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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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