27 citations · 38 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…