1 citations · 3 across the 9 of their papers we have counts for
4 papers · 1 filter
Rare anomalies require large datasets: About proving the existence of anomalies
Simon Klüttermann, Emmanuel Müller
Detecting whether any anomalies exist within a dataset is crucial for effective anomaly detection, yet it remains surprisingly underexplored in anomaly detection literature. This p…
Polyra Swarms: A Shape-Based Approach to Machine Learning
Simon Klüttermann, Emmanuel Müller
We propose Polyra Swarms, a novel machine-learning approach that approximates shapes instead of functions. Our method enables general-purpose learning with very low bias. In partic…
Unsupervised Surrogate Anomaly Detection
Simon Klüttermann, Tim Katzke, Emmanuel Müller
In this paper, we study unsupervised anomaly detection algorithms that learn a neural network representation, i.e. regular patterns of normal data, which anomalies are deviating fr…
A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany
Ina Dormuth, Sven Franke, Marlies Hafer +6
In this study, we examine the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information. Our analysis…