The research group "Reliable Machine Learning" studies the properties of machine learning algorithms.
In view of the recent success of deep learning methods in applications like image recognition, speech recognition, and automatic translation, the group especially focuses on properties of deep neural networks.
Although a neural network trained e.g. for an image classification task might work well on "real inputs", it has been repeatedly shown empirically that such networks are vulnerable to adversarial examples:
a minimal perturbation (impercetible to a human) of the input data can cause the network to misclassify the input.
Thus, an important research area of the group is to mathematically understand the reasons for the existence of such adversarial examples (i.e., the instability of trained neural networks),
and - building on that understanding - to develop improved methods that yield provably robust neural networks.
The research group is supported by the Emmy Noether project "Stability and Solvability in Deep Learning".
The magazine "Wirtschaftswoche" ranks three KU scientists among the most research-intensive business economists in the german-speaking world. One of them is Prof. Fontaine, who is an associate member of MIDS.
His research focuses on urban logistics both in the area of goods transportation and mobility. He is currently investigating, for example, how the route planning of call buses can be optimized efficiently and in a customer-friendly manner using mathematical tools.
The Chair of Reliable Maschine Learning is part of the Mathematical Institute for Machine Learning and Data Science, MIDS.
Learn more about MIDS here.
