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This argument just doesn’t sound very compelling to me. It can also be placed on actually any equipment Discovering algorithm; I don’t see why That is distinct to neural nets. If This can be just meant to clarify why it really is ok to overparameterize neural nets, then that makes additional perception to me, while then I’d say some thing like “with overparameterized neural nets, a number of parameterizations instantiate precisely the same function, and And so the ‘helpful parameterization’ is reduced than You could have believed”, rather than declaring something about Kolmogorov complexity.

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Hence, on a log-log plot I'd hope overtraining to 'tilt' the correspondence between SGD and random sampling likelihoods downward.

Scott's “So how exactly does Gradient Descent Communicate with Goodhart?” looks remarkably pertinent right here. Potentially these results could function a partial reply to that question, within the perception that SGD will not seem to vary greatly from random research (that has a Gaussian prior on the weights) for deep neural networks on MNIST.

I'd be interested to hear your take on why What this means is NN's are only performing interpolation. Exactly what does it necessarily mean, to only do interpolation and never extrapolation?

There are still some stray items, but I am now usually confident the headline assert is appropriate: the overwhelming majority of inductive bias originates from the implicit prior within the community's parameter House.

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Falsifiable Hypothesis: Look at SGD with overtaining to the random sampling algorithm. You will see that features which are not likely to be generated by random sampling might be more probably beneath SGD with overtraining.

Chris (writer with the connected article) messaged me about these, And that i also checked out the entire paper and considered it some extra.

This confusion is a lot more prosaic: I'm not persuaded the summary Pβ(file

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