Many algorithms for performing inference in graphical models have complexity that is exponential in the treewidth - a parameter of the underlying graph structure. Computing the (m...
Markov random fields are designed to represent structured dependencies among large collections of random variables, and are well-suited to capture the structure of real-world sign...
Tanya Roosta, Martin J. Wainwright, Shankar S. Sas...
The design of programmable logic architectures and supporting computer-aided design tools fundamentally requires both a good understanding of the combinatorial nature of netlist gr...
Michael D. Hutton, Jonathan Rose, Derek G. Corneil
We propose an algorithm for the binarization of document images degraded by uneven light distribution, based on the Markov Random Field modeling with Maximum A Posteriori probabil...
abstract such additional information as network annotations. We introduce a network topology modeling framework that treats annotations as an extended correlation profile of a net...
Xenofontas A. Dimitropoulos, Dmitri V. Krioukov, A...