In this paper we propose a new hierarchical non stationary image prior for image restoration. This prior captures the directional edges using a continuous model and regularizes acc...
John Chantas, Nikolas P. Galatsanos, Aristidis Lik...
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating th...
Abstract. Building on the current understanding of neural architecture of the visual cortex, we present a graphical model for learning and classification of motion patterns in vid...
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...