I'm trying to construct minimal neural networks for a set of functions. the functions are complete - I'm trying to replicate them completely in NN, with minimal resources (non-zero weights). (Obviously the term "minimal" is very loose, there will be heavy constraints on the contents/structure of the network, but im fine with that) I saw a few algorithms that do this, but the best algorithm i saw (though it is very old - 1989) is the "Regular partitioning" (link to paper) Is there a better algorithm for generating minimal networks? surely there is an algorithm for this that's been suggested within the last 30 years If there isn't an obvious candidate (and even if there is) - is there an implementation in code for the RP algorithm? I couldnt find anything with a quick search, but I'm not sure where to look further. In general, where should I look when I want to find code implementations of algorithms? github? Continue reading...
