Jean-Roch Vlimant

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CERN

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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Jean-Roch Vlimant

Featured Co-authors

Wei Wei

Gilles Louppe

Kyle Cranmer

Cheng Chen

Prabhat

Alexander Schwing

Andrey Ustyuzhanin

Maurizio Pierini

Michela Paganini

Stefano Carrazza

Eli Upfal

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

Distributed Training and Optimization Of Neural Networks

Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics

Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

Track Seeding and Labelling with Embedded-space Graph Neural Networks

Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics

Quantum adiabatic machine learning with zooming

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Machine Learning in High Energy Physics Community White Paper

Topology classification with deep learning to improve real-time event selection at the LHC

An MPI-Based Python Framework for Distributed Training with Keras