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The second In the direction of Facts Science post references this paper, that's also the key reference by which Levin's result is mentioned in the initial paper you publish. So I think looking at these references ought to be enough to get the gist.

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I am not sure I agree with this -- I think Kolmogorov complexity is often a related Idea of complexity On this context.

I do even so disagree that All those arguments may be placed on "pretty much any device Studying algorithm", Whilst they unquestionably do utilize to some much larger class of ML algorithms than simply neural networks. Even so, I also Will not Feel this is always a foul point. The image which the AIT arguments give makes it reasonably unsurprising that you'd obtain the double-descent phenomenon while you increase the measurement of the design (at smaller dimensions VC-dimensionality mechanisms dominate, but at much larger measurements the overparameterisation starts to induce a simplicity bias, which at some point starts to dominate).

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The Levin sure isn't going to utilize on to neural networks, mainly because it assumes that P is finite and discrete, nonetheless it gives some more backing on the instinct earlier mentioned.

So, In the event the Bayesian posterior absolutely accounts for generalization error and SGD is not contributing any even more inductive bias at all, we would be expecting to determine generalization mistake on the get of five% from SGD.

SI is bayesianism + universal prior, IIRC. Just how that solomonoff induction generalizes to new info is actually that it's going to take the prior and cuts out every one of the functions that contradict The brand new details. (i.e. it does bayesian update). So insofar mainly because it generalizes effectively, it's because it is made up of functions in the initial universal prior that generalize perfectly, and they've got somewhat higher prior as compared to the capabilities that don't.

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