关于图形变压器用于解决图形问题的理论表达力
Giannis Nikolentzos1, Dimitrios Kelesis2, Michalis Vazirgiannis3
1Department of Informatics and Telecommunications, University of Peloponnese, Akadimaikou G.K. Vlachou Street, Tripoli, 22131, Greece.
概括
图形变压器是一种新的神经网络架构,显示图灵通用性,可以解决超出图形神经网络 (GNN) 能力的问题. 这些发现突显了它们在基于图形的机器学习任务中的潜力.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 变压器主导NLP和计算机视觉.
- 图形转换器 (GTs) 是信息传递图形神经网络 (MPNNs) 的新兴替代品.
- 与MPNN不同,GT的表达力未被充分探索.
研究的目的:
- 了解图形变压器架构的优势和局限性.
- 为了将图形变压器连接到分布式计算中的拥挤群组模型.
- 分析图形变压器的理论能力.
主要方法:
- 导出了图形变压器与拥挤群组模型之间的连接.
- 将分布式计算的理论结果翻译成图形转换器.
- 在分子数据集上经验评估图形变压器和MPNN.
主要成果:
- 深度为2的图形变压器在某些条件下证明了图灵通用性.
- 识别了能够解决MPNN难以解决的问题的图形变压器.
- 经验结果表明,GT有效地解决了MPNN失败的分子图任务.
结论:
- 图形转换器拥有比MPNN更大的表达力.
- 深度2图形变压器是图灵通用,提供显著的计算能力.
- 对于基于图形的复杂机器学习问题,GT显示出前景,特别是在化学领域.
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