Data-Centric AI Requires Rethinking Data Notion
arXiv:2110.02491
Abstract
The transition towards data-centric AI requires revisiting data notions from mathematical and implementational standpoints to obtain unified data-centric machine learning packages. Towards this end, this work proposes unifying principles offered by categorical and cochain notions of data, and discusses the importance of these principles in data-centric AI transition. In the categorical notion, data is viewed as a mathematical structure that we act upon via morphisms to preserve this structure. As for cochain notion, data can be viewed as a function defined in a discrete domain of interest and acted upon via operators. While these notions are almost orthogonal, they provide a unifying definition to view data, ultimately impacting the way machine learning packages are developed, implemented, and utilized by practitioners.
References in corpus (8)
- Scikit-learn: Machine Learning in Python
- Neural Message Passing for Quantum Chemistry
- Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
- What are higher-order networks?
- Ologs: a categorical framework for knowledge representation
- Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
- A simple and complete discrete exterior calculus on general polygonal meshes
- Simplicial 2-Complex Convolutional Neural Nets