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Model Comparisons: XNet Outperforms KAN

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arxiv 2410.02033 v1 pith:746EGUJO submitted 2024-10-02 cs.LG cs.AI

Xin Li , Zhihong Jeff Xia , Xiaotao Zheng This is my paper · ORCID
classification cs.LGcs.AI
keywords xnetmodeltasksaccuracyacrossalgorithmarchitectureartificial
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Complex Physics-Informed Neural Network

    cs.LG 2025-02 conditional novelty 4.0 of 10

    A single-layer PINN using the trainable Cauchy activation function matches or beats larger multi-layer PINN baselines on smooth analytic PDE benchmarks.

  2. SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

    eess.IV 2024-12 conditional novelty 4.0 of 10

    A new segmentation architecture combining Fourier-based KAN convolution and a gated recurrent patch-sequence module improves hepatic vessel CT segmentation Dice by 1.78 points over TransUNet on one benchmark.

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