Speaker
Kim Albertsson
(Lulea University of Technology (SE))
Description
In this talk, we will describe the latest additions to the Toolkit for Multivariate Analysis (TMVA), the machine learning package integrated into the ROOT framework. In particular, we will focus on the new deep learning module that contains robust fully-connected, convolutional and recurrent deep neural networks implemented on CPU and GPU architectures. We will present performance of these new libraries on benchmark datasets from high-energy physics. Additionally, we will discuss new developments in parallelization, cross-validation, regression and unsupervised learning and new interfaces to external machine learning frameworks, such as Tensorflow and scikit-learn.
Primary authors
Lorenzo Moneta
(CERN)
Omar Andres Zapata Mesa
(University of Antioquia & Metropolitan Institute of Technology)
Dr
Sergei Gleyzer
(University of Florida (US))
Vladimir Ilievski
(EPFL)
Kim Albertsson
(Lulea University of Technology (SE))
Stefan Wunsch
(KIT - Karlsruhe Institute of Technology (DE))
Saurav Shekhar
(ETH Zurich)
Akshay Vashistha
Marc Huwiler
(EPFL - Ecole Polytechnique Federale Lausanne (CH))
