@inproceedings{ac17575a97314981b303ba74e6239468,
title = "Towards a Bayesian Approach for Assessing Fault Tolerance of Deep Neural Networks",
abstract = "This paper presents Bayesian Deep Learning based Fault Injection (BDLFI), a novel methodology for fault injection in neural networks (NNs) and more generally differentiable programs. BDLFI uses (1) Bayesian Deep Learning to model the propagation of faults, and (2) Markov Chain Monte Carlo inference to quantify the effect of faults on the outputs of a NN. We demonstrate BDLFI on two representative networks and present our results that challenge pre-existing results in the field.",
keywords = "Fault Injection, Neural Networks",
author = "Banerjee, {Subho S.} and James Cyriac and Saurabh Jha and Kalbarczyk, {Zbigniew T.} and Iyer, {Ravishankar K.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019 ; Conference date: 24-06-2019 Through 27-06-2019",
year = "2019",
month = jun,
doi = "10.1109/DSN-S.2019.00018",
language = "English (US)",
series = "Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "25--26",
booktitle = "Proceedings - 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2019",
address = "United States",
}