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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Veröffentlicht in:Nature reviews physics Jg. 4; H. 5; S. 284 - 286
Hauptverfasser: Grojean, Christophe, Paul, Ayan, Qian, Zhuoni, Strümke, Inga
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group 01.05.2022
Schlagworte:
Algorithms
Artificial intelligence
Decision trees
Kinematics
Machine learning
Neural networks
Particle physics
Variables
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.
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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Snippet Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is...
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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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