Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T04:38:38.949993Z
Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 2 inbound Pith citation observations for arXiv:2412.19844.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T04:38:38.949993Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:40:35.374309Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T23:52:17.214177Z
81 of 81 outbound references displayed
External citation measurements
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Observation 6fdd937d-583b-4c06-b16c-2ffd85e8b469 · outbound
A Review of Latent Representation Models in Neuroimaging Medgan: Medical image translation using gans
Reference 1
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A Review of Latent Representation Models in Neuroimaging 7t-guided super- resolution of 3t mri
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A Review of Latent Representation Models in Neuroimaging Early prediction of alzheimer’s disease progression using variational autoencoders
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Reference 5
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A Review of Latent Representation Models in Neuroimaging Dissecting psychiatric spectrum disorders by generative embedding
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A Review of Latent Representation Models in Neuroimaging t-bne: Tensor-based brain network embedding
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Reference 12
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A Review of Latent Representation Models in Neuroimaging Abductive inference and delusional belief.Cognitive neuropsychiatry, 15(1-3):261–287, 2010
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A Review of Latent Representation Models in Neuroimaging Reduced prefrontal-parietal effective connectivity and working memory deficits in schizophrenia
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A Review of Latent Representation Models in Neuroimaging A disentangled latent space for cross-site mri harmonization
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A Review of Latent Representation Models in Neuroimaging Discover- ing functional brain networks with 3d residual autoencoder (resae)
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A Review of Latent Representation Models in Neuroimaging A theory of cortical responses
Reference 22
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A Review of Latent Representation Models in Neuroimaging Computational and dynamic models in neuroimaging
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A Review of Latent Representation Models in Neuroimaging Fusing multimodal neuroimaging data with a variational autoencoder
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A Review of Latent Representation Models in Neuroimaging The generative adversarial brain
Reference 26
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A Review of Latent Representation Models in Neuroimaging Bridging imaging, genetics, and diagnosis in a coupled low-dimensional framework
Reference 27
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Reference 28
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A Review of Latent Representation Models in Neuroimaging Decoding natural image stimuli from fMRI data with a surface-based convolutional network
Reference 29
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A Review of Latent Representation Models in Neuroimaging Mindldm: Reconstruct visual stimuli from fmri using latent diffusion model
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A Review of Latent Representation Models in Neuroimaging Variational autoencoder: An unsupervised model for encoding and decoding fmri activity in visual cortex
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A Review of Latent Representation Models in Neuroimaging Gene-to-image: Decoding brain images from genetics via latent diffusion models
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Reference 43
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Reference 50
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Reference 61
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Reference 62
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Reference 69
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Reference 77
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Reference 78
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A Review of Latent Representation Models in Neuroimaging Make- a-volume: Leveraging latent diffusion models for cross-modality 3d brain mri synthesis
Reference 79
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A Review of Latent Representation Models in Neuroimaging Unsupervised anomaly localization using variational auto-encoders
Reference 80
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Observation 68c435fd-235e-42de-ae52-dcdca4de55ea · outbound
A Review of Latent Representation Models in Neuroimaging Unsupervised mr harmonization by learning disentangled representations using information bottleneck theory
Reference 81
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Observation e8ed3f1d-980c-43a9-af6d-c94ea5033a17 · inbound
Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance A Review of Latent Representation Models in Neuroimaging
Reference 77
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Observation 02bb0e23-9d31-4fcb-b8a3-2dd47946863f · inbound
How brains build higher order representations of uncertainty A Review of Latent Representation Models in Neuroimaging
Reference 620
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