from https://twitter.com/Miles_Brundage
https://arxiv.org/abs/1710.07535 https://arxiv.org/abs/1709.01041 On Mon, Oct 23, 2017 at 10:39 AM, Darren Cook <dar...@dcook.org> wrote: > > The source of AlphaGo Zero is really of zero interest (pun intended). > > The source code is the first-hand account of how it works, whereas an > academic paper is a second-hand account. So, definitely not zero use. > > > So yes, the database of 29M self-play games would be immensely more > > valuable. (Probably like the last 5M or so is fine, too). I prefer the > > games over the network - with the games it's easier to train a smaller > > network that gives better results on PC's that don't have 4 TPUs in them. > > Does anyone know of research/code on the topic of reducing the > size/complexity of deep learning networks? I think it should be possible > to reduce either the number of layers, or the size of each layer, with > only a small drop in accuracy, but it seems like the two fully-connected > networks at the top will then need retraining? > > However, this article is showing results, beyond what I thought would be > possible, even on the very deep image networks: > > https://www.oreilly.com/ideas/compressing-and-regularizing- > deep-neural-networks > > BTW, I notice his PhD thesis has just been published. Might have to add > it to my reading list: http://stanford.edu/~songhan/ > > Darren > _______________________________________________ > 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
