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Machine learning safety

Huang, Xiaowei.

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Machine learning safety

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning safety/ by Xiaowei Huang, Gaojie Jin, Wenjie Ruan.
Author:	Huang, Xiaowei.
other author:	Jin, Gaojie.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xvii, 321 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Safety of Simple Machine Learning Models -- 3. Safety of Deep Learning -- 4. Robustness Verification of Deep Learning -- 5. Enhancement to Robustness and Generalization -- 6. Probabilistic Graph Model -- A. Mathematical Foundations -- B. Competitions.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Safety measures. -
Online resource:	https://doi.org/10.1007/978-981-19-6814-3
ISBN:	9789811968143

Machine learning safety
Huang, Xiaowei.

Machine learning safety[electronic resource] /by Xiaowei Huang, Gaojie Jin, Wenjie Ruan. - Singapore :Springer Nature Singapore :2023. - xvii, 321 p. :ill., digital ;24 cm. - Artificial intelligence: foundations, theory, and algorithms,2365-306X. - Artificial intelligence: foundations, theory, and algorithms..

1. Introduction -- 2. Safety of Simple Machine Learning Models -- 3. Safety of Deep Learning -- 4. Robustness Verification of Deep Learning -- 5. Enhancement to Robustness and Generalization -- 6. Probabilistic Graph Model -- A. Mathematical Foundations -- B. Competitions.

Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities. The book aims to improve readers' awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.

ISBN: 9789811968143

Standard No.: 10.1007/978-981-19-6814-3doiSubjects--Topical Terms:

3630740
Machine learning
--Safety measures.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning safety
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505 0 $a 1. Introduction -- 2. Safety of Simple Machine Learning Models -- 3. Safety of Deep Learning -- 4. Robustness Verification of Deep Learning -- 5. Enhancement to Robustness and Generalization -- 6. Probabilistic Graph Model -- A. Mathematical Foundations -- B. Competitions.
520 $a Machine learning algorithms allow computers to learn without being explicitly programmed. Their application is now spreading to highly sophisticated tasks across multiple domains, such as medical diagnostics or fully autonomous vehicles. While this development holds great potential, it also raises new safety concerns, as machine learning has many specificities that make its behaviour prediction and assessment very different from that for explicitly programmed software systems. This book addresses the main safety concerns with regard to machine learning, including its susceptibility to environmental noise and adversarial attacks. Such vulnerabilities have become a major roadblock to the deployment of machine learning in safety-critical applications. The book presents up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities of machine learning models; formal verification, which is used to determine if a trained machine learning model is free of vulnerabilities; and adversarial training, which is used to enhance the training process and reduce vulnerabilities. The book aims to improve readers' awareness of the potential safety issues regarding machine learning models. In addition, it includes up-to-date techniques for dealing with these issues, equipping readers with not only technical knowledge but also hands-on practical skills.
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700 1 $a Ruan, Wenjie. $3 3630739
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