Every Model Learned by Gradient Descent Is Approximately a Kernel Machine
I do have a tangential technical question for someone who knows more math than I do.A kernel SVM always finds (one-of) the global best fit lines in the kernel space.A gradient descent model explicitly converges to one of the nearest local minimas by definition.Does this paper conclude that the local minimas that neural networks converge to are one of the many equivalent global maxima ? Won't this be a major revelation by itself ?