Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Using Pre-Training Can Improve Model Robustness and Uncertainty
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1901.09960.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T23:44:53.384423Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T05:30:23.456663Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
429
pith, observed 2026-08-10T05:30:23.456663Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation dd4bc780-0c0f-4cc1-943a-4b642361a96d · inbound
The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology cites this paper.
The model is the message: Lightweight convolutional autoencoders applied to noisy imaging data for planetary science and astrobiology Using Pre-Training Can Improve Model Robustness and Uncertainty
Reference 26
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T17:14:33.175244Z
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 7cc678e4-ffd8-4a8f-91f1-b7b2e1f48c4e · inbound
TLDChoiceNet: Quantitatively Choosing a Transfer Learning Dataset cites this paper.
TLDChoiceNet: Quantitatively Choosing a Transfer Learning Dataset Using Pre-Training Can Improve Model Robustness and Uncertainty
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:44:53.384423Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.