Position: Topological Deep Learning Is the New Frontier for Relational Learning
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
Text
Papamarkou et al. "Position: Topological Deep Learning Is the New Frontier for Relational Learning." International Conference on Machine Learning, 2024.Markdown
[Papamarkou et al. "Position: Topological Deep Learning Is the New Frontier for Relational Learning." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/papamarkou2024icml-position-a/)BibTeX
@inproceedings{papamarkou2024icml-position-a,
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 Veličković, Petar and Wang, Bei and Wang, Yusu and Wei, Guowei and Zamzmi, Ghada},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {39529-39555},
volume = {235},
url = {https://mlanthology.org/icml/2024/papamarkou2024icml-position-a/}
}