(Also: I don’t think the Idea of “small powerful parameterisation†definitely captures what’s happening in this article, but for The main reason you point out by yourself.)
With this context, smoothness is a single such appropriate measure: clean features have low Kolmogorov complexity, but you will discover other ways to acquire small Kolmogorov complexity without having getting sleek. I don't understand about the Levin sure specially, but in math these forms of theorems tend to be about smoothness.
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Essentially the point in this article is generalization performance is spelled out a lot more through the neural network architecture, instead of the framework of stochastic gradient descent, due to the fact we could see that stochastic gradient descent has a tendency to behave in the same way to (an approximation of) random sampling.
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The 파워볼 above mentioned publish offers empirical evidence that there isn't Significantly difference between the generalization overall performance of “executing SGD on DNNs right until you get some amount of effectiveness†and “randomly sampling DNN weights until you receive some amount of general performance.â€
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This is not exact ample to rule out modest contributions of SGD on the inductive bias, but it is really potent evidence that the Bayesian posterior is the main variable.
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This alternative is smart if A, B are definitely unstructured collections of objects without intrinsic that means.
The paper talks a bunch about things like SGD staying (Nearly) 파워볼 Bayesian plus the neural community prior getting very low Kolmogorov complexity; I found these to get distractions from the principle point. Over and above that, approximating the random sampling probability by using a Gaussian system is a reasonably delicate affair and I've concerns in 파워볼사ì´íŠ¸ regards to the applicability to serious neural networks.
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