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On the Partition Function and Random Maximum A-Posteriori Perturbations

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sword-2019-05-31T16:07:46.original.xml (130 B)
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Author(s)
Hazan, Tamir
•
Jaakkola, Tommi
Date Issued
2012
Citation
Hazan, Tamir and Jaakkola, Tommi. 2012. "On the Partition Function and Random Maximum A-Posteriori Perturbations."
Version
Author's final manuscript
Abstract
In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As a result, we can use efficient MAP solvers such as graph-cuts to evaluate the corresponding partition function. We show that our method excels in the typical "high signal - high coupling" regime that results in ragged energy landscapes difficult for alternative approaches. Copyright 2012 by the author(s)/owner(s).
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
http://creativecommons.org/licenses/by-nc-sa/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/137613
DOI of Published Version
https://icml.cc/2012/papers.1.html
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