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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.
Christopher Clark [1]

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 [2] 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 [3] [4] [5] [6] [7].

Selected Publications

[8] [9]

External Links


References

  1. ^ Christopher Clark | LinkedIn
  2. ^ Convolutional neural network - Playing Go - Wikipedia
  3. ^ Christopher Clark, Amos Storkey (2014). Teaching Deep Convolutional Neural Networks to Play Go. arXiv:1412.3409
  4. ^ Teaching Deep Convolutional Neural Networks to Play Go by Hiroshi Yamashita, The Computer-go Archives, December 14, 2014
  5. ^ Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time | MIT Technology Review, December 15, 2014
  6. ^ Teaching Deep Convolutional Neural Networks to Play Go by Michel Van den Bergh, CCC, December 16, 2014
  7. ^ Teaching Deep Convolutional Neural Networks to Play Go by Hiroshi Yamashita, The Computer-go Archives, December 19, 2014
  8. ^ dblp: Christopher Clark
  9. ^ arXiv.org Christopher Clark

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