Sylvain Arlot
Statistics and Probability · 2009–2019
A researcher in the Science corpus: 5 papers; trusts 42 researchers and is trusted by 51 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 52 citations into it; #1,020 of 12,932 by trust here, #1,112 by citations
- Artificial Intelligence — 29 citations into it
- Statistical Learning Theory — 26 citations into it
- Control and Systems Engineering — 10 citations into it; #4,814 of 8,257 by trust here, #6,083 by citations
- Optimization & Training — 10 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Computational Mechanics — 8 citations into it
- Representation & Self-Supervised Learning — 6 citations into it
Whom Sylvain Arlot trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Leo Breiman
- Eric R. Ziegel
- Niko Speybroeck
- Colin L. Mallows
- Lucien Birgé
- Ali Rahimi
- Bradley Efron
- Gérard Biau
- Francis R. Bach
- Robert Tibshirani
Who trusts Sylvain Arlot overall
- Charles Truong
- Nicholas Carlini
- Max Tabord‐Meehan
- Kwetishe Joro Danjuma
- Yu Zhang
- Aki Vehtari
- Christophe Giraud
- Gaël Varoquaux
- Csaba Szepesvari
- Yichen Zhou
Papers from Sylvain Arlot's perspective
Ordered by how strongly the researchers Sylvain Arlot trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Regression Shrinkage and Selection via the Lasso
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- A survey of cross-validation procedures for model selection (2009) — 3,932 citations
- Data-driven calibration of linear estimators with minimal penalties (2009) — 61 citations
- Rejoinder on: Minimal penalties and the slope heuristics: a survey. (2019) — 48 citations
- Analysis of purely random forests bias (2014) — 81 citations
- Multi-task regression using minimal penalties (2011) — 60 citations