DFG Priority Programme 2298 - Theoretical Foundations of Deep Learning
Raitenhaslach, Germany | 5 - 8 October 2026
The aim is to make young researchers familiar with a broad range of theoretical concepts that are relevant in the field of deep learning. At the same time, it seeks to strengthen exchange and foster collaboration among the projects funded within the SPP.
Participation does not require prior knowledge of the covered topics. In addition to the lecture programme, participants will have the opportunity to present their research in a poster session.
The summer school is funded by the Deutsche Forschungsgemeinschaft (DFG) within the Priority Programme 2298 “Theoretical Foundations of Deep Learning".
Register here until 15th of May: https://forms.gle/kmhnetXgP11gmzw56
The summer school features three introductory lecture courses:
High-dimensional Optimal Feedback Control with Neural Networks
Lars Grüne (University of Bayreuth)
Generalization Bounds for Deep Learning
Johannes Schmidt-Hieber (University of Twente)
Generative Modeling and Optimal Transport
Christian Wald (INSA Lyon)
In addition, the programme includes a special lecture:
Adversarial Robustness Evaluations: From Computer Vision to Large Language Models
Leo Schwinn (Technical University of Munich)
Christopher Bülte (LMU Munich)
Samira Kabri (DESY Hamburg)
Sara-Viola Kuntz (Technical University of Munich)
Lucas Schmitt (University of Würzburg)
Mario Sperl (University of Bayreuth)
For any questions, please send an e-mail to spp2298@math.lmu.de