arXiv Machine Learning August 17, 2026 By Youness Boutaib

Separation capacity of linear reservoirs with random connectivity matrix

Read the original on arXiv Machine Learning →

arXiv:2404. 17429v4 Announce Type: replace-cross Abstract: A natural hypothesis for the success of reservoir computing in generic tasks is the ability of the untrained reservoir to map distinct input time series to separable reservoir states, a property we term separation capacity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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