Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
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.
Cite
Text
Tang et al. "Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning." International Conference on Machine Learning, 2023.Markdown
[Tang et al. "Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/tang2023icml-autodifferentiation/)BibTeX
@inproceedings{tang2023icml-autodifferentiation,
title = {{Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning}},
author = {Tang, Yuxin and Ding, Zhimin and Jankov, Dimitrije and Yuan, Binhang and Bourgeois, Daniel and Jermaine, Chris},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {33581-33598},
volume = {202},
url = {https://mlanthology.org/icml/2023/tang2023icml-autodifferentiation/}
}