Philip Thomas

Reinforcement Learning · 2015

A researcher in the Science corpus: 1 paper; trusts 44 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 24 citations into it; #369 of 2,242 by trust here, #1,519 by citations
• Artificial Intelligence — 17 citations into it
• Management Science and Operations Research — 12 citations into it
• Statistics and Probability — 7 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Philip Thomas trusts overall

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

1. Richard S. Sutton
2. Philip S. Thomas
3. Dimitri P. Bertsekas
4. Andrew Barto
5. Bradley Efron
6. Nikolaus Hansen
7. K. Åström
8. Doina Precup
9. Georgios Theocharous
10. M. Powell

Who trusts Philip Thomas overall

1. Philip S. Thomas
2. Josiah P. Hanna
3. Joseph D. Futoma
4. Kimia Nadjahi
5. Romain Laroche
6. Emma Brunskill
7. Justin Fu
8. Nan Jiang
9. Aditya Grover
10. Ilya Kostrikov

Papers from Philip Thomas's perspective

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

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Safe Reinforcement Learning (2015) — 162 citations