BTW, by improvement, I don't mean higher Go playing skill...I mean appearing close to the same level of Go playing skill _per_ _move_ with far less computational cost. It's the total game outcomes that will fall.
On Sun, Jun 12, 2016 at 3:55 PM, Jim O'Flaherty <jim.oflaherty...@gmail.com> wrote: > The purpose is to see if there is some sort of "simplification" available > to the emerged complex functions encoded in the weights. It is a typical > reductionist strategy, especially where there is an attempt to converge on > human conceptualization. Given the complexity of the nuances in Go, my > intuition says that it will show excellent improvement in short term play > at the cost of nuance in longer term play. > > On Sun, Jun 12, 2016 at 6:05 AM, Álvaro Begué <alvaro.be...@gmail.com> > wrote: > >> I don't understand the point of using the deeper network to train the >> shallower one. If you had enough data to be able to train a model with many >> parameters, you have enough to train a model with fewer parameters. >> >> Álvaro. >> >> >> On Sun, Jun 12, 2016 at 5:52 AM, Michael Markefka < >> michael.marke...@gmail.com> wrote: >> >>> Might be worthwhile to try the faster, shallower policy network as a >>> MCTS replacement if it were fast enough to support enough breadth. >>> Could cut down on some of the scoring variations that confuse rather >>> than inform the score expectation. >>> >>> On Sun, Jun 12, 2016 at 10:56 AM, Stefan Kaitschick >>> <skaitsch...@gmail.com> wrote: >>> > I don't know how the added training compares to direct training of the >>> > shallow network. >>> > It's prob. not so important, because both should be much faster than >>> the >>> > training of the deep NN. >>> > Accuracy should be slightly improved. >>> > >>> > Together, that might not justify the effort. But I think the fact that >>> you >>> > can create the mimicking NN, after the deep NN has been refined with >>> self >>> > play, is important. >>> > >>> > On Sun, Jun 12, 2016 at 9:51 AM, Petri Pitkanen < >>> petri.t.pitka...@gmail.com> >>> > wrote: >>> >> >>> >> Would the expected improvement be reduced training time or improved >>> >> accuracy? >>> >> >>> >> >>> >> 2016-06-11 23:06 GMT+03:00 Stefan Kaitschick >>> >> <stefan.kaitsch...@hamburg.de>: >>> >>> >>> >>> If I understood it right, the playout NN in AlphaGo was created by >>> using >>> >>> the same training set as the one used for the large NN that is used >>> in the >>> >>> tree. There would be an alternative though. I don't know if this is >>> the best >>> >>> source, but here is one example: https://arxiv.org/pdf/1312.6184.pdf >>> >>> The idea is to teach a shallow NN to mimic the outputs of a deeper >>> net. >>> >>> For one thing, this seems to give better results than direct >>> training on the >>> >>> same set. But also, more importantly, this could be done after the >>> large NN >>> >>> has been improved with selfplay. >>> >>> And after that, the selfplay could be restarted with the new playout >>> NN. >>> >>> So it seems to me, there is real room for improvement here. >>> >>> >>> >>> Stefan >>> >>> >>> >>> _______________________________________________ >>> >>> 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 >>> >> >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> > >
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