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AAAI Workshop on Engineering Dependable and Secure Machine Learning Systems (2020 :)

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Engineering dependable and secure machine learning systems = third international workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020 : revised selected papers /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Engineering dependable and secure machine learning systems/ edited by Onn Shehory, Eitan Farchi, Guy Barash.
Reminder of title:	third international workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020 : revised selected papers /
remainder title:	EDSMLS 2020
other author:	Shehory, Onn.
corporate name:	AAAI Workshop on Engineering Dependable and Secure Machine Learning Systems
Published:	Cham :Springer International Publishing : : 2020.,
Description:	ix, 141 p. :ill., digital ;24 cm.
[NT 15003449]:	Quality Management of Deep Learning Systems -- Can Attention Masks Improve Adversarial Robustness? -- Learner-Independent Data Omission Attacks -- Extraction of Complex DNN Models: Real Threat or Boogeyman? -- Principal Component Properties of Adversarial Samples -- FreaAI: Automated extraction of data slices to test machine learning models -- Density estimation in representation space to predict model uncertainty -- Automated detection of drift in deep learning based classifiers using network embedding -- Quality of syntactic implication of RL-based sentence summarization -- Dependable Neural Networks for Safety Critical Tasks.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-030-62144-5
ISBN:	9783030621445

Engineering dependable and secure machine learning systems = third international workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020 : revised selected papers /
Engineering dependable and secure machine learning systemsthird international workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020 : revised selected papers /[electronic resource] :EDSMLS 2020edited by Onn Shehory, Eitan Farchi, Guy Barash. - Cham :Springer International Publishing :2020. - ix, 141 p. :ill., digital ;24 cm. - Communications in computer and information science,12721865-0929 ;. - Communications in computer and information science ;1272..

Quality Management of Deep Learning Systems -- Can Attention Masks Improve Adversarial Robustness? -- Learner-Independent Data Omission Attacks -- Extraction of Complex DNN Models: Real Threat or Boogeyman? -- Principal Component Properties of Adversarial Samples -- FreaAI: Automated extraction of data slices to test machine learning models -- Density estimation in representation space to predict model uncertainty -- Automated detection of drift in deep learning based classifiers using network embedding -- Quality of syntactic implication of RL-based sentence summarization -- Dependable Neural Networks for Safety Critical Tasks.

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.

ISBN: 9783030621445

Standard No.: 10.1007/978-3-030-62144-5doiSubjects--Topical Terms:

576368
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3

Engineering dependable and secure machine learning systems = third international workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020 : revised selected papers /
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520 $a This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.
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