Precision Learning
Precision Learning is a research direction, seeking
to integrate known operators into machine learning models to improve
generalization und efficiency.
Known operators have been shown to hold the
potential of reducing maximal error bounds when incorporated into deep
neural networks. This suggests their inclusion could allow models to
learn from less data and increase robustness.
Colloqium timetable
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Projects
Publications
A New Scale Space Total Variation Algorithm for Limited Angle Tomography
CT-Meeting 2016 (Bamberg, Germany, April 13, 2016 - April 16, 2016)
In: Marc Kachelrieß (ed.): CT-Meeting 2016 Proceedings (The 4th International Meeting on Image Formation in X-Ray Computed Tomography) 2016
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2016/Huang16-ANS.pdf
BibTeX: Download , , , , , :
Papoulis-Gerchberg Algorithms for Limited Angle Tomography Using Data Consistency Conditions
the 5th International Conference on Image Formation in X-ray Computed Tomography (Salt Lake City, Utah, the USA, May 20, 2018 - May 23, 2018)
In: Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography, Salt Lake City, Utah, the USA: 2018
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Huang18-PAF.pdf
BibTeX: Download , , , , :
Make the Most of Time: Temporal Extension of the iTV Algorithm for 4D Cardiac C-Arm CT
Bildverarbeitung für die Medizin 2016 (Berlin)
In: Bildverarbeitung für die Medizin 2016, Berlin Heidelberg: 2016
DOI: 10.1007/978-3-662-49465-3_31
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2016/Haase16-MTM.pdf
BibTeX: Download , , , , , , :
Persons
Leonid Mill, M. Sc.
- Phone number: +49 9131 85-25247
- Email: leonid.mill@fau.de
- Website: https://lme.tf.fau.de/person/mill/