Jean-Roch Vlimant

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CERN

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The Tracking Machine Learning challenge : Throughput phase

This paper reports on the second "Throughput" phase of the Tracking Mach...
05/03/2021 ∙ by Sabrina Amrouche, et al. ∙ 0 ∙ share

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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. ∙ 31 ∙ share

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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 ∙ share

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Distributed Training and Optimization Of Neural Networks

Deep learning models are yielding increasingly better performances thank...
12/03/2020 ∙ by Jean-Roch Vlimant, et al. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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,...
10/05/2020 ∙ by Cheng Chen, et al. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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 ...
05/04/2020 ∙ by Oliver Knapp, et al. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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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. ∙ 0 ∙ share

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