On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case-Reference-Cited by-同舟云学术

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On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

Published:2020-09-19 Issue:4 Volume:1 Page:045006
ISSN:2632-2153
Container-title:Machine Learning: Science and Technology
language:
Short-container-title:Mach. Learn.: Sci. Technol.

Author:

Chatham Strong Giles^ORCID

Publisher

IOP Publishing

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://iopscience.iop.org/article/10.1088/2632-2153/ab983a/pdf

Reference107 articles.

1. Search for the standard model Higgs boson in e+e− interactions at

2. Search for single top quark production at DØ using neural networks

3. Run II physics at the Fermilab Tevatron and advanced analysis methods

4. The CDF detector: an overview

5. The DØ detector

Cited by 4 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. ML_INFN project: Status report and future perspectives;EPJ Web of Conferences;2024

2. Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider;Reviews in Physics;2021-12

3. How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders;Applied Sciences;2021-11-22

4. Advanced multivariate analysis methods for use by the experiments at the Large Hadron Collider*;Physica Scripta;2021-02-26

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