Erwan Scornet

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 107 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #5,099 of 35,345 by trust here, #8,810 by citations
• Statistics and Probability — 77 citations into it; #3,794 of 12,932 by trust here, #8,740 by citations
• Statistical Learning Theory — 56 citations into it; #605 of 1,038 by trust here, #560 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #5,409 of 21,575 by trust here, #6,873 by citations
• Information Systems — 21 citations into it
• Molecular Biology — 21 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it

Whom Erwan Scornet trusts overall

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

1. Leo Breiman
2. Trevor J. Hastie
3. Nicolai Meinshausen
4. C. Stone
5. Stefan Wager
6. Gérard Biau
7. Luc Devroye
8. Lucas K. Mentch
9. Niko Speybroeck
10. Jerome H. Friedman

Who trusts Erwan Scornet overall

1. Gérard Biau
2. Stefan Wager
3. Velibor V. Misic
4. Yuantian Sun
5. Susan Athey
6. Emilio Carrizosa
7. Hristos Tyralis
8. Marcelo C. Medeiros
9. Guenther Walther
10. Ricardo P. Masini

Papers from Erwan Scornet's perspective

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

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

Their papers

• On the asymptotics of random forests (2014) — 107 citations
• Interpretable Random Forests via Rule Extraction (2020) — 77 citations
• SIRUS: making random forests interpretable (2019) — 10 citations
• Neural Random Forests (2016) — 122 citations
• A random forest guided tour (2015) — 3,741 citations
• Consistency of Random Forests (2014) — 619 citations