Conference paper Open Access

Continual Learning with Echo State Networks

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


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        <foaf:name>Claudio Gallicchio</foaf:name>
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        <foaf:name>Vincenzo Lomonaco</foaf:name>
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    <dct:title>Continual Learning with Echo State Networks</dct:title>
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    <dcat:keyword>continual learning; echo state networks; recurrent neural networks</dcat:keyword>
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    <dct:description>&lt;p&gt;Continual Learning (CL) refers to a learning setup where&lt;br&gt; data is non stationary and the model has to learn without forgetting ex-&lt;br&gt; isting knowledge. The study of CL for sequential patterns revolves around&lt;br&gt; trained recurrent networks. In this work, instead, we introduce CL in the&lt;br&gt; context of Echo State Networks (ESNs), where the recurrent component&lt;br&gt; is kept fixed. We provide the first evaluation of catastrophic forgetting in&lt;br&gt; ESNs and we highlight the benefits in using CL strategies which are not&lt;br&gt; applicable to trained recurrent models. Our results confirm the ESN as a&lt;br&gt; promising model for CL and open to its use in streaming scenarios.&lt;/p&gt;</dct:description>
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