Syllabus
cme296.stanford.edu · 264 words · saved by 1 readers
Here, you will find slides and recordings of class lectures, along with suggested readings.
DateTopicsVideo from playlist April 3rd, 2026[slides] Lecture 1: Diffusion • Background on vision • Motivation behind diffusion • Diffusion in DDPM • Training derivation, ELBO • Inference • Faster sampling with DDIM 1:46:26 April 10th, 2026[slides] Lecture 2: Score matching • Motivation behind score matching • Score estimation • Denoising score matching • SDE formulation • Training, inference • Probability flows • Parallel with diffusion 1:48:48 April 17th, 2026[slides] Lecture 3: Flow matching • Motivation behind flows • History on flows • Conditional flow matching •…
saved by
related reading
- What are Diffusion Models? | Lil'Loglilianweng.github.io
- What are Diffusion Models?lilianweng.github.io
- Flow Matching and Diffusion Models — 2026 Versiondiffusion.csail.mit.edu
- An Introduction to Flow Matching and Diffusion Modelsdiffusion.csail.mit.edu
- ⭐️ Diffusion Modelsandrewkchan.dev
- Diffusion Meets Flow Matchingdiffusionflow.github.io
- Diffusion model - Wikipediaen.wikipedia.org
- Diffusion models from scratchchenyang.co
- https://arxiv.org/pdf/2006.11239arxiv.org
- [2010.02502] Denoising Diffusion Implicit Modelsarxiv.org
- [2006.11239] Denoising Diffusion Probabilistic Modelsarxiv.org
- Yang Songyang-song.net