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Connecting reservoir computing with statistical forecasting and deep neural networks
Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate th...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752734/ https://www.ncbi.nlm.nih.gov/pubmed/35017470 http://dx.doi.org/10.1038/s41467-021-27715-5 |
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author | Jaurigue, Lina Lüdge, Kathy |
author_facet | Jaurigue, Lina Lüdge, Kathy |
author_sort | Jaurigue, Lina |
collection | PubMed |
description | Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems. |
format | Online Article Text |
id | pubmed-8752734 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87527342022-01-20 Connecting reservoir computing with statistical forecasting and deep neural networks Jaurigue, Lina Lüdge, Kathy Nat Commun Comment Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems. Nature Publishing Group UK 2022-01-11 /pmc/articles/PMC8752734/ /pubmed/35017470 http://dx.doi.org/10.1038/s41467-021-27715-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Comment Jaurigue, Lina Lüdge, Kathy Connecting reservoir computing with statistical forecasting and deep neural networks |
title | Connecting reservoir computing with statistical forecasting and deep neural networks |
title_full | Connecting reservoir computing with statistical forecasting and deep neural networks |
title_fullStr | Connecting reservoir computing with statistical forecasting and deep neural networks |
title_full_unstemmed | Connecting reservoir computing with statistical forecasting and deep neural networks |
title_short | Connecting reservoir computing with statistical forecasting and deep neural networks |
title_sort | connecting reservoir computing with statistical forecasting and deep neural networks |
topic | Comment |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752734/ https://www.ncbi.nlm.nih.gov/pubmed/35017470 http://dx.doi.org/10.1038/s41467-021-27715-5 |
work_keys_str_mv | AT jauriguelina connectingreservoircomputingwithstatisticalforecastinganddeepneuralnetworks AT ludgekathy connectingreservoircomputingwithstatisticalforecastinganddeepneuralnetworks |