Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Daniele Baccega"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detect
Externí odkaz:
https://doaj.org/article/4095c8bf4a244ed1a022935e7f521c8c
Autor:
Andrea Saglietto, Daniele Baccega, Roberto Esposito, Matteo Anselmino, Veronica Dusi, Attilio Fiandrotti, Gaetano Maria De Ferrari
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 11 (2024)
BackgroundArtificial intelligence (AI) has shown promise in the early detection of various cardiac conditions from a standard 12-lead electrocardiogram (ECG). However, the ability of AI to identify abnormalities from single-lead recordings across a r
Externí odkaz:
https://doaj.org/article/52303fbdb09b4f3fbbe4b2beb20d4a48
Autor:
Daniele Baccega, Simone Pernice, Pietro Terna, Paolo Castagno, Giovenale Moirano, Lorenzo Richiardi, Matteo Sereno, Sergio Rabellino, Milena Maria Maule, Marco Beccuti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e671368f8a10c5a0e6ed9ed12432bd9
http://hdl.handle.net/2318/1875158
http://hdl.handle.net/2318/1875158
Publikováno v:
PDP
This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2dfe373305d0483bedf47ac9615053f2
https://hdl.handle.net/2318/1695211
https://hdl.handle.net/2318/1695211