Fabrice Clérot

Artificial Intelligence · 2002–2015

A researcher in the Science corpus: 4 papers; trusts 32 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #15,702 of 35,345 by trust here, #20,377 by citations
• Management Science and Operations Research — 32 citations into it; #6,144 of 11,946 by trust here, #6,563 by citations
• Reinforcement Learning — 24 citations into it; #801 of 2,242 by trust here, #1,455 by citations
• Computer Networks and Communications — 12 citations into it; #5,066 of 9,886 by trust here, #7,492 by citations
• Information Systems — 7 citations into it
• Statistical Learning Theory — 7 citations into it
• Information Retrieval & Search — 6 citations into it
• Statistics and Probability — 5 citations into it

Whom Fabrice Clérot trusts overall

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

1. Yisong Yue
2. Eyal Even-Dar
3. Peter Auer
4. R´emi Munos
5. Thorsten Joachims
6. Jean-Yves Audibert
7. Sébastien Bubeck
8. T. L. Lai Andherbertrobbins
9. Nicolò Cesa-Bianchi
10. Shie Mannor

Who trusts Fabrice Clérot overall

1. Aadirupa Saha
2. Masrour Zoghi
3. Róbert Istvan Busa-Fekete
4. Raphaël Féraud
5. Chorng-Shyong Ong
6. Shimon Whiteson
7. Akash Kumar Mohankumar
8. Huasen Wu
9. Maarten de Rijke
10. Eyke Hüllermeier

Papers from Fabrice Clérot's perspective

Ordered by how strongly the researchers Fabrice Clérot 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Reinforcement Learning: An Introduction
10. Probability Inequalities for Sums of Bounded Random Variables

Their papers

• Random Forest for the Contextual Bandit Problem (2015) — 50 citations
• A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits (2015) — 54 citations
• A methodology to explain neural network classification (2002) — 158 citations
• Generic Exploration and K-armed Voting Bandits (2013) — 111 citations