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 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 AFAICS.

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.

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