Variational Autoencoders

This lecture covers Variational Autoencoders (VAEs), a probabilistic spin on traditional autoencoders that allow for sampling from the model to generate data. It explains the Bayesian approach to autoencoders, the challenges of Bayesian inference in deep models, and the reparameterization trick to backpropagate through stochastic models. The lecture also delves into the intractability issues of VAEs, the concept of variational lower bound, and the process of maximizing the likelihood lower bound. It concludes with the applications of VAEs in Natural Language Processing, focusing on text generation and the active research areas in the field.