Garrett Thomas

Reinforcement Learning · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 198 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 149 citations into it; #756 of 2,242 by trust here, #494 by citations
• Artificial Intelligence — 144 citations into it; #7,119 of 35,345 by trust here, #4,979 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Control and Systems Engineering — 34 citations into it
• Computational Theory and Mathematics — 26 citations into it; #10,360 of 12,182 by trust here, #2,298 by citations
• Management Science and Operations Research — 20 citations into it; #10,178 of 11,946 by trust here, #2,355 by citations
• Electrical and Electronic Engineering — 17 citations into it
• Evaluation & Benchmarks — 15 citations into it

Whom Garrett Thomas trusts overall

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

1. Sergey Levine
2. Richard S. Sutton
3. Chelsea Finn
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Pieter Abbeel
7. J. Urgen Schmidhuber
8. Ronald J. Williams
9. Yoshua Bengio
10. E. Todorov

Who trusts Garrett Thomas overall

1. Michael Janner
2. Sergey Levine
3. Tianhe Yu
4. Jessica B. Hamrick
5. Arthur Argenson
6. Wonjoon Goo
7. Rafael Rafailov
8. Qiyang Li
9. Byung-Jun Lee
10. Karthik Narasimhan

Papers from Garrett Thomas's perspective

Ordered by how strongly the researchers Garrett Thomas 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Value Iteration Networks (2016) — 697 citations
• MOPO: Model-based Offline Policy Optimization (2020) — 951 citations
• A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning (2019) — 19 citations
• Model-based Adversarial Meta-Reinforcement Learning (2020) — 57 citations