通过变化的自动编码器生成符号表达式
Sergei Popov1,2, Mikhail Lazarev1, Vladislav Belavin1
1Department of Computer Science, Higher School of Economics, Moscow, Russia.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种新的深度学习方法,用于使用变化自编码器 (VAE) 进行符号回归. 这种方法提高了科学模型的可解释性,并改善了公式的发现,特别是在杂的数据条件下.
科学领域:
- 物理 物理学 物理
- 生物学 生物学 生物学
- 自然科学 自然科学自然科学
- 机器学习 机器学习
背景情况:
- 符号回归提供了对自然规律的可解释的见解,与深度神经网络不同.
- 当前的符号回归方法缺乏主导解决方案,因此需要改进算法.
研究的目的:
- 开发一种新的深度学习框架,用于符号表达式生成.
- 在符号回归任务中解决现有方法的局限性.
主要方法:
- 一个变量自编码器 (VAE) 用于生成数学表达式.
- 培训策略确保生成的公式准确地适合给定的数据集.
- 先验知识被编码到快速检查预言中,以加速优化.
主要成果:
- 拟议的方法,SEGVAE,超过现有的象征回归基准,特别是在噪音条件下.
- 在10%的噪音下,在Nguyen数据集上实现了65%的恢复率,比之前的最先进状态有20%的改进.
- 证明性能因数据集特征而有所不同,有可能实现更高的恢复率.
结论:
- 基于VAE的新框架有效地产生了科学发现的象征性表达.
- 该方法在符号回归中提供了显著的进步,特别是对于复杂和杂的数据集.
- 未来的工作可以在各种科学领域探索进一步的优化和应用.
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