XNet用复数积分公式提升模型速度与精度，超越MLP和KAN。
Model Comparisons: XNet Outperforms KAN
- 基于复数柯西积分公式的新型网络架构
- 在高低维空间中均显著提升速度与准确率
- 适合需要高精度建模的时序预测任务
在计算数学与人工智能领域，精确数据建模对预测性机器学习任务至关重要。本文深入研究XNet，一种采用复值柯西积分公式的新型算法，其网络架构优于传统的多层感知机（MLPs）和科尔莫戈罗夫-阿诺德网络（KANs）。XNet在低维与高维空间的各种任务中均显著提升速度与准确性，重新定义了数据驱动模型开发的边界，并在性能上大幅超越如LSTM等成熟的时间序列模型。
原文摘要 · Abstract (English)
In the fields of computational mathematics and artificial intelligence, the need for precise data modeling is crucial, especially for predictive machine learning tasks. This paper explores further XNet, a novel algorithm that employs the complex-valued Cauchy integral formula, offering a superior network architecture that surpasses traditional Multi-Layer Perceptrons (MLPs) and Kolmogorov-Arnold Networks (KANs). XNet significant improves speed and accuracy across various tasks in both low and high-dimensional spaces, redefining the scope of data-driven model development and providing substantial improvements over established time series models like LSTMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。