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Paper Citation Record · LEDGER

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA

As of 13 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2507.09091.

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

pith.paper-citation-record.v1
2507.09091 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:14:06.267871Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:13:04.839923Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T18:14:06.376344Z

Reference resolution

26 of 26 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 438c1972-ea93-4376-80b6-93cc6bc4a05d · outbound

This paper cites Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA

Reference 1

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Observation 7e9bbab4-11d6-48f8-8eef-38db5979833e · outbound

This paper cites columns” of the operator “matrix.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA columns” of the operator “matrix

Reference 2

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Observation 1e4ef8e1-3fa2-418a-8f4d-3c144f4336e1 · outbound

This paper cites First, we examine the case of extracting face features via PCA (eigenfaces), but from irregularly sampled images.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA First, we examine the case of extracting face features via PCA (eigenfaces), but from irregularly sampled images

Reference 3

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Observation 14edbdb6-2e7b-4b01-8826-130e06297f5b · outbound

This paper cites This general setup allows performing these decompositions on, and extract latent source signals from, irregularly sampled signals where it otherwise would not have been possible.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA This general setup allows performing these decompositions on, and extract latent source signals from, irregularly sampled signals where it otherwise would not have been possible

Reference 4

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Observation 0104ccd8-3a1a-417c-b3ca-c6147b6aeedf · outbound

This paper cites A Tutorial on Principal Component Analy- sis,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA A Tutorial on Principal Component Analy- sis,

Reference 5

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Observation f3adb1b2-e060-49ae-83f6-bcb9502887c9 · outbound

This paper cites EM Algorithms for PCA and SPCA,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA EM Algorithms for PCA and SPCA,

Reference 6

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Observation 0d6ea245-8fe0-4d8e-a1e0-f251eda4065f · outbound

This paper cites Wiley, New York, 2001.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Wiley, New York, 2001

Reference 7

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Observation 8cf3bd71-a721-4fa6-a5b8-f35d07c2e8bf · outbound

This paper cites A Unifying Review of Linear Gaussian Models,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA A Unifying Review of Linear Gaussian Models,

Reference 8

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Observation 3c87a2e6-2565-48df-8568-c55889f121f7 · outbound

This paper cites A New Learning Algorithm for Blind Signal Separation,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA A New Learning Algorithm for Blind Signal Separation,

Reference 9

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Observation 4b963ec4-cbbd-40b1-bb47-7b23707437f6 · outbound

This paper cites Source separation using higher order mo- ments,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Source separation using higher order mo- ments,

Reference 10

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Observation 44031c81-3d01-44c9-9ee7-9d1431557d4b · outbound

This paper cites Nonlinear indepen- dent component analysis for discrete-time and continuous-time signals,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Nonlinear indepen- dent component analysis for discrete-time and continuous-time signals,

Reference 11

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Observation 66e17b40-53e3-49fb-9f2e-fc60cacce4dd · outbound

This paper cites Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference,

Reference 12

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Observation 0940f398-efa3-4bbc-8ee3-009407c81694 · outbound

This paper cites Func- tional data analysis for sparse longitudinal data,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Func- tional data analysis for sparse longitudinal data,

Reference 13

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Observation 81c481fe-314d-4719-ad22-a3b5cbc444b8 · outbound

This paper cites Nonlinear Functional Principal Component Analysis Using Neural Networks.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Nonlinear Functional Principal Component Analysis Using Neural Networks

Reference 14

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Source-reported events for the cited work

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Observation 007369fd-77b8-4852-950f-10c8be423eab · outbound

This paper cites Random functions with orthogonal exponen- tial decomposition,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Random functions with orthogonal exponen- tial decomposition,

Reference 15

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Observation ecd310f6-7f98-4e1a-9946-fa587fe1f2b6 · outbound

This paper cites Levy, Karhunen Loeve Expansion of Gaussian Pro- cesses, pp.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Levy, Karhunen Loeve Expansion of Gaussian Pro- cesses, pp

Reference 16

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Observation 8544b113-1312-444e-aae6-864e4d81d826 · outbound

This paper cites Fourier Fea- tures Let Networks Learn High Frequency Functions in Low Dimensional Domains,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Fourier Fea- tures Let Networks Learn High Frequency Functions in Low Dimensional Domains,

Reference 17

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Observation 78f7872b-6ac8-46d3-945b-d76455969645 · outbound

This paper cites Implicit neural representa- tions with periodic activation functions,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Implicit neural representa- tions with periodic activation functions,

Reference 18

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This paper cites Nerf: representing scenes as neural radiance fields for view synthe- sis,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Nerf: representing scenes as neural radiance fields for view synthe- sis,

Reference 19

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Observation 659e6a86-d2b0-4f14-afe7-18db94816ecd · outbound

This paper cites Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations

Reference 20

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Observation 5d4c1a4b-5659-442c-96b2-c8df4dffcc75 · outbound

This paper cites Point Cloud Audio Processing,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Point Cloud Audio Processing,

Reference 21

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Observation c8cb1049-f0cb-44ed-bda9-f249b4e605bf · outbound

This paper cites Robust learn- ing algorithm for blind separation of signals,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Robust learn- ing algorithm for blind separation of signals,

Reference 22

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Source-reported events for the cited work

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Observation 73faeeb1-9470-485c-9b96-5d0a1dcaace2 · outbound

This paper cites Blind source separation- semiparametric statistical approach,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Blind source separation- semiparametric statistical approach,

Reference 23

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Observation ed905120-6fc3-4cfe-8269-a97c2bcb427e · outbound

This paper cites CBCL Face Database #1, MIT Center For Biological and Computation Learning,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA CBCL Face Database #1, MIT Center For Biological and Computation Learning,

Reference 24

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Observation e843cac9-8565-421f-bc64-cf16c280ec46 · outbound

This paper cites Calculation of a constant Q spectral trans- form,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Calculation of a constant Q spectral trans- form,

Reference 25

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Source-reported events for the cited work

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Observation 9c0cbdc7-b41f-40fe-85f4-4f242cf2d36d · outbound

This paper cites Constructing an invertible constant-Q trans- form with nonstationary Gabor frames,.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Constructing an invertible constant-Q trans- form with nonstationary Gabor frames,

Reference 26

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Source-reported events for the cited work

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Pith citing papers

Observation 438c1972-ea93-4376-80b6-93cc6bc4a05d · inbound

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA cites this paper.

Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA Continuous-Time Signal Decomposition: An Implicit Neural Generalization of PCA and ICA

Reference 1

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Source-reported events for the cited work

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