arXiv Machine Learning
Exploring Learning Models for Topological Relationship Recognition from Image Data
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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.
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.
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.
arXiv Computer Vision
Sep 23
Combinatorial Network-Based Manifold Topological Deep Learning for Image Analysis
Combinatorial Network-Based Manifold Topological Deep Learning (CNMTDL) is a new framework that represents medical images as discrete manifolds and decomposes them into three Hodge components. Features from these components are concatenated and fed into a combinatorial complex architecture, enabling higher‑order message passing between 0‑cells and 2‑cells via attention‑based blocks. CNMTDL was evaluated on six 2D and 3D datasets from the MedMNIST v2 benchmark, showing improved performance for medical image analysis.
arXiv Machine Learning
1d ago
TopoEmbedX: A General Framework for Representation Learning on Topological Domains
arXiv:2609.37884v1 Announce Type: new Abstract: Topological structures such as simplicial complexes, hypergraphs, and cell complexes extend standard graph models by modeling higher-order relationship...
arXiv AI
Sep 16
Repurposing Unified Topological Signatures for Graph Representation Learning
arXiv:2609.17061v1 Announce Type: cross Abstract: Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn g...