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Interpretable Active Learning

Published in arXiv (Cornell University) • Jul 31, 2017
NobleIDNI9P58W67R01S75
Authors:
Richard L. Phillips
,
Kyu Hyun Chang
,
Sorelle A. Friedler

Abstract

Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns a...

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