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An Incremental Subgradient Algorithm for Approximate MAP Estimation in Graphical Models
This video was recorded at NIPS Workshops, Whistler 2010. We present an incremental subgradient algorithm for approximate computation of maximum-a-posteriori (MAP) states in cyclic graphical models. Its most striking property is its immense simplicity: each iteration requires only the solution of a sequence of trivial optimization problems. The algorithm can be equally understood as a degenerated dual decomposition scheme or as minimization of a degenerated tree-reweighted upper bound and assumes a form that is reminiscent of message-passing. Despite (or due to) its conceptual simplicity, it is equipped with important theoretical guarantees and exposes strong empirical performance.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
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