A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling
It provides a structured overview for researchers in machine learning, but is incremental as it synthesizes existing knowledge without new results.
This paper reviews deep generative models (DGMs) from a graphical modeling perspective, organizing them by directed and undirected types and separating model definitions from learning algorithms to clarify methodological differences.
This document aims to provide a review on learning with deep generative models (DGMs), which is an highly-active area in machine learning and more generally, artificial intelligence. This review is not meant to be a tutorial, but when necessary, we provide self-contained derivations for completeness. This review has two features. First, though there are different perspectives to classify DGMs, we choose to organize this review from the perspective of graphical modeling, because the learning methods for directed DGMs and undirected DGMs are fundamentally different. Second, we differentiate model definitions from model learning algorithms, since different learning algorithms can be applied to solve the learning problem on the same model, and an algorithm can be applied to learn different models. We thus separate model definition and model learning, with more emphasis on reviewing, differentiating and connecting different learning algorithms. We also discuss promising future research directions.