most citedNon-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates

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math.NA2023

The necessity of depth for artificial neural networks to approximate certain classes of smooth and bounded functions without the curse of dimensionality

Lukas Gonon, Robin Graeber, Arnulf Jentzen

In this article we study high-dimensional approximation capacities of shallow and deep artificial neural networks (ANNs) with the rectified linear unit (ReLU) activation. In partic…