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The paper talks a bunch about such things as SGD remaining (Pretty much) Bayesian plus the neural network prior getting low Kolmogorov complexity; I found these to be distractions from the principle place.

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We have now done overtraining, which should really enable SGD to penetrate into the area. This doesn’t seem to make Substantially change for the probabilities we get.

I concur it's not vacuous. It feels like you happen to be mainly stating the identical argument I gave but in several phrases. Is it possible to notify me what is actually Erroneous or lacking from my summary of your argument?

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The Levin certain would not implement straight to neural networks, since it assumes that P is finite and discrete, however it gives some further backing into the instinct higher than.

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The leading place, as I see it, is essentially that functions with very good generalisation correspond to huge volumes in parameter-Place, Which SGD finds capabilities with a probability roughly proportional to their volume.

The principle stage, as I see it, is essentially that capabilities with excellent generalisation correspond to large volumes in parameter-Place, and that SGD finds functions which has a likelihood approximately proportional for their quantity.

The point that NNs and GPs both of those perform, and The actual fact that there is a fairly sturdy correspondence among them, ensures that It might be style of Strange when they labored for totally diverse reasons.

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