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Papamarkou, T., Birdal, T., Bronstein, M., Carlsson, G., Curry, J., Gao, Y., Hajij, M., Kwitt, R., Liò, P., Di Lorenzo, P., Maroulas, V., Miolane, N., Nasrin, F., Natesan Ramamurthy, K., Rieck, B., Scardapane, S., T. Schaub, M., Veličković, P., Wang, B., Wang, Y., Wei, G., Zamzmi, G.
This paper explores topological deep learning as a complementary approach to graph and geometric learning, outlining key open problems, potential solutions, and future research directions in the field.
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