generative – 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: 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: Game Theory, AI

By Andrew Fairless on July 25, 2024May 24, 2024

https://www.quantamagazine.org/game-theory-can-make-ai-more-correct-and-efficient-20240509 Game Theory Can Make AI More Correct and EfficientSteve NadisMay 9, 2024 “Researchers are drawing on ideas from game theory to improve large language models and make them moreContinue readingWhat I Read: Game Theory, AI

What I Read: LLMs, Open Source

By Andrew Fairless on July 15, 2024May 6, 2024

https://www.aidancooper.co.uk/how-to-beat-proprietary-llms How to Beat Proprietary LLMs With Smaller Open Source ModelsAidan CooperApr 26, 2024 “…we explore the unique advantages of open source LLMs, and how you can leverage them toContinue readingWhat I Read: LLMs, Open Source

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: Structured Generation, Constrained Decoding

By Andrew Fairless on June 20, 2024April 16, 2024

https://www.aidancooper.co.uk/constrained-decoding A Guide to Structured Generation Using Constrained DecodingApr 8, 2024 “…there are techniques that ensure language models only return outputs that conform to your requirements. This article serves asContinue readingWhat I Read: Structured Generation, Constrained Decoding

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

Tag: generative