Topic models have been applied to everything from books to newspapers to social media
posts in an effort to identify the most prevalent themes of a text corpus. We provide an in …

Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

This paper assesses topic coherence and human topic ranking of uncovered latent topics
from scientific publications when utilizing the topic model latent Dirichlet allocation (LDA) on …

Topic models have been widely explored as probabilistic generative models of documents.
Traditional inference methods have sought closed-form derivations for updating the models …

There have been rapid developments in model-based clustering of graphs, also known as
block modelling, over the last ten years or so. We review different approaches and …

We develop stochastic variational inference, a scalable algorithm for approximating
posterior distributions. We develop this technique for a large class of probabilistic models …

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

Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of
information for a wide spectrum of users. In Twitter, popular information that is deemed …

Mean-field variational inference is a method for approximate Bayesian posterior inference. It
approximates a full posterior distribution with a factorized set of distributions by maximizing a …