Machine Learning with ROOT/TMVA
| Autor: | Sergei Gleyzer, Kim Albertsson, Lorenzo Moneta, Sitong An, Omar Andres Zapata Mesa, Luca Zampieri, Joana Niermann, Stefan Wunsch |
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| Rok vydání: | 2020 |
| Předmět: |
010308 nuclear & particles physics
business.industry Physics QC1-999 Deep learning Interoperability Training time Inference Benchmarking Python (programming language) Machine learning computer.software_genre 01 natural sciences Convolutional neural network Computing and Computers 0103 physical sciences Artificial intelligence 010306 general physics business computer computer.programming_language |
| Zdroj: | EPJ Web of Conferences, Vol 245, p 06019 (2020) |
| ISSN: | 2100-014X |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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