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Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields

Schmidt, Mark W.; Babanezhad, Reza; Ahemd, Mohamed Osama; Defazio, Aaron; Clifton, Ann; Sarkar, Anoop

Description

We apply stochastic average gradient (SAG) algorithms for training conditional random fields (CRFs). We describe a practical implementation that uses structure in the CRF gradient to reduce the memory requirement of this linearly-convergent stochastic gradient method, propose a non-uniform sampling scheme that substantially improves practical performance, and analyze the rate of convergence of the SAGA variant under non-uniform sampling. Our experimental results reveal that our method...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
URI: http://hdl.handle.net/1885/103837
Source: Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields

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