Ludwig：基于类型的声明性深度学习工具箱
Ludwig: a type-based declarative deep learning toolbox
摘要：
在这项工作中，我们提出了路德维希，一个灵活的，可扩展的和易于使用的工具箱，允许用户训练深度学习模型，并使用它们来获得预测，而无需编写代码。Ludwig实现了一种基于两个主要抽象的深度学习模型构建的新方法：数据类型和声明性配置文件。数据类型抽象允许更容易的代码和子模型重用，并且由该抽象强加的标准化接口允许封装并使代码易于扩展。声明式模型定义配置文件使没有经验的用户能够获得有效的模型，并提高专家用户的生产力。除了这两项创新，Ludwig还引入了一种通用的模块化深度学习架构，称为编码器-组合器-解码器，可以实例化以执行大量的机器学习任务。这些创新使工程师、其他领域的科学家以及更广泛的受众能够采用深度学习模型来完成他们的任务，具体地帮助其民主化。
英文摘要：
In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code. Ludwig implements a novel approach to deep learning model building based on two main abstractions: data types and declarative configuration files. The data type abstraction allows for easier code and sub-model reuse, and the standardized interfaces imposed by this abstraction allow for encapsulation and make the code easy to extend. Declarative model definition configuration files enable inexperienced users to obtain effective models and increase the productivity of expert users. Alongside these two innovations, Ludwig introduces a general modularized deep learning architecture called Encoder-Combiner-Decoder that can be instantiated to perform a vast amount of machine learning tasks. These innovations make it possible for engineers, scientists from other fields and, in general, a much broader audience to adopt deep learning models for their tasks, concretely helping in its democratization.
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。
