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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