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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