从数据中自动发现算法
Paul J Blazek1,2,3, Kesavan Venkatesh1,4, Milo M Lin5,6,7,8
1Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Nature computational science
|February 20, 2024
概括
深度蒸是一种新的AI方法,可以从数据中学习明确的科学规则,而无需搜索广的功能空间. 这种人工智能方法产生了紧的,人类可读的代码,它超出了训练数据的泛化范围,并且可以超过人类设计的算法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 科学发现 科学发现 发现
背景情况:
- 自动化科学和工程原理的发现需要人工智能从实验数据中提取明确的规则.
- 目前的方法由于潜在函数的巨大搜索空间而扎.
- 发现可概括的原则仍然是人工智能研究中的一个重大挑战.
研究的目的:
- 引入一种新的机器学习方法,即深度蒸,用于自动化科学原理的发现.
- 开发一种人工智能方法,可以从数据中学习,而不需要广泛的功能空间搜索.
- 从神经网络参数创建人类可以理解的算法.
主要方法:
- 深度蒸方法利用符号本质神经网络从数据中学习.
- 网络参数被无损地缩小成简洁,人类可读的计算机代码.
- 蒸代码可以包含复杂的结构,如循环和嵌套逻辑.
主要成果:
- 蒸的代码比原来的神经网络更加紧.
- 生成的算法展示了算术,视觉和优化任务的分布外系统概括.
- 蒸算法成功地解决了比训练数据更大,更复杂的问题.
结论:
- 深度蒸使人工智能能够从数据中发现可概括的科学原理.
- 该方法产生了人类可以理解和高度紧的算法.
- 发现的算法可以匹配或超过人类设计的算法的性能,补充人类的专业知识.
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