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Partition-Merge: Distributed Inference and Modularity Optimization
Vincent Blondel, Kyomin Jung, Pushmeet Kohli +1
This paper presents a novel meta algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster. In a nuts…
Efficient Energy Minimization for Enforcing Statistics
Yongsub Lim, Kyomin Jung, Pushmeet Kohli
Energy minimization algorithms, such as graph cuts, enable the computation of the MAP solution under certain probabilistic models such as Markov random fields. However, for many co…
Multi-dimensional Parametric Mincuts for Constrained MAP Inference
Yongsub Lim, Kyomin Jung, Pushmeet Kohli
In this paper, we propose novel algorithms for inferring the Maximum a Posteriori (MAP) solution of discrete pairwise random field models under multiple constraints. We show how th…
A two-layer Conditional Random Field for the classification of partially occluded objects
Sergey Kosov, Pushmeet Kohli, Franz Rottensteiner +1
Conditional Random Fields (CRF) are among the most popular techniques for image labelling because of their flexibility in modelling dependencies between the labels and the image fe…