26 citations · 96 across the 22 of their papers we have counts for
5 papers · 1 filter
Non-convergence to the optimal risk for Adam and stochastic gradient descent optimization in the training of deep neural networks
Thang Do, Arnulf Jentzen, Adrian Riekert
Despite the omnipresent use of stochastic gradient descent (SGD) optimization methods in the training of deep neural networks (DNNs), it remains, in basically all practically relev…
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions
Martin Hutzenthaler, Arnulf Jentzen, Katharina Pohl +2
In many numerical simulations stochastic gradient descent (SGD) type optimization methods perform very effectively in the training of deep neural networks (DNNs) but till this day…
Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation
Simon Eberle, Arnulf Jentzen, Adrian Riekert +1
The training of artificial neural networks (ANNs) with rectified linear unit (ReLU) activation via gradient descent (GD) type optimization schemes is nowadays a common industrially…
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation
Arnulf Jentzen, Adrian Riekert
Gradient descent (GD) type optimization schemes are the standard methods to train artificial neural networks (ANNs) with rectified linear unit (ReLU) activation. Such schemes can b…
Strong overall error analysis for the training of artificial neural networks via random initializations
Arnulf Jentzen, Adrian Riekert
Although deep learning based approximation algorithms have been applied very successfully to numerous problems, at the moment the reasons for their performance are not entirely und…