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Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction
| 67 |
neurips
| 17 | 16 |
2023-06-16 15:10:52.399000
|
https://github.com/simpleig/Geo-PIFu
| 107 |
Geo-pifu: Geometry and pixel aligned implicit functions for single-view human reconstruction
|
https://scholar.google.com/scholar?cluster=413072927263183494&hl=en&as_sdt=0,5
| 9 | 2,020 |
Optimal visual search based on a model of target detectability in natural images
| 11 |
neurips
| 0 | 0 |
2023-06-16 15:10:52.591000
|
https://github.com/rashidis/bio_based_detectability
| 2 |
Optimal visual search based on a model of target detectability in natural images
|
https://scholar.google.com/scholar?cluster=5184014170685749857&hl=en&as_sdt=0,31
| 2 | 2,020 |
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures
| 48 |
neurips
| 8 | 2 |
2023-06-16 15:10:52.784000
|
https://github.com/lightonai/dfa-scales-to-modern-deep-learning
| 72 |
Direct feedback alignment scales to modern deep learning tasks and architectures
|
https://scholar.google.com/scholar?cluster=12044831412271008828&hl=en&as_sdt=0,47
| 10 | 2,020 |
Bayesian Optimization for Iterative Learning
| 18 |
neurips
| 1 | 0 |
2023-06-16 15:10:52.976000
|
https://github.com/ntienvu/BOIL
| 6 |
Bayesian optimization for iterative learning
|
https://scholar.google.com/scholar?cluster=10842170699487519102&hl=en&as_sdt=0,40
| 3 | 2,020 |
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
| 97 |
neurips
| 40 | 10 |
2023-06-16 15:10:53.169000
|
https://github.com/ryanchankh/mcr2
| 168 |
Learning diverse and discriminative representations via the principle of maximal coding rate reduction
|
https://scholar.google.com/scholar?cluster=14992071413759250566&hl=en&as_sdt=0,32
| 7 | 2,020 |
Learning Rich Rankings
| 9 |
neurips
| 0 | 0 |
2023-06-16 15:10:53.361000
|
https://github.com/arjunsesh/lrr-neurips
| 3 |
Learning rich rankings
|
https://scholar.google.com/scholar?cluster=14598696558067197575&hl=en&as_sdt=0,5
| 2 | 2,020 |
Color Visual Illusions: A Statistics-based Computational Model
| 5 |
neurips
| 1 | 0 |
2023-06-16 15:10:53.571000
|
https://github.com/eladhi/VI-Glow
| 2 |
Color visual illusions: A statistics-based computational model
|
https://scholar.google.com/scholar?cluster=12501118116923270593&hl=en&as_sdt=0,29
| 1 | 2,020 |
The Pitfalls of Simplicity Bias in Neural Networks
| 197 |
neurips
| 8 | 2 |
2023-06-16 15:10:53.802000
|
https://github.com/harshays/simplicitybiaspitfalls
| 32 |
The pitfalls of simplicity bias in neural networks
|
https://scholar.google.com/scholar?cluster=13128598861891549872&hl=en&as_sdt=0,5
| 2 | 2,020 |
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
| 18 |
neurips
| 3 | 0 |
2023-06-16 15:10:53.995000
|
https://github.com/guaguakai/surrogate-optimization-learning
| 9 |
Automatically learning compact quality-aware surrogates for optimization problems
|
https://scholar.google.com/scholar?cluster=914584928870458413&hl=en&as_sdt=0,44
| 2 | 2,020 |
Empirical Likelihood for Contextual Bandits
| 9 |
neurips
| 1 | 0 |
2023-06-16 15:10:54.192000
|
https://github.com/pmineiro/elfcb
| 12 |
Empirical likelihood for contextual bandits
|
https://scholar.google.com/scholar?cluster=2477802205256797409&hl=en&as_sdt=0,5
| 2 | 2,020 |
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?
| 29 |
neurips
| 20 | 0 |
2023-06-16 15:10:54.385000
|
https://github.com/NVIDIA/GraphQSat
| 47 |
Can Q-learning with graph networks learn a generalizable branching heuristic for a SAT solver?
|
https://scholar.google.com/scholar?cluster=14134895481476323546&hl=en&as_sdt=0,11
| 4 | 2,020 |
Listening to Sounds of Silence for Speech Denoising
| 30 |
neurips
| 21 | 3 |
2023-06-16 15:10:54.577000
|
https://github.com/henryxrl/Listening-to-Sound-of-Silence-for-Speech-Denoising
| 41 |
Listening to sounds of silence for speech denoising
|
https://scholar.google.com/scholar?cluster=15043544639901416404&hl=en&as_sdt=0,5
| 2 | 2,020 |
BoxE: A Box Embedding Model for Knowledge Base Completion
| 111 |
neurips
| 4 | 0 |
2023-06-16 15:10:54.770000
|
https://github.com/ralphabb/BoxE
| 39 |
Boxe: A box embedding model for knowledge base completion
|
https://scholar.google.com/scholar?cluster=10965427098747336243&hl=en&as_sdt=0,5
| 2 | 2,020 |
Coherent Hierarchical Multi-Label Classification Networks
| 43 |
neurips
| 16 | 1 |
2023-06-16 15:10:54.962000
|
https://github.com/EGiunchiglia/C-HMCNN
| 64 |
Coherent hierarchical multi-label classification networks
|
https://scholar.google.com/scholar?cluster=10722017253343281593&hl=en&as_sdt=0,11
| 4 | 2,020 |
Federated Bayesian Optimization via Thompson Sampling
| 57 |
neurips
| 4 | 1 |
2023-06-16 15:10:55.155000
|
https://github.com/daizhongxiang/Federated_Bayesian_Optimization
| 16 |
Federated Bayesian optimization via Thompson sampling
|
https://scholar.google.com/scholar?cluster=16578927726167332521&hl=en&as_sdt=0,43
| 1 | 2,020 |
Neural Complexity Measures
| 178 |
neurips
| 0 | 0 |
2023-06-16 15:10:55.347000
|
https://github.com/yoonholee/neural-complexity
| 8 |
Architectural complexity measures of recurrent neural networks
|
https://scholar.google.com/scholar?cluster=9430461092837132372&hl=en&as_sdt=0,14
| 2 | 2,020 |
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
| 346 |
neurips
| 10 | 0 |
2023-06-16 15:10:55.540000
|
https://github.com/HumamAlwassel/XDC
| 83 |
Self-supervised learning by cross-modal audio-video clustering
|
https://scholar.google.com/scholar?cluster=7902775526850966872&hl=en&as_sdt=0,47
| 3 | 2,020 |
Generalization Bound of Gradient Descent for Non-Convex Metric Learning
| 4 |
neurips
| 1 | 0 |
2023-06-16 15:10:55.733000
|
https://github.com/xyang6/SMILE
| 1 |
Generalization bound of gradient descent for non-convex metric learning
|
https://scholar.google.com/scholar?cluster=2089980921102731438&hl=en&as_sdt=0,15
| 1 | 2,020 |
GANSpace: Discovering Interpretable GAN Controls
| 610 |
neurips
| 248 | 27 |
2023-06-16 15:10:55.925000
|
https://github.com/harskish/ganspace
| 1,731 |
Ganspace: Discovering interpretable gan controls
|
https://scholar.google.com/scholar?cluster=1986716991541343890&hl=en&as_sdt=0,33
| 41 | 2,020 |
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
| 140 |
neurips
| 319 | 64 |
2023-06-16 15:10:56.118000
|
https://github.com/pytorch/botorch
| 2,664 |
Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization
|
https://scholar.google.com/scholar?cluster=8750355730430207793&hl=en&as_sdt=0,5
| 51 | 2,020 |
Neuron-level Structured Pruning using Polarization Regularizer
| 81 |
neurips
| 11 | 7 |
2023-06-16 15:10:56.312000
|
https://github.com/polarizationpruning/PolarizationPruning
| 72 |
Neuron-level structured pruning using polarization regularizer
|
https://scholar.google.com/scholar?cluster=11036870209312598760&hl=en&as_sdt=0,23
| 2 | 2,020 |
Field-wise Learning for Multi-field Categorical Data
| 7 |
neurips
| 2 | 0 |
2023-06-16 15:10:56.504000
|
https://github.com/lzb5600/Field-wise-Learning
| 6 |
Field-wise learning for multi-field categorical data
|
https://scholar.google.com/scholar?cluster=11839494695393533500&hl=en&as_sdt=0,10
| 2 | 2,020 |
Continual Learning in Low-rank Orthogonal Subspaces
| 60 |
neurips
| 3 | 0 |
2023-06-16 15:10:56.697000
|
https://github.com/arslan-chaudhry/orthog_subspace
| 22 |
Continual learning in low-rank orthogonal subspaces
|
https://scholar.google.com/scholar?cluster=6781823175035595745&hl=en&as_sdt=0,5
| 2 | 2,020 |
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
| 2,291 |
neurips
| 262 | 36 |
2023-06-16 15:10:56.889000
|
https://github.com/facebookresearch/swav
| 1,800 |
Unsupervised learning of visual features by contrasting cluster assignments
|
https://scholar.google.com/scholar?cluster=13209348926291080860&hl=en&as_sdt=0,5
| 41 | 2,020 |
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
| 54 |
neurips
| 11 | 3 |
2023-06-16 15:10:57.080000
|
https://github.com/nv-tlabs/DefTet
| 117 |
Learning deformable tetrahedral meshes for 3d reconstruction
|
https://scholar.google.com/scholar?cluster=6266590920859751769&hl=en&as_sdt=0,33
| 33 | 2,020 |
Self-supervised learning through the eyes of a child
| 61 |
neurips
| 14 | 0 |
2023-06-16 15:10:57.276000
|
https://github.com/eminorhan/baby-vision
| 136 |
Self-supervised learning through the eyes of a child
|
https://scholar.google.com/scholar?cluster=5608715260418451299&hl=en&as_sdt=0,39
| 7 | 2,020 |
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
| 20 |
neurips
| 4 | 1 |
2023-06-16 15:10:57.473000
|
https://github.com/taohan10200/USADTM
| 27 |
Unsupervised semantic aggregation and deformable template matching for semi-supervised learning
|
https://scholar.google.com/scholar?cluster=9057482761003517439&hl=en&as_sdt=0,10
| 3 | 2,020 |
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
| 4 |
neurips
| 2 | 0 |
2023-06-16 15:10:57.667000
|
https://github.com/instadeepai/EGTA-NMARL
| 13 |
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=18296902167525929783&hl=en&as_sdt=0,24
| 5 | 2,020 |
Data Diversification: A Simple Strategy For Neural Machine Translation
| 64 |
neurips
| 6 | 2 |
2023-06-16 15:10:57.859000
|
https://github.com/nxphi47/data_diversification
| 23 |
Data diversification: A simple strategy for neural machine translation
|
https://scholar.google.com/scholar?cluster=4075963785993246098&hl=en&as_sdt=0,33
| 2 | 2,020 |
CoSE: Compositional Stroke Embeddings
| 25 |
neurips
| 6 | 0 |
2023-06-16 15:10:58.055000
|
https://github.com/eth-ait/cose
| 29 |
Cose: Compositional stroke embeddings
|
https://scholar.google.com/scholar?cluster=17699683888953268299&hl=en&as_sdt=0,5
| 8 | 2,020 |
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
| 24 |
neurips
| 8 | 0 |
2023-06-16 15:10:58.248000
|
https://github.com/XuJing1022/HiT-MAC
| 22 |
Learning multi-agent coordination for enhancing target coverage in directional sensor networks
|
https://scholar.google.com/scholar?cluster=3761066005883890135&hl=en&as_sdt=0,31
| 3 | 2,020 |
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
| 86 |
neurips
| 9 | 10 |
2023-06-16 15:10:58.441000
|
https://github.com/DTaoo/Discriminative-Sounding-Objects-Localization
| 52 |
Discriminative sounding objects localization via self-supervised audiovisual matching
|
https://scholar.google.com/scholar?cluster=2914811188248897245&hl=en&as_sdt=0,43
| 4 | 2,020 |
Learning Multi-Agent Communication through Structured Attentive Reasoning
| 24 |
neurips
| 8 | 2 |
2023-06-16 15:10:58.634000
|
https://github.com/caslab-vt/SARNet
| 21 |
Learning multi-agent communication through structured attentive reasoning
|
https://scholar.google.com/scholar?cluster=17079361444341989269&hl=en&as_sdt=0,33
| 4 | 2,020 |
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
| 16 |
neurips
| 5 | 0 |
2023-06-16 15:10:58.826000
|
https://github.com/KyunghyunLee/aes-rl
| 15 |
An efficient asynchronous method for integrating evolutionary and gradient-based policy search
|
https://scholar.google.com/scholar?cluster=16110755870648938289&hl=en&as_sdt=0,5
| 2 | 2,020 |
MetaSDF: Meta-Learning Signed Distance Functions
| 149 |
neurips
| 16 | 2 |
2023-06-16 15:10:59.018000
|
https://github.com/shaohua0116/MultiDigitMNIST
| 80 |
Metasdf: Meta-learning signed distance functions
|
https://scholar.google.com/scholar?cluster=14779381084072333819&hl=en&as_sdt=0,33
| 5 | 2,020 |
Model-based Adversarial Meta-Reinforcement Learning
| 30 |
neurips
| 6 | 1 |
2023-06-16 15:10:59.210000
|
https://github.com/LinZichuan/AdMRL
| 31 |
Model-based adversarial meta-reinforcement learning
|
https://scholar.google.com/scholar?cluster=13462874924828322027&hl=en&as_sdt=0,5
| 5 | 2,020 |
Graph Policy Network for Transferable Active Learning on Graphs
| 36 |
neurips
| 9 | 1 |
2023-06-16 15:10:59.403000
|
https://github.com/ShengdingHu/GraphPolicyNetworkActiveLearning
| 37 |
Graph policy network for transferable active learning on graphs
|
https://scholar.google.com/scholar?cluster=2017577530575623285&hl=en&as_sdt=0,34
| 2 | 2,020 |
Towards a Better Global Loss Landscape of GANs
| 23 |
neurips
| 4 | 1 |
2023-06-16 15:10:59.596000
|
https://github.com/AilsaF/RS-GAN
| 27 |
Towards a better global loss landscape of GANs
|
https://scholar.google.com/scholar?cluster=11884432475948197511&hl=en&as_sdt=0,5
| 3 | 2,020 |
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
| 198 |
neurips
| 25 | 5 |
2023-06-16 15:10:59.799000
|
https://github.com/oxwhirl/wqmix
| 92 |
Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=164177538324943983&hl=en&as_sdt=0,33
| 3 | 2,020 |
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
| 20 |
neurips
| 29 | 81 |
2023-06-16 15:10:59.992000
|
https://github.com/ThrunGroup/BanditPAM
| 597 |
Banditpam: Almost linear time k-medoids clustering via multi-armed bandits
|
https://scholar.google.com/scholar?cluster=17391343875111249867&hl=en&as_sdt=0,28
| 8 | 2,020 |
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging
| 68 |
neurips
| 9 | 8 |
2023-06-16 15:11:00.185000
|
https://github.com/ChaoningZhang/Universal-Deep-Hiding
| 76 |
Udh: Universal deep hiding for steganography, watermarking, and light field messaging
|
https://scholar.google.com/scholar?cluster=10741692453903980438&hl=en&as_sdt=0,5
| 3 | 2,020 |
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders
| 14 |
neurips
| 2 | 0 |
2023-06-16 15:11:00.377000
|
https://github.com/sisl/EvidentialSparsification
| 6 |
Evidential sparsification of multimodal latent spaces in conditional variational autoencoders
|
https://scholar.google.com/scholar?cluster=15564375391911668745&hl=en&as_sdt=0,39
| 8 | 2,020 |
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
| 49 |
neurips
| 13 | 4 |
2023-06-16 15:11:00.570000
|
https://github.com/mlvlab/SELAR
| 50 |
Self-supervised auxiliary learning with meta-paths for heterogeneous graphs
|
https://scholar.google.com/scholar?cluster=14092642603369641339&hl=en&as_sdt=0,5
| 5 | 2,020 |
Can Graph Neural Networks Count Substructures?
| 180 |
neurips
| 3 | 1 |
2023-06-16 15:11:00.762000
|
https://github.com/leichen2018/GNN-Substructure-Counting
| 31 |
Can graph neural networks count substructures?
|
https://scholar.google.com/scholar?cluster=15397526244877086732&hl=en&as_sdt=0,23
| 4 | 2,020 |
Stable and expressive recurrent vision models
| 27 |
neurips
| 0 | 1 |
2023-06-16 15:11:00.954000
|
https://github.com/c-rbp/panoptic_segmentation
| 0 |
Stable and expressive recurrent vision models
|
https://scholar.google.com/scholar?cluster=9835747249429440415&hl=en&as_sdt=0,39
| 2 | 2,020 |
BRP-NAS: Prediction-based NAS using GCNs
| 128 |
neurips
| 10 | 1 |
2023-06-16 15:11:01.147000
|
https://github.com/thomasccp/eagle
| 55 |
Brp-nas: Prediction-based nas using gcns
|
https://scholar.google.com/scholar?cluster=2963733122689341897&hl=en&as_sdt=0,36
| 6 | 2,020 |
Deep Shells: Unsupervised Shape Correspondence with Optimal Transport
| 53 |
neurips
| 6 | 1 |
2023-06-16 15:11:01.339000
|
https://github.com/marvin-eisenberger/deep-shells
| 35 |
Deep shells: Unsupervised shape correspondence with optimal transport
|
https://scholar.google.com/scholar?cluster=7877199401266840564&hl=en&as_sdt=0,44
| 6 | 2,020 |
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
| 52 |
neurips
| 8 | 3 |
2023-06-16 15:11:01.531000
|
https://github.com/iboing/ISTA-NAS
| 29 |
Ista-nas: Efficient and consistent neural architecture search by sparse coding
|
https://scholar.google.com/scholar?cluster=6611041012150582812&hl=en&as_sdt=0,5
| 3 | 2,020 |
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
| 25 |
neurips
| 1 | 0 |
2023-06-16 15:11:01.723000
|
https://github.com/princeton-vl/Rel3D
| 25 |
Rel3d: A minimally contrastive benchmark for grounding spatial relations in 3d
|
https://scholar.google.com/scholar?cluster=2314911618125318907&hl=en&as_sdt=0,5
| 6 | 2,020 |
Regularizing Black-box Models for Improved Interpretability
| 35 |
neurips
| 1 | 0 |
2023-06-16 15:11:01.915000
|
https://github.com/GDPlumb/ExpO
| 13 |
Regularizing black-box models for improved interpretability
|
https://scholar.google.com/scholar?cluster=8791844934310569033&hl=en&as_sdt=0,18
| 3 | 2,020 |
Semi-Supervised Neural Architecture Search
| 59 |
neurips
| 2 | 0 |
2023-06-16 15:11:02.109000
|
https://github.com/renqianluo/SemiNAS
| 23 |
Semi-supervised neural architecture search
|
https://scholar.google.com/scholar?cluster=18063848254529842085&hl=en&as_sdt=0,43
| 2 | 2,020 |
Consistency Regularization for Certified Robustness of Smoothed Classifiers
| 42 |
neurips
| 3 | 0 |
2023-06-16 15:11:02.303000
|
https://github.com/jh-jeong/smoothing-consistency
| 30 |
Consistency regularization for certified robustness of smoothed classifiers
|
https://scholar.google.com/scholar?cluster=15871796108252532947&hl=en&as_sdt=0,5
| 2 | 2,020 |
Make One-Shot Video Object Segmentation Efficient Again
| 30 |
neurips
| 5 | 2 |
2023-06-16 15:11:02.496000
|
https://github.com/dvl-tum/e-osvos
| 35 |
Make one-shot video object segmentation efficient again
|
https://scholar.google.com/scholar?cluster=4861842359633775782&hl=en&as_sdt=0,44
| 5 | 2,020 |
Depth Uncertainty in Neural Networks
| 71 |
neurips
| 11 | 2 |
2023-06-16 15:11:02.688000
|
https://github.com/cambridge-mlg/DUN
| 67 |
Depth uncertainty in neural networks
|
https://scholar.google.com/scholar?cluster=8829822844552583626&hl=en&as_sdt=0,5
| 9 | 2,020 |
Non-Euclidean Universal Approximation
| 50 |
neurips
| 0 | 0 |
2023-06-16 15:11:02.883000
|
https://github.com/AnastasisKratsios/NeurIPS2020_Non_Euclidean_Universal_Approximation_Example_DNN_Layer_Comparisons
| 2 |
Non-euclidean universal approximation
|
https://scholar.google.com/scholar?cluster=154021427834857784&hl=en&as_sdt=0,5
| 1 | 2,020 |
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
| 18 |
neurips
| 2 | 0 |
2023-06-16 15:11:03.076000
|
https://github.com/jacobdeasy/geometric-js
| 16 |
Constraining variational inference with geometric jensen-shannon divergence
|
https://scholar.google.com/scholar?cluster=7731569928986898287&hl=en&as_sdt=0,14
| 4 | 2,020 |
Monotone operator equilibrium networks
| 84 |
neurips
| 4 | 0 |
2023-06-16 15:11:03.283000
|
https://github.com/locuslab/monotone_op_net
| 48 |
Monotone operator equilibrium networks
|
https://scholar.google.com/scholar?cluster=17782936577976444731&hl=en&as_sdt=0,39
| 6 | 2,020 |
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
| 44 |
neurips
| 7 | 0 |
2023-06-16 15:11:03.498000
|
https://github.com/DesmondZhong/Lagrangian_caVAE
| 14 |
Unsupervised learning of lagrangian dynamics from images for prediction and control
|
https://scholar.google.com/scholar?cluster=5340883116879003000&hl=en&as_sdt=0,5
| 1 | 2,020 |
Learning Compositional Rules via Neural Program Synthesis
| 75 |
neurips
| 17 | 0 |
2023-06-16 15:11:03.692000
|
https://github.com/mtensor/rulesynthesis
| 53 |
Learning compositional rules via neural program synthesis
|
https://scholar.google.com/scholar?cluster=3160670555314650508&hl=en&as_sdt=0,5
| 5 | 2,020 |
Incorporating BERT into Parallel Sequence Decoding with Adapters
| 50 |
neurips
| 8 | 4 |
2023-06-16 15:11:03.884000
|
https://github.com/lemmonation/abnet
| 32 |
Incorporating bert into parallel sequence decoding with adapters
|
https://scholar.google.com/scholar?cluster=5170178385408287500&hl=en&as_sdt=0,5
| 3 | 2,020 |
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks
| 15 |
neurips
| 2 | 0 |
2023-06-16 15:11:04.077000
|
https://github.com/kazukiosawa/ngd_in_wide_nn
| 10 |
Understanding approximate fisher information for fast convergence of natural gradient descent in wide neural networks
|
https://scholar.google.com/scholar?cluster=7137081707264639377&hl=en&as_sdt=0,34
| 1 | 2,020 |
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
| 26 |
neurips
| 1 | 0 |
2023-06-16 15:11:04.279000
|
https://github.com/nasiryahm/GAIT-prop
| 7 |
Gait-prop: A biologically plausible learning rule derived from backpropagation of error
|
https://scholar.google.com/scholar?cluster=15875049954561764197&hl=en&as_sdt=0,3
| 3 | 2,020 |
SCOP: Scientific Control for Reliable Neural Network Pruning
| 97 |
neurips
| 45 | 2 |
2023-06-16 15:11:04.487000
|
https://github.com/huawei-noah/Pruning
| 238 |
Scop: Scientific control for reliable neural network pruning
|
https://scholar.google.com/scholar?cluster=10691651773549756733&hl=en&as_sdt=0,5
| 10 | 2,020 |
Discovering conflicting groups in signed networks
| 15 |
neurips
| 2 | 0 |
2023-06-16 15:11:04.680000
|
https://github.com/rutzeng/SCG-NeurIPS2020
| 12 |
Discovering conflicting groups in signed networks
|
https://scholar.google.com/scholar?cluster=16214693394380212585&hl=en&as_sdt=0,34
| 1 | 2,020 |
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
| 27 |
neurips
| 4 | 0 |
2023-06-16 15:11:04.874000
|
https://github.com/anishazaveri/austen_plots
| 20 |
Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding
|
https://scholar.google.com/scholar?cluster=11433667847814374249&hl=en&as_sdt=0,50
| 4 | 2,020 |
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
| 0 |
neurips
| 0 | 1 |
2023-06-16 15:11:05.065000
|
https://github.com/matthewfaw/mixnmatch
| 1 |
Mix and match: an optimistic tree-search approach for learning models from mixture distributions
|
https://scholar.google.com/scholar?cluster=9708198695831582690&hl=en&as_sdt=0,5
| 1 | 2,020 |
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
| 102 |
neurips
| 25 | 0 |
2023-06-16 15:11:05.258000
|
https://github.com/jsyoon0823/VIME
| 112 |
Vime: Extending the success of self-and semi-supervised learning to tabular domain
|
https://scholar.google.com/scholar?cluster=6759722027373902233&hl=en&as_sdt=0,5
| 3 | 2,020 |
Phase retrieval in high dimensions: Statistical and computational phase transitions
| 35 |
neurips
| 0 | 0 |
2023-06-16 15:11:05.450000
|
https://github.com/sphinxteam/PhaseRetrieval_demo
| 0 |
Phase retrieval in high dimensions: Statistical and computational phase transitions
|
https://scholar.google.com/scholar?cluster=12300381021684314628&hl=en&as_sdt=0,36
| 5 | 2,020 |
Soft Contrastive Learning for Visual Localization
| 13 |
neurips
| 1 | 1 |
2023-06-16 15:11:05.642000
|
https://github.com/janinethoma/soft_contrastive_learning
| 20 |
Soft contrastive learning for visual localization
|
https://scholar.google.com/scholar?cluster=416644308863323258&hl=en&as_sdt=0,5
| 2 | 2,020 |
Fine-Grained Dynamic Head for Object Detection
| 26 |
neurips
| 8 | 5 |
2023-06-16 15:11:05.835000
|
https://github.com/StevenGrove/DynamicHead
| 79 |
Fine-grained dynamic head for object detection
|
https://scholar.google.com/scholar?cluster=17089335587335369004&hl=en&as_sdt=0,5
| 3 | 2,020 |
Modeling and Optimization Trade-off in Meta-learning
| 24 |
neurips
| 1 | 0 |
2023-06-16 15:11:06.027000
|
https://github.com/intel-isl/MetaLearningTradeoffs
| 4 |
Modeling and optimization trade-off in meta-learning
|
https://scholar.google.com/scholar?cluster=6968213922312284457&hl=en&as_sdt=0,5
| 9 | 2,020 |
SnapBoost: A Heterogeneous Boosting Machine
| 6 |
neurips
| 2 | 0 |
2023-06-16 15:11:06.221000
|
https://github.com/IBM/snapboost-neurips
| 8 |
Snapboost: A heterogeneous boosting machine
|
https://scholar.google.com/scholar?cluster=11504245933861702155&hl=en&as_sdt=0,10
| 5 | 2,020 |
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
| 44 |
neurips
| 1 | 0 |
2023-06-16 15:11:06.413000
|
https://github.com/hyenal/relate
| 31 |
RELATE: Physically plausible multi-object scene synthesis using structured latent spaces
|
https://scholar.google.com/scholar?cluster=10197798109184209151&hl=en&as_sdt=0,5
| 4 | 2,020 |
GreedyFool: Distortion-Aware Sparse Adversarial Attack
| 36 |
neurips
| 5 | 2 |
2023-06-16 15:11:06.606000
|
https://github.com/LightDXY/GreedyFool
| 29 |
Greedyfool: Distortion-aware sparse adversarial attack
|
https://scholar.google.com/scholar?cluster=9173830500471022220&hl=en&as_sdt=0,36
| 1 | 2,020 |
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
| 40 |
neurips
| 6 | 0 |
2023-06-16 15:11:06.799000
|
https://github.com/microsoft/VAEM
| 12 |
VAEM: a deep generative model for heterogeneous mixed type data
|
https://scholar.google.com/scholar?cluster=1707955127597658267&hl=en&as_sdt=0,5
| 4 | 2,020 |
RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist
| 67 |
neurips
| 16 | 7 |
2023-06-16 15:11:06.991000
|
https://github.com/uta-smile/RetroXpert
| 48 |
Retroxpert: Decompose retrosynthesis prediction like a chemist
|
https://scholar.google.com/scholar?cluster=1673974540890711426&hl=en&as_sdt=0,14
| 7 | 2,020 |
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
| 82 |
neurips
| 11 | 0 |
2023-06-16 15:11:07.184000
|
https://github.com/cambridge-mlg/weighted-retraining
| 30 |
Sample-efficient optimization in the latent space of deep generative models via weighted retraining
|
https://scholar.google.com/scholar?cluster=6526315194994935478&hl=en&as_sdt=0,44
| 6 | 2,020 |
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
| 357 |
neurips
| 66 | 13 |
2023-06-16 15:11:07.376000
|
https://github.com/yxgeee/SpCL
| 294 |
Self-paced contrastive learning with hybrid memory for domain adaptive object re-id
|
https://scholar.google.com/scholar?cluster=12125072561642183242&hl=en&as_sdt=0,3
| 7 | 2,020 |
Winning the Lottery with Continuous Sparsification
| 86 |
neurips
| 5 | 1 |
2023-06-16 15:11:07.568000
|
https://github.com/lolemacs/continuous-sparsification
| 24 |
Winning the lottery with continuous sparsification
|
https://scholar.google.com/scholar?cluster=6340697086981943139&hl=en&as_sdt=0,47
| 3 | 2,020 |
Joints in Random Forests
| 27 |
neurips
| 5 | 3 |
2023-06-16 15:11:07.760000
|
https://github.com/AlCorreia/GeFs
| 29 |
Joints in random forests
|
https://scholar.google.com/scholar?cluster=16339434295073356631&hl=en&as_sdt=0,11
| 3 | 2,020 |
Compositional Generalization by Learning Analytical Expressions
| 12 |
neurips
| 58 | 10 |
2023-06-16 15:11:07.953000
|
https://github.com/microsoft/ContextualSP
| 310 |
Compositional generalization by learning analytical expressions
|
https://scholar.google.com/scholar?cluster=14346875242399038266&hl=en&as_sdt=0,5
| 15 | 2,020 |
JAX MD: A Framework for Differentiable Physics
| 100 |
neurips
| 155 | 64 |
2023-06-16 15:11:08.155000
|
https://github.com/google/jax-md
| 941 |
Jax md: a framework for differentiable physics
|
https://scholar.google.com/scholar?cluster=10280332258260460086&hl=en&as_sdt=0,44
| 49 | 2,020 |
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
| 59 |
neurips
| 18 | 0 |
2023-06-16 15:11:08.348000
|
https://github.com/chenhsuanlin/signed-distance-SRN
| 114 |
Sdf-srn: Learning signed distance 3d object reconstruction from static images
|
https://scholar.google.com/scholar?cluster=11067846104623774002&hl=en&as_sdt=0,47
| 4 | 2,020 |
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
| 6 |
neurips
| 1 | 0 |
2023-06-16 15:11:08.541000
|
https://github.com/JWoong148/MetaPerturb
| 13 |
Metaperturb: Transferable regularizer for heterogeneous tasks and architectures
|
https://scholar.google.com/scholar?cluster=7151677939304906463&hl=en&as_sdt=0,23
| 2 | 2,020 |
Learning to solve TV regularised problems with unrolled algorithms
| 10 |
neurips
| 3 | 0 |
2023-06-16 15:11:08.734000
|
https://github.com/hcherkaoui/carpet
| 10 |
Learning to solve TV regularised problems with unrolled algorithms
|
https://scholar.google.com/scholar?cluster=7897340009151799054&hl=en&as_sdt=0,5
| 2 | 2,020 |
Improving robustness against common corruptions by covariate shift adaptation
| 225 |
neurips
| 4 | 5 |
2023-06-16 15:11:08.927000
|
https://github.com/bethgelab/robustness
| 107 |
Improving robustness against common corruptions by covariate shift adaptation
|
https://scholar.google.com/scholar?cluster=3624568905947100464&hl=en&as_sdt=0,5
| 16 | 2,020 |
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
| 14 |
neurips
| 1 | 0 |
2023-06-16 15:11:09.121000
|
https://github.com/srambhatla/TensorNOODL
| 1 |
Provable online CP/PARAFAC decomposition of a structured tensor via dictionary learning
|
https://scholar.google.com/scholar?cluster=16029493978579736845&hl=en&as_sdt=0,36
| 1 | 2,020 |
Look-ahead Meta Learning for Continual Learning
| 77 |
neurips
| 17 | 0 |
2023-06-16 15:11:09.315000
|
https://github.com/montrealrobotics/La-MAML
| 63 |
Look-ahead meta learning for continual learning
|
https://scholar.google.com/scholar?cluster=17815879397506747892&hl=en&as_sdt=0,3
| 5 | 2,020 |
A polynomial-time algorithm for learning nonparametric causal graphs
| 21 |
neurips
| 0 | 0 |
2023-06-16 15:11:09.521000
|
https://github.com/MingGao97/NPVAR
| 4 |
A polynomial-time algorithm for learning nonparametric causal graphs
|
https://scholar.google.com/scholar?cluster=14706924750789311400&hl=en&as_sdt=0,47
| 3 | 2,020 |
Proximal Mapping for Deep Regularization
| 1 |
neurips
| 1 | 0 |
2023-06-16 15:11:09.714000
|
https://github.com/learndeep2019/ProxNet
| 6 |
Proximal mapping for deep regularization
|
https://scholar.google.com/scholar?cluster=7235863984702530816&hl=en&as_sdt=0,33
| 2 | 2,020 |
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
| 51 |
neurips
| 10 | 1 |
2023-06-16 15:11:09.912000
|
https://github.com/OATML/ucate
| 24 |
Identifying causal-effect inference failure with uncertainty-aware models
|
https://scholar.google.com/scholar?cluster=15948587746481389105&hl=en&as_sdt=0,3
| 2 | 2,020 |
Deep active inference agents using Monte-Carlo methods
| 58 |
neurips
| 10 | 2 |
2023-06-16 15:11:10.105000
|
https://github.com/zfountas/deep-active-inference-mc
| 56 |
Deep active inference agents using Monte-Carlo methods
|
https://scholar.google.com/scholar?cluster=6913305558243551046&hl=en&as_sdt=0,33
| 4 | 2,020 |
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
| 13 |
neurips
| 0 | 0 |
2023-06-16 15:11:10.299000
|
https://github.com/aritchie9590/NDIGO
| 1 |
Consistent estimation of identifiable nonparametric mixture models from grouped observations
|
https://scholar.google.com/scholar?cluster=17764309851292828713&hl=en&as_sdt=0,3
| 1 | 2,020 |
In search of robust measures of generalization
| 58 |
neurips
| 5 | 0 |
2023-06-16 15:11:10.521000
|
https://github.com/nitarshan/robust-generalization-measures
| 27 |
In search of robust measures of generalization
|
https://scholar.google.com/scholar?cluster=14875253410055834291&hl=en&as_sdt=0,5
| 4 | 2,020 |
Softmax Deep Double Deterministic Policy Gradients
| 41 |
neurips
| 4 | 2 |
2023-06-16 15:11:10.714000
|
https://github.com/ling-pan/SD3
| 36 |
Softmax deep double deterministic policy gradients
|
https://scholar.google.com/scholar?cluster=11974289959292119279&hl=en&as_sdt=0,34
| 2 | 2,020 |
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
| 19 |
neurips
| 8 | 0 |
2023-06-16 15:11:10.907000
|
https://github.com/deep-spin/sparse-marginalization-lvm
| 24 |
Efficient marginalization of discrete and structured latent variables via sparsity
|
https://scholar.google.com/scholar?cluster=16514108199949566387&hl=en&as_sdt=0,5
| 4 | 2,020 |
DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs
| 11 |
neurips
| 2 | 1 |
2023-06-16 15:11:11.100000
|
https://github.com/yaxingwang/DeepI2I
| 25 |
Deepi2i: Enabling deep hierarchical image-to-image translation by transferring from gans
|
https://scholar.google.com/scholar?cluster=13982918844141309648&hl=en&as_sdt=0,33
| 6 | 2,020 |
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
| 359 |
neurips
| 57 | 10 |
2023-06-16 15:11:11.293000
|
https://github.com/alinlab/CSI
| 254 |
Csi: Novelty detection via contrastive learning on distributionally shifted instances
|
https://scholar.google.com/scholar?cluster=7033158044687417724&hl=en&as_sdt=0,5
| 7 | 2,020 |
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
| 86 |
neurips
| 11 | 0 |
2023-06-16 15:11:11.505000
|
https://github.com/mzho7212/LICA
| 47 |
Learning implicit credit assignment for cooperative multi-agent reinforcement learning
|
https://scholar.google.com/scholar?cluster=3915814748065478142&hl=en&as_sdt=0,44
| 1 | 2,020 |
MATE: Plugging in Model Awareness to Task Embedding for Meta Learning
| 10 |
neurips
| 3 | 0 |
2023-06-16 15:11:11.696000
|
https://github.com/VITA-Group/MATE
| 7 |
MATE: plugging in model awareness to task embedding for meta learning
|
https://scholar.google.com/scholar?cluster=6157757074915250340&hl=en&as_sdt=0,33
| 2 | 2,020 |
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