This paper describes a high performance sampling architecture for inference of latent topic models on a cluster of workstations. Our system is faster than previous work by over an...
This paper presents a novel approach for exploiting the global context for the task of word sense disambiguation (WSD). This is done by using topic features constructed using the ...
As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most...
Daniel Ramage, Susan T. Dumais, Daniel J. Liebling
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...
There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it ...
Daniel M. Romero, Brendan Meeder, Jon M. Kleinberg