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This argument just doesn’t seem extremely powerful to me. It also can be applied to virtually any machine Mastering algorithm; I don’t see why That is specific to neural nets. If this is just intended to explain why it's ok to overparameterize neural nets, then that makes far more perception to me, even though then I’d say anything like “with overparameterized neural nets, many different parameterizations instantiate the exact same function, and Hence the ‘efficient parameterization’ is decrease than You could have assumed”, instead of saying nearly anything about Kolmogorov complexity.
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Also: given that we’re estimating various probabilities by sampling, we in essence really need to discretise the functionality House. In case you have any concrete ideas for ways to get around this then we’re all ears!
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The primary adjust is entropy would come to be relative entropy (as is regular when using facts concept bounds to the continual limit). Intuitively, It is just utilizing the normal translation involving chance principle and minimal description size, and implementing it towards the probability density of parameter House.
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As an example, in the event you examine Determine six in the write-up I link, it is possible to see that diverse variations of SGD do offer a slightly different inductive bias. However, this outcome is apparently fairly small relative to what's provided by the "prior".
This alternative is sensible if A, B are truly unstructured collections of objects with no intrinsic this means.
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Do you do have a superior reference with the Levin certain? My tries at locating a applicable paper all unsuccessful.
Additionally, features which have been extra probable with random sampling might be turn into less likely under SGD with overtraining.