Model Comparisons: XNet Outperforms KAN
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 2 Oct 2024.
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References · 10
- 2017Attention Is All You Needcited by 59,932
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
- 2023Sparse Autoencoders Find Highly Interpretable Features in Language Modelscited by 1,359
- 2017DGM: A deep learning algorithm for solving partial differential equationscited by 674
- 2022Transformers in Time Series: A Surveycited by 489
- 2024Transolver: A Fast Transformer Solver for PDEs on General Geometriescited by 299
- 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
- 2024Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretabilitycited by 69
- 2022ExSpliNet: An interpretable and expressive spline-based neural networkcited by 33
- 2024Cauchy activation function and XNetcited by 4
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