Herr Adrian Riekert, Angewandte Mathematik Münster: Institut für Analysis und Numerik / Mathematics Münster

Member of Mathematics Münster
Member of Mathematics Münster Graduate School
Current PublicationsJentzen A, Riekert A Strong overall error analysis for the training of artificial neural networks via random initializations. Communications in Mathematics and Statistics Vol. 0, 2023 online
Jentzen A, Riekert A Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation. Journal of Mathematical Analysis and Applications Vol. 517 (2), 2023 online
Jentzen, A.; Riekert, A.; von Wurstemberger, P. Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations. , 2023 online
Eberle, Simon; Jentzen, Arnulf; Riekert, Adrian; Weiss, Georg S. Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation. Electronic Research Archive Vol. 31 (5), 2023 online
Jentzen A, Riekert A On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks. Journal of Machine Learning Vol. 1 (2), 2022 online
Jentzen, Arnulf; Riekert, Adrian A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions. Journal of Machine Learning Research Vol. 23 (260), 2022 online
Jentzen, Arnulf; Riekert, Adrian A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions. Zeitschrift für Angewandte Mathematik und Physik Vol. 73 (5), 2022 online
Cheridito, Patrick; Jentzen, Arnulf; Riekert, Adrian; Rossmannek, Florian A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions. Journal of Complexity Vol. 72, 2022 online
Riekert, Adrian Convergence rates for empirical measures of Markov chains in dual and Wasserstein distances. Statistics and Probability Letters Vol. 189, 2022 online
E-Mailariekert at uni-muenster dot de
Phone+49 251 83-32079
FAX+49 251 83-32729
Room110.009
Secretary   Sekretariat Claudia Giesbert
Frau Claudia Giesbert
Telefon +49 251 83-33792
Fax +49 251 83-32729
Zimmer 120.002
AddressHerr Adrian Riekert
Angewandte Mathematik Münster: Institut für Analysis und Numerik
Fachbereich Mathematik und Informatik der Universität Münster
Orléans-Ring 10
48149 Münster
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