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.
The Geometric Space, Architecture and Learning Objective for Large Pre-Trained Models (GALOP) workshop is an accepted half-day KDD 2026 workshop that examines how geometric principles can make large pretrained models more expressive, robust, interpretable, and efficient.
Meng-Lin Yang, Jia-Hong Liu, Lucas Vinh Tran et al.· Proceedings of the 32nd ACM...· 1 citation
This paper initiates a systematic approach to handling mathematical data structured as (truncated) infinite $q-series, or equivalently, infinite series of integers, and demonstrates that neural networks can reliably extract essential topological information, such as homology class and underlying graph structure, direct...
Brandon Robinson, Shimal Harichurn, Fabian Ruehle et al.· 0 citations
The Active Learning Framework (ALF), an open-source Python package designed to streamline the design and deployment of MLIP training datasets on High Performance Computing resources, is introduced, illustrating ALF’s effectiveness in compiling datasets that capture essential chemical and structural regimes.
V. Grizzi, P. Lohr, Nikita Fedik et al.· Journal of Chemical Theory a...· 1 citation
Morphological transforms are long-standing tools for shape and mask processing, but the de facto reference implementation in the Python ecosystem, i.e. scipy.ndimage, is CPU-only, single-array, and therefore unusable inside a GPU training loop without an expensive device-to-host round trip. GPU vision libraries built o...
This thesis builds on an existing diagnostics toolkit mainly for t-SNE and UMAP and turns it into a more accessible package for interested practitioners, while also extending it with diagnostics tools.
Kasra Amirani, S. Huisman, E. V. van Nieuwenburg· 0 citations
This tutorial provides a comprehensive introduction to rotational equivariance, starting from the physical and geometric intuition behind coordinate independence and building up the necessary machinery from geometric deep learning, group theory, and representation theory.
Peter Lippmann, Fred A. Hamprecht· 0 citations
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