Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks

Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovativ...

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Detalles Bibliográficos
Autores principales: Bonnet, Djohan, Hirtzlin, Tifenn, Majumdar, Atreya, Dalgaty, Thomas, Esmanhotto, Eduardo, Meli, Valentina, Castellani, Niccolo, Martin, Simon, Nodin, Jean-François, Bourgeois, Guillaume, Portal, Jean-Michel, Querlioz, Damien, Vianello, Elisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661910/
https://www.ncbi.nlm.nih.gov/pubmed/37985669
http://dx.doi.org/10.1038/s41467-023-43317-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661910/
https://www.ncbi.nlm.nih.gov/pubmed/37985669
http://dx.doi.org/10.1038/s41467-023-43317-9

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