Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning Mixtures of Gaussians Using Diffusion Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2404.18869.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T04:47:30.330316Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T03:29:30.816322Z
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 9fd820e9-6c9f-4c1d-ab9b-dc06f0d92388 · inbound
Masked Autoencoders Are Effective Tokenizers for Diffusion Models cites this paper.
Masked Autoencoders Are Effective Tokenizers for Diffusion Models Learning Mixtures of Gaussians Using Diffusion Models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T04:47:30.330316Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18f9a165-f426-420f-b017-285abcd15137 · 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 Mixtures of Gaussians Using Diffusion Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T22:01:57.526465Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f259e60-f672-481b-90cf-7ac6c5c38a1b · inbound
The two clocks and the innovation window: When and how generative models learn rules cites this paper.
The two clocks and the innovation window: When and how generative models learn rules Learning Mixtures of Gaussians Using Diffusion Models
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T03:16:18.650739Z
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.
Observation 14b5edda-47a8-4e4f-b481-b6e53cab2641 · inbound
Couple to Control: Joint Initial Noise Design in Diffusion Models cites this paper.
Couple to Control: Joint Initial Noise Design in Diffusion Models Learning Mixtures of Gaussians Using Diffusion Models
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T02:17:06.570481Z
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.
Observation 3c5247ae-0c6c-45f1-bd74-eb1cc939f609 · 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 Mixtures of Gaussians Using Diffusion Models
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T08:01:16.037059Z
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.
Observation eb6a549d-d6da-42fd-8f94-7fbd6dc8217b · inbound
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models cites this paper.
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models Learning Mixtures of Gaussians Using Diffusion Models
Reference 17
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T05:56:40.355605Z
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.
Observation 4080465f-47e4-4894-92fc-698345b93e78 · 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 Mixtures of Gaussians Using Diffusion Models
Reference 6
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T03:29:30.818744Z
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.