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Nov 4 – 8, 2019
Adelaide Convention Centre
Australia/Adelaide timezone

Machine Learning with ROOT/TMVA

Nov 4, 2019, 11:15 AM
15m
Hall G (Adelaide Convention Centre)

Hall G

Adelaide Convention Centre

Oral Track 6 – Physics Analysis Track 6 – Physics Analysis

Speaker

Stefan Wunsch (KIT - Karlsruhe Institute of Technology (DE))

Description

ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. In this talk, 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.

Consider for promotion Yes

Primary authors

Lorenzo Moneta (CERN) Kim Albertsson (Lulea University of Technology (SE)) Sitong An (CERN, Carnegie Mellon University (US)) Stefan Wunsch (KIT - Karlsruhe Institute of Technology (DE)) Sergei Gleyzer (University of Florida (US)) Omar Andres Zapata Mesa (University of Antioquia & Metropolitan Institute of Technology)

Presentation materials