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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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