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Dataset Distillation with Infinitely Wide Convolutional Networks
| 87 |
neurips
| 7,321 | 1,026 |
2023-06-16 16:05:52.916000
|
https://github.com/google-research/google-research
| 29,786 |
Dataset distillation with infinitely wide convolutional networks
|
https://scholar.google.com/scholar?cluster=5517336236766100405&hl=en&as_sdt=0,39
| 727 | 2,021 |
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search
| 16 |
neurips
| 559 | 118 |
2023-06-16 16:05:53.117000
|
https://github.com/Microsoft/SPTAG
| 4,539 |
Spann: Highly-efficient billion-scale approximate nearest neighborhood search
|
https://scholar.google.com/scholar?cluster=17393178550199669476&hl=en&as_sdt=0,5
| 140 | 2,021 |
Analysis of one-hidden-layer neural networks via the resolvent method
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:05:53.317000
|
https://github.com/wirhabenzeit/nonlinearRMT
| 0 |
Analysis of one-hidden-layer neural networks via the resolvent method
|
https://scholar.google.com/scholar?cluster=1141084690647947388&hl=en&as_sdt=0,43
| 1 | 2,021 |
Grounding Spatio-Temporal Language with Transformers
| 10 |
neurips
| 0 | 0 |
2023-06-16 16:05:53.516000
|
https://github.com/flowersteam/spatio-temporal-language-transformers
| 8 |
Grounding spatio-temporal language with transformers
|
https://scholar.google.com/scholar?cluster=7814702552809480292&hl=en&as_sdt=0,14
| 7 | 2,021 |
Learning where to learn: Gradient sparsity in meta and continual learning
| 29 |
neurips
| 4 | 1 |
2023-06-16 16:05:53.716000
|
https://github.com/johswald/learning_where_to_learn
| 31 |
Learning where to learn: Gradient sparsity in meta and continual learning
|
https://scholar.google.com/scholar?cluster=15647321533147892633&hl=en&as_sdt=0,5
| 2 | 2,021 |
Domain Invariant Representation Learning with Domain Density Transformations
| 35 |
neurips
| 1 | 0 |
2023-06-16 16:05:53.917000
|
https://github.com/atuannguyen/dirt
| 9 |
Domain invariant representation learning with domain density transformations
|
https://scholar.google.com/scholar?cluster=12877601023534457317&hl=en&as_sdt=0,33
| 1 | 2,021 |
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning
| 10 |
neurips
| 5 | 2 |
2023-06-16 16:05:54.117000
|
https://github.com/microsoft/Playvirtual
| 14 |
Playvirtual: Augmenting cycle-consistent virtual trajectories for reinforcement learning
|
https://scholar.google.com/scholar?cluster=13710133509096551909&hl=en&as_sdt=0,38
| 5 | 2,021 |
Efficient Equivariant Network
| 12 |
neurips
| 1 | 0 |
2023-06-16 16:05:54.320000
|
https://github.com/LingshenHe/Efficient-Equivariant-Network
| 9 |
Efficient equivariant network
|
https://scholar.google.com/scholar?cluster=547182555419234548&hl=en&as_sdt=0,33
| 2 | 2,021 |
Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation
| 4 |
neurips
| 0 | 0 |
2023-06-16 16:05:54.519000
|
https://github.com/Kennethborup/self_distillation
| 15 |
Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation
|
https://scholar.google.com/scholar?cluster=8987467992945921645&hl=en&as_sdt=0,5
| 4 | 2,021 |
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
| 21 |
neurips
| 22 | 9 |
2023-06-16 16:05:54.719000
|
https://github.com/lucaslie/torchprune
| 146 |
Compressing neural networks: Towards determining the optimal layer-wise decomposition
|
https://scholar.google.com/scholar?cluster=11443977889418286525&hl=en&as_sdt=0,15
| 5 | 2,021 |
Accurate Point Cloud Registration with Robust Optimal Transport
| 13 |
neurips
| 13 | 2 |
2023-06-16 16:05:54.919000
|
https://github.com/uncbiag/shapmagn
| 84 |
Accurate point cloud registration with robust optimal transport
|
https://scholar.google.com/scholar?cluster=15753020473046072321&hl=en&as_sdt=0,14
| 6 | 2,021 |
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:05:55.119000
|
https://github.com/zib-iol/fw-generalized-selfconcordant
| 0 |
Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions
|
https://scholar.google.com/scholar?cluster=1532722627115622764&hl=en&as_sdt=0,48
| 1 | 2,021 |
Automatic Data Augmentation for Generalization in Reinforcement Learning
| 38 |
neurips
| 19 | 1 |
2023-06-16 16:05:55.319000
|
https://github.com/rraileanu/auto-drac
| 97 |
Automatic data augmentation for generalization in reinforcement learning
|
https://scholar.google.com/scholar?cluster=11787479877857738831&hl=en&as_sdt=0,50
| 6 | 2,021 |
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration
| 17 |
neurips
| 6 | 0 |
2023-06-16 16:05:55.519000
|
https://github.com/inria-thoth/t3sc
| 18 |
A trainable spectral-spatial sparse coding model for hyperspectral image restoration
|
https://scholar.google.com/scholar?cluster=14845341365243064096&hl=en&as_sdt=0,5
| 0 | 2,021 |
MarioNette: Self-Supervised Sprite Learning
| 28 |
neurips
| 6 | 0 |
2023-06-16 16:05:55.718000
|
https://github.com/dmsm/MarioNette
| 29 |
Marionette: Self-supervised sprite learning
|
https://scholar.google.com/scholar?cluster=4806143850107186086&hl=en&as_sdt=0,5
| 1 | 2,021 |
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
| 8 |
neurips
| 4,890 | 2,916 |
2023-06-16 16:05:55.917000
|
https://github.com/ray-project/ray
| 26,189 |
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
|
https://scholar.google.com/scholar?cluster=4240571206448451235&hl=en&as_sdt=0,4
| 450 | 2,021 |
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:05:56.116000
|
https://github.com/nvlabs/bcts
| 2 |
Improve agents without retraining: Parallel tree search with off-policy correction
|
https://scholar.google.com/scholar?cluster=15142203700069682566&hl=en&as_sdt=0,44
| 6 | 2,021 |
Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems
| 5 |
neurips
| 2 | 0 |
2023-06-16 16:05:56.316000
|
https://github.com/lcs2-iiitd/transevolve
| 10 |
Redesigning the transformer architecture with insights from multi-particle dynamical systems
|
https://scholar.google.com/scholar?cluster=10864040246145849746&hl=en&as_sdt=0,5
| 3 | 2,021 |
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
| 50 |
neurips
| 1 | 0 |
2023-06-16 16:05:56.515000
|
https://github.com/HanxunH/RobustWRN
| 30 |
Exploring architectural ingredients of adversarially robust deep neural networks
|
https://scholar.google.com/scholar?cluster=17017038540474728130&hl=en&as_sdt=0,5
| 1 | 2,021 |
Center Smoothing: Certified Robustness for Networks with Structured Outputs
| 8 |
neurips
| 1 | 0 |
2023-06-16 16:05:56.714000
|
https://github.com/aounon/center-smoothing
| 4 |
Center smoothing: Certified robustness for networks with structured outputs
|
https://scholar.google.com/scholar?cluster=6774778402376683053&hl=en&as_sdt=0,5
| 1 | 2,021 |
Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex
| 12 |
neurips
| 3 | 2 |
2023-06-16 16:05:56.913000
|
https://github.com/colinconwell/deepmousetrap
| 16 |
Neural regression, representational similarity, model zoology & neural taskonomy at scale in rodent visual cortex
|
https://scholar.google.com/scholar?cluster=14703235667751909226&hl=en&as_sdt=0,39
| 1 | 2,021 |
Parameter Inference with Bifurcation Diagrams
| 1 |
neurips
| 2 | 6 |
2023-06-16 16:05:57.113000
|
https://github.com/gszep/BifurcationInference.jl
| 26 |
Parameter Inference with Bifurcation Diagrams
|
https://scholar.google.com/scholar?cluster=11587125408302818135&hl=en&as_sdt=0,20
| 3 | 2,021 |
Similarity and Matching of Neural Network Representations
| 25 |
neurips
| 1 | 0 |
2023-06-16 16:05:57.312000
|
https://github.com/renyi-ai/drfrankenstein
| 9 |
Similarity and matching of neural network representations
|
https://scholar.google.com/scholar?cluster=18028760850112175257&hl=en&as_sdt=0,5
| 4 | 2,021 |
DOCTOR: A Simple Method for Detecting Misclassification Errors
| 19 |
neurips
| 3 | 2 |
2023-06-16 16:05:57.511000
|
https://github.com/doctor-public-submission/DOCTOR
| 19 |
Doctor: A simple method for detecting misclassification errors
|
https://scholar.google.com/scholar?cluster=17068138253074503270&hl=en&as_sdt=0,34
| 2 | 2,021 |
Contrastive Laplacian Eigenmaps
| 21 |
neurips
| 2 | 1 |
2023-06-16 16:05:57.711000
|
https://github.com/allenhaozhu/coles
| 18 |
Contrastive laplacian eigenmaps
|
https://scholar.google.com/scholar?cluster=17149806302685325367&hl=en&as_sdt=0,5
| 1 | 2,021 |
Shape Registration in the Time of Transformers
| 19 |
neurips
| 5 | 0 |
2023-06-16 16:05:57.910000
|
https://github.com/GiovanniTRA/transmatching
| 21 |
Shape registration in the time of transformers
|
https://scholar.google.com/scholar?cluster=7252503647497259902&hl=en&as_sdt=0,5
| 4 | 2,021 |
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
| 29 |
neurips
| 4 | 1 |
2023-06-16 16:05:58.110000
|
https://github.com/yifeiwang77/dgc
| 12 |
Dissecting the diffusion process in linear graph convolutional networks
|
https://scholar.google.com/scholar?cluster=953644699740016159&hl=en&as_sdt=0,10
| 1 | 2,021 |
Dynamic Grained Encoder for Vision Transformers
| 12 |
neurips
| 2 | 2 |
2023-06-16 16:05:58.310000
|
https://github.com/stevengrove/vtpack
| 28 |
Dynamic grained encoder for vision transformers
|
https://scholar.google.com/scholar?cluster=2925930572923827932&hl=en&as_sdt=0,1
| 1 | 2,021 |
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
| 28 |
neurips
| 0 | 0 |
2023-06-16 16:05:58.509000
|
https://github.com/nzw0301/Understanding-Negative-Samples
| 6 |
Understanding negative samples in instance discriminative self-supervised representation learning
|
https://scholar.google.com/scholar?cluster=280361585391691198&hl=en&as_sdt=0,6
| 1 | 2,021 |
On UMAP's True Loss Function
| 18 |
neurips
| 2 | 0 |
2023-06-16 16:05:58.709000
|
https://github.com/hci-unihd/UMAPs-true-loss
| 6 |
On UMAP's true loss function
|
https://scholar.google.com/scholar?cluster=13625192232753067686&hl=en&as_sdt=0,39
| 1 | 2,021 |
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:05:58.908000
|
https://github.com/fengliu90/MetaTesting
| 5 |
Meta two-sample testing: Learning kernels for testing with limited data
|
https://scholar.google.com/scholar?cluster=3537368320170973148&hl=en&as_sdt=0,11
| 1 | 2,021 |
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:05:59.107000
|
https://github.com/aiqz/bype-vae
| 6 |
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
|
https://scholar.google.com/scholar?cluster=11014089413900793097&hl=en&as_sdt=0,5
| 1 | 2,021 |
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
| 19 |
neurips
| 4 | 0 |
2023-06-16 16:05:59.307000
|
https://github.com/wustl-cig/pnp-recovery
| 7 |
Recovery analysis for plug-and-play priors using the restricted eigenvalue condition
|
https://scholar.google.com/scholar?cluster=6589504408297538842&hl=en&as_sdt=0,34
| 3 | 2,021 |
Group Equivariant Subsampling
| 10 |
neurips
| 1 | 0 |
2023-06-16 16:05:59.506000
|
https://github.com/jinxu06/gsubsampling
| 15 |
Group equivariant subsampling
|
https://scholar.google.com/scholar?cluster=5738105186247068728&hl=en&as_sdt=0,5
| 2 | 2,021 |
Data Sharing and Compression for Cooperative Networked Control
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:05:59.705000
|
https://github.com/chengjiangnan/cooperative_networked_control
| 3 |
Data sharing and compression for cooperative networked control
|
https://scholar.google.com/scholar?cluster=14181089307501854409&hl=en&as_sdt=0,3
| 1 | 2,021 |
Hyperbolic Procrustes Analysis Using Riemannian Geometry
| 4 |
neurips
| 1 | 1 |
2023-06-16 16:05:59.904000
|
https://github.com/ronentalmonlab/hyperbolicprocrustesanalysis
| 2 |
Hyperbolic Procrustes Analysis Using Riemannian Geometry
|
https://scholar.google.com/scholar?cluster=7536383024640437829&hl=en&as_sdt=0,47
| 1 | 2,021 |
Improving Contrastive Learning on Imbalanced Data via Open-World Sampling
| 16 |
neurips
| 2 | 0 |
2023-06-16 16:06:00.103000
|
https://github.com/vita-group/mak
| 26 |
Improving contrastive learning on imbalanced data via open-world sampling
|
https://scholar.google.com/scholar?cluster=3568757840495479770&hl=en&as_sdt=0,44
| 7 | 2,021 |
Multi-Person 3D Motion Prediction with Multi-Range Transformers
| 16 |
neurips
| 5 | 1 |
2023-06-16 16:06:00.303000
|
https://github.com/jiashunwang/MRT
| 52 |
Multi-person 3D motion prediction with multi-range transformers
|
https://scholar.google.com/scholar?cluster=10505346865379052907&hl=en&as_sdt=0,39
| 2 | 2,021 |
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds
| 1 |
neurips
| 2 | 3 |
2023-06-16 16:06:00.502000
|
https://github.com/pearsonlab/bubblewrap
| 4 |
Bubblewrap: Online tiling and real-time flow prediction on neural manifolds
|
https://scholar.google.com/scholar?cluster=10067153401508770550&hl=en&as_sdt=0,31
| 4 | 2,021 |
Learning to Combine Per-Example Solutions for Neural Program Synthesis
| 5 |
neurips
| 2 | 0 |
2023-06-16 16:06:00.702000
|
https://github.com/shrivastavadisha/N-PEPS
| 18 |
Learning to combine per-example solutions for neural program synthesis
|
https://scholar.google.com/scholar?cluster=1667137904448964441&hl=en&as_sdt=0,41
| 1 | 2,021 |
On Success and Simplicity: A Second Look at Transferable Targeted Attacks
| 43 |
neurips
| 8 | 0 |
2023-06-16 16:06:00.901000
|
https://github.com/ZhengyuZhao/Targeted-Tansfer
| 38 |
On success and simplicity: A second look at transferable targeted attacks
|
https://scholar.google.com/scholar?cluster=8748504809749727274&hl=en&as_sdt=0,5
| 1 | 2,021 |
Learning Causal Semantic Representation for Out-of-Distribution Prediction
| 45 |
neurips
| 3 | 0 |
2023-06-16 16:06:01.101000
|
https://github.com/changliu00/causal-semantic-generative-model
| 62 |
Learning causal semantic representation for out-of-distribution prediction
|
https://scholar.google.com/scholar?cluster=8202256397627886972&hl=en&as_sdt=0,31
| 2 | 2,021 |
Conformal Time-series Forecasting
| 39 |
neurips
| 11 | 1 |
2023-06-16 16:06:01.300000
|
https://github.com/kamilest/conformal-rnn
| 45 |
Conformal time-series forecasting
|
https://scholar.google.com/scholar?cluster=5073869937636714274&hl=en&as_sdt=0,3
| 3 | 2,021 |
A 3D Generative Model for Structure-Based Drug Design
| 55 |
neurips
| 37 | 6 |
2023-06-16 16:06:01.499000
|
https://github.com/luost26/3d-generative-sbdd
| 139 |
A 3D generative model for structure-based drug design
|
https://scholar.google.com/scholar?cluster=6836358933346454027&hl=en&as_sdt=0,5
| 15 | 2,021 |
Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference
| 9 |
neurips
| 2 | 1 |
2023-06-16 16:06:01.702000
|
https://github.com/kennethwdk/pinet
| 14 |
Robust pose estimation in crowded scenes with direct pose-level inference
|
https://scholar.google.com/scholar?cluster=9963375473361085203&hl=en&as_sdt=0,47
| 1 | 2,021 |
Conformal Prediction using Conditional Histograms
| 24 |
neurips
| 2 | 1 |
2023-06-16 16:06:01.903000
|
https://github.com/msesia/chr
| 16 |
Conformal prediction using conditional histograms
|
https://scholar.google.com/scholar?cluster=18022084762703462978&hl=en&as_sdt=0,5
| 1 | 2,021 |
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization
| 4 |
neurips
| 0 | 0 |
2023-06-16 16:06:02.103000
|
https://github.com/rghosh92/n2n
| 0 |
Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization
|
https://scholar.google.com/scholar?cluster=10271494152241252872&hl=en&as_sdt=0,5
| 2 | 2,021 |
Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement
| 28 |
neurips
| 1 | 1 |
2023-06-16 16:06:02.304000
|
https://github.com/amingwu/svd-dictionary-enhancement
| 10 |
Generalized and discriminative few-shot object detection via SVD-dictionary enhancement
|
https://scholar.google.com/scholar?cluster=5723968759372478905&hl=en&as_sdt=0,5
| 3 | 2,021 |
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
| 18 |
neurips
| 3 | 2 |
2023-06-16 16:06:02.506000
|
https://github.com/wjmaddox/online_vargp
| 19 |
Conditioning sparse variational gaussian processes for online decision-making
|
https://scholar.google.com/scholar?cluster=4727485038673276351&hl=en&as_sdt=0,11
| 1 | 2,021 |
Roto-translated Local Coordinate Frames For Interacting Dynamical Systems
| 11 |
neurips
| 1 | 0 |
2023-06-16 16:06:02.705000
|
https://github.com/mkofinas/locs
| 20 |
Roto-translated local coordinate frames for interacting dynamical systems
|
https://scholar.google.com/scholar?cluster=4389798723436017716&hl=en&as_sdt=0,5
| 4 | 2,021 |
Retiring Adult: New Datasets for Fair Machine Learning
| 154 |
neurips
| 14 | 3 |
2023-06-16 16:06:02.910000
|
https://github.com/zykls/folktables
| 168 |
Retiring adult: New datasets for fair machine learning
|
https://scholar.google.com/scholar?cluster=4475275989640781366&hl=en&as_sdt=0,5
| 6 | 2,021 |
Cardinality constrained submodular maximization for random streams
| 6 |
neurips
| 0 | 0 |
2023-06-16 16:06:03.157000
|
https://github.com/where-is-paul/submodular-streaming
| 0 |
Cardinality constrained submodular maximization for random streams
|
https://scholar.google.com/scholar?cluster=3566616688572088469&hl=en&as_sdt=0,48
| 1 | 2,021 |
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
| 15 |
neurips
| 2 | 1 |
2023-06-16 16:06:03.357000
|
https://github.com/jixuan-wang/grad2task
| 14 |
Grad2task: Improved few-shot text classification using gradients for task representation
|
https://scholar.google.com/scholar?cluster=16326528771354336170&hl=en&as_sdt=0,10
| 3 | 2,021 |
A variational approximate posterior for the deep Wishart process
| 1 |
neurips
| 6 | 2 |
2023-06-16 16:06:03.558000
|
https://github.com/LaurenceA/bayesfunc
| 12 |
A variational approximate posterior for the deep Wishart process
|
https://scholar.google.com/scholar?cluster=12807465440035985886&hl=en&as_sdt=0,33
| 3 | 2,021 |
Neural Tangent Kernel Maximum Mean Discrepancy
| 15 |
neurips
| 1 | 0 |
2023-06-16 16:06:03.757000
|
https://github.com/xycheng/NTK-MMD
| 2 |
Neural tangent kernel maximum mean discrepancy
|
https://scholar.google.com/scholar?cluster=12192272068722232202&hl=en&as_sdt=0,43
| 1 | 2,021 |
Subgraph Federated Learning with Missing Neighbor Generation
| 46 |
neurips
| 12 | 2 |
2023-06-16 16:06:03.957000
|
https://github.com/zkhku/fedsage
| 46 |
Subgraph federated learning with missing neighbor generation
|
https://scholar.google.com/scholar?cluster=6545450769549258065&hl=en&as_sdt=0,34
| 2 | 2,021 |
Sub-Linear Memory: How to Make Performers SLiM
| 13 |
neurips
| 7,321 | 1,026 |
2023-06-16 16:06:04.156000
|
https://github.com/google-research/google-research
| 29,786 |
Sub-linear memory: How to make performers slim
|
https://scholar.google.com/scholar?cluster=1235739226041970723&hl=en&as_sdt=0,22
| 727 | 2,021 |
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
| 9 |
neurips
| 4 | 0 |
2023-06-16 16:06:04.356000
|
https://github.com/devnkong/VQ-GNN
| 19 |
VQ-GNN: A universal framework to scale up graph neural networks using vector quantization
|
https://scholar.google.com/scholar?cluster=7465359431482590053&hl=en&as_sdt=0,33
| 2 | 2,021 |
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
| 42 |
neurips
| 7 | 7 |
2023-06-16 16:06:04.559000
|
https://github.com/moukamisama/f2m
| 29 |
Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima
|
https://scholar.google.com/scholar?cluster=13513065360011314265&hl=en&as_sdt=0,14
| 3 | 2,021 |
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
| 19 |
neurips
| 1 | 0 |
2023-06-16 16:06:04.758000
|
https://github.com/tolgabirdal/phdimgeneralization
| 17 |
Intrinsic dimension, persistent homology and generalization in neural networks
|
https://scholar.google.com/scholar?cluster=6053095805266781547&hl=en&as_sdt=0,47
| 4 | 2,021 |
GemNet: Universal Directional Graph Neural Networks for Molecules
| 95 |
neurips
| 23 | 0 |
2023-06-16 16:06:04.957000
|
https://github.com/TUM-DAML/gemnet_pytorch
| 139 |
Gemnet: Universal directional graph neural networks for molecules
|
https://scholar.google.com/scholar?cluster=17365183675502729479&hl=en&as_sdt=0,34
| 4 | 2,021 |
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
| 9 |
neurips
| 3 | 0 |
2023-06-16 16:06:05.157000
|
https://github.com/mandt-lab/variational-beam-search
| 7 |
Detecting and adapting to irregular distribution shifts in bayesian online learning
|
https://scholar.google.com/scholar?cluster=8682460145444593023&hl=en&as_sdt=0,11
| 3 | 2,021 |
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
| 12 |
neurips
| 8 | 0 |
2023-06-16 16:06:05.357000
|
https://github.com/mahuanaaa/monig
| 26 |
Trustworthy multimodal regression with mixture of normal-inverse gamma distributions
|
https://scholar.google.com/scholar?cluster=4055725857500470289&hl=en&as_sdt=0,11
| 2 | 2,021 |
Does Knowledge Distillation Really Work?
| 89 |
neurips
| 2 | 1 |
2023-06-16 16:06:05.556000
|
https://github.com/samuelstanton/gnosis
| 28 |
Does knowledge distillation really work?
|
https://scholar.google.com/scholar?cluster=14465818591986091867&hl=en&as_sdt=0,15
| 5 | 2,021 |
Teachable Reinforcement Learning via Advice Distillation
| 0 |
neurips
| 4 | 2 |
2023-06-16 16:06:05.756000
|
https://github.com/rll-research/teachable
| 14 |
Teachable Reinforcement Learning via Advice Distillation
|
https://scholar.google.com/scholar?cluster=2130873946833920299&hl=en&as_sdt=0,5
| 1 | 2,021 |
Antipodes of Label Differential Privacy: PATE and ALIBI
| 21 |
neurips
| 3 | 0 |
2023-06-16 16:06:05.959000
|
https://github.com/facebookresearch/label_dp_antipodes
| 22 |
Antipodes of label differential privacy: Pate and alibi
|
https://scholar.google.com/scholar?cluster=8767021277999281936&hl=en&as_sdt=0,47
| 11 | 2,021 |
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:06:06.158000
|
https://github.com/kreimanlab/VisualSearchAsymmetry
| 2 |
Visual search asymmetry: Deep nets and humans share similar inherent biases
|
https://scholar.google.com/scholar?cluster=4659542011867306284&hl=en&as_sdt=0,5
| 4 | 2,021 |
On the Universality of Graph Neural Networks on Large Random Graphs
| 18 |
neurips
| 1 | 0 |
2023-06-16 16:06:06.357000
|
https://github.com/nkeriven/random-graph-gnn
| 12 |
On the universality of graph neural networks on large random graphs
|
https://scholar.google.com/scholar?cluster=16885293553955687964&hl=en&as_sdt=0,39
| 1 | 2,021 |
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation
| 9 |
neurips
| 5 | 1 |
2023-06-16 16:06:06.560000
|
https://github.com/xingchenwan/grabnel
| 12 |
Adversarial attacks on graph classifiers via bayesian optimisation
|
https://scholar.google.com/scholar?cluster=13672846858663173728&hl=en&as_sdt=0,39
| 2 | 2,021 |
Do Wider Neural Networks Really Help Adversarial Robustness?
| 54 |
neurips
| 92 | 18 |
2023-06-16 16:06:06.759000
|
https://github.com/fra31/auto-attack
| 525 |
Do wider neural networks really help adversarial robustness?
|
https://scholar.google.com/scholar?cluster=11340118178463211034&hl=en&as_sdt=0,36
| 9 | 2,021 |
ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning
| 39 |
neurips
| 12 | 0 |
2023-06-16 16:06:06.960000
|
https://github.com/leehyuck/abc
| 27 |
Abc: Auxiliary balanced classifier for class-imbalanced semi-supervised learning
|
https://scholar.google.com/scholar?cluster=866707790595664862&hl=en&as_sdt=0,5
| 2 | 2,021 |
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
| 25 |
neurips
| 3 | 1 |
2023-06-16 16:06:07.160000
|
https://github.com/ermongroup/bcd-nets
| 16 |
Bcd nets: Scalable variational approaches for bayesian causal discovery
|
https://scholar.google.com/scholar?cluster=11629795294538646215&hl=en&as_sdt=0,5
| 7 | 2,021 |
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
| 4 |
neurips
| 2 | 1 |
2023-06-16 16:06:07.361000
|
https://github.com/bit-ml/dyreg-gnn
| 13 |
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
|
https://scholar.google.com/scholar?cluster=2805094251623106386&hl=en&as_sdt=0,31
| 7 | 2,021 |
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
| 25 |
neurips
| 2 | 0 |
2023-06-16 16:06:07.561000
|
https://github.com/XuZhengzhuo/Prior-LT
| 18 |
Towards calibrated model for long-tailed visual recognition from prior perspective
|
https://scholar.google.com/scholar?cluster=6176357525682720979&hl=en&as_sdt=0,44
| 1 | 2,021 |
Learning to Draw: Emergent Communication through Sketching
| 8 |
neurips
| 1 | 0 |
2023-06-16 16:06:07.760000
|
https://github.com/Ddaniela13/LearningToDraw
| 20 |
Learning to draw: Emergent communication through sketching
|
https://scholar.google.com/scholar?cluster=7936219275341815856&hl=en&as_sdt=0,47
| 2 | 2,021 |
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose
| 11 |
neurips
| 3 | 0 |
2023-06-16 16:06:07.959000
|
https://github.com/angtian/neuralvs
| 20 |
Neural view synthesis and matching for semi-supervised few-shot learning of 3d pose
|
https://scholar.google.com/scholar?cluster=7966798121022187733&hl=en&as_sdt=0,33
| 2 | 2,021 |
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
| 85 |
neurips
| 34 | 1 |
2023-06-16 16:06:08.158000
|
https://github.com/Princeton-SysML/GradAttack
| 163 |
Evaluating gradient inversion attacks and defenses in federated learning
|
https://scholar.google.com/scholar?cluster=921667981702285218&hl=en&as_sdt=0,44
| 4 | 2,021 |
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
| 43 |
neurips
| 96 | 3 |
2023-06-16 16:06:08.358000
|
https://github.com/amzn/pecos
| 442 |
Fast multi-resolution transformer fine-tuning for extreme multi-label text classification
|
https://scholar.google.com/scholar?cluster=3453341538236618558&hl=en&as_sdt=0,5
| 20 | 2,021 |
HRFormer: High-Resolution Vision Transformer for Dense Predict
| 64 |
neurips
| 63 | 19 |
2023-06-16 16:06:08.558000
|
https://github.com/HRNet/HRFormer
| 423 |
Hrformer: High-resolution vision transformer for dense predict
|
https://scholar.google.com/scholar?cluster=929504162912042332&hl=en&as_sdt=0,5
| 14 | 2,021 |
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.
| 11 |
neurips
| 0 | 1 |
2023-06-16 16:06:08.757000
|
https://github.com/ilyatrofimov/mtopdiv
| 11 |
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.
|
https://scholar.google.com/scholar?cluster=17211466672120196882&hl=en&as_sdt=0,33
| 2 | 2,021 |
Weak-shot Fine-grained Classification via Similarity Transfer
| 15 |
neurips
| 9 | 0 |
2023-06-16 16:06:08.957000
|
https://github.com/bcmi/SimTrans-Weak-Shot-Classification
| 60 |
Weak-shot fine-grained classification via similarity transfer
|
https://scholar.google.com/scholar?cluster=9671426641005762258&hl=en&as_sdt=0,39
| 8 | 2,021 |
Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:06:09.156000
|
https://github.com/boschresearch/gmm_dae
| 13 |
Shape your space: A gaussian mixture regularization approach to deterministic autoencoders
|
https://scholar.google.com/scholar?cluster=4949577002012723077&hl=en&as_sdt=0,11
| 4 | 2,021 |
Regret Bounds for Gaussian-Process Optimization in Large Domains
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:06:09.356000
|
https://github.com/mwuethri/regret-bounds-for-gaussian-process-optimization-in-large-domains
| 0 |
Regret Bounds for Gaussian-Process Optimization in Large Domains
|
https://scholar.google.com/scholar?cluster=13958128142984191002&hl=en&as_sdt=0,5
| 1 | 2,021 |
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem
| 49 |
neurips
| 10 | 3 |
2023-06-16 16:06:09.555000
|
https://github.com/liangxinedu/neurolkh
| 36 |
NeuroLKH: Combining deep learning model with Lin-Kernighan-Helsgaun heuristic for solving the traveling salesman problem
|
https://scholar.google.com/scholar?cluster=15742552904375770583&hl=en&as_sdt=0,3
| 1 | 2,021 |
Meta-learning with an Adaptive Task Scheduler
| 20 |
neurips
| 1 | 0 |
2023-06-16 16:06:09.755000
|
https://github.com/huaxiuyao/ATS
| 17 |
Meta-learning with an adaptive task scheduler
|
https://scholar.google.com/scholar?cluster=7034157580850953271&hl=en&as_sdt=0,33
| 1 | 2,021 |
Edge Representation Learning with Hypergraphs
| 24 |
neurips
| 5 | 1 |
2023-06-16 16:06:09.954000
|
https://github.com/harryjo97/EHGNN
| 38 |
Edge representation learning with hypergraphs
|
https://scholar.google.com/scholar?cluster=13386857555344208572&hl=en&as_sdt=0,10
| 2 | 2,021 |
One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval
| 27 |
neurips
| 11 | 2 |
2023-06-16 16:06:10.155000
|
https://github.com/AkariAsai/CORA
| 63 |
One question answering model for many languages with cross-lingual dense passage retrieval
|
https://scholar.google.com/scholar?cluster=2691643624683077982&hl=en&as_sdt=0,5
| 3 | 2,021 |
LEADS: Learning Dynamical Systems that Generalize Across Environments
| 11 |
neurips
| 4 | 0 |
2023-06-16 16:06:10.354000
|
https://github.com/yuan-yin/leads
| 17 |
LEADS: Learning dynamical systems that generalize across environments
|
https://scholar.google.com/scholar?cluster=14202840426672915694&hl=en&as_sdt=0,47
| 2 | 2,021 |
Storchastic: A Framework for General Stochastic Automatic Differentiation
| 9 |
neurips
| 5 | 53 |
2023-06-16 16:06:10.554000
|
https://github.com/HEmile/storchastic
| 155 |
Storchastic: A framework for general stochastic automatic differentiation
|
https://scholar.google.com/scholar?cluster=400914295796581713&hl=en&as_sdt=0,34
| 7 | 2,021 |
Robustness of Graph Neural Networks at Scale
| 41 |
neurips
| 6 | 0 |
2023-06-16 16:06:10.757000
|
https://github.com/sigeisler/robustness_of_gnns_at_scale
| 20 |
Robustness of graph neural networks at scale
|
https://scholar.google.com/scholar?cluster=2310809073193622200&hl=en&as_sdt=0,5
| 4 | 2,021 |
Random Noise Defense Against Query-Based Black-Box Attacks
| 24 |
neurips
| 8 | 1 |
2023-06-16 16:06:10.959000
|
https://github.com/SCLBD/BlackboxBench
| 47 |
Random noise defense against query-based black-box attacks
|
https://scholar.google.com/scholar?cluster=5823403933289238841&hl=en&as_sdt=0,33
| 2 | 2,021 |
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
| 16 |
neurips
| 9 | 0 |
2023-06-16 16:06:11.161000
|
https://github.com/dmirlab-group/sadga
| 30 |
Sadga: Structure-aware dual graph aggregation network for text-to-sql
|
https://scholar.google.com/scholar?cluster=1414568396267987258&hl=en&as_sdt=0,5
| 4 | 2,021 |
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
| 42 |
neurips
| 2 | 0 |
2023-06-16 16:06:11.362000
|
https://github.com/IDSIA/recurrent-fwp
| 40 |
Going beyond linear transformers with recurrent fast weight programmers
|
https://scholar.google.com/scholar?cluster=7454464025962811538&hl=en&as_sdt=0,44
| 10 | 2,021 |
Proper Value Equivalence
| 22 |
neurips
| 0 | 0 |
2023-06-16 16:06:11.564000
|
https://github.com/chrisgrimm/proper_value_equivalence
| 5 |
Proper value equivalence
|
https://scholar.google.com/scholar?cluster=9083466870698024082&hl=en&as_sdt=0,5
| 2 | 2,021 |
Neural Scene Flow Prior
| 31 |
neurips
| 9 | 2 |
2023-06-16 16:06:11.765000
|
https://github.com/lilac-lee/neural_scene_flow_prior
| 99 |
Neural scene flow prior
|
https://scholar.google.com/scholar?cluster=8188256741599180302&hl=en&as_sdt=0,5
| 9 | 2,021 |
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift
| 38 |
neurips
| 5 | 2 |
2023-06-16 16:06:11.966000
|
https://github.com/automl/nes
| 26 |
Neural ensemble search for uncertainty estimation and dataset shift
|
https://scholar.google.com/scholar?cluster=11225734588910887046&hl=en&as_sdt=0,5
| 11 | 2,021 |
Finding Bipartite Components in Hypergraphs
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:06:12.165000
|
https://github.com/pmacg/hypergraph-bipartite-components
| 5 |
Finding Bipartite Components in Hypergraphs
|
https://scholar.google.com/scholar?cluster=6321982817275178738&hl=en&as_sdt=0,26
| 1 | 2,021 |
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
| 30 |
neurips
| 1 | 0 |
2023-06-16 16:06:12.399000
|
https://github.com/hongxin001/ODNL
| 16 |
Open-set label noise can improve robustness against inherent label noise
|
https://scholar.google.com/scholar?cluster=18714998357358816&hl=en&as_sdt=0,33
| 1 | 2,021 |
Relational Self-Attention: What's Missing in Attention for Video Understanding
| 16 |
neurips
| 6 | 4 |
2023-06-16 16:06:12.606000
|
https://github.com/KimManjin/RSA
| 45 |
Relational self-attention: What's missing in attention for video understanding
|
https://scholar.google.com/scholar?cluster=11774709697468302185&hl=en&as_sdt=0,33
| 2 | 2,021 |
Towards Enabling Meta-Learning from Target Models
| 1 |
neurips
| 0 | 0 |
2023-06-16 16:06:12.806000
|
https://github.com/njulus/ST
| 7 |
Towards enabling meta-learning from target models
|
https://scholar.google.com/scholar?cluster=18110537945582791730&hl=en&as_sdt=0,39
| 1 | 2,021 |
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