Paper Citation Record · LEDGER

Combining Discrete Wavelet and Cosine Transforms for Efficient Sentence Embedding

As of 7 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.

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

Reference 8

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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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Observation f09cd25f-7481-4bf7-9f4e-8283c8bc57f2 · outbound

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

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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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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

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Reference 17

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

Reference 18

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This paper cites Recent Advances in Convolutional Neural Networks.

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

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This paper cites Mining and summarizing customer reviews.

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

Reference 21

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

Reference 22

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This paper cites EigenSent: Spectral sentence embeddings using higher-order dynamic mode decomposition.

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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This paper cites A survey on wavelet applications in data mining.

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

Reference 25

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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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This paper cites Advances in pre-training distributed word representations.

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

Reference 28

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This paper cites Linguistic regularities in continuous space word representations.

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

Reference 29

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

Reference 31

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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

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

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

Reference 35

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

Reference 36

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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

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

Reference 38

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

Reference 39

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This paper cites Voorhees and Dawn M.

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

Reference 40

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Reference 41

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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

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Reference 43

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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

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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

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Pith citing papers

No inbound Pith citation observations are available.