Indico has been updated to v3.3. See our blog post for details on this release. (OTG0146394)

Oct 10 – 14, 2016
San Francisco Marriott Marquis
America/Los_Angeles timezone

Machine Learning with TensorFlow as an alternative to TMVA

Oct 12, 2016, 12:15 PM
GG A+B (San Francisco Mariott Marquis)


San Francisco Mariott Marquis

Oral Track 5: Software Development Track 5: Software Development


Prof. Martin Sevior (University of Melbourne)


The Toolkit for Multivariate Analysis (TMVA) is a component of the ROOT data analysis framework and is widely used for classification problems. For example, TMVA might be used for the binary classification problem of distinguishing signal from background events.

The classification methods included in TMVA are standard, well-known machine learning techniques which can be implemented in other languages and hardware architectures. The recently released open source package “TensorFlow” from Google, offers the opportunity to test an alternative implementation. In particular, TensorFlow has the capability to transparently interface GPU acceleration for machine learning with the potential for orders of magnitude increase in performance. Furthermore, TensorFlow enables the construction of sophisticated artificial neural networks capable of “Deep Learning”. We have investigated the use of TensorFlow for general purpose machine learning applications in Particle Physics by interfacing it to the root data format and implementing the TMVA API within the TensorFlow framework. In addition, we have investigated recurrent neural net-
works (RNN) using TensorFlow.

The presentation will report the performance of TensorFlow compared to TMVA for both general purpose CPUs' and a high-performance GPU cluster. We will also report on the effectiveness of RNN for particle physics applications.

Primary Keyword (Mandatory) Analysis tools and techniques

Primary author

Prof. Martin Sevior (University of Melbourne)


Mr Aidan Dang (University of Melbourne) Mr Anton Hawthorne (University of Melbourne)

Presentation materials