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观点:拓扑深度学习是关系学习的新前沿。

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

作者信息

Papamarkou Theodore, Birdal Tolga, Bronstein Michael, Carlsson Gunnar, Curry Justin, Gao Yue, Hajij Mustafa, Kwitt Roland, Liò Pietro, Di Lorenzo Paolo, Maroulas Vasileios, Miolane Nina, Nasrin Farzana, Ramamurthy Karthikeyan Natesan, Rieck Bastian, Scardapane Simone, Schaub Michael T, Veličković Petar, Wang Bei, Wang Yusu, Wei Guo-Wei, Zamzmi Ghada

机构信息

Department of Mathematics, The University of Manchester, Manchester, UK.

Department of Computing, Imperial College London, London, UK.

出版信息

Proc Mach Learn Res. 2024 Jul;235:39529-39555.

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.

摘要

拓扑深度学习(TDL)是一个快速发展的领域,它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可以通过纳入拓扑概念来补充图表示学习和几何深度学习,从而为各种机器学习场景提供自然的选择。为此,本文讨论了TDL中的开放问题,从实际益处到理论基础。针对每个问题,本文概述了潜在的解决方案和未来的研究机会。同时,本文邀请科学界积极参与TDL研究,以释放这一新兴领域的潜力。

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本文引用的文献

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Topology-Aware Uncertainty for Image Segmentation.用于图像分割的拓扑感知不确定性
Adv Neural Inf Process Syst. 2024;36:8186-8207. Epub 2024 May 30.
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A topological deep learning framework for neural spike decoding.用于神经尖峰解码的拓扑深度学习框架。
Biophys J. 2024 Sep 3;123(17):2781-2789. doi: 10.1016/j.bpj.2024.01.025. Epub 2024 Feb 22.
3
Persistent spectral theory-guided protein engineering.持久光谱理论指导的蛋白质工程。
Nat Comput Sci. 2023 Feb;3(2):149-163. doi: 10.1038/s43588-022-00394-y. Epub 2023 Feb 20.
4
Topological deep learning based deep mutational scanning.基于拓扑深度学习的深度突变扫描。
Comput Biol Med. 2023 Sep;164:107258. doi: 10.1016/j.compbiomed.2023.107258. Epub 2023 Jul 17.
5
De novo design of protein structure and function with RFdiffusion.利用 RFdiffusion 从头设计蛋白质结构和功能。
Nature. 2023 Aug;620(7976):1089-1100. doi: 10.1038/s41586-023-06415-8. Epub 2023 Jul 11.
6
Everything is connected: Graph neural networks.万物皆相连:图神经网络。
Curr Opin Struct Biol. 2023 Apr;79:102538. doi: 10.1016/j.sbi.2023.102538. Epub 2023 Feb 9.
8
Generative hypergraph models and spectral embedding.生成超图模型和谱嵌入。
Sci Rep. 2023 Jan 11;13(1):540. doi: 10.1038/s41598-023-27565-9.
9
Graph neural networks for materials science and chemistry.用于材料科学与化学的图神经网络
Commun Mater. 2022;3(1):93. doi: 10.1038/s43246-022-00315-6. Epub 2022 Nov 26.

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