Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T15:03:35.864728Z
Paper Citation Record · LEDGER
As of 18 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 3 inbound Pith citation observations for arXiv:2501.14636.
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 2026-08-10T15:03:35.864728Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:44:40.274196Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T23:19:31.380219Z
43 of 43 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 53387536-f280-4734-972c-13afc8bbc105 · outbound
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 1
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 2
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Arridge, P
Reference 3
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Atkinson, A
Reference 4
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 5
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Baldi and K
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Benner, S
Reference 8
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Bourlard and Y
Reference 9
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Bulatov , NotMNIST dataset , Google (Books/OCR), Tech
Reference 10
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Chung and M
Reference 11
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Chung and S
Reference 12
Source-reported events for the cited work
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Observation 3ac1ea0b-f3dc-40ce-a91b-497a4d97dfa7 · outbound
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Chung , Randomized Shepp-Logan phantom , 2020, https://github.com/matthiaschung/ Random-Shepp-Logan-Phantom
Reference 13
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Sparse $L^1$-Autoencoders for Scientific Data Compression
Reference 14
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Observation c0e90d2c-a18e-4736-aec4-7aa027790383 · outbound
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Chung, E
Reference 15
Source-reported events for the cited work
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Observation 7504eb1d-4c94-4c19-84f7-e1b8eff0720c · outbound
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Deng , The MNIST database of handwritten digit images for machine learning research , IEEE Signal Processing Magazine, 29 (2012), pp
Reference 16
Source-reported events for the cited work
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Observation e475e2af-d8eb-41e3-afa1-10d38a4a6a77 · outbound
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 17
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 18
Source-reported events for the cited work
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Observation e03fc77d-1fb4-4d90-a105-af478a9b66b4 · outbound
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Auto-Linear Phenomenon in Subsurface Imaging
Reference 19
Source-reported events for the cited work
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization On a Hidden Property in Computational Imaging
Reference 20
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Gazzola, P
Reference 21
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Solving Bayesian Inverse Problems via Variational Autoencoders
Reference 22
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 23
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Goodfellow, Y
Reference 24
Source-reported events for the cited work
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Hadamard, Lectures on Cauchy’s Problem in Linear Differential Equations, Yale University Press, New Haven, 1923
Reference 25
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 26
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 27
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Neural Operator: Learning Maps Between Function Spaces
Reference 28
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Kulkarni, S
Reference 29
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
Reference 30
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Reference 31
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Unresolved cited work
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Reference 33
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Pichi, B
Reference 34
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Pukelsheim, Optimal design of experiments , SIAM, 2006
Reference 37
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Reference 38
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Sanz-Alonso, A
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization Torralba, R
Reference 40
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Reference 41
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Reference 42
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A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization W ang, H
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