Paul Garnier

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it
• Optimization & Training — 6 citations into it

Whom Paul Garnier trusts overall

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

1. Nikolaus Hansen
2. Richard S. Sutton
3. Sepp Hochreiter
4. John Schulman
5. J. Urgen Schmidhuber
6. Ronald J. Williams
7. G. Tesauro
8. Geoffrey E. Hinton
9. Kimin Lee
10. Yoshua Bengio

Who trusts Paul Garnier overall

1. Ji-chao Li
2. Xiaosong Du

Papers from Paul Garnier's perspective

Ordered by how strongly the researchers Paul Garnier trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Asynchronous Methods for Deep Reinforcement Learning
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A review on Deep Reinforcement Learning for Fluid Mechanics (2019) — 330 citations