| Description |
Abstract:
Diffusion models are a class of generative models in machine learning that iteratively transform data into noise through a forward diffusion process, typically converging toward a Gaussian distribution. A corresponding reverse process then reconstructs data-like samples by denoising these vectors step-by-step. This pair of processes resembles coarse-graining and fine-graining operations. In this talk, I will provide a brief introduction to the principles and mechanics of diffusion models, focusing on how they generate realistic samples from noise. |
QLS Seminar - An introduction to diffusion models in generative machine learning
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