<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-21T02:46:28Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/121632" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/121632</identifier><datestamp>2026-06-06T00:48:46Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Joshua B. Tenenbaum.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Morales, Lucas Eduardo.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department" lang="en_US">Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2019-07-15T20:29:35Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2019-07-15T20:29:35Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2018</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2018</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">https://hdl.handle.net/1721.1/121632</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">1098177598</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from student-submitted PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (pages 133-145).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">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.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Lucas Eduardo Morales.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">M.Eng.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="collection" lang="en_US">M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">145 pages</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Electrical Engineering and Computer Science.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">On the representation and learning of concepts : programs, types, and bayes</dim:field>
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   	&lt;Title>On the representation and learning of concepts : programs, types, and bayes&lt;/Title>
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    &lt;Keyword>Electrical Engineering and Computer Science.&lt;/Keyword>
   	&lt;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.&lt;/Abstract>
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