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Augmenting Dual Decomposition for MAP Inference

Augmenting Dual Decomposition for MAP Inference

This video was recorded at NIPS Workshops, Whistler 2010. In this paper, we propose combining augmented Lagrangian optimization with the dual decomposition method to obtain a fast algorithm for approximate MAP (maximum a posteriori) inference on factor graphs. We also show how the proposed algorithm can efficiently handle problems with (possibly global) structural constraints. The experimental results reported testify for the state-of-the-art performance of the proposed approach.

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