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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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