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Neural networks: deep, shallow, or in between?
Guergana Petrova, Przemyslaw Wojtaszczyk · 2023-10-11 · via stat.ML updates on arXiv.org

We give estimates from below for the error of approximation of a compact subset from a Banach space by the outputs of feed-forward neural networks with width W, depth l and Lipschitz activation functions. We show that, modulo logarithmic factors, rates better that entropy numbers' rates are possibly attainable only for neural networks for which the depth l goes to infinity, and that there is no gain if we fix the depth and let the width W go to infinity.

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