Compressing neural nets

How to make neural nets smaller while still preserving their performance. This is a subtle problem, As we suspect that part of their special sauce is precisely that they are overparameterized which is to say, one reason they work is precisely that they are bigger than they “need” to be. The problem of finding the network that is smaller than the bigger one that it seems to need to be is tricky. My instinct is to use some sparse regularisation but this does not carry over to the deep network setting, at least naïvely.

Lottery tickets

Kim Martineau’s summary of the state of the art in “Lottery ticket” (Frankle and Carbin 2019) pruning strategies is fun; See also You et al. (2019) for an elaboration.

Edge ML

A.k.a. Tiny ML, Mobile ML etc. A major consumer of compressing neural nets, since small devices cannot fit large nerual nets. See Edge ML


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