TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning

Abstract:Over the last decade, neural networks have been applied to an increasingly diverse range of applications, including data with rich geometric, topological, or symmetry-related structure. As a result, researchers have increasingly drawn inspiration from topology, algebra, and geometry. Despite this rich algorithmic development, the supporting software ecosystem remains fragmented. Many important methods exist only as research prototypes in unmaintained repositories. We address this by introducing Topology, Algebra, and Geometry Torch (TAGTorch), an open-source, PyTorch-based library that unifies tools inspired by topology, algebra, and geometry, including data-preprocessing methods, architectures, training techniques, and model analysis tools. We describe the design philosophy of TAGTorch and then discuss its current architecture and capabilities, highlighting areas where it can fill gaps in the current software ecosystem. We conclude with a discussion of our future development priorities for the library.

Machine Learning (cs.LG)

arXiv:2607.28755 [cs.LG]

(or arXiv:2607.28755v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.28755 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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