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 Networks (I)
• Lecture 4: Message Passing and Graph Neural Networks (II)
• Lecture 5: Variational Inference and Variational Autoencoder (I)
• Lecture 6: Variational Inference and Variational Autoencoder (II)
• Lecture 7: MCMC Sampling (I)
• Lecture 8: MCMC Sampling (II)

Learning

• Lecture 9: Maximum Likelihood and EM Algorithm
• Lecture 10: Structure Learning and Neural Architecture Search

Deep Generative Models

• Lecture 11: Generative Adversarial Networks
• Lecture 12: Normalizing Flows
• Lecture 13: Diffusion Models
• Lecture 14: Autoregressive Models and GPT4
• Lecture 15: Evaluation of Deep Generative Models

Applications

• Lecture 16: Image Generation and Stable Diffusion
• Lecture 17: Text Generation and ChatGPT (I)
• Lecture 18: Text Generation and ChatGPT (II)
• Lecture 19: Graph Generation and Drug Design