Paper Citation Record · LEDGER

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

As of 18 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 4 inbound Pith citation observations for arXiv:2501.18913.

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

pith.paper-citation-record.v1
2501.18913 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:03:03.675377Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:36:26.525335Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T17:57:33.501777Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation 7374ec49-89bd-4889-b7c1-ed755df1cdf3 · outbound

This paper cites Universal guidance for diffusion models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Universal guidance for diffusion models

Reference 1

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Observation 5c3980fd-6787-40b1-b5ae-3fb6b25f1553 · outbound

This paper cites Demystifying MMD GANs.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Demystifying MMD GANs

Reference 2

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Observation 3ef8b8de-7c07-4520-8ab2-5f0853a0d537 · outbound

This paper cites Tweedie Moment Projected Diffusions For Inverse Problems.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Tweedie Moment Projected Diffusions For Inverse Problems

Reference 3

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Observation 8a4a3e95-e96d-49d4-8143-f5857bc0be78 · outbound

This paper cites Monte carlo guided denoising diffusion models for bayesian linear inverse problems.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Monte carlo guided denoising diffusion models for bayesian linear inverse problems

Reference 4

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Observation e7e9d2b7-967f-4d34-ae6c-16bf4cc44cb3 · outbound

This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Diffusion Posterior Sampling for General Noisy Inverse Problems

Reference 5

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Observation fc9fc221-5b8d-42aa-8915-bf9083550b98 · outbound

This paper cites Improving Diffusion Models for Inverse Problems using Manifold Constraints.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Improving Diffusion Models for Inverse Problems using Manifold Constraints

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T22:03:03.462293Z

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Observation 81b28e05-ddcb-446b-968b-9aca3fc77d30 · outbound

This paper cites Prompt-tuning latent diffusion models for inverse problems.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Prompt-tuning latent diffusion models for inverse problems

Reference 7

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Observation 6bad0aba-94a0-47fd-838a-9bc0119cd55f · outbound

This paper cites From Posterior Sampling to Meaningful Diversity in Image Restoration.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior From Posterior Sampling to Meaningful Diversity in Image Restoration

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T22:03:03.471760Z

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Observation f0095b0b-5d52-402e-9ca7-9d58efa1ac65 · outbound

This paper cites Elements of information theory.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Elements of information theory

Reference 9

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Observation c5b6a65b-1432-4746-a53f-727141de461c · outbound

This paper cites Closing the ODE-SDE gap in score-based diffusion models through the Fokker-Planck equation.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Closing the ODE-SDE gap in score-based diffusion models through the Fokker-Planck equation

Reference 10

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Observation 3f5450df-dc39-4fb6-abbd-d63bda45aaf3 · outbound

This paper cites Diffusion Models Beat GANs on Image Synthesis.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Diffusion Models Beat GANs on Image Synthesis

Reference 11

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Observation 83f673df-20a5-4588-b241-1935f13fce7d · outbound

This paper cites Diffusion posterior sampling for linear inverse problem solving: A filtering perspective.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Diffusion posterior sampling for linear inverse problem solving: A filtering perspective

Reference 12

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Observation e677aac5-1b84-40ce-83bf-c359ec1b0b36 · outbound

This paper cites Diffusion posterior sampling for linear inverse problem solving: A filtering perspective.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Diffusion posterior sampling for linear inverse problem solving: A filtering perspective

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T22:03:03.492952Z

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Observation 7c84933c-99b6-4fb8-84e5-085b3bca2ad9 · outbound

This paper cites Score-Based Diffusion Models as Principled Priors for Inverse Imaging.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Score-Based Diffusion Models as Principled Priors for Inverse Imaging

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T22:03:03.496861Z

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Observation e1745474-ba14-45b9-b783-e67705b01f12 · outbound

This paper cites Inverse Problems with Diffusion Models: A MAP Estimation Perspective.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Inverse Problems with Diffusion Models: A MAP Estimation Perspective

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-09T22:03:03.973834Z

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Observation 1be50626-7b12-46ab-8801-2403585fa6d8 · outbound

This paper cites Fast and stable diffusion inverse solver with history gradient update.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Fast and stable diffusion inverse solver with history gradient update

Reference 16

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Observation 61e88fef-ae40-483c-9f76-a1d749b31725 · outbound

This paper cites Manifold preserving guided diffusion.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Manifold preserving guided diffusion

Reference 17

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Source-reported events for the cited work

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Observation 3d604d60-afc7-4130-9319-776a50f672eb · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:03:04.361553Z

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Observation d137bd01-df16-4dc6-baba-3bcd4a729cd1 · outbound

This paper cites Denoising diffusion probabilistic models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Denoising diffusion probabilistic models

Reference 19

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Observation bed286e8-6d8c-4cda-aa32-22be3d542a7d · outbound

This paper cites Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-09T22:03:03.953232Z

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Observation f8b89cc1-8fd3-458e-9fa9-a1edde8f276e · outbound

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

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Elucidating the Design Space of Diffusion-Based Generative Models

Reference 21

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Observation 7a44025a-19ed-46f2-b277-f14179588813 · outbound

This paper cites Denoising Diffusion Restoration Models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Denoising Diffusion Restoration Models

Reference 22

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Observation 69be5633-3982-4f7f-a2aa-c2ab0837521f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Adam: A Method for Stochastic Optimization

Reference 23

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Observation cecc490f-8b54-424b-9d81-e3e76d682ce1 · outbound

This paper cites Indoor scene layout estimation from a single image.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Indoor scene layout estimation from a single image

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:03:04.337467Z

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Observation f358045b-93e3-4eeb-a12e-96cf1b46f716 · outbound

This paper cites Flow Matching for Generative Modeling.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Flow Matching for Generative Modeling

Reference 25

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Observation 56e74ee1-96f8-4f4a-8baf-e25191bacfb5 · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Repaint: Inpainting using denoising diffusion probabilistic models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:03:04.323755Z

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Observation 74a13cb2-7d78-45a8-a2f9-d63583e464dd · outbound

This paper cites A Variational Perspective on Solving Inverse Problems with Diffusion Models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior A Variational Perspective on Solving Inverse Problems with Diffusion Models

Reference 28

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Observation b57befb4-8a44-4b9e-9b9e-0fb5de6d2ec3 · outbound

This paper cites Pulse: Self-supervised photo upsampling via latent space exploration of generative models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Pulse: Self-supervised photo upsampling via latent space exploration of generative models

Reference 29

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Observation c2dc667e-104d-4260-a0e3-c7ed5fb51261 · outbound

This paper cites Variational inference for monte carlo objectives.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Variational inference for monte carlo objectives

Reference 30

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Observation c1abb10c-364b-4df5-91ed-435f53e15275 · outbound

This paper cites Monte carlo gradient estimation in machine learning.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Monte carlo gradient estimation in machine learning

Reference 31

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Observation c0bae980-74aa-41e4-83ad-cc6f9c001f91 · outbound

This paper cites Particle Denoising Diffusion Sampler.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Particle Denoising Diffusion Sampler

Reference 32

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Observation 2a49ab3b-d6e0-428f-9abc-55796ff64b9d · outbound

This paper cites Training-free Linear Image Inverses via Flows.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Training-free Linear Image Inverses via Flows

Reference 33

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Observation b28e8408-1894-4b74-9946-d5a91d652ce9 · outbound

This paper cites Muckley, Ricky T.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Muckley, Ricky T

Reference 34

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Observation a72e1a13-bba2-4adb-8a0e-2901d4708ec4 · outbound

This paper cites Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion

Reference 35

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Observation 4ea16cb3-767f-4393-b6d6-eda40ff2e1cf · outbound

This paper cites Solving linear inverse problems provably via posterior sampling with latent diffusion models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Solving linear inverse problems provably via posterior sampling with latent diffusion models

Reference 36

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Observation a15c5f3c-566c-447e-895e-18b3002fbe7b · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Deep unsupervised learning using nonequilibrium thermodynamics

Reference 37

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Observation 6988c5a3-27d4-45c4-98d6-cade3367f98c · outbound

This paper cites Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency

Reference 38

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Observation 1b668e2e-3d33-4096-9d9f-baf854171f1d · outbound

This paper cites Pseudoinverse-guided diffusion models for inverse problems.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Pseudoinverse-guided diffusion models for inverse problems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:03:04.237202Z

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Observation 86ad822d-82a6-4f40-abdd-423f7fb57475 · outbound

This paper cites Pseudoinverse-guided diffusion models for inverse problems.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Pseudoinverse-guided diffusion models for inverse problems

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:03:04.223751Z

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Observation c696d237-c751-476b-ad6f-f2b7beb1dce8 · outbound

This paper cites Loss-guided diffusion models for plug-and-play controllable generation.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Loss-guided diffusion models for plug-and-play controllable generation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:03:04.210266Z

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Observation 2215dcf7-04fe-46fd-b20a-0395df677c43 · outbound

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

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Score-Based Generative Modeling through Stochastic Differential Equations

Reference 42

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This paper cites Exploiting diffusion prior for real-world image super-resolution.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Exploiting diffusion prior for real-world image super-resolution

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:03:04.196336Z

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Observation 6426d84b-606c-456d-8051-81d87db6dba4 · outbound

This paper cites Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

Reference 44

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This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Simple statistical gradient-following algorithms for connectionist reinforcement learning

Reference 45

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This paper cites Practical and asymptotically exact conditional sampling in diffusion models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Practical and asymptotically exact conditional sampling in diffusion models

Reference 46

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Observation 90c7cb65-9bcc-45e1-b254-0d4a07d78c8d · outbound

This paper cites Guidance with Spherical Gaussian Constraint for Conditional Diffusion.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Guidance with Spherical Gaussian Constraint for Conditional Diffusion

Reference 47

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This paper cites FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model

Reference 48

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This paper cites Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing

Reference 49

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This paper cites Adding Conditional Control to Text-to-Image Diffusion Models.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Adding Conditional Control to Text-to-Image Diffusion Models

Reference 50

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This paper cites Efros, Eli Shechtman, and Oliver Wang.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Efros, Eli Shechtman, and Oliver Wang

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:03:04.163404Z

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Observation b0a0ddd2-aead-452f-ad25-0ca690591e34 · outbound

This paper cites write newline.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior write newline

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Observation 82887540-f108-41c6-bf97-118b1779918b · outbound

This paper cites @esa (Ref.

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior @esa (Ref

Reference 53

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Observation 7fe8e2df-c950-4a0f-b690-9f8ee85999e7 · outbound

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Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Unresolved cited work

Reference 54

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Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior Unresolved cited work

Reference 55

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Pith citing papers

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Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation cites this paper.

Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

Reference 31

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Local MAP Sampling for Diffusion Models cites this paper.

Local MAP Sampling for Diffusion Models Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

Reference 19

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Proximal-Based Generative Modeling for Bayesian Inverse Problems cites this paper.

Proximal-Based Generative Modeling for Bayesian Inverse Problems Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

Reference 116

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DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models cites this paper.

DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

Reference 36

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