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 …

In this paper, we propose a fully autonomous, local-modes-based data partitioning
algorithm, which is able to automatically recognize local maxima of the data density from …

We present a novel hierarchical distance-dependent Bayesian model for event coreference
resolution. While existing generative models for event coreference resolution are completely …

One of the most used priors in Bayesian clustering is the Dirichlet prior. It can be expressed
as a Chinese Restaurant Process. This process allows nonparametric estimation of the …

This paper introduces a novel framework for modeling temporal events with complex
longitudinal dependency that are generated by dependent sources. This framework takes …

The Dirichlet process is one of the most widely used priors in Bayesian clustering. This
process allows for a nonparametric estimation of the number of clusters when partitioning …

Dirichlet process mixture (DPM) models tend to produce many small clusters regardless of
whether they are needed to accurately characterize the data-this is particularly true for large …

The cooperative hierarchical structure is a common and significant data structure observed
in, or adopted by, many research areas, such as: text mining (author–paper–word) and multi …

Markov random fields (MRFs) are prominent in modelling image to handle image processing
problems. However, they confront the bottleneck of model selection in further improving the …

With the rapid growth of text data on the Web and on personal devices, there is an
increasing need to automatically process text and unlock different types of information from …