2 citations · 4 across the 5 of their papers we have counts for
6 papers
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation
Nishant Panda, Natalie Klein, Dominic Yang +2
Given two probability densities on related data spaces, we seek a map pushing one density to the other while satisfying application-dependent constraints. For maps to have utility…
Generative structured normalizing flow Gaussian processes applied to spectroscopic data
Natalie Klein, Nishant Panda, Patrick Gasda +1
In this work, we propose a novel generative model for mapping inputs to structured, high-dimensional outputs using structured conditional normalizing flows and Gaussian process reg…
Denoising neural networks for magnetic resonance spectroscopy
Natalie Klein, Amber J. Day, Harris Mason +2
In many scientific applications, measured time series are corrupted by noise or distortions. Traditional denoising techniques often fail to recover the signal of interest, particul…
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
Katiana Kontolati, Natalie Klein, Nishant Panda +1
Constructing probability densities for inference in high-dimensional spectral data is often intractable. In this work, we use normalizing flows on structured spectral latent spaces…
Cross-population coupling of neural activity based on Gaussian process current source densities
Natalie Klein, Joshua H. Siegle, Tobias Teichert +1
Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a tria…
Torus Graphs for Multivariate Phase Coupling Analysis
Natalie Klein, Josue Orellana, Scott Brincat +2
Angular measurements are often modeled as circular random variables, where there are natural circular analogues of moments, including correlation. Because a product of circles is a…