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Paper Citation Record · LEDGER

Training with Quantization Noise for Extreme Model Compression

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2004.07320.

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

pith.paper-citation-record.v1
2004.07320 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:54:28.750525Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T12:04:25.715325Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 97f93f63-7f8b-48b5-91fb-338f2a9213c7 · inbound

Linformer: Self-Attention with Linear Complexity cites this paper.

Linformer: Self-Attention with Linear Complexity Training with Quantization Noise for Extreme Model Compression

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T00:37:42.288471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T00:37:42.175821Z digest=sha256:ae7f92e621d2ae85144c6b6e49d8a91f8539802f39e51c35348cc3b7fa2d2e65

Observation ff526104-01cb-49ef-a38b-fa953b01eb25 · inbound

Multi-Modality Distillation via Learning the teacher's modality-level Gram Matrix cites this paper.

Multi-Modality Distillation via Learning the teacher's modality-level Gram Matrix Training with Quantization Noise for Extreme Model Compression

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-24T12:04:25.718558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-24T12:03:25.464802Z digest=sha256:75fa9cf830bdeb672c0ec2eae0fbb3c4520be899d04565e00ab4f39d0db3cc14

Observation 1c7294ce-49be-4510-8a0e-a4ccf9e17df2 · inbound

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale Training with Quantization Noise for Extreme Model Compression

Reference 132

Resolution
verified exact
arxiv_id, observed 2026-05-13T13:35:36.076011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-13T13:35:35.972596Z digest=sha256:7f33350536bfeaf7e1578cd0b16baae4806b83b6fafbafa6f47cfca59977298d

Observation 9a7ac72a-9452-4b6b-b9f9-f946a7edfc0b · inbound

BiVM: Accurate Binarized Neural Network for Efficient Video Matting cites this paper.

BiVM: Accurate Binarized Neural Network for Efficient Video Matting Training with Quantization Noise for Extreme Model Compression

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T19:54:28.750525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:28.750525Z digest=sha256:356b6b6adad7e3f8359df52bfff1732f731a4bb832ba2855880df4b94e8e9527

Observation d78a2921-d3eb-447f-a442-5581b5785f5a · inbound

Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimization cites this paper.

Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimization Training with Quantization Noise for Extreme Model Compression

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T11:27:32.153748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:27:32.153748Z digest=sha256:94399e14d58ad8bb5941629ac3e432509dcfc7b881288a0199d7a7c9c8dd5d2e

Observation a941c690-1d9d-4874-a205-c40ba3671575 · inbound

SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models cites this paper.

SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models Training with Quantization Noise for Extreme Model Compression

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T00:05:49.240830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T18:43:15.746399Z digest=sha256:25f8411ca9cff1cc353f8ce6035dbcc81ab9d55c157ca6a2217ddedf33148e2f

Observation 31be0655-4a3f-4c32-b4a3-2c7bcff80d58 · inbound

Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization cites this paper.

Multi-Modal Learning meets Genetic Programming: Analyzing Alignment in Latent Space Optimization Training with Quantization Noise for Extreme Model Compression

Reference 10

Resolution
unresolved
no resolver link, observed 2026-07-12T23:58:40.131335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:58:40.131335Z digest=sha256:4e32ae56345e13037b6896f5d53a1bda11718af7eee47eb2221abbdebdfdce57

Observation 3f783469-edc3-4774-9491-bd6995b8b9e0 · inbound

Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility cites this paper.

Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility Training with Quantization Noise for Extreme Model Compression

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-15T05:39:47.981218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-15T05:35:09.705532Z digest=sha256:50c042b5349d120e235c2e06ca87f7f2b0126ef7f6a64c991e02ca820b9dbc58