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Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update | 65 | neurips | 3 | 0 | 2023-06-15 23:44:42.445000 | https://github.com/suyoung-lee/Episodic-Backward-Update | 16 | Sample-efficient deep reinforcement learning via episodic backward update | https://scholar.google.com/scholar?cluster=4339423520544824474&hl=en&as_sdt=0,31 | 1 | 2,019 |
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior | 143 | neurips | 14 | 11 | 2023-06-15 23:44:42.628000 | https://github.com/chengchunhsu/WSIS_BBTP | 93 | Weakly supervised instance segmentation using the bounding box tightness prior | https://scholar.google.com/scholar?cluster=16279253940935119442&hl=en&as_sdt=0,5 | 9 | 2,019 |
Copula-like Variational Inference | 4 | neurips | 1 | 0 | 2023-06-15 23:44:42.810000 | https://github.com/marcelah/copula-like-vi | 2 | Copula-like variational inference | https://scholar.google.com/scholar?cluster=11673032824816999908&hl=en&as_sdt=0,33 | 1 | 2,019 |
Towards Hardware-Aware Tractable Learning of Probabilistic Models | 7 | neurips | 2 | 0 | 2023-06-15 23:44:42.998000 | https://github.com/laurago894/HwAwareProb | 5 | Towards hardware-aware tractable learning of probabilistic models | https://scholar.google.com/scholar?cluster=3228255888644610265&hl=en&as_sdt=0,5 | 3 | 2,019 |
Incremental Few-Shot Learning with Attention Attractor Networks | 167 | neurips | 27 | 8 | 2023-06-15 23:44:43.181000 | https://github.com/renmengye/inc-few-shot-attractor-public | 115 | Incremental few-shot learning with attention attractor networks | https://scholar.google.com/scholar?cluster=13601757233344695275&hl=en&as_sdt=0,5 | 8 | 2,019 |
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations | 54 | neurips | 2 | 0 | 2023-06-15 23:44:43.363000 | https://github.com/JerryLingjieMei/ADEPT-Model-Release | 18 | Modeling expectation violation in intuitive physics with coarse probabilistic object representations | https://scholar.google.com/scholar?cluster=13697103313826084802&hl=en&as_sdt=0,33 | 10 | 2,019 |
Efficient Convex Relaxations for Streaming PCA | 4 | neurips | 0 | 0 | 2023-06-15 23:44:43.546000 | https://github.com/tmarino2/Streaming_PCA | 1 | Efficient convex relaxations for streaming PCA | https://scholar.google.com/scholar?cluster=4848852433077315561&hl=en&as_sdt=0,11 | 2 | 2,019 |
Deep Model Transferability from Attribution Maps | 48 | neurips | 4 | 1 | 2023-06-15 23:44:43.729000 | https://github.com/zju-vipa/TransferbilityFromAttributionMaps | 19 | Deep model transferability from attribution maps | https://scholar.google.com/scholar?cluster=4823918589598291923&hl=en&as_sdt=0,29 | 5 | 2,019 |
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging | 8 | neurips | 2 | 1 | 2023-06-15 23:44:43.912000 | https://github.com/imagingofthings/DeepWave | 7 | Deepwave: a recurrent neural-network for real-time acoustic imaging | https://scholar.google.com/scholar?cluster=8909154303117580680&hl=en&as_sdt=0,33 | 3 | 2,019 |
Meta Architecture Search | 36 | neurips | 1 | 1 | 2023-06-15 23:44:44.095000 | https://github.com/ashaw596/meta_architecture_search | 21 | Meta architecture search | https://scholar.google.com/scholar?cluster=11889304968518770704&hl=en&as_sdt=0,14 | 3 | 2,019 |
Graph Structured Prediction Energy Networks | 11 | neurips | 1 | 2 | 2023-06-15 23:44:44.277000 | https://github.com/cgraber/GSPEN | 8 | Graph structured prediction energy networks | https://scholar.google.com/scholar?cluster=4956777384539332368&hl=en&as_sdt=0,5 | 3 | 2,019 |
Universal Invariant and Equivariant Graph Neural Networks | 216 | neurips | 4 | 1 | 2023-06-15 23:44:44.460000 | https://github.com/nkeriven/univgnn | 8 | Universal invariant and equivariant graph neural networks | https://scholar.google.com/scholar?cluster=9485621363684643376&hl=en&as_sdt=0,5 | 3 | 2,019 |
PIDForest: Anomaly Detection via Partial Identification | 20 | neurips | 6 | 11 | 2023-06-15 23:44:44.642000 | https://github.com/vatsalsharan/pidforest | 25 | Pidforest: anomaly detection via partial identification | https://scholar.google.com/scholar?cluster=16154054441639592175&hl=en&as_sdt=0,33 | 3 | 2,019 |
Face Reconstruction from Voice using Generative Adversarial Networks | 43 | neurips | 32 | 4 | 2023-06-15 23:44:44.824000 | https://github.com/cmu-mlsp/reconstructing_faces_from_voices | 171 | Face reconstruction from voice using generative adversarial networks | https://scholar.google.com/scholar?cluster=2028677097849623866&hl=en&as_sdt=0,15 | 13 | 2,019 |
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning | 28 | neurips | 10 | 1 | 2023-06-15 23:44:45.007000 | https://github.com/microsoft/logrl | 26 | Using a logarithmic mapping to enable lower discount factors in reinforcement learning | https://scholar.google.com/scholar?cluster=13664515477486389545&hl=en&as_sdt=0,5 | 8 | 2,019 |
PRNet: Self-Supervised Learning for Partial-to-Partial Registration | 263 | neurips | 27 | 7 | 2023-06-15 23:44:45.190000 | https://github.com/WangYueFt/prnet | 104 | Prnet: Self-supervised learning for partial-to-partial registration | https://scholar.google.com/scholar?cluster=2200668442123135001&hl=en&as_sdt=0,5 | 7 | 2,019 |
Learning to Optimize in Swarms | 46 | neurips | 10 | 0 | 2023-06-15 23:44:45.372000 | https://github.com/Shen-Lab/LOIS | 14 | Learning to optimize in swarms | https://scholar.google.com/scholar?cluster=14460959149503655029&hl=en&as_sdt=0,5 | 4 | 2,019 |
A Little Is Enough: Circumventing Defenses For Distributed Learning | 245 | neurips | 6 | 2 | 2023-06-15 23:44:45.555000 | https://github.com/moranant/attacking_distributing_learning | 19 | A little is enough: Circumventing defenses for distributed learning | https://scholar.google.com/scholar?cluster=5802076485972034054&hl=en&as_sdt=0,10 | 2 | 2,019 |
Statistical Model Aggregation via Parameter Matching | 28 | neurips | 5 | 0 | 2023-06-15 23:44:45.738000 | https://github.com/IBM/SPAHM | 6 | Statistical model aggregation via parameter matching | https://scholar.google.com/scholar?cluster=4576666574864292124&hl=en&as_sdt=0,19 | 10 | 2,019 |
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees | 1 | neurips | 1 | 0 | 2023-06-15 23:44:45.923000 | https://github.com/Muhammad-Osama/uncertainty_spatial_point_process | 0 | Prediction of spatial point processes: regularized method with out-of-sample guarantees | https://scholar.google.com/scholar?cluster=14802758782588566999&hl=en&as_sdt=0,33 | 0 | 2,019 |
STREETS: A Novel Camera Network Dataset for Traffic Flow | 22 | neurips | 3 | 5 | 2023-06-15 23:44:46.106000 | https://github.com/corey-snyder/STREETS | 28 | Streets: A novel camera network dataset for traffic flow | https://scholar.google.com/scholar?cluster=12192449479723633961&hl=en&as_sdt=0,5 | 3 | 2,019 |
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions | 54 | neurips | 1 | 0 | 2023-06-15 23:44:46.289000 | https://github.com/cpempire/pSVN | 2 | Projected Stein variational Newton: A fast and scalable Bayesian inference method in high dimensions | https://scholar.google.com/scholar?cluster=5374015985674763908&hl=en&as_sdt=0,5 | 1 | 2,019 |
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction | 54 | neurips | 0 | 0 | 2023-06-15 23:44:46.473000 | https://github.com/ganguli-lab/deep-retina-reduction | 2 | From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction | https://scholar.google.com/scholar?cluster=12641169667609562982&hl=en&as_sdt=0,5 | 16 | 2,019 |
Abstract Reasoning with Distracting Features | 55 | neurips | 4 | 1 | 2023-06-15 23:44:46.655000 | https://github.com/zkcys001/distracting_feature | 25 | Abstract reasoning with distracting features | https://scholar.google.com/scholar?cluster=12802844100612242645&hl=en&as_sdt=0,10 | 3 | 2,019 |
Deep Scale-spaces: Equivariance Over Scale | 126 | neurips | 5 | 1 | 2023-06-15 23:44:46.838000 | https://github.com/deworrall92/deep-scale-spaces | 21 | Deep scale-spaces: Equivariance over scale | https://scholar.google.com/scholar?cluster=5786613009740480936&hl=en&as_sdt=0,5 | 3 | 2,019 |
Generalized Sliced Wasserstein Distances | 202 | neurips | 11 | 0 | 2023-06-15 23:44:47.022000 | https://github.com/kimiandj/gsw | 31 | Generalized sliced wasserstein distances | https://scholar.google.com/scholar?cluster=16864660898326164591&hl=en&as_sdt=0,10 | 2 | 2,019 |
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference | 11 | neurips | 0 | 0 | 2023-06-15 23:44:47.205000 | https://github.com/colehurwitz/vae_spike_localization | 2 | Scalable spike source localization in extracellular recordings using amortized variational inference | https://scholar.google.com/scholar?cluster=7317962235981887067&hl=en&as_sdt=0,5 | 0 | 2,019 |
A General Framework for Symmetric Property Estimation | 10 | neurips | 0 | 0 | 2023-06-15 23:44:47.388000 | https://github.com/shiragur/CodeForPseudoPML | 0 | A general framework for symmetric property estimation | https://scholar.google.com/scholar?cluster=17182237778285852187&hl=en&as_sdt=0,5 | 2 | 2,019 |
CondConv: Conditionally Parameterized Convolutions for Efficient Inference | 384 | neurips | 1,790 | 294 | 2023-06-15 23:44:47.596000 | https://github.com/tensorflow/tpu | 5,127 | Condconv: Conditionally parameterized convolutions for efficient inference | https://scholar.google.com/scholar?cluster=12029837360807310242&hl=en&as_sdt=0,5 | 369 | 2,019 |
Towards a Zero-One Law for Column Subset Selection | 26 | neurips | 0 | 0 | 2023-06-15 23:44:47.779000 | https://github.com/zpl7840/general_loss_column_subset_selection | 0 | Towards a zero-one law for column subset selection | https://scholar.google.com/scholar?cluster=5184402617939346172&hl=en&as_sdt=0,5 | 1 | 2,019 |
Nonzero-sum Adversarial Hypothesis Testing Games | 13 | neurips | 0 | 0 | 2023-06-15 23:44:47.962000 | https://github.com/sarath1789/ahtg_neurips2019 | 0 | Nonzero-sum adversarial hypothesis testing games | https://scholar.google.com/scholar?cluster=2106859842031488052&hl=en&as_sdt=0,6 | 1 | 2,019 |
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks | 143 | neurips | 5 | 2 | 2023-06-15 23:44:48.144000 | https://github.com/DingXiaoH/GSM-SGD | 40 | Global sparse momentum sgd for pruning very deep neural networks | https://scholar.google.com/scholar?cluster=17035988967323546960&hl=en&as_sdt=0,48 | 5 | 2,019 |
Quantum Wasserstein Generative Adversarial Networks | 66 | neurips | 10 | 1 | 2023-06-15 23:44:48.328000 | https://github.com/yiminghwang/qWGAN | 45 | Quantum wasserstein generative adversarial networks | https://scholar.google.com/scholar?cluster=13035971902912722342&hl=en&as_sdt=0,5 | 5 | 2,019 |
Deep Learning without Weight Transport | 105 | neurips | 4 | 1 | 2023-06-15 23:44:48.511000 | https://github.com/makrout/Deep-Learning-without-Weight-Transport | 30 | Deep learning without weight transport | https://scholar.google.com/scholar?cluster=16021016757478630175&hl=en&as_sdt=0,34 | 3 | 2,019 |
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks | 107 | neurips | 0 | 0 | 2023-06-15 23:44:48.693000 | https://github.com/GauthierGidel/Implicit-Regularization-of-Discrete-Gradient-Dynamics-in-Linear-Neural-Networks | 0 | Implicit regularization of discrete gradient dynamics in linear neural networks | https://scholar.google.com/scholar?cluster=3335747216116083173&hl=en&as_sdt=0,14 | 2 | 2,019 |
Generative Models for Graph-Based Protein Design | 271 | neurips | 46 | 7 | 2023-06-15 23:44:48.876000 | https://github.com/jingraham/neurips19-graph-protein-design | 186 | Generative models for graph-based protein design | https://scholar.google.com/scholar?cluster=8179315795887115217&hl=en&as_sdt=0,47 | 7 | 2,019 |
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks | 104 | neurips | 2 | 0 | 2023-06-15 23:44:49.060000 | https://github.com/stonezwr/ST-RSBP | 11 | Spike-train level backpropagation for training deep recurrent spiking neural networks | https://scholar.google.com/scholar?cluster=15180879194749277106&hl=en&as_sdt=0,33 | 1 | 2,019 |
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity | 31 | neurips | 1 | 10 | 2023-06-15 23:44:49.243000 | https://github.com/ariaaay/NeuralTaskonomy | 15 | Neural taskonomy: Inferring the similarity of task-derived representations from brain activity | https://scholar.google.com/scholar?cluster=2336960988937785366&hl=en&as_sdt=0,33 | 2 | 2,019 |
Adaptive Gradient-Based Meta-Learning Methods | 269 | neurips | 1 | 0 | 2023-06-15 23:44:49.426000 | https://github.com/mkhodak/ARUBA | 10 | Adaptive gradient-based meta-learning methods | https://scholar.google.com/scholar?cluster=12829613586326997125&hl=en&as_sdt=0,5 | 1 | 2,019 |
Compositional generalization through meta sequence-to-sequence learning | 153 | neurips | 1 | 0 | 2023-06-15 23:44:49.612000 | https://github.com/brendenlake/meta_seq2seq | 44 | Compositional generalization through meta sequence-to-sequence learning | https://scholar.google.com/scholar?cluster=10650832284960970235&hl=en&as_sdt=0,39 | 8 | 2,019 |
Meta-Learning Representations for Continual Learning | 258 | neurips | 27 | 4 | 2023-06-15 23:44:49.795000 | https://github.com/Khurramjaved96/mrcl | 181 | Meta-learning representations for continual learning | https://scholar.google.com/scholar?cluster=8778557720740141982&hl=en&as_sdt=0,5 | 7 | 2,019 |
A Composable Specification Language for Reinforcement Learning Tasks | 66 | neurips | 4 | 0 | 2023-06-15 23:44:49.978000 | https://github.com/keyshor/spectrl_tool | 10 | A composable specification language for reinforcement learning tasks | https://scholar.google.com/scholar?cluster=11872644767058709693&hl=en&as_sdt=0,5 | 1 | 2,019 |
On the Utility of Learning about Humans for Human-AI Coordination | 190 | neurips | 98 | 4 | 2023-06-15 23:44:50.160000 | https://github.com/HumanCompatibleAI/overcooked_ai | 468 | On the utility of learning about humans for human-ai coordination | https://scholar.google.com/scholar?cluster=17425854259950271984&hl=en&as_sdt=0,11 | 16 | 2,019 |
Park: An Open Platform for Learning-Augmented Computer Systems | 71 | neurips | 43 | 17 | 2023-06-15 23:44:50.343000 | https://github.com/park-project/park | 189 | Park: An open platform for learning-augmented computer systems | https://scholar.google.com/scholar?cluster=11372767626321679465&hl=en&as_sdt=0,33 | 13 | 2,019 |
Compression with Flows via Local Bits-Back Coding | 47 | neurips | 5 | 2 | 2023-06-15 23:44:50.527000 | https://github.com/hojonathanho/localbitsback | 35 | Compression with flows via local bits-back coding | https://scholar.google.com/scholar?cluster=9614859180156738260&hl=en&as_sdt=0,5 | 4 | 2,019 |
On Adversarial Mixup Resynthesis | 54 | neurips | 3 | 1 | 2023-06-15 23:44:50.709000 | https://github.com/christopher-beckham/amr | 32 | On adversarial mixup resynthesis | https://scholar.google.com/scholar?cluster=3310014081611030550&hl=en&as_sdt=0,34 | 4 | 2,019 |
Certifying Geometric Robustness of Neural Networks | 100 | neurips | 5 | 6 | 2023-06-15 23:44:50.893000 | https://github.com/eth-sri/deepg | 15 | Certifying geometric robustness of neural networks | https://scholar.google.com/scholar?cluster=9475017515216465786&hl=en&as_sdt=0,14 | 7 | 2,019 |
MAVEN: Multi-Agent Variational Exploration | 268 | neurips | 21 | 4 | 2023-06-15 23:44:51.076000 | https://github.com/AnujMahajanOxf/MAVEN | 49 | Maven: Multi-agent variational exploration | https://scholar.google.com/scholar?cluster=3641019168324212820&hl=en&as_sdt=0,5 | 6 | 2,019 |
The continuous Bernoulli: fixing a pervasive error in variational autoencoders | 71 | neurips | 7 | 0 | 2023-06-15 23:44:51.260000 | https://github.com/cunningham-lab/cb | 31 | The continuous Bernoulli: fixing a pervasive error in variational autoencoders | https://scholar.google.com/scholar?cluster=13640532786864289225&hl=en&as_sdt=0,5 | 4 | 2,019 |
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters | 17 | neurips | 2 | 0 | 2023-06-15 23:44:51.443000 | https://github.com/albertometelli/wql | 8 | Propagating uncertainty in reinforcement learning via wasserstein barycenters | https://scholar.google.com/scholar?cluster=2109934115378775122&hl=en&as_sdt=0,21 | 2 | 2,019 |
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters | 27 | neurips | 3 | 1 | 2023-06-15 23:44:51.633000 | https://github.com/wokas36/DFNets | 10 | DFNets: Spectral CNNs for graphs with feedback-looped filters | https://scholar.google.com/scholar?cluster=6084314422726553090&hl=en&as_sdt=0,23 | 4 | 2,019 |
Multiclass Learning from Contradictions | 14 | neurips | 1 | 0 | 2023-06-15 23:44:51.816000 | https://github.com/LGE-ARC-AdvancedAI/MU-SVM | 1 | Multiclass learning from contradictions | https://scholar.google.com/scholar?cluster=5633591904420775490&hl=en&as_sdt=0,47 | 1 | 2,019 |
Multi-relational Poincaré Graph Embeddings | 250 | neurips | 30 | 1 | 2023-06-15 23:44:52 | https://github.com/ibalazevic/multirelational-poincare | 150 | Multi-relational poincaré graph embeddings | https://scholar.google.com/scholar?cluster=9000210112086695185&hl=en&as_sdt=0,5 | 7 | 2,019 |
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization | 38 | neurips | 7 | 4 | 2023-06-15 23:44:52.183000 | https://github.com/csyhhu/MetaQuant | 51 | Metaquant: Learning to quantize by learning to penetrate non-differentiable quantization | https://scholar.google.com/scholar?cluster=15883471795192638746&hl=en&as_sdt=0,5 | 5 | 2,019 |
Normalization Helps Training of Quantized LSTM | 39 | neurips | 7 | 2 | 2023-06-15 23:44:52.366000 | https://github.com/houlu369/Normalized-Quantized-LSTM | 27 | Normalization helps training of quantized LSTM | https://scholar.google.com/scholar?cluster=11640994388027903274&hl=en&as_sdt=0,5 | 1 | 2,019 |
Multi-Agent Common Knowledge Reinforcement Learning | 75 | neurips | 7 | 1 | 2023-06-15 23:44:52.550000 | https://github.com/schroederdewitt/mackrl | 31 | Multi-agent common knowledge reinforcement learning | https://scholar.google.com/scholar?cluster=6084747952815676289&hl=en&as_sdt=0,5 | 2 | 2,019 |
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections | 26 | neurips | 1 | 0 | 2023-06-15 23:44:52.733000 | https://github.com/BorisMuzellec/SubspaceOT | 1 | Subspace detours: Building transport plans that are optimal on subspace projections | https://scholar.google.com/scholar?cluster=2998304691038707291&hl=en&as_sdt=0,14 | 2 | 2,019 |
The Broad Optimality of Profile Maximum Likelihood | 26 | neurips | 0 | 0 | 2023-06-15 23:44:52.916000 | https://github.com/ucsdyi/PML | 1 | The broad optimality of profile maximum likelihood | https://scholar.google.com/scholar?cluster=4063268634884804791&hl=en&as_sdt=0,5 | 0 | 2,019 |
Efficient online learning with kernels for adversarial large scale problems | 15 | neurips | 0 | 0 | 2023-06-15 23:44:53.103000 | https://github.com/Remjez/kernel-online-learning | 0 | Efficient online learning with kernels for adversarial large scale problems | https://scholar.google.com/scholar?cluster=424533984591498235&hl=en&as_sdt=0,44 | 1 | 2,019 |
On the Downstream Performance of Compressed Word Embeddings | 23 | neurips | 4 | 0 | 2023-06-15 23:44:53.298000 | https://github.com/HazyResearch/smallfry | 18 | On the downstream performance of compressed word embeddings | https://scholar.google.com/scholar?cluster=10444272090155128399&hl=en&as_sdt=0,5 | 12 | 2,019 |
Primal-Dual Block Generalized Frank-Wolfe | 11 | neurips | 0 | 0 | 2023-06-15 23:44:53.481000 | https://github.com/CarlsonZhuo/primal_dual_frank_wolfe | 1 | Primal-dual block generalized frank-wolfe | https://scholar.google.com/scholar?cluster=4125673740157136146&hl=en&as_sdt=0,48 | 3 | 2,019 |
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models | 37 | neurips | 2 | 0 | 2023-06-15 23:44:53.679000 | https://github.com/sphinxteam/spiked_matrix-tensor_T0 | 2 | Who is afraid of big bad minima? analysis of gradient-flow in spiked matrix-tensor models | https://scholar.google.com/scholar?cluster=5757410499012237876&hl=en&as_sdt=0,5 | 4 | 2,019 |
Differential Privacy Has Disparate Impact on Model Accuracy | 324 | neurips | 11 | 0 | 2023-06-15 23:44:53.862000 | https://github.com/ebagdasa/differential-privacy-vs-fairness | 31 | Differential privacy has disparate impact on model accuracy | https://scholar.google.com/scholar?cluster=4704572033718664713&hl=en&as_sdt=0,44 | 2 | 2,019 |
Fair Algorithms for Clustering | 202 | neurips | 5 | 12 | 2023-06-15 23:44:54.045000 | https://github.com/nicolasjulioflores/fair_algorithms_for_clustering | 10 | Fair algorithms for clustering | https://scholar.google.com/scholar?cluster=15890260769740780525&hl=en&as_sdt=0,34 | 3 | 2,019 |
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers | 17 | neurips | 236 | 513 | 2023-06-15 23:44:54.228000 | https://github.com/zenodo/zenodo | 793 | The cells out of sample (coos) dataset and benchmarks for measuring out-of-sample generalization of image classifiers | https://scholar.google.com/scholar?cluster=664729084222698681&hl=en&as_sdt=0,47 | 42 | 2,019 |
On Tractable Computation of Expected Predictions | 40 | neurips | 2 | 0 | 2023-06-15 23:44:54.411000 | https://github.com/UCLA-StarAI/mc2 | 9 | On tractable computation of expected predictions | https://scholar.google.com/scholar?cluster=7393033356171648134&hl=en&as_sdt=0,5 | 4 | 2,019 |
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering | 20 | neurips | 0 | 1 | 2023-06-15 23:44:54.602000 | https://github.com/Biwei-Huang/Specific-and-Shared-Causal-Relation-Modeling-and-Mechanism-Based-Clustering | 2 | Specific and shared causal relation modeling and mechanism-based clustering | https://scholar.google.com/scholar?cluster=16092569721082830349&hl=en&as_sdt=0,5 | 1 | 2,019 |
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks | 154 | neurips | 13 | 0 | 2023-06-15 23:44:54.786000 | https://github.com/thuml/TransNorm | 73 | Transferable normalization: Towards improving transferability of deep neural networks | https://scholar.google.com/scholar?cluster=9221290800687054760&hl=en&as_sdt=0,5 | 4 | 2,019 |
Semi-Implicit Graph Variational Auto-Encoders | 86 | neurips | 9 | 2 | 2023-06-15 23:44:54.969000 | https://github.com/sigvae/SIGraphVAE | 22 | Semi-implicit graph variational auto-encoders | https://scholar.google.com/scholar?cluster=10588276767934139650&hl=en&as_sdt=0,34 | 1 | 2,019 |
GOT: An Optimal Transport framework for Graph comparison | 77 | neurips | 4 | 0 | 2023-06-15 23:44:55.153000 | https://github.com/Hermina/GOT | 28 | GOT: an optimal transport framework for graph comparison | https://scholar.google.com/scholar?cluster=17969024179191140070&hl=en&as_sdt=0,5 | 3 | 2,019 |
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss | 34 | neurips | 0 | 0 | 2023-06-15 23:44:55.335000 | https://github.com/JunYan65/DRCR_NIPS2019_Code | 0 | Multivariate distributionally robust convex regression under absolute error loss | https://scholar.google.com/scholar?cluster=7458836863119383276&hl=en&as_sdt=0,44 | 1 | 2,019 |
A Benchmark for Interpretability Methods in Deep Neural Networks | 469 | neurips | 7,320 | 1,025 | 2023-06-15 23:44:55.519000 | https://github.com/google-research/google-research | 29,776 | A benchmark for interpretability methods in deep neural networks | https://scholar.google.com/scholar?cluster=1845943296865459984&hl=en&as_sdt=0,33 | 727 | 2,019 |
Zero-shot Knowledge Transfer via Adversarial Belief Matching | 162 | neurips | 18 | 1 | 2023-06-15 23:44:55.702000 | https://github.com/polo5/ZeroShotKnowledgeTransfer | 122 | Zero-shot knowledge transfer via adversarial belief matching | https://scholar.google.com/scholar?cluster=14084992756090695507&hl=en&as_sdt=0,5 | 5 | 2,019 |
Discrete Object Generation with Reversible Inductive Construction | 27 | neurips | 4 | 1 | 2023-06-15 23:44:55.884000 | https://github.com/PrincetonLIPS/reversible-inductive-construction | 29 | Discrete object generation with reversible inductive construction | https://scholar.google.com/scholar?cluster=13201286911892635677&hl=en&as_sdt=0,5 | 3 | 2,019 |
Adaptively Aligned Image Captioning via Adaptive Attention Time | 56 | neurips | 15 | 4 | 2023-06-15 23:44:56.067000 | https://github.com/husthuaan/AAT | 47 | Adaptively aligned image captioning via adaptive attention time | https://scholar.google.com/scholar?cluster=6529707515477430169&hl=en&as_sdt=0,5 | 5 | 2,019 |
Fully Dynamic Consistent Facility Location | 31 | neurips | 0 | 0 | 2023-06-15 23:44:56.250000 | https://github.com/NikosParotsidis/Fully-dynamic_facility_location-NeurIPS2019 | 4 | Fully dynamic consistent facility location | https://scholar.google.com/scholar?cluster=4359801201128958247&hl=en&as_sdt=0,5 | 1 | 2,019 |
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method | 37 | neurips | 6 | 0 | 2023-06-16 15:09:49.371000 | https://github.com/BensonRen/BDIMNNA | 17 | Benchmarking deep inverse models over time, and the neural-adjoint method | https://scholar.google.com/scholar?cluster=10303492890298321577&hl=en&as_sdt=0,6 | 3 | 2,020 |
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift | 24 | neurips | 0 | 0 | 2023-06-16 15:09:49.585000 | https://github.com/MasaKat0/OPE_CS | 1 | Off-policy evaluation and learning for external validity under a covariate shift | https://scholar.google.com/scholar?cluster=11932511552912814820&hl=en&as_sdt=0,5 | 4 | 2,020 |
Neural Methods for Point-wise Dependency Estimation | 22 | neurips | 2 | 0 | 2023-06-16 15:09:49.777000 | https://github.com/yaohungt/Pointwise_Dependency_Neural_Estimation | 17 | Neural methods for point-wise dependency estimation | https://scholar.google.com/scholar?cluster=3025466449129186225&hl=en&as_sdt=0,5 | 4 | 2,020 |
Fast and Flexible Temporal Point Processes with Triangular Maps | 18 | neurips | 7 | 0 | 2023-06-16 15:09:49.969000 | https://github.com/shchur/triangular-tpp | 23 | Fast and flexible temporal point processes with triangular maps | https://scholar.google.com/scholar?cluster=7206682078029107173&hl=en&as_sdt=0,5 | 2 | 2,020 |
Backpropagating Linearly Improves Transferability of Adversarial Examples | 58 | neurips | 5 | 4 | 2023-06-16 15:09:50.161000 | https://github.com/qizhangli/linbp-attack | 39 | Backpropagating linearly improves transferability of adversarial examples | https://scholar.google.com/scholar?cluster=1816302577038884057&hl=en&as_sdt=0,5 | 1 | 2,020 |
Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering | 6 | neurips | 2 | 0 | 2023-06-16 15:09:50.353000 | https://github.com/SoftWiser-group/CFDebug | 6 | Trading personalization for accuracy: Data debugging in collaborative filtering | https://scholar.google.com/scholar?cluster=12342608985408864603&hl=en&as_sdt=0,5 | 7 | 2,020 |
Cascaded Text Generation with Markov Transformers | 11 | neurips | 8 | 0 | 2023-06-16 15:09:50.545000 | https://github.com/harvardnlp/cascaded-generation | 123 | Cascaded text generation with markov transformers | https://scholar.google.com/scholar?cluster=12660981170581586406&hl=en&as_sdt=0,3 | 12 | 2,020 |
Deep reconstruction of strange attractors from time series | 33 | neurips | 28 | 0 | 2023-06-16 15:09:50.737000 | https://github.com/williamgilpin/fnn | 111 | Deep reconstruction of strange attractors from time series | https://scholar.google.com/scholar?cluster=13942188603498560541&hl=en&as_sdt=0,5 | 9 | 2,020 |
Reciprocal Adversarial Learning via Characteristic Functions | 4 | neurips | 2 | 0 | 2023-06-16 15:09:50.929000 | https://github.com/ShengxiLi/rcf_gan | 10 | Reciprocal adversarial learning via characteristic functions | https://scholar.google.com/scholar?cluster=4107964100222082951&hl=en&as_sdt=0,5 | 2 | 2,020 |
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach | 107 | neurips | 4 | 13 | 2023-06-16 15:09:51.120000 | https://github.com/amirhk/recourse | 28 | Algorithmic recourse under imperfect causal knowledge: a probabilistic approach | https://scholar.google.com/scholar?cluster=4986898048327715369&hl=en&as_sdt=0,48 | 5 | 2,020 |
Minimax Classification with 0-1 Loss and Performance Guarantees | 16 | neurips | 1 | 0 | 2023-06-16 15:09:51.312000 | https://github.com/MachineLearningBCAM/Minimax-risk-classifiers-NeurIPS-2020 | 3 | Minimax classification with 0-1 loss and performance guarantees | https://scholar.google.com/scholar?cluster=3844746042992599379&hl=en&as_sdt=0,5 | 1 | 2,020 |
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization | 23 | neurips | 7 | 0 | 2023-06-16 15:09:51.504000 | https://github.com/nnaisense/MAGE | 29 | How to learn a useful critic? Model-based action-gradient-estimator policy optimization | https://scholar.google.com/scholar?cluster=12964689647322845845&hl=en&as_sdt=0,32 | 8 | 2,020 |
Coresets for Regressions with Panel Data | 12 | neurips | 0 | 0 | 2023-06-16 15:09:51.695000 | https://github.com/huanglx12/Coresets-for-regressions-with-panel-data | 0 | Coresets for regressions with panel data | https://scholar.google.com/scholar?cluster=9096294393329532403&hl=en&as_sdt=0,39 | 1 | 2,020 |
Achieving Equalized Odds by Resampling Sensitive Attributes | 23 | neurips | 4 | 0 | 2023-06-16 15:09:51.887000 | https://github.com/yromano/fair_dummies | 3 | Achieving equalized odds by resampling sensitive attributes | https://scholar.google.com/scholar?cluster=6396740997740111580&hl=en&as_sdt=0,39 | 3 | 2,020 |
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates | 52 | neurips | 6 | 1 | 2023-06-16 15:09:52.079000 | https://github.com/wenhaol/PrSBC | 10 | Multi-robot collision avoidance under uncertainty with probabilistic safety barrier certificates | https://scholar.google.com/scholar?cluster=16415958684564416079&hl=en&as_sdt=0,10 | 1 | 2,020 |
Hard Shape-Constrained Kernel Machines | 24 | neurips | 1 | 0 | 2023-06-16 15:09:52.271000 | https://github.com/PCAubin/Hard-Shape-Constraints-for-Kernels | 2 | Hard shape-constrained kernel machines | https://scholar.google.com/scholar?cluster=5312947070123746678&hl=en&as_sdt=0,5 | 1 | 2,020 |
A Closer Look at the Training Strategy for Modern Meta-Learning | 22 | neurips | 0 | 0 | 2023-06-16 15:09:52.463000 | https://github.com/jiaxinchen666/meta-theory | 9 | A closer look at the training strategy for modern meta-learning | https://scholar.google.com/scholar?cluster=1508062348687769372&hl=en&as_sdt=0,36 | 1 | 2,020 |
Flows for simultaneous manifold learning and density estimation | 104 | neurips | 22 | 2 | 2023-06-16 15:09:52.655000 | https://github.com/johannbrehmer/manifold-flow | 217 | Flows for simultaneous manifold learning and density estimation | https://scholar.google.com/scholar?cluster=12827214460848825511&hl=en&as_sdt=0,39 | 8 | 2,020 |
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee | 29 | neurips | 2 | 0 | 2023-06-16 15:09:52.847000 | https://github.com/JinchengBai/sparse-variational-bnn | 4 | Efficient variational inference for sparse deep learning with theoretical guarantee | https://scholar.google.com/scholar?cluster=10814248748579550273&hl=en&as_sdt=0,19 | 1 | 2,020 |
One-bit Supervision for Image Classification | 7 | neurips | 0 | 0 | 2023-06-16 15:09:53.048000 | https://github.com/huhengtong/one-bit-supervision | 6 | One-bit supervision for image classification | https://scholar.google.com/scholar?cluster=8819536365045393695&hl=en&as_sdt=0,5 | 1 | 2,020 |
What is being transferred in transfer learning? | 265 | neurips | 11 | 0 | 2023-06-16 15:09:53.240000 | https://github.com/google-research/understanding-transfer-learning | 40 | What is being transferred in transfer learning? | https://scholar.google.com/scholar?cluster=13447249673581194617&hl=en&as_sdt=0,5 | 7 | 2,020 |
Neural Networks with Recurrent Generative Feedback | 30 | neurips | 8 | 0 | 2023-06-16 15:09:53.458000 | https://github.com/yjhuangcd/CNNF | 19 | Neural networks with recurrent generative feedback | https://scholar.google.com/scholar?cluster=18302025610474575461&hl=en&as_sdt=0,25 | 3 | 2,020 |
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction | 59 | neurips | 11 | 0 | 2023-06-16 15:09:53.651000 | https://github.com/JinheonBaek/GEN | 51 | Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction | https://scholar.google.com/scholar?cluster=1470927861111828133&hl=en&as_sdt=0,6 | 2 | 2,020 |
Neuron Merging: Compensating for Pruned Neurons | 21 | neurips | 11 | 4 | 2023-06-16 15:09:53.843000 | https://github.com/friendshipkim/neuron-merging | 35 | Neuron merging: Compensating for pruned neurons | https://scholar.google.com/scholar?cluster=8238161891344439767&hl=en&as_sdt=0,31 | 4 | 2,020 |
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