Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
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DOI：
10.48550/arxiv.2306.00088
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Yu-Shuen Tang;Zhimin Ding;Dimitrije Jankov;Binhang Yuan;Daniel Bourgeois;C. Jermaine
中科院分区：
文献类型：
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作者：
Yu-Shuen Tang;Zhimin Ding;Dimitrije Jankov;Binhang Yuan;Daniel Bourgeois;C. Jermaine
关系数据模型旨在促进大规模的数据管理和分析。我们考虑了如何区分相关表达的计算的问题。我们在实验上表明，运行自动分化的关系算法的关系引擎可以轻松扩展到非常大的数据集，并且具有用于大规模分布式机器学习的最先进的特殊用途系统的竞争。
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.