面向模块化和可编程体系结构的研究
Towards modular and programmable architecture search
摘要：
神经架构搜索方法能够以专家的最小努力找到高性能深度学习架构。然而，当前的系统专注于特定的用例（例如，卷积图像分类器和递归语言模型），使得它们不适合专家可能希望编写的一般用例。超参数优化系统是通用的，但缺乏易于应用于架构搜索所需的构造。在这项工作中，我们提出了一个形式语言编码搜索空间一般计算图。语言结构允许我们编写模块化的、可组合的和可重用的搜索空间编码，并对搜索空间设计进行推理。我们使用我们的语言编码的搜索空间的架构搜索文献。该语言允许我们解耦搜索空间和搜索算法的实现，允许我们通过一致的接口向搜索算法公开搜索空间。我们的实验表明，我们可以很容易地尝试不同的组合搜索空间和搜索算法，而不必从头开始实现每个组合。我们在本文中发布了我们语言的实现。
英文摘要：
Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-cases (e.g. convolutional image classifiers and recurrent language models), making them unsuitable for general use-cases that an expert might wish to write. Hyperparameter optimization systems are general-purpose but lack the constructs needed for easy application to architecture search. In this work, we propose a formal language for encoding search spaces over general computational graphs. The language constructs allow us to write modular, composable, and reusable search space encodings and to reason about search space design. We use our language to encode search spaces from the architecture search literature. The language allows us to decouple the implementations of the search space and the search algorithm, allowing us to expose search spaces to search algorithms through a consistent interface. Our experiments show the ease with which we can experiment with different combinations of search spaces and search algorithms without having to implement each combination from scratch. We release an implementation of our language with this paper.
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。
