I'm curious to determine what you necessarily mean by "give attention to the architecture instead." My guess is usually that Should the OP is correct, virtually any neural net architecture can have a simplicity bias.
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The above mentioned write-up offers empirical proof that there isn't Significantly difference between the generalization overall performance of “performing SGD on DNNs until finally you receive some amount of functionality†and “randomly sampling DNN weights until eventually you have some volume of effectiveness.â€
As it is feasible to compress superior-probability activities using an ideal code for that chance distribution, you could possibly íŒŒì›Œë³¼ë¶„ì„ hope that features with superior likelihood during the neural network prior could be compressed in excess of capabilities with reduced likelihood.
I am unsure how you can reconcile that with one other arguments in that put up for why it should be distinct, even though, including the experiments that Peter And that i did.
Such as, for those who look at Determine six in the write-up I backlink, you'll be able to see that distinct variations of SGD do present a rather different inductive bias. Nonetheless, this result is apparently pretty compact relative to what's supplied by the "prior".
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So it doesn't make sense to claim that two features are 파워볼 equivalent (in the best way your speaking about not less than), since they always differ on some input (or they'd be the exact same functionality). However, two packages is often equal in which they output the same things (they compute equivalent features).
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