Strong overall error analysis for the training of artificial neural networks via random initializations Jentzen A, Riekert A (2023) In: Communications in Mathematics and Statistics, 0 Type of Publication: Research article (journal)
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation Jentzen A, Riekert A (2023) In: Journal of Mathematical Analysis and Applications, 517(2) Type of Publication: Research article (journal)
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Jentzen, A.; Riekert, A.; von Wurstemberger, P. (2023) In: arXiv.org:2302.03286. Type of Publication: Research article in digital collection
Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation Eberle, Simon; Jentzen, Arnulf; Riekert, Adrian; Weiss, Georg S. (2023) In: Electronic Research Archive, 31(5) Type of Publication: Research article (journal)
On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks Jentzen A, Riekert A (2022) In: Journal of Machine Learning, 1(2) Type of Publication: Research article (journal)