Statistical topic models are a class of Bayesian latent variable models, originally developed for analyzing the semantic content of large document corpora. With the increasing availability of other large, heterogeneous data collections, topic models have been adapted to model data from fields as diverse as computer vision, finance, bioinformatics, cognitive science, music, and the social sciences. While the underlying models are often extremely similar, these communities use topic models in different ways in order to achieve different goals. This one-day workshop will bring together topic modeling researchers from multiple disciplines, providing an opportunity for attendees to meet, present their work and share ideas, as well as inform the wider NIPS community about current research in topic modeling. This workshop will address the following specific goals:
The workshop will consist of invited talks by established researchers from multiple research communities, contributed talks, a poster session, and a panel session.