Paper Citation Record · LEDGER

Group Fisher Pruning for Practical Network Compression

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2108.00708.

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

pith.paper-citation-record.v1
2108.00708 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:46:38.287873Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

25
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 123729c9-0cec-4f2b-8e62-afa6312fd0be · inbound

On Accelerating Edge AI: Optimizing Resource-Constrained Environments cites this paper.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Group Fisher Pruning for Practical Network Compression

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.287873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e404fb81-d970-468a-afcb-4cac69586276 · inbound

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study cites this paper.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Group Fisher Pruning for Practical Network Compression

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:46.284667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02e6edda-dbbe-4cde-b92f-c64e37675c87 · inbound

Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression cites this paper.

Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression Group Fisher Pruning for Practical Network Compression

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-18T08:41:08.020901Z

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.

Observation c6e0f972-e783-4f52-b350-b1b57ed924d8 · inbound

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency cites this paper.

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency Group Fisher Pruning for Practical Network Compression

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:56:12.959101Z

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.