<ul><li>KANs offer a novel framework for function approximation by replacing traditional neural network weights with learnable univariate functions.</li><li>To improve memory efficiency and training costs associated with KANs, a smaller meta-learner named MetaKANs is proposed to generate weights for KANs.</li><li>By training KANs and MetaKANs together in an end-to-end differentiable manner, MetaKANs achieve comparable or superior performance with fewer trainable parameters.</li><li>Experiments on various tasks show that MetaKANs can enhance parameter efficiency and reduce memory usage, providing a more scalable and cost-effective training method for KANs.</li></ul>

Improving Memory Efficiency for Training KANs via Meta Learning

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