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U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
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U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models

Song Mei
ArXiv (abs)PDFHTML

Papers citing "U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models"

5 / 5 papers shown
Title
Learning curves theory for hierarchically compositional data with power-law distributed features
Learning curves theory for hierarchically compositional data with power-law distributed features
Francesco Cagnetta
Hyunmo Kang
Matthieu Wyart
130
1
0
11 May 2025
Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data
Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data
Zisheng Yao
Yuhe Zhang
Zhe Hu
Robert Klöfkorn
Tobias Ritschel
Pablo Villanueva-Perez
AI4CE
136
1
0
04 Apr 2025
How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
Alessandro Favero
Antonio Sclocchi
Francesco Cagnetta
Pascal Frossard
Matthieu Wyart
DiffMCoGe
110
6
0
17 Feb 2025
Probing the Latent Hierarchical Structure of Data via Diffusion Models
Probing the Latent Hierarchical Structure of Data via Diffusion Models
Antonio Sclocchi
Alessandro Favero
Noam Itzhak Levi
Matthieu Wyart
DiffM
121
5
0
17 Oct 2024
How transformers learn structured data: insights from hierarchical filtering
How transformers learn structured data: insights from hierarchical filtering
Jerome Garnier-Brun
Marc Mézard
Emanuele Moscato
Luca Saglietti
167
6
0
27 Aug 2024

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