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Found 5 papers, 1 papers with code
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation
We compare our method (parOT) to related optimal transport approaches in the context of domain adaptation and domain translation on benchmark data sets.
Generative structured normalizing flow Gaussian processes applied to spectroscopic data
In particular, in the physical sciences, limited training data may not adequately characterize future observed data; it is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate.
Denoising neural networks for magnetic resonance spectroscopy
Our motivating example is magnetic resonance spectroscopy, in which a primary goal is to detect the presence of short-duration, low-amplitude radio frequency signals that are often obscured by strong interference that can be difficult to separate from the signal using traditional methods.
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
Constructing probability densities for inference in high-dimensional spectral data is often intractable.
Torus Graphs for Multivariate Phase Coupling Analysis
Angular measurements are often modeled as circular random variables, where there are natural circular analogues of moments, including correlation.
