Dennis Elbrächter
Dennis Elbrächter
Verified email at univie.ac.at
Title
Cited by
Cited by
Year
Deep neural network approximation theory
D Elbrächter, D Perekrestenko, P Grohs, H Bölcskei
arXiv preprint arXiv:1901.02220, 2019
672019
DNN expression rate analysis of high-dimensional PDEs: Application to option pricing
D Elbrächter, P Grohs, A Jentzen, C Schwab
arXiv preprint arXiv:1809.07669, 2018
452018
The universal approximation power of finite-width deep ReLU networks
D Perekrestenko, P Grohs, D Elbrächter, H Bölcskei
arXiv preprint arXiv:1806.01528, 2018
222018
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, ...
medRxiv, 2020
162020
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
112019
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
52019
The Oracle of DLphi
D Alfke, W Baines, J Blechschmidt, MJ Sarmina, A Drory, D Elbrächter, ...
arXiv preprint arXiv:1901.05744, 2019
2019
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