Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
We present a Bayesian search algorithm for learning the structure of latent variable models of continuous variables. We stress the importance of applying search operators designed...
Although the notion of generality is central in mathematics and science, being able to identify and express general patterns and/or articulating structures is one of the main difï...
—We present a structured model of context that supports an integrated approach to language acquisition and use. The model extends an existing formal notation, Embodied Constructi...
Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For...