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 article “Architecture of soil microaggregates: Advanced methodologies to explore properties and functions” in the Journal of Plant Nutrition and Soil Science is one of the 10% most read articles in 2023.
Her publication “Investigations of effective dispersion models for electroosmotic flow with rigid and free boundaries in a thin strip”, published in the journal Mathematical Methods in the Applied Sciences, is also among the most read in 2023.
We congratulate Prof. Ray on this award.
The Chair of Reliable Maschine Learning is part of the Mathematical Institute for Machine Learning and Data Science, MIDS.
Learn more about MIDS here.
