79 citations · 424 across the 54 of their papers we have counts for
16 papers · 1 filter
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…
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…
Zero-Shot Conditioning of Score-Based Diffusion Models by Neuro-Symbolic Constraints
Davide Scassola, Sebastiano Saccani, Ginevra Carbone +1
Score-based diffusion models have emerged as effective approaches for both conditional and unconditional generation. Still conditional generation is based on either a specific trai…
Frequency maps reveal the correlation between Adversarial Attacks and Implicit Bias
Lorenzo Basile, Nikos Karantzas, Alberto d'Onofrio +4
Despite their impressive performance in classification tasks, neural networks are known to be vulnerable to adversarial attacks, subtle perturbations of the input data designed to…
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
Luca Bortolussi, Ginevra Carbone, Luca Laurenti +3
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical a…
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking
Luca Bortolussi, Francesca Cairoli, Ginevra Carbone +1
Parametric verification of linear temporal properties for stochastic models can be expressed as computing the satisfaction probability of a certain property as a function of the pa…
Neural Predictive Monitoring under Partial Observability
Francesca Cairoli, Luca Bortolussi, Nicola Paoletti
We consider the problem of predictive monitoring (PM), i.e., predicting at runtime future violations of a system from the current state. We work under the most realistic settings w…
Abstraction of Markov Population Dynamics via Generative Adversarial Nets
Francesca Cairoli, Ginevra Carbone, Luca Bortolussi
Markov Population Models are a widespread formalism used to model the dynamics of complex systems, with applications in Systems Biology and many other fields. The associated Markov…
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks
Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi
We consider the problem of the stability of saliency-based explanations of Neural Network predictions under adversarial attacks in a classification task. Saliency interpretations o…
Random Projections for Improved Adversarial Robustness
Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi
We propose two training techniques for improving the robustness of Neural Networks to adversarial attacks, i.e. manipulations of the inputs that are maliciously crafted to fool net…
A kernel function for Signal Temporal Logic formulae
Luca Bortolussi, Giuseppe Maria Gallo, Laura Nenzi
We discuss how to define a kernel for Signal Temporal Logic (STL) formulae. Such a kernel allows us to embed the space of formulae into a Hilbert space, and opens up the use of ker…
Density-embedding layers: a general framework for adaptive receptive fields
Francesco Cicala, Luca Bortolussi
The effectiveness and performance of artificial neural networks, particularly for visual tasks, depends in crucial ways on the receptive field of neurons. The receptive field itsel…
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Ginevra Carbone, Matthew Wicker, Luca Laurenti +3
Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical a…
Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence
Luca Bortolussi, Guido Sanguinetti
The success of modern Artificial Intelligence (AI) technologies depends critically on the ability to learn non-linear functional dependencies from large, high dimensional data sets…