# Topological Understanding of Neural Networks, a survey

arXiv: https://arxiv.org/abs/2301.09742 · HTML: https://mlsanity.com/paper/2301.09742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 23 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [Topology of deep neural networks](https://mlsanity.com/paper/2004.06093/) (2020)
- [Tropical Geometry of Deep Neural Networks](https://mlsanity.com/paper/1805.07091/) (2018)

## Abstract (arXiv)

We look at the internal structure of neural networks which is usually treated as a black box. The easiest and the most comprehensible thing to do is to look at a binary classification and try to understand the approach a neural network takes. We review the significance of different activation functions, types of network architectures associated to them, and some empirical data. We find some interesting observations and a possibility to build upon the ideas to verify the process for real datasets. We suggest some possible experiments to look forward to in three different directions.