Lessons on interpretable machine learning from particle physics
Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods...
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| Published in: | Nature reviews physics Vol. 4; no. 5; pp. 284 - 286 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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London
Nature Publishing Group
01.05.2022
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| ISSN: | 2522-5820 |
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| Abstract | Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods commonly used in particle physics. |
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| AbstractList | Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods commonly used in particle physics. |
| Author | Qian, Zhuoni Strümke, Inga Paul, Ayan Grojean, Christophe |
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| Copyright | Copyright Nature Publishing Group May 2022 |
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| DOI | 10.1038/s42254-022-00456-0 |
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| SubjectTerms | Algorithms Artificial intelligence Decision trees Kinematics Machine learning Neural networks Particle physics Variables |
| Title | Lessons on interpretable machine learning from particle physics |
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