> 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???
Maybe something like: each filter consists of eg 16 weights, which are assigned randomly over all input-output pairs, such that each pair is assign to exactly one of these shared weights, and then somehow: - either just fix the sharing assignment, a little like how echo state networks fix many of their weights, to keep the number of learnable parameters down, - or, have some way of optimizing the filters to learn the most useful sharing assigments, eg: - randomly modify them, genetic-type algorithm, or - some kind of Dirichlet-process type sampling? :-P - something else? _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
