Michael C. Yip

Reinforcement Learning · 2014–2019

A researcher in the Science corpus: 4 papers; trusts 114 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 42 citations into it
• Artificial Intelligence — 41 citations into it; #14,305 of 35,345 by trust here, #30,347 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Control and Systems Engineering — 25 citations into it; #2,322 of 8,257 by trust here, #5,227 by citations
• Aerospace Engineering — 15 citations into it
• Biomedical Engineering — 13 citations into it; #2,122 of 10,734 by trust here, #8,749 by citations
• Robotics & Control — 11 citations into it
• Optimization & Training — 10 citations into it

Whom Michael C. Yip trusts overall

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

1. Sergey Levine
2. Pieter Abbeel
3. Chelsea Finn
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. John Schulman
7. Bryan A. Jones
8. D. C. Rucker
9. Andrew Y. Ng
10. Steven M. LaValle

Who trusts Michael C. Yip overall

1. Igor Mordatch
2. Jessica Burgner-Kahrs
3. Anusha Nagabandi
4. Paul Christiano
5. Nikhil Mishra
6. Faraz Torabi
7. D. C. Rucker
8. Gregory Kahn
9. Akanksha Saran
10. Sam Toyer

Papers from Michael C. Yip's perspective

Ordered by how strongly the researchers Michael C. Yip 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Motion Planning Networks (2018) — 294 citations
• Adversarial Imitation via Variational Inverse Reinforcement Learning (2018) — 65 citations
• Model-Less Feedback Control of Continuum Manipulators in Constrained Environments (2014) — 253 citations
• Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion Planners (2019) — 318 citations