The application of machine learning in sciences has seen exciting advances in recent years. As a widely-applicable technique, anomaly detection has been long studied in the machine learning community. Especially, deep neural nets-based out-of-distribution detection has made great progress for high-dimensional data. Recently, these techniques have been showing their potential in scientific disciplines. We take a critical look at their applicative prospects including data universality, experimental protocols, model robustness, etc. We discuss examples that display transferable practices and domain-specific challenges simultaneously, providing a starting point for establishing a novel interdisciplinary research paradigm in the near future.
翻译:近些年来,机器学习在科学方面的应用取得了令人振奋的进展。作为一种广泛应用的技术,机器学习界长期研究异常现象的发现。特别是,基于深神经网的分布外探测在高维数据方面取得了巨大进步。最近,这些技术在科学学科中展现了潜力。我们批判地审视了它们应用的前景,包括数据的普遍性、实验协议、模型坚固性等等。我们讨论了同时展示可转让做法和特定领域挑战的例子,为在不久的将来建立新的跨学科研究模式提供了一个起点。