The performance of deep learning models is dependent on the precise configuration of many layers and parameters. However, there are currently few systematic guidelines for how to configure a successful model. This means model builders often have to experiment with different configurations by manually programming different architectures (which is tedious and time consuming) or rely on purely automated approaches to generate and train the architectures (which is expensive). In this paper, we present Rapid Exploration of Model Architectures and Parameters, or REMAP, a visual analytics tool that allows a model builder to discover a deep learning model quickly via exploration and rapid experimentation of neural network architectures. In REMAP, the user explores the large and complex parameter space for neural network architectures using a combination of global inspection and local experimentation. Through a visual overview of a set of models, the user identifies interesting clusters of architectures. Based on their findings, the user can run ablation and variation experiments to identify the effects of adding, removing, or replacing layers in a given architecture and generate new models accordingly. They can also handcraft new models using a simple graphical interface. As a result, a model builder can build deep learning models quickly, efficiently, and without manual programming. We inform the design of REMAP through a design study with four deep learning model builders. Through a use case, we demonstrate that REMAP allows users to discover performant neural network architectures efficiently using visual exploration and user-defined semi-automated searches through the model space.

中文翻译：

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消融，变异和考虑：用于发现神经体系结构的可视化分析。

深度学习模型的性能取决于许多层和参数的精确配置。但是，目前很少有关于如何配置成功模型的系统指南。这意味着模型构建者通常必须通过手动编程不同的体系结构来进行不同的配置试验（这既繁琐又耗时），或者依靠纯自动化方法来生成和训练体系结构（这很昂贵）。在本文中，我们介绍了模型架构和参数的快速探索（REMAP），这是一种可视化分析工具，允许模型构建者通过探索和快速试验神经网络架构来快速发现深度学习模型。在REMAP中，用户结合了全局检查和局部实验，探索了神经网络体系结构的庞大而复杂的参数空间。通过一组模型的可视化概览，用户可以识别出有趣的体系结构集群。根据他们的发现，用户可以进行消融和变化实验，以确定在给定体系结构中添加，删除或替换层的效果，并相应地生成新模型。他们还可以使用简单的图形界面来手工制作新模型。结果，模型构建者可以快速，有效地构建深度学习模型，而无需手动编程。我们通过与四个深度学习模型构建者的设计研究为REMAP的设计提供信息。通过用例，