Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Demystifying Representation Learning with Non-contrastive Self-supervision
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2110.04947.
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 2026-08-06T19:58:42.117531Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T08:30:57.478057Z
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 7495a7be-a721-4756-818c-3e9f76467152 · inbound
Clustering via Self-Supervised Diffusion cites this paper.
Clustering via Self-Supervised Diffusion Towards Demystifying Representation Learning with Non-contrastive Self-supervision
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:58:42.117531Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8117746d-d9db-4868-ab76-b004028ef9ee · inbound
A Minimal Model of Representation Collapse: Frustration, Stop-Gradient, and Dynamics cites this paper.
A Minimal Model of Representation Collapse: Frustration, Stop-Gradient, and Dynamics Towards Demystifying Representation Learning with Non-contrastive Self-supervision
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:30:57.480337Z
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.
Observation f48f5e78-7ac4-4f86-bf40-c668821c3291 · inbound
Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus cites this paper.
Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus Towards Demystifying Representation Learning with Non-contrastive Self-supervision
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T10:09:08.439990Z
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.