We observe the following factorization, Neural approximation aims at minimizing the Kullback-Leibler divergence between the q φ (Z|X, y) and p θ (Z|X, y).
instead of optimizing it directly, we observe that the following equation naturally holds, where D KL [q p] is the Kullback-Leibler divergence between the distributions q and p. we equivalently maximize the following variational recurrent lower bound by plugging Eq.