{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/123119"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/123119","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Efficient fixed-radius near neighbors for machine learning","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_html":"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_has_math":false,"creators":["Walter, David Porter,III"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Tomaso A. Poggio."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:21Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/123119","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Tomaso A. Poggio."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Efficient fixed-radius near neighbors for machine learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Tomaso A. Poggio."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. 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