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.
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.
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.
To explore the contents of this repository, follow the steps below:
-
Clone the Repository:
git clone https://github.com/dambrosidenis/Turing_Completeness_of_Neural_Network_Architectures.git cd Turing_Completeness_of_Neural_Network_Architectures
-
View the Article:
Open the
article/article.pdf
file to read the full article. -
View the Presentation:
Open the
presentation/slides.pdf
file to review the presentation.
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
.
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.
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}
}
This project is licensed under the MIT License. See the LICENSE file for more details.