Model Comparisons: XNet Outperforms KAN
XNet;Kolmogorov Arnold networks;function approximations;PDE;time series;Cauchy integral theorem
Violating anonymity/double-blind policy -- the first sentence in the Introduction section directly reveals the identities of the authors.
Submission Desk Rejected by Program Chairs
This paper presents XNet, a novel algorithm based on the Cauchy integral formula, which significantly enhances speed and accuracy in predictive machine learning tasks over MLPs, KANs, and LSTMs.
@misc{
li2024model,
title={Model Comparisons: {XN}et Outperforms {KAN}},
author={Xin Li and Zhihong Jeff Xia and Xiaotao Zheng},
year={2024},
url={https://openreview.net/forum?id=eIK4ojL2QM}
}
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.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
~Xin_Li73 ~Zhihong_Jeff_Xia1 ~Xiaotao_Zheng1
Xin Li Zhihong Jeff Xia Xiaotao Zheng
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XNet Kolmogorov Arnold networks function approximations PDE time series Cauchy integral theorem
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li|model_comparisons_xnet_outperforms_kan
/pdf/6f5816ce0f484ae1ed45f680865599d11bc7292c.pdf
learning theory
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Model Comparisons: XNet Outperforms KAN
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission