how_strange
Re:Every Model Learned by Gradient Descent Is Approximately a Kernel M...
on Sunday December 06, 2020 @11:34AM (#25324531)
If, as in this paper, we allow ourselves to set the kernel after seeing the data, then the statement in the title is trivial: if my learning algorithm outputs function f, I can take the kernel K(x,x')=f(x)*f(x').
The result is interesting insofar as the path kernel is interesting, which requires some more thought.