I'm curious to know very well what you mean by "focus on the architecture alternatively." My guess is always that In the event the OP is true, essentially any neural Internet architecture could have a simplicity bias.
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The 2nd Towards Information Science publish references this paper, which can be also the main reference through which Levin's result is described in the initial paper you write-up. So I suppose looking through these references ought to be more than enough to get the gist.
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Also: given that we’re estimating many probabilities by sampling, we generally ought to discretise the function Area. When you have any concrete solutions for how to get about this then we’re all ears!
Scott's “How can Gradient Descent Connect with Goodhart?†appears remarkably related right here. Maybe these results could serve as a partial answer to that concern, inside the perception that SGD does not seem to differ greatly from random look for (by using a Gaussian prior to the weights) for deep neural networks on MNIST.
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From there, the pure subsequent question is just what exactly generality is stored/dropped, and if the loss of generality in fact matters for things in the true globe.
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I find those results suggestive but not conclusive, and am much more persuaded by the paper summarized here, so I don’t hope to summarize them.
All right, I do think a confusion below is always that I (as well as the OP AFAIK) Really don't mention precisely the same items as you do when utilizing the phrase "function". Solomonoff induction is about courses (commonly Turing devices) and from your comment it looks as if the sense of capabilities you're taking. But capabilities as I'm utilizing in this article (and i am pretty sure This can be the which means in the estimate) is simply an input/output relation.
If the former, how does a gaussian course of action bias toward brief programs apart from the compressibility offered by smoothness?
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