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
Art der Publikation: Forschungsartikel (Zeitschrift)

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)
Art der Publikation: Forschungsartikel (Zeitschrift)

Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality

Grohs P, Ibragimov S, Jentzen A, Koppensteiner S (2023)
In: Journal of Complexity, 0
Art der Publikation: Forschungsartikel (Zeitschrift)

Strong convergence rates for explicit space-time discrete numerical approximations of stochastic Allen--Cahn equations

Becker S, Gess B, Jentzen A, Kloeden PE (2023)
In: Stochastics and Partial Differential Equations: Analysis and Computations, 11(1)
Art der Publikation: Forschungsartikel (Zeitschrift)

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.
Art der Publikation: Forschungsartikel in Online-Sammlung

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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 Art der Publikation: Forschungsartikel (Zeitschrift)
An overview on deep learning-based approximation methods for partial differential equations Beck C, Hutzenthaler M, Jentzen A, Kuckuck B (2023) In: Discrete and Continuous Dynamical Systems - Series B, 28(6) Art der Publikation: Forschungsartikel (Zeitschrift)
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) Art der Publikation: Forschungsartikel (Zeitschrift)
Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations Hutzenthaler M, Jentzen A, Kruse T, Nguyen TA (2023) In: Journal of Numerical Mathematics, 31(2) Art der Publikation: Forschungsartikel (Zeitschrift)
Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality Grohs P, Ibragimov S, Jentzen A, Koppensteiner S (2023) In: Journal of Complexity, 0 Art der Publikation: Forschungsartikel (Zeitschrift)
Space-time error estimates for deep neural network approximations for differential equations Grohs P, Hornung F, Jentzen A, Zimmermann P (2023) In: Advances in Computational Mathematics, 49(1) Art der Publikation: Forschungsartikel (Zeitschrift)
Strong convergence rates for explicit space-time discrete numerical approximations of stochastic Allen--Cahn equations Becker S, Gess B, Jentzen A, Kloeden PE (2023) In: Stochastics and Partial Differential Equations: Analysis and Computations, 11(1) Art der Publikation: Forschungsartikel (Zeitschrift)
The necessity of depth for artificial neural networks to approximate certain classes of smooth bounded functions without the curse of dimensionality Gonon, L.; Graeber, R.; Jentzen, A. (2023) In: arXiv.org:2212.13111. Art der Publikation: Forschungsartikel in Online-Sammlung
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. Art der Publikation: Forschungsartikel in Online-Sammlung
On the existence of minimizers in shallow residual ReLU neural network optimization landscapes Dereich, S.; Jentzen, A.; Kassing, S. (2023) In: arXiv.org:2302.14690. Art der Publikation: Forschungsartikel in Online-Sammlung