基于证据的指导框架,用于神经网络系统图表
Guy Marshall1, André Freitas1,2, Caroline Jay1
1Department of Computer Science, University of Manchester, Manchester, United Kingdom.
PloS one
|March 18, 2025
概括
我们开发了一个框架,用于在研究论文中格式化神经网络架构图. 遵循这些基于证据的指导方针可以提高图表的清晰度,增加论文引用量,增强科学沟通.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 准确的研究传播对于科学进步至关重要.
- 神经网络架构图对于理解新系统至关重要,但缺乏标准化的呈现惯例.
- 现有的图表经常存在模两可和变异,阻碍了解释.
研究的目的:
- 建立第一个基于证据的框架,用于在学术出版物中格式化神经网络架构图.
- 解决在介绍神经网络设计时的可解释性和一致性的挑战.
- 提高机器学习研究沟通的清晰度和影响.
主要方法:
- 进行了用户研究,包括采访和卡片排序,以了解图表的使用和偏好.
- 分析了顶级神经网络场所的现有图形,使用基于语料库的方法.
- 根据用户反,设计原则和经验数据,推导和评估了一个框架.
主要成果:
- 在当前神经网络图表的呈现和解释中发现了显著的模糊性和多样性.
- 开发了一个具有高可用性和实用性的框架,提高了图表清晰度.
- 证明了遵守框架指导方针的论文得到了更多的引用.
结论:
- 拟议的框架提高了神经网络图的解释性和实用性.
- 遵守标准化图表格式准则对研究的可见性和影响力产生积极影响.
- 这项工作为在机器学习研究中进行一致和有效的视觉沟通提供了基础.
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