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Planning for Sample Efficient Imitation Learning | 1 | neurips | 2 | 0 | 2023-06-16 22:57:11.706000 | https://github.com/zhaohengyin/EfficientImitate | 23 | Planning for Sample Efficient Imitation Learning | https://scholar.google.com/scholar?cluster=5323017540550695246&hl=en&as_sdt=0,5 | 1 | 2,022 |
Towards Safe Reinforcement Learning with a Safety Editor Policy | 7 | neurips | 0 | 1 | 2023-06-16 22:57:11.917000 | https://github.com/hnyu/seditor | 8 | Towards safe reinforcement learning with a safety editor policy | https://scholar.google.com/scholar?cluster=5028356496095011487&hl=en&as_sdt=0,21 | 1 | 2,022 |
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty | 0 | neurips | 7 | 0 | 2023-06-16 22:57:12.128000 | https://github.com/sungnyun/understanding-cdfsl | 18 | Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty | https://scholar.google.com/scholar?cluster=5252362604705009687&hl=en&as_sdt=0,47 | 2 | 2,022 |
Sustainable Online Reinforcement Learning for Auto-bidding | 0 | neurips | 3 | 0 | 2023-06-16 22:57:12.339000 | https://github.com/nobodymx/sorl-for-auto-bidding | 13 | Sustainable Online Reinforcement Learning for Auto-bidding | https://scholar.google.com/scholar?cluster=6790569068711156469&hl=en&as_sdt=0,11 | 1 | 2,022 |
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs | 11 | neurips | 15 | 3 | 2023-06-16 22:57:12.550000 | https://github.com/fundamentalvision/Uni-Perceiver | 195 | Uni-perceiver-moe: Learning sparse generalist models with conditional moes | https://scholar.google.com/scholar?cluster=8405812116415915225&hl=en&as_sdt=0,5 | 10 | 2,022 |
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning | 2 | neurips | 3 | 0 | 2023-06-16 22:57:12.761000 | https://github.com/thudzj/ella | 13 | Accelerated Linearized Laplace Approximation for Bayesian Deep Learning | https://scholar.google.com/scholar?cluster=8567091747078651114&hl=en&as_sdt=0,5 | 1 | 2,022 |
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures | 3 | neurips | 1 | 0 | 2023-06-16 22:57:12.973000 | https://github.com/aryol/booleanpvr | 9 | Learning to reason with neural networks: Generalization, unseen data and boolean measures | https://scholar.google.com/scholar?cluster=5899767711713652917&hl=en&as_sdt=0,5 | 2 | 2,022 |
Training and Inference on Any-Order Autoregressive Models the Right Way | 1 | neurips | 2 | 0 | 2023-06-16 22:57:13.183000 | https://github.com/andyshih12/mac | 7 | Training and Inference on Any-Order Autoregressive Models the Right Way | https://scholar.google.com/scholar?cluster=17556958914030914345&hl=en&as_sdt=0,5 | 2 | 2,022 |
Lazy and Fast Greedy MAP Inference for Determinantal Point Process | 0 | neurips | 0 | 0 | 2023-06-16 22:57:13.400000 | https://github.com/Alnusjaponica/DPP-MAP-Inference | 1 | Lazy and Fast Greedy MAP Inference for Determinantal Point Process | https://scholar.google.com/scholar?cluster=6823574159955750227&hl=en&as_sdt=0,5 | 2 | 2,022 |
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant | 2 | neurips | 5 | 0 | 2023-06-16 22:57:13.610000 | https://github.com/Jin-Ying/GTA-Seg | 22 | Semi-supervised semantic segmentation via gentle teaching assistant | https://scholar.google.com/scholar?cluster=4347716352468380052&hl=en&as_sdt=0,5 | 1 | 2,022 |
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods | 22 | neurips | 0 | 0 | 2023-06-16 22:57:13.824000 | https://github.com/serre-lab/meta-predictor | 3 | What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods | https://scholar.google.com/scholar?cluster=5412890546069619633&hl=en&as_sdt=0,5 | 16 | 2,022 |
TransTab: Learning Transferable Tabular Transformers Across Tables | 15 | neurips | 13 | 4 | 2023-06-16 22:57:14.084000 | https://github.com/ryanwangzf/transtab | 93 | Transtab: Learning transferable tabular transformers across tables | https://scholar.google.com/scholar?cluster=5025075385855240360&hl=en&as_sdt=0,5 | 6 | 2,022 |
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop | 3 | neurips | 0 | 0 | 2023-06-16 22:57:14.296000 | https://github.com/zwx8981/PerceptualAttack_BIQA | 7 | Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop | https://scholar.google.com/scholar?cluster=8403042660344902079&hl=en&as_sdt=0,33 | 1 | 2,022 |
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks | 5 | neurips | 2 | 0 | 2023-06-16 22:57:14.507000 | https://github.com/chingyaoc/tmd | 32 | Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks | https://scholar.google.com/scholar?cluster=15681270444210282135&hl=en&as_sdt=0,5 | 2 | 2,022 |
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum | 5 | neurips | 3 | 0 | 2023-06-16 22:57:14.718000 | https://github.com/liun-online/spco | 21 | Revisiting graph contrastive learning from the perspective of graph spectrum | https://scholar.google.com/scholar?cluster=9580149588228619113&hl=en&as_sdt=0,5 | 2 | 2,022 |
(De-)Randomized Smoothing for Decision Stump Ensembles | 0 | neurips | 0 | 0 | 2023-06-16 22:57:14.929000 | https://github.com/eth-sri/drs | 2 | (De-) Randomized Smoothing for Decision Stump Ensembles | https://scholar.google.com/scholar?cluster=9534504421648606260&hl=en&as_sdt=0,5 | 5 | 2,022 |
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation | 7 | neurips | 4 | 0 | 2023-06-16 22:57:15.172000 | https://github.com/Jxu-Thu/DITTO | 19 | Learning to break the loop: Analyzing and mitigating repetitions for neural text generation | https://scholar.google.com/scholar?cluster=305884743851229055&hl=en&as_sdt=0,10 | 1 | 2,022 |
Integral Probability Metrics PAC-Bayes Bounds | 4 | neurips | 0 | 0 | 2023-06-16 22:57:15.399000 | https://github.com/ron-amit/pac_bayes_reg | 0 | Integral Probability Metrics PAC-Bayes Bounds | https://scholar.google.com/scholar?cluster=17771180228755273754&hl=en&as_sdt=0,11 | 0 | 2,022 |
Self-explaining deep models with logic rule reasoning | 3 | neurips | 5 | 3 | 2023-06-16 22:57:15.610000 | https://github.com/archon159/selor | 34 | Self-explaining deep models with logic rule reasoning | https://scholar.google.com/scholar?cluster=17380550052737130818&hl=en&as_sdt=0,11 | 2 | 2,022 |
Contrastive Neural Ratio Estimation | 3 | neurips | 0 | 0 | 2023-06-16 22:57:15.820000 | https://github.com/bkmi/cnre | 1 | Contrastive neural ratio estimation | https://scholar.google.com/scholar?cluster=10243773059505759044&hl=en&as_sdt=0,5 | 1 | 2,022 |
EgoTaskQA: Understanding Human Tasks in Egocentric Videos | 5 | neurips | 0 | 1 | 2023-06-16 22:57:16.030000 | https://github.com/Buzz-Beater/EgoTaskQA | 17 | Egotaskqa: Understanding human tasks in egocentric videos | https://scholar.google.com/scholar?cluster=2618582324466290943&hl=en&as_sdt=0,5 | 1 | 2,022 |
C-Mixup: Improving Generalization in Regression | 7 | neurips | 0 | 1 | 2023-06-16 22:57:16.241000 | https://github.com/huaxiuyao/c-mixup | 45 | C-mixup: Improving generalization in regression | https://scholar.google.com/scholar?cluster=15175213809542606261&hl=en&as_sdt=0,33 | 3 | 2,022 |
Generalised Mutual Information for Discriminative Clustering | 2 | neurips | 0 | 0 | 2023-06-16 22:57:16.453000 | https://github.com/oshillou/gemini | 2 | Generalised Mutual Information for Discriminative Clustering | https://scholar.google.com/scholar?cluster=17126945082306251507&hl=en&as_sdt=0,33 | 2 | 2,022 |
Pseudo-Riemannian Graph Convolutional Networks | 5 | neurips | 0 | 0 | 2023-06-16 22:57:16.663000 | https://github.com/xiongbo010/qgcn | 4 | Pseudo-Riemannian Graph Convolutional Networks | https://scholar.google.com/scholar?cluster=8375225836111812142&hl=en&as_sdt=0,5 | 0 | 2,022 |
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion | 3 | neurips | 0 | 0 | 2023-06-16 22:57:16.874000 | https://github.com/naver/croco | 23 | CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion | https://scholar.google.com/scholar?cluster=16202141712210963294&hl=en&as_sdt=0,5 | 5 | 2,022 |
Sound and Complete Verification of Polynomial Networks | 2 | neurips | 0 | 0 | 2023-06-16 22:57:17.085000 | https://github.com/megaelius/PNVerification | 2 | Sound and Complete Verification of Polynomial Networks | https://scholar.google.com/scholar?cluster=4570751371641493882&hl=en&as_sdt=0,14 | 2 | 2,022 |
CalFAT: Calibrated Federated Adversarial Training with Label Skewness | 4 | neurips | 1 | 1 | 2023-06-16 22:57:17.295000 | https://github.com/cc233/calfat | 0 | CalFAT: Calibrated federated adversarial training with label skewness | https://scholar.google.com/scholar?cluster=16082019978611352733&hl=en&as_sdt=0,13 | 0 | 2,022 |
Rethinking Generalization in Few-Shot Classification | 7 | neurips | 3 | 3 | 2023-06-16 22:57:17.506000 | https://github.com/mrkshllr/FewTURE | 32 | Rethinking generalization in few-shot classification | https://scholar.google.com/scholar?cluster=2312996917630319931&hl=en&as_sdt=0,5 | 4 | 2,022 |
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing | 2 | neurips | 0 | 0 | 2023-06-16 22:57:17.717000 | https://github.com/sunshine-ye/nips22-st | 6 | Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing | https://scholar.google.com/scholar?cluster=11081706588643944252&hl=en&as_sdt=0,5 | 2 | 2,022 |
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations | 42 | neurips | 8 | 2 | 2023-06-16 22:57:17.927000 | https://github.com/ML-GSAI/EGSDE | 111 | Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations | https://scholar.google.com/scholar?cluster=8785482238856182484&hl=en&as_sdt=0,33 | 3 | 2,022 |
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems | 4 | neurips | 1 | 0 | 2023-06-16 22:57:18.138000 | https://github.com/m3rg-iitd/benchmarking_graph | 3 | Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems | https://scholar.google.com/scholar?cluster=12287057853788727578&hl=en&as_sdt=0,41 | 0 | 2,022 |
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning | 3 | neurips | 1 | 0 | 2023-06-16 22:57:18.368000 | https://github.com/MrHuff/GWI | 5 | Generalized variational inference in function spaces: Gaussian measures meet Bayesian deep learning | https://scholar.google.com/scholar?cluster=15952581512655430688&hl=en&as_sdt=0,20 | 1 | 2,022 |
Communicating Natural Programs to Humans and Machines | 18 | neurips | 6 | 0 | 2023-06-16 22:57:18.579000 | https://github.com/samacqua/LARC | 49 | Communicating natural programs to humans and machines | https://scholar.google.com/scholar?cluster=13373939616457240114&hl=en&as_sdt=0,39 | 4 | 2,022 |
SCAMPS: Synthetics for Camera Measurement of Physiological Signals | 10 | neurips | 2 | 4 | 2023-06-16 22:57:18.790000 | https://github.com/danmcduff/scampsdataset | 34 | Scamps: Synthetics for camera measurement of physiological signals | https://scholar.google.com/scholar?cluster=15226072589725201524&hl=en&as_sdt=0,23 | 3 | 2,022 |
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases | 5 | neurips | 53 | 45 | 2023-06-16 22:57:19.002000 | https://github.com/google/learned_optimization | 649 | A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases | https://scholar.google.com/scholar?cluster=10651202979674165812&hl=en&as_sdt=0,5 | 11 | 2,022 |
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions | 0 | neurips | 0 | 0 | 2023-06-16 22:57:19.213000 | https://github.com/haanvid/kmis | 1 | Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions | https://scholar.google.com/scholar?cluster=11353779178954280803&hl=en&as_sdt=0,44 | 1 | 2,022 |
Flare7K: A Phenomenological Nighttime Flare Removal Dataset | 7 | neurips | 6 | 1 | 2023-06-16 22:57:19.424000 | https://github.com/ykdai/Flare7K | 63 | Flare7K: A Phenomenological Nighttime Flare Removal Dataset | https://scholar.google.com/scholar?cluster=4666672639396573877&hl=en&as_sdt=0,22 | 6 | 2,022 |
USB: A Unified Semi-supervised Learning Benchmark for Classification | 5 | neurips | 116 | 24 | 2023-06-16 22:57:19.635000 | https://github.com/microsoft/semi-supervised-learning | 804 | Usb: A unified semi-supervised learning benchmark for classification | https://scholar.google.com/scholar?cluster=10960877857326492306&hl=en&as_sdt=0,5 | 14 | 2,022 |
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world | 5 | neurips | 20 | 20 | 2023-06-16 22:57:19.846000 | https://github.com/facebookresearch/nocturne | 202 | Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world | https://scholar.google.com/scholar?cluster=10789605761114029551&hl=en&as_sdt=0,5 | 11 | 2,022 |
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency | 23 | neurips | 55 | 7 | 2023-06-16 22:57:20.057000 | https://github.com/mims-harvard/tfc-pretraining | 235 | Self-supervised contrastive pre-training for time series via time-frequency consistency | https://scholar.google.com/scholar?cluster=18283822055997916844&hl=en&as_sdt=0,5 | 5 | 2,022 |
Uncalibrated Models Can Improve Human-AI Collaboration | 7 | neurips | 1 | 0 | 2023-06-16 22:57:20.268000 | https://github.com/kailas-v/human-ai-interactions | 7 | Uncalibrated models can improve human-ai collaboration | https://scholar.google.com/scholar?cluster=12469546917170199830&hl=en&as_sdt=0,33 | 1 | 2,022 |
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces | 8 | neurips | 0 | 0 | 2023-06-16 22:57:20.478000 | https://github.com/CSML-IIT-UCL/kooplearn | 7 | Learning dynamical systems via koopman operator regression in reproducing kernel hilbert spaces | https://scholar.google.com/scholar?cluster=12157593222906117840&hl=en&as_sdt=0,10 | 4 | 2,022 |
A Policy-Guided Imitation Approach for Offline Reinforcement Learning | 7 | neurips | 2 | 0 | 2023-06-16 22:57:20.689000 | https://github.com/ryanxhr/por | 44 | A policy-guided imitation approach for offline reinforcement learning | https://scholar.google.com/scholar?cluster=17364397345225831453&hl=en&as_sdt=0,47 | 3 | 2,022 |
On the Convergence Theory for Hessian-Free Bilevel Algorithms | 3 | neurips | 2 | 0 | 2023-06-16 22:57:20.899000 | https://github.com/sowmaster/esjacobians | 6 | On the convergence theory for hessian-free bilevel algorithms | https://scholar.google.com/scholar?cluster=15140633553551921538&hl=en&as_sdt=0,47 | 1 | 2,022 |
Spartan: Differentiable Sparsity via Regularized Transportation | 0 | neurips | 0 | 0 | 2023-06-16 22:57:21.111000 | https://github.com/facebookresearch/spartan | 19 | Spartan: Differentiable Sparsity via Regularized Transportation | https://scholar.google.com/scholar?cluster=15812271986166410699&hl=en&as_sdt=0,46 | 3 | 2,022 |
Focal Modulation Networks | 39 | neurips | 51 | 10 | 2023-06-16 22:57:21.322000 | https://github.com/microsoft/FocalNet | 552 | Focal modulation networks | https://scholar.google.com/scholar?cluster=12867511582517934835&hl=en&as_sdt=0,10 | 16 | 2,022 |
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces | 5 | neurips | 2 | 0 | 2023-06-16 22:57:21.533000 | https://github.com/leoqli/hsurf-net | 25 | HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces | https://scholar.google.com/scholar?cluster=8622760401117810211&hl=en&as_sdt=0,5 | 4 | 2,022 |
Robust Streaming PCA | 1 | neurips | 0 | 0 | 2023-06-16 22:57:21.743000 | https://github.com/MinchanJeong/Robust-Streaming-PCA | 1 | Robust Streaming PCA | https://scholar.google.com/scholar?cluster=9313897813400392144&hl=en&as_sdt=0,10 | 1 | 2,022 |
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression | 10 | neurips | 0 | 0 | 2023-06-16 22:57:21.954000 | https://github.com/haoyuzhao123/soteriafl | 4 | SoteriaFL: A unified framework for private federated learning with communication compression | https://scholar.google.com/scholar?cluster=6684992000278225554&hl=en&as_sdt=0,5 | 2 | 2,022 |
Your Transformer May Not be as Powerful as You Expect | 12 | neurips | 1 | 1 | 2023-06-16 22:57:22.165000 | https://github.com/lsj2408/urpe | 21 | Your transformer may not be as powerful as you expect | https://scholar.google.com/scholar?cluster=13623285884170722320&hl=en&as_sdt=0,5 | 2 | 2,022 |
Diffusion-LM Improves Controllable Text Generation | 139 | neurips | 104 | 45 | 2023-06-16 22:57:22.377000 | https://github.com/xiangli1999/diffusion-lm | 836 | Diffusion-lm improves controllable text generation | https://scholar.google.com/scholar?cluster=17910853149942433121&hl=en&as_sdt=0,36 | 18 | 2,022 |
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure | 7 | neurips | 1 | 1 | 2023-06-16 22:57:22.587000 | https://github.com/paulnovello/hsic-attribution-method | 10 | Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure | https://scholar.google.com/scholar?cluster=7791180788979429607&hl=en&as_sdt=0,21 | 1 | 2,022 |
Energy-Based Contrastive Learning of Visual Representations | 1 | neurips | 0 | 1 | 2023-06-16 22:57:22.798000 | https://github.com/1202kbs/ebclr | 5 | Energy-Based Contrastive Learning of Visual Representations | https://scholar.google.com/scholar?cluster=14002446974731282321&hl=en&as_sdt=0,10 | 2 | 2,022 |
On the Generalizability and Predictability of Recommender Systems | 0 | neurips | 1 | 5 | 2023-06-16 22:57:23.009000 | https://github.com/naszilla/reczilla | 20 | On the Generalizability and Predictability of Recommender Systems | https://scholar.google.com/scholar?cluster=17151097798328031409&hl=en&as_sdt=0,22 | 5 | 2,022 |
Divert More Attention to Vision-Language Tracking | 3 | neurips | 71 | 25 | 2023-06-16 22:57:23.221000 | https://github.com/JudasDie/SOTS | 417 | Divert More Attention to Vision-Language Tracking | https://scholar.google.com/scholar?cluster=6209180784126725956&hl=en&as_sdt=0,1 | 11 | 2,022 |
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning | 15 | neurips | 8 | 2 | 2023-06-16 22:57:23.432000 | https://github.com/ist-daslab/obc | 38 | Optimal Brain Compression: A framework for accurate post-training quantization and pruning | https://scholar.google.com/scholar?cluster=2227477302772250547&hl=en&as_sdt=0,5 | 4 | 2,022 |
Association Graph Learning for Multi-Task Classification with Category Shifts | 2 | neurips | 1 | 1 | 2023-06-16 22:57:23.642000 | https://github.com/autumn9999/mtc-with-category-shifts | 6 | Association graph learning for multi-task classification with category shifts | https://scholar.google.com/scholar?cluster=8917197566031875925&hl=en&as_sdt=0,44 | 1 | 2,022 |
A Unified Model for Multi-class Anomaly Detection | 8 | neurips | 12 | 0 | 2023-06-16 22:57:23.853000 | https://github.com/zhiyuanyou/uniad | 136 | A Unified Model for Multi-class Anomaly Detection | https://scholar.google.com/scholar?cluster=11558725855987199082&hl=en&as_sdt=0,15 | 1 | 2,022 |
Learning with little mixing | 8 | neurips | 7,321 | 1,026 | 2023-06-16 22:57:24.064000 | https://github.com/google-research/google-research | 29,788 | Learning with little mixing | https://scholar.google.com/scholar?cluster=55245308812869418&hl=en&as_sdt=0,45 | 727 | 2,022 |
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming | 2 | neurips | 1 | 0 | 2023-06-16 22:57:24.275000 | https://github.com/elemisi/vael | 14 | VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming | https://scholar.google.com/scholar?cluster=10135207146367765358&hl=en&as_sdt=0,39 | 3 | 2,022 |
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks | 9 | neurips | 0 | 0 | 2023-06-16 22:57:24.486000 | https://github.com/leirunlin/evennet | 7 | Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks | https://scholar.google.com/scholar?cluster=15300270171268425828&hl=en&as_sdt=0,5 | 1 | 2,022 |
Differentiable Analog Quantum Computing for Optimization and Control | 4 | neurips | 2 | 0 | 2023-06-16 22:57:24.697000 | https://github.com/yilingqiao/diffquantum | 16 | Differentiable Analog Quantum Computing for Optimization and Control | https://scholar.google.com/scholar?cluster=2405301331103163699&hl=en&as_sdt=0,5 | 2 | 2,022 |
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images | 6 | neurips | 1 | 0 | 2023-06-16 22:57:24.908000 | https://github.com/vlar-group/unsupobjseg | 26 | Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images | https://scholar.google.com/scholar?cluster=1761568637963446015&hl=en&as_sdt=0,47 | 1 | 2,022 |
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation | 7 | neurips | 1 | 3 | 2023-06-16 22:57:25.119000 | https://github.com/usr922/fst | 29 | Learning from Future: A Novel Self-Training Framework for Semantic Segmentation | https://scholar.google.com/scholar?cluster=6027127191801048854&hl=en&as_sdt=0,43 | 2 | 2,022 |
How Powerful are K-hop Message Passing Graph Neural Networks | 15 | neurips | 3 | 0 | 2023-06-16 22:57:25.331000 | https://github.com/JiaruiFeng/KP-GNN | 48 | How powerful are k-hop message passing graph neural networks | https://scholar.google.com/scholar?cluster=3067212826478566297&hl=en&as_sdt=0,47 | 2 | 2,022 |
Exploitability Minimization in Games and Beyond | 4 | neurips | 0 | 0 | 2023-06-16 22:57:25.543000 | https://github.com/denizalp/exploit-min | 0 | Exploitability Minimization in Games and Beyond | https://scholar.google.com/scholar?cluster=4856157037704483082&hl=en&as_sdt=0,44 | 1 | 2,022 |
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps | 23 | neurips | 10 | 8 | 2023-06-16 22:57:25.753000 | https://github.com/mediabrain-sjtu/where2comm | 75 | Where2comm: Communication-efficient collaborative perception via spatial confidence maps | https://scholar.google.com/scholar?cluster=15169095000176396543&hl=en&as_sdt=0,41 | 2 | 2,022 |
Your Out-of-Distribution Detection Method is Not Robust! | 5 | neurips | 1 | 0 | 2023-06-16 22:57:25.964000 | https://github.com/rohban-lab/atd | 9 | Your Out-of-Distribution Detection Method is Not Robust! | https://scholar.google.com/scholar?cluster=1414819434166798732&hl=en&as_sdt=0,5 | 2 | 2,022 |
NaturalProver: Grounded Mathematical Proof Generation with Language Models | 4 | neurips | 1 | 0 | 2023-06-16 22:57:26.175000 | https://github.com/wellecks/naturalprover | 23 | Naturalprover: Grounded mathematical proof generation with language models | https://scholar.google.com/scholar?cluster=7878492470641044970&hl=en&as_sdt=0,41 | 2 | 2,022 |
One for All: Simultaneous Metric and Preference Learning over Multiple Users | 1 | neurips | 1 | 0 | 2023-06-16 22:57:26.386000 | https://github.com/gregcanal/multiuser-metric-preference | 0 | One for all: Simultaneous metric and preference learning over multiple users | https://scholar.google.com/scholar?cluster=4938147600895831412&hl=en&as_sdt=0,25 | 1 | 2,022 |
SegViT: Semantic Segmentation with Plain Vision Transformers | 13 | neurips | 5 | 4 | 2023-06-16 22:57:26.597000 | https://github.com/zbwxp/SegVit | 67 | Segvit: Semantic segmentation with plain vision transformers | https://scholar.google.com/scholar?cluster=4636047207088039334&hl=en&as_sdt=0,21 | 1 | 2,022 |
Unsupervised Learning From Incomplete Measurements for Inverse Problems | 3 | neurips | 2 | 1 | 2023-06-16 22:57:26.808000 | https://github.com/edongdongchen/moi | 7 | Unsupervised Learning From Incomplete Measurements for Inverse Problems | https://scholar.google.com/scholar?cluster=14843076631440223178&hl=en&as_sdt=0,36 | 1 | 2,022 |
Redeeming intrinsic rewards via constrained optimization | 2 | neurips | 6 | 2 | 2023-06-16 22:57:27.018000 | https://github.com/improbable-ai/eipo | 59 | Redeeming intrinsic rewards via constrained optimization | https://scholar.google.com/scholar?cluster=1760121311943802855&hl=en&as_sdt=0,5 | 5 | 2,022 |
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks | 9 | neurips | 14 | 5 | 2023-06-16 22:57:27.230000 | https://github.com/thunlp/openbackdoor | 94 | A unified evaluation of textual backdoor learning: Frameworks and benchmarks | https://scholar.google.com/scholar?cluster=12638294460038796289&hl=en&as_sdt=0,5 | 8 | 2,022 |
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models | 2 | neurips | 0 | 0 | 2023-06-16 22:57:27.441000 | https://github.com/ErdunGAO/MissDAG | 2 | MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models | https://scholar.google.com/scholar?cluster=8771512698541826516&hl=en&as_sdt=0,25 | 1 | 2,022 |
A Theoretical Study on Solving Continual Learning | 6 | neurips | 1 | 0 | 2023-06-16 22:57:27.652000 | https://github.com/k-gyuhak/wptp | 8 | A Theoretical Study on Solving Continual Learning | https://scholar.google.com/scholar?cluster=11651266848032744688&hl=en&as_sdt=0,5 | 1 | 2,022 |
Misspecified Phase Retrieval with Generative Priors | 0 | neurips | 0 | 0 | 2023-06-16 22:57:27.862000 | https://github.com/jiulongliu/MPRG | 0 | Misspecified Phase Retrieval with Generative Priors | https://scholar.google.com/scholar?cluster=1648135207641613717&hl=en&as_sdt=0,5 | 2 | 2,022 |
Data-Efficient Augmentation for Training Neural Networks | 1 | neurips | 2 | 0 | 2023-06-16 22:57:28.074000 | https://github.com/tianyu139/data-efficient-augmentation | 2 | Data-Efficient Augmentation for Training Neural Networks | https://scholar.google.com/scholar?cluster=16120463592327015292&hl=en&as_sdt=0,33 | 2 | 2,022 |
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning | 9 | neurips | 2 | 6 | 2023-06-16 22:57:28.284000 | https://github.com/zyezhang/dac | 29 | Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning | https://scholar.google.com/scholar?cluster=14836332941736923065&hl=en&as_sdt=0,33 | 6 | 2,022 |
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning | 17 | neurips | 34 | 17 | 2023-06-16 22:57:28.496000 | https://github.com/pku-marl/dexteroushands | 319 | Towards human-level bimanual dexterous manipulation with reinforcement learning | https://scholar.google.com/scholar?cluster=3451546095013207545&hl=en&as_sdt=0,26 | 13 | 2,022 |
Local-Global MCMC kernels: the best of both worlds | 3 | neurips | 2 | 0 | 2023-06-16 22:57:28.706000 | https://github.com/svsamsonov/ex2mcmc_new | 2 | Local-Global MCMC kernels: the best of both worlds | https://scholar.google.com/scholar?cluster=6444779825968376973&hl=en&as_sdt=0,14 | 3 | 2,022 |
The computational and learning benefits of Daleian neural networks | 0 | neurips | 0 | 0 | 2023-06-16 22:57:28.917000 | https://github.com/adamhaber/daleian_networks | 0 | The computational and learning benefits of Daleian neural networks | https://scholar.google.com/scholar?cluster=7045665223313726154&hl=en&as_sdt=0,15 | 1 | 2,022 |
Efficient and Modular Implicit Differentiation | 99 | neurips | 55 | 80 | 2023-06-16 22:57:29.127000 | https://github.com/google/jaxopt | 713 | Efficient and modular implicit differentiation | https://scholar.google.com/scholar?cluster=17447288700726145942&hl=en&as_sdt=0,23 | 19 | 2,022 |
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations | 17 | neurips | 1 | 0 | 2023-06-16 22:57:29.339000 | https://github.com/AI4LIFE-GROUP/lfa | 1 | Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations | https://scholar.google.com/scholar?cluster=14882559489186994501&hl=en&as_sdt=0,5 | 2 | 2,022 |
Accelerating Certified Robustness Training via Knowledge Transfer | 0 | neurips | 0 | 0 | 2023-06-16 22:57:29.550000 | https://github.com/ethos-lab/crt-neurips22 | 1 | Accelerating Certified Robustness Training via Knowledge Transfer | https://scholar.google.com/scholar?cluster=16137440255270375978&hl=en&as_sdt=0,5 | 2 | 2,022 |
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings | 13 | neurips | 14 | 23 | 2023-06-16 22:57:29.761000 | https://github.com/owkin/flamby | 158 | FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings | https://scholar.google.com/scholar?cluster=7376958489532657973&hl=en&as_sdt=0,32 | 8 | 2,022 |
Blackbox Attacks via Surrogate Ensemble Search | 2 | neurips | 3 | 0 | 2023-06-16 22:57:29.972000 | https://github.com/csiplab/bases | 5 | Blackbox attacks via surrogate ensemble search | https://scholar.google.com/scholar?cluster=3551879013092176593&hl=en&as_sdt=0,6 | 2 | 2,022 |
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking | 10 | neurips | 8 | 0 | 2023-06-16 22:57:30.182000 | https://github.com/vita-group/large_scale_gcn_benchmarking | 42 | A comprehensive study on large-scale graph training: Benchmarking and rethinking | https://scholar.google.com/scholar?cluster=1620706562706665630&hl=en&as_sdt=0,31 | 10 | 2,022 |
Scale-invariant Learning by Physics Inversion | 0 | neurips | 0 | 0 | 2023-06-16 22:57:30.394000 | https://github.com/tum-pbs/sip | 9 | Scale-invariant Learning by Physics Inversion | https://scholar.google.com/scholar?cluster=11653236116859810051&hl=en&as_sdt=0,11 | 2 | 2,022 |
Sample Constrained Treatment Effect Estimation | 1 | neurips | 0 | 0 | 2023-06-16 22:57:30.604000 | https://github.com/raddanki/sample-constrained-treatment-effect-estimation | 1 | Sample Constrained Treatment Effect Estimation | https://scholar.google.com/scholar?cluster=8394950395338055772&hl=en&as_sdt=0,14 | 2 | 2,022 |
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks | 10 | neurips | 0 | 0 | 2023-06-16 22:57:30.816000 | https://github.com/xlhex/cater_neurips | 3 | CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks | https://scholar.google.com/scholar?cluster=14890378325788554569&hl=en&as_sdt=0,34 | 2 | 2,022 |
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs | 2 | neurips | 1 | 0 | 2023-06-16 22:57:31.027000 | https://github.com/seijimaekawa/empirical-study-of-gnns | 2 | Beyond real-world benchmark datasets: An empirical study of node classification with GNNs | https://scholar.google.com/scholar?cluster=6075046742984586862&hl=en&as_sdt=0,10 | 1 | 2,022 |
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps | 158 | neurips | 90 | 12 | 2023-06-16 22:57:31.237000 | https://github.com/luchengthu/dpm-solver | 1,022 | Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps | https://scholar.google.com/scholar?cluster=2427327523938680723&hl=en&as_sdt=0,44 | 19 | 2,022 |
Active Exploration for Inverse Reinforcement Learning | 2 | neurips | 1 | 0 | 2023-06-16 22:57:31.449000 | https://github.com/lasgroup/aceirl | 4 | Active Exploration for Inverse Reinforcement Learning | https://scholar.google.com/scholar?cluster=2422293204605820403&hl=en&as_sdt=0,5 | 2 | 2,022 |
Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences | 8 | neurips | 3 | 0 | 2023-06-16 22:57:31.661000 | https://github.com/wangsiwei2010/neurips22-fmvacc | 12 | Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences | https://scholar.google.com/scholar?cluster=6953275277959692680&hl=en&as_sdt=0,33 | 1 | 2,022 |
Geodesic Graph Neural Network for Efficient Graph Representation Learning | 4 | neurips | 2 | 0 | 2023-06-16 22:57:31.871000 | https://github.com/woodcutter1998/gdgnn | 13 | Geodesic Graph Neural Network for Efficient Graph Representation Learning | https://scholar.google.com/scholar?cluster=15655553108751060031&hl=en&as_sdt=0,51 | 1 | 2,022 |
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams | 12 | neurips | 177 | 12 | 2023-06-16 22:57:32.083000 | https://github.com/google-research/federated | 555 | Improved differential privacy for sgd via optimal private linear operators on adaptive streams | https://scholar.google.com/scholar?cluster=7562865688859267077&hl=en&as_sdt=0,5 | 26 | 2,022 |
On Privacy and Personalization in Cross-Silo Federated Learning | 8 | neurips | 1 | 0 | 2023-06-16 22:57:32.294000 | https://github.com/kenziyuliu/private-cross-silo-fl | 21 | On privacy and personalization in cross-silo federated learning | https://scholar.google.com/scholar?cluster=5435954743553051960&hl=en&as_sdt=0,47 | 2 | 2,022 |
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning | 4 | neurips | 10 | 0 | 2023-06-16 22:57:32.506000 | https://github.com/hsvgbkhgbv/shapley-q-learning | 27 | Shaq: Incorporating shapley value theory into multi-agent q-learning | https://scholar.google.com/scholar?cluster=5920175691861441269&hl=en&as_sdt=0,5 | 2 | 2,022 |
Trajectory balance: Improved credit assignment in GFlowNets | 18 | neurips | 67 | 8 | 2023-06-16 22:57:32.717000 | https://github.com/gfnorg/gflownet | 457 | Trajectory balance: Improved credit assignment in gflownets | https://scholar.google.com/scholar?cluster=6680117776194765384&hl=en&as_sdt=0,5 | 10 | 2,022 |
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