Philip Thomas

Reinforcement Learning · with Jagannatha · 2018

A researcher in the Science corpus: 1 paper; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 13 citations into it
• Artificial Intelligence — 10 citations into it

Whom Philip Thomas trusts overall

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

1. Damien Ernst
2. Doina Precup
3. Richard S. Sutton
4. R´emi Munos
5. Shamim Nemati
6. Shixiang Shane Gu
7. Aniruddh Raghu
8. Marzyeh Ghassemi

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. ImageNet classification with deep convolutional neural networks
2. Reinforcement Learning: An Introduction
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Eligibility Traces for Off-Policy Policy Evaluation
8. Attention is All you Need (Neural Information Processing Systems)
9. Tree-Based Batch Mode Reinforcement Learning
10. ImageNet: A large-scale hierarchical image database

Their papers

• Towards High Confidence Off-Policy Reinforcement Learning for Clinical Applications (2018)