Philippe Hansen-Estruch

Artificial Intelligence · 2022–2023

A researcher in the Science corpus: 3 papers; trusts 48 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it
• Reinforcement Learning — 70 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Control and Systems Engineering — 27 citations into it
• Computational Theory and Mathematics — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Optimization & Training — 8 citations into it

Whom Philippe Hansen-Estruch trusts overall

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

1. Sascha Lange
2. Geoffrey E. Hinton
3. Sergey Levine
4. Kaiming He
5. Diederik P. Kingma
6. Jimmy Ba
7. Ilya Kostrikov
8. Martin A. Riedmiller
9. Alex Krizhevsky
10. Richard S. Sutton

Who trusts Philippe Hansen-Estruch overall

1. Kevin Black
2. Homer Rich Walke

Papers from Philippe Hansen-Estruch's perspective

Ordered by how strongly the researchers Philippe Hansen-Estruch 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• BridgeData V2: A Dataset for Robot Learning at Scale (2023) — 762 citations
• IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies (2023) — 320 citations
• Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning (2022) — 47 citations