From Shallow to Deep Learning for Inverse Imaging Problems - Carola-Bibiane Schönlieb, Cambridge
Explore the intersection of mathematics and data science in this 36-minute conference talk by Carola-Bibiane Schönlieb from Cambridge University. Delve into inverse imaging problems, progressing from shallow to deep learning techniques. Examine dose-imposed problems, variation regularization, and their applications. Understand key concepts like regularizers, deep neural networks, and bilevel optimization. Learn about pseudoinverse methods, training regularizers, and gradient descent steps in neural networks. Analyze results and takeaway messages, while also considering the disadvantages of these approaches. Gain insights from this cutting-edge research presented at the Isaac Newton Institute's workshop on "Approximation, sampling and compression in data science," which aims to foster collaboration among experts in mathematics, statistics, computer science, and engineering.