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Aren't all of these (including convolution) specific instances of Graph Neural Networks?

I don't have any specific experience in ML but I always liked the formalism presented in https://www.youtube.com/watch?v=J1YCdVogd14 as a way to unify everything

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Yeah, I mean, a lot of things can express attention, because attention is mechanically extremely simple. The "trick" of attention is that it's "relatively easy" to train. Or if not "relatively easy", we at least have a lot of collective experience doing it.

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