“KAN，你能听到我吗？”探索用于口语语言理解的Kolmogorov-Arnold网络
"KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding
AI总结：
本研究首次将Kolmogorov-Arnold网络应用于口语语言理解，通过五种配置集成KAN层，发现其可替代线性层并在多数数据集上达到相当或更优性能，同时揭示了KAN与线性层对原始波形关注区域的差异。
摘要：
Kolmogorov-Arnold网络（KANs）最近作为传统神经架构的一种有前景的替代方案出现，但其在语音处理中的应用仍未得到充分探索。本研究首次对KANs在口语语言理解（SLU）任务中的应用进行了调查。我们在两个数据集上使用2D-CNN模型进行实验，在密集块内以五种不同配置集成KAN层。表现最佳的设置将KAN层置于两个线性层之间，该设置被直接应用于基于Transformer的模型，并在五个复杂度递增的SLU数据集上进行了评估。我们的结果表明，KAN层可以有效替代线性层，在大多数情况下达到相当或更优的性能。最后，我们提供了关于Transformer之上的KAN层与线性层如何不同地关注原始波形输入区域的见解。
英文摘要：
Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional neural architectures, yet their application to speech processing remains under explored. This work presents the first investigation of KANs for Spoken Language Understanding (SLU) tasks. We experiment with 2D-CNN models on two datasets, integrating KAN layers in five different configurations within the dense block. The best-performing setup, which places a KAN layer between two linear layers, is directly applied to transformer-based models and evaluated on five SLU datasets with increasing complexity. Our results show that KAN layers can effectively replace the linear layers, achieving comparable or superior performance in most cases. Finally, we provide insights into how KAN and linear layers on top of transformers differently attend to input regions of the raw waveforms.
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