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We have now accomplished overtraining, which must allow for SGD to penetrate into the region. This doesn’t seem to make Substantially distinction with the probabilities we get.
The second Toward Information Science article references this paper, which happens to be also the most crucial reference through which Levin's result is talked about in the initial paper you article. So I presume looking at these references should be ample to find the gist.
I feel The perfect theoretical result could well be to show that SGD on neural nets can be an approximation of Solomonoff Induction (or some thing like SI), plus the approximation will get superior since the NNs get more substantial and deeper. But I have nonetheless to determine any concept that connects neural nets/ SGD to anything like brief programs.
Fundamentally the point in this article is that generalization performance is discussed much more because of the neural network architecture, rather then the structure of stochastic gradient descent, due to the fact we are able to see that stochastic gradient descent has a tendency to behave similarly to (an approximation of) random sampling.
Such as, when the argument doesn’t utilize to all ML algorithms, it does apply to all neural community architectures in the exact same way, but Evidently there are crucial differences within the inductive bias of eg CNNs and RNNs.
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The key place, as I see it, is actually that functions with excellent generalisation correspond to substantial volumes in parameter-House, Which SGD finds capabilities which has a probability around 파워볼사ì´íŠ¸ proportional for their volume.
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