Comments (2)
rusty1s
commented on June 1, 2024
1
Hi and thanks for your interest!
k_i refers to the kernel size/the number of basis functions in dimension i(0 < i < d). This results in product_i(k_i) overall basis functions/trainable parameters.
When talking about uniform knot vectors, we mean that knot vectors are placed uniformly inside each kernel. For example, in case you have a one-dimensional kernel with kernel size = 5, knot vectors are placed at [-1, -0.5, 0, 0.5, 1]. Each knot vector refers to a single basis function/kernel weight, it is not dependent on the number of vertices.
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jannessm
commented on June 1, 2024
thank you for your answer! this helped a lot!
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