Dynamic Graphical Models

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Deep Learning: Methods and Applications (2014, background): “The above deepstructured generative models of speech can be shown as special cases of the more general dynamic network model and even more general dynamic graphical models [35, 34].”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “The above deep-structured generative models of speech can be shown as special cases of the more general dynamic Bayesian network model and even more general dynamic graphical models [77, 78].”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, background): “Overviews on the general use of dynamic Bayesian networks, which belong to directed form of graphical models, for ASR have been provided in [4], [57], [58].”
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs (2018, background): “Probabilistic Graphical Models (PGMs) are a principled instantiation of graphical models [7, 13, 35, 47, 48].”
An Overview of Deep-Structured Learning for Information Processing (2011, background): “All the above deep-structured generative models of speech can be shown as special cases of the more general dynamic Bayesian network model and even more general dynamic graphical models ( Bilmes and Bartels , 2005; Bilmes, 2010).”