arXiv Machine Learning July 28, 2026 By Denis Mayr Lima Martins, Gottfried Vossen

Queryable Self-Organizing Maps: A Database Abstraction for Topology-Driven Data Exploration

Read the original on arXiv Machine Learning →

arXiv:2607. 22843v1 Announce Type: cross Abstract: Self-Organizing Maps (SOMs) have long been used as exploratory tools for high-dimensional data: they organize objects into a two-dimensional topology that reveals clusters, gradients, sparse regions, dense regions, and boundaries.

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.

data-engineering

arXiv Machine Learning

Jul 13

Cluster and then Embed: A Modular Approach for Visualization

arXiv:2509. 03373v2 Announce Type: replace Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure.

By Elizabeth Coda, Ery Arias-Castro, Gal Mishne

rag

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 AI

Jul 10

TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing

arXiv:2510. 04100v2 Announce Type: replace-cross Abstract: Topological mapping offers a compact and robust representation for navigation, but progress in the field is hindered by the lack of standardized evaluation metrics, datasets, and protocols.

By Jiaming Wang, Jizhuo Chen, Diwen Liu, Harold Soh

benchmarks

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

Sep 3

From topology learning to graph generation: A unifying perspective

The article reviews the problem of learning graph structures from data, noting that research has traditionally split into two paths: inferring the topology of a single graph from observations on it, and learning a generative distribution from multiple observed graphs to sample new ones. It proposes a unified framework that treats both as inverse problems of a common graph generation process, reviews key methods, and discusses their interrelations, strengths, and limitations. The review highlights opportunities for cross‑paradigm integration and outlines future research directions.

By Xiaowen Dong, Hoi-To Wai, Siheng Chen, Laura Toni, Dorina Thanou

One email a morning, machine-written

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Cluster and then Embed: A Modular Approach for Visualization

torchsom: The Reference PyTorch Library for Self-Organizing Maps

Unreduced Persistence Diagrams for Topological Machine Learning

TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing

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From topology learning to graph generation: A unifying perspective