Deep Regression Bayesian Network and Its Applications

TL;DR

This paper reviews deep directed generative models with a focus on Bayesian Network structures, proposing a direct inference method that preserves latent variable dependencies and demonstrates effectiveness on benchmark datasets.

Contribution

It introduces a direct inference approach for deep Bayesian Network models that avoids auxiliary networks and maintains latent dependencies, enhancing data representation and feature learning.

Findings

Effective inference without auxiliary networks

Preservation of latent variable dependencies

Strong performance on benchmark datasets

Abstract

Deep directed generative models have attracted much attention recently due to their generative modeling nature and powerful data representation ability. In this paper, we review different structures of deep directed generative models and the learning and inference algorithms associated with the structures. We focus on a specific structure that consists of layers of Bayesian Networks due to the property of capturing inherent and rich dependencies among latent variables. The major difficulty of learning and inference with deep directed models with many latent variables is the intractable inference due to the dependencies among the latent variables and the exponential number of latent variable configurations. Current solutions use variational methods often through an auxiliary network to approximate the posterior probability inference. In contrast, inference can also be performed directly without using any auxiliary network to maximally preserve the dependencies among the latent variables. Specifically, by exploiting the sparse representation with the latent space, max-max instead of max-sum operation can be used to overcome the exponential number of latent configurations. Furthermore, the max-max operation and augmented coordinate ascent are applied to both supervised and unsupervised learning as well as to various inference. Quantitative evaluations on benchmark datasets of different models are given for both data representation and feature learning tasks.