Analysis of learning a flow-based generative model from limited sample
complexity

Analysis of learning a flow-based generative model from limited sample complexity

5 October 2023

Florent Krzakala

Eric Vanden-Eijnden

Lenka Zdeborová

Papers citing "Analysis of learning a flow-based generative model from limited sample complexity"

7 / 7 papers shown

Title
Understanding Classifier-Free Guidance: High-Dimensional Theory and Non-Linear Generalizations Krunoslav Lehman Pavasovic Jakob Verbeek Giulio Biroli Marc Mézard 59 0 0 11 Feb 2025
A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models Gen Li Yuting Wei Yuejie Chi Yuxin Chen DiffM 33 21 0 05 Aug 2024
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models Song Mei 3DV AI4CE DiffM 34 11 0 29 Apr 2024
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions M. S. Albergo Nicholas M. Boffi Eric Vanden-Eijnden DiffM 244 261 0 15 Mar 2023
Diffusion Models are Minimax Optimal Distribution Estimators Kazusato Oko Shunta Akiyama Taiji Suzuki DiffM 64 85 0 03 Mar 2023
Convergence of score-based generative modeling for general data distributions Holden Lee Jianfeng Lu Yixin Tan DiffM 177 128 0 26 Sep 2022
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions Sitan Chen Sinho Chewi Jungshian Li Yuanzhi Li Adil Salim Anru R. Zhang DiffM 123 245 0 22 Sep 2022