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arXiv 1807.01437cond-mat.soft
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数据有限状态下的机器学习:用合成数据增强实验,揭示了皱巴巴的纸张中的顺序

Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets

Jordan Hoffmann, Yohai Bar-Sinai, Lisa Lee, Jovana Andrejevic, Shruti Mishra, Shmuel M. Rubinstein, Chris H. Rycroft

摘要:

机器学习作为识别复杂、高维数据结构的有力工具,已经得到了广泛的关注。然而,这些技术表面上不适用于实验系统中的数据是稀缺的或昂贵的获得。在这里,我们介绍了一种策略来解决这个僵局,通过增加实验数据集与合成生成的数据的一个更简单的姐妹系统。具体来说,我们研究了褶皱薄片折痕网络中自发出现的局部秩序,这是空间复杂性的一个典型例子,并表明机器学习技术即使在数据有限的情况下也是有效的。这是通过增加稀缺的实验数据集与取之不尽的刚性平折片,这是简单的模拟和共享共同的统计特性的模拟数据。这显著提高了模式完成测试问题的预测能力,并证明了机器学习在数据良好但稀缺的台式实验中的有用性。

英文摘要:

Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data. However, these techniques are ostensibly inapplicable for experimental systems where data is scarce or expensive to obtain. Here we introduce a strategy to resolve this impasse by augmenting the experimental dataset with synthetically generated data of a much simpler sister system. Specifically, we study spontaneously emerging local order in crease networks of crumpled thin sheets, a paradigmatic example of spatial complexity, and show that machine learning techniques can be effective even in a data-limited regime. This is achieved by augmenting the scarce experimental dataset with inexhaustible amounts of simulated data of rigid flat-folded sheets, which are simple to simulate and share common statistical properties. This significantly improves the predictive power in a test problem of pattern completion and demonstrates the usefulness of machine learning in bench-top experiments where data is good but scarce.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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