Karl Schrader

Position: Research Assistant E-mail: schrader -at- mia.uni-saarland.de (please replace anti-spam -at- by @) Address: Mathematical Image Analysis Group Faculty of Mathematics and Computer Science, Saarland University Campus E1.7 66041 Saarbrücken, Germany Office: Room 4.04 Building E1.7, Saarbrücken Campus see also Contact

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

Conference Papers

1. 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.

Technical Reports

2. 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.
3. 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.

Theses

4. K. Schrader:
   Translating Anisotropic Diffusion into Residual Networks.
   M.Sc. Thesis in Visual Computing,
   Saarland University, Saarbrücken, Germany, October 2020.
   


Teaching

Courses

• 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



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