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Paper Citation Record · LEDGER

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2605.28992.

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

pith.paper-citation-record.v1
2605.28992 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T09:20:13.379131Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T07:57:54.664179Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T05:56:40.468761Z

Reference resolution

33 of 33 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved30
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e5b3c43a-8546-4483-b633-179974d30f96 · outbound

This paper cites VVC complexity and software implementation analysis,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder VVC complexity and software implementation analysis,

Reference 1

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:40dfd1219d905a86e48d981ca2334617fb70a3f63f0c13d3826e12aee12ebb2f

Observation 677b3194-3292-4b75-af69-3f4b91f15e4e · outbound

This paper cites Mcucoder: Adaptive bitrate learned video compression for IoT devices,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Mcucoder: Adaptive bitrate learned video compression for IoT devices,

Reference 2

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:32936a51281d90eb36ecd86b34675a4cc2d0cebdfc9d2b622fe7bd0e45611a0d

Observation 3cee761a-ff69-460a-983c-44c94f260396 · outbound

This paper cites Learned compression for com- pressed learning,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Learned compression for com- pressed learning,

Reference 3

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:4ac7814f19166807f7e1c7be6640575a0d38ad0d0f1e38a4459f6e4dc76e4357

Observation 4259a594-78c7-4d73-afd7-6be96e0eb0cc · outbound

This paper cites LiVeAction: Lightweight, versatile, and asymmetric codec design for real-time operation,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder LiVeAction: Lightweight, versatile, and asymmetric codec design for real-time operation,

Reference 4

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:64f0671a47458873dd298b3a641f6e1c094ba775462e745a540d4b7c08bdfef7

Observation 64359b55-b560-4932-a238-d2a32f5caf10 · outbound

This paper cites Variable Rate Image Compression with Recurrent Neural Networks.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Variable Rate Image Compression with Recurrent Neural Networks

Reference 5

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local_arxiv, observed 2026-06-29T09:23:16.102038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:81ac4d2044deca2e057573081be4c47c90c3ebd07ebcf4db98f2199875e76e87

Observation ac6517e5-607a-453e-9689-a10fbfc61533 · outbound

This paper cites Full resolution image compression with recurrent neural networks,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Full resolution image compression with recurrent neural networks,

Reference 6

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:de4f5db34ae2518dbee5ee87516a24dd9e29d9e20c986696cd19c1c50d20cc5c

Observation 5a78c257-6657-4cf8-ac6a-93399527d55a · outbound

This paper cites SoundStream: An end-to-end neural audio codec,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder SoundStream: An end-to-end neural audio codec,

Reference 7

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:fd3d50ad9671232d9ff6825384f0aabc2f77ccd4eba8143203fd5d23cc5a2deb

Observation c055ca48-058e-466d-87fd-e7ccb0c8f283 · outbound

This paper cites High fidelity neural audio compression,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder High fidelity neural audio compression,

Reference 8

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:ea7723066366402089e2dbaf4d24eac347c371c307995bcb5c22b6142bccbae1

Observation fb4e99ac-6c45-4891-808e-8fa8102653aa · outbound

This paper cites High- fidelity audio compression with improved RVQGAN,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder High- fidelity audio compression with improved RVQGAN,

Reference 9

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:029af52a8c0176b56136c654125a42d8a9a2751128c785bc142196dd108c9b2d

Observation 6f91a5a6-3b38-403f-ae13-a899087941ca · outbound

This paper cites Forward stagewise additive modeling,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Forward stagewise additive modeling,

Reference 10

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:33a083c69edb54ae60b8d689f2398e5005c721904196e307c79e05a2bc453437

Observation 4401a031-c86c-47e8-a843-9d845e8f29dd · outbound

This paper cites Matching pursuits with time-frequency dictionaries,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Matching pursuits with time-frequency dictionaries,

Reference 11

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:658df347ee79480716d72b9073575ba1195c3845abf7440545a143669e39d1ff

Observation 04295190-902b-4f05-9f74-52529fe84500 · outbound

This paper cites Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition,

Reference 12

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:26cf09a26a6cd3affabfb1d7d788f68d48e2d9476c9200ec77f0d7ed44123a75

Observation a0a0fb66-0cec-46fd-a111-3ca491ad057d · outbound

This paper cites The recurrent cascade-correlation architecture,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder The recurrent cascade-correlation architecture,

Reference 13

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:3ba6295d34dfc5611d30ba41a819648613bfbae4a2e54870a06dafcc96a78e18

Observation 3cdb726b-9968-4251-9749-5d8eac9fa14a · outbound

This paper cites Greedy layer- wise training of deep networks,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Greedy layer- wise training of deep networks,

Reference 14

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:fcad5a5841c2e3fab49ec3774cd1cedb2bbacf15acbb1605f7fd3c65579acb5c

Observation 1b6d133b-d866-4530-83f1-244f3a697974 · outbound

This paper cites The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features,

Reference 15

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:9efdddb79e8c2d3b4c48692a2d4f222ebf74380bf8442db100942ba4ebe87270

Observation ee3aaf26-c35a-4a52-9443-1a4d8fe8655e · outbound

This paper cites The gifi system of descriptive multivariate analysis,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder The gifi system of descriptive multivariate analysis,

Reference 16

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:940356d9b9e46bd1bb5409789878800be38499f0eb403fd3040bf6c1896065b2

Observation 68a8befa-d583-48b5-a143-4eac3c853d4a · outbound

This paper cites Canonical correlation analysis: A general parametric significance-testing system.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Canonical correlation analysis: A general parametric significance-testing system

Reference 17

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:5960f6cc8419bac835280d09059abd61472c0691073f5ab90d3058465b1e3ca2

Observation 0655a8e0-f655-4bbb-a3d3-efc23da3fef3 · outbound

This paper cites Chapter 16: Canonical correlation analysis,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Chapter 16: Canonical correlation analysis,

Reference 18

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:dc419957126882bacc81d1451aa04b2ab50a44ee7b7ed3ce09f404a585b573a5

Observation 21e7befc-ce84-422d-86bb-35315905b0e2 · outbound

This paper cites A fast fixed-point algorithm for independent component analysis,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder A fast fixed-point algorithm for independent component analysis,

Reference 19

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:8e60e3f64f6f7be7cd6528624bfa75bfe342bdb769955fad881517395248f325

Observation 6dd9889b-450a-412a-885d-789bd87a84f5 · outbound

This paper cites Fast and robust fixed-point algorithms for independent component analysis,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Fast and robust fixed-point algorithms for independent component analysis,

Reference 20

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:86a323a988dd26a09d1b45ab81b4ceb201d4b165ec351b360a71c620b0a4ad09

Observation e7504e1e-b440-4e04-b8b5-4f1c541df39b · outbound

This paper cites A projection pursuit algorithm for exploratory data analysis,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder A projection pursuit algorithm for exploratory data analysis,

Reference 21

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:30a4b456af8bd7688b4e74286bb98fea225e7e0a56c5ef5f2182d82d327d67eb

Observation 31985b6b-a8ff-420c-b692-0e50babc6fd6 · outbound

This paper cites Projection pursuit regression,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Projection pursuit regression,

Reference 22

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:d10c5dbc2f02ba5d52de9b29f62e425203311d7d8a4544261c2625853b944179

Observation 909a341e-f671-43f9-846b-8c47b94a56d5 · outbound

This paper cites Projection pursuit regression,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Projection pursuit regression,

Reference 23

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:e7c14fd3b0abae75836caf6408273626f95a802582cfc4af6ee7391257835797

Observation 24926b93-34dd-4106-9aab-25a936adfefb · outbound

This paper cites A ConvNet for the 2020s,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder A ConvNet for the 2020s,

Reference 24

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:556869bfcfe2fa06744bcbdaeb47ad35effc6ae69c040d969928edacd74dca22

Observation 289e7c15-5cf7-4692-9607-12d24ae9831b · outbound

This paper cites Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video,

Reference 25

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:0654f0b9e8d40fac85ab50ec68e1a999390f40b2ff8d43dfc758d55465ea423d

Observation f2774d1d-233c-4e8a-9a6f-176590dc1280 · outbound

This paper cites The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG- LS,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG- LS,

Reference 26

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:1798968d1d24e61d8b1defa2b7b54e79ea16db09a5f3cc4ce28f19f1d10dca2d

Observation a78786c0-67a9-4e1a-b556-d3b964b14647 · outbound

This paper cites Lsdir: A large scale dataset for image restoration,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Lsdir: A large scale dataset for image restoration,

Reference 27

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:a1dbcdfe5751615ea3fcce52e8ecad6442834be4f6c83768cf537cea4194a227

Observation a22e63d1-badc-47b9-afbe-ee71a05d7af7 · outbound

This paper cites Adan: Adaptive nesterov momentum algorithm for faster optimizing deep models,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Adan: Adaptive nesterov momentum algorithm for faster optimizing deep models,

Reference 28

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:ff8580a5aed0693ac93e2acf06e36e2b1169295f4270af316e82f64307e12c6c

Observation 48ff27c0-a893-4e79-a986-3b7e67a5fd51 · outbound

This paper cites Joint autoregressive and hierarchical priors for learned image compression,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Joint autoregressive and hierarchical priors for learned image compression,

Reference 29

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:ddce288b22f2e305ab839664b58749272cc41b0e0e3aa353e6c5173b57fc0a4d

Observation 760fa6b8-8859-46fc-bc2e-bf2d935804e3 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Image quality assessment: from error visibility to structural similarity,

Reference 30

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:134382bd991994d727708d589d04eb3140228ac8979c19a29782400d83ac6104

Observation 89678787-d379-4da5-aa14-bfb0ecaed446 · outbound

This paper cites Image quality assessment: Unifying structure and texture similarity,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Image quality assessment: Unifying structure and texture similarity,

Reference 31

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:92478b9a2577dbbfe67e1e8d96dfab4f0ff9822a063c5731fd868ab0f40576d6

Observation c85b7456-0f9c-43c7-acba-42a393b1483b · outbound

This paper cites Towards practical real-time neural video compression,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Towards practical real-time neural video compression,

Reference 32

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source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:745e736a3de30d69d198ff176676c50c84c8a210e8d08fef9a0bf7ac1eabc42b

Observation 1ea6e5c6-72c8-4af3-b7f9-13d7f58f9ae7 · outbound

This paper cites Improvements of the BD-PSNR model,.

FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder Improvements of the BD-PSNR model,

Reference 33

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arxiv_id, observed 2026-06-29T09:23:16.098758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T09:20:13.379131Z digest=sha256:117189d4a159a6547337d0823a5bd480da555fefa3f2cc67a43f5be306bb9b41

Pith citing papers

Observation 06f079fe-d21e-4d90-b7a1-338bdc61f925 · inbound

SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction cites this paper.

SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction FRAPPE: Full Input, Residual Output Autoencoding with Projection Pursuit Encoder

Reference 18

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local_arxiv, observed 2026-07-02T05:56:40.470226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-28T07:57:54.664179Z digest=sha256:73cb810db8a36118ddc04edb8e0542cb960db2730f06315cf09f1680c0477f6c