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We can easily sometimes ensure that a Understanding algorithm will generalise properly if it might only Specific a restricted set of capabilities (see VC dimensionality). Even so, a neural network is just too expressive for this clarification to work (as they can Specific all All those ten^10k features).
As it is achievable to compress superior-likelihood functions using an best code for your likelihood distribution, you might count on that capabilities with large likelihood during the neural network prior might be compressed more than functions with lower chance.
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