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相关概念视频

Concepts and Prototypes01:24

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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X-CHAR:一个基于概念的可解释的复杂人类活动识别模型.

Jeya Vikranth Jeyakumar1, Ankur Sarker1, Luis Antonio Garcia2

  • 1University of California Los Angeles, USA.

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
|March 26, 2024
PubMed
概括
此摘要是机器生成的。

X-CHAR提供可解释的人类活动识别 (HAR),而不需要精确的低级活动注释. 这种深度学习模型产生了可理解的概念序列和反事实,减少了专家的工作量,提高了对安全关键应用程序的信任.

关键词:
活动认可 活动认可可解释的人工智能可以解释性 解释性神经网络的神经网络的神经网络

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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:

  • 人类活动识别 (HAR) 的深度学习模型对于安全关键应用至关重要,但往往缺乏可解释性.
  • 现有的可解释的HAR方法需要广泛,精确的低级活动注释,增加专家工作量和错误潜力.
  • 弥合高性能深度学习和可信,可解释的HAR之间的差距仍然是一个重大挑战.

研究的目的:

  • 介绍X-CHAR,一个可解释的复杂人类活动识别模型.
  • 开发HAR的深度学习方法,提供人类可以理解的解释,而不需要精确的低级活动注释.
  • 减轻领域专家的负担,同时保持强大的预测准确度.

主要方法:

  • X-CHAR将复杂的活动模型作为高级概念的序列.
  • 使用连接式时间分类 (CTC) 损失来处理序列信息,而无需对低级注释进行确切的开始/结束时间.
  • 以概念序列和反事实示例的形式生成解释.

主要成果:

  • 对于时间序列数据,X-CHAR实现了与基线端到端深度学习模型可比的强大性能.
  • 该模型成功地以可理解的高级概念的形式提供了解释.
  • 一项机械土耳其研究证实,X-CHAR的解释比现有方法的解释更容易理解.

结论:

  • X-CHAR为可解释复杂的 HAR 提供了可行的解决方案,减少了开发人员的负担并增强了模型的信任.
  • 该方法有效地平衡了时间序列数据的可解释解释的预测准确性.
  • 这项工作推动了可解释AI在安全关键的HAR应用领域的发展.