Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Training Sparse Neural Networks
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1611.06694.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T15:17:02.093340Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T13:57:37.804308Z
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 bb587366-efbd-4798-8701-a6cc1980e5db · inbound
Architecture-aware Network Pruning for Vision Quality Applications cites this paper.
Architecture-aware Network Pruning for Vision Quality Applications Training Sparse Neural Networks
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:17:02.093340Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30a87c3f-716d-4a33-88c1-9e398403f81b · inbound
Smaller Models, Better Generalization cites this paper.
Smaller Models, Better Generalization Training Sparse Neural Networks
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:25:09.295317Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 11dcd3d5-9971-466d-acac-e083c272c802 · inbound
Principled Approximation Methods for Efficient and Scalable Deep Learning cites this paper.
Principled Approximation Methods for Efficient and Scalable Deep Learning Training Sparse Neural Networks
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T13:57:37.943742Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.