Owen Jow

Reinforcement Learning · 2017

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 31 citations into it; #1,339 of 2,242 by trust here, #895 by citations
• Artificial Intelligence — 24 citations into it; #18,194 of 35,345 by trust here, #9,760 by citations
• Control and Systems Engineering — 22 citations into it; #3,689 of 8,257 by trust here, #906 by citations

Whom Owen Jow trusts overall

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

1. Stefan Schaal
2. Sergey Levine
3. Diederik P. Kingma
4. Pieter Abbeel
5. Jan Peters
6. Geoffrey E. Hinton
7. Jimmy Ba
8. Christian Szegedy
9. Andrew Y. Ng
10. Richard S. Sutton

Who trusts Owen Jow overall

1. Ajay Mandlekar
2. Corey Lynch
3. Jonathan Booher
4. Peter R. Florence
5. Chelsea Finn

Papers from Owen Jow's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Going deeper with convolutions
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Human-level control through deep reinforcement learning
10. Asynchronous Methods for Deep Reinforcement Learning

Their papers

• Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation (2017) — 778 citations