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

Proceedings of machine learning research Pub Date : 2024-07-01

Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, 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, Guo-Wei Wei, Ghada Zamzmi

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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.

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观点：拓扑深度学习是关系学习的新前沿。

拓扑深度学习（TDL）是一个快速发展的领域，利用拓扑特征来理解和设计深度学习模型。本文认为TDL是关系学习的新前沿。TDL可以通过结合拓扑概念来补充图表示学习和几何深度学习，因此可以为各种机器学习设置提供自然选择。为此，本文从实践利益到理论基础等方面探讨了TDL的开放性问题。对于每个问题，它都概述了潜在的解决方案和未来的研究机会。同时，本文也作为对科学界积极参与TDL研究的邀请，以释放这一新兴领域的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of machine learning research

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