New Machine Learning Developments in ROOT/TMVA
| Autor: | Lorenzo Moneta, Marc Huwiler, Stefan Wunsch, Saurav Shekar, Omar Andres Zapata Mesa, Akshay Vashistha, Victor Estrade, Kim Albertsson, Sergei Gleyzer, Vladimir Ilievski |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Multivariate statistics
Root (linguistics) Speedup 010308 nuclear & particles physics business.industry Deep learning Physics QC1-999 Machine learning computer.software_genre 01 natural sciences Cross-validation Computing and Computers Reduction (complexity) 0103 physical sciences Gradient boosting Artificial intelligence 010306 general physics Focus (optics) business computer |
| Zdroj: | EPJ Web of Conferences, Vol 214, p 06014 (2019) |
| Popis: | 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. Both dense and convo-lutional layers are shown to be competitive on small-scale networks suitable for high-level physics analyses in both training and in single-event evaluation. Par-allelisation efforts show an asymptotical 3-fold reduction in boosted decision tree training time while the cross validation implementation shows significant speed up with parallel fold evaluation. |
| Databáze: | OpenAIRE |
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