I'm under impression is what gives evolution based learning paired with guess/check modeling an edge - jumps out of local minimums easier?
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Perhaps we can find a more general learning strategy that discovers gradient descent as a sub solution where appropriate?
End of conversation
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That's probably why learning by failure is so important: they give feedback to the model.
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Can you suggest reading material about representation, please? (The gap btwn semantic networks and deep neural networks is to large for me.)
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