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Grave'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
by deepnet 11y ago
Grave'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...