Then, the goal of learning is to maximize the log-probability of the original reference Y ⇤ given the corrupted version.
to minimize Once this cost J DAE is minimized, we can recursively perform the maximum-a-posterior inference, i.e.,Ŷ = arg max Y log p ✓ (Y |X), to findŶ that (approximately) maximizes log p(Y |X).