Neural networks can learn to utilize correlated auxiliary noise

Abstract We demonstrate that neural networks that process noisy data can learn to exploit, when available, access to auxiliary noise that is correlated with the noise on the data. In effect, the network learns to use the correlated auxiliary noise as an approximate key to decipher its noisy input da...

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Bibliographic Details
Main Authors: Aida Ahmadzadegan, Petar Simidzija, Ming Li, Achim Kempf
Format: Article
Language:English
Published: Nature Portfolio 2021-11-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-00502-4