Large Language Models (LLMs) - Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 2 - Score matching

Learn more details about this course: https://online.stanford.edu/courses/cme296-diffusion-and-large-vision-models To follow along with the course schedule and syllabus, visit: https://cme296.stanford.edu/syllabus/ Chapters: 00:00:00 Introduction 00:04:33 Motivation behind score matching 00:14:30 Lanvegin sampling 00:18:43 Score estimation 00:19:50 Implicit score matching, sliced score matching 00:20:56 Score of a Gaussian distribution 00:29:58 Denoising score matching 00:40:44 Limitations of DSM 00:49:49 Noise conditional score networks 00:52:49 Annealed Langevin dynamics 00:58:09 Parallel between DDPM and score 01:04:37 Continuous derivation 01:14:31 SDE formulation 01:22:54 Training 01:24:29 Reverse SDE 01:28:46 Inference with Euler-Maruyama 01:30:32 PF-ODE 01:41:24 DPM-Solver For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education Afshine Amidi is an Adjunct Lecturer at Stanford University. Shervine Amidi is an Adjunct Lecturer at Stanford University. View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNdy8rt2rZ4T2xM0OjADnfu