# Recurrent Spectral Network (RSN): shaping the basin of attraction of a discrete map to reach automated classification

arXiv: https://arxiv.org/abs/2202.04497 · HTML: https://mlsanity.com/paper/2202.04497/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/1904.00310/) (2019)
- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Machine learning in spectral domain](https://mlsanity.com/paper/2005.14436/) (2020)
- [On the training of sparse and dense deep neural networks: less parameters, same performance](https://mlsanity.com/paper/2106.09021/) (2021)

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

- [How a student becomes a teacher: learning and forgetting through Spectral methods](https://mlsanity.com/paper/2310.12612/) (2023)
- [Complex Recurrent Spectral Network](https://mlsanity.com/paper/2312.07296/) (2023)
- [Estimating Global Input Relevance and Enforcing Sparse Representations with a Scalable Spectral Neural Network Approach](https://mlsanity.com/paper/2406.01183/) (2024)
- [Stochastic Gradient Descent-like relaxation is equivalent to Metropolis dynamics in discrete optimization and inference problems](https://mlsanity.com/paper/2309.05337/) (2023)

## Abstract (arXiv)

A novel strategy to automated classification is introduced which exploits a fully trained dynamical system to steer items belonging to different categories toward distinct asymptotic attractors. These latter are incorporated into the model by taking advantage of the spectral decomposition of the operator that rules the linear evolution across the processing network. Non-linear terms act for a transient and allow to disentangle the data supplied as initial condition to the discrete dynamical system, shaping the boundaries of different attractors. The network can be equipped with several memory kernels which can be sequentially activated for serial datasets handling. Our novel approach to classification, that we here term Recurrent Spectral Network (RSN), is successfully challenged against a simple test-bed model, created for illustrative purposes, as well as a standard dataset for image processing training.