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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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