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.
Table of Contents
|2 Plenary Session||3-6|
|3 Adversarial Attacks||7-12|
|4 Detection and Mitigation of Adversarial Attacks and Anomalies||13-18|
|5 Enablers of Machine Learning Algorithms and Systems||19-22|
|6 Recent Trends in Machine Learning, Parts 1 and 2||23-34|
|7 Plenary Session||35-38|
|8 Recent Trends in Machine Learning, Part 3||39-45|
|9 Machine Learning Systems||46-52|
|Appendix A: Biographical Sketches of Workshop Planning Committee||57-61|
|Appendix B: Workshop Agenda||62-64|
|Appendix C: Workshop Statement of Task||65-65|
|Appendix D: Capability Technology Matrix||66-68|
|Appendix E: Acronyms||69-70|
The National Academies Press and the Transportation Research Board have partnered with Copyright Clearance Center to offer a variety of options for reusing our content. You may request permission to:
For most Academic and Educational uses no royalties will be charged although you are required to obtain a license and comply with the license terms and conditions.
For information on how to request permission to translate our work and for any other rights related query please click here.
For questions about using the Copyright.com service, please contact:
Copyright Clearance Center
22 Rosewood Drive
Danvers, MA 01923
Tel (toll free): 855/239-3415 (select option 1)
E-mail: [email protected]
Loading stats for Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop...