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This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC).
2017
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Qasim, S., Kieseler, J., Iiyama, Y. & Pierini, M. Learning representations of irregular particle-detector geometry with distance-weighted graph networks. The European Physical Journal C
2019
Earlier work this paper cites.
Xuan, T., Borca-Tasciuc, G., Zhu, Y., Sun, Y., Dean, C., Shi, Z. & Yu, D. Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer. Machine Learning And Knowledge Discovery In Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part III
2022
Cited alongside, same era.
2022
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Cited in the paper.
R. Sarkar, S. Abi-Karam, Y. He, L. Sathidevi and C. Hao, FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference , 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
2023
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