1 citations · 1 across the 3 of their papers we have counts for
3 papers
Asymptotic stability properties and a priori bounds for Adam and other gradient descent optimization methods
Steffen Dereich, Robin Graeber, Arnulf Jentzen +1
Gradient descent (GD) based optimization methods are these days the standard tools to train deep neural networks in artificial intelligence systems. In optimization procedures in d…
Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates
Steffen Dereich, Robin Graeber, Arnulf Jentzen
Deep learning algorithms - typically consisting of a class of deep neural networks trained by a stochastic gradient descent (SGD) optimization method - are nowadays the key ingredi…
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…