2 citations · 2 across the 8 of their papers we have counts for
8 papers
Combinatorial Network-Based Manifold Topological Deep Learning for Image Analysis
Alice Wachira, Xiang Liu, Zhe Su +3
Medical image analysis remains fundamentally challenging because of the intricate geometric and topological structures present in medical data. Conventional convolutional neural ne…
Weighted Hodge Laplacians on Manifolds with Boundary
Zhe Su, Yiying Tong, Guo-Wei Wei
The spectrum of the Hodge Laplacian on differential manifolds encodes rich topological and geometric information and thus provides a powerful tool for analyzing data on manifolds.…
Persistent Manifold Learning of Protein Properties
Xingjian Xu, Zhe Su, Guo-Wei Wei +1
Predicting how tightly two biomolecules bind remains a major challenge, in part because different interaction classes present dissimilar interfaces, from compact metal-coordinated…
A vector field induced de Rham-Hodge theory on manifolds
Zhe Su
We introduce a de Rham-Hodge framework induced by a vector field on a compact, oriented smooth manifold. Using a vector field induced bundle isomorphism on differential forms, we d…
Topological Data Analysis and Topological Deep Learning Beyond Persistent Homology -- A Review
Zhe Su, Xiang Liu, Layal Bou Hamdan +4
Topological data analysis (TDA) is a rapidly evolving field in applied mathematics and data science that leverages tools from topology to uncover robust, shape-driven insights in c…
Manifold Topological Deep Learning for Biomedical Data
Xiang Liu, Zhe Su, Yongyi Shi +3
Recently, topological deep learning (TDL), which integrates algebraic topology with deep neural networks, has achieved tremendous success in processing point-cloud data, emerging a…
Topology-preserving Hodge Decomposition in the Eulerian Representation
Zhe Su, Yiying Tong, Guo-Wei Wei
The Hodge decomposition is a fundamental result in differential geometry and algebraic topology, particularly in the study of differential forms on a Riemannian manifold. Despite e…
Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning
Zhe Su, Yiying Tong, Guo-Wei Wei
Recently, topological data analysis has become a trending topic in data science and engineering. However, the key technique of topological data analysis, i.e., persistent homology,…