Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

2015-01-10 Thread Hugh Perkins
On 12/27/14, Aja Huang wrote: > We were doing exploratory work that optimized performance not training > time, so we don't know how symmetry affects training time. In terms of > performance it seems not have an effect. You are using 3x3. Clarke and Storkey are using 5x5 (section 4.2, first sente

Re: [Computer-go] January KGS bot tournament, 19x19

2015-01-10 Thread Nick Wedd
Reminder - it's tomorrow On 5 January 2015 at 20:52, Nick Wedd wrote: > The January KGS bot tournament will be held next Sunday, January > 11th, starting at 08:00 UTC and ending at 15:00 UTC. It will use > 19x19 boards, with time limits of 29 minutes each plus very fast > Canadian overtime, and

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

2015-01-10 Thread Stefan Kaitschick
> > Let's be pragmatic - humans heavily use the information about the last > move too. If they take a while, they don't need to know the last move > of the opponent when reviewing a position, but when reading a tactical > sequence the previous move in the sequence is essential piece of > informati

Re: [Computer-go] Evaluation function through Deep Convolutional Neural Networks

2015-01-10 Thread Stefan Kaitschick
To me, that's the core lesson of MCTS - take your hands off that evaluation button. On Sat, Jan 10, 2015 at 12:00 AM, Darren Cook wrote: > > The discussion on move evaluation via CNNs got me wondering: has anyone > > tried to make an evaluation function with CNNs ? > > My first thought was a hum