This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
There has been a recent surge in work in probabilistic databases, propelled in large part by the huge increase in noisy data sources -from sensor data, experimental data, data fro...
We present a class of richly structured, undirected hidden variable models suitable for simultaneously modeling text along with other attributes encoded in different modalities. O...