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New Machine Learning Developments in ROOT/TMVA
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. CERN.ORCID iD: 0000-0002-5052-9629
University of Florida.
EPFL.
EPFL.
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2019 (English)In: 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), EDP Sciences, 2019, Vol. 214, article id 06014Conference paper, Published paper (Refereed)
Abstract [en]

The Toolkit for Multivariate Analysis, TMVA, the machine learning package integrated into the ROOT data analysis framework, has recently seen improvements to its deep learning module, parallelisation of multivariate methods and cross validation. Performance benchmarks on datasets from high-energy physics are presented with a particular focus on the new deep learning module which contains robust fully-connected, convolutional and recurrent deep neural networks implemented on CPU and GPU architectures. Both dense and convo-lutional layers are shown to be competitive on small-scale networks suitable for high-level physics analyses in both training and in single-event evaluation. Par-allelisation efforts show an asymptotical 3-fold reduction in boosted decision tree training time while the cross validation implementation shows significant speed up with parallel fold evaluation.

Place, publisher, year, edition, pages
EDP Sciences, 2019. Vol. 214, article id 06014
Series
EPJ Web of Conferences, E-ISSN 2100-014X
Keywords [en]
ROOT, TMVA, Machine Learning
National Category
Software Engineering Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-76550DOI: 10.1051/epjconf/201921406014OAI: oai:DiVA.org:ltu-76550DiVA, id: diva2:1366548
Conference
23rd International Conference on Computing in High Energy Physics and Nuclear Physics, Sofia, Bulgaria, 9-13 July 2018
Available from: 2019-10-29 Created: 2019-10-29 Last updated: 2019-11-15Bibliographically approved

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Albertsson, Kim

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