Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2404.02954.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T20:35:23.477363Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T20:03:56.273033Z
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 af827495-8bfb-48f2-9314-743ced20a950 · inbound
Variational Rank Reduction Autoencoders for Generative Thermal Design cites this paper.
Variational Rank Reduction Autoencoders for Generative Thermal Design Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:35:23.477363Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7398303d-4f4b-4d6f-81c6-f1b96cc21a8e · inbound
Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics cites this paper.
Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T10:09:01.735841Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab26f6fe-c7de-4efb-99d4-f0f5a5571088 · inbound
Why Code, Why Now: An Information-Theoretic Perspective on the Limits of Machine Learning cites this paper.
Why Code, Why Now: An Information-Theoretic Perspective on the Limits of Machine Learning Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Reference 28
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T21:56:40.617030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 74a49d7a-a5e4-48c0-a60f-f52444c1543f · inbound
Bi-Lipschitz Autoencoder With Injectivity Guarantee cites this paper.
Bi-Lipschitz Autoencoder With Injectivity Guarantee Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T00:15:55.045437Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 242f04eb-6302-4759-8776-7a50607a7521 · inbound
What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion cites this paper.
What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Reference 54
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T04:05:57.588972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b5755060-f21c-4554-b27b-bb38217347ea · 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 Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:39:49.358876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b2701598-7096-40ac-bf93-cf166a78cfa1 · inbound
Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences cites this paper.
Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T20:03:56.274419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.