Model Comparisons: XNet Outperforms KAN

Xin Li, Zhihong Jeff Xia, Xiaotao Zheng

Keywords: XNet, Kolmogorov Arnold networks, function approximations, PDE, time series, Cauchy integral theorem

TL;DR: 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.

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.

Primary Area: learning theory

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 13403