[Computer-go] Fwd: Representing Komi for neural network

[Hugh Perkins]

>  But then, the komi wont really
participate in the hierarchical representation we are hoping that the
network will build, that I suppose we are hoping is the key to
obtaining human-comparable results?

Well... it seems that Hinton, in his dropout paper
http://arxiv.org/pdf/1207.0580.pdf , get kindof ok results with
'permutation-invariant' networks, basically consisting of 2-3 fc
layers, so maybe a bunch of conv layers feeding into 2-3 fc layers,
and the non-image inputs going into the fc-layers too is reasonable.

Perhaps what we want is a compromise between convnets and fcs though?
ie, either take an fc and make it a bit more sparse, and / or take an
fc and randomly link sets of weights together???