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".
Last week, the board of the “Förderverein Georgianum” met with its advisory board.
The committee decided to fund a “Deutschlandstipendium” for new data science students next year. This is a great support, and MIDS would like to express its sincere thanks for the funding.
The committee includes the following people, front row, from left to right:
Vice Chairman Prof. Ludwig Mödl
Ingolstadt´s mayor Dr. Michael Kern
Dr. Gerhard Schmidt, Chairman
Götz Pfander, MIDS spokesperson
The Chair of Reliable Maschine Learning is part of the Mathematical Institute for Machine Learning and Data Science, MIDS.
Learn more about MIDS here.
