Flow matching
A simplified and elegant approach to diffusion models proposed by Meta, focusing on global velocity instead of intermediate steps.
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The ML Technique Every Founder Should Know
Y Combinator
A simplified and elegant approach to diffusion models proposed by Meta, focusing on global velocity instead of intermediate steps.
Mistral: Voxtral TTS, Forge, Leanstral, & Mistral 4 — w/ Pavan Kumar Reddy & Guillaume Lample
Latent Space
A machine learning technique used in Voxal TTS, offering an alternative to autoregressive methods for modeling distributions in audio generation.
Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 1 - Diffusion
Stanford Online
One of the main image generation paradigms covered in lectures one, two, and three.
Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 3 - Flow matching
Stanford Online
The primary generation paradigm discussed in this lecture, which aims to transport an initial data distribution to a target distribution by learning a vector field.
Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 4 - Latent Space & Guidance
Stanford Online
A generation paradigm that views the process as transporting probability mass from an initial to a target distribution by predicting a vector field or velocity.
Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 6 - Model Training
Stanford Online
A third perspective that interprets the problem of going from noise to a target distribution as a transport problem, focusing on the vector field which can be solved using an ODE solver like Euler method. It is now widely preferred for its loss function characteristics.