As graph models are applied to more widely varying fields, researchers struggle with tools for exploring and analyzing these structures. We describe GUESS, a novel system for grap...
We propose a method to improve approximate inference methods by correcting for the influence of loops in the graphical model. The method is a generalization and alternative implem...
We consider graph drawing algorithms for learning spaces, a type of st-oriented partial cube derived from an antimatroid and used to model states of knowledge of students. We show...
We consider the estimation problem in Gaussian graphical models with arbitrary structure. We analyze the Embedded Trees algorithm, which solves a sequence of problems on tractable...
Venkat Chandrasekaran, Jason K. Johnson, Alan S. W...
We present a novel approach for learning patterns (sub-images) shared by multiple images without prior knowledge about the number and the positions of the patterns in the images. ...