Generative Models

Introduction

Generative models are used in different fields of machine learning, e.g., image processing, natural language processing, representation learning, and multimodal learning just to name a few. Advances in parameterizing these models using deep neural networks have enabled scalable modeling of complex and high-dimensional data. This course focuses on Variational Autoencoders and Variational Diffusion models. The course consists of 5 days of teaching with both lectures and practical components.

Course content

• Variational Inference
  • Latent Variable Models
  • Variational Inference
  • Mean field
  • Stochastic variational inference
  • Amortized inference
• Variational Autoencoder (VAE)
  • Deriving and interpreting the evidence lower bound
  • The representation trick
  • Representation Learning
  • Semi-supervised VAE
  • Supervised VAE
  • Multimodal Learning
  • Well known problems: Posterior collapse, beta-annealing, upper-bound on mutual information
• Diffusion Models
  • Denoising Diffusion Models
  • Forward and Backward Diffusion Process
  • Deriving the ELBO