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Talk20 min

Machine-learning preservation from ATLAS' view

Tomasz Procter · CERN

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About

As machine learning becomes increasingly embedded in ATLAS analyses, ensuring the long-term usability of ML-based results poses several challenges. This talk will examine different aspects of ML preservation, including storing and documenting trained models, handling evolving software dependencies, and ensuring accessibility of input features tied to detector conditions. We will discuss the difficulties of rerunning ML-based analyses years after publication and highlight key areas where improvements are needed to support reinterpretation and reproducibility in the future..

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The talk

  1. Machine-learning preservation from ATLAS' view 2025-02-26 · 20 min · CERN's player

Credits and licence

Institution
CERN
Department
Workshops
Speaker
Tomasz Procter
Year
2025
Language
English
Subjects
Physics
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
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