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Making centralized (graph) computation faster, distributed and (at times) better

Making centralized (graph) computation faster, distributed and (at times) better

This video was recorded at Workshop on Statistical Physics of Inference and Control Theory, Granada 2012. I will introduce a generic method for approximate inference in graphical models using graph partitioning. The resulting algorithm is linear time and provides an excellent approximation for the maximum a posteriori assignment (MAP) in a larger class of graphical model including any graph with "polynomial growth" and graph that exclude fixed minors (e.g. planar graphs). In general, the algorithm can be thought of as a "meta" algorithm that can be used to speed up any existing inference algorithm without losing performance. The goal of the talk is to primarily introduce the algorithm and provide insights into why such a simplistic algorithm works. Time permitting, I will also discuss its implication for "modularity clustering" that has been popularly utilized in processing networked data.


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