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Machine Learning with ROOT/TMVA
ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables an...
Autores principales: | , , , , , , , |
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Lenguaje: | eng |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1051/epjconf/202024506019 http://cds.cern.ch/record/2752303 |
_version_ | 1780969334683729920 |
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author | Albertsson, Kim An, Sitong Gleyzer, Sergei Moneta, Lorenzo Niermann, Joana Wunsch, Stefan Zampieri, Luca Mesa, Omar Andres Zapata |
author_facet | Albertsson, Kim An, Sitong Gleyzer, Sergei Moneta, Lorenzo Niermann, Joana Wunsch, Stefan Zampieri, Luca Mesa, Omar Andres Zapata |
author_sort | Albertsson, Kim |
collection | CERN |
description | ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. The new developments are paired with newly designed C++ and Python interfaces supporting modern C++ paradigms and full interoperability in the Python ecosystem. We present as well a new deep learning implementation for convolutional neural network using the cuDNN library for GPU. We show benchmarking results in term of training time and inference time, when comparing with other machine learning libraries such as Keras/Tensorflow. |
id | oai-inspirehep.net-1832217 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
record_format | invenio |
spelling | oai-inspirehep.net-18322172021-02-18T19:23:11Zdoi:10.1051/epjconf/202024506019http://cds.cern.ch/record/2752303engAlbertsson, KimAn, SitongGleyzer, SergeiMoneta, LorenzoNiermann, JoanaWunsch, StefanZampieri, LucaMesa, Omar Andres ZapataMachine Learning with ROOT/TMVAComputing and ComputersROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. The new developments are paired with newly designed C++ and Python interfaces supporting modern C++ paradigms and full interoperability in the Python ecosystem. We present as well a new deep learning implementation for convolutional neural network using the cuDNN library for GPU. We show benchmarking results in term of training time and inference time, when comparing with other machine learning libraries such as Keras/Tensorflow.oai:inspirehep.net:18322172020 |
spellingShingle | Computing and Computers Albertsson, Kim An, Sitong Gleyzer, Sergei Moneta, Lorenzo Niermann, Joana Wunsch, Stefan Zampieri, Luca Mesa, Omar Andres Zapata Machine Learning with ROOT/TMVA |
title | Machine Learning with ROOT/TMVA |
title_full | Machine Learning with ROOT/TMVA |
title_fullStr | Machine Learning with ROOT/TMVA |
title_full_unstemmed | Machine Learning with ROOT/TMVA |
title_short | Machine Learning with ROOT/TMVA |
title_sort | machine learning with root/tmva |
topic | Computing and Computers |
url | https://dx.doi.org/10.1051/epjconf/202024506019 http://cds.cern.ch/record/2752303 |
work_keys_str_mv | AT albertssonkim machinelearningwithroottmva AT ansitong machinelearningwithroottmva AT gleyzersergei machinelearningwithroottmva AT monetalorenzo machinelearningwithroottmva AT niermannjoana machinelearningwithroottmva AT wunschstefan machinelearningwithroottmva AT zampieriluca machinelearningwithroottmva AT mesaomarandreszapata machinelearningwithroottmva |