(Also: I don’t Feel the notion of “very low powerful parameterisation†genuinely captures what’s happening here, but for The main reason you indicate yourself.)
The rest of the comment is about stuff that I did not have a robust belief on, so I'll depart it for Zach to reply if he desires.
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Essentially The purpose below is always that generalization performance is described a lot more through the neural network architecture, as opposed to the composition of stochastic gradient descent, considering that we will see that stochastic gradient descent tends to behave similarly to (an approximation of) random sampling.
The authors consider (a amount equivalent to) the fraction of inputs in S for which a provided purpose outputs one.
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From there, the purely natural up coming query is precisely what generality is saved/missing, and if the lack of generality really issues for points in the true 파워볼 world.
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Falsifiable Hypothesis: Review SGD with overtaining to the random sampling algorithm. You will notice that features which can be unlikely to get created by random sampling will be much more very likely beneath SGD with overtraining.
The main position, as I see it, is essentially that features with superior generalisation correspond to big volumes in parameter-Area, and that SGD finds features that has a likelihood about proportional to their volume.
The points Joar is saying there typically match my intuitions, Whilst 파워볼 there are a few factors unsaid wherever my intuitions might diverge much more.
Do you've got a 파워볼 good reference with the Levin certain? My attempts at getting a pertinent paper all unsuccessful.
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