Enki:一种多样性驱动的方法来测试和训练健壮的学习支持系统

IF 2.2 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2021-01-01 DOI:10.1145/3460959

Michael Austin Langford, B. Cheng

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引用次数: 6

摘要

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Enki: A Diversity-driven Approach to Test and Train Robust Learning-enabled Systems

ing with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Association for Computing Machinery. 1556-4665/2021/05-ART5 $15.00 https://doi.org/10.1145/3460959 ACM Transactions on Autonomous and Adaptive Systems, Vol. 15, No. 2, Article 5. Publication date: May 2021. 5:2 M. A. Langford and B. H. C. Cheng

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来源期刊

ACM Transactions on Autonomous and Adaptive Systems 工程技术-计算机：理论方法

CiteScore

4.80

自引率

7.40%

发文量

审稿时长

>12 weeks

期刊介绍： TAAS addresses research on autonomous and adaptive systems being undertaken by an increasingly interdisciplinary research community -- and provides a common platform under which this work can be published and disseminated. TAAS encourages contributions aimed at supporting the understanding, development, and control of such systems and of their behaviors. TAAS addresses research on autonomous and adaptive systems being undertaken by an increasingly interdisciplinary research community - and provides a common platform under which this work can be published and disseminated. TAAS encourages contributions aimed at supporting the understanding, development, and control of such systems and of their behaviors. Contributions are expected to be based on sound and innovative theoretical models, algorithms, engineering and programming techniques, infrastructures and systems, or technological and application experiences.