Jonathan Lee

Reinforcement Learning · with Dragan · 2016–2021

A researcher in the Science corpus: 3 papers; trusts 50 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 76 citations into it; #1,992 of 2,242 by trust here, #1,155 by citations
• Artificial Intelligence — 59 citations into it; #30,180 of 35,345 by trust here, #12,580 by citations
• Management Science and Operations Research — 32 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Control and Systems Engineering — 14 citations into it; #7,026 of 8,257 by trust here, #1,592 by citations
• Computer Networks and Communications — 10 citations into it
• Statistical Learning Theory — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Jonathan Lee trusts overall

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

1. Stéphane Ross
2. Aadirupa Saha
3. Christopher K. I. Williams
4. Christian Wirth
5. Richard S. Sutton
6. Dean A. Pomerleau
7. John Schulman
8. Jonathan Ho
9. Geoffrey E. Hinton
10. Sergey Levine

Who trusts Jonathan Lee overall

1. Jeffrey Mahler
2. Mayank Bansal
3. Yuping Luo
4. Yasasa Abeysirigoonawardena

Papers from Jonathan Lee's perspective

Ordered by how strongly the researchers Jonathan Lee 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Reinforcement Learning: An Introduction
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• DART: Noise Injection for Robust Imitation Learning (2017) — 328 citations
• Dueling RL: Reinforcement Learning with Trajectory Preferences (2021) — 120 citations
• Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations (2016) — 80 citations