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Found 9 papers, 3 papers with code
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes.
Hodge-Compositional Edge Gaussian Processes
We propose principled Gaussian processes (GPs) for modeling functions defined over the edge set of a simplicial 2-complex, a structure similar to a graph in which edges may form triangular faces.
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
Generalized signals on simplicial complexes
Topological signal processing (TSP) over simplicial complexes typically assumes observations associated with the simplicial complexes are real scalars.
Convolutional Learning on Simplicial Complexes
We propose a simplicial complex convolutional neural network (SCCNN) to learn data representations on simplicial complexes.
Convolutional Filtering in Simplicial Complexes
This paper proposes convolutional filtering for data whose structure can be modeled by a simplicial complex (SC).
Simplicial Convolutional Filters
We study linear filters for processing signals supported on abstract topological spaces modeled as simplicial complexes, which may be interpreted as generalizations of graphs that account for nodes, edges, triangular faces etc.
Simplicial Convolutional Neural Networks
Graphs can model networked data by representing them as nodes and their pairwise relationships as edges.
Finite Impulse Response Filters for Simplicial Complexes
In this paper, we study linear filters to process signals defined on simplicial complexes, i. e., signals defined on nodes, edges, triangles, etc.
