Bridging Machine Learning and Sciences: Opportunities and Challenges
It addresses the problem of integrating machine learning into sciences for researchers, but is incremental as it reviews existing techniques rather than introducing new methods.
The paper examines the application of machine learning, particularly anomaly detection techniques like deep neural nets for out-of-distribution detection, to scientific disciplines, highlighting both opportunities and challenges such as data universality and model robustness.
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.