We propose the Dirichlet Process - Naive Bayes model (DPNB) that can simultaneously impute missing values and address classification problems.
We have been studying the theoretical properties of Bayesian neural network (BNN) models and their applications.
We proposed an end-to-end deep learning model combining BNN with Korean speech recognition.
We propose a scalable Bayesian method for sparse covariance matrix estimation by incorporating a continuous shrinkage prior with a screening procedure.
