>approaching optimal architectures that do the job but have no superfluous, pre-conceived notions of the (modeled) world Usually, modelling efficiency is relat

>approaching optimal architectures that do the job but have no superfluous, pre-conceived notions of the (modeled) world Usually, modelling efficiency is related to the amount of priors. The more (correct) priors, the faster a model learns, and from less data. A prior-poor model just spends data learning those correct priors in the first place. There are countless examples I could give to support intuition of this. There isn't, AFAIK, any theoretical work yet. That the priors are correctly aligned with reality is vital. A model with fixed bad priors cannot unlearn them. If the priors are bad but not fixed, you're just spending time and data to correct those priors.