Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.08082.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:42:19.942620Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-12T14:42:20.684869Z
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 c8cf9d1f-a952-4800-bd5f-f612ab9646c1 · inbound
Dimension-independent rates for structured neural density estimation cites this paper.
Dimension-independent rates for structured neural density estimation Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T14:42:20.690196Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 032d42b4-6367-4866-bb2e-49b162d50e87 · inbound
Diffusion Bootstrap for High-Dimensional Linear Models cites this paper.
Diffusion Bootstrap for High-Dimensional Linear Models Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T18:14:56.711767Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.