Placing spline knots in neural networks using splines as activation functions.

When using feed-forward neural networks with spline activation functions, the quality of
approximation depends on the knot placement of spline functions. We demonstrate a method of
choosing equidistant knots in each subdivision of the space when an arbitrary initial division is
given, in order to keep the approximation error under a predefined limit.