I think it's clear that there's a strong relationship between all of these methods. Papers proving that Neural Networks are GPs came out a year or two ago, for

I think it's clear that there's a strong relationship between all of these methods. Papers proving that Neural Networks are GPs came out a year or two ago, for example. Deep learning is more useful in the end because kernel machines and gaussian processes (classically) store all samples in a dataset and require expensive pairwise comparison. Deep learning/gradient descent doesn't.