Deep neural networks reduce fitting uncertainties in CW-NMR polarization measurements for dynamically polarized targets.
Michelucci.An Introduction to Autoencoders
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A two-stage CNN reconstructs pseudo 6D phase space from 16 x-y images taken at varying rotation angles in the KEK-ATF injector.
Autoencoder uses latent space to estimate parameters of multi-component damped sinusoids in noise with high accuracy even for weak or opposing-phase components.
citing papers explorer
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Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks
Deep neural networks reduce fitting uncertainties in CW-NMR polarization measurements for dynamically polarized targets.
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Two-stage Convolutional Neural Network for pseudo six-dimensional phase space reconstruction
A two-stage CNN reconstructs pseudo 6D phase space from 16 x-y images taken at varying rotation angles in the KEK-ATF injector.
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Autoencoder-Based Parameter Estimation for Superposed Multi-Component Damped Sinusoidal Signals
Autoencoder uses latent space to estimate parameters of multi-component damped sinusoids in noise with high accuracy even for weak or opposing-phase components.