Generative Diffusion Models

Last update: 21 Apr 2025 21:17
First version: 14 April 2025

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Yet Another Inadequate Placeholder

for yet another topic I need to master before I become obsolete, this one living where stochastic differential equations meets density estimation and neural networks.

Recommended:
• Xianghao Kong, Rob Brekelmans, and Greg Ver Steeg, "Information-Theoretic Diffusion", ICLR 2023

To read:
• Giulio Biroli, Tony Bonnaire, Valentin de Bortoli, Marc Mézard, "Dynamical Regimes of Diffusion Models", arxiv:2402.18491
• Z. I. Botev, J. F. Grotowski, D. P. Kroese, "Kernel density estimation via diffusion", Annals of Statistics 38/strong> (2010): 2916--2957, arxiv:1011.2602
• "Count Bayesie", Linear Diffusion: Building a Diffusion Model from linear Components [I think this would be a nice class exercise, but I need to re-read it]
• Reza Ghane, Anthony Bao, Danil Akhtiamov, Babak Hassibi, "Concentration of Measure for Distributions Generated via Diffusion Models", arxiv:2501.07741
• Negar Kamali, Karyn Nakamura, Angelos Chatzimparmpas, Jessica Hullman, Matthew Groh, "How to Distinguish AI-Generated Images from Authentic Photographs", arxiv:2406.08651
• Maxim Raginsky, Stochastic Differential Equations: A Systems-Theoretic Approach [PDF draft via Prof. Raginsky. In this context, for the last chapter.]
• Pau Rodriguez, Arno Blaas, Michal Klein, Luca Zappella, Nicholas Apostoloff, Marco Cuturi, Xavier Suau, "Controlling Language and Diffusion Models by Transporting Activations", arxiv:2410.23054
• Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli, "Deep Unsupervised Learning using Nonequilibrium Thermodynamics", arxiv:1503.03585