<ul><li>Copresheaf Topological Neural Networks (CTNNs) introduced as a deep learning framework for structured data like images, point clouds, graphs, meshes, and topological manifolds.</li><li>CTNNs are designed to address challenges in neural architecture design by leveraging copresheaves from algebraic topology.</li><li>The formulation of CTNNs provides a rich design space for theoretically sound and practically effective solutions, improving representation learning in deep learning models.</li><li>Empirical results show that CTNNs outperform conventional baselines in structured data benchmarks, particularly excelling in tasks requiring hierarchical or localized sensitivity.</li></ul>

Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework

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