The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
We extend previous work on fully unsupervised part-of-speech tagging. Using a non-parametric version of the HMM, called the infinite HMM (iHMM), we address the problem of choosing...
Jurgen Van Gael, Andreas Vlachos, Zoubin Ghahraman...
A new hierarchical nonparametric Bayesian framework is proposed for the problem of multi-task learning (MTL) with sequential data. The models for multiple tasks, each characterize...
Kai Ni, John William Paisley, Lawrence Carin, Davi...