Balancing accuracy with computational efficiency is paramount in machine learning, particularly when dealing with high-dimensional data, such as spatial-temporal datasets. This study introduces ST-MambaSync, an innovative framework that integrates a streamlined attention layer with a simplified state-space layer. The model achieves competitive accuracy in spatial-temporal prediction tasks. We delve into the relationship between attention mechanisms and the Mamba component, revealing that Mamba functions akin to attention within a residual network structure. This comparative analysis underpins the efficiency of state-space models, elucidating their capability to deliver superior performance at reduced computational costs.

翻译：摘要：在机器学习中，平衡准确性与计算效率至关重要，尤其是在处理高维数据（如时空数据集）时。本研究提出了ST-MambaSync，一种创新框架，该框架将精简的注意力层与简化的状态空间层相结合。该模型在时空预测任务中实现了具有竞争力的准确性。我们深入探究了注意力机制与Mamba组件之间的关系，揭示了Mamba在残差网络结构中起到类似注意力的作用。这一比较分析为状态空间模型的效率提供了理论基础，阐明了它们以更低计算成本实现优越性能的能力。