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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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