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

arXiv: https://arxiv.org/abs/2104.10010 · HTML: https://mlsanity.com/paper/2104.10010/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [Convolutional Neural Fabrics](https://mlsanity.com/paper/1606.02492/) (2016)
- [Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks](https://mlsanity.com/paper/1706.00046/) (2017)

## Abstract (arXiv)

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.