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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2020-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06019.pdf |
| _version_ | 1819123926358294528 |
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| author | Albertsson Kim An Sitong Gleyzer Sergei Moneta Lorenzo Niermann Joana Wunsch Stefan Zampieri Luca Zapata Mesa Omar Andres |
| author_facet | Albertsson Kim An Sitong Gleyzer Sergei Moneta Lorenzo Niermann Joana Wunsch Stefan Zampieri Luca Zapata Mesa Omar Andres |
| author_sort | Albertsson Kim |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-22T07:16:06Z |
| format | Article |
| id | doaj.art-1f42177b23164469b9219e717fb4b60d |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-12-22T07:16:06Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-1f42177b23164469b9219e717fb4b60d2022-12-21T18:34:23ZengEDP SciencesEPJ Web of Conferences2100-014X2020-01-012450601910.1051/epjconf/202024506019epjconf_chep2020_06019Machine Learning with ROOT/TMVAAlbertsson KimAn SitongGleyzer Sergei0Moneta Lorenzo1Niermann Joana2Wunsch StefanZampieri Luca3Zapata Mesa Omar Andres4University of AlabamaCERNCERNCERNCERNROOT 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.https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06019.pdf |
| spellingShingle | Albertsson Kim An Sitong Gleyzer Sergei Moneta Lorenzo Niermann Joana Wunsch Stefan Zampieri Luca Zapata Mesa Omar Andres Machine Learning with ROOT/TMVA EPJ Web of Conferences |
| 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 |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2020/21/epjconf_chep2020_06019.pdf |
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