Linear and Parallel Learning for Markov Random Fields
We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded degree, its complexity is linear in the number of cliques. Unlike it...
| Main Authors: | , , |
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| Format: | Conference item |
| Published: |
2014
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