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.<p>A kernel SVM always finds (one-of) the <i>global best fit lines</i> in the kernel space.<p>A gradient descent model explicitly converges to one of the nearest <i>local minimas</i> by definition.<p>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 ?