FAILS: a tool for assessing risk in ML systems-Reference-Cited by-同舟云学术

FAILS: a tool for assessing risk in ML systems

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 28th Asia-Pacific Software Engineering Conference Workshops (APSEC Workshops)
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Author:

Dominguez Gonzalo Aguirre¹,Kawaai Keigo¹,Maruyama Hiroshi¹

Affiliation:

1. Preferred Networks Inc.,Tokyo,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9719592/9719880/09719942.pdf?arnumber=9719942

Reference10 articles.

1. Guaranteeing Deep Neural Network Outputs in a Feasible Region;maruyama;Proceedings of the International Workshop on Evidence-based Security and Privacy in the Wild and the 1st International Workshop on Machine Learning Systems Engineering,2018

2. The Marabou Framework for Verification and Analysis of Deep Neural Networks

3. The ML test score: A rubric for ML production readiness and technical debt reduction

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