107 citations · 372 across the 19 of their papers we have counts for
20 papers
Unity is strength: Improving the Detection of Adversarial Examples with Ensemble Approaches
Francesco Craighero, Fabrizio Angaroni, Fabio Stella +3
A key challenge in computer vision and deep learning is the definition of robust strategies for the detection of adversarial examples. Here, we propose the adoption of ensemble app…
OG-SPACE: Optimized Stochastic Simulation of Spatial Models of Cancer Evolution
Fabrizio Angaroni, Marco Antoniotti, Alex Graudenzi
Algorithmic strategies for the spatio-temporal simulation of multi-cellular systems are crucial to generate synthetic datasets for bioinformatics tools benchmarking, as well as to…
Investigating the Compositional Structure Of Deep Neural Networks
Francesco Craighero, Fabrizio Angaroni, Alex Graudenzi +2
The current understanding of deep neural networks can only partially explain how input structure, network parameters and optimization algorithms jointly contribute to achieve the s…
Metabolic enrichment through functional gene rules
Davide Maspero, Claudio Isella, Marzia Di Filippo +6
It is well known that tumors originating from the same tissue have different prognosis and sensitivity to treatments. Over the last decade, cancer genomics consortia like the Cance…
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Daniele Ramazzotti, Alex Graudenzi, Luca De Sano +2
Background. A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple…
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti +1
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures.…