Position: Topological Deep Learning is the New Frontier for Relational Learning

Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T Schaub, Petar Veličković, Bei Wang, Yusu Wang, Guowei Wei, Ghada Zamzmi
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:39529-39555, 2024.

Abstract

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-papamarkou24a, title = {Position: Topological Deep Learning is the New Frontier for Relational Learning}, author = {Papamarkou, Theodore and Birdal, Tolga and Bronstein, Michael M. and Carlsson, Gunnar E. and Curry, Justin and Gao, Yue and Hajij, Mustafa and Kwitt, Roland and Lio, Pietro and Di Lorenzo, Paolo and Maroulas, Vasileios and Miolane, Nina and Nasrin, Farzana and Natesan Ramamurthy, Karthikeyan and Rieck, Bastian and Scardapane, Simone and Schaub, Michael T and Veli\v{c}kovi\'{c}, Petar and Wang, Bei and Wang, Yusu and Wei, Guowei and Zamzmi, Ghada}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {39529--39555}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/papamarkou24a/papamarkou24a.pdf}, url = {https://proceedings.mlr.press/v235/papamarkou24a.html}, abstract = {Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.} }
Endnote
%0 Conference Paper %T Position: Topological Deep Learning is the New Frontier for Relational Learning %A Theodore Papamarkou %A Tolga Birdal %A Michael M. Bronstein %A Gunnar E. Carlsson %A Justin Curry %A Yue Gao %A Mustafa Hajij %A Roland Kwitt %A Pietro Lio %A Paolo Di Lorenzo %A Vasileios Maroulas %A Nina Miolane %A Farzana Nasrin %A Karthikeyan Natesan Ramamurthy %A Bastian Rieck %A Simone Scardapane %A Michael T Schaub %A Petar Veličković %A Bei Wang %A Yusu Wang %A Guowei Wei %A Ghada Zamzmi %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-papamarkou24a %I PMLR %P 39529--39555 %U https://proceedings.mlr.press/v235/papamarkou24a.html %V 235 %X Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.
APA
Papamarkou, T., Birdal, T., Bronstein, M.M., Carlsson, G.E., Curry, J., Gao, Y., Hajij, M., Kwitt, R., Lio, P., Di Lorenzo, P., Maroulas, V., Miolane, N., Nasrin, F., Natesan Ramamurthy, K., Rieck, B., Scardapane, S., Schaub, M.T., Veličković, P., Wang, B., Wang, Y., Wei, G. & Zamzmi, G.. (2024). Position: Topological Deep Learning is the New Frontier for Relational Learning. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:39529-39555 Available from https://proceedings.mlr.press/v235/papamarkou24a.html.

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