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1) It's definitely not the first. Other methods have universal guarantees of some form or other with well quantified rates of convergence, e.g. k-NN would be the best known example…

1) Indeed, GLNs don't learn features... but I would claim they do learn some notion of an intermediate representation, it's just different from the DL mainstream -- in particular i…

The result here is stronger, in the sense that typical NN universality results are statements with respect to just capacity (and not how you optimise them). Here, the result holds …

Convolution is a linear operation; in the case of images, you can view it as a multiplication with a doubly block circulant matrix. I can't see any barriers to hybrid approaches he…