论文标题
桥接机器学习和科学：机遇和挑战
Bridging Machine Learning and Sciences: Opportunities and Challenges
论文作者
论文摘要
近年来，机器学习在科学中的应用令人兴奋。作为一种广泛适用的技术，在机器学习社区中长期研究了异常检测。特别是，基于深度神经网的分布外检测已在高维数据方面取得了长足的进步。最近，这些技术已经显示出它们在科学学科中的潜力。我们仔细研究了他们的应用前景，包括数据通用性，实验协议，模型鲁棒性等。我们讨论了同时显示可转移实践和特定领域的挑战的示例，为建立新颖的跨学科研究范式提供了一个起点。
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.
