Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
This addresses the challenge of scaling machine learning on relational data for data scientists and engineers, offering a novel integration rather than an incremental improvement.
The paper tackled the problem of differentiating computations expressed in relational algebra for large-scale machine learning, showing that a relational engine with auto-differentiation can scale to very large datasets and be competitive with state-of-the-art distributed systems.
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.