Search Results for author: Fabrizio Angaroni

Found 4 papers, 2 papers with code

EAD: an ensemble approach to detect adversarial examples from the hidden features of deep neural networks

no code implementations24 Nov 2021 Francesco Craighero, Fabrizio Angaroni, Fabio Stella, Chiara Damiani, Marco Antoniotti, Alex Graudenzi

One of the key challenges in Deep Learning is the definition of effective strategies for the detection of adversarial examples.

OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution

1 code implementation13 Oct 2021 Fabrizio Angaroni, Marco Antoniotti, Alex Graudenzi

We introduce the Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE), a computational framework for the simulation of the spatio-temporal evolution of cancer subpopulations and of the experimental procedures of both bulk andsingle-cell sequencing.

Investigating the Compositional Structure Of Deep Neural Networks

no code implementations17 Feb 2020 Francesco Craighero, Fabrizio Angaroni, Alex Graudenzi, Fabio Stella, Marco Antoniotti

By defining a direct acyclic graph representing the composition of activation patterns through the network layers, it is possible to characterize the instances of the input data with respect to both the predicted label and the specific (linear) transformation used to perform predictions.

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power

2 code implementations26 Aug 2014 Fabrizio Angaroni, Kevin Chen, Chiara Damiani, Giulio Caravagna, Alex Graudenzi, Daniele Ramazzotti

Motivation: Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse.

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