RecurrentLayers

RecurrentLayers.jl extends Flux.jl recurrent layers offering by providing implementations of additional recurrent layers not available in base deep learning libraries.

Features

The package offers multiple layers for Flux.jl. Currently there are 30+ cells implemented, together with multiple higher level implementations:

• Additional wrappers: Stacked RNNs,

Multiplicative RNN, and FastSlow.

Installation

You can install RecurrentLayers using either of:

using Pkg
Pkg.add("RecurrentLayers")

julia> ]
pkg> add RecurrentLayers

Citation

If you use RecurrentLayers.jl in your work, please consider citing

@misc{martinuzzi2025unified,
  doi = {10.48550/ARXIV.2510.21252},
  url = {https://arxiv.org/abs/2510.21252},
  author = {Martinuzzi,  Francesco},
  keywords = {Machine Learning (cs.LG),  Software Engineering (cs.SE),  FOS: Computer and information sciences,  FOS: Computer and information sciences},
  title = {Unified Implementations of Recurrent Neural Networks in Multiple Deep Learning Frameworks},
  publisher = {arXiv},
  year = {2025},
  copyright = {Creative Commons Attribution 4.0 International}
}

Contributing

Contributions are always welcome! We specifically look for :

• Recurrent cells you would like to see implemented
• Benchmarks
• Fixes for any bugs/errors
• Documentation, in any form: examples, how tos, docstrings

Please consider the following guidelines before opening a pull request:

• The code should be formatted according to the format file provided
• Variable names should be meaningful: please no single letter variables, and try to avoid double letters variables too. I know at the moment there are some in the codebase, but I will need a breaking change in order to fix the majority of them.
• The format file does not format markdown. If you are adding docs, or docstrings please take care of not going over 92 cols.

For any clarification feel free to contact me directly (@MartinuzziFrancesco) either in the julia slack, by email or X/bluesky.