Dependency tracking for risk mitigation in machine learning (ML) systems-Reference-Cited by-同舟云学术

Dependency tracking for risk mitigation in machine learning (ML) systems

Published:2022-05-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice
language:
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Author:

Xu Xiwei¹,Wang Chen¹,Wang Zhen (Jef)¹,Lu Qinghua¹,Zhu Liming¹

Affiliation:

1. University of New South Wales, Sydney, Australia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3510457.3513058

Reference7 articles.

1. Saleema Amershi , Andrew Begel , Christian Bird , Robert DeLine , Harald Gall , Ece Kamar , Nachiappan Nagappan , Besmira Nushi , and Thomas Zimmermann . 2019 . Software Engineering for Machine Learning: A Case Study. In ICSE2019-SEIP. 291--300 . Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. 2019. Software Engineering for Machine Learning: A Case Study. In ICSE2019-SEIP. 291--300.

2. Transparency in the Financial System: Rollover Risk and Crises

3. Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In international conference on machine learning. PMLR, 1050--1059 . Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. PMLR, 1050--1059.

4. Establishing a Framework for Dynamic Risk Management in ‘Intelligent’ Aero-Engine Control

5. Continuous validation for data analytics systems

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