The following auto-regressive decoding process is usually used: Parameters ✓ are typically learned by maximizing the marginal log likelihood log p However, this marginal term is intractable to compute for many decoder choices.
variational inference is considered, and the true posterior p ✓ (z|x) / p ✓ (x|z)p(z) is approximated via the variational distribution q (z|x) is (often known as the inference model or encoder), implemented via a -parameterized neural network.