Clamping Improves TRWand Mean Field Approximations
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Authors
Weller, Adrian
Domke, Justin
Publication Date
2016-05-02Journal Title
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics
Publisher
Microtome Publishing
Pages
38-46
Language
English
Type
Article
This Version
VoR
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Weller, A., & Domke, J. (2016). Clamping Improves TRWand Mean Field Approximations. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 38-46. https://doi.org/10.17863/CAM.270
Description
This is the author accepted manuscript. The final version is available from Microtome Publishing via http://jmlr.org/proceedings/papers/v51/weller16a.html.
Abstract
We examine the effect of clamping variables for approximate inference in undirected graphical models with pairwise relationships and discrete variables. For any number of variable labels, we demonstrate that clamping and summing approximate sub-partition functions can lead only to a decrease in the partition function estimate for TRW, and an increase for the naive mean field method, in each case guaranteeing an improvement in the approximation and bound. We next focus on binary variables, add the Bethe approximation to consideration and examine ways to choose good variables to clamp, introducing new methods. We show the importance of identifying highly frustrated cycles, and of checking the singleton entropy of a variable. We explore the value of our methods by empirical analysis and draw lessons to guide practitioners.
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.270
This record's URL: https://www.repository.cam.ac.uk/handle/1810/256328