PRKAN：参数减少的科尔莫戈罗夫-阿诺德网络
PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks
AI总结：
针对现有科尔莫戈罗夫-阿诺德网络（KANs）参数过多的问题，本文提出PRKANs，通过多种方法减少KAN层参数数量，在MNIST和Fashion-MNIST数据集上性能优于现有KANs，带注意力机制的变体可与MLPs媲美。
摘要：
科尔莫戈罗夫-阿诺德网络（KANs）是神经网络架构的一项创新，在卷积神经网络（CNNs）、循环神经网络（RNNs）和Transformer等模型中，为多层感知机（MLPs）提供了一种有吸引力的替代方案。通过推进网络设计，KANs推动了突破性研究，并在涉及神经网络的各个科学领域实现了变革性应用。然而，现有KANs的网络层通常需要比MLPs多得多的参数。为解决这一局限性，本文提出PRKANs（参数减少的科尔莫戈罗夫-阿诺德网络），其采用多种方法减少KAN层的参数数量，使其与MLP层相当。在MNIST和Fashion-MNIST数据集上的实验结果表明，PRKANs优于多种现有KANs，其带注意力机制的变体虽训练时间略长，但性能可与MLPs媲美。此外，研究还强调了高斯径向基函数（GRBFs）和层归一化在KAN设计中的优势。本研究的代码库可在https://github.com/hoangthangta/All-KAN获取。
英文摘要：
Kolmogorov-Arnold Networks (KANs) represent an innovation in neural network architectures, offering a compelling alternative to Multi-Layer Perceptrons (MLPs) in models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. By advancing network design, KANs drive groundbreaking research and enable transformative applications across various scientific domains involving neural networks. However, existing KANs often require significantly more parameters in their network layers than MLPs. To address this limitation, this paper introduces PRKANs (Parameter-Reduced Kolmogorov-Arnold Networks), which employ several methods to reduce the parameter count in KAN layers, making them comparable to MLP layers. Experimental results on the MNIST and Fashion-MNIST datasets demonstrate that PRKANs outperform several existing KANs, and their variant with attention mechanisms rivals the performance of MLPs, albeit with slightly longer training times. Furthermore, the study highlights the advantages of Gaussian Radial Basis Functions (GRBFs) and layer normalization in KAN designs. The repository for this work is available at: https://github.com/hoangthangta/All-KAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
