# guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network

<https://repo.masters-masters.com/en/repos/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network>

[Home](https://repo.masters-masters.com/en) › [guillaume-chevalier](https://repo.masters-masters.com/en/owners/guillaume-chevalier) › [Linear-Attention-Recurrent-Neural-Network](https://repo.masters-masters.com/en/repos/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network)

guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network has 148 stars on GitHub. Its RepoMasters card: Prototype tier. A recurrent attention module…

| Field | Value |
| --- | --- |
| Owner | [guillaume-chevalier](https://repo.masters-masters.com/en/owners/guillaume-chevalier) |
| Stars | 148 |
| Forks | 32 |
| Language | [Jupyter Notebook](https://repo.masters-masters.com/en/languages/jupyter-notebook) |
| License | MIT |
| Created | 2018 |
| Topics | rnn, lstm, attention-mechanism, attention-model, attention-is-all-you-need, recurrent-neural-networks, pytorch |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network](https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network) |

## Repository

A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, ju…

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.