I thoroughly enjoyed reading your article on Kolmogorov-Arnold Networks (KANs). The exploration of this novel architecture as a potential alternative to Multi-Layer Perceptrons (MLPs) was both insightful and exciting.

To address the challenges mentioned, especially the slower training times of KANs compared to MLPs, consider focusing on optimizing the hyperparameters and using parallel computing resources to speed up the training process. Additionally, leveraging transfer learning techniques can help pre-train KANs on smaller datasets before applying them to more complex problems. Incorporating more efficient optimization algorithms like Adam or RMSprop could also enhance performance. Engaging with the research community to share findings and gather diverse insights will further refine the approach.

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