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Of these ten^10k capabilities, the wide vast majority could have really inadequate accuracy on the test established. Hence, if we tried to do inference by buying a random perform that fits the schooling facts, we'd Virtually definitely get really weak generalisation (in fact, we would be expecting to find the identical precision just like random guessing).

This argument just doesn’t audio very powerful to me. It also can be placed on actually any equipment Discovering algorithm; I don’t see why That is precise to neural nets. If This is often just intended to elucidate why it can be alright to overparameterize neural nets, then that makes a lot more perception to me, nevertheless then I’d say a little something like “with overparameterized neural nets, a number of parameterizations instantiate precisely the same function, and Therefore the ‘successful parameterization’ is lessen than You may have believed”, instead of stating something about Kolmogorov complexity.

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As for the rest of your comment -- Whatever you’re indicating here looks genuine to me, but I’m unsure I see how any of this is a counterpoint to something we’re indicating?

Which is accurately appropriate. That actual case in point can be a situation of superior circuit measurement but small Kolmogorov complexity.

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So, If your Bayesian posterior fully accounts for generalization error and SGD just isn't contributing any further more inductive bias at all, we would be expecting to see generalization error about the get of 5% from SGD.

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There has been some current analysis which (I feel) sheds some gentle on this issue. I want to phone awareness to this blog site write-up: