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Abstract
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Kolmogorov–Arnold Networks (KANs) are a novel class of neural networks inspired by with learnable univariate function modules, KANs provide a fundamentally different approach to modeling multivariate functions. This paper introduces the core concepts behind KANs, compares their structure and performance to traditional multilayer perceptrons (MLPs), and demonstrates their advantages in both prediction tasks.
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