A package for recurrent neural networks in PyTorch
Project description
torchrecurrent
Pytorch compatible implementation of various recurrent layers
found in the literature.
Disclaimer: torchrecurrent is an independent project
and is not affiliated with the PyTorch project or Meta AI.
The name reflects compatibility with PyTorch, not any official endorsement.
Installation
pip install torchrecurrent
Features
| Short name | Publication venue | Official implementation |
|---|---|---|
| AntisymmetricRNN/GatedAntisymmetricRNN | ICLR 2019 | – |
| ATR | EMNLP 2018 | bzhangGo/ATR |
| BR/BRC | PLOS ONE 2021 | nvecoven/BRC |
| CFN | ICLR 2017 | – |
| coRNN | ICLR 2021 | tk-rusch/coRNN |
| FastRNN/FastGRNN | NeurIPS 2018 | Microsoft/EdgeML |
| FSRNN | NeurIPS 2017 | amujika/Fast-Slow-LSTM |
| IndRNN | CVPR 2018 | Sunnydreamrain/IndRNN_Theano_Lasagne |
| JANET | arXiv 2018 | JosvanderWesthuizen/janet |
| LEM | ICLR 2022 | tk-rusch/LEM |
| LiGRU | IEEE Transactions on Emerging Topics in Computing 2018 | mravanelli/theano-kaldi-rnn |
| LightRU | MDPI Electronics 2023 | – |
| MinimalRNN | NeurIPS 2017 | – |
| MultiplicativeLSTM | Workshop ICLR 2017 | benkrause/mLSTM |
| MGU | International Journal of Automation and Computing 2016 | – |
| MUT1/MUT2/MUT3 | ICML 2015 | – |
| NAS | arXiv 2016 | tensorflow_addons/rnn |
| OriginalLSTM | Neural Computation 1997 | - |
| PeepholeLSTM | JMLR 2002 | – |
| RAN | arXiv 2017 | kentonl/ran |
| RHN | ICML 2017 | jzilly/RecurrentHighwayNetworks |
| SCRN | ICLR 2015 | facebookarchive/SCRNNs |
| SGRN | IET 2018 | – |
| STAR | IEEE Transactions on Pattern Analysis and Machine Intelligence 2022 | 0zgur0/STAckable-Recurrent-network |
| Typed RNN / GRU / LSTM | ICML 2016 | – |
| UGRNN | ICLR 2017 | - |
| UnICORNN | ICML 2021 | tk-rusch/unicornn |
| WMCLSTM | Neural Networks 2021 | – |
See also
LuxRecurrentLayers.jl: Provides recurrent layers for Lux.jl in Julia.
RecurrentLayers.jl: Provides recurrent layers for Flux.jl in Julia.
ReservoirComputing.jl: Reservoir computing utilities for scientific machine learning. Essentially gradient free trained recurrent neural networks.
License
This project’s own code is distributed under the MIT License (see LICENSE). The primary intent of this software is academic research.
Third-party Attributions
Some cells are re-implementations of published methods that carry their own licenses:
- NASCell: originally available under Apache 2.0 — see LICENSE-Apache2.0.txt.
Please consult each of those licenses for your obligations when using this code in commercial or closed-source settings.
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