6 papers
Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semantics
Irene Ferfoglia, Simone Silvetti, Gaia Saveri +2
Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box dee…
Towards Interpretable Concept Learning over Time Series via Temporal Logic Semantics
Irene Ferfoglia, Simone Silvetti, Gaia Saveri +2
Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box dee…
Monitoring Spatially Distributed Cyber-Physical Systems with Alternating Finite Automata
Anand Balakrishnan, Sheryl Paul, Simone Silvetti +2
Modern cyber-physical systems (CPS) can consist of various networked components and agents interacting and communicating with each other. In the context of spatially distributed CP…
ECATS: Explainable-by-design concept-based anomaly detection for time series
Irene Ferfoglia, Gaia Saveri, Laura Nenzi +1
Deep learning methods for time series have already reached excellent performances in both prediction and classification tasks, including anomaly detection. However, the complexity…
stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic
Gaia Saveri, Laura Nenzi, Luca Bortolussi +1
Integrating symbolic knowledge and data-driven learning algorithms is a longstanding challenge in Artificial Intelligence. Despite the recognized importance of this task, a notable…
Bayesian Machine Learning meets Formal Methods: An application to spatio-temporal data
Laura Vana, Ennio Visconti, Laura Nenzi +2
We propose an interdisciplinary framework that combines Bayesian predictive inference, a well-established tool in Machine Learning, with Formal Methods rooted in the computer scien…