I believe that investigation of this sort is rather relevant for AI alignment. It seems fairly plausible that neural networks, or some thing comparable to them, will likely be made use of for a ingredient of AGI. If that's the scenario, then we would like to be able to reliably predict and motive about how neural networks behave in new predicaments, and how they connect with other techniques, and it is difficult to imagine how that might be feasible with out a deep knowledge of the dynamics at play when neural networks understand from information.
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I have not viewed the paper you hyperlink, so I am unable to touch upon it exclusively, but I would like to Observe which the declare "SGD is around Bayesian" would not suggest "Bayesian inference would give greater generalisation than SGD". It can simultaneously be the case that the neural community "prior" is biased toward lower-complexity functions, that SGD roughly follows the "prior", Which SGD supplies some more bias in the direction of very low-complexity functions (about and higher than exactly what is supplied by the "prior").
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Basically The purpose below is usually that generalization efficiency is defined far more by the neural community architecture, as opposed to the structure of stochastic gradient descent, because we will see 파워볼 that stochastic gradient descent has a tendency to behave in the same way to (an approximation of) random sampling.
And The rationale This is certainly correct would be that the prior is predicated on complexity, and when you dig íŒŒì›Œë³¼ë¶„ì„ into the small print you encounter the argument I just sketched, the place the prior is definitely based on weighting each perform Similarly but proving that simpler features tend to be more various.
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You will find about a gazillion steps of "simplicity". If we decide some simplicity evaluate M, the standard rule is that points with reduced M-complexity have minimal Kolmogorov complexity, but factors with lower Kolmogorov complexity You should not necessarily have low M-complexity.
SI is bayesianism + common prior, IIRC. How that solomonoff induction generalizes to new facts is actually that it will require the prior and cuts out every one of the features that contradict The brand new details. (i.e. it does bayesian update). So insofar because it generalizes effectively, it's mainly because it contains functions in the initial universal prior that generalize very well, and they've rather significant prior when compared to the features that don't.
So it does not seem sensible to claim that two capabilities are equal (in the way your talking about at the least), since they essentially differ on some input (or they might be a similar operate). Conversely, two applications is often equal in which they output precisely the same matters (they compute similar functions).
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