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Found 2 papers, 1 papers with code
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and inverse uncertainty propagation.
Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation
Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method.
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