Mathematics of Deep Generative Models

In this talk, I will focus on the mathematical point of view of Variational Autoencoders (VAE), which is the most common approach of Deep Generative Models (another one is Generative Adversarial Networks (GAN)). A Generative Model is a powerful way of learning any kind of data distribution using unsupervised learning and it has achieved tremendous success in just few years. All types of generative models aim at learning the true data distribution of the training set so as to generate new data points with some variations.