Bridging Machine Learning and Sciences: Opportunities and Challenges

• URL: http://arxiv.org/abs/2210.13441v2
• Date: Thu, 2 Nov 2023 13:24:36 GMT
• Title: Bridging Machine Learning and Sciences: Opportunities and Challenges
• Authors: Taoli Cheng
• Abstract summary: Application of machine learning in sciences has seen exciting advances in recent years. Recently, deep neural nets-based out-of-distribution detection has made great progress for high-dimensional data. We take a critical look at their applicative prospects including data universality, experimental protocols, model robustness, etc.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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