>Is it really such a burden?
Well, I have to place my bets on some things and not on others. It seems to me that the costs of a NN must be higher than a system based on decision trees. The convolution NN has a very large parameter space if my reading of the paper is correct. Specifically, it can represent all patterns translated and rotated and matched against all points in parallel. To me, that seems like a good way to mimic the visual cortex, but an inefficient way to match patterns on a Go board. So my bet is on decision trees. The published research on NN will help me to understand the opportunities much better, and I have every expectation that the performance of decision trees should be >= NN in every way. E.g., faster, more accurate, easier and faster to tune. I recognize that my approach is full of challenges. E.g., a NN would automatically infer "soft" qualities such as "wall", "influence" that would have to be provided to a DT as inputs. No free lunch, but again, this is about betting that one technology is (overall) more suitable than another. From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Stefan Kaitschick Sent: Monday, December 15, 2014 6:37 PM To: computer-go@computer-go.org Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go Finally, I am not a fan of NN in the MCTS architecture. The NN architecture imposes a high CPU burden (e.g., compared to decision trees), and this study didn't produce such a breakthrough in accuracy that I would give away performance. Is it really such a burden? Supporting the move generator with the NN result high up in the decision tree can't be that expensive.
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