# BraidNet: procedural generation of neural networks for image classification problems using braid theory

Source: [BraidNet: procedural generation of neural networks for image classification problems using braid theory](<https://www.sota2.com/research/paper/68fa580a1e209131ce9aaaf0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:55:57.361Z

Authors: Olga Lukyanova, Oleg Nikitin, Alex Kunin

Published: 2021-04-20T14:40:30.000Z

## Abstract

In this article, we propose the approach to procedural optimization of a neural network, based on the combination of information theory and braid theory. The network studied in the article implemented with the intersections between the braid strands, as well as simplified networks (a network with strands without intersections and a simple convolutional deep neural network), are used to solve various problems of multiclass image classification that allow us to analyze the comparative effectiveness of the proposed architecture. The simulation results showed BraidNet's comparative advantage in learning speed and classification accuracy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.10010>)

[PDF](<https://arxiv.org/pdf/2104.10010.pdf>)