Foundations of Model-Based Deep Learning: Applications, Interpretability and Performance Guarantees

In this thesis we lay the foundations of model-based deep learning with applications in various signal processing fields such as communication systems, compressive sensing, phase retrieval, radar signal processing, and image processing. Furthermore, we provide theoretical analysis of the proposed ideas, and specifically, presenting performance guarantees for the obtained model-based deep architectures.