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cs.LG2025★ 2 cited
Autoencoders for Anomaly Detection are Unreliable
Roel Bouman, Tom Heskes
Autoencoders are frequently used for anomaly detection, both in the unsupervised and semi-supervised settings. They rely on the assumption that when trained using the reconstructio…
cs.LG2024
Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements
Roel Bouman, Linda Schmeitz, Luco Buise +3
In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods wit…
cs.LG2023
Unsupervised anomaly detection algorithms on real-world data: how many do we need?
Roel Bouman, Zaharah Bukhsh, Tom Heskes
In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly det…