Yeah I agree with that. But there is also a sense in which some (many?) features will be inherently sparse.
A token is either the first one of multi-token word or it isn’t.
A word is either a noun, a verb or something else.
A word belongs to language LANG and not to any other language/has other meanings in those languages.
A H×W image can only contain so many objects which can only contain so many sub-aspects.
I don’t know what it would mean to go “out of distribution” in any of these cases.
This means that any network that has an incentive to conserve parameter usage (however we want to define that), might want to use superposition.
Yeah I agree with that. But there is also a sense in which some (many?) features will be inherently sparse.
A token is either the first one of multi-token word or it isn’t.
A word is either a noun, a verb or something else.
A word belongs to language LANG and not to any other language/has other meanings in those languages.
A H×W image can only contain so many objects which can only contain so many sub-aspects.
I don’t know what it would mean to go “out of distribution” in any of these cases.
This means that any network that has an incentive to conserve parameter usage (however we want to define that), might want to use superposition.