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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...
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Distributed Training and Optimization Of Neural Networks
Deep learning models are yielding increasingly better performances thank...
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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...
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Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
We present a fast simulation application based on a Deep Neural Network,...
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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 ...
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Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to ...
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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...
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Quantum adiabatic machine learning with zooming
Recent work has shown that quantum annealing for machine learning (QAML)...
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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...
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Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
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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...
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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...
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