1 citations · 3 across the 9 of their papers we have counts for
9 papers
Distilling Formal Logic into Neural Spaces: A Kernel Alignment Approach for Signal Temporal Logic
Sara Candussio, Gabriele Sarti, Gaia Saveri +1
We introduce a framework for learning continuous neural representations of formal specifications by distilling the geometry of their semantics into a latent space. Existing approac…
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…
Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
Sara Candussio, Gaia Saveri, Gabriele Sarti +1
Continuous representations of logic formulae allow us to integrate symbolic knowledge into data-driven learning algorithms. If such embeddings are semantically consistent, i.e. if…
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…
Retrieval-Augmented Mining of Temporal Logic Specifications from Data
Gaia Saveri, Luca Bortolussi
The integration of cyber-physical systems (CPS) into everyday life raises the critical necessity of ensuring their safety and reliability. An important step in this direction is re…
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…
Towards Invertible Semantic-Preserving Embeddings of Logical Formulae
Gaia Saveri, Luca Bortolussi
Logic is the main formal language to perform automated reasoning, and it is further a human-interpretable language, at least for small formulae. Learning and optimising logic requi…
Graph Neural Networks for Propositional Model Counting
Gaia Saveri, Luca Bortolussi
Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks. Nevertheless, counting problems such as propositional model counting (#SAT) are…