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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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