Efficient fixed-radius near neighbors for machine learning
Name
1128186935-MIT.pdf
Size
1.54 MB
Format
Adobe PDF
Checksum (MD5)
9a4aa0720951285425b9f0d85a97ff45
Author(s)
Walter, David Porter,III
Advisor(s)
Tomaso A. Poggio.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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.
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, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-63).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
Persistent DSpace Link
