Towards Interpretable Explanations for Transfer Learning in Sequential Tasks

People increasingly rely on machine learning (ML) to make intelligent decisions. However, the ML results are often difficult to interpret and the algorithms do not support interaction to solicit clarification or explanation. In this paper, we highlight an emerging research area of interpretable expl...

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Bibliographic Details
Main Authors: Ramakrishnan, Ramya (Contributor), Shah, Julie A (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Association for the Advancement of Artificial Intelligence, 2017-01-27T14:49:58Z.
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