Spline KAN Pocket compares a learnable edge-spline network with an exactly
parameter-matched tanh MLP on nonlinear symbolic regression. Both models contain
641 trainable parameters and receive the same number of optimization steps.
The benchmark separates interpolation inside the training square from
extrapolation in the surrounding ring. The Space renders the true and learned
surfaces side by side.
This is a compact piecewise-linear spline KAN experiment, not… See the full description on the dataset page:
https://huggingface.co/datasets/ARotting/spline-kan-surface.
