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In-database machine learning has been very popular, almost being a cliche.However, can we do it the other way around? In this work, we say "yes" byapplying plain old SQL to deep learning, in a sense implementing deep learningalgorithms with SQL. Most deep learning frameworks, as well as generic machinelearning ones, share a de facto standard of multidimensional array operations,underneath fancier infrastructure such as automatic differentiation. As SQLtables can be regarded as generalisations of (multi-dimensional) arrays, wehave found a way to express common deep learning operations in SQL, encouraginga different way of thinking and thus potentially novel models. In particular,one of the latest trend in deep learning was the introduction of sparsity inthe name of graph convolutional networks, whereas we take sparsity almost forgranted in the database world. As both databases and machine learning involvetransformation of datasets, we hope this work can inspire further worksutilizing the large body of existing wisdom, algorithms and technologies in thedatabase field to advance the state of the art in machine learning, rather thanmerely integerating machine learning into databases.

In-Machine-Learning Database: Reimagining Deep Learning with Old-School SQL