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Tabular :
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Gordon, Andrew D.
(author)
Tabular : a schema-driven probabilistic programming language
- Article/chapterEnglish2014
Publisher, publication year, extent ...
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2014-01-08
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New York :ACM Press,2014
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electronicrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:uu-220064
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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-220064URI
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https://doi.org/10.1145/2535838.2535850DOI
Supplementary language notes
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Language:English
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Summary in:English
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Classification
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Subject category:ref swepub-contenttype
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Subject category:kon swepub-publicationtype
Notes
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We propose a new kind of probabilistic programming language for machine learning. We write programs simply by annotating existing relational schemas with probabilistic model expressions. We describe a detailed design of our language, Tabular, complete with formal semantics and type system. A rich series of examples illustrates the expressiveness of Tabular. We report an implementation, and show evidence of the succinctness of our notation relative to current best practice. Finally, we describe and verify a transformation of Tabular schemas so as to predict missing values in a concrete database. The ability to query for missing values provides a uniform interface to a wide variety of tasks, including classification, clustering, recommendation, and ranking.
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Added entries (persons, corporate bodies, meetings, titles ...)
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Graepel, Thore
(author)
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Rolland, Nicolas
(author)
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Russo, Claudio
(author)
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Borgström, JohannesUppsala universitet,Datalogi,Mobility(Swepub:uu)johbo484
(author)
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Guiver, John
(author)
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Uppsala universitetDatalogi
(creator_code:org_t)
Related titles
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In:Proc. 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming LanguagesNew York : ACM Press, s. 321-3349781450325448
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