Vincent François-Lavet

Reinforcement Learning · with Pineau · 2018

A researcher in the Science corpus: 3 papers; trusts 181 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 225 citations into it
• Artificial Intelligence — 214 citations into it
• Computer Vision and Pattern Recognition — 61 citations into it
• Management Science and Operations Research — 34 citations into it
• Control and Systems Engineering — 32 citations into it
• Computational Theory and Mathematics — 26 citations into it
• Optimization & Training — 21 citations into it
• Evaluation & Benchmarks — 19 citations into it

Whom Vincent François-Lavet trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Sergey Levine
4. Yoshua Bengio
5. Alex Krizhevsky
6. Pieter Abbeel
7. J. Urgen Schmidhuber
8. Diederik P. Kingma
9. Yann LeCun
10. Marc G. Bellemare

Who trusts Vincent François-Lavet overall

1. Ben M. Hambly
2. Ji-chao Li
3. Marc G. Bellemare
4. Carles Gelada
5. Yash Chandak
6. Max Schwarzer
7. K. Liang
8. Renyuan Xu
9. Xiaosong Du
10. Saurabh Kumar

Papers from Vincent François-Lavet's perspective

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

Their papers

• An Introduction to Deep Reinforcement Learning (2018) — 1,498 citations
• Reward Estimation for Variance Reduction in Deep Reinforcement Learning (2018) — 45 citations
• Combined Reinforcement Learning via Abstract Representations (2018) — 99 citations