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Boche, Holger; Fono, Adalbert und Kutyniok, Gitta 
ORCID: https://orcid.org/0000-0001-9738-2487
(2025):
Mathematical algorithm design for deep learning under societal and judicial constraints: The algorithmic transparency requirement.
In: Applied and Computational Harmonic Analysis, Bd. 77, 101763
[PDF, 1MB]
ORCID: https://orcid.org/0000-0001-9738-2487
(2025):
Mathematical algorithm design for deep learning under societal and judicial constraints: The algorithmic transparency requirement.
In: Applied and Computational Harmonic Analysis, Bd. 77, 101763
[PDF, 1MB]
Boche, Holger; Fono, Adalbert und Kutyniok, Gitta ORCID: https://orcid.org/0000-0001-9738-2487
(2025):
Inverse problems are solvable on real number signal processing hardware.
In: Applied and Computational Harmonic Analysis, Bd. 74, 101719
[PDF, 918kB]
ORCID: https://orcid.org/0000-0001-9738-2487
(2025):
Inverse problems are solvable on real number signal processing hardware.
In: Applied and Computational Harmonic Analysis, Bd. 74, 101719
[PDF, 918kB]
Boche, Holger ORCID: https://orcid.org/0000-0002-8375-8946; Fono, Adalbert ORCID: https://orcid.org/0000-0002-4302-8762 und Kutyniok, Gitta ORCID: https://orcid.org/0000-0001-9738-2487
(2023):
Limitations of Deep Learning for Inverse Problems on Digital Hardware.
In: IEEE Transactions on Information Theory, Bd. 69, Nr. 12: S. 7887-7908
ORCID: https://orcid.org/0000-0002-8375-8946; Fono, Adalbert ORCID: https://orcid.org/0000-0002-4302-8762 und Kutyniok, Gitta ORCID: https://orcid.org/0000-0001-9738-2487
(2023):
Limitations of Deep Learning for Inverse Problems on Digital Hardware.
In: IEEE Transactions on Information Theory, Bd. 69, Nr. 12: S. 7887-7908