10-14 October 2016
San Francisco Marriott Marquis
America/Los_Angeles timezone

New Machine Learning Developments in ROOT

10 Oct 2016, 15:15
Sierra A (San Francisco Mariott Marquis)

Sierra A

San Francisco Mariott Marquis

Oral Track 5: Software Development Track 5: Software Development


Dr Sergei Gleyzer (University of Florida (US))


ROOT provides advanced statistical methods needed by the LHC experiments to analyze their data. These include machine learning tools for classification, regression and clustering. TMVA, a toolkit for multi-variate analysis in ROOT, provides these machine learning methods.

We will present new developments in TMVA, including parallelisation, deep-learning neural networks, new features and
additional interfaces to external machine learning packages.
We will show the new modular design of the new version of TMVA, cross-validation and hyper parameter tuning capabilities, feature engineering and deep learning.
We will further describe new parallelisation features including multi-threading, multi-processing, cluster parallelisation and present GPU support for intensive machine learning applications, such as deep learning.

Secondary Keyword (Optional) Algorithms
Primary Keyword (Mandatory) Artificial intelligence/Machine learning
Tertiary Keyword (Optional) Analysi tools and techniques

Primary authors

Lorenzo Moneta (CERN) Dr Sergei Gleyzer (University of Florida (US))


Omar Andres Zapata Mesa (University of Antioquia & Metropolitan Institute of Technology)

Presentation Materials

Your browser is out of date!

Update your browser to view this website correctly. Update my browser now