The topic will be: "Mathematical Insights in Machine Learning and Data Science." The special issue editors are Profs Mikhail Belkin, Götz Pfander, Holger Rauhut and David Walnut. Submissions are possible via https://www.sciencedirect.com/journal/applied-and-computational-harmonic-analysis. As article type, please select MLDS-ICCHA2022.
Welcome to the website of the Chair of Reliable Machine Learning
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".
About us
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Mathematical Insights in Machine Learning and Data Science
Mathematical Institute for Machine Learning and Data Science
The Chair of Reliable Maschine Learning is part of the Mathematical Institute for Machine Learning and Data Science, MIDS.
Learn more about MIDS here.