Paper Citation Record · LEDGER

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance

As of 3 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2605.06553.

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

pith.paper-citation-record.v1
2605.06553 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T12:29:16.215618Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-02T06:30:47.504484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:33:07.718877Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

51 of 51 outbound references displayed

verified exact5
verified fuzzy39
unresolved5
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 499bfaf8-d9e4-4c93-b45e-df786ba1ba06 · outbound

This paper cites URL https://huggingface.co/black-forest-labs/ FLUX.1-dev.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance URL https://huggingface.co/black-forest-labs/ FLUX.1-dev

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.536796Z

Source-reported events for the cited work

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Observation 0c42d4e2-33c9-4c63-852d-cdc8bbd7fc45 · outbound

This paper cites Building normalizing flows with stochastic interpolants.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Building normalizing flows with stochastic interpolants

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.526217Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 2dcc1c73-57c1-4860-bcae-46dbb4daabce · outbound

This paper cites Anderson.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Anderson

Reference 3

Resolution: verified exact; doi, observed 2026-05-08T21:39:15.303899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation c4a9e3f2-c4b3-49f0-81e3-236766f365e6 · outbound

This paper cites Structured denoising diffusion models in discrete state-spaces.Advances in neural information processing systems, 34:17981–17993.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Structured denoising diffusion models in discrete state-spaces.Advances in neural information processing systems, 34:17981–17993

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.511251Z

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Observation f4324867-bc68-40e1-9a87-c19a179e3e4c · outbound

This paper cites Jaakkola.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Jaakkola

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.514893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation d861d945-e0e6-48cc-96d3-d197f1f94cbc · outbound

This paper cites Jaakkola.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Jaakkola

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.503964Z

Source-reported events for the cited work

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Observation 3eb039c3-87aa-4e8f-83ea-cc9bcc3f7095 · outbound

This paper cites Vision transformers need registers.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Vision transformers need registers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.511007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 09588002-00a9-4f6d-84b4-9cd2ad717230 · outbound

This paper cites Diffusion models beat GANs on image synthesis.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Diffusion models beat GANs on image synthesis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.500598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 19992eea-4398-455d-98e3-91f18ec5b68e · outbound

This paper cites Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:16:07.381790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 7050fc7f-f2a7-4624-910d-4475222a3418 · outbound

This paper cites It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-11T19:16:07.397917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 95eace32-f837-4051-a008-09c2c145332b · outbound

This paper cites doi: 10.18653/v1/2021.emnlp-main.595.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance doi: 10.18653/v1/2021.emnlp-main.595

Reference 11

Resolution: metadata mismatch; doi, observed 2026-05-08T21:39:15.307608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation d48732ea-536f-4e36-996f-36abe86c135e · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equi- librium.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equi- librium

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.507711Z

Source-reported events for the cited work

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Observation fa40809a-334e-4f5d-9db4-0987fa88019b · outbound

This paper cites Classifier-free diffusion guidance.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Classifier-free diffusion guidance

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.518462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 049ad4b5-a9ca-4978-b7ae-f671c83f82af · outbound

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

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.525955Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 046e35ea-9d7e-4e59-b38d-806b3711907f · outbound

This paper cites Equivariant diffusion for molecule generation in 3D.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Equivariant diffusion for molecule generation in 3D

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.557254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 637b3449-64fd-47c3-830a-de210229a88d · outbound

This paper cites Simulation-free differential dynamics through neural conservation laws.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Simulation-free differential dynamics through neural conservation laws

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.547287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation e2eb905d-27c1-4508-a430-4ffa74e79013 · outbound

This paper cites An information-theoretic evaluation of generative models in learning multi-modal distributions.Advances in Neural Information Processing Systems, 36:9931–9943.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance An information-theoretic evaluation of generative models in learning multi-modal distributions.Advances in Neural Information Processing Systems, 36:9931–9943

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.483449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 8ef16559-a3fa-434b-8f73-4a3b655d4dd4 · outbound

This paper cites SPARKE: Scalable prompt- aware diversity and novelty guidance in diffusion models via RKE score.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance SPARKE: Scalable prompt- aware diversity and novelty guidance in diffusion models via RKE score

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.495457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 5754901b-d96a-48d7-b872-4f0f6f1b2c8b · outbound

This paper cites Rethinking FID: Towards a better evaluation metric for image generation.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Rethinking FID: Towards a better evaluation metric for image generation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.472780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation f463b6b1-41f6-4d21-9c5e-ba5a6ce77c57 · outbound

This paper cites Torsional diffusion for molecular conformer generation.Advances in neural information processing systems, 35:24240–24253.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Torsional diffusion for molecular conformer generation.Advances in neural information processing systems, 35:24240–24253

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.543935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 5190adef-6bc7-41c9-9800-3b51fe62ee13 · outbound

This paper cites Diverse text-to-image generation via contrastive noise optimization.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Diverse text-to-image generation via contrastive noise optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.503263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation ce92016d-7ea4-4ded-8015-930fef555931 · outbound

This paper cites an unresolved cited work.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:17:49.514644Z

Source-reported events for the cited work

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Observation c14ea867-ad5c-42f5-b994-44ca9fa98be7 · outbound

This paper cites Shielded diffusion: Generating novel and diverse images using sparse repellency.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Shielded diffusion: Generating novel and diverse images using sparse repellency

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.461459Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 2bf8ade4-a2e2-485c-bace-387436f54df4 · outbound

This paper cites Feedback guidance of diffusion models.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Feedback guidance of diffusion models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.570244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 924a9cda-d3c8-4a4f-a549-b8eb5f2bc75f · outbound

This paper cites Applying guidance in a limited interval improves sample and distribution quality in diffusion models.Advances in Neural Information Processing Systems, 37:122458–122483.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Applying guidance in a limited interval improves sample and distribution quality in diffusion models.Advances in Neural Information Processing Systems, 37:122458–122483

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.485128Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation dc6ce5eb-8e8d-4a35-b93e-ba7b672aa5d1 · outbound

This paper cites Laion-aesthetics predictor v2.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Laion-aesthetics predictor v2

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.529059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 383634c4-10f8-4c6d-88db-590d3a08c9c6 · outbound

This paper cites Diffusion-LM improves controllable text generation.Advances in neural information processing systems, 35:4328–4343.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Diffusion-LM improves controllable text generation.Advances in neural information processing systems, 35:4328–4343

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.448471Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 8a9a9324-fff0-4b13-9eaa-30bb19360c85 · outbound

This paper cites Microsoft COCO: Common objects in context.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Microsoft COCO: Common objects in context

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.450244Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation dbd1362d-04fb-4e36-a3c4-376678f371ba · outbound

This paper cites an unresolved cited work.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:17:49.481804Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 9b5eb67b-be0a-4717-a448-48046e213906 · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.464968Z

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Observation 0e4aa639-949c-41b5-a764-5438ff611700 · outbound

This paper cites Score-Regularized Joint Sampling with Importance Weights for Flow Matching.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Score-Regularized Joint Sampling with Importance Weights for Flow Matching

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-07T03:18:10.688058Z

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Observation f9a719dd-b39f-46d8-a7f8-f95a9e7c18ba · outbound

This paper cites Discrete diffusion modeling by estimating the ratios of the data distribution.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Discrete diffusion modeling by estimating the ratios of the data distribution

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.529799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation c5033e4b-c2b2-4788-8a5d-efd0578de277 · outbound

This paper cites an unresolved cited work.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:17:49.563387Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 8dcf6799-1b44-4807-be4b-2dca44fda744 · outbound

This paper cites ProCreate, don’t reproduce! propulsive energy diffusion for creative generation.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance ProCreate, don’t reproduce! propulsive energy diffusion for creative generation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.493831Z

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No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation a2000be2-3789-4643-a814-f97b8ada5849 · outbound

This paper cites DiverseFlow: Sample-efficient diverse mode coverage in flows.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance DiverseFlow: Sample-efficient diverse mode coverage in flows

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.548578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 2f026a3e-d779-461f-a91a-b9dc78399822 · outbound

This paper cites Narcowich and Joseph D.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Narcowich and Joseph D

Reference 36

Resolution: verified exact; doi, observed 2026-05-08T21:39:15.300324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation cea16668-34d2-4442-a683-6c72bc6fdc85 · outbound

This paper cites Representing flow fields with divergence-free kernels for reconstruction.Proceedings of the ACM on Computer Graphics and Interactive Techniques, 8(4):1–21.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Representing flow fields with divergence-free kernels for reconstruction.Proceedings of the ACM on Computer Graphics and Interactive Techniques, 8(4):1–21

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.540296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 7dd7db10-653d-4e10-8b91-d8da4c59f3dd · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Representation Learning with Contrastive Predictive Coding

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-11T19:16:07.366309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 4ffa6def-d012-4e7e-8257-bcd1372fc9ce · outbound

This paper cites Inequalities for differential and integral equations.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Inequalities for differential and integral equations

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.432188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 74660522-67a3-4e29-8065-b960c324218b · outbound

This paper cites Scaling group inference for diverse and high-quality generation.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Scaling group inference for diverse and high-quality generation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.522673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation a9547ca2-3550-41d8-a5e1-e8075f9751ae · outbound

This paper cites SDXL: Improving latent diffusion models for high-resolution image synthesis.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance SDXL: Improving latent diffusion models for high-resolution image synthesis

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.457304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation ae7fa3d9-3760-477a-89cc-d6a81b7e93cc · outbound

This paper cites Learning transferable visual models from natural language supervision.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Learning transferable visual models from natural language supervision

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.469903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation b0c05e5d-903a-49ac-9f93-5dc3340a78ac · outbound

This paper cites an unresolved cited work.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:17:49.545332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 89f096f5-87d9-46cf-96e5-42b26f398659 · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance High- resolution image synthesis with latent diffusion models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.551698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation e84d2b52-0d78-4e1c-adf0-b5fd3b6ec47d · outbound

This paper cites an unresolved cited work.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:17:49.554385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation cefca909-d681-4126-b8ee-828153fcab87 · outbound

This paper cites Simple and effective masked diffusion language models.Advances in Neural Information Processing Systems, 37:130136–130184.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Simple and effective masked diffusion language models.Advances in Neural Information Processing Systems, 37:130136–130184

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.560105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation a648fb18-a3a4-4b6a-bd92-0c8015ae8702 · outbound

This paper cites Deep unsuper- vised learning using nonequilibrium thermodynamics.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Deep unsuper- vised learning using nonequilibrium thermodynamics

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.566967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 03ca509d-a330-4700-8cb0-fcb1b8d5174b · outbound

This paper cites Denoising diffusion implicit models.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Denoising diffusion implicit models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.542124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 4edb5c9a-9743-47dd-8e28-f86d923e2fca · outbound

This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.532214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 96b04749-8650-4dac-9c7e-cbce6772af83 · outbound

This paper cites A bound for the error in the normal approximation to the distribution of a sum of dependent random variables.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance A bound for the error in the normal approximation to the distribution of a sum of dependent random variables

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.535493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 524a4381-67da-4193-9279-6b028a2bfa2a · outbound

This paper cites GeoDiff: A geometric diffusion model for molecular conformation generation.

Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance GeoDiff: A geometric diffusion model for molecular conformation generation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:17:49.538917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Pith citing papers

Observation 5608cc70-e42c-42ae-976e-e058fd87ead4 · inbound

Manifold-Constrained Noise Optimization for Diverse Diffusion Sampling cites this paper.

Manifold-Constrained Noise Optimization for Diverse Diffusion Sampling Diverse Sampling in Diffusion Models with Marginal Preserving Particle Guidance

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T23:33:07.718877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.