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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...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| 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 |