Paper Citation Record · LEDGER

Analysis of learning a flow-based generative model from limited sample complexity

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.03575.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.03575 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:53:00.137222Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T05:39:40.865663Z

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 cc9fb163-d20f-4cfa-b356-210fa11c8f99 · inbound

Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms cites this paper.

Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms Analysis of learning a flow-based generative model from limited sample complexity

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T12:53:00.137222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f8f1251-a559-488b-a729-d186e4e963f6 · inbound

Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models cites this paper.

Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models Analysis of learning a flow-based generative model from limited sample complexity

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T14:57:50.901824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3465dce-c227-49a4-b445-915bcd5ed73d · inbound

The Interplay of Data Structure and Imbalance in the Learning Dynamics of Diffusion Models cites this paper.

The Interplay of Data Structure and Imbalance in the Learning Dynamics of Diffusion Models Analysis of learning a flow-based generative model from limited sample complexity

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:36:14.072331Z

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 f3668808-b881-46fc-9e82-fe0a1c9cb804 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Analysis of learning a flow-based generative model from limited sample complexity

Reference 261

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T05:39:40.867303Z

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 9dd32621-0d0b-4c77-aa34-41135e8fa5bf · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Analysis of learning a flow-based generative model from limited sample complexity

Reference 261

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T21:57:25.629491Z

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 87aedc47-65a3-4afa-89de-d3a973da0660 · inbound

Provable diffusion-based posterior sampling for linear inverse problems via DDIM cites this paper.

Provable diffusion-based posterior sampling for linear inverse problems via DDIM Analysis of learning a flow-based generative model from limited sample complexity

Reference 233

Resolution: unresolved; no resolver link, observed 2026-08-01T12:53:08.348302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.