Adversarially-learned inference via an ensemble of discrete undirected graphical models
Name
1227275913-MIT.pdf
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1.55 MB
Format
Adobe PDF
Checksum (MD5)
f3b4c24c84483746f2078e3d61370438
Author(s)
Jeewajee, Adarsh Keshav S.
Advisor(s)
Leslie Pack Kaelbling.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Undirected graphical models are compact representations of joint probability distributions over random variables. To carry out an inference task of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, when faced with new tasks, these models (EGMs) often need to be re-trained. Instead, we propose an inference-agnostic adversarial training framework for producing an ensemble of graphical models (AGMs). The ensemble is optimized to generate data, and inference is learned as a by-product of this endeavor. AGMs perform comparably with EGMs on inference tasks that the latter were specifically optimized for. Most importantly, AGMs show significantly better generalization capabilities across inference tasks. AGMs are also on par with GibbsNet, a state-of-the-art deep neural architecture, which like AGMs, allows conditioning on any subset of random variables. Finally, AGMs allow fast data sampling, competitive with Gibbs sampling from EGMs.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 50-55).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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