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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".

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Math News

12-19-2023

Data Science Christmas Party

Once again this year, the professors of MIDS and the consulting firm d-fine have invited to a Christmas mulled wine evening. This year, the event took place for the first time in the beautiful rooms of the Georgianum. Around 40 Data Science students from the first and third semesters used the cozy evening with mulled wine, punch and gingerbread to get together, have interesting conversations with d-fine and exchange ideas with the MIDS staff. Mathematical topics were also discussed without further ado on a PostIt.

We really enjoyed the evening and would like to take this opportunity to wish all Data Science students a Merry Christmas and a Happy New Year. Let's start together again in 2024.

MIDS Seminar

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.