Trustworthiness Score for Echo State Networks by Analysis of the Reservoir Dynamics

  1. Enguita-Gonzalez, Jose M. 1
  2. Garcia-Perez, Diego 1
  3. Cuadrado-Vega, Abel Alberto 1
  4. García-Peña, Daniel
  5. Rodríguez-Ossorio, José Ramón
  6. Diaz-Blanco, Ignacio 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Actes de conférence:
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2024

Année de publication: 2024

Pages: 455-460

Congreso: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (32th. 2024. Bruges, Bélgica)

Type: Communication dans un congrès

DOI: 10.14428/ESANN/2024.ES2024-38 GOOGLE SCHOLAR lock_openAccès ouvert editor

Résumé

Epistemic uncertainty arises from input data areas where models lack exposure during training and may result in significant performance degradation in deployment. Echo State Networks are often used as virtual sensors or digital twins processing temporal input data, so their robustness against this degradation is crucial. This paper addresses this challenge by proposing a score comparing the similarity between the dynamic evolution of the reservoir in training and in inference. This research aims to enhance model confidence and adaptability in evolving circumstances.

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Information sur le financement

This work is part of Grant PID2020-115401GB-I00 funded by MCIN/AEI/ 10.13039/501100011033.

Financeurs