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Created page with "'''Home * People * Peter Dayan''' FILE:Peter Dayan Royal Society.jpg|border|right|thumb| Peter Dayan <ref>Peter Dayan at the [https://en.wikipedia.org/wik..."
'''[[Main Page|Home]] * [[People]] * Peter Dayan'''
[[FILE:Peter Dayan Royal Society.jpg|border|right|thumb| Peter Dayan <ref>Peter Dayan at the [https://en.wikipedia.org/wiki/Royal_Society Royal Society] [https://en.wikipedia.org/wiki/Fellow_of_the_Royal_Society#Admission admissions day], [https://en.wikipedia.org/wiki/London London], July 13, 2018, by [https://commons.wikimedia.org/wiki/User:Duncan.Hull Duncan.Hull], [https://en.wikipedia.org/wiki/Wikimedia_Commons Wikimedia Commons]</ref> ]]
'''Peter Dayan''',<br/>
a British mathematician, computer scientist and neuroscientist, and director at the [https://en.wikipedia.org/wiki/Max_Planck_Institute_for_Biological_Cybernetics Max Planck Institute for Biological Cybernetics] in [https://en.wikipedia.org/wiki/T%C3%BCbingen Tübingen], Germany, since early 2019 also affiliated with the ''SMARTStart'' training program of the [https://en.wikipedia.org/wiki/Bernstein_Network Bernstein Network Computational Neuroscience] <ref>[https://www.smartstart-compneuro.de/about/peter-dayan-and-li-zhaoping-join-the-faculty-1 Peter Dayan and Li Zhaoping join the faculty] — [https://en.wikipedia.org/wiki/Bernstein_Network SMART START], January 28, 2019</ref> <ref>[https://www.bernstein-network.de/en/study-training-options/smart-start?set_language=en SMARTStart — Bernstein Netzwerk Computational Neuroscience]</ref>. From 1998 until 2018, he was professor of [https://en.wikipedia.org/wiki/Computational_neuroscience computational neuroscience] at [https://en.wikipedia.org/wiki/University_College_London University College London], and director of UCL's [https://en.wikipedia.org/wiki/Gatsby_Charitable_Foundation Gatsby Computational Neuroscience Unit] <ref>[http://www.gatsby.ucl.ac.uk/~dayan/ Gatsby Computational Neuroscience Unit | Professor Peter Dayan]</ref>.
Peter Dayan obtained a B.Sc. in mathematics from [https://en.wikipedia.org/wiki/University_of_Cambridge University of Cambridge] and a Ph.D. in [[Artificial Intelligence|artificial intelligence]] from [[University of Edinburgh]] under [[Mathematician#DJWallace|David Wallace]], which focused on [https://en.wikipedia.org/wiki/Bayesian_network Bayesian network] and [[Neural Networks|neural network]] models of [[Learning|machine learning]] <ref>[[Peter Dayan]] ('''1991'''). ''[https://www.era.lib.ed.ac.uk/handle/1842/14754 Reinforcing Connectionism: Learning the Statistical Way]''. Ph.D. thesis, [[University of Edinburgh]]</ref>. He was postdoctoral researcher at the [https://en.wikipedia.org/wiki/Salk_Institute_for_Biological_Studies Salk Institute for Biological Studies] working with [[Terrence J. Sejnowski]], and at the [[University of Toronto]] with [[Mathematician#GEHinton|Geoffrey E. Hinton]], and was further assistant professor at [[Massachusetts Institute of Technology|MIT]] before relocating to UCL.
=Work=
Peter Dayan's work has been influential in several fields impinging on [[Cognition|cognitive science]], including [[Learning|machine learning]], [https://en.wikipedia.org/wiki/Mathematical_statistics mathematical statistics], [https://en.wikipedia.org/wiki/Neuroscience neuroscience] and [[Psychology|psychology]] - he has articulated a view in which [[Neural Networks|neural computation]] is akin to a [https://en.wikipedia.org/wiki/Bayesian_inference Bayesian inference] process <ref>[https://cognitivesciencesociety.org/rumelhart-prize/ 2012 Recipient Peter Dayan] | [https://en.wikipedia.org/wiki/Rumelhart_Prize The David E. Rumelhart Prize 2012]</ref>. His research centers around [[Supervised Learning|self-supervised learning]], [[Reinforcement Learning|reinforcement learning]], [[Temporal Difference Learning|temporal difference learning]] and [https://en.wikipedia.org/wiki/Neural_coding#Population_coding population coding].
He researched and published on [https://en.wikipedia.org/wiki/Q-learning Q-learning] with [[Chris Watkins]] <ref> [[Chris Watkins]], [[Peter Dayan]] ('''1992'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/papers/wd92.html Q-learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 2</ref>,
and provided a proof of convergence of [[Temporal Difference Learning#TDLamba|TD(λ)]] for arbitrary λ <ref>[[Peter Dayan]] ('''1992'''). ''[https://link.springer.com/article/10.1023/A:1022632907294 The convergence of TD (λ) for general λ]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 3</ref>.
=Learning Go=
Along with [[Nicol N. Schraudolph]] and [[Terrence J. Sejnowski]], Peter Dayan worked and published on [[Temporal Difference Learning|temporal difference learning]] to [[Evaluation|evaluate]] positions in [[Go]] <ref>[[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1993'''). ''[https://papers.nips.cc/paper/820-temporal-difference-learning-of-position-evaluation-in-the-game-of-go Temporal Difference Learning of Position Evaluation in the Game of Go]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 NIPS 1993]</ref> <ref>[http://satirist.org/learn-game/systems/go-net.html Nici Schraudolph’s go networks], review by [[Jay Scott]]</ref>.
=Selected Publications=
<ref>[https://dblp.uni-trier.de/pers/hd/d/Dayan:Peter dblp: Peter Dayan]</ref>
==1990 ...==
* [[Peter Dayan]] ('''1990'''). ''[https://papers.nips.cc/paper/428-navigating-through-temporal-difference Navigating Through Temporal Difference]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-3-1990 NIPS 1990]
* [[Peter Dayan]] ('''1991'''). ''[https://www.era.lib.ed.ac.uk/handle/1842/14754 Reinforcing Connectionism: Learning the Statistical Way]''. Ph.D. thesis, [[University of Edinburgh]]
* [[Chris Watkins]], [[Peter Dayan]] ('''1992'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/papers/wd92.html Q-learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 2
* [[Peter Dayan]] ('''1992'''). ''[https://www.researchgate.net/publication/227208155_The_Convergence_of_TDl_for_General_l The convergence of TD (λ) for general λ]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 3
* [[Peter Dayan]], [[Mathematician#GEHinton|Geoffrey E. Hinton]] ('''1992'''). ''Feudal reinforcement learning''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-5-1992 NIPS 1992], [http://www.gatsby.ucl.ac.uk/~Dayan/papers/dh93.pdf pdf]
* [[Peter Dayan]] ('''1993'''). ''Improving generalisation for temporal difference learning: The successor representation''. [https://en.wikipedia.org/wiki/Neural_Computation_(journal) Neural Computation], Vol. 5, [http://www.gatsby.ucl.ac.uk/~dayan/papers/sr93.pdf pdf]
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1993'''). ''[https://papers.nips.cc/paper/820-temporal-difference-learning-of-position-evaluation-in-the-game-of-go Temporal Difference Learning of Position Evaluation in the Game of Go]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 NIPS 1993]
* [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1994'''). ''TD(λ) converges with Probability 1''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 14, No. 1, [https://www.researchgate.net/profile/Terrence_Sejnowski/publication/228392650_TD_X_Converges_with_Probability/links/54a4afea0cf256bf8bb327a9.pdf?origin=publication_detail pdf]
* [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1996'''). ''[https://link.springer.com/article/10.1023/A:1018357105171 Exploration Bonuses and Dual Control]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 25, No. 1, [http://www.gatsby.ucl.ac.uk/~dayan/papers/ds96.pdf pdf]
* [[Peter Dayan]] ('''1999'''). ''Recurrent Sampling Models for the Helmholtz Machine''. [https://en.wikipedia.org/wiki/Neural_Computation_(journal) Neural Computation], Vol. 11, No. 3, [http://www.gatsby.ucl.ac.uk/~dayan/papers/rechelm99.pdf pdf] <ref>[https://en.wikipedia.org/wiki/Helmholtz_machine Helmholtz machine from Wikipedia]</ref>
==2000 ...==
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''2001'''). ''[https://link.springer.com/chapter/10.1007/978-3-7908-1833-8_4 Learning to Evaluate Go Positions via Temporal Difference Methods]''. [http://jasss.soc.surrey.ac.uk/7/1/reviews/takama.html Computational Intelligence in Games, Studies in Fuzziness and Soft Computing]. [http://www.springer.com/economics?SGWID=1-165-6-73481-0 Physica-Verlag], [https://papers.cnl.salk.edu/PDFs/Learning%20to%20Evaluate%20Go%20Positions%20Via%20Temporal%20Difference%20Methods%202001-3244.pdf pdf]
* [[Peter Dayan]], [https://en.wikipedia.org/wiki/Larry_Abbott Laurence F. Abbott] ('''2001, 2005'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/book/index.html Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Peter Dayan]] ('''2008'''). ''[https://papers.nips.cc/paper/3516-load-and-attentional-bayes Load and Attentional Bayes]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-21-2008 NIPS 2008]
==2010 ...==
* [[Peter Dayan]] ('''2012'''). ''How to set the switches on this thing''. [https://www.journals.elsevier.com/current-opinion-in-neurobiology Current Opinion in Neurobiology], Vol. 22, [http://www.gatsby.ucl.ac.uk/~dayan/papers/dayanswitch2012.pdf pdf]
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2012'''). ''[https://papers.nips.cc/paper/4767-efficient-bayes-adaptive-reinforcement-learning-using-sample-based-search Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-25-2012 NIPS 2012]
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2012'''). ''Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search''. [https://arxiv.org/abs/1205.3109 arXiv:1205.3109]
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2013'''). ''[https://www.jair.org/index.php/jair/article/view/10853 Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research Journal of Artificial Intelligence Research], Vol. 48
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2014'''). ''Better Optimism By Bayes: Adaptive Planning with Rich Models''. [https://arxiv.org/abs/1402.1958 arXiv:1402.1958v1]
* [[Arthur Guez]], [[Nicolas Heess]], [[David Silver]], [[Peter Dayan]] ('''2014'''). ''Bayes-Adaptive Simulation-based Search with Value Function Approximation''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-27-2014 NIPS 2014], [https://papers.nips.cc/paper/5501-bayes-adaptive-simulation-based-search-with-value-function-approximation.pdf pdf]
* [https://dblp.org/pers/hd/r/Rae:Jack_W= Jack W. Rae], [https://scholar.google.com/citations?user=W2DsnAkAAAAJ&hl=en Chris Dyer], [[Peter Dayan]], [[Timothy Lillicrap]] ('''2018'''). ''Fast Parametric Learning with Activation Memorization''. [https://arxiv.org/abs/1803.10049 arXiv:1803.10049]
* [https://scholar.google.co.uk/citations?user=OAkRr-YAAAAJ&hl=en Sanjeevan Ahilan], [[Peter Dayan]] ('''2018'''). ''Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning''. [https://arxiv.org/abs/1901.08492 arXiv:1901.08492]
=External Links=
* [https://en.wikipedia.org/wiki/Peter_Dayan Peter Dayan from Wikipedia]
* [https://www.smartstart-compneuro.de/about/peter-dayan-and-li-zhaoping-join-the-faculty-1 Peter Dayan and Li Zhaoping join the faculty] — [https://en.wikipedia.org/wiki/Bernstein_Network SMART START], January 28, 2019
* [https://www.mpg.de/12300126/appointment-dayan-li Peter Dayan and Li Zhaoping appointed to the Max Planck Institute for Biological Cybernetics] | [https://en.wikipedia.org/wiki/Max_Planck_Society Max Planck Society], September 25, 2018
* [http://www.gatsby.ucl.ac.uk/~dayan/ Gatsby Computational Neuroscience Unit | Professor Peter Dayan]
=References=
<references />
'''[[People|Up one level]]'''
[[Category:Researcher|Dayan]]
[[FILE:Peter Dayan Royal Society.jpg|border|right|thumb| Peter Dayan <ref>Peter Dayan at the [https://en.wikipedia.org/wiki/Royal_Society Royal Society] [https://en.wikipedia.org/wiki/Fellow_of_the_Royal_Society#Admission admissions day], [https://en.wikipedia.org/wiki/London London], July 13, 2018, by [https://commons.wikimedia.org/wiki/User:Duncan.Hull Duncan.Hull], [https://en.wikipedia.org/wiki/Wikimedia_Commons Wikimedia Commons]</ref> ]]
'''Peter Dayan''',<br/>
a British mathematician, computer scientist and neuroscientist, and director at the [https://en.wikipedia.org/wiki/Max_Planck_Institute_for_Biological_Cybernetics Max Planck Institute for Biological Cybernetics] in [https://en.wikipedia.org/wiki/T%C3%BCbingen Tübingen], Germany, since early 2019 also affiliated with the ''SMARTStart'' training program of the [https://en.wikipedia.org/wiki/Bernstein_Network Bernstein Network Computational Neuroscience] <ref>[https://www.smartstart-compneuro.de/about/peter-dayan-and-li-zhaoping-join-the-faculty-1 Peter Dayan and Li Zhaoping join the faculty] — [https://en.wikipedia.org/wiki/Bernstein_Network SMART START], January 28, 2019</ref> <ref>[https://www.bernstein-network.de/en/study-training-options/smart-start?set_language=en SMARTStart — Bernstein Netzwerk Computational Neuroscience]</ref>. From 1998 until 2018, he was professor of [https://en.wikipedia.org/wiki/Computational_neuroscience computational neuroscience] at [https://en.wikipedia.org/wiki/University_College_London University College London], and director of UCL's [https://en.wikipedia.org/wiki/Gatsby_Charitable_Foundation Gatsby Computational Neuroscience Unit] <ref>[http://www.gatsby.ucl.ac.uk/~dayan/ Gatsby Computational Neuroscience Unit | Professor Peter Dayan]</ref>.
Peter Dayan obtained a B.Sc. in mathematics from [https://en.wikipedia.org/wiki/University_of_Cambridge University of Cambridge] and a Ph.D. in [[Artificial Intelligence|artificial intelligence]] from [[University of Edinburgh]] under [[Mathematician#DJWallace|David Wallace]], which focused on [https://en.wikipedia.org/wiki/Bayesian_network Bayesian network] and [[Neural Networks|neural network]] models of [[Learning|machine learning]] <ref>[[Peter Dayan]] ('''1991'''). ''[https://www.era.lib.ed.ac.uk/handle/1842/14754 Reinforcing Connectionism: Learning the Statistical Way]''. Ph.D. thesis, [[University of Edinburgh]]</ref>. He was postdoctoral researcher at the [https://en.wikipedia.org/wiki/Salk_Institute_for_Biological_Studies Salk Institute for Biological Studies] working with [[Terrence J. Sejnowski]], and at the [[University of Toronto]] with [[Mathematician#GEHinton|Geoffrey E. Hinton]], and was further assistant professor at [[Massachusetts Institute of Technology|MIT]] before relocating to UCL.
=Work=
Peter Dayan's work has been influential in several fields impinging on [[Cognition|cognitive science]], including [[Learning|machine learning]], [https://en.wikipedia.org/wiki/Mathematical_statistics mathematical statistics], [https://en.wikipedia.org/wiki/Neuroscience neuroscience] and [[Psychology|psychology]] - he has articulated a view in which [[Neural Networks|neural computation]] is akin to a [https://en.wikipedia.org/wiki/Bayesian_inference Bayesian inference] process <ref>[https://cognitivesciencesociety.org/rumelhart-prize/ 2012 Recipient Peter Dayan] | [https://en.wikipedia.org/wiki/Rumelhart_Prize The David E. Rumelhart Prize 2012]</ref>. His research centers around [[Supervised Learning|self-supervised learning]], [[Reinforcement Learning|reinforcement learning]], [[Temporal Difference Learning|temporal difference learning]] and [https://en.wikipedia.org/wiki/Neural_coding#Population_coding population coding].
He researched and published on [https://en.wikipedia.org/wiki/Q-learning Q-learning] with [[Chris Watkins]] <ref> [[Chris Watkins]], [[Peter Dayan]] ('''1992'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/papers/wd92.html Q-learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 2</ref>,
and provided a proof of convergence of [[Temporal Difference Learning#TDLamba|TD(λ)]] for arbitrary λ <ref>[[Peter Dayan]] ('''1992'''). ''[https://link.springer.com/article/10.1023/A:1022632907294 The convergence of TD (λ) for general λ]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 3</ref>.
=Learning Go=
Along with [[Nicol N. Schraudolph]] and [[Terrence J. Sejnowski]], Peter Dayan worked and published on [[Temporal Difference Learning|temporal difference learning]] to [[Evaluation|evaluate]] positions in [[Go]] <ref>[[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1993'''). ''[https://papers.nips.cc/paper/820-temporal-difference-learning-of-position-evaluation-in-the-game-of-go Temporal Difference Learning of Position Evaluation in the Game of Go]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 NIPS 1993]</ref> <ref>[http://satirist.org/learn-game/systems/go-net.html Nici Schraudolph’s go networks], review by [[Jay Scott]]</ref>.
=Selected Publications=
<ref>[https://dblp.uni-trier.de/pers/hd/d/Dayan:Peter dblp: Peter Dayan]</ref>
==1990 ...==
* [[Peter Dayan]] ('''1990'''). ''[https://papers.nips.cc/paper/428-navigating-through-temporal-difference Navigating Through Temporal Difference]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-3-1990 NIPS 1990]
* [[Peter Dayan]] ('''1991'''). ''[https://www.era.lib.ed.ac.uk/handle/1842/14754 Reinforcing Connectionism: Learning the Statistical Way]''. Ph.D. thesis, [[University of Edinburgh]]
* [[Chris Watkins]], [[Peter Dayan]] ('''1992'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/papers/wd92.html Q-learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 2
* [[Peter Dayan]] ('''1992'''). ''[https://www.researchgate.net/publication/227208155_The_Convergence_of_TDl_for_General_l The convergence of TD (λ) for general λ]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 3
* [[Peter Dayan]], [[Mathematician#GEHinton|Geoffrey E. Hinton]] ('''1992'''). ''Feudal reinforcement learning''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-5-1992 NIPS 1992], [http://www.gatsby.ucl.ac.uk/~Dayan/papers/dh93.pdf pdf]
* [[Peter Dayan]] ('''1993'''). ''Improving generalisation for temporal difference learning: The successor representation''. [https://en.wikipedia.org/wiki/Neural_Computation_(journal) Neural Computation], Vol. 5, [http://www.gatsby.ucl.ac.uk/~dayan/papers/sr93.pdf pdf]
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1993'''). ''[https://papers.nips.cc/paper/820-temporal-difference-learning-of-position-evaluation-in-the-game-of-go Temporal Difference Learning of Position Evaluation in the Game of Go]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-6-1993 NIPS 1993]
* [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1994'''). ''TD(λ) converges with Probability 1''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 14, No. 1, [https://www.researchgate.net/profile/Terrence_Sejnowski/publication/228392650_TD_X_Converges_with_Probability/links/54a4afea0cf256bf8bb327a9.pdf?origin=publication_detail pdf]
* [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''1996'''). ''[https://link.springer.com/article/10.1023/A:1018357105171 Exploration Bonuses and Dual Control]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 25, No. 1, [http://www.gatsby.ucl.ac.uk/~dayan/papers/ds96.pdf pdf]
* [[Peter Dayan]] ('''1999'''). ''Recurrent Sampling Models for the Helmholtz Machine''. [https://en.wikipedia.org/wiki/Neural_Computation_(journal) Neural Computation], Vol. 11, No. 3, [http://www.gatsby.ucl.ac.uk/~dayan/papers/rechelm99.pdf pdf] <ref>[https://en.wikipedia.org/wiki/Helmholtz_machine Helmholtz machine from Wikipedia]</ref>
==2000 ...==
* [[Nicol N. Schraudolph]], [[Peter Dayan]], [[Terrence J. Sejnowski]] ('''2001'''). ''[https://link.springer.com/chapter/10.1007/978-3-7908-1833-8_4 Learning to Evaluate Go Positions via Temporal Difference Methods]''. [http://jasss.soc.surrey.ac.uk/7/1/reviews/takama.html Computational Intelligence in Games, Studies in Fuzziness and Soft Computing]. [http://www.springer.com/economics?SGWID=1-165-6-73481-0 Physica-Verlag], [https://papers.cnl.salk.edu/PDFs/Learning%20to%20Evaluate%20Go%20Positions%20Via%20Temporal%20Difference%20Methods%202001-3244.pdf pdf]
* [[Peter Dayan]], [https://en.wikipedia.org/wiki/Larry_Abbott Laurence F. Abbott] ('''2001, 2005'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/book/index.html Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems]''. [https://en.wikipedia.org/wiki/MIT_Press MIT Press]
* [[Peter Dayan]] ('''2008'''). ''[https://papers.nips.cc/paper/3516-load-and-attentional-bayes Load and Attentional Bayes]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-21-2008 NIPS 2008]
==2010 ...==
* [[Peter Dayan]] ('''2012'''). ''How to set the switches on this thing''. [https://www.journals.elsevier.com/current-opinion-in-neurobiology Current Opinion in Neurobiology], Vol. 22, [http://www.gatsby.ucl.ac.uk/~dayan/papers/dayanswitch2012.pdf pdf]
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2012'''). ''[https://papers.nips.cc/paper/4767-efficient-bayes-adaptive-reinforcement-learning-using-sample-based-search Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search]''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-25-2012 NIPS 2012]
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2012'''). ''Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search''. [https://arxiv.org/abs/1205.3109 arXiv:1205.3109]
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2013'''). ''[https://www.jair.org/index.php/jair/article/view/10853 Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research Journal of Artificial Intelligence Research], Vol. 48
* [[Arthur Guez]], [[David Silver]], [[Peter Dayan]] ('''2014'''). ''Better Optimism By Bayes: Adaptive Planning with Rich Models''. [https://arxiv.org/abs/1402.1958 arXiv:1402.1958v1]
* [[Arthur Guez]], [[Nicolas Heess]], [[David Silver]], [[Peter Dayan]] ('''2014'''). ''Bayes-Adaptive Simulation-based Search with Value Function Approximation''. [https://papers.nips.cc/book/advances-in-neural-information-processing-systems-27-2014 NIPS 2014], [https://papers.nips.cc/paper/5501-bayes-adaptive-simulation-based-search-with-value-function-approximation.pdf pdf]
* [https://dblp.org/pers/hd/r/Rae:Jack_W= Jack W. Rae], [https://scholar.google.com/citations?user=W2DsnAkAAAAJ&hl=en Chris Dyer], [[Peter Dayan]], [[Timothy Lillicrap]] ('''2018'''). ''Fast Parametric Learning with Activation Memorization''. [https://arxiv.org/abs/1803.10049 arXiv:1803.10049]
* [https://scholar.google.co.uk/citations?user=OAkRr-YAAAAJ&hl=en Sanjeevan Ahilan], [[Peter Dayan]] ('''2018'''). ''Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning''. [https://arxiv.org/abs/1901.08492 arXiv:1901.08492]
=External Links=
* [https://en.wikipedia.org/wiki/Peter_Dayan Peter Dayan from Wikipedia]
* [https://www.smartstart-compneuro.de/about/peter-dayan-and-li-zhaoping-join-the-faculty-1 Peter Dayan and Li Zhaoping join the faculty] — [https://en.wikipedia.org/wiki/Bernstein_Network SMART START], January 28, 2019
* [https://www.mpg.de/12300126/appointment-dayan-li Peter Dayan and Li Zhaoping appointed to the Max Planck Institute for Biological Cybernetics] | [https://en.wikipedia.org/wiki/Max_Planck_Society Max Planck Society], September 25, 2018
* [http://www.gatsby.ucl.ac.uk/~dayan/ Gatsby Computational Neuroscience Unit | Professor Peter Dayan]
=References=
<references />
'''[[People|Up one level]]'''
[[Category:Researcher|Dayan]]
