Research on Bayesian nonparametric deep learning method

Presented a Bayesian nonparametric deep learning method called deep graph random process (DGP) that can generate an infinite number of probabilistic graphs representing percepts.

Applied the presented DGP for physiological time-series classification, which are able to capture the relation between different channels in the multivariate signals.