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Global Variational Learning for Graphical Models with Latent Variables
Probabilistic Graphical Models have been used intensively for developing Machine Learning applications including Computer Vision, Natural Language processing, Collaborative Filtering, and Bioinformatics. Moreover, Graphical ...
Advances in Message-Passing Algorithms in Propositional and Lifted Graphical Models
With the profusion of data across new and complicated domains, the compactness and expressivity of PGMs have made them a cornerstone of modern, data-driven technologies. Unfortunately, PGMs suffer one substantial drawback; ...
Scalable Learning Approaches for Sum-Product-Cutset Networks
Tractable models are a subclass of probabilistic graphical models (PGMs) in which exact inference can be performed tractably – a very desirable property missing from arbitrary PGMs like Bayesian and Markov networks – exact ...
Parameter Tying and Dissociation in Graphical Models
Understanding the implications of today’s deluge and high velocity of data is a challenging problem facing researchers across multiple disciplines and domains. Data are typically highdimensional, unstructured, and noisy; ...
Variational Inference Methods for Continuous Probabilistic Graphical Models
Graphical models provide a general framework for representing and reasoning about data. Once these models are fit to data, they can be used to answer statistical queries about the observed data. Unfortunately these answering ...