Residual KANs are claimed to approximate and learn Besov functions at optimal rates, but the proof's width count is invalid in dimension d.
Extending sobolev func- tions with partially vanishing traces from locally (ε,δ)-domains and applications to mixed boundary problems
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Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
Residual KANs are claimed to approximate and learn Besov functions at optimal rates, but the proof's width count is invalid in dimension d.