
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
We consider the NPhard problem of MAPinference for undirected discrete...
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Exact MAPInference by Confining Combinatorial Search with LP Relaxation
We consider the MAPinference problem for graphical models, which is a v...
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Higher Order Maximum Persistency and Comparison Theorems
We address combinatorial problems that can be formulated as minimization...
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Maximum A Posteriori Inference in SumProduct Networks
Sumproduct networks (SPNs) are a class of probabilistic graphical model...
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Exact MAP inference in general higherorder graphical models using linear programming
This paper is concerned with the problem of exact MAP inference in gener...
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Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem
We consider a variational convex relaxation of a class of optimal partit...
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Efficient Energy Minimization for Enforcing Statistics
Energy minimization algorithms, such as graph cuts, enable the computati...
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Partial Optimality by Pruning for MAPInference with General Graphical Models
We consider the energy minimization problem for undirected graphical models, also known as MAPinference problem for Markov random fields which is NPhard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal nonrelaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAPinference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. The method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method's runtime is determined by the runtime of the convex relaxation solver for the MAPinference problem.
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