[gamegoblin]

I don't think they've done it yet -- in the conclusion they say:

    The most obvious next step is to integrate a 
    DCNN into a full fledged Go playing system. For
    example, a DCNN could be run on a GPU in parallel with
    a MCTS Go program and be used to provide highly quality
    priors for what the strongest moves to consider are. Such
    a system would both be the first to bring sophisticated pat-
    tern recognitions abilities to playing Go, and have a strong
    potential ability to surpass current computer Go programs.

I agree that the integration should be exceedingly straightforward. I've written MCTS implementations (though not a Go implementation -- I used it on Connect 4 and other easy-to-code games), and it seems like you'd just plug it into your already-existing bias function.

The authors didn't mention my idea about using the probability distribution output from the network to guide the random playouts, which I would also be interested in.