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Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

Yuxin Tang · Zhimin Ding · Dimitrije Jankov · Binhang Yuan · Daniel Bourgeois · Chris Jermaine

Exhibit Hall 1 #316
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The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. We show experimentally that a relational engine running an auto-differentiated relational algorithm can easily scale to very large datasets, and is competitive with state-of-the-art, special-purpose systems for large-scale distributed machine learning.

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