Structure Learning for Sum-Product Networks KEC 1003 Mon, 10/19/2015 - 4:00pm
Daniel Lowd Assistant Professor, Department of Computer and Information Science, University of Oregon Abstract: Probabilistic graphical models have been applied to many domains, including computer vision, natural language processing, and bioinformatics. However, their effectiveness is limited by the complexity of inference, which is generally intractable. An appealing alternative is to work with tractable probabilistic models, in which exact inference is efficient. Sum-product networks (SPNs) are a deep, tractable probabilistic representation that generalize many other tractable model classes. SPNs have achieved state-of-the-art results on computer vision and density estimation problems, but selecting a good structure for an SPN is challenging. In this talk, I will provide a brief introduction to SPNs and then discuss several recent approaches to learning SPN structures from data. The first approach is to adapt standard graphical model structure learning algorithms, resulting in SPNs that represent tractable graphical models. The second approach is to recursively cluster instances and variables, resulting in SPNs that represent hierarchical mixture models. These two approaches capture different types of patterns in the data. The ID-SPN algorithm uses a combined approach, leading to much better structures on a variety of benchmark domains. In many cases, ID-SPN learns SPNs that are more accurate than intractable Bayesian networks, demonstrating that SPNs can maintain tractability without sacrificing accuracy. Bio: URL: http://eecs.oregonstate.edu/colloquium/structure-learning-sum-product-networks _______________________________________________ Colloquium mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/colloquium
