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Robust machine learning algorithms and systems for detection and mitigation of adversarial attacks and anomalies : proceedings of a workshop / Linda Casola and Dionna Ali, rapporteurs ; Intelligence Community Studies Board ; Computer Science and Telecommunications Board, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine.

By: Contributor(s): Material type: TextTextPublication details: Washington, DC : the National Academies Press, (c)2019..Description: 1 online resource (xii, 69 pages) : color illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780309496124
Subject(s): Genre/Form: LOC classification:
  • HV6773 .R638 2019
  • Q325
Online resources: Available additional physical forms:
Contents:
Plenary session -- Adversarial attacks -- Detection and mitigation of adversarial attacks and anomalies -- Enablers of machine learning algorithms and systems -- Recent trends i machine learning, parts 1 and 2 -- Plenary session -- Recent trends in machine learning, part 3 -- Machine learning systems --References -- Appendixes
Subject: "The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11-12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop"--Publisher's description
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Includes bibliographies and index.

Introduction -- Plenary session -- Adversarial attacks -- Detection and mitigation of adversarial attacks and anomalies -- Enablers of machine learning algorithms and systems -- Recent trends i machine learning, parts 1 and 2 -- Plenary session -- Recent trends in machine learning, part 3 -- Machine learning systems --References -- Appendixes

"The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11-12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop"--Publisher's description

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