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Tomaso A. Poggio. Walter, David Porter,III Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-12-05T18:04:32Z 2019-12-05T18:04:32Z 2019 2019 https://hdl.handle.net/1721.1/123119 1128186935 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 61-63). Deep learning has enabled artificial intelligence systems to move away from manual feature engineering and toward feature learning and better performance. Convolutional neural networks (CNNs) have especially demonstrated super-human performance in many vision tasks. One big reason for the success of CNNs is due to the use of parallelizable software and hardware to run these models, making their use computationally practical. This work is focused in the design and implementation of an efficient and parallel fixed-radius near neighbors program (FRNN). FRNN is a core component in a new type of machine learning model, object oriented deep learning (OODL), serving as a replacement for CNNs with goals of invariance, equivariance, interpretability, and computational efficiency that improve upon the abilities of CNNs. This efficient implementation of FRNN is a critical step in making OODL computationally efficient and practical. by David Porter Walter, III. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 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. Efficient fixed-radius near neighbors for machine learning Thesis application/pdf 2019-12-05T18:04:32Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="57b244a9-384d-43a7-aaf9-83527138dac8"> <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>Efficient fixed-radius near neighbors for machine learning</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Walter, David Porter,III</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>Deep learning has enabled artificial intelligence systems to move away from manual feature engineering and toward feature learning and better performance. Convolutional neural networks (CNNs) have especially demonstrated super-human performance in many vision tasks. One big reason for the success of CNNs is due to the use of parallelizable software and hardware to run these models, making their use computationally practical. This work is focused in the design and implementation of an efficient and parallel fixed-radius near neighbors program (FRNN). FRNN is a core component in a new type of machine learning model, object oriented deep learning (OODL), serving as a replacement for CNNs with goals of invariance, equivariance, interpretability, and computational efficiency that improve upon the abilities of CNNs. This efficient implementation of FRNN is a critical step in making OODL computationally efficient and practical.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>