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Paper Citation Record · LEDGER

Convergence of Continuous Normalizing Flows for Learning Probability Distributions

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2404.00551.

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

pith.paper-citation-record.v1
2404.00551 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:56:24.164688Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
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  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0fb56a2c-638b-47c3-b5b2-809b98d1244b · inbound

Distribution learning via neural differential equations: minimal energy regularization and approximation theory cites this paper.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-09T00:56:24.164688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:56:24.164688Z digest=sha256:360d4f6a326552efdef648e7ffe15932931ddfd8b6c69657aa77581b5df2bd95

Observation 3dae8e99-7908-4d8b-a877-4181574450d0 · inbound

Distribution learning via neural differential equations: minimal energy regularization and approximation theory cites this paper.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-09T00:56:24.160700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:56:24.160700Z digest=sha256:c4e4d3f392844a1ec2cf92baeec7a443ea4f2dbbfe07db9a26332a54e5bef118

Observation cf3bee2c-0e53-4cea-9a84-77d00e847132 · inbound

Non-asymptotic convergence bound of conditional diffusion models cites this paper.

Non-asymptotic convergence bound of conditional diffusion models Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-05T20:56:33.499799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T20:56:33.499799Z digest=sha256:aa9157b8257dce6beb6e195e69ccaec0290b4bd6cc75293bc4f1f8f1a09e2dd5

Observation 3a6a4cb7-77eb-4ad8-97c4-68c2d6cd2743 · inbound

Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities cites this paper.

Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T20:30:47.473061Z

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.

source=pdf_text observed=2026-05-10T18:15:58.587798Z digest=sha256:02b1f472f02e54293d2a99a6ce321ccd9f8bf45600edf5b31424231c6f9b7db8

Observation 0a63b6b0-fdda-4f1f-bb25-647a6c5e5017 · inbound

RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation cites this paper.

RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 64

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T16:41:14.855566Z

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.

source=arxiv_source observed=2026-05-09T15:24:59.445688Z digest=sha256:803a42a069a177c25e66170650ac8bd39a4b7210096235df46bd10bade630b2e

Observation 95fcbade-640b-4095-aa61-9c03a8792f99 · inbound

dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models cites this paper.

dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 97

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T06:51:29.657464Z

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.

source=arxiv_source observed=2026-05-12T03:52:05.779559Z digest=sha256:e733e700a8849b3549090144017e69bddc2c1469bc380d179350aa8ebcdee4fc

Observation 003a3f11-9ecd-457c-b465-87af59edb463 · inbound

Ergodic Trajectory Design by Learned Pushforward Maps: Provable Coverage via Conditional Flow Matching cites this paper.

Ergodic Trajectory Design by Learned Pushforward Maps: Provable Coverage via Conditional Flow Matching Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-14T20:09:26.345755Z

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.

source=pdf_text observed=2026-05-14T20:08:15.501474Z digest=sha256:c682b39eb465acef1a1326974a616ef51bbbabf7cbf64094b2ec2b7bae62fb27

Observation 767f599b-1f5c-4ddb-89a5-59f5c7054d21 · inbound

Panel Flow Matching: A Generative Approach to Learning Distributions of Longitudinal Data cites this paper.

Panel Flow Matching: A Generative Approach to Learning Distributions of Longitudinal Data Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 55

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T08:24:26.496061Z

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.

source=arxiv_source observed=2026-06-30T08:20:03.575151Z digest=sha256:5656d3e592e69d6a658488d88fb35b6d86b465fed92b7f2f1653e436fda91bf5

Observation 41059bd0-ef53-4b3f-ad56-b39d70f01169 · inbound

Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations cites this paper.

Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-01T20:13:45.161874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T20:13:45.161874Z digest=sha256:606fd13e88e065b4728f77a6dbef6d2bb52b454b211c23201bcb9d34994c3ad8

Observation 28585768-8f44-498c-a8d1-eff692d7b4d0 · inbound

Diffusion Bootstrap for High-Dimensional Linear Models cites this paper.

Diffusion Bootstrap for High-Dimensional Linear Models Convergence of Continuous Normalizing Flows for Learning Probability Distributions

Reference 27

Resolution
unresolved
no resolver link, observed 2026-07-31T18:14:56.787787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T18:14:56.787787Z digest=sha256:d487b490e609dd4821a8b34f59a714521291a77b6f6c9b3a1594b8645b20644b