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A Generative Restricted Boltzmann Machine Based Method for High-Dimensional Motion Data Modeling
A learning method is then proposed to perform efficient learning for the proposed model.
Deep Regression Bayesian Network and Its Applications
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.
Latent Regression Bayesian Network for Data Representation
Deep directed generative models have attracted much attention recently due to their expressive representation power and the ability of ancestral sampling.
Advances in Learning Bayesian Networks of Bounded Treewidth
This work presents novel algorithms for learning Bayesian network structures with bounded treewidth.
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