News - Data Science Chair

Our paper "Higher-Order DeepTrails: Unified Approach to *Trails" presents an alternative approach using autoregressive language models to the Bayesian methods HypTrails, MixedTrails and SubTrails.

In this technical report, we detail the process of setting an automated speech detection pipeline on a low-power device. We expand work done previously at our chair and deploy a trained Siamese network together with a k-NN-classifier.

We have successfully acquired funding for a three year DFG project in cooperation with the Chair for Computer Philology and History of Contemporary German Literature (Prof. Fotis Jannidis). The goal of the project is to improve the capabilities of Language Models to model characters in literary texts - an area where even current models often still struggle to provide a good representation of the text's content, the story. If you're interested in joining, please contact us!

We show that architectures from the machine learning domain of continual learning can help language models to procedurally learn facts about the world.

We design a multi-agent simulation that can produce company data that also contains hidden fraud cases.

We rigorously evaluate commonly used approaches that explain the decisions of network attack detection systems.

In our work we propose a method for learning explicit representations of PDEs from non-grid data

Mehr kooperierende Betriebe, höhere Förderung, ein neuer Partner innerhalb der Uni: BigData@Geo geht in die zweite Runde und will mit Hilfe von Klimadaten konkrete Handlungsempfehlungen für Unternehmen mit Naturbezug erstellen.

In our work we present a benchmark dataset for learning forecasts of dynamical systems on non-grid structured data

In our paper we propose a method allowing the comparison of human behaviour across behavioural networks with different properties.