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- Mathematical Foundations of Deep Learning
- Connections between Partial Differential Equations and Convolutional Neural Networks
Conference Papers
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T. Alt, P. Peter, J. Weickert, K. Schrader:
Translating numerical concepts for PDEs into neural architectures.
To appear in A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2021.
Also available as arXiv:2103.15419 [math.NA], March 2021. -
T. Alt, K. Schrader, M. Augustin, P. Peter, J. Weickert:
Connections between Numerical Algorithms for PDEs and Neural Networks.
arXiv:2107.14742 [math.NA], July 2021. -
T. Alt, K. Schrader, J. Weickert, P. Peter, M. Augustin:
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods.
arXiv:2108.13993 [cs.LG], August 2021. -
K. Schrader:
Translating Anisotropic Diffusion into Residual Networks.
M.Sc. Thesis in Visual Computing,
Saarland University, Saarbrücken, Germany, October 2020.
- Winter term 2020:
Seminar Deep Learning and Optimisation for Visual Computing - Summer term 2021:
Seminar Connections of Deep Learning and PDEs for Visual Computing
Proseminar Naturinspirierte Optimierung - Winter term 2021:
Seminar Milestones and Advances in Image Analysis
Proseminar Simulation der Welt - Summer term 2022:
Lecture Model-driven Deep Learning Lab for Image Analysis
Technical Reports
Theses
Courses