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Found 5 papers, 1 papers with code
Universal Functional Regression with Neural Operator Flows
We empirically study the performance of OpFlow on regression and generation tasks with data generated from Gaussian processes with known posterior forms and non-Gaussian processes, as well as real-world earthquake seismograms with an unknown closed-form distribution.
Discovering Structure From Corruption for Unsupervised Image Reconstruction
We show that such a set of inverse problems can be solved simultaneously without the use of a spatial image prior by instead inferring a shared image generator with a low-dimensional latent space.
Image Reconstruction without Explicit Priors
We show that such a set of inverse problems can be solved simultaneously by learning a shared image generator with a low-dimensional latent space.
Seismic wave propagation and inversion with Neural Operators
We develop a scheme to train Neural Operators on an ensemble of simulations performed with random velocity models and source locations.
Generalized Unsupervised Clustering of Hyperspectral Images of Geological Targets in the Near Infrared
The application of infrared hyperspectral imagery to geological problems is becoming more popular as data become more accessible and cost-effective.
