Understanding Deep Learning with Activation Pattern Diagrams. - Francesco Craighero; Alex Graudenzi; Fabrizio Angaroni; Fabio Stella; Marco Antoniotti (XAI.it@AI*IA, 2020)

Investigating the Compositional Structure of Deep Neural Networks. - Francesco Craighero; Fabrizio Angaroni; Alex Graudenzi; Fabio Stella; Marco Antoniotti (LOD (1), 2020)

EAD: an ensemble approach to detect adversarial examples from the hidden features of deep neural networks. - Francesco Craighero; Fabrizio Angaroni; Fabio Stella; Chiara Damiani; Marco Antoniotti; Alex Graudenzi (CoRR, 2021)

Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems. - Lucrezia Patruno; Francesco Craighero; Davide Maspero; Alex Graudenzi; Chiara Damiani (Inf. Comput., 2021)

Unity is strength: Improving the detection of adversarial examples with ensemble approaches. - Francesco Craighero; Fabrizio Angaroni; Fabio Stella; Chiara Damiani; Marco Antoniotti; Alex Graudenzi (Neurocomputing, 2023)

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution. - Gianluca Ascolani; Fabrizio Angaroni; Davide Maspero; Francesco Craighero; Narra Lakshmi Sai Bhavesh; Rocco Piazza; Chiara Damiani; Daniele Ramazzotti; Marco Antoniotti; Alex Graudenzi (BMC Bioinform., 2023)

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations. - Mohan Vamsi Nallapareddy; Francesco Craighero; Cédric Gobet; Felix Naef; Pierre Vandergheynst (MLCB, 2024)