Hi Mark, 2014-12-20 19:17 GMT+00:00 Mark Wagner <wagner.mar...@gmail.com>:
> Thanks for sharing. I'm intrigued by your strategy for integrating > with MCTS. It's clear that latency is a challenge for integration. Do > you have any statistics on how many searches new nodes had been > through by the time the predictor comes back with an estimation? Did > you try any prefetching techniques? Because the CNN will guide much of > the search at the frontier of the tree, prefetching should be > tractable. Did you do any comparisons between your MCTS with and w/o CNN? That's > the direction that many of us will be attempting over the next few > months it seems :) I'm glad you like the paper and would consider to attempt. :) Thanks for the interesting suggestions. Regards, Aja > > - Mark > > On Sat, Dec 20, 2014 at 10:43 AM, Álvaro Begué <alvaro.be...@gmail.com> > wrote: > > If you start with a 19x19 grid and you take convolutional filters of size > > 5x5 (as an example), you'll end up with a board of size 15x15, because a > 5x5 > > box can be placed inside a 19x19 board in 15x15 different locations. We > can > > get 19x19 outputs if we allow the 5x5 box to be centered on any point, > but > > then you need to do multiply by values outside of the original 19x19 > board. > > Zero-padding just means you'll use 0 as the value coming from outside the > > board. You can either prepare a 23x23 matrix with two rows of zeros along > > the edges, or you can just keep the 19x19 input and do your math > carefully > > so terms outside the board are ignored. > > > > > > > > On Sat, Dec 20, 2014 at 12:01 PM, Detlef Schmicker <d...@physik.de> > wrote: > >> > >> Hi, > >> > >> I am still fighting with the NN slang, but why do you zero-padd the > >> output (page 3: 4 Architecture & Training)? > >> > >> From all I read up to now, most are zero-padding the input to make the > >> output fit 19x19?! > >> > >> Thanks for the great work > >> > >> Detlef > >> > >> Am Freitag, den 19.12.2014, 23:17 +0000 schrieb Aja Huang: > >> > Hi all, > >> > > >> > > >> > We've just submitted our paper to ICLR. We made the draft available at > >> > http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf > >> > > >> > > >> > > >> > I hope you enjoy our work. Comments and questions are welcome. > >> > > >> > > >> > Regards, > >> > Aja > >> > _______________________________________________ > >> > Computer-go mailing list > >> > Computer-go@computer-go.org > >> > http://computer-go.org/mailman/listinfo/computer-go > >> > >> > >> _______________________________________________ > >> Computer-go mailing list > >> Computer-go@computer-go.org > >> http://computer-go.org/mailman/listinfo/computer-go > > > > > > > > _______________________________________________ > > Computer-go mailing list > > Computer-go@computer-go.org > > http://computer-go.org/mailman/listinfo/computer-go > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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