50 citations · 54 across the 8 of their papers we have counts for
4 papers · 1 filter
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
Valentina Zaccaria, Chiara Masiero, David Dandolo +1
While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challeng…
AcME-AD: Accelerated Model Explanations for Anomaly Detection
Valentina Zaccaria, David Dandolo, Chiara Masiero +1
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications. Traditional Anomaly Detection methods excel…
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
Davide Dalle Pezze, Denis Deronjic, Chiara Masiero +3
Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classi…
AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box
David Dandolo, Chiara Masiero, Mattia Carletti +2
In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the…