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Camero, Andrés and Toutouh, Jamal and Alba, Enrique (2021) Reliable and Fast Recurrent Neural Network Architecture Optimization. In: Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence, pp. 219-220. XIX Conference of the Spanish Association for Artificial Intelligence, 2021-09-22 - 2021-09-24, Malaga, Spain. ISBN 978-84-09-30514-8.
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Official URL: https://caepia20-21.uma.es/proceedings.html
Abstract
This article introduces Random Error Sampling-based Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures. RESN combines an evolutionary algorithm with a training-free evaluation approach. The results show that RESN achieves state-of-the-art error performance while reducing by half the computational time.
| Item URL in elib: | https://elib.dlr.de/188382/ | ||||||||||||||||
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| Document Type: | Conference or Workshop Item (Speech) | ||||||||||||||||
| Title: | Reliable and Fast Recurrent Neural Network Architecture Optimization | ||||||||||||||||
| Authors: |
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| Date: | 22 September 2021 | ||||||||||||||||
| Journal or Publication Title: | Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence | ||||||||||||||||
| Refereed publication: | No | ||||||||||||||||
| Open Access: | Yes | ||||||||||||||||
| Gold Open Access: | No | ||||||||||||||||
| In SCOPUS: | No | ||||||||||||||||
| In ISI Web of Science: | No | ||||||||||||||||
| Page Range: | pp. 219-220 | ||||||||||||||||
| ISBN: | 978-84-09-30514-8 | ||||||||||||||||
| Status: | Published | ||||||||||||||||
| Keywords: | neuroevolution, evolutionary algorithms, metaheuristics, recurrent neural networks | ||||||||||||||||
| Event Title: | XIX Conference of the Spanish Association for Artificial Intelligence | ||||||||||||||||
| Event Location: | Malaga, Spain | ||||||||||||||||
| Event Type: | international Conference | ||||||||||||||||
| Event Start Date: | 22 September 2021 | ||||||||||||||||
| Event End Date: | 24 September 2021 | ||||||||||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||
| HGF - Program: | Space | ||||||||||||||||
| HGF - Program Themes: | Earth Observation | ||||||||||||||||
| DLR - Research area: | Raumfahrt | ||||||||||||||||
| DLR - Program: | R EO - Earth Observation | ||||||||||||||||
| DLR - Research theme (Project): | R - Artificial Intelligence | ||||||||||||||||
| Location: | Oberpfaffenhofen | ||||||||||||||||
| Institutes and Institutions: | Remote Sensing Technology Institute > EO Data Science | ||||||||||||||||
| Deposited By: | Camero, Dr Andres | ||||||||||||||||
| Deposited On: | 27 Sep 2022 13:32 | ||||||||||||||||
| Last Modified: | 24 Apr 2024 20:49 |
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