Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1901.08278.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:45:20.114139Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T14:23:21.532347Z
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 73cdb024-2bcd-4ca9-a7c3-34f95977c62d · inbound
SETOL: A Semi-Empirical Theory of (Deep) Learning cites this paper.
SETOL: A Semi-Empirical Theory of (Deep) Learning Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks
Reference 81
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:45:20.114139Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a42ed313-77e0-443f-bf30-c15941d7c762 · inbound
A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions cites this paper.
A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T14:23:21.533803Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.