SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions
A new neural network architecture, SechKAN, introduces hyperbolic secant basis functions into Kolmogorov-Arnold Networks (KANs) to provide smooth, localized responses and stable gradients. Experiments show that SechKAN outperforms multilayer perceptrons (MLPs) and other KAN variants with similar parameter counts on tasks such as function fitting, partial differential equation (PDE) problems, and image classification benchmarks, though it runs slightly slower than MLPs.
Why it matters: SechKAN demonstrates a meaningful advance in neural network design by achieving better performance than established architectures on multiple tasks without increasing model size.
Full story at: arXiv Machine Learning ↗