Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
Yuxin Tang,u00a0Zhimin Ding,u00a0Dimitrije Jankov,u00a0Binhang Yuan,u00a0Daniel Bourgeois,u00a0Chris Jermaine
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.
