The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure

ICLR 2019 Frederic KoehlerAndrej Risteski

There has been a large amount of interest, both in the past and particularly recently, into the relative advantage of different families of universal function approximators, for instance neural networks, polynomials, rational functions, etc. However, current research has focused almost exclusively on understanding this problem in a worst case setting: e.g. characterizing the best L1 or L_{infty} approximation in a box (or sometimes, even under an adversarially constructed data distribution.).. (read more)

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