26 citations · 96 across the 22 of their papers we have counts for
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math.NA2021★ 13 cited
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions
Arnulf Jentzen, Adrian Riekert
In this article we study the stochastic gradient descent (SGD) optimization method in the training of fully-connected feedforward artificial neural networks with ReLU activation. T…
math.NA2021★ 26 cited
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions
Patrick Cheridito, Arnulf Jentzen, Adrian Riekert +1
Gradient descent optimization algorithms are the standard ingredients that are used to train artificial neural networks (ANNs). Even though a huge number of numerical simulations i…