 | https://github.com/facebookresearch/rebel | 554 | Combining deep reinforcement learning and search for imperfect-information games | https://scholar.google.com/scholar?cluster=4530917614847709299&hl=en&as_sdt=0,5 | 26 | 2,020 |
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