# Attention-Enhanced Reservoir Computing

arXiv: https://arxiv.org/abs/2312.16503 · HTML: https://mlsanity.com/paper/2312.16503/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Designing spontaneous behavioral switching via chaotic itinerancy](https://mlsanity.com/paper/2002.08332/) (2020)
- [Adaptive model selection in photonic reservoir computing by reinforcement learning](https://mlsanity.com/paper/2004.12575/) (2020)

## Built on by (most cited first, 4 of 4)

- [Reservoir observer enhanced with residual calibration and attention mechanism](https://mlsanity.com/paper/2604.08592/) (2026)
- [Reservoir Computing as a Language Model](https://mlsanity.com/paper/2507.15779/) (2025)
- [Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computing](https://mlsanity.com/paper/2510.14287/) (2025)
- [Adaptive Sensing of Continuous Physical Systems for Machine Learning](https://mlsanity.com/paper/2603.03650/) (2026)

## Abstract (arXiv)

Photonic reservoir computing has been successfully utilized in time-series prediction as the need for hardware implementations has increased. Prediction of chaotic time series remains a significant challenge, an area where the conventional reservoir computing framework encounters limitations of prediction accuracy. We introduce an attention mechanism to the reservoir computing model in the output stage. This attention layer is designed to prioritize distinct features and temporal sequences, thereby substantially enhancing the prediction accuracy. Our results show that a photonic reservoir computer enhanced with the attention mechanism exhibits improved prediction capabilities for smaller reservoirs. These advancements highlight the transformative possibilities of reservoir computing for practical applications where accurate prediction of chaotic time series is crucial.