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Does it mean then that neural networks start with a completely crazy model of the real world, and slowly modify this model to better fit the data, as opposed to jumping between model sets that fit the data perfectly, as Solomonoff induction does?
This seems like a good description to me.
I'm not an expert in Solomonoff induction, but my impression is that each model set is a subset of the model set from the last step. That is, you consider every possible output string (implicitly) by considering every possible program that could generate those strings, ... (read more)