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Engineering Dependable and Secure Machine Learning Systems: Communications in Computer and Information Science, cartea 1272

Editat de Onn Shehory, Eitan Farchi, Guy Barash
en Limba Engleză Paperback – 8 noi 2020
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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Specificații

ISBN-13: 9783030621438
ISBN-10: 303062143X
Pagini: 152
Ilustrații: IX, 141 p. 44 illus., 34 illus. in color.
Dimensiuni: 155 x 235 x 9 mm
Greutate: 0.24 kg
Ediția:1st edition 2020
Editura: Springer
Colecția Communications in Computer and Information Science
Seria Communications in Computer and Information Science

Locul publicării:Cham, Switzerland

Cuprins

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.