Jean Rabault
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 42 citations into it
- Reinforcement Learning — 39 citations into it
- Optimization & Training — 9 citations into it
- Computational Mechanics — 7 citations into it
- Computational Theory and Mathematics — 6 citations into it
- Electrical and Electronic Engineering — 6 citations into it
- Statistical and Nonlinear Physics — 5 citations into it
Whom Jean Rabault trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yann LeCun
- Timothy A. Davis
- Geoffrey E. Hinton
- John Schulman
- Yoshua Bengio
- Steven L. Brunton
- Volodymyr Mnih
- J. Urgen Schmidhuber
- Alex Krizhevsky
- Richard S. Sutton
Who trusts Jean Rabault overall
Papers from Jean Rabault's perspective
Ordered by how strongly the researchers Jean Rabault 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- A review on Deep Reinforcement Learning for Fluid Mechanics (2019) — 330 citations
- Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control (2018) — 553 citations