Please use this identifier to cite or link to this item:

Deep Learning Through the Lens of Classical SQL

File Size Format  
0677.pdf 299.14 kB Adobe PDF View/Open

Item Summary

Title:Deep Learning Through the Lens of Classical SQL
Authors:Du, Len
Keywords:Computational Intelligence and State-of-the-Art Data Analytics
data analytics
deep learning
graph convolutional network
show 1 moremachine learning
show less
Date Issued:05 Jan 2021
Abstract:In-database machine learning has been very popular, almost being a cliche. However, can we do it the other way around? In this work, we say “yes” by applying plain old SQL to Deep Learning (DL), in a sense, hypothetically implementing deep learning algorithms with SQL. Most deep learning frameworks, as well as generic machine learning ones, share a de facto standard of multidimensional array operations, underneath fancier infrastructure such as automatic differentiation. As SQL tables can be regarded as generalizations of (multi-dimensional) arrays, we have found a way to express common deep learning operations in SQL, encouraging a different way of thinking and thus potentially novel models. In particular, one of the latest trend in deep learning was the introduction of sparsity in the name of Graph Convolutional Networks (GCNs), whereas we take sparsity almost for granted in the database world. As both databases and machine learning involve transformation of datasets, we hope this work can inspire further works utilizing the large body of existing wisdom, algorithms and technologies in the database field to advance the state-of-the-art in machine learning, rather than merely integrating machine learning into databases.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Computational Intelligence and State-of-the-Art Data Analytics

Please email if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons