1.
|
New machine learning developments in ROOT/TMVA
/ Albertsson, Kim (CERN ; Lulea U. Technol. (main)) ; Gleyzer, Sergei (U. Florida, Gainesville (main)) ; Huwiler, Marc (Ecole Polytechnique, Lausanne) ; Ilievski, Vladimir (Ecole Polytechnique, Lausanne) ; Moneta, Lorenzo (CERN) ; Shekar, Saurav (ETH, Zurich (main)) ; Estrade, Victor (CERN) ; Vashistha, Akshay (CERN) ; Wunsch, Stefan (CERN ; KIT, Karlsruhe) ; Zapata Mesa, Omar Andres (Antioquia U. ; Inst. Tech. Metro., Medellin)
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. [...]
2019 - 8 p.
- Published in : EPJ Web Conf. 214 (2019) 06014
Fulltext from publisher: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.06014
|
|
2.
|
Machine Learning with ROOT/TMVA
/ Albertsson, Kim (CERN ; Lulea U.) ; An, Sitong (CERN ; Carnegie Mellon U.) ; Gleyzer, Sergei (Alabama U.) ; Moneta, Lorenzo (CERN) ; Niermann, Joana (CERN) ; Wunsch, Stefan (CERN ; KIT, Karlsruhe) ; Zampieri, Luca (CERN) ; Mesa, Omar Andres Zapata (CERN)
ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. [...]
2020 - 7 p.
- Published in : EPJ Web Conf. 245 (2020) 06019
Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.06019
|
|
3.
|
Fast Inference for Machine Learning in ROOT/TMVA
/ Albertsson, Kim (CERN ; Lulea U.) ; An, Sitong (CERN ; Carnegie Mellon U.) ; Moneta, Lorenzo (CERN) ; Wunsch, Stefan (CERN ; KIT, Karlsruhe) ; Zampieri, Luca (Ecole Polytechnique, Lausanne)
ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. However, with the rapidly evolving ecosystem for machine learning, the focus of TMVA is shifting. [...]
2020 - 8 p.
- Published in : EPJ Web Conf. 245 (2020) 06008
Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.06008
|
|
4.
|
C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA
/ An, Sitong (CERN ; Carnegie Mellon U.) ; Moneta, Lorenzo (CERN)
We report the latest development in ROOT/TMVA, a new system that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. We present an overview of the current solutions for conducting inference in C++ production environment, discuss the technical details and examples of the generated code, and demonstrates its development status with a preliminary benchmark against popular tools..
2021 - 8 p.
- Published in : EPJ Web Conf. 251 (2021) 03040
Fulltext: PDF;
In : 25th International Conference on Computing in High-Energy and Nuclear Physics (CHEP), Online, Online, 17 - 21 May 2021, pp.03040
|
|
5.
|
C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA
/ An, Sitong (CERN ; Carnegie Mellon U.) ; Moneta, Lorenzo (CERN) ; Sengupta, Sanjiban (Bhubaneswar, Inst. Phys.) ; Hamdan, Ahmat (Yaounde U.) ; Sossai, Federico (U. Padua (main)) ; Saxena, Aaradhya (IIT, Roorkee)
We report the latest development in ROOT/TMVA, a new tool that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. An introduction to SOFIE (System for Optimized Fast Inference code Emit) is presented, with examples of interface and generated code. [...]
2023 - 5 p.
- Published in : J. Phys. : Conf. Ser. 2438 (2023) 012013
Fulltext: PDF;
In : 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), Daejeon, Korea, 29 Nov - 3 Dec 2021, pp.012013
|
|
6.
|
|
C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA
/ An, Sitong (speaker) (CERN, Carnegie Mellon University (US))
We report the latest development in ROOT/TMVA, a new system that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. We present an overview of the current solutions for conducting inference in C++ production environment, discuss the technical details and examples of the generated code, and demonstrates its development status with a preliminary benchmark against popular tools..
2021 - 733.
Conferences; 25th International Conference on Computing in High Energy & Nuclear Physics
External links: Talk details; Event details
In : 25th International Conference on Computing in High Energy & Nuclear Physics
|
|
7.
|
TMVA: Toolkit for Multivariate Data Analysis with ROOT
/ Voss, H (Max-Planck-Inst. für Kernphysik, Heidelberg, Germany) ; Höcker, H (CERN) ; Stelzer, J (CERN) ; Tegenfeldt, F (Iowa State Univ., USA)
2007
- Published in : PoS: ACAT (2007) , pp. 040
Published version from PoS: PDF;
In : 11th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Amsterdam, The Netherlands, 23 - 27 Apr 2007, pp.040
|
|
8.
|
|
9.
|
Machine learning developments in ROOT
/ Bagoly, A (Eotvos U.) ; Bevan, A (Queen Mary, U. of London) ; Carnes, A (Florida U.) ; Gleyzer, S V (Florida U.) ; Moneta, L (CERN) ; Moudgil, A (Hyderabad, IIIT) ; Pfreundschuh, S (Chalmers U. Tech.) ; Stevenson, T (Queen Mary, U. of London) ; Wunsch, S (KIT, Karlsruhe) ; Zapata, O (Antioquia U.)
ROOT is a software framework for large-scale data analysis that provides basic and advanced statistical methods used by high-energy physics experiments. It includes machine learning tools from the ROOT-integrated Toolkit for Multivariate Analysis (TMVA). [...]
2017 - 8 p.
- Published in : J. Phys.: Conf. Ser. 898 (2017) 072046
Fulltext: PDF;
In : 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, San Francisco, Usa, 10 - 14 Oct 2016, pp.072046
|
|
10.
|
|