30 citations · 83 across the 11 of their papers we have counts for
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hep-th2023★ 4 cited
Renormalizing Diffusion Models
Jordan Cotler, Semon Rezchikov
We explain how to use diffusion models to learn inverse renormalization group flows of statistical and quantum field theories. Diffusion models are a class of machine learning mode…
cs.CV2023★ 23 cited
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision
Ayush Tewari, Tianwei Yin, George Cazenavette +5
Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals. However, their applicability is limited to scenario…