Distributed inference : combining variational inference with distributed computing
Name
870308346-MIT.pdf
Description
Full printable version
Size
6.66 MB
Format
Adobe PDF
Checksum (MD5)
3da7c9891ad232bb3e9d2ee9879aadd4
Author(s)
Calabrese, Chris, M. Eng. Massachusetts Institute of Technology
Advisor(s)
David Wingate.
Alternative Title
Combining variational inference with distributed computing
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
The study of inference techniques and their use for solving complicated models has taken off in recent years, but as the models we attempt to solve become more complex, there is a worry that our inference techniques will be unable to produce results. Many problems are difficult to solve using current approaches because it takes too long for our implementations to converge on useful values. While coming up with more efficient inference algorithms may be the answer, we believe that an alternative approach to solving this complicated problem involves leveraging the computation power of multiple processors or machines with existing inference algorithms. This thesis describes the design and implementation of such a system by combining a variational inference implementation (Variational Message Passing) with a high-level distributed framework (Graphlab) and demonstrates that inference is performed faster on a few large graphical models when using this system.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 95-97).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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