Bridging Machine Learning and Sciences: Opportunities and Challenges

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-d...

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Bibliographic Details
Published in:arXiv.org 2023-11
Main Author: Cheng, Taoli
Format: Article
Language:English
Subjects:
Anomalies
Interdisciplinary studies
Machine learning
Neural networks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422