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Paper Citation Record · LEDGER

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

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 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 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:29:40.920453Z

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 538bc8b5-e609-4dd5-a91d-363a39e1f9ff · inbound

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models cites this paper.

Phase-aware Training Schedule Simplifies Learning in Flow-Based Generative Models Analysis of learning a flow-based generative model from limited sample complexity

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T18:29:40.920453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:29:40.920453Z digest=sha256:816fd00c9b9b2093fccbd8384d0f5e5e5f6f3308fbec53d10d9bafdc233a43f9

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.

source=pdf_text observed=2026-08-08T12:53:00.137222Z digest=sha256:bc900fc27f1437542f4dfcb669dd710243526002193c4d46eaed37d4b387234b

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.

source=pdf_text observed=2026-08-07T14:57:50.901824Z digest=sha256:8513e7062beabb53d7060d9ff6c7e3a5857d12da701652e00dac25d30b413958

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-08T05:00:23.008831Z digest=sha256:ead696f7b217eb2235137bc0714aa474e9dff74f4d58795be86fccd7d6ed5bc0

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-26T15:35:51.654392Z digest=sha256:66d9451784e8ba4324a06a8d8d6ea5ebf6f0809b543da1ca3d803796a67957b6

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 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-07-02T21:51:13.457071Z digest=sha256:223e5b2769d7af6c2f8527cfe8e222c180ae1511564c5676783d081fb5b05822

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.

source=arxiv_source observed=2026-08-01T12:53:08.348302Z digest=sha256:188a51d3a4527f889a38ab363de38ac379ed13124ca0ff90f037ce39d95aaa65