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arXiv · 2306.00088

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

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

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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Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine. 2023-05-31. Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning. https://arxiv.org/abs/2306.00088

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