我们是否已经关闭了循环? 在VIS4ML研究的概括性上的差距
IEEE transactions on visualization and computer graphics
|October 23, 2023
概括
机器学习可视化 (VIS4ML) 研究旨在改进人工智能模型,但目前的研究往往过度适应研究结果. 需要更多可通用的研究来弥合VIS4ML愿望和实际应用之间的差距.
科学领域:
- 人与计算机的交互
- 机器学习 机器学习
- 数据可视化 数据可视化
背景情况:
- 机器学习可视化 (VIS4ML) 研究旨在将人类专业知识纳入人工智能开发.
- 关键领域包括理解人类知识,设计可解释的可视化和评估人与模型的互动.
研究的目的:
- 评估VIS4ML中的研究贡献和声称在人类循环中的机器学习中的概括性.
- 识别当前VIS4ML研究范围和实际应用愿望之间的差距.
主要方法:
- 对最近VIS4ML研究论文的调查.
- 分析研究贡献,主张及其在特定研究条件之外的适用性.
主要成果:
- 确定了VIS4ML研究范围和实际应用之间的潜在差距.
- 发现结论往往过度适应特定场景,使用有限的专家/数据集,缺乏合理的决策.
- 突出了声明的概括性和实际研究约束之间的断开.
结论:
- 当前的VIS4ML研究声明可能不像所呈现的那样可以概括.
- 需要改进文档实践,以报告一般性约束.
- 为更广泛的适用性,建议将研究主张与经验证据对齐的方法.
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