Adrian Riekert

No current affiliations available for this person. The person is no longer active at the university.

Publications

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. doi:10.1007/s40304-022-00292-9 Research article (journal) \| Peer reviewed \| accepted / in press (not yet published)
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), 126601. doi:10.1016/j.jmaa.2022.126601 Research article (journal) \| Peer reviewed \| Published
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. Research article in digital collection \| Preprint \| submitted / under review
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), 2519-2554. doi:10.3934/era.2023128 Research article (journal) \| Peer reviewed \| Published
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), 141-246. doi:10.4208/jml.220114a Research article (journal) \| Peer reviewed \| Published

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