  In this article a surprising result is demonstrated using the neural tangent
kernel. This kernel is defined as the inner product of the vector of the
gradient of an underlying model evaluated at training points. This kernel is
used to perform kernel regression. The surprising thing is that the accuracy of
that regression is independent of the accuracy of the underlying network.
