Variational Autoencoders: an introduction to new applications and a new regularization approach.
Date:
In this presentation, we discuss the Variational AutoEncodeur (VAE): a latent variable model emerging from the machine learning community. To begin, we introduce the theoretical foundations of the model and its relationship with well-established statistical models. Then, we discuss how we used VAEs to solve two widely different problems. First, we tackled a classic statistical problem, survival analysis, and then a classic machine learning problems, image analysis and image generation. We conclude with a short discussion of our latest research project where we establish a new metric for the evaluation or regularization of latent variable models such a Gaussian Mixture Models and VAEs.
This is invited talk given at the University of Victoria Statistics Seminar. You can download the slides here.
The talk was recorded and is freely available as a lecture here.