Theoretical Foundations of Deep Learning completed

Deep learning is revolutionizing real-world applications and science, replacing or complementing classical model-based methods in solving mathematical problems. Despite successes, deep neural networks lack strong theoretical-mathematical foundations. This program aims to develop a comprehensive theoretical foundation of deep learning from three perspectives: statistical, application, and mathematical-methodological. The research is interdisciplinary, combining mathematics, statistics, and theoretical computer science to address complex questions.