Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1909.08174.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T13:22:48.083495Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T21:56:14.094008Z
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 4fcd6331-8720-4d8d-9447-6aee4685d197 · 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 Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:22:48.083495Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 478d80d8-523c-4991-ab56-b7080ec2e7cd · 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 Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:56:14.098688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.