Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Mascolini, Alessio"'
Autor:
Mascolini, Alessio, Gaiardelli, Sebastiano, Ponzio, Francesco, Dall'Ora, Nicola, Macii, Enrico, Vinco, Sara, Di Cataldo, Santa, Fummi, Franco
Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues
Externí odkaz:
http://arxiv.org/abs/2409.14816
Autor:
Capogrosso, Luigi, Mascolini, Alessio, Girella, Federico, Skenderi, Geri, Gaiardelli, Sebastiano, Dall'Ora, Nicola, Ponzio, Francesco, Fraccaroli, Enrico, Di Cataldo, Santa, Vinco, Sara, Macii, Enrico, Fummi, Franco, Cristani, Marco
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, In
Externí odkaz:
http://arxiv.org/abs/2307.06975
Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper we present GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture
Externí odkaz:
http://arxiv.org/abs/2107.07761
Publikováno v:
In Biocybernetics and Biomedical Engineering January-March 2022 42(1):426-436
Autor:
Mascolini, Alessio1 (AUTHOR) alessio.mascolini@polito.it, Cardamone, Dario2,3 (AUTHOR), Ponzio, Francesco1 (AUTHOR), Di Cataldo, Santa1 (AUTHOR), Ficarra, Elisa4 (AUTHOR)
Publikováno v:
BMC Bioinformatics. 7/25/2022, Vol. 23 Issue 1, p1-17. 17p.
Additional file 1. The file contains additional information on the experimental setup and dose response curves obtained using our technique.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::012b7bd95e3fc102a1c23e324112667c