Andrei Alexandrescu Wrote: > > So are you saying there are neural networks with thresholds that are > trained using evolutionary algorithms instead of e.g. backprop? I found > this: The moment a network is just a bit recurrent, any gradient descent algo will be a hell.
> > https://docs.google.com/viewer?url=http://www.cs.rutgers.edu/~mlittman/courses/ml03/iCML03/papers/batchis.pdf > > which does seem to support the point. I'd have to give it a closer look > to see whether precision would affect training. > I would love to see your results :) But even in the basic 3 layer sigmoid network the question is: Will two outputs which are exactly the same(for a certain input) stay the same if you change the precision. When the calculations leading up to the two outputs are totally different ( for instance fully dependent on separated subsets of the input; separated paths), changing the precision could influence them differently leading to different outputs ?
