Variance component estimation (VCE), herein called joint inversion, is a widely used
approach to weigh the contributions of different data sets. Traditionally, the random errors of …

In cooperative intelligent transportation systems (C-ITSs), vehicular positioning performance
can be augmented by network-shared data and cooperative positioning (CP) algorithms …

Large scale Bayesian nonparametrics (BNP) learner such as stochastic variational inference
(SVI) can handle datasets with large class number and large training size at fractional cost …

Deep clustering is an emerging topic in deep learning where traditional clustering is
performed in deep learning feature space. However, clustering and deep learning are often …

Deep clustering is an emerging topic in deep learning where traditional clustering is
performed in deep learning feature space. However, clustering and deep learning are often …

Scalable algorithms of variational posterior approximation allow Bayesian nonparametrics
such as Dirichlet process mixture to scale up to larger dataset at fractional cost. Recent …

Bayesian mixture models are increasingly used for model‐based clustering and the follow‐
up analysis on the clusters identified. As such, they are of particular interest for analyzing …

Motivation Mammalian genomes can contain thousands of enhancers but only a subset are
actively driving gene expression in a given cellular context. Integrated genomic datasets can …

Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational
Inference (SVI) can handle datasets with large class number and large training size at …

The variational inference of Bayesian mixture models such as the Dirichlet process mixture
is not scalable to very large datasets, since the learning is based on computing the entire …