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Paper Citation Record · LEDGER

Convergence of Continuous Normalizing Flows for Learning Probability Distributions

As of 10 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 2026-08-10T06:31:04.303077+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

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  • verified fuzzy0
  • unresolved0
  • 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=sha256:8d8296c639d1c8bf0151231ce63d5550b3b4b186d19821c231795d74c23b6152

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:facf9e801082cbe2c46be2ffe9513ebaf1e21c56e0f40bd7db57a78e1853aada

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:c771e20522628531910e211e789c8ec4a8a2f12cb2ac214b9177251ddec31e9a

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T18:15:58.587798Z digest=sha256:a2190c66313c8a13c5ff9c7ee8a1c12f6bfb1b3eedfb97e384bf0c62a46184a9

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-09T15:24:59.445688Z digest=sha256:129ca26e8988ecacfa39452425f75eabdb1dac1c925c4b39b5e2de061b149568

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-12T03:52:05.779559Z digest=sha256:d44265ad11b34085c6587bd9dd2abec9c5e6ceea36a78c26d07a6612f89fe4f2

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T20:08:15.501474Z digest=sha256:ed493bc6188a2fa16e8666b401f2768b4478daccbc1c5d21f66d4b55a03768df

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-30T08:20:03.575151Z digest=sha256:e9318f96548cf979c49cb0a6b58f153d41250d6324103238c2b2d2ff423bec05

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:f2e97c99dbdadeb827b8f7df3e682953b0b2addcb995a542bc758409f8d4cbd8

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=sha256:2b67d9d5f2b58531d5accf8c5c02260c7ece662845daacdb3f7ed41e39cf2187