Whenever this topic comes up I like to provide a citation to some work I've done: https://towardsdatascience.com/gradient-kernel-regression-e431f0f29750 https:

Whenever this topic comes up I like to provide a citation to some work I've done: https://towardsdatascience.com/gradient-kernel-regression-e431f0f29750 https://towardsdatascience.com/gradient-kernel-regression-e4... Not out of vanity (ok, a little) but because I think the idea has importance that has not been fully explored. The article's Bayesian perspective may be the whole story but somehow I don't think so. Unlike the article's author, my work left me feeling model architecture was the most important thing (behind training data) whereas they seem to feel it is ancillary.