The Sum-Product Network (SPN) is a new type of machine learning model with fast exact probabilistic inference over many layers. Unlike graphical models, SPNs are tractable over high treewidth. SPNs are a deep architecture. With full probabilistic semantics. SPNs can incorporate features. Into an expressive model without requiring approximate inference. SPNs have achieved impressive results on numerous datasets, including:.
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Sum-Product Networks | spn.cs.washington.edu Reviews
https://spn.cs.washington.edu
The Sum-Product Network (SPN) is a new type of machine learning model with fast exact probabilistic inference over many layers. Unlike graphical models, SPNs are tractable over high treewidth. SPNs are a deep architecture. With full probabilistic semantics. SPNs can incorporate features. Into an expressive model without requiring approximate inference. SPNs have achieved impressive results on numerous datasets, including:.
spn.cs.washington.edu
Sum-Product Networks
http://spn.cs.washington.edu/index.shtml
The Sum-Product Network (SPN) is a new type of machine learning model with fast exact probabilistic inference over many layers. Unlike graphical models, SPNs are tractable over high treewidth. SPNs are a deep architecture. With full probabilistic semantics. SPNs can incorporate features. Into an expressive model without requiring approximate inference. SPNs have achieved impressive results on numerous datasets, including:.
Sum-Product Networks
http://spn.cs.washington.edu/faq.shtml
SPN Frequently Asked Questions. Can you convert between SPNs and graphical models? How do you create an SPN from a junction tree? If you convert a graphical model to an SPN, is it just a single product layer that computes the product of all the factors? Are SPNs just trees? What are the drawbacks of tree-structured SPNs? Are they similar to tree-structured graphical models? Can SPNs represent convolution? What are the drawbacks of SPNs? Can you convert between SPNs and graphical models? GM ∪ SPN. These a...
Sum-Product Networks
http://spn.cs.washington.edu/code.shtml
We are working to produce a comprehensive SPN software package. In the meantime, code is available from the following papers:. Language Modeling with Sum-Product Networks. Wei-Chen Cheng, Stanley Kok, Hoai Vu Pham, Hai Leong Chieu, and Kian Ming A. Chai. Annual Conference of the International Speech Communication Association 15 (INTERSPEECH 2014). Learning the Structure of Sum-Product Networks. Code and supplemental results. Robert Gens and Pedro Domingos. Sum-Product Networks: A New Deep Architecture.
Sum-Product Networks
http://spn.cs.washington.edu/pubs.shtml
Robert Gens and Pedro Domingos. International Conference on Learning Representations 5 (ICLR 2017) Workshop Track. Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions. Alejandro Molina, Sriraam Natarajan, and Kristian Kersting. AAAI Conference on Artificial Intelligence 31 (AAAI 2017). The Sum-Product Theorem: A Foundation for Learning Tractable Models. Abram Friesen and Pedro Domingos. International Conference on Machine Learning 33 (ICML 2016). European Co...
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Sum-Product Networks
The Sum-Product Network (SPN) is a new type of machine learning model with fast exact probabilistic inference over many layers. Unlike graphical models, SPNs are tractable over high treewidth. SPNs are a deep architecture. With full probabilistic semantics. SPNs can incorporate features. Into an expressive model without requiring approximate inference. SPNs have achieved impressive results on numerous datasets, including:.
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