机器学习数据库：用老派的SQL重新构想深度学习
In-Machine-Learning Database: Reimagining Deep Learning with Old-School SQL
摘要：
数据库内机器学习已经非常流行，几乎是陈词滥调。但是，我们是否可以反过来做呢？在这项工作中，我们通过将普通的旧SQL应用于深度学习，在某种意义上用SQL实现深度学习算法来回答“是”。大多数深度学习框架，以及通用的机器学习框架，都共享一个事实上的多维数组操作标准，在更花哨的基础设施下，如自动微分。由于SQL表可以被视为（多维）数组的泛化，我们已经找到了一种在SQL中表达常见深度学习操作的方法，鼓励不同的思维方式，因此可能是新颖的模型。特别是，深度学习的最新趋势之一是以图卷积网络的名义引入稀疏性，而我们在数据库世界中几乎认为稀疏性是理所当然的。由于数据库和机器学习都涉及数据集的转换，我们希望这项工作可以激发进一步的工作，利用数据库领域现有的大量智慧，算法和技术来推进机器学习的最新技术，而不仅仅是将机器学习集成到数据库中。
英文摘要：
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, in a sense 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 generalisations 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, 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 integerating machine learning into databases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
