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Found 4 papers, 1 papers with code
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks
In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient goal-oriented uncertainty quantification for inverse problems.
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
In the absence of theoretical guidelines or prior experience on similar tasks, this requires solving many training problems, which can be time-consuming and demanding on computational resources.
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks
We emphasize that the key advantage of using DNNs for learning regularization parameters, compared to previous works on learning via optimal experimental design or empirical Bayes risk minimization, is greater generalizability.
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e. g., the size may exceed computer memory or data are collected in real-time).
