NeuRaLaTeX: A machine learning library written in pure LaTeX

• URL: http://arxiv.org/abs/2503.24187v2
• Date: Wed, 02 Apr 2025 10:46:42 GMT
• Title: NeuRaLaTeX: A machine learning library written in pure LaTeX
• Authors: James A. D. Gardner, Will Rowan, William A. P. Smith,
• Abstract summary: We introduce NeuRaLa, which we believe to be the first deep learning library written entirely in rhyme.<n>As part of your document you can specify the architecture of a neural network and its loss functions.<n>When the document is compiled, the compiler will generate or load training data, train the network, run experiments, and generate figures.<n>The paper took 48 hours to compile and the entire source code for NeuRaLa is contained within the source code of the paper.
• Score: 15.978130916451295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce NeuRaLaTeX, which we believe to be the first deep learning library written entirely in LaTeX. As part of your LaTeX document you can specify the architecture of a neural network and its loss functions, define how to generate or load training data, and specify training hyperparameters and experiments. When the document is compiled, the LaTeX compiler will generate or load training data, train the network, run experiments, and generate figures. This paper generates a random 100 point spiral dataset, trains a two layer MLP on it, evaluates on a different random spiral dataset, produces plots and tables of results. The paper took 48 hours to compile and the entire source code for NeuRaLaTeX is contained within the source code of the paper. We propose two new metrics: the Written In Latex (WIL) metric measures the proportion of a machine learning library that is written in pure LaTeX, while the Source Code Of Method in Source Code of Paper (SCOMISCOP) metric measures the proportion of a paper's implementation that is contained within the paper source. We are state-of-the-art for both metrics, outperforming the ResNet and Transformer papers, as well as the PyTorch and Tensorflow libraries. Source code, documentation, videos, crypto scams and an invitation to invest in the commercialisation of NeuRaLaTeX are available at https://www.neuralatex.com

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