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广泛的语言表征来预测可解释的气味混合物可辨别性

Amit Dhurandhar¹, Hongyang Li², Guillermo A Cecchi²

¹Foundations of Trusted Artificial Intelligence, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, United States.

Chemical senses

|June 1, 2023

概括

这项研究表明,基于语言的语义气味描述器可以预测气味混合物的辨别能力. 这种语义模型通过改进气味可分辨性预测来推进嗅觉科学.

科学领域:

嗅觉科学是一种嗅觉科学.
计算化学是一种计算化学.
认知科学是一种认知科学.

背景情况:

传统方法难以准确量化气味和嗅觉属性.
现有的气味剂模型往往侧重于单个分子,而不是混合物.
语言通常被认为不足以详细描述气味.

研究的目的:

开发一种使用语义气味描述器来预测气味混合物的可辨别性模型.
扩展现有的结构感知模型,包括复杂的嗅觉混合物.
为了研究语言在量化嗅觉空间中的实用性.

主要方法:

适应了对气味混合物的结构感知模型.
使用化学描述符来预测语义属性 (例如",鱼"",烧"",甜") 和强度.
在语义表示上使用度量学习来预测气味混合物的可区别性.
使用10个精选的语义描述符来预测可歧视性和相似性.

主要成果:

语义模型成功地预测了气味混合物的可区分性.
这种方法在气味可分辨性预测方面优于现有的最先进的方法.
该模型利用人类感知数据来增强预测的概括性.
证明了快速获得气味混合物的可解释属性的能力.

结论:

来自语言的语义描述符可以有效量化嗅觉属性,如混合物可区分性.
开发的语义模型为气味相似性和可辨别性提供了改进和概括的预测.
这项研究证实了语言的使用作为一种工具,用于在嗅觉空间内建立可区分度量.

关键词:

歧视性歧视性机器学习是机器学习.它们是元母体,元母体.计量学学习学习的方法自然语言处理自然语言处理.嗅觉混合物嗅觉混合物

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