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20242026
most citedReconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks

3 citations · 3 across the 5 of their papers we have counts for

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hep-ex2026

RINO: Renormalization Group Distillation with No Labels

Zichun Hao, Raghav Kansal, Abhijith Gandrakota +6

Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics…

hep-ex2024

Large-Scale Pretraining and Finetuning for Efficient Jet Classification in Particle Physics

Zihan Zhao, Farouk Mokhtar, Raghav Kansal +2

This study introduces an innovative approach to analyzing unlabeled data in high-energy physics (HEP) through the application of self-supervised learning (SSL). Faced with the incr…