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Sum-Product Networks: A Survey

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dc.contributor.authorSánchez Cauce, Raquel
dc.contributor.authorParís Fernández, Iago
dc.contributor.authorDíez Vegas, Francisco Javier
dc.date.accessioned2024-05-20T11:43:13Z
dc.date.available2024-05-20T11:43:13Z
dc.date.issued2021-02-25
dc.description.abstractA sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions. They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies. Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of edges in the graph. They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding. This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, several applications, a brief review of software libraries, and a comparison with related models.en
dc.description.versionversión publicada
dc.identifier.doi10.1109/TPAMI.2021.3061898
dc.identifier.issn1939-3539
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12464
dc.journal.issue7
dc.journal.titleIEEE Transactions on Pattern Analysis and Machine Intelligence
dc.journal.volume44
dc.language.isoen
dc.publisherIEEE
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsAtribución 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subject.keywordsSum-product networks
dc.subject.keywordsprobabilistic graphical models
dc.subject.keywordsBayesian networks
dc.subject.keywordsmachine learning
dc.subject.keywordsdeep neural networks
dc.titleSum-Product Networks: A Surveyes
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationc6032e20-a1d0-49b9-92e3-5c9f624ab143
relation.isAuthorOfPublication.latestForDiscoveryc6032e20-a1d0-49b9-92e3-5c9f624ab143
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