A logifold structure on measure space
Not on the map. Why?
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.
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
5 of 30 references.
References · 5
- 2021A Survey on Ensemble Learning under the Era of Deep Learningcited by 47
- 2020The Representation Theory of Neural Networkscited by 19
- 2016CIFAR-10: KNN-based Ensemble of Classifierscited by 16
- 2022Efficient Adaptive Ensembling for Image Classificationcited by 8
- 2022Noncommutative Geometry of Computational Models and Uniformization for Framed Quiver Varietiescited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.
- 2026Laws of Learning Dynamics and the Core of Learners4 Feb 2026
- 2024Logifold: A Geometrical Foundation of Ensemble Machine Learning23 Jul 2024