Christopher+Clark

a British web developer, programmer and research assistant at the psychology department, University of Edinburgh with expertise neural networks, in particular in collaboration with Amos Storkey on deep convolutional neural networks to represent and learn a move evaluation function for the game of Go. || toc =DCNNs in Go= As reported in their 2014 paper //Teaching Deep Convolutional Neural Networks to Play Go//, Clark and Storkey 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:ChristopherClark.jpg link="https://uk.linkedin.com/in/thechrisclark"]] ||~ || **Christopher Clark**,
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=Selected Publications=
 * [|Mohammad Pourhomayoun], [|Peter Dugan], [|Marian Popescu], Christopher Clark (**2013**). //Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network//. [|arXiv:1305.3635]
 * Christopher Clark, Amos Storkey (**2014**). //Teaching Deep Convolutional Neural Networks to Play Go//. [|arXiv:1412.3409]

=External Links=
 * [|Christopher Clark | LinkedIn]

=References= =What links here?= include page="Christopher Clark" component="backlinks" limit="40"
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