Conference paper Open Access

Continual Learning with Echo State Networks

Andrea Cossu; Davide Bacciu; Antonio Carta; Claudio Gallicchio; Vincenzo Lomonaco


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    "description": "<p>Continual Learning (CL) refers to a learning setup where<br>\ndata is non stationary and the model has to learn without forgetting ex-<br>\nisting knowledge. The study of CL for sequential patterns revolves around<br>\ntrained recurrent networks. In this work, instead, we introduce CL in the<br>\ncontext of Echo State Networks (ESNs), where the recurrent component<br>\nis kept fixed. We provide the first evaluation of catastrophic forgetting in<br>\nESNs and we highlight the benefits in using CL strategies which are not<br>\napplicable to trained recurrent models. Our results confirm the ESN as a<br>\npromising model for CL and open to its use in streaming scenarios.</p>", 
    "language": "eng", 
    "title": "Continual Learning with Echo State Networks", 
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    "keywords": [
      "continual learning; echo state networks; recurrent neural networks"
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    "publication_date": "2021-08-05", 
    "creators": [
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        "affiliation": "University of Pisa", 
        "name": "Andrea Cossu"
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      {
        "affiliation": "University of Pisa", 
        "name": "Davide Bacciu"
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      {
        "affiliation": "University of Pisa", 
        "name": "Antonio Carta"
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      {
        "affiliation": "University of Pisa", 
        "name": "Claudio Gallicchio"
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      {
        "affiliation": "University of Pisa", 
        "name": "Vincenzo Lomonaco"
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      "acronym": "ESANN", 
      "title": "European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning"
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