The main author of KANs did a tutorial session yesterday at MLCAD, an academic conference focused on the intersection of hardware / semiconductor design and ML / deep learning.
It was super fascinating and seems really good for what they advertise it for, gaining insight and interpret for physical systems (symbolic expressions, conserved quantities , symmetries). For science and mathematics this can be useful but for engineering this might not be the main priority of an ML / deep learning (to some extent).
There are still unknowns for leaning hard tasks and learning capacity over harder problems. Even choices in for things like the chosen basis function used for the KAN “activations” and what other architectures these layers can be plugged into with some gain is still unexplored. I think as people mess around with KANs we’ll get better answers to these questions.
There are still unknowns for leaning hard tasks and learning capacity over harder problems. Even choices in for things like the chosen basis function used for the KAN “activations” and what other architectures these layers can be plugged into with some gain is still unexplored. I think as people mess around with KANs we’ll get better answers to these questions.