Groups
Dropout can be interpreted as variational inference in a Bayesian neural network, where applying random masks approximates sampling from a posterior over weights.
Mean field variational family assumes the joint posterior over latent variables factorizes into independent pieces q(z) = ∏ q_i(z_i).