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 learningmethods 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.
Now that the MIDS has found its home this semester, the KU-LMU-TUM Data Seminar took place in the Georgianum for the first time. Prof. Krahmer (TUM), Prof. Maly and Prof Rauhut (LMU) and colleagues attended the lectures by Götz Pfander on cube lattices and Hannes Matt on implicit regularization of artificial neural networks. The day ended with a wrap-up session at Gasthaus Daniel.
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.
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