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Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients | 2 | neurips | 0 | 0 | 2023-06-16 23:00:23.432000 | https://github.com/red-portal/klpqvi.jl | 0 | Markov chain score ascent: A unifying framework of variational inference with Markovian gradients | https://scholar.google.com/scholar?cluster=9999896485416486947&hl=en&as_sdt=0,5 | 2 | 2,022 |
Rethinking Value Function Learning for Generalization in Reinforcement Learning | 0 | neurips | 1 | 0 | 2023-06-16 23:00:23.644000 | https://github.com/snu-mllab/dcpg | 9 | Rethinking Value Function Learning for Generalization in Reinforcement Learning | https://scholar.google.com/scholar?cluster=17768972917538912915&hl=en&as_sdt=0,5 | 4 | 2,022 |
Improving Certified Robustness via Statistical Learning with Logical Reasoning | 2 | neurips | 0 | 1 | 2023-06-16 23:00:23.857000 | https://github.com/sensing-reasoning/sensing-reasoning-pipeline | 3 | Improving certified robustness via statistical learning with logical reasoning | https://scholar.google.com/scholar?cluster=12962831424296042350&hl=en&as_sdt=0,46 | 1 | 2,022 |
Understanding Robust Learning through the Lens of Representation Similarities | 2 | neurips | 0 | 0 | 2023-06-16 23:00:24.069000 | https://github.com/uchicago-sandlab/robust_representation_similarity | 1 | Understanding robust learning through the lens of representation similarities | https://scholar.google.com/scholar?cluster=13729841239622676756&hl=en&as_sdt=0,47 | 0 | 2,022 |
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization | 3 | neurips | 0 | 0 | 2023-06-16 23:00:24.346000 | https://github.com/weijiazhang24/causalmil | 6 | Multi-instance causal representation learning for instance label prediction and out-of-distribution generalization | https://scholar.google.com/scholar?cluster=5803800343677787178&hl=en&as_sdt=0,5 | 1 | 2,022 |
PerfectDou: Dominating DouDizhu with Perfect Information Distillation | 11 | neurips | 21 | 0 | 2023-06-16 23:00:24.606000 | https://github.com/netease-games-ai-lab-guangzhou/perfectdou | 89 | Perfectdou: Dominating doudizhu with perfect information distillation | https://scholar.google.com/scholar?cluster=10276583276169438358&hl=en&as_sdt=0,34 | 5 | 2,022 |
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech | 5 | neurips | 2 | 3 | 2023-06-16 23:00:24.818000 | https://github.com/chorowski-lab/hcpc | 16 | Variable-rate hierarchical CPC leads to acoustic unit discovery in speech | https://scholar.google.com/scholar?cluster=15342183140020352170&hl=en&as_sdt=0,5 | 3 | 2,022 |
Learning Neural Set Functions Under the Optimal Subset Oracle | 0 | neurips | 0 | 0 | 2023-06-16 23:00:25.039000 | https://github.com/SubsetSelection/EquiVSet | 16 | Learning Neural Set Functions Under the Optimal Subset Oracle | https://scholar.google.com/scholar?cluster=14074525399634060470&hl=en&as_sdt=0,5 | 1 | 2,022 |
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models | 5 | neurips | 1 | 0 | 2023-06-16 23:00:25.252000 | https://github.com/naver-ai/mid.metric | 23 | Mutual Information Divergence: A Unified Metric for Multimodal Generative Models | https://scholar.google.com/scholar?cluster=7729417185496732731&hl=en&as_sdt=0,33 | 2 | 2,022 |
Delving into Out-of-Distribution Detection with Vision-Language Representations | 13 | neurips | 3 | 2 | 2023-06-16 23:00:25.484000 | https://github.com/deeplearning-wisc/mcm | 27 | Delving into Out-of-Distribution Detection with Vision-Language Representations | https://scholar.google.com/scholar?cluster=5820179747828691857&hl=en&as_sdt=0,47 | 4 | 2,022 |
Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve | 0 | neurips | 1 | 0 | 2023-06-16 23:00:25.696000 | https://github.com/giannisdaras/multilingual_robustness | 10 | Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve | https://scholar.google.com/scholar?cluster=1557526934945118330&hl=en&as_sdt=0,47 | 2 | 2,022 |
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding | 7 | neurips | 8 | 1 | 2023-06-16 23:00:25.907000 | https://github.com/deepgraphlearning/peer_benchmark | 51 | Peer: a comprehensive and multi-task benchmark for protein sequence understanding | https://scholar.google.com/scholar?cluster=14330854305087707376&hl=en&as_sdt=0,5 | 4 | 2,022 |
Deep Counterfactual Estimation with Categorical Background Variables | 1 | neurips | 1 | 1 | 2023-06-16 23:00:26.120000 | https://github.com/edebrouwer/cfqp | 7 | Deep Counterfactual Estimation with Categorical Background Variables | https://scholar.google.com/scholar?cluster=16244902668087959747&hl=en&as_sdt=0,33 | 2 | 2,022 |
Self-Supervised Learning with an Information Maximization Criterion | 6 | neurips | 4 | 2 | 2023-06-16 23:00:26.332000 | https://github.com/serdarozsoy/corinfomax-ssl | 16 | Self-supervised learning with an information maximization criterion | https://scholar.google.com/scholar?cluster=3815127622526777729&hl=en&as_sdt=0,47 | 3 | 2,022 |
TwiBot-22: Towards Graph-Based Twitter Bot Detection | 12 | neurips | 25 | 8 | 2023-06-16 23:00:26.544000 | https://github.com/luoundergradxjtu/twibot-22 | 90 | TwiBot-22: Towards graph-based Twitter bot detection | https://scholar.google.com/scholar?cluster=6456058773715528503&hl=en&as_sdt=0,5 | 5 | 2,022 |
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF | 4 | neurips | 0 | 0 | 2023-06-16 23:00:26.756000 | https://github.com/jayneelparekh/l2i-code | 3 | Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF | https://scholar.google.com/scholar?cluster=12104450137353790860&hl=en&as_sdt=0,5 | 2 | 2,022 |
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression | 2 | neurips | 2 | 0 | 2023-06-16 23:00:26.978000 | https://github.com/xk-huang/OrdinalCLIP | 18 | OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression | https://scholar.google.com/scholar?cluster=3053611634838674005&hl=en&as_sdt=0,33 | 2 | 2,022 |
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control | 1 | neurips | 15 | 0 | 2023-06-16 23:00:27.190000 | https://github.com/microsoft/MoCapAct | 92 | MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control | https://scholar.google.com/scholar?cluster=11298263061250398476&hl=en&as_sdt=0,5 | 9 | 2,022 |
On the Effectiveness of Persistent Homology | 5 | neurips | 0 | 1 | 2023-06-16 23:00:27.402000 | https://github.com/renata-turkes/turkevs2022on | 4 | On the effectiveness of persistent homology | https://scholar.google.com/scholar?cluster=17747599099493045319&hl=en&as_sdt=0,5 | 1 | 2,022 |
Flowification: Everything is a normalizing flow | 3 | neurips | 1 | 0 | 2023-06-16 23:00:27.615000 | https://github.com/balintmate/flowification | 3 | Flowification: Everything is a normalizing flow | https://scholar.google.com/scholar?cluster=10643002561590578659&hl=en&as_sdt=0,32 | 1 | 2,022 |
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective | 10 | neurips | 2 | 0 | 2023-06-16 23:00:27.826000 | https://github.com/naver-ai/hmix-gmix | 16 | A unified analysis of mixed sample data augmentation: A loss function perspective | https://scholar.google.com/scholar?cluster=14554827738828101158&hl=en&as_sdt=0,3 | 6 | 2,022 |
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings | 3 | neurips | 2 | 0 | 2023-06-16 23:00:28.038000 | https://github.com/yiren-jian/NonLing-CSE | 18 | Non-linguistic supervision for contrastive learning of sentence embeddings | https://scholar.google.com/scholar?cluster=5735790098682052651&hl=en&as_sdt=0,5 | 2 | 2,022 |
4D Unsupervised Object Discovery | 4 | neurips | 1 | 3 | 2023-06-16 23:00:28.250000 | https://github.com/robertwyq/lsmol | 46 | 4d unsupervised object discovery | https://scholar.google.com/scholar?cluster=15078826490225309292&hl=en&as_sdt=0,10 | 3 | 2,022 |
Deep invariant networks with differentiable augmentation layers | 1 | neurips | 0 | 0 | 2023-06-16 23:00:28.462000 | https://github.com/cedricrommel/augnet | 14 | Deep invariant networks with differentiable augmentation layers | https://scholar.google.com/scholar?cluster=6037019697272911487&hl=en&as_sdt=0,33 | 1 | 2,022 |
Reinforcement Learning with a Terminator | 2 | neurips | 0 | 0 | 2023-06-16 23:00:28.674000 | https://github.com/guytenn/terminator | 3 | Reinforcement Learning with a Terminator | https://scholar.google.com/scholar?cluster=7563547842459702948&hl=en&as_sdt=0,33 | 1 | 2,022 |
A Multilabel Classification Framework for Approximate Nearest Neighbor Search | 0 | neurips | 0 | 0 | 2023-06-16 23:00:28.886000 | https://github.com/vioshyvo/a-multilabel-classification-framework | 1 | A Multilabel Classification Framework for Approximate Nearest Neighbor Search | https://scholar.google.com/scholar?cluster=2936492944429726858&hl=en&as_sdt=0,10 | 1 | 2,022 |
Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks | 1 | neurips | 1 | 0 | 2023-06-16 23:00:29.098000 | https://github.com/raymondyeh07/learnable_polyphase_sampling | 8 | Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks | https://scholar.google.com/scholar?cluster=12661870794117490476&hl=en&as_sdt=0,5 | 4 | 2,022 |
Deep Generative Model for Periodic Graphs | 13 | neurips | 1 | 3 | 2023-06-16 23:00:29.311000 | https://github.com/shi-yu-wang/pgd-vae | 5 | Deep generative model for periodic graphs | https://scholar.google.com/scholar?cluster=12918861137062671900&hl=en&as_sdt=0,43 | 2 | 2,022 |
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models | 7 | neurips | 1,128 | 230 | 2023-06-16 23:00:29.523000 | https://github.com/NVIDIA/Megatron-LM | 5,442 | Exploring the limits of domain-adaptive training for detoxifying large-scale language models | https://scholar.google.com/scholar?cluster=13821301846979103824&hl=en&as_sdt=0,5 | 114 | 2,022 |
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo | 7 | neurips | 0 | 0 | 2023-06-16 23:00:29.735000 | https://github.com/ipeis/HH-VAEM | 9 | Missing data imputation and acquisition with deep hierarchical models and hamiltonian monte carlo | https://scholar.google.com/scholar?cluster=8364326333884223136&hl=en&as_sdt=0,5 | 1 | 2,022 |
DNA: Proximal Policy Optimization with a Dual Network Architecture | 0 | neurips | 3 | 1 | 2023-06-16 23:00:29.946000 | https://github.com/maitchison/PPO | 10 | DNA: Proximal Policy Optimization with a Dual Network Architecture | https://scholar.google.com/scholar?cluster=14725366901420334322&hl=en&as_sdt=0,39 | 2 | 2,022 |
Masked Autoencoders As Spatiotemporal Learners | 135 | neurips | 18 | 7 | 2023-06-16 23:00:30.159000 | https://github.com/facebookresearch/mae_st | 167 | Masked autoencoders as spatiotemporal learners | https://scholar.google.com/scholar?cluster=5215096183189163093&hl=en&as_sdt=0,48 | 6 | 2,022 |
On the Parameterization and Initialization of Diagonal State Space Models | 19 | neurips | 161 | 22 | 2023-06-16 23:00:30.372000 | https://github.com/hazyresearch/state-spaces | 1,217 | On the parameterization and initialization of diagonal state space models | https://scholar.google.com/scholar?cluster=7664274811979401457&hl=en&as_sdt=0,43 | 42 | 2,022 |
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork | 2 | neurips | 0 | 1 | 2023-06-16 23:00:30.584000 | https://github.com/vita-group/trap-and-replace-backdoor-defense | 8 | Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork | https://scholar.google.com/scholar?cluster=9232182512273650158&hl=en&as_sdt=0,33 | 10 | 2,022 |
Cluster and Aggregate: Face Recognition with Large Probe Set | 3 | neurips | 2 | 3 | 2023-06-16 23:00:30.796000 | https://github.com/mk-minchul/caface | 23 | Cluster and aggregate: Face recognition with large probe set | https://scholar.google.com/scholar?cluster=1137447088637227795&hl=en&as_sdt=0,33 | 7 | 2,022 |
GLIPv2: Unifying Localization and Vision-Language Understanding | 57 | neurips | 125 | 52 | 2023-06-16 23:00:31.009000 | https://github.com/microsoft/GLIP | 1,330 | Glipv2: Unifying localization and vision-language understanding | https://scholar.google.com/scholar?cluster=4160517527641475312&hl=en&as_sdt=0,5 | 44 | 2,022 |
Rethinking Alignment in Video Super-Resolution Transformers | 9 | neurips | 3 | 3 | 2023-06-16 23:00:31.221000 | https://github.com/xpixelgroup/rethinkvsralignment | 60 | Rethinking alignment in video super-resolution transformers | https://scholar.google.com/scholar?cluster=13813872909195716054&hl=en&as_sdt=0,39 | 2 | 2,022 |
Learning to Scaffold: Optimizing Model Explanations for Teaching | 6 | neurips | 4 | 0 | 2023-06-16 23:00:31.433000 | https://github.com/coderpat/learning-scaffold | 18 | Learning to scaffold: Optimizing model explanations for teaching | https://scholar.google.com/scholar?cluster=6201332313543501646&hl=en&as_sdt=0,19 | 3 | 2,022 |
AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints | 3 | neurips | 5 | 0 | 2023-06-16 23:00:31.645000 | https://github.com/xingzhehe/AutoLink-Self-supervised-Learning-of-Human-Skeletons-and-Object-Outlines-by-Linking-Keypoints | 26 | Autolink: Self-supervised learning of human skeletons and object outlines by linking keypoints | https://scholar.google.com/scholar?cluster=290662636948878015&hl=en&as_sdt=0,5 | 2 | 2,022 |
Giving Feedback on Interactive Student Programs with Meta-Exploration | 2 | neurips | 1 | 0 | 2023-06-16 23:00:31.857000 | https://github.com/ezliu/dreamgrader | 7 | Giving Feedback on Interactive Student Programs with Meta-Exploration | https://scholar.google.com/scholar?cluster=7333217017498365852&hl=en&as_sdt=0,33 | 1 | 2,022 |
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network | 0 | neurips | 1 | 0 | 2023-06-16 23:00:32.069000 | https://github.com/stat-ml/nuq | 5 | Nonparametric Uncertainty Quantification for Single Deterministic Neural Network | https://scholar.google.com/scholar?cluster=5318025374154758978&hl=en&as_sdt=0,36 | 7 | 2,022 |
Decoupling Features in Hierarchical Propagation for Video Object Segmentation | 12 | neurips | 6 | 0 | 2023-06-16 23:00:32.287000 | https://github.com/z-x-yang/AOT | 91 | Decoupling Features in Hierarchical Propagation for Video Object Segmentation | https://scholar.google.com/scholar?cluster=9093499936003644917&hl=en&as_sdt=0,47 | 13 | 2,022 |
Chain of Thought Imitation with Procedure Cloning | 5 | neurips | 7,321 | 1,026 | 2023-06-16 23:00:32.499000 | https://github.com/google-research/google-research | 29,788 | Chain of thought imitation with procedure cloning | https://scholar.google.com/scholar?cluster=11561247381511573929&hl=en&as_sdt=0,5 | 727 | 2,022 |
ResT V2: Simpler, Faster and Stronger | 1 | neurips | 27 | 10 | 2023-06-16 23:00:32.711000 | https://github.com/wofmanaf/ResT | 233 | Rest v2: simpler, faster and stronger | https://scholar.google.com/scholar?cluster=7008614846201767249&hl=en&as_sdt=0,10 | 6 | 2,022 |
Learning Partial Equivariances From Data | 11 | neurips | 0 | 0 | 2023-06-16 23:00:32.923000 | https://github.com/merlresearch/partial_gcnn | 7 | Learning partial equivariances from data | https://scholar.google.com/scholar?cluster=13426434973387392229&hl=en&as_sdt=0,5 | 0 | 2,022 |
A Simple Decentralized Cross-Entropy Method | 0 | neurips | 0 | 0 | 2023-06-16 23:00:33.135000 | https://github.com/vincentzhang/decentcem | 2 | A Simple Decentralized Cross-Entropy Method | https://scholar.google.com/scholar?cluster=11544076991942656328&hl=en&as_sdt=0,5 | 2 | 2,022 |
MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification | 0 | neurips | 1 | 0 | 2023-06-16 23:00:33.347000 | https://github.com/hciilab/msds | 28 | MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification | https://scholar.google.com/scholar?cluster=16618815475951417675&hl=en&as_sdt=0,19 | 2 | 2,022 |
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks | 1 | neurips | 2 | 1 | 2023-06-16 23:00:33.559000 | https://github.com/guanjiyang/sac | 9 | Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks | https://scholar.google.com/scholar?cluster=1273042545223201349&hl=en&as_sdt=0,5 | 1 | 2,022 |
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning | 4 | neurips | 1 | 0 | 2023-06-16 23:00:33.780000 | https://github.com/t6-thu/H2O | 42 | When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning | https://scholar.google.com/scholar?cluster=17890075669123951660&hl=en&as_sdt=0,25 | 2 | 2,022 |
Data-Efficient Structured Pruning via Submodular Optimization | 2 | neurips | 2 | 0 | 2023-06-16 23:00:33.993000 | https://github.com/marwash25/subpruning | 5 | Data-efficient structured pruning via submodular optimization | https://scholar.google.com/scholar?cluster=16143049953682779562&hl=en&as_sdt=0,33 | 1 | 2,022 |
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification | 4 | neurips | 1 | 0 | 2023-06-16 23:00:34.205000 | https://github.com/mpatacchiola/contextual-squeeze-and-excitation | 21 | Contextual squeeze-and-excitation for efficient few-shot image classification | https://scholar.google.com/scholar?cluster=12106343171515681246&hl=en&as_sdt=0,5 | 3 | 2,022 |
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation | 42 | neurips | 4 | 1 | 2023-06-16 23:00:34.418000 | https://github.com/jiyuanfeng/amos2022 | 16 | Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation | https://scholar.google.com/scholar?cluster=10453212939134874202&hl=en&as_sdt=0,5 | 3 | 2,022 |
Scalable Interpretability via Polynomials | 8 | neurips | 11 | 2 | 2023-06-16 23:00:34.630000 | https://github.com/facebookresearch/nbm-spam | 67 | Scalable Interpretability via Polynomials | https://scholar.google.com/scholar?cluster=11992772218251377209&hl=en&as_sdt=0,33 | 7 | 2,022 |
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes | 21 | neurips | 10 | 2 | 2023-06-16 23:00:34.843000 | https://github.com/showlab/devrf | 158 | Devrf: Fast deformable voxel radiance fields for dynamic scenes | https://scholar.google.com/scholar?cluster=11949927249170979085&hl=en&as_sdt=0,23 | 9 | 2,022 |
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints | 12 | neurips | 2 | 2 | 2023-06-16 23:00:35.054000 | https://github.com/heathcliff-saku/viewfool_ | 17 | ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints | https://scholar.google.com/scholar?cluster=4486454263174539234&hl=en&as_sdt=0,33 | 1 | 2,022 |
Latency-aware Spatial-wise Dynamic Networks | 2 | neurips | 1 | 0 | 2023-06-16 23:00:35.272000 | https://github.com/leaplabthu/lasnet | 9 | Latency-aware Spatial-wise Dynamic Networks | https://scholar.google.com/scholar?cluster=7885868681172675457&hl=en&as_sdt=0,21 | 2 | 2,022 |
Towards Versatile Embodied Navigation | 1 | neurips | 1 | 0 | 2023-06-16 23:00:35.493000 | https://github.com/hanqingwangai/vxn | 14 | Towards versatile embodied navigation | https://scholar.google.com/scholar?cluster=1358245884279440150&hl=en&as_sdt=0,10 | 3 | 2,022 |
Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes | 0 | neurips | 1 | 0 | 2023-06-16 23:00:35.706000 | https://github.com/griver/memup | 0 | Explain My Surprise: Learning Efficient Long-Term Memory by Predicting Uncertain Outcomes | https://scholar.google.com/scholar?cluster=14873450018728548996&hl=en&as_sdt=0,5 | 1 | 2,022 |
Transformers from an Optimization Perspective | 8 | neurips | 0 | 0 | 2023-06-16 23:00:35.917000 | https://github.com/fftyyy/transformers-from-optimization | 2 | Transformers from an optimization perspective | https://scholar.google.com/scholar?cluster=3271621775430662676&hl=en&as_sdt=0,23 | 1 | 2,022 |
Amortized Projection Optimization for Sliced Wasserstein Generative Models | 13 | neurips | 0 | 0 | 2023-06-16 23:00:36.129000 | https://github.com/ut-austin-data-science-group/amortizedsw | 7 | Amortized projection optimization for sliced Wasserstein generative models | https://scholar.google.com/scholar?cluster=4767006857593439261&hl=en&as_sdt=0,33 | 0 | 2,022 |
DART: Articulated Hand Model with Diverse Accessories and Rich Textures | 5 | neurips | 7 | 2 | 2023-06-16 23:00:36.342000 | https://github.com/DART2022/DART | 97 | DART: Articulated Hand Model with Diverse Accessories and Rich Textures | https://scholar.google.com/scholar?cluster=7571309201531991447&hl=en&as_sdt=0,5 | 3 | 2,022 |
BadPrompt: Backdoor Attacks on Continuous Prompts | 5 | neurips | 1 | 2 | 2023-06-16 23:00:36.555000 | https://github.com/paperspapers/badprompt | 17 | Badprompt: Backdoor attacks on continuous prompts | https://scholar.google.com/scholar?cluster=12437827439430094599&hl=en&as_sdt=0,7 | 1 | 2,022 |
Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding | 3 | neurips | 2 | 0 | 2023-06-16 23:00:36.766000 | https://github.com/eslambakr/LAR-Look-Around-and-Refer | 17 | Look around and refer: 2d synthetic semantics knowledge distillation for 3d visual grounding | https://scholar.google.com/scholar?cluster=4825555452150751793&hl=en&as_sdt=0,33 | 2 | 2,022 |
DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body | 1 | neurips | 0 | 0 | 2023-06-16 23:00:36.978000 | https://github.com/amathislab/dmap | 13 | DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body | https://scholar.google.com/scholar?cluster=17998464088526482192&hl=en&as_sdt=0,5 | 1 | 2,022 |
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks | 5 | neurips | 1 | 1 | 2023-06-16 23:00:37.190000 | https://github.com/1170300521/DiFa | 37 | Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks | https://scholar.google.com/scholar?cluster=15242817500819796035&hl=en&as_sdt=0,47 | 3 | 2,022 |
Deep Bidirectional Language-Knowledge Graph Pretraining | 28 | neurips | 29 | 1 | 2023-06-16 23:00:37.402000 | https://github.com/michiyasunaga/dragon | 201 | Deep bidirectional language-knowledge graph pretraining | https://scholar.google.com/scholar?cluster=3831570526448132220&hl=en&as_sdt=0,47 | 5 | 2,022 |
A Theoretical Framework for Inference Learning | 3 | neurips | 0 | 0 | 2023-06-16 23:00:37.613000 | https://github.com/nalonso2/iltheory | 1 | A theoretical framework for inference learning | https://scholar.google.com/scholar?cluster=2593807461259318440&hl=en&as_sdt=0,11 | 1 | 2,022 |
Cross-modal Learning for Image-Guided Point Cloud Shape Completion | 1 | neurips | 4 | 3 | 2023-06-16 23:00:37.825000 | https://github.com/diegovalsesia/xmfnet | 21 | Cross-modal Learning for Image-Guided Point Cloud Shape Completion | https://scholar.google.com/scholar?cluster=8948872736066673993&hl=en&as_sdt=0,33 | 4 | 2,022 |
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning | 2 | neurips | 1 | 1 | 2023-06-16 23:00:38.037000 | https://github.com/uoe-agents/robust_onpolicy_data_collection | 3 | Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning | https://scholar.google.com/scholar?cluster=1440594365083807013&hl=en&as_sdt=0,47 | 2 | 2,022 |
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection | 6 | neurips | 3 | 0 | 2023-06-16 23:00:38.249000 | https://github.com/jacobyuan7/rlip | 49 | RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection | https://scholar.google.com/scholar?cluster=15237439848602268466&hl=en&as_sdt=0,10 | 4 | 2,022 |
The Implicit Delta Method | 123 | neurips | 0 | 0 | 2023-06-16 23:00:38.461000 | https://github.com/jamesmcinerney/implicit-delta | 1 | A delta method for implicitly defined random variables | https://scholar.google.com/scholar?cluster=4313312882856489116&hl=en&as_sdt=0,5 | 1 | 2,022 |
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning | 11 | neurips | 5 | 0 | 2023-06-16 23:00:38.674000 | https://github.com/syp2ysy/SVF | 58 | Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning | https://scholar.google.com/scholar?cluster=12823222114383862400&hl=en&as_sdt=0,34 | 3 | 2,022 |
On the relationship between variational inference and auto-associative memory | 0 | neurips | 0 | 0 | 2023-06-16 23:00:38.887000 | https://github.com/sino7/predictive_coding_associative_memories | 3 | On the Relationship Between Variational Inference and Auto-Associative Memory | https://scholar.google.com/scholar?cluster=2785842017536639&hl=en&as_sdt=0,10 | 1 | 2,022 |
A Closer Look at Weakly-Supervised Audio-Visual Source Localization | 12 | neurips | 3 | 3 | 2023-06-16 23:00:39.098000 | https://github.com/stonemo/slavc | 10 | A Closer Look at Weakly-Supervised Audio-Visual Source Localization | https://scholar.google.com/scholar?cluster=13873896709239769203&hl=en&as_sdt=0,16 | 3 | 2,022 |
Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion | 1 | neurips | 0 | 0 | 2023-06-16 23:00:39.310000 | https://github.com/hong-ming/scaledsgd | 0 | Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion | https://scholar.google.com/scholar?cluster=505692052743334879&hl=en&as_sdt=0,33 | 2 | 2,022 |
Lifting Weak Supervision To Structured Prediction | 0 | neurips | 0 | 0 | 2023-06-16 23:00:39.522000 | https://github.com/sprocketlab/ws-structured-prediction | 2 | Lifting Weak Supervision To Structured Prediction | https://scholar.google.com/scholar?cluster=17266476389711347506&hl=en&as_sdt=0,31 | 4 | 2,022 |
A Lagrangian Duality Approach to Active Learning | 6 | neurips | 0 | 0 | 2023-06-16 23:00:39.735000 | https://github.com/juanelenter/ally | 2 | A lagrangian duality approach to active learning | https://scholar.google.com/scholar?cluster=11681313256965630916&hl=en&as_sdt=0,5 | 2 | 2,022 |
Understanding the Failure of Batch Normalization for Transformers in NLP | 0 | neurips | 0 | 0 | 2023-06-16 23:00:39.947000 | https://github.com/wjxts/regularizedbn | 13 | Understanding the Failure of Batch Normalization for Transformers in NLP | https://scholar.google.com/scholar?cluster=6560684434761979086&hl=en&as_sdt=0,5 | 2 | 2,022 |
Exploration via Elliptical Episodic Bonuses | 6 | neurips | 9 | 0 | 2023-06-16 23:00:40.159000 | https://github.com/facebookresearch/e3b | 66 | Exploration via Elliptical Episodic Bonuses | https://scholar.google.com/scholar?cluster=2613239820780112903&hl=en&as_sdt=0,14 | 8 | 2,022 |
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup | 4 | neurips | 0 | 0 | 2023-06-16 23:00:40.371000 | https://github.com/tencentailabhealthcare/umix | 10 | Umix: Improving importance weighting for subpopulation shift via uncertainty-aware mixup | https://scholar.google.com/scholar?cluster=9446890541395197883&hl=en&as_sdt=0,33 | 2 | 2,022 |
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging | 25 | neurips | 70 | 0 | 2023-06-16 23:00:40.583000 | https://github.com/caiyuanhao1998/MST | 386 | Degradation-aware unfolding half-shuffle transformer for spectral compressive imaging | https://scholar.google.com/scholar?cluster=7746611837210116803&hl=en&as_sdt=0,5 | 7 | 2,022 |
FLAIR: Federated Learning Annotated Image Repository | 1 | neurips | 11 | 0 | 2023-06-16 23:00:40.795000 | https://github.com/apple/ml-flair | 62 | FLAIR: Federated Learning Annotated Image Repository | https://scholar.google.com/scholar?cluster=3690272585566553585&hl=en&as_sdt=0,15 | 8 | 2,022 |
Detecting Abrupt Changes in Sequential Pairwise Comparison Data | 1 | neurips | 0 | 1 | 2023-06-16 23:00:41.007000 | https://github.com/mountlee/cpd_bt | 0 | Detecting Abrupt Changes in Sequential Pairwise Comparison Data | https://scholar.google.com/scholar?cluster=6701386184904567179&hl=en&as_sdt=0,32 | 1 | 2,022 |
Rethinking Resolution in the Context of Efficient Video Recognition | 3 | neurips | 1 | 0 | 2023-06-16 23:00:41.220000 | https://github.com/cvmi-lab/reskd | 28 | Rethinking resolution in the context of efficient video recognition | https://scholar.google.com/scholar?cluster=9701240362700437697&hl=en&as_sdt=0,33 | 4 | 2,022 |
Deep Equilibrium Approaches to Diffusion Models | 2 | neurips | 3 | 0 | 2023-06-16 23:00:41.432000 | https://github.com/locuslab/deq-ddim | 50 | Deep equilibrium approaches to diffusion models | https://scholar.google.com/scholar?cluster=14854015404116338033&hl=en&as_sdt=0,5 | 2 | 2,022 |
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network? | 23 | neurips | 3 | 2 | 2023-06-16 23:00:41.644000 | https://github.com/NeuralCollapseApplications/ImbalancedLearning | 29 | Do we really need a learnable classifier at the end of deep neural network? | https://scholar.google.com/scholar?cluster=13915965631648718729&hl=en&as_sdt=0,3 | 1 | 2,022 |
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation | 9 | neurips | 1 | 3 | 2023-06-16 23:00:41.855000 | https://github.com/liuyuanwei98/ipmt | 15 | Intermediate prototype mining transformer for few-shot semantic segmentation | https://scholar.google.com/scholar?cluster=9369835073666589032&hl=en&as_sdt=0,10 | 2 | 2,022 |
Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning | 8 | neurips | 13 | 0 | 2023-06-16 23:00:42.067000 | https://github.com/microsoft/xpretrain | 290 | Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning | https://scholar.google.com/scholar?cluster=14516544053429726965&hl=en&as_sdt=0,21 | 13 | 2,022 |
Neural Conservation Laws: A Divergence-Free Perspective | 8 | neurips | 1 | 0 | 2023-06-16 23:00:42.278000 | https://github.com/facebookresearch/neural-conservation-law | 29 | Neural conservation laws: A divergence-free perspective | https://scholar.google.com/scholar?cluster=11358706941570605831&hl=en&as_sdt=0,5 | 3 | 2,022 |
Model Zoos: A Dataset of Diverse Populations of Neural Network Models | 4 | neurips | 0 | 0 | 2023-06-16 23:00:42.507000 | https://github.com/ModelZoos/ModelZooDataset | 21 | Model Zoos: A Dataset of Diverse Populations of Neural Network Models | https://scholar.google.com/scholar?cluster=11134475911805065050&hl=en&as_sdt=0,33 | 3 | 2,022 |
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation | 6 | neurips | 3 | 0 | 2023-06-16 23:00:42.719000 | https://github.com/peihaochen/ws-mgmap | 13 | Weakly-supervised multi-granularity map learning for vision-and-language navigation | https://scholar.google.com/scholar?cluster=10538814385598827849&hl=en&as_sdt=0,5 | 1 | 2,022 |
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination | 2 | neurips | 0 | 0 | 2023-06-16 23:00:42.931000 | https://github.com/dmksjfl/CABI | 2 | Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination | https://scholar.google.com/scholar?cluster=360756721662557774&hl=en&as_sdt=0,47 | 2 | 2,022 |
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy | 8 | neurips | 2 | 0 | 2023-06-16 23:00:43.143000 | https://github.com/woodyx218/private_vision | 4 | Scalable and efficient training of large convolutional neural networks with differential privacy | https://scholar.google.com/scholar?cluster=2508850479410885483&hl=en&as_sdt=0,29 | 2 | 2,022 |
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks | 2 | neurips | 0 | 0 | 2023-06-16 23:00:43.356000 | https://github.com/hongjoon0805/halo | 5 | Descent steps of a relation-aware energy produce heterogeneous graph neural networks | https://scholar.google.com/scholar?cluster=18379331258021041231&hl=en&as_sdt=0,33 | 1 | 2,022 |
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | 27 | neurips | 12 | 0 | 2023-06-16 23:00:43.570000 | https://github.com/Lee-Gihun/FedNTD | 37 | Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | https://scholar.google.com/scholar?cluster=17418553757029920054&hl=en&as_sdt=0,51 | 2 | 2,022 |
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning | 0 | neurips | 0 | 0 | 2023-06-16 23:00:43.783000 | https://github.com/lionellee9089/metamask | 6 | MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning | https://scholar.google.com/scholar?cluster=14621291428401560306&hl=en&as_sdt=0,33 | 1 | 2,022 |
On Feature Learning in the Presence of Spurious Correlations | 15 | neurips | 2 | 0 | 2023-06-16 23:00:43.995000 | https://github.com/izmailovpavel/spurious_feature_learning | 27 | On feature learning in the presence of spurious correlations | https://scholar.google.com/scholar?cluster=8309037915604326672&hl=en&as_sdt=0,5 | 3 | 2,022 |
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection | 4 | neurips | 3 | 1 | 2023-06-16 23:00:44.209000 | https://github.com/stevewongv/sparse2dense | 57 | Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection | https://scholar.google.com/scholar?cluster=13399141401949023952&hl=en&as_sdt=0,5 | 5 | 2,022 |
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation | 75 | neurips | 118 | 58 | 2023-06-16 23:00:44.421000 | https://github.com/vitae-transformer/vitpose | 765 | Vitpose: Simple vision transformer baselines for human pose estimation | https://scholar.google.com/scholar?cluster=9439766841533136382&hl=en&as_sdt=0,5 | 19 | 2,022 |
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules | 4 | neurips | 2 | 0 | 2023-06-16 23:00:44.640000 | https://github.com/idsia/neuraldiffeq-fwp | 13 | Neural differential equations for learning to program neural nets through continuous learning rules | https://scholar.google.com/scholar?cluster=8895930076370351035&hl=en&as_sdt=0,31 | 4 | 2,022 |
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