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no code implementations • 31 Aug 2023 • Davide Scassola, Sebastiano Saccani, Ginevra Carbone, Luca Bortolussi

Score-based and diffusion models have emerged as effective approaches for both conditional and unconditional generation.

1 code implementation • 25 May 2023 • Emanuele Ballarin, Alessio Ansuini, Luca Bortolussi

In this work, we propose a novel adversarial defence mechanism for image classification - CARSO - blending the paradigms of adversarial training and adversarial purification in a mutually-beneficial, robustness-enhancing way.

1 code implementation • 24 May 2023 • Lorenzo Basile, Nikos Karantzas, Alberto D'Onofrio, Luca Bortolussi, Alex Rodriguez, Fabio Anselmi

Despite their impressive performance in classification, neural networks are known to be vulnerable to adversarial attacks.

no code implementations • 3 May 2023 • Gaia Saveri, Luca Bortolussi

State of the art Machine Learning (ML) approaches are mostly based on gradient descent optimisation in continuous spaces, while learning logic is framed in the discrete syntactic space of formulae.

1 code implementation • 4 Nov 2022 • Francesca Cairoli, Nicola Paoletti, Luca Bortolussi

We consider the problem of predictive monitoring (PM), i. e., predicting at runtime the satisfaction of a desired property from the current system's state.

1 code implementation • 13 Jul 2022 • Luca Bortolussi, Ginevra Carbone, Luca Laurenti, Andrea Patane, Guido Sanguinetti, Matthew Wicker

Despite significant efforts, both practical and theoretical, training deep learning models robust to adversarial attacks is still an open problem.

no code implementations • 11 May 2022 • Luca Bortolussi, Francesca Cairoli, Ginevra Carbone, Paolo Pulcini

As observations are costly and noisy, smMC is framed as a Bayesian inference problem so that the estimates have an additional quantification of the uncertainty.

no code implementations • 9 May 2022 • Gaia Saveri, Luca Bortolussi

Graph Neural Networks (GNNs) have been recently leveraged to solve several logical reasoning tasks.

no code implementations • 24 Jan 2022 • Luca Bortolussi, Giuseppe Maria Gallo, Jan Křetínský, Laura Nenzi

We introduce a similarity function on formulae of signal temporal logic (STL).

1 code implementation • 16 Aug 2021 • 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.

1 code implementation • 24 Jun 2021 • 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.

no code implementations • 3 May 2021 • Michael Backenköhler, Luca Bortolussi, Gerrit Großmann, Verena Wolf

To understand the long-run behavior of Markov population models, the computation of the stationary distribution is often a crucial part.

1 code implementation • 22 Feb 2021 • Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi

We empirically show that interpretations provided by Bayesian Neural Networks are considerably more stable under adversarial perturbations of the inputs and even under direct attacks to the explanations.

no code implementations • 18 Feb 2021 • 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 networks into incorrect predictions.

1 code implementation • 20 Oct 2020 • Michael Backenköhler, Luca Bortolussi, Gerrit Großmann, Verena Wolf

Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints.

no code implementations • 11 Sep 2020 • Luca Bortolussi, Giuseppe Maria Gallo, Laura Nenzi

We discuss how to define a kernel for Signal Temporal Logic (STL) formulae.

no code implementations • 4 Sep 2020 • Luca Bortolussi, Francesca Cairoli, Ginevra Carbone, Francesco Franchina, Enrico Regolin

We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests.

no code implementations • 23 Jun 2020 • Francesco Cicala, Luca Bortolussi

In this paper, we propose the novel theoretical framework of density-embedded layers, generalizing the transformation represented by a neuron.

no code implementations • 12 Jun 2020 • Tabea Waizmann, Luca Bortolussi, Andrea Vandin, Mirco Tribastone

The deterministic rate equation (DRE) gives a macroscopic approximation as a compact system of differential equations that estimate the average populations for each species, but it may be inaccurate in the case of nonlinear interaction dynamics.

1 code implementation • NeurIPS 2020 • Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patane, Luca Bortolussi, Guido Sanguinetti

Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications.

no code implementations • 25 Sep 2019 • Luca Laurenti, Andrea Patane, Matthew Wicker, Luca Bortolussi, Luca Cardelli, Marta Kwiatkowska

We investigate global adversarial robustness guarantees for machine learning models.

no code implementations • 8 Nov 2018 • 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.

no code implementations • 13 Nov 2017 • Laura Nenzi, Simone Silvetti, Ezio Bartocci, Luca Bortolussi

We consider the problem of mining signal temporal logical requirements from a dataset of regular (good) and anomalous (bad) trajectories of a dynamical system.

no code implementations • 7 May 2016 • Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti

Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study.

no code implementations • 29 Dec 2013 • Ezio Bartocci, Luca Bortolussi, Guido Sanguinetti

We present a novel approach to learn the formulae characterising the emergent behaviour of a dynamical system from system observations.

no code implementations • 3 Sep 2013 • Ezio Bartocci, Luca Bortolussi, Laura Nenzi, Guido Sanguinetti

By discussing two examples, we show how to approximate the distribution of the robustness score and its key indicators: the average robustness and the conditional average robustness.

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