arXiv AI

Recovering topological information of light by topological learning

arXiv AI

Jun 30

TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging

arXiv:2606. 29763v1 Announce Type: cross Abstract: Topological data analysis (TDA), particularly persistent homology (PH), captures geometric structural properties in medical images (e.

By Guangyu Meng, Pengfei Gu, Xueyang Li, Yiyu Shi, Erin Wolf Chambers, Danny Z. Chen

llms agents

arXiv AI

Jun 29

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation

arXiv:2606. 28268v1 Announce Type: cross Abstract: Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models.

By Ali Zia, Usman Ali, Abdul Rehman, Umer Ramzan, Kang Han, Muhammad Faheem, Shahnawaz Qureshi, Wei Xiang

computer-vision benchmarks

arXiv Machine Learning

Jun 26

Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery

arXiv:2606. 26204v1 Announce Type: new Abstract: Floods frequently impact regions around the world.

By Sophia Li, Max Zhao, Raghu G. Raj, Tianyu Chen

llms computer-vision safety

arXiv Machine Learning

Jul 16

Quantum Topological Data Encoding

arXiv:2607. 13847v1 Announce Type: cross Abstract: Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations.

By Adam Weso{\l}owski, Dimitrios Thanos, Daniel Leykam, Lirand\"e Pira

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 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

Jun 5

Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis

arXiv:2606. 06342v1 Announce Type: cross Abstract: Topological Data Analysis (TDA) offers a principled, intrinsic lens for comparing neural representations.

By Yan Wang, Tianyang Hu

llms benchmarks

arXiv AI

Jun 9

Topological Neural Operators

arXiv:2606. 09806v1 Announce Type: cross Abstract: We introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or edges to topological domains.

By Lennart Bastian, Samuel Leventhal, Mustafa Hajij, Tolga Birdal

benchmarks

arXiv Machine Learning

2d ago

TopTimeNet: Topologically-assisted time-series classification model

arXiv:2609.39792v2 Announce Type: new Abstract: Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering. Yet, end-to-end learned arch...

By Sharareh Sayyad, Sophia Bazzi

llms rag benchmarks

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

llms rag benchmarks

arXiv Machine Learning

Aug 18

Machine Learning Approaches to Decoding Topological Quantum Codes

arXiv:2608. 15760v1 Announce Type: cross Abstract: Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information.

By Changwon Lee, Tak Hur, Jeongwoo Jae, Daniel K. Park

benchmarks

arXiv Machine Learning

Jul 22

Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration

arXiv:2505. 04346v2 Announce Type: replace Abstract: Clustering aims at partitioning data points into groups of similar objects without knowing about the class labels.

By Arghya Pratihar, Kushal Bose, Swagatam Das

benchmarks

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