Álvaro, this is exactly something that I have been thinking about as
well (the last part about MC+NN and feedback between the two). It
seems like the authors of that paper are also thinking about
something similar.

I currently have the very basics of an implementation as well but
performance is bad and I don't have access to any (reasonable) GPUs and
currently only a very slow CPU. It would be nice to try to figure
something out together if you're interested.

Regards, Mikael Simberg


On Mon, Dec 15, 2014, at 02:54, Álvaro Begué wrote:
>
> When I had an opportunity to talk to Yann LeCun about a month ago, I
> asked him if anybody had used convolutional neural networks to play go
> and he wasn't aware of any efforts in that direction. This is
> precisely what I had in mind. Thank you very much for the link,
> Hiroshi!
>
> I have been learning about neural networks recently, I have some basic
> implementation (e.g., no convolutional layers yet), and I am working
> hard on getting GPU acceleration.
>
> One of the things I want to do with NNs is almost exactly what's
> described in this paper (but including number of liberties and chain
> length as inputs). I also want to try to use a similar NN to
> initialize win/loss counts in new nodes in the tree in a MCTS program.
> One last thing I want to experiment with is providing the results of
> MC simulations as inputs to the network to improve its performance. I
> think there is potential for MC to help NNs and viceversa.
>
> Does anyone else in this list have an interest in applying these
> techniques to computer go?
>
>
> Álvaro.
>
>
>
>
> On Sun, Dec 14, 2014 at 6:53 PM, Hiroshi Yamashita
> <y...@bd.mbn.or.jp> wrote:
>> Hi,
>>
>>
This paper looks very cool.
>>
>>
Teaching Deep Convolutional Neural Networks to Play Go
>> http://arxiv.org/pdf/1412.__3409v1.pdf
>>
>>
Thier move prediction got 91% winrate against GNU Go and 14%
>>
against Fuego in 19x19.
>>
>>
Regards,
>>
Hiroshi Yamashita
>>
>>
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