论文标题
机内学习数据库：用老式的SQL重新构想深度学习
In-Machine-Learning Database: Reimagining Deep Learning with Old-School SQL
论文作者
论文摘要
数据库机器学习非常流行，几乎是陈词滥调。但是，我们可以反过来做吗？在这项工作中，我们通过将普通的旧SQL应用于深度学习中，从某种意义上说是“是”，从某种意义上说，使用SQL实施深度学习算法。大多数深度学习框架以及通用的机器学习框架，它们在诸如自动分化之类的高级基础架构下都具有事实上的多维阵列操作标准。由于SQL表可以被视为（多维）阵列的概括，因此我们找到了一种表达SQL中常见深度学习操作的方法，鼓励了另一种思维方式，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形卷积网络的名义引入了稀疏性，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，因此我们希望这项工作能够通过数据库字段中现有的智慧，算法和技术来激发进一步的工作，从而在机器学习中推进了艺术的状态，而不仅仅是将机器学习整合到数据库中。
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.
