Raphaël Féraud

Artificial Intelligence · 2001–2018

A researcher in the Science corpus: 6 papers; trusts 50 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #12,761 of 35,345 by trust here, #21,878 by citations
• Management Science and Operations Research — 34 citations into it; #4,376 of 11,946 by trust here, #7,638 by citations
• Reinforcement Learning — 26 citations into it; #1,030 of 2,242 by trust here, #1,663 by citations
• Computer Networks and Communications — 9 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it; #11,250 of 21,575 by trust here, #13,269 by citations
• Statistical Learning Theory — 8 citations into it
• Statistics and Probability — 8 citations into it
• Scaling & Learning Theory — 7 citations into it

Whom Raphaël Féraud trusts overall

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

1. Eyal Even-Dar
2. Peter Auer
3. Vladimir Naumovich Vapni
4. Emilie Kaufmann
5. Shie Mannor
6. Leslie G. Valiant
7. Sébastien Bubeck
8. R´emi Munos
9. Sham M. Kakade
10. J. Langford

Who trusts Raphaël Féraud overall

1. Carlos Riquelme
2. Chorng-Shyong Ong
3. Cha Zhang
4. Christophe Garcia
5. Thiago D. Simão
6. Masrour Zoghi
7. Romain Laroche
8. Dongruo Zhou
9. Jeremy Schiff
10. George Tucker

Papers from Raphaël Féraud's perspective

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

Their papers

• A Fast and Accurate Face Detector Based on Neural Networks (2001) — 365 citations
• A Neural Networks Committee for the Contextual Bandit Problem (2014) — 93 citations
• Random Forest for the Contextual Bandit Problem (2015) — 50 citations
• A methodology to explain neural network classification (2002) — 158 citations
• Decentralized Exploration in Multi-Armed Bandits (2018) — 26 citations
• Generic Exploration and K-armed Voting Bandits (2013) — 111 citations