Amos+Storkey

a British computer scientist and [|reader] (associate professor) at School of Informatics, University of Edinburgh. He holds a Ph.D. in 1999 on neural networks from the Neural Systems Group, Department of Electrical Engineering, [|Imperial College London]. His research covers machine learning applied to [|prediction market], [|astronomy] and changing [|environments], [|bayesian modeling] in [|neuroimaging], learning and [|inference], dynamical [|Boltzmann machine] models, and scalable deep learning. || toc =DCNNs in Go= As reported in their 2014 paper //Teaching Deep Convolutional Neural Networks to Play Go//, Amos Storkey along with Christopher Clark trained an 8-layer convolutional neural network by supervised learning from a database of human professional games to predict the moves made by expert Go players. They introduced a number of novel techniques, including a method of tying weights in the network to 'hard code' symmetries that are expect to exist in the target function, and demonstrated in an ablation study they considerably improve performance. Their final networks can consistently defeat Gnu Go, indicating it is state of the art among programs that do not use Monte-Carlo Tree Search, and was also able to win some games against Fuego while using a fraction of the play time.
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 * [[image:AmosStorkey.jpg link="http://homepages.inf.ed.ac.uk/amos/introduction.html"]] ||~ || **Amos Storkey**,
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=Selected Publications=
 * Amos Storkey (**1999**). //Efficient Covariance Matrix Methods for Bayesian Gaussian Processes and Hopfield Neural Networks//. Ph.D. thesis. [|Imperial College London]
 * Amos Storkey (**2011**). //Machine Learning Markets//. [|arXiv:1106.4509]
 * Christopher Clark, Amos Storkey (**2014**). //Teaching Deep Convolutional Neural Networks to Play Go//. [|arXiv:1412.3409]

=External Links=
 * [|Amos Storkey]
 * [|Amos Storkey - Google Scholar Citations]

=References= =What links here?= include page="Amos Storkey" component="backlinks" limit="80"
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