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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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