Papers

• Track Seeding and Labelling with Embedded-space Graph Neural Networks (2020)
  Nicholas Choma et al.
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• Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics (2020)
  Yutaro Iiyama et al.
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• Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs (2020)
  Aneesh Heintz and Vesal Razavimaleki and Javier Duarte and Gage DeZoort and Isobel Ojalvo and Savannah Thais and Markus Atkinson and Mark Neubauer and Lindsey Gray and Sergo Jindariani and Nhan Tran and Philip Harris and Dylan Rankin and Thea Aarrestad and Vladimir Loncar and Maurizio Pierini and Sioni Summers and Jennifer Ngadiuba and Mia Liu and Edward Kreinar and Zhenbin Wu
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• Explaining machine-learned particle-flow reconstruction (2021)
  Farouk Mokhtar et al.
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• Graph Neural Networks for Charged Particle Tracking on FPGAs (2021)
  Abdelrahman Elabd and Vesal Razavimaleki and Shi-Yu Huang and Javier Duarte and Markus Atkinson and Gage DeZoort and Peter Elmer and Scott Hauck and Jin-Xuan Hu and Shih-Chieh Hsu and Bo-Cheng Lai and Mark Neubauer and Isobel Ojalvo and Savannah Thais and Matthew Trahms
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• End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks (2022)
  Shah Rukh Qasim et al.
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• Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube (2022)
  R. Abbasi et al.
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• LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics (2022)
  Zhiqiang Que et al.
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• Equivariant Graph Neural Networks for Charged Particle Tracking (2023)
  Daniel Murnane et al.
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• Graph Structure from Point Clouds: Geometric Attention is All You Need (2023)
  Daniel Murnane
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• Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics (2024)
  Md Abrar Jahin et al.
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