# A logifold structure on measure space

arXiv: https://arxiv.org/abs/2405.05492 · HTML: https://mlsanity.com/paper/2405.05492/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 9 May 2024.

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

- [A Survey on Ensemble Learning under the Era of Deep Learning](https://mlsanity.com/paper/2101.08387/) (2021)
- [The Representation Theory of Neural Networks](https://mlsanity.com/paper/2007.12213/) (2020)
- [CIFAR-10: KNN-based Ensemble of Classifiers](https://mlsanity.com/paper/1611.04905/) (2016)
- [Efficient Adaptive Ensembling for Image Classification](https://mlsanity.com/paper/2206.07394/) (2022)
- [Noncommutative Geometry of Computational Models and Uniformization for Framed Quiver Varieties](https://mlsanity.com/paper/2201.05900/) (2022)

## Built on by (most cited first, 2 of 2)

- [Logifold: A Geometrical Foundation of Ensemble Machine Learning](https://mlsanity.com/paper/2407.16177/) (2024)
- [Laws of Learning Dynamics and the Core of Learners](https://mlsanity.com/paper/2602.05026/) (2026)

## Abstract (arXiv)

In this paper,we develop a local-to-global and measure-theoretical approach to understand datasets. The idea is to take network models with restricted domains as local charts of datasets. We develop the mathematical foundations for these structures, and show in experiments how it can be used to find fuzzy domains and to improve accuracy in data classification problems.