- Mathematical Foundations of Deep Learning
- Connections between Partial Differential Equations and Convolutional Neural Networks
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.
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