https://github.com/hayden-donnelly/febam

PyTorch implementation of Feature Extracting Bidirectional Associative Memory (FEBAM).
https://github.com/hayden-donnelly/febam

cognitive-modeling neural-network pytorch

Last synced: about 10 hours ago
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PyTorch implementation of Feature Extracting Bidirectional Associative Memory (FEBAM).

• Host: GitHub
• URL: https://github.com/hayden-donnelly/febam
• Owner: hayden-donnelly
• License: mit
• Created: 2023-05-16T23:27:06.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-27T06:06:15.000Z (8 months ago)
• Last Synced: 2024-03-27T07:25:14.943Z (8 months ago)
• Topics: cognitive-modeling, neural-network, pytorch
• Language: Jupyter Notebook
• Homepage: https://ieeexplore.ieee.org/document/4371210
• Size: 152 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# FEBAM

PyTorch implementation of Feature Extracting Bidirectional Associative Memory (FEBAM).



## Docker Environment

Building image:

```

docker-compose build

```

Starting container/environment:

```

docker-compose up -d

```

Opening a shell in container:

```

docker-compose exec febam bash

```

Instead of opening a shell, you can also go to http://localhost:8888/ to access a Jupyter Lab instance running inside the container.

Stopping container/environment:

```

docker-compose down

```

## Citation

```bibtex

@INPROCEEDINGS{chartier_giguere_renaud_lina_proulx,

    author={Chartier, Sylvain and Giguere, Gyslain and Renaud, Patrice and Lina, Jean-Marc and Proulx, Robert},

    booktitle={2007 International Joint Conference on Neural Networks}, 

    title={FEBAM: A Feature-Extracting Bidirectional Associative Memory}, 

    year={2007},

    pages={1679-1684},

    doi={10.1109/IJCNN.2007.4371210}

}

```