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Diffusion model tutorial

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RCLW03 - Accelerating statistical inference and experimental design with machine learning

This tutorial is an introduction to diffusion models, a class of parametric generative processes that produces samples by iterative local refinement or denoising. In this tutorial, we will consider high-dimensional Euclidean spaces, although variants for discrete spaces and other Riemannian manifolds exist. The exposition will be self-contained assuming basic familiarity with latent variable models and principles of deep learning. I will present the main ideas from the point of view of hierarchical variational inference, with connections mentioned to stochastic differential equations and annealed Langevin dynamics. The talk will serve as background for my subsequent talk on diffusion modelling for amortised inference, i.e., in the absence of a ground-truth dataset.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Average on weekdays: 8945.67939815 kWh Webinar & FW11, Computer Laboratory, William Gates Building. The Stevenage Bioscience Catalyst, Stevenage, UK

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