Formulating flexible probabilistic models

Thomas Schön, Uppsala University

Abstract: One of the key lessons to take away from contemporary machine learning is that flexible models offer the best predictive performance. This has implications in many situations. In this lecture I will try to make this concrete by looking at a few constructions that we are working with. I will start with a classification task from ECG interpretation and then continue to the more under-researched area of how to formulate and solve regression problems using deep learning. There are currently several different approaches used for deep regression and there is still room for innovation. I will illustrate this landscape in general and introduce our rather general deep regression method which has a clear probabilistic interpretation. We show good performance on several computer vision regression tasks, system identification problems and 3D object detection using laser data.

Bio: Thomas B. Schön is the Beijer Professor Artificial Intelligence in the Department of Information Technology at Uppsala University. In 2018, he was elected to The Royal Swedish Academy of Engineering Sciences (IVA) and The Royal Society of Sciences at Uppsala. He received the Tage Erlander prize for natural sciences and technology in 2017 and the Arnberg prize in 2016, both awarded by the Royal Swedish Academy of Sciences (KVA). He was awarded the best PhD thesis award by The European Association for Signal Processing in 2013. He received the best teacher award at the Institute of Technology, Linköping University in 2009.