Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
Amortized Bethe Free Energy Minimization for Learning MRFs
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Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
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Notably, most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns, exhibiting a structural disparity.
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The Functional Neural Process
Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling
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Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
Priyank Jaini, Pascal Poupart, Yaoliang Yu
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Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Dominik Linzner, Michael Schmidt, Heinz Koeppl
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Instead ofsampling andscoring allpossible structures individually,we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures.
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