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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I mentioned in a different subthread- it's easy to run but hard to define the distribution over input data to do the analysis
At large risk of sounding like an idiot because I haven’t looked at this stuff seriously in 25 years… isn’t dropout pretty much a method for minimizing mutual info? Not explicitly, but that’s more-or-less the understanding of what it does?
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