Engineering Dependable and Secure Machine Learning Systems
Third International Workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020, Revised Selected Papers
Published on 7. November 2020
IX, 141 pages
978-3-030-62144-5 (ISBN)
Description
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.
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.
More details
Series
Edition
1st ed. 2020
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
IX, 141 p. 44 illus., 34 illus. in color.
ISBN-13
978-3-030-62144-5 (9783030621445)
DOI
10.1007/978-3-030-62144-5
Schweitzer Classification
Other editions
Additional editions
Onn Shehory | Eitan Farchi | Guy Barash
Engineering Dependable and Secure Machine Learning Systems
Third International Workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020, Revised Selected Papers
Book
11/2020
Springer
€53.49
Shipment within 7-9 days
Content
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.
