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Paper Citation Record · LEDGER

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding

As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2508.00420.

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

pith.paper-citation-record.v1
2508.00420 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:13:22.304677Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7e5a18ef-03e4-49cf-a9fb-9564fcbea929 · outbound

This paper cites SemEval-2012 task 6: A pilot on semantic textual similarity.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding SemEval-2012 task 6: A pilot on semantic textual similarity

Reference 1

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Observation 836a3a02-835d-4938-854d-72621c9c78af · outbound

This paper cites Efficient sentence embedding using dis- crete cosine transform.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Efficient sentence embedding using dis- crete cosine transform

Reference 2

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Observation e7e1c399-ca9c-4fff-9d1e-4898ce89f296 · outbound

This paper cites Attributes in lexical acquisition.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Attributes in lexical acquisition

Reference 3

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Observation 7c959b6a-4d42-42ac-a8cf-06c27aac3037 · outbound

This paper cites A simple but tough-to-beat baseline for sen- tence embeddings.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding A simple but tough-to-beat baseline for sen- tence embeddings

Reference 4

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Observation d95e4c79-cb59-4564-89bd-e3b6f0bc4df6 · outbound

This paper cites Don’t count, predict! a systematic comparison of context-counting vs.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Don’t count, predict! a systematic comparison of context-counting vs

Reference 5

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Observation 182b0200-5348-4441-9842-30ba11da0f58 · outbound

This paper cites How we blessed distributional semantic evaluation.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding How we blessed distributional semantic evaluation

Reference 6

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Observation 4d39cc8e-1f17-42d9-b8ab-522db5975706 · outbound

This paper cites The Wavelet Transform for Image Processing Appli- cations.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding The Wavelet Transform for Image Processing Appli- cations

Reference 7

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Source-reported events for the cited work

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Observation 8ae7abea-31c7-4a40-a192-58a0790ba664 · outbound

This paper cites Multimodal distributional semantics.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Multimodal distributional semantics

Reference 8

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Observation 05282207-b001-470a-a5cd-b2f76b8d57aa · outbound

This paper cites Brunton and J.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Brunton and J

Reference 9

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This paper cites Signal processing and compression with wavelet packets.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Signal processing and compression with wavelet packets

Reference 10

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Observation c6176051-dd1f-4bf2-82a4-162173434d98 · outbound

This paper cites SentEval: An Evaluation Toolkit for Universal Sentence Representations.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding SentEval: An Evaluation Toolkit for Universal Sentence Representations

Reference 11

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This paper cites A wavelet-packets based algorithm for eeg signal compression.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding A wavelet-packets based algorithm for eeg signal compression

Reference 12

Resolution
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Observation 258483cc-d7dc-4814-9699-9bcdada33fae · outbound

This paper cites Ten Lectures on Wavelets.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Ten Lectures on Wavelets

Reference 13

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Observation 1821cc5e-e283-4b4a-8349-e761ef8eeea2 · outbound

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Unresolved cited work

Reference 14

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This paper cites Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources

Reference 15

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Observation 12647bff-6ecc-425e-b0f9-e593b2e9369e · outbound

This paper cites Placing search in context: The concept revisited.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Placing search in context: The concept revisited

Reference 16

Resolution
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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Unresolved cited work

Reference 17

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Observation 8e0f470f-5dda-444c-ad9a-c6f38131b9d5 · outbound

This paper cites Grgic, M.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Grgic, M

Reference 18

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Source-reported events for the cited work

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Observation c5a36f91-effd-44cd-9cc9-18f580a8fca2 · outbound

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Recent Advances in Convolutional Neural Networks

Reference 19

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This paper cites SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation

Reference 20

Resolution
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Observation c8b15d03-89b0-499c-9b78-3b7258c26229 · outbound

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Mining and summarizing customer reviews

Reference 21

Resolution
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Observation 6ca4f147-f673-4814-93c6-b12aa2b8d757 · outbound

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Unresolved cited work

Reference 22

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding EigenSent: Spectral sentence embeddings using higher-order dynamic mode decomposition

Reference 23

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Source-reported events for the cited work

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Unresolved cited work

Reference 24

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding A survey on wavelet applications in data mining

Reference 25

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This paper cites Word Embedding for Understanding Natural Language: A Survey, volume 26.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Word Embedding for Understanding Natural Language: A Survey, volume 26

Reference 26

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Madhavan

Reference 27

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Advances in pre-training distributed word representations

Reference 28

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Linguistic regularities in continuous space word representations

Reference 29

Resolution
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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding All-but-the-Top: Simple and Effective Postprocessing for Word Representations

Reference 30

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Source-reported events for the cited work

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Selecting corpus-semantic models for neurolinguistic decoding

Reference 31

Resolution
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This paper cites A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts

Reference 33

Resolution
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Source-reported events for the cited work

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding GloVe: Global vectors for word representation

Reference 34

Resolution
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Source-reported events for the cited work

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Observation da04c8a2-d324-4d31-9f07-b10ada7358df · outbound

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Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Effective dimensionality reduction for word embeddings

Reference 35

Resolution
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Source-reported events for the cited work

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Observation 3ddef009-7195-44aa-ba6a-2f9656ec8d10 · outbound

This paper cites Rioul and M.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Rioul and M

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9ac470fa-8c2a-455a-a433-9a0ed2d5c3ba · outbound

This paper cites Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T10:13:21.633547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:13:21.633547Z digest=sha256:9fc2441bfe0ac26ed33d590e3fd3d8c5f551278bcb280d8bfeb1594e27a75b7c

Observation 4b19b72f-8d2d-4c04-af40-6e254d91832e · outbound

This paper cites Chapter 10 - compression.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Chapter 10 - compression

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T10:13:24.074558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a2422b4a-0e18-4a7f-9814-10169663f5ac · outbound

This paper cites Speech recognition by wavelet analysis.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Speech recognition by wavelet analysis

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T10:13:23.927934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c89ba6e8-df2d-401c-9fe3-2ec2e24af602 · outbound

This paper cites Voorhees and Dawn M.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Voorhees and Dawn M

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T10:13:23.791312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 29f5548d-e178-4c98-9f90-22b46981c9d9 · outbound

This paper cites an unresolved cited work.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-06T10:13:23.599188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 8643b5a1-3500-4cb6-995f-05bd562bce38 · outbound

This paper cites On the dimensionality of sentence embed- dings.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding On the dimensionality of sentence embed- dings

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T10:13:23.430852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T10:13:22.068650Z digest=sha256:8aaf7cff201662dc6edd1a8f9ee16d3cf5e4a1f866e78fd70b1b04b5935c57f8

Observation 4492e1e7-e7f6-4c83-b615-b2000cac082d · outbound

This paper cites an unresolved cited work.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-06T10:13:23.262206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c4949823-a1c2-4dff-87b7-153c1661faa9 · outbound

This paper cites An edge detection approach based on directional wavelet transform.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding An edge detection approach based on directional wavelet transform

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T10:13:23.095361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 68163a22-8dcc-435d-b8dd-b87892010a58 · outbound

This paper cites Sentence analogies: Exploring linguistic relationships and regularities in sentence embeddings, 2020.

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding Sentence analogies: Exploring linguistic relationships and regularities in sentence embeddings, 2020

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T10:13:22.981844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Pith citing papers

No inbound Pith citation observations are available.