arXiv:2502.06018v3 Announce Type: replace Abstract: Although Kolmogorov-Arnold-based interpretable networks (KANs) possess strong theoretical expressiveness, they suffer from severe parameter explosion and limited ability to capture high-frequency features in high-dimensional tasks. To address these issues, we propose the Kolmogorov-Arnold Fourier Network (KAF), which fundamentally redefines the KAN paradigm through spectral reparameterization. Our key contributions include: (1) proposing a fundamental basis transformation from the local, grid-based B-spline representation to a global, adaptiv
The continuous drive for more efficient and expressive neural network architectures is pushing research towards addressing limitations of current models like KANs.
Improved AI architectures can lead to more powerful and interpretable models, accelerating advancements across various AI applications and potentially reducing compute requirements.
The proposed Kolmogorov-Arnold Fourier Network (KAF) offers a potential solution to the parameter explosion and high-frequency capture issues in KANs, indicating a new direction for interpretable neural networks.
- · AI researchers
- · High-dimensional data analysis
- · Generative AI
- · Pattern recognition applications
- · Less efficient neural network architectures
- · Compute-intensive research methods
More accurate and efficient AI models become feasible for complex tasks.
Reduced computational cost for training and inference allows for broader AI adoption in resource-constrained environments.
Advances in AI interpretability could foster greater trust and accelerate deployment in critical industries like healthcare and finance.
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Read at arXiv cs.LG