NN training goes through two distinct phases: (1) reducing classification error through drift (2) optimally compressing mutual information of hidden layers through diffusion https://arxiv.org/abs/1703.00810 pic.twitter.com/He7dNY3NSv
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grump is good! that said I think there's a couple things here: (1) it's hard to expand this analysis past toy-ish data since you need to be able to define an actual distribution over the inputs, and NNs are usually used in cases where we don't have one (images, for instance)
(2) I think the explanation accounts for both why NNs are so successful _and_ for why they're so unsuccessful: they can only compress in the dumbest way possible (by randomly jiggling weights)
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