I am very much looking forward to your paper. I do see the CNN research as a new idea that has great potential.
> Linear models like what MM is using are ... far less powerful than CNN My mathematical objection is that this cannot be. The "no free lunch" theorem applies, and besides, both representations are provably asymptotically perfect given sufficiently accurate training data. I am skeptical about the practical claims, but maybe your paper will sway me. Best, Brian. From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Aja Huang Sent: Tuesday, December 16, 2014 10:23 AM To: computer-go@computer-go.org Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go Hi Brian, I understand your points, but deep convolutional neural networks are very powerful in the sense that they can represent very complicated functions and are still trainable. Linear models like what MM is using are much simpler and easier to train, but far less powerful than CNN. In our paper we have presented the theoretical background and very good results using CNN on Go. Once approved I'll post the paper and I hope you will enjoy reading it. Regards, Aja On Tue, Dec 16, 2014 at 2:03 PM, Brian Sheppard <sheppar...@aol.com> wrote: My impression is that each feature gets a single weight in Crazy-stone. The team-of-features aspect arises because a single point can match several patterns, so you need a model to assign credit when tuning. The paper that I remember used fixed receptive fields to define the patterns. (E.g., from 3x3 through 5x10, or some such.) The easiest way to match those is to use a hash function. My impression is that both NN and DT are capable of asymptotically learning the entire game. (Also true if you use fixed receptive fields.) They should be equally powerful, though they differ in terms of the degree of understanding required by the programmer. IMO, MCTS should always be the "outermost loop" in the system. MCTS provides asymptotic optimality guarantees under remarkably general conditions. From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of René van de Veerdonk Sent: Monday, December 15, 2014 11:47 PM To: computer-go Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go Correct me if I am wrong, but I believe that the CrazyStone approach of team-of-features can be cast in terms of a shallow neural network. The inputs are matched patterns on the board and other local information on atari, previous moves, ko situation, and such. Remi alluded as much on this list sometime after his paper got published. Without having studied the Deep Learning papers in detail, it seems that these are the types of "smart features" that could be learned by a Deep Neural Net in the first few layers if the input is restricted to just the raw board, but could equally well be provided as domain specific features in order to improve computational efficiency (and perhaps enforce correctness). These approaches may not be all that far apart, other than the depth of the net and the domain specific knowledge used directly. Remi recently mentioned that the number of patterns in more recent versions of CrazyStone also number in the millions. I think the prediction rates for these two approaches are also pretty close. Compare the Deep Learning result to the other recent study of a German group quoted in the Deep Learning paper. The bigger questions to me are related to engine architecture. Are you going to use this as an input to a search? Or are you going to use this directly to play? If the former, it had better be reasonably fast. The latter approach can be far slower, but requires the predictions to be of much higher quality. And the biggest question, how can you make these two approaches interact efficiently? René On Mon, Dec 15, 2014 at 8:00 PM, Brian Sheppard <sheppar...@aol.com> wrote: >Is it really such a burden? Well, I have to place my bets on some things and not on others. It seems to me that the costs of a NN must be higher than a system based on decision trees. The convolution NN has a very large parameter space if my reading of the paper is correct. Specifically, it can represent all patterns translated and rotated and matched against all points in parallel. To me, that seems like a good way to mimic the visual cortex, but an inefficient way to match patterns on a Go board. So my bet is on decision trees. The published research on NN will help me to understand the opportunities much better, and I have every expectation that the performance of decision trees should be >= NN in every way. E.g., faster, more accurate, easier and faster to tune. I recognize that my approach is full of challenges. E.g., a NN would automatically infer "soft" qualities such as "wall", "influence" that would have to be provided to a DT as inputs. No free lunch, but again, this is about betting that one technology is (overall) more suitable than another. From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Stefan Kaitschick Sent: Monday, December 15, 2014 6:37 PM To: computer-go@computer-go.org Subject: Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go Finally, I am not a fan of NN in the MCTS architecture. The NN architecture imposes a high CPU burden (e.g., compared to decision trees), and this study didn't produce such a breakthrough in accuracy that I would give away performance. Is it really such a burden? Supporting the move generator with the NN result high up in the decision tree can't be that expensive. _______________________________________________ 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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