A two-stage CNN reconstructs pseudo 6D phase space from 16 x-y images taken at varying rotation angles in the KEK-ATF injector.
Michelucci.An Introduction to Autoencoders
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Deep neural networks trained on simulated Q-meter NMR spectra can extract target polarization with lower fitting uncertainty than conventional least-squares lineshape fitting, at least when the test data come from the same simulator.
Autoencoder uses latent space to estimate parameters of multi-component damped sinusoids in noise with high accuracy even for weak or opposing-phase components.
Denoising autoencoder pretraining on corrupted visual embeddings yields more robust Med-VQA performance on SLAKE and PathVQA while using LoRA for efficient LLM adaptation.
citing papers explorer
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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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Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks
Deep neural networks trained on simulated Q-meter NMR spectra can extract target polarization with lower fitting uncertainty than conventional least-squares lineshape fitting, at least when the test data come from the same simulator.
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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.
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Noise-Aware Visual Representation Learning for Medical Visual Question Answering
Denoising autoencoder pretraining on corrupted visual embeddings yields more robust Med-VQA performance on SLAKE and PathVQA while using LoRA for efficient LLM adaptation.