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Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations.
A relational model of data for large shared data banks
Edgar F Codd · 1970
Earlier work this paper cites.
Sequel: A structured english query language
Donald D Chamberlin and Raymond F Boyce · 1974
Earlier work this paper cites.
A framework for representing knowledge, 1974
Marvin Minsky · 1974
Earlier work this paper cites.
Inductive logic programming
Nada Lavrac and Saso Dzeroski · 1994
Earlier work this paper cites.
National Center for Biotechnology Information, U.S. National Library of Medicine, 1996
PubMed · 1996
Earlier work this paper cites.
Learning probabilistic relational models
Lise Getoor, Nir Friedman, Daphne Koller, and Avi Pfeffer · 2001
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Deep speech 2: End-to-end speech recognition in english and mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al · 2016
Earlier work this paper cites.
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Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
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