William T. B. Uther

Reinforcement Learning · 1997–2009

A researcher in the Science corpus: 7 papers; trusts 60 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 75 citations into it; #1,920 of 2,242 by trust here, #1,864 by citations
• Artificial Intelligence — 55 citations into it; #4,146 of 35,345 by trust here, #18,586 by citations
• Management Science and Operations Research — 44 citations into it; #736 of 11,946 by trust here, #4,746 by citations
• Computer Networks and Communications — 21 citations into it
• Statistics and Probability — 20 citations into it
• Statistical Learning Theory — 15 citations into it
• Optimization & Training — 10 citations into it
• Game & Decision Theory — 9 citations into it

Whom William T. B. Uther trusts overall

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

1. Andrew W. Moore
2. Michael L. Littman
3. Andrew McCallum
4. Leemon C. Baird III
5. Leslie Pack Kaelbling
6. J. Ross Quinlan
7. Justin A. Boyan
8. Richard S. Sutton
9. David Chapman
10. Thomas G. Dietterich

Who trusts William T. B. Uther overall

1. Manuela M. Veloso
2. Ziyang Tang
3. Warrren B Powell
4. Aaron M. Roth
5. Matthew Riemer
6. Michael H. Bowling
7. Mohammad Ghavamzadeh
8. Khimya Khetarpal
9. Pascal Klink
10. He He

Papers from William T. B. Uther's perspective

Ordered by how strongly the researchers William T. B. Uther 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. Learning Multiple Layers of Features from Tiny Images
9. Reinforcement Learning: An Introduction
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Markov Decision Processes (2004) — 1,276 citations
• The Lumberjack Algorithm for Learning Linked Decision Forests (2000) — 13 citations
• Bootstrapping from Game Tree Search (2009) — 67 citations
• TTree: Tree-Based State Generalization with Temporally Abstract Actions (2002) — 14 citations
• Tree based hierarchical reinforcement learning (2002) — 28 citations
• Tree Based Discretization for Continuous State Space Reinforcement Learning (1998)
• Adversarial Reinforcement Learning (1997)