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MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
In general-purpose particle detectors, the particle flow algorithm may b...
01/21/2021 ∙ by Joosep Pata, et al. ∙
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Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
We develop a graph generative adversarial network to generate sparse dat...
11/30/2020 ∙ by Raghav Kansal, et al. ∙
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Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Graph neural networks have been shown to achieve excellent performance f...
08/08/2020 ∙ by Yutaro Iiyama, et al. ∙
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GPU coprocessors as a service for deep learning inference in high energy physics
In the next decade, the demands for computing in large scientific experi...
07/20/2020 ∙ by Jeffrey Krupa, et al. ∙
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Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
We present the implementation of binary and ternary neural networks in t...
03/11/2020 ∙ by Giuseppe Di Guglielmo, et al. ∙
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Fast inference of Boosted Decision Trees in FPGAs for particle physics
We describe the implementation of Boosted Decision Trees in the hls4ml l...
02/05/2020 ∙ by Sioni Summers, et al. ∙
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Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
07/08/2018 ∙ by Kim Albertsson, et al. ∙
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Fast inference of deep neural networks in FPGAs for particle physics
Recent results at the Large Hadron Collider (LHC) have pointed to enhanc...
04/16/2018 ∙ by Javier Duarte, et al. ∙
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