Preprint

PUBDB-2022-01415

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Interpretable machine learning in Physics

Grojean, C.DESY* ; Paul, A.DESY* ; Qian, Z. ; Strümke, I. (Corresponding author)

2022

This record in other databases:    

Please use a persistent id in citations: doi:10.3204/PUBDB-2022-01415

Report No.: DESY-22-038; arXiv:2203.08021

Abstract: Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of clarity in the underlying dynamics of the system.

Keyword(s): correlation, higher-order

Note: Submitted version of invited Comment Article for Nature Reviews Physics

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Contributing Institute(s):

1. Theorie-Gruppe (T)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2022

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Database coverage:
; Published

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Journal Article Grojean, C.DESY* ; Paul, A.DESY* ; Qian, Z.Extern* ; Struemke, I. (Corresponding author)Extern*
Comment: Lessons on interpretable machine learning from particle physics
Nature Reviews Physics 4(5), 284 - 286 (2022) [10.1038/s42254-022-00456-0]2022     Files  Fulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS

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