Deep Generative Models (Pengtao Xie)

Graphical Models

• Lecture 1: Introduction and Bayesian Network
• Lecture 2: Markov Random Field

Inference

• Lecture 3: Message Passing and Graph Neural Network (I)
• Lecture 4: Message Passing and Graph Neural Network (II)
• Lecture 5: Variational Inference
• Lecture 6: MCMC Sampling (I)
• Lecture 7: MCMC Sampling (II)

Learning

• Lecture 8: Maximum Likelihood and EM Algorithm
• Lecture 9: Structure Learning and Neural Architecture Search

Deep Generative Models

• Lecture 10: Variational Autoencoder
• Lecture 11: Generative Adversarial Networks
• Lecture 12: Normalizing Flows
• Lecture 13: Evaluation of Deep Generative Models

Applications

• Lecture 14: Image Generation
• Lecture 15: Text Generation (I)
• Lecture 16: Text Generation (II)
• Lecture 17: Graph Generation
• Lecture 18: Gaussian Process on Functions
• Lecture 19: Determinantal Point Process on Sets