Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.18444.
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 2026-08-15T22:33:21.719274Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T18:23:50.490435Z
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 a750fd93-4be7-42e3-aae3-39acc916f719 · inbound
Learning curves theory for hierarchically compositional data with power-law distributed features cites this paper.
Learning curves theory for hierarchically compositional data with power-law distributed features U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:33:21.719274Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 24e503dc-c7c6-4687-9857-e197085ce378 · inbound
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures cites this paper.
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:31:44.318560Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8a026a36-be02-4411-82b4-92845da3c93c · inbound
Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models cites this paper.
Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
Reference 73
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:57:51.655432Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation caea22b4-6e9a-4649-814f-2c1281acea59 · inbound
CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processing cites this paper.
CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processing U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T17:44:17.207031Z
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 2dc81298-0f60-478f-a3b1-23be752f4342 · inbound
Learn from your own latents and not from tokens: A sample-complexity theory cites this paper.
Learn from your own latents and not from tokens: A sample-complexity theory U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T18:23:50.492124Z
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.