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Gaussian Processes

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 11: Diffusion Models Jul 31, 2026 · 1:22:31 Diffusion models are generative frameworks used to generate realistic images, videos, and actions. The process involves a fixed forward noising step ($X_0 o X_T$) and a learned reverse denoising step $p_ heta(X_{t-1}|X_t)$, optimized using the Evidence Lower Bound (ELBO).

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Stanford CS229 Machine Learning | Spring 2026 | Lecture 11: Diffusion Models

Diffusion models are generative frameworks used to generate realistic images, videos, and actions. The process involves a fixed forward noising step ($X_0 o X_T$) and a learned reverse denoising step $p_ heta(X_{t-1}|X_t)$, optimized using the Evidence Lower Bound (ELBO).

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