ChebMixer:使用MLP混合器学习高效的图形表示
概括
ChebMixer是一种新的图形神经网络架构,通过使用切比舍夫多项式来提高图形表示学习,以有效地提取令牌. 这种方法可以提高节点分类和医疗图像细分任务的性能.
科学领域:
- 图形神经网络 (GNN) 是一个神经网络.
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 图形变压器实现了高性能,但却受到二次复杂性的困扰.
- 图形MLP混合器提供效率,但受到慢令牌提取的限制.
研究的目的:
- 介绍ChebMixer,一个新的图形MLP混合器架构.
- 提高图形表示学习的效率和性能.
主要方法:
- 使用快速的基于Chebyshev多项式的光谱过来实现多尺度节点表示.
- 使用MLP Mixer来改进节点表示.
- 使用切比舍夫插值汇总多尺度表示.
主要成果:
- ChebMixer在同质和异构图节点分类方面取得了显著的改进.
- 与NAGphormer相比,在同质和异构图上分别获得了1.45%和4.15%的平均性能增长.
- 与VM-UNet.net相比,医疗图像细分性能提高了1.39%.
结论:
- ChebMixer提供强大的表示功能和快速计算.
- 该架构有效地提取下游任务的信息节点表示.
- ChebMixer在各种图形挖掘和细分任务中显示了广泛的适用性.
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