It has always been very difficult to recognize realistic actions from unconstrained videos because there are tremendous variations from camera motion, background clutter, object a...
We consider the use of Bayesian topic models in the analysis of computer network traffic. Our approach utilizes latent Dirichlet allocation and time-varying dynamic latent Dirich...
Collapsed Gibbs sampling is a frequently applied method to approximate intractable integrals in probabilistic generative models such as latent Dirichlet allocation. This sampling ...
This paper presents a user recommendation system that recommends to a user new friends having similar interests. We automatically discover users’ interests using Latent Dirichle...
Given a movie comment, does it contain a spoiler? A spoiler is a comment that, when disclosed, would ruin a surprise or reveal an important plot detail. We study automatic methods...
This paper presents preliminary results on the detection of cultural differences from people's experiences in various countries from two perspectives: tourists and locals. Ou...
Classification of users' whereabouts patterns is important for many emerging ubiquitous computing applications. Latent Dirichlet Allocation (LDA) is a powerful mechanism to e...
Models of latent document semantics such as the mixture of multinomials model and Latent Dirichlet Allocation have received substantial attention for their ability to discover top...
Daniel David Walker, William B. Lund, Eric K. Ring...
This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modell...
Latent Dirichlet allocation is a fully generative statistical language model that has been proven to be successful in capturing both the content and the topics of a corpus of docum...