Mones Raslan
Mones Raslan
Applied Scientist at Zalando
Verified email at
Cited by
Cited by
A theoretical analysis of deep neural networks and parametric PDEs
G Kutyniok, P Petersen, M Raslan, R Schneider
Constructive Approximation 55 (1), 73-125, 2022
Topological properties of the set of functions generated by neural networks of fixed size
P Petersen, M Raslan, F Voigtlaender
Foundations of Computational Mathematics, 1-70, 2020
Numerical solution of the parametric diffusion equation by deep neural networks
M Geist, P Petersen, M Raslan, R Schneider, G Kutyniok
Journal of Scientific Computing 88 (1), 22, 2021
Approximation rates for neural networks with encodable weights in smoothness spaces
I Gühring, M Raslan
Neural Networks, 2020
Expressivity of deep neural networks
I Gühring, M Raslan, G Kutyniok
arXiv preprint arXiv:2007.04759 34, 2020
Anisotropic multiscale systems on bounded domains
P Grohs, G Kutyniok, J Ma, P Petersen, M Raslan
arXiv preprint arXiv:1510.04538, 2015
Unfavorable structural properties of the set of neural networks with fixed architecture
P Petersen, M Raslan, F Voigtlaender
Proceedings of International Conference on Sampling Theory and Applications …, 2019
Deep Learning based Forecasting: a case study from the online fashion industry
M Kunz, S Birr, M Raslan, L Ma, Z Li, A Gouttes, M Koren, T Naghibi, ...
arXiv preprint arXiv:2305.14406, 2023
Solving parametric PDEs with neural networks: unfavorable structure vs. expressive power
M Raslan
TU Berlin, Institut für Mathematik, 2021
Approximation properties of hybrid shearlet-wavelet frames for Sobolev spaces
P Petersen, M Raslan
Advances in Computational Mathematics 45, 1581-1606, 2019
The structure of spaces of neural network functions
P Petersen, M Raslan, F Voigtlaender
Wavelets and Sparsity XVIII 11138, 144-151, 2019
MD 21218, USA
K Kawaguchi, G Kutyniok, Y Levine, Q Li, T Merkh, G Montavon, ...
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