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Paper Citation Record · LEDGER

Learning general Gaussian mixtures with efficient score matching

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.18893.

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

pith.paper-citation-record.v1
2404.18893 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:01:57.455219Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:29:30.787035Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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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 1f971f30-7b3b-426f-93a5-61824580e16c · inbound

CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation cites this paper.

CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation Learning general Gaussian mixtures with efficient score matching

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-08T22:01:57.455219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T22:01:57.455219Z digest=sha256:a90c6c18f3be16213ea9f1925a66c440ab33984552877ac56182a7e516937682

Observation f4245277-e141-4315-a5a3-7f301de08a3a · inbound

Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis cites this paper.

Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis Learning general Gaussian mixtures with efficient score matching

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T20:15:03.000215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:15:03.000215Z digest=sha256:5aacbfe2e4058153cc36d8f3fa237990fe2e741868eca3c05433c3f00cca6606

Observation b3392d0b-9594-4895-a049-e7ee509c85b7 · inbound

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective cites this paper.

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective Learning general Gaussian mixtures with efficient score matching

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-22T08:01:16.042174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-22T07:56:46.688712Z digest=sha256:615f71ef845c0c858697b73c32ce64386103ba5b2e58596b90d764ff3fe87500

Observation 7551f3ef-de3a-48b8-9b09-2b28a5ed0a10 · inbound

Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation cites this paper.

Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Learning general Gaussian mixtures with efficient score matching

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:36:39.424227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-25T05:34:36.228155Z digest=sha256:e40fe597fadba3da85dc5ad11f2685206a546b87ebbe1e7561501ad21a4cc058

Observation c57de5b4-bd0d-4b2d-a350-fa09350bed2e · inbound

Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence cites this paper.

Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence Learning general Gaussian mixtures with efficient score matching

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:29:30.789609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-26T17:59:23.282231Z digest=sha256:d32cf8d539d5b8586143aabf3b08d1132020777802496c3a8beed798013e7e12