> Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. - https://en.wikipedia.org/wiki/Kernel_method

As it relates to this: https://en.wikipedia.org/wiki/Neural_tangent_kernel

To me, this is JFM. Not sure if I'm connecting the dots right either. I just don't know of anything else claiming to solve the curse.

Does “possible” in your statement refer to the inherent constraints of the architecture as specified by the researcher, or something else?