Priority Programme “Theoretical Foundations of Deep Learning” (SPP 2298)
Montag 30. November 2020
Deadline: 30. November 2020
We currently witness the impressive success of deep learning in real-world applications, ranging from autonomous driving over game intelligence to the health care sector. At the same time, deep learning-based methods have a similarly strong impact on science, often replacing state-of-the-art classical model-based methods to solve mathematical problems such as inverse problems or partial differential equations
The key goal of this Priority Programme is the development of a comprehensive theoretical foundation of deep learning and will be structured along three complementary points of view:
- the statistical point of view regarding neural network training as a statistical learning problem and studying expressivity, learning, optimisation, and generalisation,
- the applications point of view focusing on safety, robustness, interpretability, and fairness, and
- the mathematical methodologies point of view developing and theoretically analysing novel deep learning-based approaches to solve inverse problems and partial differential equations.
