Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian
framework for modeling axial data (ie, observations are axes of direction) that can be …

From the celebrated Gaussian mixture, model averaging estimators to the cutting-edge multi-
Bernoulli mixture of various forms, finite mixture models offer a fundamental and flexible …

In this paper, we develop a novel variational Bayesian learning method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very …

For the purpose of enhancing the discriminability of convolutional neural networks (CNNs)
and facilitating the optimization, we investigate the activation function for a neural network …

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two
fundamental invertible transformations, namely, serial nonlinear transformation and parallel …

As uncertainty quantification is crucial for determining undesirable inputs and improving
decisions made by a system to acquire accurate evaluations, it has received much attention …

In recent years, clustering methods based on deep generative models have received great
attention in various unsupervised applications, due to their capabilities for learning …

For most of the non-Gaussian statistical models, the data being modeled represent strongly
structured properties, such as scalar data with bounded support (eg, beta distribution) …

In this paper, we propose a clustering-based online news topic detection and tracking (TDT)
approach based on hierarchical Bayesian nonparametric framework that allows topics to be …