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Found 4 papers, 1 papers with code
Persistence-based operators in machine learning
Artificial neural networks can learn complex, salient data features to achieve a given task.
Generalized Permutants and Graph GENEOs
In this paper we establish a bridge between Topological Data Analysis and Geometric Deep Learning, adapting the topological theory of group equivariant non-expansive operators (GENEOs) to act on the space of all graphs weighted on vertices or edges.
Beyond topological persistence: Starting from networks
Nowadays, data generation, representation and analysis occupy central roles in human society.
Combinatorics Category Theory
Comparing persistence diagrams through complex vectors
The natural pseudo-distance of spaces endowed with filtering functions is precious for shape classification and retrieval; its optimal estimate coming from persistence diagrams is the bottleneck distance, which unfortunately suffers from combinatorial explosion.
