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Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing / Amboage, Juan Pablo García (CERN ; U. Santiago de Compostela (main)) ; Wulff, Eric (CERN) ; Girone, Maria (CERN) ; Pena, Tomás F. (U. Santiago de Compostela (main))
Hyperparameter Optimization (HPO) of Deep Learning-based models tends to be a compute resource intensive process as it usually requires to train the target model with many different hyperparameter configurations. We show that integrating model performance prediction with early stopping methods holds great potential to speed up the HPO process of deep learning models. [...]
arXiv:2311.17508.- 2024 - 7 p. - Published in : EPJ Web Conf. 295 (2024) 12005 Fulltext: document - PDF; 2311.17508 - PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.12005
2.
Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC / Wulff, Eric (CERN) ; Girone, Maria (CERN) ; Southwick, David (CERN) ; Amboage, Juan Pablo García (CERN) ; Cuba, Eduard (CERN)
Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as well as scalable and resource efficient hyperparameter search algorithms. [...]
arXiv:2303.15053.
- 5 p.
Fulltext

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