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My guess is When the OP is correct, just about any neural Internet architecture should have a simplicity bias.
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As an illustration, if a file is often compressed a good deal by a ZIP file encoding, then that file has reduced Kolmogorov complexity, although not all minimal Kolmogorov complexity strings is usually compressed by ZIP particularly.
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I am Actually undecided, I just was not genuinely certain what he meant when he said which the Bayesian as well as Kolmogorov complexity things had been "distractions from the most crucial position".
You will find numerous priors more than functions for which we are able to determine the exact posterior. (E.g., Gaussian procedures.) Having said that, undertaking Bayesian inference on these priors will not carry out as well as neural networks on most datasets. So recognizing SGD is Bayesian is just interesting if we also recognize that the prior is exciting.
If SGD is run with early halting, as described earlier mentioned, then my argument is that this is roughly reminiscent of random sampling by using an appeal to focus of evaluate in large-dimensions.
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If that is what's going on, then it truly is asking "would be the distributions of qualified functions close to precisely the same on realistic X-values?", and It can be just complicated into the reader to mention these random functions as coming from the "check established". Not a substantive dilemma, just communication issues.
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