Erratum: Bayesian neural networks using magnetic tunnel junction-based probabilistic in-memory computing

An Erratum on
Bayesian neural networks using magnetic tunnel junction-based probabilistic in-memory computing

by Liu S, Xiao TP, Kwon J, Debusschere BJ, Agarwal S, Incorvia JAC and Bennett CH (2022). Front. Nanotechnol. 4:1021943. doi: 10.3389/fnano.2022.1021943

Due to a production error, an incorrect Copyright statement was provided, and a Licenses and Permissions section was omitted. The correct Copyright statement is as follows:

“© 2022 Liu, Xiao, Kwon, Debusschere, Agarwal, Incorvia and Bennett. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.”

The Licenses and Permissions statement is as follows:

“This article has been authored by employees of National Technology and Engineering Solutions of Sandia, LLC under Contract No. DENA0003525 with the US Department of Energy (DOE). These employees own all right, title and interest in and to the article and are solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan https://www.energy.gov/downloads/doe-public-access-plan.”

The publisher apologizes for this mistake. The original version of this article has been updated.