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Benjamin-Walker merged 1 commit into from
Feb 17, 2026
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Update README to point to DataSig SLiCEs repository #42

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19 changes: 17 additions & 2 deletions README.md
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<h1 align="center">Structured Linear Controlled Differential Equations: Maximally Expressive and Parallel-in-Time Sequence Models</h1>
<h2 align="center"><em>Don't be Dense, SLiCE the Cost!</em></h2>

## New SLiCEs Repository

The actively maintained SLiCEs codebase now lives at [datasig-ac-uk/SLiCEs](https://github.com/datasig-ac-uk/SLiCEs). For new projects, this is the recommended repository.

Compared to this repository, the new codebase is designed as a standalone package with cleaner installation and usage for modern workflows:

- 🏛️ Official maintained implementation
- ✅ Pip-installable package support
- ⚡ Parallel-in-time SLiCE implementations

Use this repository primarily for reproducing the original experiments described in the paper.

## Introduction

Structured Linear Controlled Differential Equations (SLiCEs) are a new class of sequence models that combine the maximal expressivity (i.e., universality) of dense, input-dependent, state-transition matrices with the computational efficiency of structured alternatives. The SLiCE framework generalises and extends architectures such as DeltaNet (which uses a diagonal-plus-low-rank structure) and input-dependent block-diagonal linear RNNs, alongside introducing two novel variants based on sparsity and the Walsh–Hadamard transform. Unlike S4 and Mamba, which rely on diagonal state-transition matrices, SLiCEs with block-diagonal, sparse, or Walsh–Hadamard structures match the maximal expressivity of dense matrices while being significantly cheaper to compute.

**Highlights:**
Expand Down Expand Up @@ -63,10 +77,11 @@ Below is a comparison of dense LNCDEs (DE-LNCDEs), diagonal LNCDEs (D-LNCDEs), d
| BD-LNCDEs | \$\mathcal{O}(d\_h \sum\_j b\_j^2)\$ | \$\mathcal{O}(n d\_h \sum\_j b\_j^2)\$ | \$\mathcal{O}(\log(n), d\_h \sum\_j b\_j^2)\$ | Yes |



## Using this Repository

This repository provides scripts to reproduce the $A_5$ and regular language experiments from the paper. Results on the UEA multivariate time series classification archive are implemented in the [Log-NCDE github repo](https://github.com/Benjamin-Walker/log-neural-cdes). All experiments in this repo are configured using JSON files in the `experiment_configs/` directory.
This repository provides scripts to reproduce the original $A_5$ and regular language experiments from the paper. Results on the UEA multivariate time series classification archive are implemented in the [Log-NCDE github repo](https://github.com/Benjamin-Walker/log-neural-cdes).

If you are looking for the current, pip-installable SLiCEs implementation, use [datasig-ac-uk/SLiCEs](https://github.com/datasig-ac-uk/SLiCEs). All experiments in this repo are configured using JSON files in the `experiment_configs/` directory.

For example, to run a block-diagonal LNCDE on the cycle navigation task, use the following configuration:

Expand Down