In this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to automata whose transitions may take varying time length...
Abstract. We describe a probabilistic model, implemented as a dynamic Bayesian network, that can be used to predict nucleosome positioning along a chromosome based on one or more g...
Sheila M. Reynolds, Zhiping Weng, Jeff A. Bilmes, ...
In this paper, we develop multilingual supervised latent Dirichlet allocation (MLSLDA), a probabilistic generative model that allows insights gleaned from one language's data...
We study graphical modeling in the case of stringvalued random variables. Whereas a weighted finite-state transducer can model the probabilistic relationship between two strings, ...
We discuss vision as a sensory modality for systems that effect actions in response to perceptions. While the internal representations informed by vision may be arbitrarily compl...