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None of the ideas are new. http://people.idsia.ch/~juergen/evolino.html http://people.idsia.ch/~juergen/evolino.html Yet Schmidhuber's nets are much more comp
by deepnet 11y ago
None of the ideas are new.
http://people.idsia.ch/~juergen/evolino.html http://people.idsia.ch/~juergen/evolino.html
Yet Schmidhuber's nets are much more complex and certainly different.
I still think Otoro's very simple RNN feedback nets are unique - especially when coupled with training by self-play.
Does Schmidhuber have any game playing agents ?
- darkmighty 11y agoI was very impressed with this handwriting demo (and the accompaning paper): http://www.cs.toronto.edu/~graves/handwriting.html http://www.cs.toronto.edu/~graves/handwriting.html Using a recurrent architecture. To be honest I haven't played with NNs, but it puzzles me as to why the non-recurrent approach is so prevalent for complex tasks. I mean, it's the basic combinatorial circuit vs sequential circuits, which we all know are much more suited for complex or large outputs. Where's everything we learned from synchronous logic synthesis?
- deepnet 11y agoGrave's Handwriting Net was trained using Backpropagation - whereby the error between the net's estimate and a training target is sent backward through the net - so the net's estimates gradually become closer to the targets. Backpropagation takes longer the deeper the net - Recurrent Neural Nets are deep in time so Back Propagation can become intractable or unstable. Otoro's Slimeball demo evolves a Recurrent Net rather than training it - this appears to be a very efficient method, less likely to get stuck in local minima. The slimes evolve through self-play which is a trial and error method and reinforcement methods seem to do better on control tasks than passive learning.
- darkmighty 11y agoAh I see. But as far as training goes the difference between the two methods ("evolution" and backprop) is a matter of locality, no? The backprop modifies weights loosely based on local gradiet towards fitness, and evolution goes in sparse random directions. In this view backprop is indeed vulnerable to local maxima if your optimization method isn't very good, but isn't it just a matter of choosing good optimization methods? In other words, combining local backprop optimization with global evolutionary methods should be the role of robust optimization algos, no?
- hardmaru 11y agoThat handwriting demo is awesome! Thanks for the pointers - I want learn more about how that works. I wish I stayed at U of T for a few more years...
- deepnet 11y ago[reply to darkmighty comment below, thread depth limitation] Indeed this is exactly it, evolution is a global method, learning is local. I am reading John Gomez's thesis where he compares and combines learning and evolution http://www.cs.utexas.edu/users/nn/downloads/papers/gomez.phdtr03.pdf http://www.cs.utexas.edu/users/nn/downloads/papers/gomez.phd... Otoro's post on the evolution of his slime volleyball thinking is well worth a read. http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/ http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/
- darkmighty 11y agoI will check those out, thanks. This is indeed a fascinating topic. I guess every scientist wants to understand learning. The connection comes up in david mckay's Information Theory book too, a reading I definitively recommend, although I haven't been through it properly myself. http://www.inference.phy.cam.ac.uk/itprnn/book.pdf http://www.inference.phy.cam.ac.uk/itprnn/book.pdf
- deepnet 11y agoMcKays Information Theory is a brilliant read so far, many thanks. Having learning couched in Information Theory terms brings it all right back to Claude Shannon's early work on Reinforcement Learning Chess programs and Alan Turing's ideas about evolving efficient machine code by bitmask genetic recombination.
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