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Timothy P. (Tim) Lillicrap,
a Canadian neuroscientist an AI researcher, adjunct professor at University College London, and staff research scientist at Google, DeepMind, where he is involved in the AlphaGo and AlphaZero projects mastering the games of Go, chess and Shogi. He holds a B.Sc. in cognitive science and artificial intelligence from University of Toronto in 2005, and a Ph.D. in systems neuroscience from Queen's University in 2014 under Stephen H. Scott [1] [2]. His research focuses on machine learning and statistics for optimal control and decision making, as well as using these mathematical frameworks to understand how the brain learns. He has developed algorithms and approaches for exploiting deep neural networks in the context of reinforcement learning, and new recurrent memory architectures for one-shot learning [3].
Timothy Lillicrap [4]

Selected Publications

[5]

2014

2015 ...

2016
2017

External Links


References

  1. ^ Timothy Lillicrap (2014). Modelling Motor Cortex using Neural Network Controls Laws. Ph.D. Systems Neuroscience Thesis, Centre for Neuroscience Studies, Queen's University, advisor: Stephen H. Scott
  2. ^ Curriculum Vitae - Timothy P. Lillicrap (pdf)
  3. ^ timothy lillicrap - research
  4. ^ Image captured from the Data efficient Deep Reinforcement Learning for Continuous Control - Video at 20:21
  5. ^ dblp: Timothy P. Lillicrap
  6. ^ Q-learning from Wikipedia
  7. ^ AlphaGo Zero: Learning from scratch by Demis Hassabis and David Silver, DeepMind, October 18, 2017

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