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Found 4 papers, 2 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.
Convergence Analysis for Training Stochastic Neural Networks via Stochastic Gradient Descent
In this paper, we carry out numerical analysis to prove convergence of a novel sample-wise back-propagation method for training a class of stochastic neural networks (SNNs).
A Backward SDE Method for Uncertainty Quantification in Deep Learning
We develop a probabilistic machine learning method, which formulates a class of stochastic neural networks by a stochastic optimal control problem.
