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We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data.
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Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin, “Emerging properties in self-supervised vision transformers,” in 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021 (IEEE, 2021) pp. 9630–9640
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Vinicius Mikuni and Florencia Canelli, “Point cloud transformers applied to collider physics,” Machine Learning: Science and Technology 2
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ATLAS Collaboration (ATLAS), Transformer Neural Networks for Identifying Boosted Higgs Bosons decaying into b b ¯ b\bar{b} and c c ¯ c\bar{c} in ATLAS , Tech. Rep. (CERN, Geneva, 2023)
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