Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing
Reservoir computing typically relies on large, randomly generated reservoirs, enabling simple, often linear readouts. Over the past two decades, most constructions have exploited the freedom to select the reservoir, constrained primarily by stability conditions based on state contraction or memory capacity. However, these designs are largely independent of the input data and learning objective, resulting in a trial-and-error methodology driven by randomness. In high dimensions, the reservoir acts as a random embedding of the input history, implicitly relying on Johnson--Lindenstrauss--type concentration phenomena to preserve information. In contrast, we develop reservoir design principles...
| Authors | G Manjunath, Juan-Pablo Ortega, Alma van der Merwe |
|---|---|
| Primary category | math.DS |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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