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Co-evolution Transformer for Protein Contact Prediction
| 8 |
neurips
| 4 | 2 |
2023-06-16 16:06:53.146000
|
https://github.com/microsoft/proteinfolding
| 7 |
Co-evolution transformer for protein contact prediction
|
https://scholar.google.com/scholar?cluster=13689461348782005120&hl=en&as_sdt=0,36
| 3 | 2,021 |
Unsupervised Foreground Extraction via Deep Region Competition
| 17 |
neurips
| 3 | 0 |
2023-06-16 16:06:53.347000
|
https://github.com/yupeiyu98/drc
| 33 |
Unsupervised foreground extraction via deep region competition
|
https://scholar.google.com/scholar?cluster=16513245695011473122&hl=en&as_sdt=0,15
| 2 | 2,021 |
Class-Incremental Learning via Dual Augmentation
| 49 |
neurips
| 4 | 0 |
2023-06-16 16:06:53.546000
|
https://github.com/impression2805/il2a
| 21 |
Class-incremental learning via dual augmentation
|
https://scholar.google.com/scholar?cluster=2287473140272807570&hl=en&as_sdt=0,5
| 1 | 2,021 |
Credal Self-Supervised Learning
| 11 |
neurips
| 1 | 0 |
2023-06-16 16:06:53.747000
|
https://github.com/julilien/cssl
| 6 |
Credal self-supervised learning
|
https://scholar.google.com/scholar?cluster=6910723304890074266&hl=en&as_sdt=0,33
| 2 | 2,021 |
Spot the Difference: Detection of Topological Changes via Geometric Alignment
| 1 |
neurips
| 0 | 1 |
2023-06-16 16:06:53.947000
|
https://github.com/steffenczolbe/topologicalchangedetection
| 3 |
Spot the Difference: Detection of Topological Changes via Geometric Alignment
|
https://scholar.google.com/scholar?cluster=5621165356317670171&hl=en&as_sdt=0,5
| 3 | 2,021 |
A PAC-Bayes Analysis of Adversarial Robustness
| 8 |
neurips
| 0 | 0 |
2023-06-16 16:06:54.150000
|
https://github.com/paulviallard/neurips21-pb-robustness
| 4 |
A pac-bayes analysis of adversarial robustness
|
https://scholar.google.com/scholar?cluster=4965785273710394143&hl=en&as_sdt=0,5
| 1 | 2,021 |
Bayesian Optimization of Function Networks
| 22 |
neurips
| 3 | 0 |
2023-06-16 16:06:54.350000
|
https://github.com/raulastudillo06/bofn
| 4 |
Bayesian optimization of function networks
|
https://scholar.google.com/scholar?cluster=4084524116407827795&hl=en&as_sdt=0,5
| 1 | 2,021 |
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
| 33 |
neurips
| 44 | 27 |
2023-06-16 16:06:54.549000
|
https://github.com/decile-team/cords
| 272 |
Retrieve: Coreset selection for efficient and robust semi-supervised learning
|
https://scholar.google.com/scholar?cluster=6090246534903910907&hl=en&as_sdt=0,5
| 10 | 2,021 |
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
| 20 |
neurips
| 4 | 1 |
2023-06-16 16:06:54.749000
|
https://github.com/pkuxmq/ide-fsnn
| 25 |
Training feedback spiking neural networks by implicit differentiation on the equilibrium state
|
https://scholar.google.com/scholar?cluster=6586041422303063440&hl=en&as_sdt=0,5
| 3 | 2,021 |
Online Selective Classification with Limited Feedback
| 5 |
neurips
| 1 | 0 |
2023-06-16 16:06:54.952000
|
https://github.com/anilkagak2/online-selective-classification
| 2 |
Online selective classification with limited feedback
|
https://scholar.google.com/scholar?cluster=15501560290765015507&hl=en&as_sdt=0,1
| 3 | 2,021 |
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
| 30 |
neurips
| 6 | 0 |
2023-06-16 16:06:55.153000
|
https://github.com/nec-research/tf-imle
| 68 |
Implicit MLE: backpropagating through discrete exponential family distributions
|
https://scholar.google.com/scholar?cluster=5081288066118060759&hl=en&as_sdt=0,21
| 6 | 2,021 |
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:06:55.354000
|
https://github.com/csy530216/pg-zoo
| 3 |
On the convergence of prior-guided zeroth-order optimization algorithms
|
https://scholar.google.com/scholar?cluster=1225343765026705119&hl=en&as_sdt=0,5
| 2 | 2,021 |
Topic Modeling Revisited: A Document Graph-based Neural Network Perspective
| 6 |
neurips
| 3 | 0 |
2023-06-16 16:06:55.555000
|
https://github.com/smilesdzgk/gntm
| 9 |
Topic modeling revisited: A document graph-based neural network perspective
|
https://scholar.google.com/scholar?cluster=13478795624326939129&hl=en&as_sdt=0,5
| 1 | 2,021 |
Hard-Attention for Scalable Image Classification
| 18 |
neurips
| 2 | 0 |
2023-06-16 16:06:55.757000
|
https://github.com/Tpap/TNet
| 12 |
Hard-attention for scalable image classification
|
https://scholar.google.com/scholar?cluster=12789329679837374584&hl=en&as_sdt=0,33
| 1 | 2,021 |
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
| 371 |
neurips
| 195 | 12 |
2023-06-16 16:06:55.958000
|
https://github.com/VITA-Group/TransGAN
| 1,549 |
Transgan: Two pure transformers can make one strong gan, and that can scale up
|
https://scholar.google.com/scholar?cluster=13264315013369292854&hl=en&as_sdt=0,5
| 32 | 2,021 |
Characterizing the risk of fairwashing
| 11 |
neurips
| 1 | 0 |
2023-06-16 16:06:56.158000
|
https://github.com/aivodji/characterizing_fairwashing
| 0 |
Characterizing the risk of fairwashing
|
https://scholar.google.com/scholar?cluster=16578546167532201637&hl=en&as_sdt=0,39
| 1 | 2,021 |
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
| 21 |
neurips
| 5 | 0 |
2023-06-16 16:06:56.360000
|
https://github.com/iamkanghyunchoi/qimera
| 24 |
Qimera: Data-free quantization with synthetic boundary supporting samples
|
https://scholar.google.com/scholar?cluster=3050831061991737197&hl=en&as_sdt=0,5
| 3 | 2,021 |
Adversarial Reweighting for Partial Domain Adaptation
| 9 |
neurips
| 1 | 0 |
2023-06-16 16:06:56.563000
|
https://github.com/xjtu-xgu/adversarial-reweighting-for-partial-domain-adaptation
| 16 |
Adversarial reweighting for partial domain adaptation
|
https://scholar.google.com/scholar?cluster=285607461307195622&hl=en&as_sdt=0,36
| 1 | 2,021 |
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information
| 22 |
neurips
| 0 | 0 |
2023-06-16 16:06:56.763000
|
https://github.com/IST-DASLab/M-FAC
| 11 |
M-fac: Efficient matrix-free approximations of second-order information
|
https://scholar.google.com/scholar?cluster=17606620249219904066&hl=en&as_sdt=0,14
| 5 | 2,021 |
Anti-Backdoor Learning: Training Clean Models on Poisoned Data
| 91 |
neurips
| 8 | 0 |
2023-06-16 16:06:56.963000
|
https://github.com/bboylyg/abl
| 61 |
Anti-backdoor learning: Training clean models on poisoned data
|
https://scholar.google.com/scholar?cluster=8704631197528357914&hl=en&as_sdt=0,5
| 3 | 2,021 |
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:06:57.164000
|
https://github.com/keunseokim91/lmpbt
| 1 |
Locally most powerful Bayesian test for out-of-distribution detection using deep generative models
|
https://scholar.google.com/scholar?cluster=16483011761242448907&hl=en&as_sdt=0,10
| 1 | 2,021 |
Robust Compressed Sensing MRI with Deep Generative Priors
| 98 |
neurips
| 11 | 2 |
2023-06-16 16:06:57.364000
|
https://github.com/utcsilab/csgm-mri-langevin
| 56 |
Robust compressed sensing mri with deep generative priors
|
https://scholar.google.com/scholar?cluster=13822397892595830206&hl=en&as_sdt=0,45
| 4 | 2,021 |
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions
| 6 |
neurips
| 2 | 0 |
2023-06-16 16:06:57.564000
|
https://github.com/JegZheng/CT-pytorch
| 11 |
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions
|
https://scholar.google.com/scholar?cluster=9036582230777928391&hl=en&as_sdt=0,6
| 2 | 2,021 |
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
| 22 |
neurips
| 3 | 1 |
2023-06-16 16:06:57.764000
|
https://github.com/zecevic-matej/ispn
| 6 |
Interventional sum-product networks: Causal inference with tractable probabilistic models
|
https://scholar.google.com/scholar?cluster=6190649913010207640&hl=en&as_sdt=0,47
| 3 | 2,021 |
PettingZoo: Gym for Multi-Agent Reinforcement Learning
| 145 |
neurips
| 303 | 18 |
2023-06-16 16:06:57.964000
|
https://github.com/Farama-Foundation/PettingZoo
| 1,859 |
Pettingzoo: Gym for multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=13783223934701922919&hl=en&as_sdt=0,33
| 20 | 2,021 |
Decision Transformer: Reinforcement Learning via Sequence Modeling
| 511 |
neurips
| 350 | 23 |
2023-06-16 16:06:58.164000
|
https://github.com/kzl/decision-transformer
| 1,731 |
Decision transformer: Reinforcement learning via sequence modeling
|
https://scholar.google.com/scholar?cluster=7704492432415173786&hl=en&as_sdt=0,5
| 25 | 2,021 |
Probability Paths and the Structure of Predictions over Time
| 0 |
neurips
| 0 | 0 |
2023-06-16 16:06:58.364000
|
https://github.com/itsmrlin/probability-paths
| 1 |
Probability Paths and the Structure of Predictions over Time
|
https://scholar.google.com/scholar?cluster=6857395478545683607&hl=en&as_sdt=0,19
| 1 | 2,021 |
Automorphic Equivalence-aware Graph Neural Network
| 8 |
neurips
| 1 | 0 |
2023-06-16 16:06:58.564000
|
https://github.com/tsinghua-fib-lab/grape
| 3 |
Automorphic equivalence-aware graph neural network
|
https://scholar.google.com/scholar?cluster=8149350483577160238&hl=en&as_sdt=0,22
| 1 | 2,021 |
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems
| 11 |
neurips
| 0 | 0 |
2023-06-16 16:06:58.766000
|
https://github.com/ItaySafran/SGD_condition_number
| 0 |
Random shuffling beats sgd only after many epochs on ill-conditioned problems
|
https://scholar.google.com/scholar?cluster=7972409612167103531&hl=en&as_sdt=0,5
| 1 | 2,021 |
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
| 22 |
neurips
| 0 | 0 |
2023-06-16 16:06:58.965000
|
https://github.com/x-zho14/VRPGE-Sparse-Training
| 4 |
Efficient neural network training via forward and backward propagation sparsification
|
https://scholar.google.com/scholar?cluster=13227182771581567481&hl=en&as_sdt=0,47
| 2 | 2,021 |
Large-Scale Wasserstein Gradient Flows
| 36 |
neurips
| 4 | 0 |
2023-06-16 16:06:59.165000
|
https://github.com/PetrMokrov/Large-Scale-Wasserstein-Gradient-Flows
| 23 |
Large-scale wasserstein gradient flows
|
https://scholar.google.com/scholar?cluster=10744565130766307878&hl=en&as_sdt=0,7
| 4 | 2,021 |
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
| 6 |
neurips
| 3 | 1 |
2023-06-16 16:06:59.365000
|
https://github.com/hengruicai/djl
| 6 |
Deep jump learning for off-policy evaluation in continuous treatment settings
|
https://scholar.google.com/scholar?cluster=6393386215888057987&hl=en&as_sdt=0,11
| 1 | 2,021 |
Attention Approximates Sparse Distributed Memory
| 18 |
neurips
| 2 | 0 |
2023-06-16 16:06:59.565000
|
https://github.com/trentbrick/attention-approximates-sdm
| 17 |
Attention approximates sparse distributed memory
|
https://scholar.google.com/scholar?cluster=18296333632073096000&hl=en&as_sdt=0,5
| 2 | 2,021 |
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
| 4 |
neurips
| 1 | 0 |
2023-06-16 16:06:59.764000
|
https://github.com/clinicalml/finding-decision-heterogeneity-regions
| 3 |
Finding regions of heterogeneity in decision-making via expected conditional covariance
|
https://scholar.google.com/scholar?cluster=3846031101866356923&hl=en&as_sdt=0,39
| 5 | 2,021 |
Identifying and Benchmarking Natural Out-of-Context Prediction Problems
| 4 |
neurips
| 0 | 0 |
2023-06-16 16:06:59.965000
|
https://github.com/dmadras/nooch
| 5 |
Identifying and benchmarking natural out-of-context prediction problems
|
https://scholar.google.com/scholar?cluster=8053844208251353066&hl=en&as_sdt=0,47
| 1 | 2,021 |
Overinterpretation reveals image classification model pathologies
| 34 |
neurips
| 6 | 3 |
2023-06-16 16:07:00.165000
|
https://github.com/gifford-lab/overinterpretation
| 18 |
Overinterpretation reveals image classification model pathologies
|
https://scholar.google.com/scholar?cluster=15064589715025215072&hl=en&as_sdt=0,43
| 2 | 2,021 |
Neural Circuit Synthesis from Specification Patterns
| 11 |
neurips
| 3 | 1 |
2023-06-16 16:07:00.365000
|
https://github.com/reactive-systems/ml2
| 3 |
Neural circuit synthesis from specification patterns
|
https://scholar.google.com/scholar?cluster=14168342810209101010&hl=en&as_sdt=0,5
| 3 | 2,021 |
Federated Multi-Task Learning under a Mixture of Distributions
| 114 |
neurips
| 25 | 0 |
2023-06-16 16:07:00.565000
|
https://github.com/omarfoq/fedem
| 116 |
Federated multi-task learning under a mixture of distributions
|
https://scholar.google.com/scholar?cluster=7523531428975949915&hl=en&as_sdt=0,5
| 3 | 2,021 |
ResT: An Efficient Transformer for Visual Recognition
| 121 |
neurips
| 27 | 10 |
2023-06-16 16:07:00.764000
|
https://github.com/wofmanaf/ResT
| 233 |
Rest: An efficient transformer for visual recognition
|
https://scholar.google.com/scholar?cluster=16023950935157352535&hl=en&as_sdt=0,34
| 6 | 2,021 |
Self-Supervised Learning with Kernel Dependence Maximization
| 35 |
neurips
| 1 | 0 |
2023-06-16 16:07:00.964000
|
https://github.com/deepmind/ssl_hsic
| 33 |
Self-supervised learning with kernel dependence maximization
|
https://scholar.google.com/scholar?cluster=13912402342615870661&hl=en&as_sdt=0,47
| 3 | 2,021 |
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:07:01.164000
|
https://github.com/chung-neuroai-lab/adversarial-manifolds
| 3 |
Neural population geometry reveals the role of stochasticity in robust perception
|
https://scholar.google.com/scholar?cluster=8334152733875926312&hl=en&as_sdt=0,10
| 2 | 2,021 |
Unsupervised Learning of Compositional Energy Concepts
| 33 |
neurips
| 8 | 2 |
2023-06-16 16:07:01.364000
|
https://github.com/yilundu/comet
| 48 |
Unsupervised learning of compositional energy concepts
|
https://scholar.google.com/scholar?cluster=13193016976136899043&hl=en&as_sdt=0,19
| 2 | 2,021 |
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach
| 6 |
neurips
| 1 | 0 |
2023-06-16 16:07:01.564000
|
https://github.com/aabbas90/COPS
| 14 |
Combinatorial optimization for panoptic segmentation: A fully differentiable approach
|
https://scholar.google.com/scholar?cluster=1192610999668447759&hl=en&as_sdt=0,10
| 2 | 2,021 |
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes
| 8 |
neurips
| 1 | 2 |
2023-06-16 16:07:01.764000
|
https://github.com/nam630/acno_mdp
| 3 |
Reinforcement learning with state observation costs in action-contingent noiselessly observable markov decision processes
|
https://scholar.google.com/scholar?cluster=7666336988392135584&hl=en&as_sdt=0,11
| 2 | 2,021 |
Iterative Amortized Policy Optimization
| 19 |
neurips
| 0 | 1 |
2023-06-16 16:07:01.965000
|
https://github.com/joelouismarino/variational_rl
| 16 |
Iterative amortized policy optimization
|
https://scholar.google.com/scholar?cluster=5877339606852616235&hl=en&as_sdt=0,8
| 3 | 2,021 |
Nested Graph Neural Networks
| 69 |
neurips
| 10 | 0 |
2023-06-16 16:07:02.165000
|
https://github.com/muhanzhang/nestedgnn
| 45 |
Nested graph neural networks
|
https://scholar.google.com/scholar?cluster=11431651511469545337&hl=en&as_sdt=0,14
| 1 | 2,021 |
Multimodal and Multilingual Embeddings for Large-Scale Speech Mining
| 14 |
neurips
| 428 | 62 |
2023-06-16 16:07:02.365000
|
https://github.com/facebookresearch/LASER
| 3,327 |
Multimodal and multilingual embeddings for large-scale speech mining
|
https://scholar.google.com/scholar?cluster=2638068290174673175&hl=en&as_sdt=0,33
| 91 | 2,021 |
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
| 7 |
neurips
| 224 | 7 |
2023-06-16 16:07:02.565000
|
https://github.com/jakobrunge/tigramite
| 926 |
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
|
https://scholar.google.com/scholar?cluster=4882214133614420018&hl=en&as_sdt=0,47
| 37 | 2,021 |
A flow-based latent state generative model of neural population responses to natural images
| 9 |
neurips
| 5 | 0 |
2023-06-16 16:07:02.765000
|
https://github.com/sinzlab/bashiri-et-al-2021
| 5 |
A flow-based latent state generative model of neural population responses to natural images
|
https://scholar.google.com/scholar?cluster=11678236070139425319&hl=en&as_sdt=0,5
| 4 | 2,021 |
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
| 84 |
neurips
| 11 | 0 |
2023-06-16 16:07:02.966000
|
https://github.com/zaixizhang/MGSSL
| 82 |
Motif-based graph self-supervised learning for molecular property prediction
|
https://scholar.google.com/scholar?cluster=18172966297950947391&hl=en&as_sdt=0,41
| 2 | 2,021 |
On Inductive Biases for Heterogeneous Treatment Effect Estimation
| 31 |
neurips
| 16 | 1 |
2023-06-16 16:07:03.166000
|
https://github.com/AliciaCurth/CATENets
| 80 |
On inductive biases for heterogeneous treatment effect estimation
|
https://scholar.google.com/scholar?cluster=8065378932248670082&hl=en&as_sdt=0,33
| 1 | 2,021 |
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
| 129 |
neurips
| 4 | 1 |
2023-06-16 16:07:03.366000
|
https://github.com/susheels/adgcl
| 68 |
Adversarial graph augmentation to improve graph contrastive learning
|
https://scholar.google.com/scholar?cluster=8871306304913199720&hl=en&as_sdt=0,5
| 2 | 2,021 |
Contrastive Reinforcement Learning of Symbolic Reasoning Domains
| 8 |
neurips
| 3 | 1 |
2023-06-16 16:07:03.567000
|
https://github.com/gpoesia/socratic-tutor
| 6 |
Contrastive reinforcement learning of symbolic reasoning domains
|
https://scholar.google.com/scholar?cluster=17064760670691302458&hl=en&as_sdt=0,34
| 3 | 2,021 |
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework
| 5 |
neurips
| 1 | 0 |
2023-06-16 16:07:03.767000
|
https://github.com/tengteng95/spatial_ensemble
| 18 |
Spatial ensemble: a novel model smoothing mechanism for student-teacher framework
|
https://scholar.google.com/scholar?cluster=16762456942955743613&hl=en&as_sdt=0,39
| 2 | 2,021 |
Probabilistic Tensor Decomposition of Neural Population Spiking Activity
| 1 |
neurips
| 0 | 1 |
2023-06-16 16:07:03.966000
|
https://github.com/hugosou/vbgcp
| 6 |
Probabilistic tensor decomposition of neural population spiking activity
|
https://scholar.google.com/scholar?cluster=10421872540300649808&hl=en&as_sdt=0,33
| 1 | 2,021 |
Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification
| 11 |
neurips
| 0 | 2 |
2023-06-16 16:07:04.177000
|
https://github.com/waltergerych/rbcc
| 1 |
Recurrent bayesian classifier chains for exact multi-label classification
|
https://scholar.google.com/scholar?cluster=4029419628080987406&hl=en&as_sdt=0,5
| 1 | 2,021 |
Adversarial Attack Generation Empowered by Min-Max Optimization
| 17 |
neurips
| 5 | 0 |
2023-06-16 16:07:04.420000
|
https://github.com/wangjksjtu/minmax-adv
| 13 |
Adversarial attack generation empowered by min-max optimization
|
https://scholar.google.com/scholar?cluster=2026570449907320771&hl=en&as_sdt=0,10
| 2 | 2,021 |
Safe Pontryagin Differentiable Programming
| 18 |
neurips
| 6 | 0 |
2023-06-16 16:07:04.639000
|
https://github.com/wanxinjin/Safe-PDP
| 52 |
Safe pontryagin differentiable programming
|
https://scholar.google.com/scholar?cluster=9197004349873168467&hl=en&as_sdt=0,4
| 2 | 2,021 |
Active 3D Shape Reconstruction from Vision and Touch
| 16 |
neurips
| 9 | 1 |
2023-06-16 16:07:04.839000
|
https://github.com/facebookresearch/Active-3D-Vision-and-Touch
| 19 |
Active 3D shape reconstruction from vision and touch
|
https://scholar.google.com/scholar?cluster=15734454491754654805&hl=en&as_sdt=0,5
| 6 | 2,021 |
DualNet: Continual Learning, Fast and Slow
| 60 |
neurips
| 7 | 0 |
2023-06-16 16:07:05.039000
|
https://github.com/phquang/DualNet
| 47 |
Dualnet: Continual learning, fast and slow
|
https://scholar.google.com/scholar?cluster=7928893258137916324&hl=en&as_sdt=0,5
| 2 | 2,021 |
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
| 6 |
neurips
| 1 | 1 |
2023-06-16 16:07:05.239000
|
https://github.com/ruilin0212/debut
| 11 |
Deformable butterfly: A highly structured and sparse linear transform
|
https://scholar.google.com/scholar?cluster=2028959433486626192&hl=en&as_sdt=0,10
| 1 | 2,021 |
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
| 43 |
neurips
| 1 | 0 |
2023-06-16 16:07:05.439000
|
https://github.com/sangmichaelxie/pretraining_analysis
| 5 |
Why do pretrained language models help in downstream tasks? an analysis of head and prompt tuning
|
https://scholar.google.com/scholar?cluster=9072064632949074229&hl=en&as_sdt=0,5
| 2 | 2,021 |
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
| 26 |
neurips
| 1 | 0 |
2023-06-16 16:07:05.639000
|
https://github.com/TLMichael/Delusive-Adversary
| 30 |
Better safe than sorry: Preventing delusive adversaries with adversarial training
|
https://scholar.google.com/scholar?cluster=3520870120153676720&hl=en&as_sdt=0,18
| 2 | 2,021 |
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
| 34 |
neurips
| 11 | 0 |
2023-06-16 16:07:05.839000
|
https://github.com/wvangansbeke/Revisiting-Contrastive-SSL
| 85 |
Revisiting contrastive methods for unsupervised learning of visual representations
|
https://scholar.google.com/scholar?cluster=735436327696041641&hl=en&as_sdt=0,5
| 6 | 2,021 |
Diffusion Normalizing Flow
| 29 |
neurips
| 9 | 2 |
2023-06-16 16:07:06.039000
|
https://github.com/qsh-zh/DiffFlow
| 92 |
Diffusion normalizing flow
|
https://scholar.google.com/scholar?cluster=14357142464181491088&hl=en&as_sdt=0,10
| 3 | 2,021 |
Introspective Distillation for Robust Question Answering
| 23 |
neurips
| 2 | 2 |
2023-06-16 16:07:06.239000
|
https://github.com/yuleiniu/introd
| 13 |
Introspective distillation for robust question answering
|
https://scholar.google.com/scholar?cluster=7828374703675153755&hl=en&as_sdt=0,23
| 1 | 2,021 |
Adaptive Machine Unlearning
| 52 |
neurips
| 1 | 0 |
2023-06-16 16:07:06.439000
|
https://github.com/ChrisWaites/adaptive-machine-unlearning
| 14 |
Adaptive machine unlearning
|
https://scholar.google.com/scholar?cluster=17284958947210206051&hl=en&as_sdt=0,5
| 2 | 2,021 |
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
| 35 |
neurips
| 12 | 3 |
2023-06-16 16:07:06.639000
|
https://github.com/zhuchen03/gradinit
| 127 |
Gradinit: Learning to initialize neural networks for stable and efficient training
|
https://scholar.google.com/scholar?cluster=15488946872563904704&hl=en&as_sdt=0,33
| 4 | 2,021 |
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
| 4 |
neurips
| 2 | 1 |
2023-06-16 16:07:06.838000
|
https://github.com/iesl/geometric_graph_embedding
| 8 |
Capacity and bias of learned geometric embeddings for directed graphs
|
https://scholar.google.com/scholar?cluster=16338786501738019143&hl=en&as_sdt=0,43
| 16 | 2,021 |
Online Learning Of Neural Computations From Sparse Temporal Feedback
| 1 |
neurips
| 0 | 0 |
2023-06-16 16:07:07.039000
|
https://github.com/lukas-braun/learning-neural-computations
| 3 |
Online learning of neural computations from sparse temporal feedback
|
https://scholar.google.com/scholar?cluster=6869448204792358463&hl=en&as_sdt=0,22
| 1 | 2,021 |
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
| 134 |
neurips
| 5 | 0 |
2023-06-16 16:07:07.240000
|
https://github.com/ysharma1126/ssl_identifiability
| 29 |
Self-supervised learning with data augmentations provably isolates content from style
|
https://scholar.google.com/scholar?cluster=7917711258655478976&hl=en&as_sdt=0,5
| 2 | 2,021 |
Instance-Conditional Knowledge Distillation for Object Detection
| 35 |
neurips
| 5 | 0 |
2023-06-16 16:07:07.440000
|
https://github.com/megvii-research/ICD
| 53 |
Instance-conditional knowledge distillation for object detection
|
https://scholar.google.com/scholar?cluster=14282697853463699011&hl=en&as_sdt=0,41
| 5 | 2,021 |
Multimodal Virtual Point 3D Detection
| 78 |
neurips
| 33 | 9 |
2023-06-16 16:07:07.640000
|
https://github.com/tianweiy/MVP
| 236 |
Multimodal virtual point 3d detection
|
https://scholar.google.com/scholar?cluster=4582080155258437560&hl=en&as_sdt=0,5
| 5 | 2,021 |
On Joint Learning for Solving Placement and Routing in Chip Design
| 23 |
neurips
| 31 | 8 |
2023-06-16 16:07:07.839000
|
https://github.com/thinklab-sjtu/eda-ai
| 125 |
On joint learning for solving placement and routing in chip design
|
https://scholar.google.com/scholar?cluster=8601523056294216341&hl=en&as_sdt=0,21
| 6 | 2,021 |
Learning with Algorithmic Supervision via Continuous Relaxations
| 17 |
neurips
| 4 | 1 |
2023-06-16 16:07:08.040000
|
https://github.com/felix-petersen/algovision
| 79 |
Learning with algorithmic supervision via continuous relaxations
|
https://scholar.google.com/scholar?cluster=6447317346907557992&hl=en&as_sdt=0,26
| 2 | 2,021 |
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
| 102 |
neurips
| 184 | 64 |
2023-06-16 16:07:08.239000
|
https://github.com/princeton-vl/droid-slam
| 1,192 |
Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras
|
https://scholar.google.com/scholar?cluster=6382749367222033389&hl=en&as_sdt=0,5
| 43 | 2,021 |
Few-Shot Object Detection via Association and DIscrimination
| 42 |
neurips
| 1 | 4 |
2023-06-16 16:07:08.441000
|
https://github.com/yhcao6/fadi
| 51 |
Few-shot object detection via association and discrimination
|
https://scholar.google.com/scholar?cluster=251363499415465218&hl=en&as_sdt=0,5
| 4 | 2,021 |
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
| 48 |
neurips
| 7 | 1 |
2023-06-16 16:07:08.640000
|
https://github.com/shiming-chen/hsva
| 20 |
Hsva: Hierarchical semantic-visual adaptation for zero-shot learning
|
https://scholar.google.com/scholar?cluster=1579442632617525911&hl=en&as_sdt=0,14
| 1 | 2,021 |
Low-Rank Subspaces in GANs
| 35 |
neurips
| 4 | 2 |
2023-06-16 16:07:08.840000
|
https://github.com/zhujiapeng/LowRankGAN
| 114 |
Low-rank subspaces in gans
|
https://scholar.google.com/scholar?cluster=12439105830629052103&hl=en&as_sdt=0,44
| 12 | 2,021 |
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
| 31 |
neurips
| 5 | 3 |
2023-06-16 16:07:09.041000
|
https://github.com/jizongFox/Self-paced-Contrastive-Learning
| 19 |
Self-paced contrastive learning for semi-supervised medical image segmentation with meta-labels
|
https://scholar.google.com/scholar?cluster=410593789873583327&hl=en&as_sdt=0,39
| 2 | 2,021 |
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
| 8 |
neurips
| 1 | 0 |
2023-06-16 16:07:09.241000
|
https://github.com/jimmysmith1919/jslds_public
| 8 |
Reverse engineering recurrent neural networks with jacobian switching linear dynamical systems
|
https://scholar.google.com/scholar?cluster=9142246519482264&hl=en&as_sdt=0,44
| 1 | 2,021 |
Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds
| 6 |
neurips
| 0 | 0 |
2023-06-16 16:07:09.442000
|
https://github.com/adampolak/dpm
| 1 |
Learning-augmented dynamic power management with multiple states via new ski rental bounds
|
https://scholar.google.com/scholar?cluster=5423257059528807595&hl=en&as_sdt=0,5
| 2 | 2,021 |
Large-Scale Unsupervised Object Discovery
| 28 |
neurips
| 2 | 1 |
2023-06-16 16:07:09.642000
|
https://github.com/huyvvo/LOD
| 19 |
Large-scale unsupervised object discovery
|
https://scholar.google.com/scholar?cluster=15236204020494676594&hl=en&as_sdt=0,5
| 4 | 2,021 |
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
| 8 |
neurips
| 3 | 0 |
2023-06-16 16:07:09.842000
|
https://github.com/gorilla-lab-scut/ss-conv
| 30 |
Sparse steerable convolutions: An efficient learning of se (3)-equivariant features for estimation and tracking of object poses in 3d space
|
https://scholar.google.com/scholar?cluster=7745307057738038854&hl=en&as_sdt=0,10
| 3 | 2,021 |
On Linear Stability of SGD and Input-Smoothness of Neural Networks
| 23 |
neurips
| 2 | 1 |
2023-06-16 16:07:10.041000
|
https://github.com/ChaoMa93/Sobolev-Reg-of-SGD
| 6 |
On linear stability of sgd and input-smoothness of neural networks
|
https://scholar.google.com/scholar?cluster=8707145438646691678&hl=en&as_sdt=0,20
| 1 | 2,021 |
Joint inference and input optimization in equilibrium networks
| 8 |
neurips
| 0 | 1 |
2023-06-16 16:07:10.243000
|
https://github.com/locuslab/jiio-deq
| 8 |
Joint inference and input optimization in equilibrium networks
|
https://scholar.google.com/scholar?cluster=16212650449337646631&hl=en&as_sdt=0,44
| 4 | 2,021 |
A unified framework for bandit multiple testing
| 7 |
neurips
| 1 | 0 |
2023-06-16 16:07:10.443000
|
https://github.com/neilzxu/e_bmt
| 0 |
A unified framework for bandit multiple testing
|
https://scholar.google.com/scholar?cluster=24267533719859223&hl=en&as_sdt=0,22
| 1 | 2,021 |
Recovering Latent Causal Factor for Generalization to Distributional Shifts
| 18 |
neurips
| 4 | 1 |
2023-06-16 16:07:10.642000
|
https://github.com/wubotong/lacim
| 20 |
Recovering latent causal factor for generalization to distributional shifts
|
https://scholar.google.com/scholar?cluster=13967586791355289063&hl=en&as_sdt=0,22
| 1 | 2,021 |
Adversarial Neuron Pruning Purifies Backdoored Deep Models
| 84 |
neurips
| 10 | 1 |
2023-06-16 16:07:10.842000
|
https://github.com/csdongxian/anp_backdoor
| 36 |
Adversarial neuron pruning purifies backdoored deep models
|
https://scholar.google.com/scholar?cluster=6050825940162092618&hl=en&as_sdt=0,5
| 2 | 2,021 |
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
| 18 |
neurips
| 4 | 2 |
2023-06-16 16:07:11.042000
|
https://github.com/caesarcai/lrpca
| 11 |
Learned robust pca: A scalable deep unfolding approach for high-dimensional outlier detection
|
https://scholar.google.com/scholar?cluster=8055867094753250128&hl=en&as_sdt=0,14
| 2 | 2,021 |
Dynamic Bottleneck for Robust Self-Supervised Exploration
| 13 |
neurips
| 1 | 0 |
2023-06-16 16:07:11.242000
|
https://github.com/baichenjia/db
| 4 |
Dynamic bottleneck for robust self-supervised exploration
|
https://scholar.google.com/scholar?cluster=11409187468169077186&hl=en&as_sdt=0,5
| 2 | 2,021 |
ProTo: Program-Guided Transformer for Program-Guided Tasks
| 21 |
neurips
| 1 | 0 |
2023-06-16 16:07:11.442000
|
https://github.com/sjtuytc/Neurips21-ProTo-Program-guided-Transformers-for-Program-guided-Tasks
| 20 |
Proto: Program-guided transformer for program-guided tasks
|
https://scholar.google.com/scholar?cluster=17831895146124544328&hl=en&as_sdt=0,24
| 2 | 2,021 |
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
| 10 |
neurips
| 5 | 0 |
2023-06-16 16:07:11.642000
|
https://github.com/tianpeiyang/maptf_code
| 10 |
An efficient transfer learning framework for multiagent reinforcement learning
|
https://scholar.google.com/scholar?cluster=982889218338734274&hl=en&as_sdt=0,34
| 3 | 2,021 |
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform
| 11 |
neurips
| 1 | 0 |
2023-06-16 16:07:11.842000
|
https://github.com/Achillethin/NEO_non_equilibrium_sampling
| 0 |
Neo: Non equilibrium sampling on the orbits of a deterministic transform
|
https://scholar.google.com/scholar?cluster=17961771076980989561&hl=en&as_sdt=0,5
| 1 | 2,021 |
Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer
| 24 |
neurips
| 60 | 10 |
2023-06-16 16:07:12.043000
|
https://github.com/microsoft/mup
| 866 |
Tuning large neural networks via zero-shot hyperparameter transfer
|
https://scholar.google.com/scholar?cluster=7493984337771588112&hl=en&as_sdt=0,36
| 26 | 2,021 |
Differentiable Simulation of Soft Multi-body Systems
| 25 |
neurips
| 1 | 0 |
2023-06-16 16:07:12.243000
|
https://github.com/yilingqiao/diff_fem
| 33 |
Differentiable simulation of soft multi-body systems
|
https://scholar.google.com/scholar?cluster=9841721368314533190&hl=en&as_sdt=0,5
| 5 | 2,021 |
Good Classification Measures and How to Find Them
| 11 |
neurips
| 0 | 0 |
2023-06-16 16:07:12.443000
|
https://github.com/yandex-research/classification-measures
| 7 |
Good classification measures and how to find them
|
https://scholar.google.com/scholar?cluster=11404788536905460119&hl=en&as_sdt=0,13
| 0 | 2,021 |
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
| 16 |
neurips
| 1 | 1 |
2023-06-16 16:07:12.644000
|
https://github.com/ByungKwanLee/Adversarial-Information-Bottleneck
| 41 |
Distilling robust and non-robust features in adversarial examples by information bottleneck
|
https://scholar.google.com/scholar?cluster=5846557975157001548&hl=en&as_sdt=0,21
| 2 | 2,021 |
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
| 36 |
neurips
| 5 | 0 |
2023-06-16 16:07:12.844000
|
https://github.com/korawat-tanwisuth/proto_da
| 38 |
A prototype-oriented framework for unsupervised domain adaptation
|
https://scholar.google.com/scholar?cluster=13706347291358706428&hl=en&as_sdt=0,33
| 1 | 2,021 |
Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation
| 1 |
neurips
| 0 | 1 |
2023-06-16 16:07:13.044000
|
https://github.com/sunyinggilly/voten
| 2 |
Discerning decision-making process of deep neural networks with hierarchical voting transformation
|
https://scholar.google.com/scholar?cluster=11689963681509960475&hl=en&as_sdt=0,5
| 1 | 2,021 |
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