Anomaly Detection for Physics Analysis and Less Than Supervised Learning-Reference-Cited by-同舟云学术

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Anomaly Detection for Physics Analysis and Less Than Supervised Learning

Published:2022-02-06 Issue: Volume: Page:85-112
ISSN:
Container-title:Artificial Intelligence for High Energy Physics
language:
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Author:

Nachman Benjamin¹

Affiliation:

1. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

Publisher

WORLD SCIENTIFIC

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unravelling physics beyond the standard model with classical and quantum anomaly detection;Machine Learning: Science and Technology;2023-11-16

2. Quantum Generative Adversarial Networks For Anomaly Detection In High Energy Physics;2023 IEEE International Conference on Quantum Computing and Engineering (QCE);2023-09-17

3. Roadmap on artificial intelligence and big data techniques for superconductivity;Superconductor Science and Technology;2023-02-24

4. Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges;Computer Modeling in Engineering & Sciences;2023

5. ‘Flux+Mutability’: a conditional generative approach to one-class classification and anomaly detection;Machine Learning: Science and Technology;2022-11-04

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