On the representation and learning of concepts : programs, types, and bayes
Name
1098177598-MIT.pdf
Size
1.02 MB
Format
Adobe PDF
Checksum (MD5)
79260bfccd68d4376fec852dfa4e66fe
Author(s)
Morales, Lucas Eduardo.
Advisor(s)
Joshua B. Tenenbaum.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
This thesis develops computational models of cognition with a focus on concept representation and learning. We start with brief philosophical discourse accompanied by empirical findings and theories from developmental science. We review many formal foundations of computation as well as modern approaches to the problem of program induction - the learning of structure within those representations. We show our own research on program induction focused on its application for language bootstrapping. We then demonstrate our approach for augmenting a class of machine learning algorithms to enable domain-general learning by applying it to a program induction algorithm. Finally, we present our own computational account of concepts and cognition.
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, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 133-145).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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