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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.
Introduces a Lindblad-inspired multi-timescale reservoir architecture that separates rotation and dissipation for independent control of mixing, memory, and stability, achieving competitive results on benchmarks like NARMA-20 and Lorenz-63.
This paper proposes a method using reservoir computing to recycle computational processes of dynamic programming for combinatorial optimization problems, achieving improved approximation accuracy and reduced computation time on traveling salesman and subset sum problems.
This paper proposes 'learnable novelty' as a unified principle underlying intelligence across statistics, complex systems, and adaptive behavior, and provides a differentiable estimator using reservoir computing that demonstrates complexity generation, abstraction, and exploration without supervision.
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 morphologically tunable mycelium chips as a substrate for physical reservoir computing, leveraging the adaptive growth of fungal networks.
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.
This article discusses how reservoir computing, a simplified type of neural network often called AI's cousin, is being applied to control soft robots, offering efficient and adaptive control solutions.
This paper presents a hybrid quantum-classical pipeline using neutral-atom reservoir computing and auto-encoders for medical image classification, specifically for polyp detection. It addresses quantum measurement non-differentiability with a surrogate model to enable end-to-end training.