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Paper Citation Record · LEDGER

Training with Quantization Noise for Extreme Model Compression

As of 10 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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T00:37:42.175821Z digest=sha256:ea6cb9c158671038f97a9cede7d452f6bd451a1d8fe5804ebdc43622be0c622f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T12:03:25.464802Z digest=sha256:f2db09568688b5c74bd30e1055f6dfd239370d4cf166b4c0414042a22f7540f8

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T13:35:35.972596Z digest=sha256:9ff377956c8aa22b30462805c9fdf5e282009a209d281da762757f07a08f8c2d

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=sha256:3fb68d3d0794b520919f0ccdfbf491ab5cddcff8f99d02a707896d277c8f54a5

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=sha256:245fcedc66669ab68fff2384838f0bb3a3fca3fd90b9a4862102bbfba60f7031

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T18:43:15.746399Z digest=sha256:3b2319c2f4f569977cd5b71f96b5d36b6a19de7d989adb22d7b3cdcb65b5b004

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=sha256:6719bb543f49cf4480f42a84660082201d5d68a528c42694c0cd619d8db56126

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-15T05:35:09.705532Z digest=sha256:addf02aa8944eeaee55bd4f45f24c372dfffe556601500100d6afe60014f36ac