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This paper introduces a deterministic hyperparameter selection method for reservoir computing using free-probability kernels, which eliminates the need for resource-intensive rollouts and achieves performance similar to exhaustive search with significantly lower cost.
Proposes a perturbation-based learning rule for online self-supervised learning in echo state networks that avoids reservoir-size-dependent variance growth by decomposing the learning cost and perturbing only the input-dependent component.
This paper introduces FRESCO, an Echo State Network architecture operating entirely in the frequency domain to achieve O(N) complexity for dense recurrent updates, matching state-of-the-art performance on benchmarks while reducing computational costs.
Introduces EARLY, an evolutionary framework for evolving multi-reservoir Echo State Networks that outperforms random search on temporal learning tasks and exhibits task-dependent structural differences.