Active learning using meta-learned priors
Name
1145013781-MIT.pdf
Size
2.57 MB
Format
Adobe PDF
Checksum (MD5)
2dee606fb946f67d95a6fd488a231748
Author(s)
Gentili, Paolo(Paolo Y.)
Advisor(s)
Leslie P. Kaelbling and Tomás Lozano-Pérez.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Deep learning models have found enormous success across a variety of displines, but training the parameters within these models generally requires huge amounts of labelled data. One technique to reduce the burden of this data requirement is meta-learning, which involves extracting knowledge from previously experienced tasks so we can train new tasks with less data. Another such technique is active learning, where we selectively query labels for examples to train on, which potentially allows us to train a new task with fewer labelled data points. We investigate the potential to use active learning in the meta-learning setting, combining the advantages of the two techniques and further reducing the requirements for labelled data. In particular, we consider several techniques for actively querying labels within the modular meta-learning framework. We apply these techniques to several empirical settings, finding significant advantages over a baseline of random queries.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 49-51).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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