Position Paper: Challenges and Opportunities in Topological Deep Learning

arXiv:2402.08871v1 Announce Type: new
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 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 …

abstract arxiv challenges concepts cs.lg deep learning design features graph graph representation natural opportunities paper representation representation learning stat.ml type