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I am not sure I agree using this -- I think Kolmogorov complexity can be a related Idea of complexity in this context.
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It turns out that the functions expressed by these models are considerably more possible compared to counting evaluate to 파워볼 assign a single label (1 or 0) to most outputs.
Moreover, most functions that in shape a supplied set of training facts won't generalise properly to new information. And nevertheless, if we train a neural network we will usually receive a function that gives fantastic generalisation. What is the mechanism driving this phenomenon?
Effectively, small-complexity features correspond to greater volumes inside the parameter-space of neural networks. If capabilities with massive volumes also have substantial basins of attraction, and if making use of SGD is roughly such as happening a random basin (weighted by its measurement), then This could effectively reveal why 파워볼사ì´íŠ¸ neural networks perform.
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I don’t find the Kolmogorov complexity things extremely convincing. Generally speaking, algorithmic data theory has a tendency to have arguments of the shape “considering that this measure simulates just about every computable process, it (finally) a minimum of matches any unique computable methodâ€. This feels really diverse from normal notions of “simplicity†or “intelligenceâ€, and so I typically attempt to exclusively taboo phrases like “Solomonoff induction†or “Kolmogorov complexity†and swap them with some thing like “by simulating just about every feasible computational procedureâ€, and find out if the argument nevertheless appears to be convincing. That mainly doesn’t appear to be the situation here.
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