Laurent Charlin

Artificial Intelligence · with Blei · 2014–2016

A researcher in the Science corpus: 4 papers; trusts 56 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it
• Information Systems — 31 citations into it; #8,256 of 8,771 by trust here, #3,315 by citations
• Probabilistic & Bayesian Methods — 29 citations into it; #870 of 1,525 by trust here, #960 by citations
• Recommender Systems — 24 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it; #9,606 of 21,575 by trust here, #13,542 by citations
• Natural Language Processing — 14 citations into it
• Signal Processing — 14 citations into it
• Statistics and Probability — 14 citations into it

Whom Laurent Charlin trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. David M. Blei
2. Ruslan Salakhutdinov
3. Radford M. Neal
4. Steffen Rendle
5. Chong Wang
6. Geoffrey E. Hinton
7. Michael I. Jordan
8. John F. Canny
9. Donald B. Rubin
10. Arthur P. Dempster

Who trusts Laurent Charlin overall

1. Dawen Liang
2. Ting-Li Chen
3. David M. Blei
4. Rajesh Ranganath
5. Xiangnan He
6. Dustin Tran
7. Osvaldo Simeone
8. Prem Gopalan
9. Maksims Volkovs
10. Francisco J. R. Ruiz

Papers from Laurent Charlin's perspective

Ordered by how strongly the researchers Laurent Charlin trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Exponential Families (2014) — 158 citations
• Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence (2016) — 277 citations
• Modeling User Exposure in Recommendation (2015) — 426 citations
• Content-based recommendations with Poisson factorization (2014) — 194 citations