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Found 4 papers, 3 papers with code
Towards trustworthy explanations with gradient-based attribution methods
In practice, gradient-based attribution methods, such as saliency maps, can yield noisy importance scores depending on model architecture and training procedure.
Deep active subspaces - a scalable method for high-dimensional uncertainty propagation
The difficulty of accurate surrogate modeling in such systems, is further compounded by data scarcity brought about by the large cost of forward model evaluations.
Simulator-free Solution of High-Dimensional Stochastic Elliptic Partial Differential Equations using Deep Neural Networks
We propose a novel methodology for high-dimensional uncertainty propagation of elliptic SPDEs which lifts the requirement for a deterministic forward solver.
Data Analysis, Statistics and Probability Computational Physics
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
One, thus, tries to construct a cheap-to-evaluate surrogate model to replace the forward model solver.
