Paper Citation Record · LEDGER

The Score-Difference Flow for Implicit Generative Modeling

As of 20 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2304.12906.

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

pith.paper-citation-record.v1
2304.12906 v5

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T08:59:02.744628Z

measured 24 of 24 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:14:31.375599Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T15:14:31.458579Z

Reference resolution

23 of 23 outbound references displayed

verified exact12
verified fuzzy10
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b4801be-6590-4380-b0f9-4d387fff5482 · outbound

This paper cites nearest neighbor.

The Score-Difference Flow for Implicit Generative Modeling nearest neighbor

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.021532Z

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.

Observation 435e3a6b-870a-4a8f-94df-2af3776112b9 · outbound

This paper cites Relative Entropy Gradient Sampler for Unnormalized Distributions.

The Score-Difference Flow for Implicit Generative Modeling Relative Entropy Gradient Sampler for Unnormalized Distributions

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:59:14.766987Z

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.

Observation 9549b372-2591-49d5-8def-68e50e2a3bbb · outbound

This paper cites Deep generative learning via variational gradient flow.

The Score-Difference Flow for Implicit Generative Modeling Deep generative learning via variational gradient flow

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.016796Z

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.

Observation 35ce0163-2738-438c-b95a-d586dcf89635 · outbound

This paper cites NIPS 2016 Tutorial: Generative Adversarial Networks.

The Score-Difference Flow for Implicit Generative Modeling NIPS 2016 Tutorial: Generative Adversarial Networks

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-24T08:59:14.800333Z

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.

Observation b17554e1-910c-4062-b416-b152d8dc6206 · outbound

This paper cites Generative Adversarial Networks.

The Score-Difference Flow for Implicit Generative Modeling Generative Adversarial Networks

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-24T08:59:14.758248Z

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.

Observation 51687bae-b44c-4e12-83ed-fe074a83e264 · outbound

This paper cites FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.

The Score-Difference Flow for Implicit Generative Modeling FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-24T08:59:14.808668Z

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.

Observation 2035eb24-c71e-4577-888f-2091581d08c7 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851.

The Score-Difference Flow for Implicit Generative Modeling Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.014438Z

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.

Observation 6ccd781e-91a4-424d-802f-4b464b1e08ea · outbound

This paper cites Elucidating the Design Space of Diffusion-Based Generative Models.

The Score-Difference Flow for Implicit Generative Modeling Elucidating the Design Space of Diffusion-Based Generative Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-24T08:59:14.781524Z

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.

Observation 927e7996-11a2-49fc-8208-c8df6c1002ce · outbound

This paper cites Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures.

The Score-Difference Flow for Implicit Generative Modeling Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:59:14.786814Z

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.

Observation 633946ad-a328-43bc-b633-c5614572de4a · outbound

This paper cites A class of markov processes associated with nonlinear parabolic equations.Proceedings of the National Academy of Sciences, 56(6):1907–1911.

The Score-Difference Flow for Implicit Generative Modeling A class of markov processes associated with nonlinear parabolic equations.Proceedings of the National Academy of Sciences, 56(6):1907–1911

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.012151Z

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.

Observation 43e8cd2a-afed-4152-ab8a-9f31dd4a2a41 · outbound

This paper cites Hopfield Networks is All You Need.

The Score-Difference Flow for Implicit Generative Modeling Hopfield Networks is All You Need

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-24T08:59:14.770812Z

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.

Observation 706d6b86-33c5-43fc-91be-8dcf9bd58642 · outbound

This paper cites How to Train Your Energy-Based Models.

The Score-Difference Flow for Implicit Generative Modeling How to Train Your Energy-Based Models

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:59:14.804969Z

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.

Observation fd3ddd5f-cc52-40b5-8ec0-86b841ee0ab5 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

The Score-Difference Flow for Implicit Generative Modeling Score-Based Generative Modeling through Stochastic Differential Equations

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-24T08:59:14.775452Z

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.

Observation 98171e40-c112-4ec8-bad2-e43e2e680f08 · outbound

This paper cites Density estimation in infinite dimensional exponential families.Journal of Machine Learning Research, 18.

The Score-Difference Flow for Implicit Generative Modeling Density estimation in infinite dimensional exponential families.Journal of Machine Learning Research, 18

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.009672Z

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.

Observation a8eee696-e3ba-4bcf-ad6e-2cd058e97574 · outbound

This paper cites Exploiting the Hidden Tasks of GANs: Making Implicit Subproblems Explicit.

The Score-Difference Flow for Implicit Generative Modeling Exploiting the Hidden Tasks of GANs: Making Implicit Subproblems Explicit

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:59:14.795603Z

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.

Observation 08809f5c-61c1-499b-9a82-1df9d79d33d6 · outbound

This paper cites Tackling the Generative Learning Trilemma with Denoising Diffusion GANs.

The Score-Difference Flow for Implicit Generative Modeling Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:59:14.762793Z

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.

Observation 8fd6119a-d5a5-432d-ace5-cc68ed36f1bd · outbound

This paper cites Improved Distribution Matching Distillation for Fast Image Synthesis.

The Score-Difference Flow for Implicit Generative Modeling Improved Distribution Matching Distillation for Fast Image Synthesis

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:59:14.791328Z

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.

Observation 4f7965f9-aee1-4b9c-bd6e-d361ac8d0883 · outbound

This paper cites In Appendix B.2, we describe the evolution of the generative distribution of a GAN underany loss.

The Score-Difference Flow for Implicit Generative Modeling In Appendix B.2, we describe the evolution of the generative distribution of a GAN underany loss

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.007123Z

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.

Observation 4d37bc5b-07e1-49f7-b207-8e97f65a31ee · outbound

This paper cites The results of Section 3.1 suggest that, in the limit of infinite data, this direction is proportional to∇ztp(zt;σ)−∇ztqt(zt;σ).

The Score-Difference Flow for Implicit Generative Modeling The results of Section 3.1 suggest that, in the limit of infinite data, this direction is proportional to∇ztp(zt;σ)−∇ztqt(zt;σ)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.019057Z

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.

Observation a6f738f1-2e3d-488d-bffb-d5b487c23cba · outbound

This paper cites mystery distribution.

The Score-Difference Flow for Implicit Generative Modeling mystery distribution

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.004747Z

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.

Observation e5ceed8f-2cac-4aee-bdfa-9437cb8426f4 · outbound

This paper cites Swiss roll.

The Score-Difference Flow for Implicit Generative Modeling Swiss roll

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.023457Z

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.

Observation 9155602a-1213-44ae-836a-996224d98c2a · outbound

This paper cites Swiss roll.

The Score-Difference Flow for Implicit Generative Modeling Swiss roll

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:59:15.002813Z

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.

Observation 9bca471f-752f-49c7-b776-2e3b0eef3fa8 · outbound

This paper cites an unresolved cited work.

The Score-Difference Flow for Implicit Generative Modeling Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-05-24T08:59:15.025712Z

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.

Pith citing papers

Observation 87cc5f03-8278-432c-95ed-4d677373f63e · inbound

Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models cites this paper.

Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models The Score-Difference Flow for Implicit Generative Modeling

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T15:14:31.466293Z

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.