PelatihA variational autoencoder (VAE) is an autoencoder (chapter 9) whose encoder outputs a distribution over a low-dimensional latent code and whose decoder outputs a distribution over the data; it is trained to maximise a lower bound on the likelihood, the reconstruction log-likelihood minus the Kullback–Leibler divergence of the encoder’s distribution from a standard normal prior, and it generates by decoding codes drawn from the prior (Kingma and Welling, 2014).
Quantitative Finance · Glosarium