Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.01965.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T23:04:31.392984Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T07:39:49.377085Z
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 27cd6aac-e215-47d3-bf11-8a4f5b025b41 · inbound
Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention cites this paper.
Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T23:04:31.392984Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation da533651-1382-4d0e-b6e8-576bab737756 · inbound
Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access cites this paper.
Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
Reference 19
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T12:37:17.410335Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 799057a6-d617-4eb6-8c3d-202f658e888c · inbound
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine cites this paper.
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
Reference 64
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:39:49.378998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.