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cs.LG2025
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…
cs.LG2025
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…
cs.LG2025★ 1 cited
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…