Johannes Schmidt-Hieber - Mathematics for Deep Neural Networks: Energy landscape and open problems (Lecture 5/5)
From Kathryn Gentilello on March 28th, 2019
To derive a theory for gradient descent methods, it is important to have some understanding of the energy landscape. In this lecture, an overview of existing results is given. The second part of the lecture is devoted to future challenges in the field. We describe important future steps needed for the future development of the statistical theory of deep networks.