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Reservoir Computing Trends

dc.contributor.authorLukoševičius, Mantas
dc.contributor.authorJaeger, Herbert
dc.contributor.authorSchrauwen, Benjamin
dc.date.accessioned2018-01-08T09:16:10Z
dc.date.available2018-01-08T09:16:10Z
dc.date.issued2012
dc.description.abstractReservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the readouts from it. It started ten years ago and is currently a prolific research area, giving important insights into RNNs, practical machine learning tools, as well as enabling computation with non-conventional hardware. Here we give a brief introduction into basic concepts, methods, insights, current developments, and highlight some applications of RC.
dc.identifier.pissn1610-1987
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/11319
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 26, No. 4
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectEcho state network
dc.subjectRecurrent neural network
dc.subjectReservoir computing
dc.titleReservoir Computing Trends
dc.typeText/Journal Article
gi.citation.endPage371
gi.citation.startPage365

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