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.
@article{arxiv.2402.08871,
title = {Position: Topological Deep Learning is the New Frontier for Relational Learning},
author = {Theodore Papamarkou and Tolga Birdal and Michael Bronstein and Gunnar Carlsson and Justin Curry and Yue Gao and Mustafa Hajij and Roland Kwitt and Pietro Liò and Paolo Di Lorenzo and Vasileios Maroulas and Nina Miolane and Farzana Nasrin and Karthikeyan Natesan Ramamurthy and Bastian Rieck and Simone Scardapane and Michael T. Schaub and Petar Veličković and Bei Wang and Yusu Wang and Guo-Wei Wei and Ghada Zamzmi},
journal= {arXiv preprint arXiv:2402.08871},
year = {2024}
}
Comments
Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024