Peter Dayan

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Peter Dayan, a British mathematician, computer scientist and neuroscientist, and director at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, since early 2019 also affiliated with the SMARTStart training program of the Bernstein Network Computational Neuroscience. From 1998 until 2018, he was professor of computational neuroscience at University College London, and director of UCL's Gatsby Computational Neuroscience Unit.

Peter Dayan obtained a B.Sc. in mathematics from University of Cambridge and a Ph.D. in artificial intelligence from University of Edinburgh under David Wallace, which focused on Bayesian network and neural network models of machine learning. He was postdoctoral researcher at the Salk Institute for Biological Studies working with Terrence J. Sejnowski, and at the University of Toronto with Geoffrey E. Hinton, and was further assistant professor at MIT before relocating to UCL.

=Work= Peter Dayan's work has been influential in several fields impinging on cognitive science, including machine learning, mathematical statistics, neuroscience and psychology - he has articulated a view in which neural computation is akin to a Bayesian inference process. His research centers around self-supervised learning, reinforcement learning, temporal difference learning and population coding. He researched and published on Q-learning with Chris Watkins , and provided a proof of convergence of TD(λ) for arbitrary λ.

=Learning Go= Along with Nicol N. Schraudolph and Terrence J. Sejnowski, Peter Dayan worked and published on temporal difference learning to evaluate positions in Go.

=Selected Publications=

1990 ...

 * Peter Dayan (1990). Navigating Through Temporal Difference. NIPS 1990
 * Peter Dayan (1991). Reinforcing Connectionism: Learning the Statistical Way. Ph.D. thesis, University of Edinburgh
 * Chris Watkins, Peter Dayan (1992). Q-learning. Machine Learning, Vol. 8, No. 2
 * Peter Dayan (1992). The convergence of TD (λ) for general λ. Machine Learning, Vol. 8, No. 3
 * Peter Dayan, Geoffrey E. Hinton (1992). Feudal reinforcement learning. NIPS 1992, pdf
 * Peter Dayan (1993). Improving generalisation for temporal difference learning: The successor representation. Neural Computation, Vol. 5, pdf
 * Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (1993). Temporal Difference Learning of Position Evaluation in the Game of Go. NIPS 1993
 * Peter Dayan, Terrence J. Sejnowski (1994). TD(λ) converges with Probability 1. Machine Learning, Vol. 14, No. 1, pdf
 * Peter Dayan, Terrence J. Sejnowski (1996). Exploration Bonuses and Dual Control. Machine Learning, Vol. 25, No. 1, pdf
 * Peter Dayan (1999). Recurrent Sampling Models for the Helmholtz Machine. Neural Computation, Vol. 11, No. 3, pdf

2000 ...

 * Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (2001). Learning to Evaluate Go Positions via Temporal Difference Methods. Computational Intelligence in Games, Studies in Fuzziness and Soft Computing. Physica-Verlag, pdf
 * Peter Dayan, Laurence F. Abbott (2001, 2005). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press
 * Peter Dayan (2008). Load and Attentional Bayes. NIPS 2008

2010 ...

 * Peter Dayan (2012). How to set the switches on this thing. Current Opinion in Neurobiology, Vol. 22, pdf
 * Arthur Guez, David Silver, Peter Dayan (2012). Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. NIPS 2012
 * Arthur Guez, David Silver, Peter Dayan (2012). Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search. arXiv:1205.3109
 * Arthur Guez, David Silver, Peter Dayan (2013). Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search. Journal of Artificial Intelligence Research, Vol. 48
 * Arthur Guez, David Silver, Peter Dayan (2014). Better Optimism By Bayes: Adaptive Planning with Rich Models. arXiv:1402.1958v1
 * Arthur Guez, Nicolas Heess, David Silver, Peter Dayan (2014). Bayes-Adaptive Simulation-based Search with Value Function Approximation. NIPS 2014, pdf
 * Jack W. Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap (2018). Fast Parametric Learning with Activation Memorization. arXiv:1803.10049
 * Sanjeevan Ahilan, Peter Dayan (2018). Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. arXiv:1901.08492

=External Links=
 * Peter Dayan from Wikipedia
 * Peter Dayan and Li Zhaoping join the faculty — SMART START, January 28, 2019
 * Peter Dayan and Li Zhaoping appointed to the Max Planck Institute for Biological Cybernetics | Max Planck Society, September 25, 2018
 * Gatsby Computational Neuroscience Unit | Professor Peter Dayan

=References= Up one level