Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks.
- DOI
- 10.1038/s41467-023-43317-9
- Published
- 2023 Nov 20
- Container
- Nature communications
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41467-023-43317-9,
title = {Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks.},
author = {Bonnet D and Hirtzlin T and Majumdar A and Dalgaty T and Esmanhotto E and Meli V and Castellani N and Martin S and Nodin JF and Bourgeois G and Portal JM and Querlioz D and Vianello E},
year = {2023},
journal = {Nature communications},
doi = {10.1038/s41467-023-43317-9},
url = {https://doi.org/10.1038/s41467-023-43317-9}
}RIS
TY - JOUR TI - Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks. AU - Bonnet D AU - Hirtzlin T AU - Majumdar A AU - Dalgaty T AU - Esmanhotto E AU - Meli V AU - Castellani N AU - Martin S AU - Nodin JF AU - Bourgeois G AU - Portal JM AU - Querlioz D AU - Vianello E PY - 2023 JO - Nature communications DO - 10.1038/s41467-023-43317-9 UR - https://doi.org/10.1038/s41467-023-43317-9 ER -
APA
D, B., T, H., A, M., T, D., E, E., V, M., N, C., S, M., JF, N., G, B., JM, P., D, Q., & E, V. (2023). Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks.. Nature communications. https://doi.org/10.1038/s41467-023-43317-9
Source records
- pubmed · retrieved 2026-09-26T15:43:34.888Z