A top-down tractable inference method for Sum-Product Networks by pruning ineffective Subnetworks

A top-down tractable inference method for Sum-Product Networks by pruning ineffective Subnetworks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A top-down tractable inference method for Sum-Product Networks by pruning ineffective Subnetworks Mohsen Ghanbarpour Jooybari, Adel Torkaman Rahmani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4323095/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Sum-product networks (SPNs) are a class of probabilistic models that were introduced for the first time in the early 2010s. SPNs belong to the intersection of deep models and probabilistic graphical models with the ability to guarantee tractable inference. SPNs can encode any probabilistic distribution in a directed acyclic graph, in which each non-leaf node is either a sum or a product node. This paper, introduces a new inference method in SPNs that can prune ineffective subnetworks for a query during inference. By keeping some additional information in sum nodes, we can prune a few nodes that have no effect on the output of the network for an input sample. Experimental results demonstrated that even 99% of the nodes could be pruned from some datasets without affecting output accuracy. Sum-Product Networks Probabilistic Graphical Models Tractable Inference Pruning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 28 May, 2024 Submission checks completed at journal 28 May, 2024 First submitted to journal 25 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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