Model Comparisons: XNet Outperforms KAN

Authors: Xin Li, Zhihong Jeff Xia, Xiaotao Zheng
Primary area: learning theory
Keywords: XNet, Kolmogorov Arnold networks, function approximations, PDE, time series, Cauchy integral theorem

Abstract:
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.

Decision: desk reject

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