L'eonard Hussenot

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 9 papers; trusts 171 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 294 citations into it
• Reinforcement Learning — 280 citations into it
• Computer Vision and Pattern Recognition — 64 citations into it
• Control and Systems Engineering — 59 citations into it
• Optimization & Training — 43 citations into it
• Evaluation & Benchmarks — 42 citations into it
• Management Science and Operations Research — 30 citations into it
• Computational Theory and Mathematics — 29 citations into it

Whom L'eonard Hussenot trusts overall

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

1. Diederik P. Kingma
2. Sergey Levine
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Andrew Y. Ng
7. Pieter Abbeel
8. Ronald J. Williams
9. Alex Krizhevsky
10. John Schulman

Who trusts L'eonard Hussenot overall

1. Tim Pearce
2. Robert Dadashi

Papers from L'eonard Hussenot's perspective

Ordered by how strongly the researchers L'eonard Hussenot 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. GENERATIVE ADVERSARIAL NETS
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study (2020) — 283 citations
• Offline Reinforcement Learning with Pseudometric Learning (2021) — 44 citations
• Primal Wasserstein Imitation Learning (2020) — 157 citations
• Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 citations
• What Matters for Adversarial Imitation Learning? (2021) — 102 citations
• Hyperparameter Selection for Imitation Learning (2021) — 21 citations
• Continuous Control with Action Quantization from Demonstrations (2021) — 33 citations
• RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning (2021) — 42 citations
• What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study (2021) — 249 citations