arXiv Machine Learning August 3, 2026 By Brendan Kennedy, Tegan Emerson, Gregory Roek, Emilie Purvine, Henry Kvinge

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

Read the original on arXiv Machine Learning →

arXiv:2607. 28755v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 17

Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers

arXiv:2602. 07429v2 Announce Type: replace-cross Abstract: Boundary representation (B-rep) is the industry standard for computer-aided design (CAD).

By Yuanxu Sun, Yuezhou Ma, Haixu Wu, Guanyang Zeng, Muye Chen, Jianmin Wang, Mingsheng Long

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arXiv Machine Learning

1d ago

Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning

arXiv:2608. 15388v1 Announce Type: new Abstract: Topological deep learning (TDL) methods rely on lifting raw data into higher-order discrete domains such as simplicial complexes, cell complexes, and hypergraphs.

By Mathilde Papillon, Guillermo Bern\'ardez, \'Alvaro Ball\'on Barreiro, Marco Montagna, R\'emi Devaux, Antoine Jardin, Nina Miolane

arXiv Machine Learning

Jul 31

TopoFormer: Topology Meets Attention for Graph Learning

arXiv:2607. 28259v1 Announce Type: new Abstract: We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences.

By Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora, Baris Coskunuzer

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arXiv Machine Learning

Jul 24

torchsom: The Reference PyTorch Library for Self-Organizing Maps

arXiv:2510. 11147v2 Announce Type: replace-cross Abstract: This paper introduces torchsom, an open-source Python library that provides a reference implementation of the Self-Organizing Map (SOM) in PyTorch.

By Louis Berthier, Ahmed Shokry, Maxime Moreaud, Guillaume Ramelet, Eric Moulines

arXiv Machine Learning

Jun 18

Unreduced Persistence Diagrams for Topological Machine Learning

arXiv:2507. 07156v2 Announce Type: replace-cross Abstract: Supervised machine learning pipelines trained on features derived from persistent homology have been experimentally observed to ignore much of the information contained in a persistence diagram.

By Nicole Abreu, Parker B. Edwards, Francis Motta

arXiv Machine Learning

6d ago

Weaves, Wires, and Morphisms: Formalizing and Implementing the Algebra of Deep Learning

arXiv:2604. 07242v3 Announce Type: replace Abstract: Despite deep learning models running well-defined mathematical functions, we lack a formal mathematical framework for describing model architectures.

By Vincent Abbott, Gioele Zardini

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Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers

Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning

TopoFormer: Topology Meets Attention for Graph Learning

torchsom: The Reference PyTorch Library for Self-Organizing Maps

Unreduced Persistence Diagrams for Topological Machine Learning

Weaves, Wires, and Morphisms: Formalizing and Implementing the Algebra of Deep Learning