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Paper Citation Record · LEDGER

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective

As of 22 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2506.11641.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.11641 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:10:13.517314Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T08:02:44.126467Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T08:06:47.702779Z

Reference resolution

30 of 30 outbound references displayed

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  • unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c2c7414-6685-477f-8bbe-7b4d2bf64299 · outbound

This paper cites Establishing strong imputation per- formance of a denoising autoencoder in a wide range of missing data problems.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Establishing strong imputation per- formance of a denoising autoencoder in a wide range of missing data problems

Reference 1

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation c61ba711-f9b4-4bfd-ab01-74325c87864c · outbound

This paper cites The gap between theory and practice in function approximation with deep neural networks.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective The gap between theory and practice in function approximation with deep neural networks

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation cb6505b1-3cbe-4afc-89a6-0b627260d356 · outbound

This paper cites Simoncelli.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Simoncelli

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation d6fecc3d-d8cf-41f4-8aa8-eae41d1eae51 · outbound

This paper cites An introduction to the proper orthogonal decomposition.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective An introduction to the proper orthogonal decomposition

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation bc5bfe6f-262a-4acd-b92f-3639917bd07b · outbound

This paper cites Autoencoder-based network anomaly detection.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Autoencoder-based network anomaly detection

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 30823648-d7f3-44b2-a90b-b7db262c0c9a · outbound

This paper cites On the properties of neural machine translation: Encoder–decoder approaches.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective On the properties of neural machine translation: Encoder–decoder approaches

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 7612f7c7-7b2b-4f9f-aa46-527f04164bbb · outbound

This paper cites A course in functional analysis , volume 96.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective A course in functional analysis , volume 96

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 96612a51-42d2-40ee-8e8e-cfa70c041169 · outbound

This paper cites Convergence rates for learning linear operators from noisy data.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Convergence rates for learning linear operators from noisy data

Reference 8

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 4a891cf9-838a-4d2e-b1b1-f51ca272aceb · outbound

This paper cites Real analysis and probability.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Real analysis and probability

Reference 9

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation a3e881ab-d876-41cc-b88e-d3b7315992db · outbound

This paper cites The approximation of one matrix by another of lower rank.Psychometrika, 1(3):211–218, Sep 1936.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective The approximation of one matrix by another of lower rank.Psychometrika, 1(3):211–218, Sep 1936

Reference 10

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Source-reported events for the cited work

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Observation 8d99d16f-e829-4a96-952c-1a8791fe5cf6 · outbound

This paper cites A deep learning approach to reduced order mod- elling of parameter dependent partial differential equations.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective A deep learning approach to reduced order mod- elling of parameter dependent partial differential equations

Reference 11

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 732d05d5-c2b2-457c-a3e1-5a61523c0da7 · outbound

This paper cites A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized pdes.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized pdes

Reference 12

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 5b304994-4c8f-4945-a71d-047a7148403a · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, 2015.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, 2015

Reference 13

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 63e16739-52ea-499c-be81-dcb8b87a58b4 · outbound

This paper cites an unresolved cited work.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Unresolved cited work

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5d57e109-abe0-4455-94b1-d79d6cf96fd4 · outbound

This paper cites Autoencoders, minimum description length and helmholtz free energy.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Autoencoders, minimum description length and helmholtz free energy

Reference 15

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 6037f3dd-3508-4ff6-83d9-02a1f87574cd · outbound

This paper cites an unresolved cited work.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Unresolved cited work

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation bf3033a7-f31d-4f9d-9808-87a588492a9d · outbound

This paper cites Nonlinear principal component analysis using autoassociative neural networks.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Nonlinear principal component analysis using autoassociative neural networks

Reference 17

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 04b29845-5ec2-44a0-8b43-9676ce0e96a7 · outbound

This paper cites Error estimates for deeponets: A deep learning framework in infinite dimensions.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Error estimates for deeponets: A deep learning framework in infinite dimensions

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 6909f766-6e80-4a98-bece-bc9957a88dab · outbound

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Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Unresolved cited work

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 51daa8c6-e0cf-4889-8fee-3dfd3f872d45 · outbound

This paper cites M¨ ucke, Sander M.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective M¨ ucke, Sander M

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 5d3e6670-82de-4f7d-9b28-5aa695a9b2e7 · outbound

This paper cites Nguyen, Raymond K.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Nguyen, Raymond K

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 5004cb5d-fce3-4a71-8c54-e7811e05c348 · outbound

This paper cites Otto, Gregory R.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Otto, Gregory R

Reference 22

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation e652200c-b8d9-4ee7-bcb7-4e06494c05c1 · outbound

This paper cites Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 5775224c-85a1-46e5-8faf-8b6faef23f44 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Pytorch: An imperative style, high-performance deep learning library

Reference 24

Resolution
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no resolver link, observed 2026-08-07T04:10:13.500510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation aaa789cd-6924-488e-88f6-7a29014aad74 · outbound

This paper cites A graph convolutional autoencoder approach to model order reduction for parametrized pdes.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective A graph convolutional autoencoder approach to model order reduction for parametrized pdes

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T04:10:13.503598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 909e8ad7-ca47-455e-8c7a-b849abe64075 · outbound

This paper cites Reduced basis methods for partial differential equations: an introduction , volume 92.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Reduced basis methods for partial differential equations: an introduction , volume 92

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T04:10:13.507429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 29ca52e3-3677-4695-a0fc-0336bee46490 · outbound

This paper cites Methods of modern mathematical physics.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Methods of modern mathematical physics

Reference 27

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation e2925e3e-d99d-431b-b13e-f5b3cdb96bc7 · outbound

This paper cites Zur theorie der linearen und nichtlinearen integralgleichungen.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Zur theorie der linearen und nichtlinearen integralgleichungen

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:10:13.562098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 2e50d185-8c33-4399-aeb0-d9c870b4c57f · outbound

This paper cites Invertible autoencoder for domain adaptation.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Invertible autoencoder for domain adaptation

Reference 29

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 80cee7c2-6c11-4546-a10e-8e8c1ed6a2af · outbound

This paper cites an unresolved cited work.

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective Unresolved cited work

Reference 1992

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Pith citing papers

Observation 795744e7-9b76-426a-bc08-eb0965814ab9 · inbound

Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry cites this paper.

Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:06:47.704177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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