Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
2 files | 1.56 MB
Add to list
  1. Search results
  2. A training algorithm for networks of ...

A training algorithm for networks of high-variability reservoirs

Matthias Freiberger (UGent) , Peter Bienstman (UGent) and Joni Dambre (UGent)
(2020) SCIENTIFIC REPORTS. 10(1).
Author
Organization
Abstract
Physical reservoir computing approaches have gained increased attention in recent years due to their potential for low-energy high-performance computing. Despite recent successes, there are bounds to what one can achieve simply by making physical reservoirs larger. Therefore, we argue that a switch from single-reservoir computing to multi-reservoir and even deep physical reservoir computing is desirable. Given that error backpropagation cannot be used directly to train a large class of multi-reservoir systems, we propose an alternative framework that combines the power of backpropagation with the speed and simplicity of classic training algorithms. In this work we report our findings on a conducted experiment to evaluate the general feasibility of our approach. We train a network of 3 Echo State Networks to perform the well-known NARMA-10 task, where we use intermediate targets derived through backpropagation. Our results indicate that our proposed method is well-suited to train multi-reservoir systems in an efficient way.
Keywords
deep reservoir computing, target signal derivation, multi-reservoir architectures, training algorithms, recurrent neural networks, NEURAL-NETWORKS, SYSTEMS

Downloads

  • Download
    s41598-020-71549-y 1 .pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 1.28 MB
  • Download
    41598 2020 71549 MOESM1 ESM.pdf
    • supplementary material
    • |
    • open access
    • |
    • PDF
    • |
    • 279.45 KB

Citation

Please use this url to cite or link to this publication:

MLA
Freiberger, Matthias, et al. “A Training Algorithm for Networks of High-Variability Reservoirs.” SCIENTIFIC REPORTS, vol. 10, no. 1, 2020, doi:10.1038/s41598-020-71549-y.
APA
Freiberger, M., Bienstman, P., & Dambre, J. (2020). A training algorithm for networks of high-variability reservoirs. SCIENTIFIC REPORTS, 10(1). https://doi.org/10.1038/s41598-020-71549-y
Chicago author-date
Freiberger, Matthias, Peter Bienstman, and Joni Dambre. 2020. “A Training Algorithm for Networks of High-Variability Reservoirs.” SCIENTIFIC REPORTS 10 (1). https://doi.org/10.1038/s41598-020-71549-y.
Chicago author-date (all authors)
Freiberger, Matthias, Peter Bienstman, and Joni Dambre. 2020. “A Training Algorithm for Networks of High-Variability Reservoirs.” SCIENTIFIC REPORTS 10 (1). doi:10.1038/s41598-020-71549-y.
Vancouver
1.
Freiberger M, Bienstman P, Dambre J. A training algorithm for networks of high-variability reservoirs. SCIENTIFIC REPORTS. 2020;10(1).
IEEE
[1]
M. Freiberger, P. Bienstman, and J. Dambre, “A training algorithm for networks of high-variability reservoirs,” SCIENTIFIC REPORTS, vol. 10, no. 1, 2020.
@article{8692664,
  abstract     = {{Physical reservoir computing approaches have gained increased attention in recent years due to their potential for low-energy high-performance computing. Despite recent successes, there are bounds to what one can achieve simply by making physical reservoirs larger. Therefore, we argue that a switch from single-reservoir computing to multi-reservoir and even deep physical reservoir computing is desirable. Given that error backpropagation cannot be used directly to train a large class of multi-reservoir systems, we propose an alternative framework that combines the power of backpropagation with the speed and simplicity of classic training algorithms. In this work we report our findings on a conducted experiment to evaluate the general feasibility of our approach. We train a network of 3 Echo State Networks to perform the well-known NARMA-10 task, where we use intermediate targets derived through backpropagation. Our results indicate that our proposed method is well-suited to train multi-reservoir systems in an efficient way.}},
  articleno    = {{14451}},
  author       = {{Freiberger, Matthias and Bienstman, Peter and Dambre, Joni}},
  issn         = {{2045-2322}},
  journal      = {{SCIENTIFIC REPORTS}},
  keywords     = {{deep reservoir computing,target signal derivation,multi-reservoir architectures,training algorithms,recurrent neural networks,NEURAL-NETWORKS,SYSTEMS}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{11}},
  title        = {{A training algorithm for networks of high-variability reservoirs}},
  url          = {{http://dx.doi.org/10.1038/s41598-020-71549-y}},
  volume       = {{10}},
  year         = {{2020}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: