diffusion – Andrew Fairless, Ph.D.

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

What I Read: Perspectives on diffusion

By Andrew Fairless on August 30, 2023July 30, 2023

https://sander.ai/2023/07/20/perspectives.html Perspectives on diffusionSander DielemanJuly 20, 2023 “Diffusion models appear to come in many shapes and forms…. these various perspectives each reveal new connections and are a breeding ground forContinue readingWhat I Read: Perspectives on diffusion

What I Read: Modern AI Art

By Andrew Fairless on March 6, 2023January 9, 2023

https://www.quantamagazine.org/the-physics-principle-that-inspired-modern-ai-art-20230105/ The Physics Principle That Inspired Modern AI ArtAnil AnanthaswamyJanuary 5, 2023 “Diffusion models generate incredible images by learning to reverse the process that, among other things, causes ink toContinue readingWhat I Read: Modern AI Art

What I Read: Poisson Flow Generative Models

By Andrew Fairless on January 5, 2023November 5, 2022

https://www.assemblyai.com/blog/an-introduction-to-poisson-flow-generative-models/ An Introduction to Poisson Flow Generative ModelsRyan O’ConnorDeveloper EducatorOct 26, 2022 “Poisson Flow Generative Models (PFGMs) are a new type of generative Deep Learning model, taking inspiration from physicsContinue readingWhat I Read: Poisson Flow Generative Models

Tag: diffusion