diffusion – Andrew Fairless, Ph.D.

What I Read: Diffusion, Flow Matching

By Andrew Fairless on March 27, 2025December 8, 2024

https://diffusionflow.github.io Diffusion Meets Flow Matching: Two Sides of the Same CoinRuiqi Gao, Emiel Hoogeboom, Jonathan Heek, Valentin De Bortoli, Kevin P. Murphy, Tim SalimansDec. 2, 2024 “Flow matching and diffusionContinue readingWhat I Read: Diffusion, Flow Matching

What I Read: Flow

By Andrew Fairless on March 18, 2025November 20, 2024

https://drscotthawley.github.io/blog/posts/FlowModels.html Flow With What You KnowScott H. HawleyNovember 13, 2024 “In this tutorial post, we provide an accessible introduction to flow-matching and rectified flow models, which are increasingly at theContinue readingWhat I Read: Flow

What I Read: Multimodal LLMs

By Andrew Fairless on March 6, 2025November 17, 2024

https://magazine.sebastianraschka.com/p/understanding-multimodal-llms Understanding Multimodal LLMsSebastian Raschka, PhDNov 03, 2024 “In this article, I aim to explain how multimodal LLMs function. Additionally, I will review and summarize roughly a dozen other recentContinue readingWhat I Read: Multimodal LLMs

What I Read: Illustrated AlphaFold

By Andrew Fairless on October 9, 2024July 14, 2024

https://elanapearl.github.io/blog/2024/the-illustrated-alphafold The Illustrated AlphaFoldElana Simon, Jake Silberg “A visual walkthrough of the AlphaFold3 architecture…”

What I Read: KL All You Need

By Andrew Fairless on August 21, 2024June 4, 2024

https://blog.alexalemi.com/kl-is-all-you-need.html KL is All You NeedAlexander A. Alemi2024-01-08 “…the core of essentially all modern machine learning methods is a single universal objective: Kullback-Leibler (KL) divergence minimization…. Understand KL, understand theContinue readingWhat I Read: KL All You Need

What I Read: Flow Matching

By Andrew Fairless on June 26, 2024April 23, 2024

https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html An introduction to Flow MatchingTor Fjelde, Emile Mathieu, Vincent Dutordoir “Flow matching (FM) is a recent generative modelling paradigm which has rapidly been gaining popularity in the deep probabilisticContinue readingWhat I Read: Flow Matching

What I Read: diffusion distillation

By Andrew Fairless on May 13, 2024March 11, 2024

https://sander.ai/2024/02/28/paradox.html The paradox of diffusion distillationSander DielemanFebruary 28, 2024 “…let’s take a closer look at the various ways in which the number of sampling steps required to get good resultsContinue readingWhat I Read: diffusion distillation

What I Read: Diffusion Model theory

By Andrew Fairless on April 23, 2024March 1, 2024

https://ayandas.me/2024/blog/diffusion-theory-from-scratch/ Building Diffusion Model’s theory from ground upAyan DasMay 7, 2024 “…we will go back and revisit the ‘fundamental ingredients’ behind the SDE formulation, and show how the idea canContinue readingWhat I Read: Diffusion Model theory

What I Read: Diffusion models, new theoretical perspective

By Andrew Fairless on April 8, 2024March 21, 2024

https://www.chenyang.co/diffusion.html Diffusion models from scratch, from a new theoretical perspectiveChenyang Yuan2023 “This tutorial aims to introduce diffusion models from an optimization perspective…”

What I Read: Physical Process, Generative AI

By Andrew Fairless on October 31, 2023October 5, 2023

https://www.quantamagazine.org/new-physics-inspired-generative-ai-exceeds-expectations-20230919/ The Physical Process That Powers a New Type of Generative AISteve Nadis9/19/23 10:15 AM “Some modern image generators rely on the principles of diffusion to create images. Alternatives basedContinue readingWhat I Read: Physical Process, Generative AI

Tag: diffusion