Also, When the simplicity prior they use is akin to kolmogorov complexity-based priors, then Which means whatever they are performing is akin to what e.g. Solomonoff Induction does. And that i've in no way read any individual try to argue that Solomonoff Induction "just interpolates" before!
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Merge that with The truth that empirically we get good generalization and we receive the thing you explained.
That is accurately right. That precise instance could well be a scenario of higher circuit sizing but low Kolmogorov complexity.
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It's true that there are additional Erroneous answers than appropriate responses, but (I claim) you can find far more capabilities that go on the sequence by giving the appropriate answer than functions that keep on the sequence by offering a Completely wrong solution.
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It's also perfectly-recognized which the inductive bias of neural networks is afflicted by things like just how long you coach for, and what action dimensions and batch measurement you employ, and so on. However, these consequences appear to be pretty compact compared to the outcome that emanates from the parameter-operate map.
If A , B are finite sets, then you will find a finite quantity of features file:A→B.  Let us generate S with the finite list of such capabilities.
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