Learning General Latent-Variable Graphical Models with Predictive Belief Propagation
national conference on artificial intelligence, 2020.
Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence. All previous methods, including the EM algorithm and the spectral algorithms, face severe limitations that largely restrict their applicability and affect their performance. In order to overcome...More
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