Dennis Elbrächter
Dennis Elbrächter
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Deep neural network approximation theory
D Elbrächter, D Perekrestenko, P Grohs, H Bölcskei
arXiv preprint arXiv:1901.02220, 2019
DNN expression rate analysis of high-dimensional PDEs: application to option pricing
D Elbrächter, P Grohs, A Jentzen, C Schwab
Constructive Approximation 55 (1), 3-71, 2022
Group testing for SARS-CoV-2 allows for up to 10-fold efficiency increase across realistic scenarios and testing strategies
CM Verdun, T Fuchs, P Harar, D Elbrächter, DS Fischer, J Berner, ...
Frontiers in Public Health 9, 583377, 2021
The universal approximation power of finite-width deep ReLU networks
D Perekrestenko, P Grohs, D Elbrächter, H Bölcskei
arXiv preprint arXiv:1806.01528, 2018
How degenerate is the parametrization of neural networks with the ReLU activation function?
J Berner, D Elbrächter, P Grohs
Advances in Neural Information Processing Systems, 7790-7801, 2019
Towards a regularity theory for ReLU networks–chain rule and global error estimates
J Berner, D Elbrächter, P Grohs, A Jentzen
2019 13th International conference on Sampling Theory and Applications …, 2019
Redistributor: Transforming Empirical Data Distributions
P Harar, D Elbrächter, M Dörfler, KD Johnson
arXiv preprint arXiv:2210.14219, 2022
The Oracle of DLphi
D Alfke, W Baines, J Blechschmidt, MJ Sarmina, A Drory, D Elbrächter, ...
arXiv preprint arXiv:1901.05744, 2019
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