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Suggested Citation:"Appendix E: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
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E

Acronyms

2D two dimensional
3D three dimensional
ACE Aggregative Contingent Estimation
AI artificial intelligence
ARIMA AutoRegressive Integrated Moving Average
CAMEO conflict and mediation event observation
CorEx Total Correlation Explanation
DoD Department of Defense
EVM extreme value machine
GAN generative adversarial network
GPU graphical processing unit
IARPA Intelligence Advanced Research Projects Activity
IC Intelligence Community
ICSB Intelligence Community Studies Board
IFP individual forecasting problem
KWIVER Kitware Imagery and Video Exploitation and Retrieval
LOTS layerwise origin-target synthesis
NOAA National Oceanic and Atmospheric Administration
ODNI Office of the Director of National Intelligence
ROC receiver operating characteristic
SAGE Synergistic Anticipation of Geopolitical Events
VIAME Video and Imagery Analytics for the Marine Environment
VQA visual question answering
XAI explainable artificial intelligence
Suggested Citation:"Appendix E: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
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Suggested Citation:"Appendix E: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
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Page 69
Suggested Citation:"Appendix E: Acronyms." National Academies of Sciences, Engineering, and Medicine. 2019. Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25534.
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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.

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