12 citations · 17 across the 3 of their papers we have counts for
3 papers
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★ 3 cited
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★ 12 cited
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…