Universal approximation just indicates existence of a suitable sequence, not that it is findable and doesn't apply with finite numbers of neurons. But MLPs are

Universal approximation just indicates existence of a suitable sequence, not that it is findable and doesn't apply with finite numbers of neurons. But MLPs are not good for everything. Where Simulated annealing works better than auto-diff is the classic example that is easier to visualize, at least for me. Even if the sequence 'exists', finding it is the problem, it doesn't matter if a method can represent an unfindable sequence. That said, IMHO, MLP vs KAN is probably safer to think of as horses for courses, they are better at different things. At least with your definition of 'usable' being undefined.