Distributed map inference for undirected graphical models
Published 1 January 2010
Sameer Singh, Amarnag Subramanya Fern, Fernando Pereira
Citations6
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TL;DR
The method allows ease in specifying structure, distribution strategy and proposal function, which enables faster convergence in approximate inference and learning methods.
Abstract
Graphical models have widespread uses in information extraction and natural language processing. Recent improvements in approximate inference techniques [1, 2, 3, 4] have allowed exploration of dense models over a large number of variables. These applications include coreference resolution [5, 6], relation extraction [7], and joint inference [8, 9, 10]. But as the graphs grow to web scale,
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