Advances in Set Function Learning: A Survey of Techniques and Applications

• URL: http://arxiv.org/abs/2501.14991v1
• Date: Fri, 24 Jan 2025 23:59:44 GMT
• Title: Advances in Set Function Learning: A Survey of Techniques and Applications
• Authors: Jiahao Xie, Guangmo Tong,
• Abstract summary: Set function learning has emerged as a crucial area in machine learning, addressing the challenge of modeling functions that take sets as inputs. This survey provides a comprehensive overview of the current development in set function learning, covering foundational theories, key methodologies, and diverse applications.
• Score: 16.57565552996801
• License:
• Abstract: Set function learning has emerged as a crucial area in machine learning, addressing the challenge of modeling functions that take sets as inputs. Unlike traditional machine learning that involves fixed-size input vectors where the order of features matters, set function learning demands methods that are invariant to permutations of the input set, presenting a unique and complex problem. This survey provides a comprehensive overview of the current development in set function learning, covering foundational theories, key methodologies, and diverse applications. We categorize and discuss existing approaches, focusing on deep learning approaches, such as DeepSets and Set Transformer based methods, as well as other notable alternative methods beyond deep learning, offering a complete view of current models. We also introduce various applications and relevant datasets, such as point cloud processing and multi-label classification, highlighting the significant progress achieved by set function learning methods in these domains. Finally, we conclude by summarizing the current state of set function learning approaches and identifying promising future research directions, aiming to guide and inspire further advancements in this promising field.

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