We address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...
We propose a new method for solving structured CSPs which generalizes and improves the Cyclic-Clustering approach [4]. First, the cutset and the tree-decomposition of the constrai...
Image parsing remains difficult due to the need to combine
local and contextual information when labeling a
scene. We approach this problem by using the epitome as a
prior over ...
Jonathan Warrell, Simon J. D. Prince, Alastair P. ...
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...