| A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions P Cheridito, A Jentzen, A Riekert, F Rossmannek Journal of Complexity 72, 101646, 2022 | 29 | 2022 |
| A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions A Jentzen, A Riekert Zeitschrift für angewandte Mathematik und Physik 73 (5), 188, 2022 | 23 | 2022 |
| Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation A Jentzen, A Riekert Journal of Mathematical Analysis and Applications 517 (2), 126601, 2023 | 22 | 2023 |
| On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks A Jentzen, A Riekert arXiv preprint arXiv:2112.09684, 2021 | 21 | 2021 |
| Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation S Eberle, A Jentzen, A Riekert, GS Weiss arXiv preprint arXiv:2108.08106, 2021 | 21 | 2021 |
| 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 … A Jentzen, A Riekert Journal of Machine Learning Research 23 (260), 1-50, 2022 | 19 | 2022 |
| Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions M Hutzenthaler, A Jentzen, K Pohl, A Riekert, L Scarpa arXiv preprint arXiv:2112.07369, 2021 | 11 | 2021 |
| Convergence to good non-optimal critical points in the training of neural networks: Gradient descent optimization with one random initialization overcomes all bad non-global … S Ibragimov, A Jentzen, A Riekert arXiv preprint arXiv:2212.13111, 2022 | 9 | 2022 |
| Convergence rates for empirical measures of Markov chains in dual and Wasserstein distances A Riekert Statistics & Probability Letters 189, 109605, 2022 | 8* | 2022 |
| On the existence of infinitely many realization functions of non-global local minima in the training of artificial neural networks with ReLU activation S Ibragimov, A Jentzen, T Kröger, A Riekert arXiv preprint arXiv:2202.11481, 2022 | 6 | 2022 |
| Strong overall error analysis for the training of artificial neural networks via random initializations A Jentzen, A Riekert Communications in Mathematics and Statistics, 1-50, 2023 | 5 | 2023 |
| Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks A Jentzen, A Riekert arXiv preprint arXiv:2402.05155, 2024 | 3 | 2024 |
| Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations A Jentzen, A Riekert, P von Wurstemberger arXiv preprint arXiv:2302.03286, 2023 | 3 | 2023 |
| Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses S Dereich, A Jentzen, A Riekert arXiv preprint arXiv:2406.14340, 2024 | 2 | 2024 |
| Normalized gradient flow optimization in the training of ReLU artificial neural networks S Eberle, A Jentzen, A Riekert, G Weiss arXiv preprint arXiv:2207.06246, 2022 | 2 | 2022 |
| Deep neural network approximation of composite functions without the curse of dimensionality A Riekert arXiv preprint arXiv:2304.05790, 2023 | 1 | 2023 |
| An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning L Gonon, A Jentzen, B Kuckuck, S Liang, A Riekert, P von Wurstemberger arXiv preprint arXiv:2408.13222, 2024 | | 2024 |
| A proof of the corrected Sister Beiter cyclotomic coefficient conjecture inspired by Zhao and Zhang B Juran, P Moree, A Riekert, D Schmitz, J Völlmecke arXiv preprint arXiv:2304.09250, 2023 | | 2023 |
Arnulf JentzenThe Chinese University of Hong Kong, Shenzhen & University of Münster在 uni-muenster.de 的电子邮件经过验证
