In this paper we propose a novel algorithm for super resolution based on total variation prior and variational distribution approximations. We formulate the problem using a hierar...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
Reduced-rank decompositions provide descriptions of the variation among the elements of a matrix or array. In such decompositions, the elements of an array are expressed as produc...
Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) have recently emerged as powerful statistical tools for text document modeling. In this pap...
Duangmanee Putthividhya, Hagai Thomas Attias, Srik...
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
We address the problem of pronunciation variation in conversational speech with a context-dependent articulatory featurebased model. The model is an extension of previous work usi...
Preethi Jyothi, Karen Livescu, Eric Fosler-Lussier