Estimating Uncertainty of Deep Learning Multi-Label Classifications Using Laplace Approximation. - Ferdinand Rewicki; Jakob Gawlikowski (IGARSS, 2022)

Is it worth it? An experimental comparison of six deep- and classical machine learning methods for unsupervised anomaly detection in time series. - Ferdinand Rewicki; Joachim Denzler; Julia Niebling (CoRR, 2022)

Leveraging Causal Information for Multivariate Timeseries Anomaly Detection. - Lukas Heppel; Andreas Gerhardus; Ferdinand Rewicki; Jan Christian Deeken; Günther Waxenegger-Wilfing (DX, 2024)

Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-Regenerative Life Support System Telemetry. - Ferdinand Rewicki; Jakob Gawlikowski; Julia Niebling; Joachim Denzler (ECML/PKDD (9), 2024)

Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data. - Ferdinand Rewicki; Joachim Denzler; Julia Niebling (CoRR, 2025)