Mainly, we care about NN generalization on issues wherever the enter Area is continuous, normally R^n.  The authors argue that the finite-set results are suitable to those challenges, mainly because one can always discretize R^n to obtain a finite set.  I don't Believe this captures the styles of functionality complexity we care about for NNs

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Also, In case the simplicity prior they use is akin to kolmogorov complexity-primarily based priors, then Which means what they are executing is akin to what e.g. Solomonoff Induction does. And that i've in no way listened to everyone seek to argue that Solomonoff Induction "simply interpolates" before!It is a little bit of a tangent, even so

I'm curious to determine what you mean by "center on the architecture instead." My guess is the fact When the OP is true, just about any neural Web architecture can have a simplicity bias.All together, Stephen Curry just needed to play for a little something at the tip immediately after Most people counted out his Golden State Warriors from th

Also, In case the simplicity prior they use is akin to kolmogorov complexity-dependent priors, then Meaning whatever they are carrying out is akin to what e.g. Solomonoff Induction does. And that i've never listened to anyone endeavor to argue that Solomonoff Induction "merely interpolates" before!You will need to sign-up before including a re