发表机构
Pacific Northwest National Laboratory; University of Texas at El Paso; University of Washington（西北太平洋国家实验室; 德克萨斯大学埃尔帕索分校; 华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对几何拓扑对称感知机器学习软件生态碎片化问题，推出基于PyTorch的开源库TAGTorch，统一相关工具并阐述其设计架构与未来开发重点。
AI 中文摘要
在过去十年中，神经网络已被应用于越来越多的多样化应用场景，包括具有丰富几何、拓扑或对称相关结构的数据。因此，研究人员日益从拓扑学、代数学和几何学中汲取灵感。尽管算法发展丰富，但支撑的软件生态系统仍处于碎片化状态，许多重要方法仅作为研究原型存在于未维护的代码仓库中。针对这一问题，我们推出Topology, Algebra, and Geometry Torch（TAGTorch），这是一个基于PyTorch的开源库，统一了受拓扑学、代数学和几何学启发的工具，包括数据预处理方法、架构、训练技术和模型分析工具。我们阐述了TAGTorch的设计理念，随后讨论其当前架构与功能，重点说明它可填补当前软件生态系统空白的领域，最后探讨该库未来的开发重点。
英文摘要
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.
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