| An overview on deep learning-based approximation methods for partial differential equations C Beck, M Hutzenthaler, A Jentzen, B Kuckuck arXiv preprint arXiv:2012.12348, 2020 | 144 | 2020 |
| Full error analysis for the training of deep neural networks C Beck, A Jentzen, B Kuckuck Infinite Dimensional Analysis, Quantum Probability and Related Topics, 2150020, 2022 | 56 | 2022 |
| Strong error analysis for stochastic gradient descent optimization algorithms A Jentzen, B Kuckuck, A Neufeld, P von Wurstemberger IMA Journal of Numerical Analysis 41 (1), 455-492, 2021 | 53 | 2021 |
| Subdirect products of groups and the n-(n+1)-(n+2) conjecture B Kuckuck The Quarterly Journal of Mathematics 65 (4), 1293-1318, 2014 | 22 | 2014 |
| Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions V Boussange, S Becker, A Jentzen, B Kuckuck, L Pellissier arXiv preprint arXiv:2205.03672, 2022 | 14 | 2022 |
| Deep neural network approximation theory for high-dimensional functions P Beneventano, P Cheridito, R Graeber, A Jentzen, B Kuckuck arXiv preprint arXiv:2112.14523, 2021 | 11 | 2021 |
| Strong -error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations M Hutzenthaler, A Jentzen, B Kuckuck, JL Padgett arXiv preprint arXiv:2110.08297, 2021 | 7 | 2021 |
| Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz … J Ackermann, A Jentzen, T Kruse, B Kuckuck, JL Padgett arXiv preprint arXiv:2309.13722, 2023 | 6 | 2023 |
| Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory A Jentzen, B Kuckuck, P von Wurstemberger arXiv preprint arXiv:2310.20360, 2023 | 5 | 2023 |
| Duplication monotonicity in the allocation of indivisible goods B Kuckuck, J Rothe AI Communications 32 (4), 253-270, 2019 | 5 | 2019 |
| The Magnus property for direct products B Klopsch, B Kuckuck Archiv der Mathematik 107 (4), 379-388, 2016 | 5 | 2016 |
| Sequential Allocation Rules are Separable: Refuting a Conjecture on Scoring-Based Allocation of Indivisible Goods. B Kuckuck, J Rothe AAMAS, 650-658, 2018 | 3 | 2018 |
| Finiteness properties of fibre products B Kuckuck University of Oxford, 2012 | 3 | 2012 |
| On the strong regularity of degenerate additive noise driven stochastic differential equations with respect to their initial values A Jentzen, B Kuckuck, T Müller-Gronbach, L Yaroslavtseva Journal of Mathematical Analysis and Applications 502 (2), 125240, 2021 | 2 | 2021 |
| Counterexamples to local Lipschitz and local Hölder continuity with respect to the initial values for additive noise driven SDEs with smooth drift coefficient functions with at … A Jentzen, B Kuckuck, T Müller-Gronbach, L Yaroslavtseva arXiv preprint arXiv:2001.03472, 2020 | 2 | 2020 |
| Refugee Allocation in the Setting of Hedonic Games B Kuckuck, J Rothe, A Weißenfeld International Conference on Algorithmic DecisionTheory, 65-80, 2019 | 1 | 2019 |
| Counterexamples to local Lipschitz and local Hölder continuity with respect to the initial values for additive noise driven stochastic differential equations with smooth drift … A Jentzen, B Kuckuck, T Müller-Gronbach, L Yaroslavtseva Discrete & Continuous Dynamical Systems-B, 2021 | | 2021 |
| Monotonicity, Duplication Monotonicity, and Pareto Optimality in the Scoring-Based Allocation of Indivisible Goods B Kuckuck, J Rothe International Conference on Agreement Technologies, 173-189, 2018 | | 2018 |
| Zusammenhangseigenschaften von Operationen nicht-endlich erzeugter Gruppen auf CAT(0)-Räumen B Kuckuck Johann Wolfgang Goethe-Universität, Frankfurt am Main, 2007 | | 2007 |
Arnulf JentzenThe Chinese University of Hong Kong, Shenzhen & University of Münster在 uni-muenster.de 的电子邮件经过验证
