Author

Taiki Yamada

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Jul 2026

Scalable Perturbation Learning for Online Self-Supervised Learning in Echo State Networks

A perturbation-based learning rule for online self-supervised learning in ESNs is proposed, derived from an orthogonal decomposition of the self-supervised learning cost, which separates an input-dependent component from a redundant component determined by the fixed ESN parameters.

Taiki Yamada, Kantaro Fujiwara · arXiv.org · 0 citations