arXiv cs.NE
· Papers
Scalable Perturbation Learning for Online Self-Supervised Echo State Networks
arXiv:2607.06079v1 Announce Type: cross Abstract: Intelligent systems should not only solve tasks but also adapt under real-world constraints. Autonomous adaptation via self-supervised learning, sequential adaptation via online learning, and memory-efficient implementation via perturbation-based learning are important