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Will a SOTA model be trained with Kolmogorov-Arnold Networks by 2029?

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Kolmogorov-Arnold Networks (KANs) are a new kind of neural network, which were proposed in the paper KAN: Kolmogorov-Arnold Networks.

From the abstract:
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs).
While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights").
KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline.
We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability.