Probabilistic Inference for Controlling Diffusion Models
An in-depth presentation on controlling diffusion models during inference, categorizing techniques into sequential and parallel families. The talk explores both approximate and exact probabilistic inference methods, highlighting their underlying principles, computational trade-offs, and applications in areas like rare event sampling and protein design. A practical roadmap is provided for selecting the most suitable control strategy based on objective and computational resources.
