Ethan Holly

Reinforcement Learning · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 45 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 57 citations into it; #440 of 2,242 by trust here, #303 by citations
• Artificial Intelligence — 45 citations into it; #6,097 of 35,345 by trust here, #6,664 by citations
• Control and Systems Engineering — 35 citations into it; #538 of 8,257 by trust here, #103 by citations
• Biomedical Engineering — 15 citations into it; #4,680 of 10,734 by trust here, #1,507 by citations
• Computational Theory and Mathematics — 13 citations into it
• Optimization & Training — 9 citations into it
• Aerospace Engineering — 6 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Ethan Holly trusts overall

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

1. Jan Peters
2. Ronald J. Williams
3. Martin A. Riedmiller
4. Richard S. Sutton
5. Diederik P. Kingma
6. Stefan Schaal
7. Geoffrey E. Hinton
8. Sergey Levine
9. Pieter Abbeel
10. Sergey Ioffe

Who trusts Ethan Holly overall

1. Yuxi Li
2. Sergey Levine
3. Ofir Nachum
4. Lei Tai
5. Abhishek Gupta
6. Shansi Zhang
7. Shixiang Shane Gu
8. Julian Ibarz
9. Benjamin Eysenbach
10. Xue Bin Peng

Papers from Ethan Holly's perspective

Ordered by how strongly the researchers Ethan Holly 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Their papers

• QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (2018) — 1,735 citations
• Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates (2016) — 1,601 citations