SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions
Quick summary
arXiv:2607.18290v3 Announce Type: replace-cross Abstract: In recent years KolmogorovArnold Networks KANs have attracted increasing attention due to their effectiveness in machine learning and scientific computing offering a new paradigm for neural network design In this paper we present SechKAN a novel KAN based on hyperbolic secant sech functions The hyperbolic secant basis is adopted for its smooth bellshaped form localized responses and wellbehaved gradients We employ a 1D linear projection to reduce the number of parameters allowing SechKAN to maintain a model size comparable to that of mu
Key takeaways
- arXiv:2607.18290v3 Announce Type: replace-cross Abstract: In recent years KolmogorovArnold Networks KANs have attracted increasing attention due to their effectiveness in machine learning and scientific computing offering a new paradigm for neural network design In this paper we present SechKAN a novel KAN based on hyperbolic secant sech functions The hyperbolic secant basis is adopted for its smooth bellshaped form localized responses and wellbehaved gradients We employ a 1D linear projection to reduce the number of parameters allowing SechKAN to maintain a model size comparable to that of mu
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