Deep neural network approximation theory for high-dimensional functions. - Pierfrancesco Beneventano; Patrick Cheridito; Robin Graeber; Arnulf Jentzen; Benno Kuckuck (CoRR, 2021)

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 (CoRR, 2023)

Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates. - Steffen Dereich; Robin Graeber; Arnulf Jentzen (CoRR, 2024)