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Literature review about the theoretical expressive capabilities of (Recurrent) Neural Networks.

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Turing Completeness of Neural Network Architectures: a Review

This repository has been created with the sole objective of sharing a comprehensive literature review about the expressive capabilities of different Neural Network architectures from a theoretical perspective.

Table of Contents

Introduction

This repository provides resources related to the study of Turing Completeness in Neural Networks. The materials include a comprehensive literature review about this topic, along with an accompanying presentation suitable for an academic seminar.

Repository Structure

The repository is organized as follows:

.
├── article
│   ├── article.pdf
│   └── src
│       ├── main.tex
│       └── references.bib
├── presentation
│   ├── slides.pdf
│   └── src
│       ├── cgru.jpg
│       ├── gru.jpg
│       ├── slides.md
│       ├── theme.css
│       └── transformers.png
├── README.md
├── LICENSE
└── CITATION.cff
  • article/: Contains the article PDF and its source files.
  • presentation/: Contains the presentation PDF and its source files.
  • README.md: This README file.
  • LICENSE: The license under which this project is distributed.
  • CITATION.cff: Citation information for this repository.

Getting Started

To explore the contents of this repository, follow the steps below:

  1. Clone the Repository:

    git clone https://github.com/dambrosidenis/Turing_Completeness_of_Neural_Network_Architectures.git
    cd Turing_Completeness_of_Neural_Network_Architectures
  2. View the Article:

    Open the article/article.pdf file to read the full article.

  3. View the Presentation:

    Open the presentation/slides.pdf file to review the presentation.

Article

The detailed article explaining the Turing Completeness of neural networks can be found in the article/ directory:

The source files for the article, including LaTeX and bibliography, are located in article/src/. The article can be compiled through pdflatex and bibtex.

Presentation

The presentation summarizing the key findings can be found in the presentation/ directory:

The source files for the presentation, including images and styles, are located in presentation/src/. The presentation is written in markdown format using the Marp extension.

Citation

If you use this work in your research, please cite it as follows:

@software{D_Ambrosi_Turing_Completeness_of_2023,
    author = {D'Ambrosi, Denis},
    month = feb,
    title = {{Turing Completeness of Neural Networks Architectures: a Review}},
    url = {https://github.com/dambrosidenis/Turing_Completeness_of_Neural_Network_Architectures/},
    version = {1.0.0},
    year = {2023}
}

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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Literature review about the theoretical expressive capabilities of (Recurrent) Neural Networks.

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