Abstract
Denoising diffusion models have led to impressive generative models in many domains. These algorithms learn from data a probability distribution in high dimension. They invert a stochastic differential which maps the probability distribution to a Gaussian. This talk will present the princple of these algorithms and provide a different perspective based on recent progress, with a focus on formulations that do not involve stochastic differential equations.
