旧瓶新酒:机器学习组件的n -版本编程

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) Pub Date : 2020-10-01 DOI:10.1109/ISSREW51248.2020.00086

A. Gujarati, S. Gopalakrishnan, K. Pattabiraman

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引用次数: 6

摘要

我们在机器学习(ML)的背景下重新审视n版本编程。生成一个ML组件的N个版本不需要额外的编程工作，只需要额外的计算。这开启了执行数百个不同副本的可能性，如果仔细部署，可以显著提高它们的整体可靠性。我们使用数学模型来评估这些好处。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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New Wine in an Old Bottle: N-Version Programming for Machine Learning Components

We revisit N-version programming in the context of machine learning (ML). Generating N versions of an ML component does not require additional programming effort, but only extra computations. This opens up the possibility of executing hundreds of diverse replicas, which, if carefully deployed, can improve their overall reliability by a significant margin. We use mathematical modeling to evaluate these benefits.

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2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)

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