Philippe Preux

Artificial Intelligence · 2010–2015

A researcher in the Science corpus: 4 papers; trusts 42 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #27,815 of 35,345 by trust here, #30,347 by citations
• Statistical Learning Theory — 35 citations into it
• Computational Mechanics — 21 citations into it
• Statistics and Probability — 18 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #17,078 of 21,575 by trust here, #19,310 by citations
• Applied Mathematics — 11 citations into it
• Computational Theory and Mathematics — 10 citations into it

Whom Philippe Preux trusts overall

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

1. Charles A. Micchelli
2. Francis R. Bach
3. Massimiliano Pontil
4. Andrea Caponnetto
5. Christopher K. I. Williams
6. Heng Lian
7. Bernhard Schölkopf
8. Theodoros Evgeniou
9. Frédéric Ferraty
10. Paul Tseng

Who trusts Philippe Preux overall

1. Hachem Kadri
2. Barnab'as P'oczos

Papers from Philippe Preux's perspective

Ordered by how strongly the researchers Philippe Preux 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Multitask Learning

Their papers

• Operator-valued Kernels for Learning from Functional Response Data (2015) — 154 citations
• A Generalized Kernel Approach to Structured Output Learning (2012) — 38 citations
• Multiple Operator-valued Kernel Learning (2012) — 19 citations
• Nonlinear functional regression: a functional RKHS approach (2010) — 62 citations