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The paper talks a bunch about such things as SGD currently being (almost) Bayesian and also the neural network prior possessing small Kolmogorov complexity; I discovered these to be interruptions from the key point.
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Especially, When the inductive bias came from SGD, we'd expect that bias to go away if we replaced SGD with random sampling. In random sampling, we sample an initialization from the neural community, and when it's zero coaching error, then we’re finished, in any other case we repeat.
"Straightforward" features in the LZ sense are Consequently kinds that reply to binary vectors in (about) a predictable way,.  Exactly 파워볼중계 what does it mean to get a operate to reply to binary vectors in the predictable way?
What's more, most capabilities that in good shape a specified established of training data will not generalise perfectly to new knowledge. And nevertheless, if we prepare a neural network we will usually receive a function that offers fantastic generalisation. What is the system driving this phenomenon?
Effectively, lower-complexity functions correspond to much larger volumes within the parameter-House of neural networks. If features with large volumes even have significant basins of attraction, and if applying SGD is around equivalent to taking place a random basin (weighted by its dimension), then This is able to effectively clarify why neural networks operate.
Okay, I believe a confusion right here is that I (plus the OP AFAIK) Will not speak about the identical things as you need to do when using the word "purpose". Solomonoff induction is about programs (ordinarily Turing devices) and from your remark it looks like the sense of features 파워볼 you're taking. But capabilities as I'm using below (and I'm pretty absolutely sure This is actually the meaning from the quote) is simply an input/output relation.
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Especially, I'm wary of the focus on measuring complexity for features in between finite sets, like binary functions.
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