[Computer-go] Neural nets for Go - chain pooling?

David Wu lightvector at gmail.com
Fri Aug 18 05:14:30 PDT 2017

While browsing the online, I found an interesting idea "chain pooling"
presented here:

The idea is to have some early layers that perform a max-pool across
solidly-connected stones. I could also imagine it being useful to perform a
sum. So the input would be a 19x19 layer, and the output would be a 19x19
layer where the output at a given position, if that position is occupied by
a stone, is equal to the maximum (or the sum of) all the values in the
input layer across all stones that are solidly connected to that group.

One might imagine going further and allowing the neural net some early
convolutional layers that determine the connectivity strength for this
pooling between groups, so that it could choose to pool across definite
single-point eyes or bamboo joints, etc. It's possible that one would not
want to force all layers through this operation, so possibly only some
feature planes would be fed through this operation, or perhaps all of them
but the identity transformation would also be an output of the layer to
feed into the next.

Speculatively, in the best case one might imagine this has a chance to
improve the ability of the neural net to evaluate large semeai or to judge
the status of large dragons, by letting it propagate liberty count
information (including virtual liberties due to approach moves) and
information about eyes across the board more rapidly than a series of local
convolutions could do so. In fact, it seems that convolutional layers
followed by an early pooling of this sort would make it unnecessary to
provide liberties as an input feature because it would become easy for the
neural net to compute it on its own, although one would still probably want
to provide it to save the network the effort of having to learn it.

Of course, this idea could also easily turn out worthless. One thing I'm
very not sure about is how GPU-friendly this kind of operation could be
made to be, since I don't understand GPUs. Any thoughts?
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