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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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

Aug 18

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 AI

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Predicting Transmembrane Protein Topology from 3D Structure

This paper introduces a new method for predicting transmembrane protein topology by employing the graph neural network SchNet. The model is trained on the same dataset used for DeepTMHMM, using 5‑fold cross‑validation, and incorporates all atom‑level embeddings rather than just sequence or alpha‑carbon features. Results indicate that GNNs hold significant promise for topological predictions without relying on pre‑trained weights.

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arXiv Machine Learning

Jul 24

torchsom: The Reference PyTorch Library for Self-Organizing Maps

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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

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