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Topological Continual Learning with Wasserstein Distance and Barycenter
Neuroscience findings suggest that continual learning success in the human brain is potentially associated with its modular structure and memory consolidation mechanisms.
Topological Classification in a Wasserstein Distance Based Vector Space
The proposed vector space is based on the Wasserstein distance between persistence barcodes.
Fast Topological Clustering with Wasserstein Distance
The topological patterns exhibited by many real-world networks motivate the development of topology-based methods for assessing the similarity of networks.
Topological Learning for Brain Networks
This paper proposes a novel topological learning framework that integrates networks of different sizes and topology through persistent homology.
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