Fig. 3: Pruning of a KAN-integrated DeepEC.
From: Interpretable Kolmogorov-Arnold networks for enzyme commission number prediction
A KAN model is pruned with varying thresholds. The number of parameters thus decreases incrementally as the thresholds increase. The rightmost point corresponds to the unpruned baseline model, serving as a reference for comparison. We observed that the best performance is yielded by a pruned model. The unpruned model contains 36.68% more parameters than the best-performing model.
