Betty J. Mohler

Reinforcement Learning · 2007–2014

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 18 citations into it
• Artificial Intelligence — 8 citations into it; #10,314 of 35,345 by trust here, #19,018 by citations
• Control and Systems Engineering — 8 citations into it; #1,646 of 8,257 by trust here, #2,890 by citations
• Cognitive Neuroscience — 5 citations into it; #2,464 of 10,852 by trust here, #5,990 by citations

Whom Betty J. Mohler trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Stefan Schaal
4. Andrew Y. Ng
5. Pieter Abbeel
6. Andrew Barto
7. Sergey Levine
8. Martin L. Puterman
9. Jan Peters
10. Warrren B Powell

Who trusts Betty J. Mohler overall

1. Jens Kober
2. Jan Peters
3. Gerhard Neumann
4. Christian Daniel
5. Katharina Muelling
6. Marc Peter Deisenroth
7. Oliver Kroemer
8. Abbas Abdolmaleki
9. Yevgen Chebotar
10. Takayuki Osa

Papers from Betty J. Mohler's perspective

Ordered by how strongly the researchers Betty J. Mohler 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. Reinforcement Learning: An Introduction
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

• Learning perceptual coupling for motor primitives (2008) — 92 citations
• Learning strategies in table tennis using inverse reinforcement learning (2014) — 81 citations
• Visual flow influences gait transition speed and preferred walking speed (2007) — 332 citations