Improving Deep Unsupervised Anomaly Detection by Exploiting VAE Latent Space Distribution. - Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina (DS, 2020)

Meta-feature Extraction Strategies for Active Anomaly Detection. - Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina; Prospero Papaleo (IDEAL, 2021)

Cooperative Deep Unsupervised Anomaly Detection. - Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina; Rosaria Spada (DS, 2022)

Detecting Anomalies with rmLatentOut: Novel Scores, Architectures, and Settings. - Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina (ISMIS, 2022)

Reconstruction Error-based Anomaly Detection with Few Outlying Examples. - Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina (CoRR, 2023)

${{ mathrm {Latent}}Out}$: an unsupervised deep anomaly detection approach exploiting latent space distribution. - Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina (Mach. Learn., 2023)

A Lightweight Meta-Feature Extraction Strategy for Deep Reinforcement Anomaly Detection (SHORT PAPER). - Simone Amirato; Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina (GreenAI@AI*IA, 2024)

On the Environmental Impact of the Algorithm LatentOut for Unsupervised Anomaly Detection (SHORT PAPER). - Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina (GreenAI@AI*IA, 2024)

Indecision-Aware Deep Active Anomaly Detection. - Simone Amirato; Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina (IDEAL (2), 2024)

A Clustering-based Approach for Interpreting Black-box Models. - Luca Ferragina; Simona Nisticò (SEBD, 2024)

Enhancing anomaly detectors with LatentOut. - Fabrizio Angiulli; Fabio Fassetti; Luca Ferragina (J. Intell. Inf. Syst., 2024)