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Caroline Uhler. Radhakrishnan, Adityanarayanan Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-02-08T16:28:14Z 2018-02-08T16:28:14Z 2017 http://hdl.handle.net/1721.1/113538 1020173762 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. Cataloged from PDF version of thesis. "May 2017." Hand written on title page: "June 2017." Includes bibliographical references (pages 69-72). With the recent availability of large training datasets and graphics processing units (GPUs), we address challenges in the application of graphical models and neural networks to prediction sensitive areas such as healthcare. We begin by presenting our work in the context of learning graphical models from biological data. Namely, we present a combinatorial perspective of Markov Equivalence Classes (MECs), which defines the size of solution spaces when attempting to learn a graphical model from data. Through our analysis, we show that the size of these MECs can be exponential with respect to features of the graph (such as average degree). We then switch contexts to address the challenge of developing interpretable complex models. Namely, we present a variational-inference-motivated neural network, PatchNet, that provides visual interpretability, and we present the application of our network to the Describable Textures Dataset (DTD), the ISIC-ISBI Melanoma Classification Challenge, and cell nucleus data. by Adityanarayanan Radhakrishnan. M.Eng. 72 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Theory and application of neural and graphical models in early cancer diagnostics Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e8bb460f-0d1a-4522-a569-5764ca860744"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Theory and application of neural and graphical models in early cancer diagnostics</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Radhakrishnan, Adityanarayanan</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>With the recent availability of large training datasets and graphics processing units (GPUs), we address challenges in the application of graphical models and neural networks to prediction sensitive areas such as healthcare. We begin by presenting our work in the context of learning graphical models from biological data. Namely, we present a combinatorial perspective of Markov Equivalence Classes (MECs), which defines the size of solution spaces when attempting to learn a graphical model from data. Through our analysis, we show that the size of these MECs can be exponential with respect to features of the graph (such as average degree). We then switch contexts to address the challenge of developing interpretable complex models. Namely, we present a variational-inference-motivated neural network, PatchNet, that provides visual interpretability, and we present the application of our network to the Describable Textures Dataset (DTD), the ISIC-ISBI Melanoma Classification Challenge, and cell nucleus data.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>