Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That
said, while in classical (ie, non-deep) clustering the benefits of the nonparametric approach …

Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has
also been successfully applied for hyperspectral image classification tasks. Training deep …

The hierarchical Dirichlet process (HDP) is a Bayesian nonparametric model that can be
used to model mixed-membership data with a potentially infinite number of components. It …

We present the nested Chinese restaurant process (nCRP), a stochastic process that
assigns probability distributions to ensembles of infinitely deep, infinitely branching trees …

In this paper we introduce a novel collapsed Gibbs sampling method for the widely used
latent Dirichlet allocation (LDA) model. Our new method results in significant speedups on …

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling.
The nHDP generalizes the nested Chinese restaurant process (nCRP) to allow each word to …

A novel data‐driven adaptive robust optimization framework that leverages big data in
process industries is proposed. A Bayesian nonparametric model—the Dirichlet process …

Demonstration trajectories collected from a supervisor in teleoperation are widely used for
robot learning, and temporally segmenting the trajectories into shorter, less-variable …

Is it possible to crowdsource categorization? Amongst the challenges:(a) each annotator has
only a partial view of the data,(b) different annotators may have different clustering criteria …

Abstract We develop\name\(STM), a nonparametric Bayesian model of parsed documents.\
Shortname\generates words that are both thematically and syntactically constrained, which …