Presentation
PBIL for Optimizing Hyperparameters of Convolutional Neural Networks and STL Decomposition
Author/s | Vasco Carofilis, Roberto A.
Gutiérrez Naranjo, Miguel Ángel ![]() ![]() ![]() ![]() ![]() ![]() ![]() Cárdenas Montes, Miguel |
Department | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Publication Date | 2020 |
Deposit Date | 2021-04-07 |
Published in |
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ISBN/ISSN | 978-3-030-61704-2 0302-9743 |
Abstract | The optimization of hyperparameters in Deep Neural Net-works is a
critical task for the final performance, but it involves a high amount of subjective
decisions based on previous researchers’ expertise. This paper presents ... The optimization of hyperparameters in Deep Neural Net-works is a critical task for the final performance, but it involves a high amount of subjective decisions based on previous researchers’ expertise. This paper presents the implementation of Population-based Incremen-tal Learning for the automatic optimization of hyperparameters in Deep Learning architectures. Namely, the proposed architecture is a combina-tion of preprocessing the time series input with Seasonal Decomposition of Time Series by Loess, a classical method for decomposing time series, and forecasting with Convolutional Neural Networks. In the past, this combination has produced promising results, but penalized by an incre-mental number of parameters. The proposed architecture is applied to the prediction of the 222Rn level at the Canfranc Underground Labora-tory (Spain). By predicting the lowlevel periods of 222Rn, the potential contamination during the maintenance operations in the experiments hosted in the laboratory could be minimized. In this paper, it is shown that Population-based Incremental Learning can be used for the choice of optimized hyperparameters in Deep Learning architectures with a reasonable computational cost. |
Funding agencies | Ministerio de Economía y Competitividad (MINECO). España |
Project ID. | MDM- 2015-0509
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Citation | Vasco Carofilis, R.A., Gutiérrez Naranjo, M.Á. y Cárdenas Montes, M. (2020). PBIL for Optimizing Hyperparameters of Convolutional Neural Networks and STL Decomposition. En HAIS 2020: 15th International Conference on Hybrid Artificial Intelligence Systems (147-159), Gijón, España: Springer. |
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