We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As s...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koll...
We address the problem of efficiently discovering the influential nodes in a social network under the susceptible/infected/susceptible (SIS) model, a diffusion model where nodes ar...
This paper presents a novel probabilistic approach to fusing multimodal metadata for event based home photo clustering. Photo events are characterized by the coherence of multimod...
Tao Mei, Bin Wang, Xian-Sheng Hua, He-Qin Zhou, Sh...
Abstract. In this paper we present a probabilistic algorithm which factorizes non-negative data. We employ entropic priors to additionally satisfy that user specified pairs of fac...
Paris Smaragdis, Madhusudana V. S. Shashanka, Bhik...