# Phase Transition Adaptation

arXiv: https://arxiv.org/abs/2104.10132 · HTML: https://mlsanity.com/paper/2104.10132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Fast and Deep Graph Neural Networks](https://mlsanity.com/paper/1911.08941/) (2019)
- [Determination of the edge of criticality in echo state networks through Fisher information maximization](https://mlsanity.com/paper/1603.03685/) (2016)
- [Investigating echo state networks dynamics by means of recurrence analysis](https://mlsanity.com/paper/1601.07381/) (2016)

## 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.