# Residual Reservoir Memory Networks

arXiv: https://arxiv.org/abs/2508.09925 · HTML: https://mlsanity.com/paper/2508.09925/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 13 Aug 2025.

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

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Dynamical Systems as Temporal Feature Spaces](https://mlsanity.com/paper/1907.06382/) (2019)
- [Input-to-State Representation in linear reservoirs dynamics](https://mlsanity.com/paper/2003.10585/) (2020)
- [Edge of stability echo state networks](https://mlsanity.com/paper/2308.02902/) (2023)

## Built on by (most cited first, 2 of 2)

- [ParalESN: Enabling parallel information processing in Reservoir Computing](https://mlsanity.com/paper/2601.22296/) (2026)
- [Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks](https://mlsanity.com/paper/2508.21172/) (2025)

## Abstract (arXiv)

We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs). ResRMN combines a linear memory reservoir with a non-linear reservoir, where the latter is based on residual orthogonal connections along the temporal dimension for enhanced long-term propagation of the input. The resulting reservoir state dynamics are studied through the lens of linear stability analysis, and we investigate diverse configurations for the temporal residual connections. The proposed approach is empirically assessed on time-series and pixel-level 1-D classification tasks. Our experimental results highlight the advantages of the proposed approach over other conventional RC models.