Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2411.10507.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:44:05.184245Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T12:53:06.046088Z
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 feb04c80-797e-413a-ac72-1b7e6e4b4828 · inbound
ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices cites this paper.
ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:44:05.184245Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ffef8bc4-d3a2-4864-8e95-fc7e914b18be · inbound
Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.
Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively
Reference 55
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T12:53:06.050449Z
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.
Observation 644b203d-df71-4c4d-ad69-bf41d1ef8ef3 · inbound
A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.
A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T12:53:14.870947Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.