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Physics and Computing Performance of the Exa.TrkX TrackML Pipeline
The Exa.TrkX project has applied geometric learning concepts such as met...
03/11/2021 ∙ by Xiangyang Ju, et al. ∙
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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. ∙
41
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Track Seeding and Labelling with Embedded-space Graph Neural Networks
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project ...
06/30/2020 ∙ by Nicholas Choma, et al. ∙
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Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
Using detailed simulations of calorimeter showers as training data, we i...
12/14/2019 ∙ by Dawit Belayneh, et al. ∙
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Quantum adiabatic machine learning with zooming
Recent work has shown that quantum annealing for machine learning (QAML)...
08/13/2019 ∙ by Alexander Zlokapa, et al. ∙
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hepaccelerate: Fast Analysis of Columnar Collider Data
At HEP experiments, processing terabytes of structured numerical event d...
06/14/2019 ∙ by Joosep Pata, et al. ∙
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Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Using variational autoencoders trained on known physics processes, we de...
11/26/2018 ∙ by Olmo Cerri, et al. ∙
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Topology classification with deep learning to improve real-time event selection at the LHC
We show how event topology classification based on deep learning could b...
06/29/2018 ∙ by Thong Q. Nguyen, et al. ∙
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An MPI-Based Python Framework for Distributed Training with Keras
We present a lightweight Python framework for distributed training of ne...
12/16/2017 ∙ by Dustin Anderson, et al. ∙
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