William H. Montgomery

Reinforcement Learning · 2016

A researcher in the Science corpus: 2 papers; trusts 58 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 32 citations into it
• Artificial Intelligence — 20 citations into it; #34,319 of 35,345 by trust here, #28,481 by citations
• Computational Theory and Mathematics — 12 citations into it
• Control and Systems Engineering — 12 citations into it; #7,984 of 8,257 by trust here, #5,784 by citations
• Biomedical Engineering — 5 citations into it
• Robotics & Control — 5 citations into it

Whom William H. Montgomery trusts overall

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

1. Sergey Levine
2. Jan Peters
3. Pieter Abbeel
4. E. Todorov
5. Ronald J. Williams
6. John Schulman
7. Stefan Schaal
8. Richard S. Sutton
9. Geoffrey E. Hinton
10. Weiwei Li

Who trusts William H. Montgomery overall

1. Marvin Zhang
2. Yevgen Chebotar
3. Sergey Levine
4. Chelsea Finn
5. Pieter Abbeel
6. Ali Yahya
7. Abbas Abdolmaleki
8. Wen Sun
9. Cathy Wu
10. Karol Hausman

Papers from William H. Montgomery's perspective

Ordered by how strongly the researchers William H. Montgomery 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states (2016) — 40 citations
• Guided Policy Search as Approximate Mirror Descent (2016) — 129 citations