Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Simple Recipe for Competitive Low-compute Self supervised Vision Models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2301.09451.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:27:06.688060Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-09T04:17:20.361661Z
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 37267cd6-9275-444c-baf4-9082af89a338 · inbound
DINOv2: Learning Robust Visual Features without Supervision cites this paper.
DINOv2: Learning Robust Visual Features without Supervision A Simple Recipe for Competitive Low-compute Self supervised Vision Models
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T04:17:20.363804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 93bd00f1-d070-4151-bbb7-5e1cfe1d9be4 · inbound
On the Surprising Effectiveness of Attention Transfer for Vision Transformers cites this paper.
On the Surprising Effectiveness of Attention Transfer for Vision Transformers A Simple Recipe for Competitive Low-compute Self supervised Vision Models
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T20:27:06.688060Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation efc92c3d-85b5-47e8-94b9-48ed67dcdc9a · inbound
Distilling foundation models for robust and efficient models in digital pathology cites this paper.
Distilling foundation models for robust and efficient models in digital pathology A Simple Recipe for Competitive Low-compute Self supervised Vision Models
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T13:40:24.306502Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.