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I believe the ideal theoretical result could be to indicate that SGD on neural nets is an approximation of Solomonoff Induction (or a little something like SI), as well as the approximation will get superior as being the NNs get bigger and deeper. But I've however to find out any concept that connects neural nets/ SGD to one thing like quick courses.

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The above publish provides empirical proof that There's not much distinction between the generalization effectiveness of “carrying out SGD on DNNs right up until you receive some volume of overall performance” and “randomly sampling DNN weights until you receive some volume of performance.”

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I'd be interested to listen to your take on this comment earlier mentioned, which happens to be talking about compressibility, which to me feels like kolmogorov complexity.

However, if we translate objects from one particular mathematical Room to a different (say by discretizing, or adding/removing a metric construction), we can't just say the complexity steps for space A on the original A-objects inevitably agree with Individuals Room B on the translated B-objects.  Whether or not This is often genuine is determined by our option of translation.

I don’t discover the Kolmogorov complexity things extremely convincing. In general, algorithmic facts concept has a tendency to have arguments of the shape “because this evaluate simulates every single computable procedure, it (inevitably) at the least matches any specific computable system”. This feels pretty diverse from regular notions of “simplicity” or “intelligence”, and so 파워볼 I normally make an effort to specifically taboo phrases like “Solomonoff induction” or “Kolmogorov complexity” and exchange them with something like “by simulating just about every feasible computational course of action”, and see In case the argument still looks convincing. That generally doesn’t seem to be the case in this article.

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