Phase Transition Adaptation
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Abstract (arXiv)
Artificial Recurrent Neural Networks are a powerful information processing abstraction, and Reservoir Computing provides an efficient strategy to build robust implementations by projecting external inputs into high dimensional dynamical system trajectories. In this paper, we propose an extension of the original approach, a local unsupervised learning mechanism we call Phase Transition Adaptation, designed to drive the system dynamics towards the `edge of stability'. Here, the complex behavior exhibited by the system elicits an enhancement in its overall computational capacity. We show experimentally that our approach consistently achieves its purpose over several datasets.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 54 references that are in the map. First posted on arXiv 20 Apr 2021.
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References · 4
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2019Fast and Deep Graph Neural Networkscited by 35
- 2016Determination of the edge of criticality in echo state networks through Fisher information maximizationcited by 12
- 2016Investigating echo state networks dynamics by means of recurrence analysiscited by 8
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