QUANOS-Reference-Cited by-同舟云学术

QUANOS

Published:2020-08-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design
language:
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Author:

Panda Priyadarshini¹

Affiliation:

1. Yale University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3370748.3406585

Reference14 articles.

1. Y. LeCun et al. "Deep learning " nature 2015. Y. LeCun et al. "Deep learning " nature 2015.

2. A. Madry et al. "Towards deep learning models resistant to adversarial attacks " arXiv:1706.06083 2017. A. Madry et al. "Towards deep learning models resistant to adversarial attacks " arXiv:1706.06083 2017.

3. N. Carlini et al. "On evaluating adversarial robustness " arXiv:1902.06705 2019. N. Carlini et al. "On evaluating adversarial robustness " arXiv:1902.06705 2019.

4. P. Panda et al. "Discretization based solutions for secure machine learning against adversarial attacks " IEEE Access 2019. P. Panda et al. "Discretization based solutions for secure machine learning against adversarial attacks " IEEE Access 2019.

5. J. Lin et al. "Defensive quantization: When efficiency meets robustness " arXiv:1904.08444 2019. J. Lin et al. "Defensive quantization: When efficiency meets robustness " arXiv:1904.08444 2019.

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