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.
Prof. Voigtlaender from MIDS has organized a special programming competition for KU Data Science students during Advent 2024. This is based on “Advent of Code”, a website run by Eric Wastl, on which two programming tasks are published every day during the Advent season in the form of an Advent calendar. As part of the “MIDS Advent of Code Challenge”, there was a special leaderboard especially for MIDS students.
In the end, there were three winners who were delighted to receive Amazon vouchers worth a total of €100.
![[Translate to Englisch:] Advent of Code](/fileadmin/_processed_/7/a/csm_Advent_of_code_8a4621a9a3.webp)
