Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1411.2404.
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 2026-08-07T00:52:37.278637Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-18T12:21:21.288860Z
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 e68ac903-43e0-4ed7-b179-7c92feecccee · inbound
AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning cites this paper.
AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:52:37.278637Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87783cf0-eaaa-46e2-a02e-7732e8a4e7df · inbound
LLM DNA: Tracing Model Evolution via Functional Representations cites this paper.
LLM DNA: Tracing Model Evolution via Functional Representations The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T12:21:21.290980Z
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 96940573-2192-4eee-99c8-3f4866c40520 · inbound
Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior cites this paper.
Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction
Reference 190
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T17:16:06.880832Z
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.