Paper Citation Record · LEDGER

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

As of 7 August 2026, this Paper Citation Record lists 100 of 269 outbound references and 11 inbound Pith citation observations for arXiv:2506.03979.

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

pith.paper-citation-record.v1
2506.03979 v2

Coverage vector

measured 100 of 269 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:54:50.012195Z

measured 111 of 111 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:18:57.787056Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 269 outbound references displayed

verified exact2
verified fuzzy0
unresolved98
parse uncertain0
malformed identifier0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation fae29518-3919-4bf5-9133-0f7ddaab0b8d · outbound

This paper cites Bayesian inverse problems for functions and applications to fluid mechanics.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Bayesian inverse problems for functions and applications to fluid mechanics

Reference 1

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Observation bad6d2fc-a493-4807-8460-e38f011256ec · outbound

This paper cites Inverse problems in free surface flows: a review.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Inverse problems in free surface flows: a review

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.685747Z

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Observation 3b9146c0-5698-4eb0-aaa4-62acb9e1a77c · outbound

This paper cites Inverse problems: Basics, theory and applications in geophysics.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Inverse problems: Basics, theory and applications in geophysics

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.689225Z

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Observation 441267c8-caa1-40d0-a752-f0b6e1c9dc10 · outbound

This paper cites Sparse mri: The application of compressed sensing for rapid mr imaging.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Sparse mri: The application of compressed sensing for rapid mr imaging

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.692651Z

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Observation 6a02a158-8c57-4b87-a3c2-aa898a2f5d6f · outbound

This paper cites Tomographic phase microscopy.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Tomographic phase microscopy

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.695915Z

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Observation 55afc8e2-15ee-4ff1-b1c9-ddb31768dd2f · outbound

This paper cites Introduction to inverse problems in imaging.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Introduction to inverse problems in imaging

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.699248Z

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Observation d953f990-31f8-4ab6-bbb1-6af544c9e597 · outbound

This paper cites Mcmc using hamiltonian dynamics.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Mcmc using hamiltonian dynamics

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.702912Z

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Observation 3e806c68-6be5-425e-8651-5eaa577adbcf · outbound

This paper cites Bayesian learning via stochastic gradient langevin dynamics.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Bayesian learning via stochastic gradient langevin dynamics

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.706351Z

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Observation 0cdb27ff-e601-455f-8e4e-23db256dd110 · outbound

This paper cites Dimension-independent likelihood- informed mcmc.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Dimension-independent likelihood- informed mcmc

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.709808Z

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Observation e79358b5-949f-44c4-a22c-70f1bf1104e0 · outbound

This paper cites Invertible gen- erative models for inverse problems: mitigating representation error and dataset bias.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Invertible gen- erative models for inverse problems: mitigating representation error and dataset bias

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.713106Z

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Observation 65cb643f-a52e-487d-b037-cbc45dc27b3e · outbound

This paper cites Solving bayesian inverse problems from the perspective of deep generative networks.Computational Mechanics, 64:395–408, 2019.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Solving bayesian inverse problems from the perspective of deep generative networks.Computational Mechanics, 64:395–408, 2019

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.716747Z

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Observation 4947eca6-f7b1-4f76-9985-9aa5d3b6e6e0 · outbound

This paper cites Multiscale invertible generative networks for high-dimensional bayesian inference.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Multiscale invertible generative networks for high-dimensional bayesian inference

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.720590Z

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Observation a717239b-f99b-47d4-b6c4-ebe5f2149773 · outbound

This paper cites Composing normalizing flows for inverse problems.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Composing normalizing flows for inverse problems

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.723588Z

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Observation 3f82eccf-d197-4080-89c7-46c37472cfff · outbound

This paper cites Solving inverse problems with a flow-based noise model.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Solving inverse problems with a flow-based noise model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.726981Z

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Observation fdfbdd92-663f-4eba-aad6-3ae0332aba98 · outbound

This paper cites Stochastic normalizing flows for in- verse problems: A markov chains viewpoint.SIAM/ASA Journal on Uncertainty Quantification, 10(3):1162–1190, 2022.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Stochastic normalizing flows for in- verse problems: A markov chains viewpoint.SIAM/ASA Journal on Uncertainty Quantification, 10(3):1162–1190, 2022

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.730167Z

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Observation d4699705-7d0b-49e2-8fc2-d227a5484ba9 · outbound

This paper cites Bayesian Inference with Generative Adversarial Network Priors.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Bayesian Inference with Generative Adversarial Network Priors

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.733373Z

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Observation d802d444-41ef-4c33-9f86-c2680b0a87ca · outbound

This paper cites Compressed sensing using generative models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Compressed sensing using generative models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.737234Z

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Observation 15c3d3a6-97b9-4c9b-8256-c176eef1b301 · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.740290Z

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Observation 92942889-0aa6-4a60-b424-9c5689507730 · outbound

This paper cites Building Normalizing Flows with Stochastic Interpolants.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Building Normalizing Flows with Stochastic Interpolants

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.743540Z

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Observation 84f558fc-6d8d-4f7c-970a-81979778070a · outbound

This paper cites Flow Matching for Generative Modeling.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Flow Matching for Generative Modeling

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.747250Z

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Observation fac53e8f-9388-4d6e-90a9-85622139f259 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.750259Z

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Observation be07d956-c583-4c8e-9204-7bb940fc5f15 · outbound

This paper cites Deep un- supervised learning using nonequilibrium thermodynamics.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Deep un- supervised learning using nonequilibrium thermodynamics

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.753848Z

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Observation 6cda063b-6b3f-4e10-941a-22a2438c9bf7 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 23

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Observation 35bd3612-0db9-4a5c-b9bf-49db0b69b029 · outbound

This paper cites Denoising Diffusion Implicit Models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Denoising Diffusion Implicit Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.760236Z

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Observation b249e178-621e-490a-ba84-431e31473c49 · outbound

This paper cites Maximum likelihood training of score-based diffusion models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Maximum likelihood training of score-based diffusion models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.763199Z

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Observation 7118c95c-c539-4b15-9c27-717e5c3174f3 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Generative modeling by estimating gradients of the data distribution

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.766157Z

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Observation 62af543d-333c-4283-bf53-cc8c3fbfd503 · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Score-Based Generative Modeling through Stochastic Differential Equations

Reference 27

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Observation fda0b2a8-7fb8-4e2e-bf28-958dce37a2a5 · outbound

This paper cites Monge-Amp\`ere Flow for Generative Modeling.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Monge-Amp\`ere Flow for Generative Modeling

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.772857Z

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Observation 247ca531-74ad-49a4-bb98-4fe877926eec · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Diffusion Posterior Sampling for General Noisy Inverse Problems

Reference 29

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Observation 575755c8-e507-483c-8312-ae42e8ff2c94 · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Pseudoinverse-guided diffusion models for inverse problems

Reference 30

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Observation 65c057d9-71ae-4895-96bf-2317af4faabe · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Practical and asymptotically exact conditional sampling in diffusion models

Reference 31

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Observation 772af08a-2d6f-47cf-8d8f-e7708a66fb56 · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Monte carlo guided denoising diffusion models for bayesian linear inverse problems

Reference 32

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Observation f3228762-ef62-4846-ab64-28f67066a4c3 · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Diffusion posterior sampling for linear inverse problem solving: A filtering perspective

Reference 33

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Observation 4ab7fc11-9f73-4b30-b36f-883c1e8e804e · outbound

This paper cites Provable probabilistic imaging using score-based generative priors.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Provable probabilistic imaging using score-based generative priors

Reference 34

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Observation a88daac2-ee69-4308-8dc0-8b077835e6d9 · outbound

This paper cites Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction

Reference 35

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Observation ea93ebd2-4c08-4c80-b4a6-001af94b99ae · outbound

This paper cites Principled probabilistic imaging using diffusion models as plug-and-play priors.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Principled probabilistic imaging using diffusion models as plug-and-play priors

Reference 36

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Observation 4c492833-32e5-46e3-bf06-9686563e3864 · outbound

This paper cites Provable posterior sampling with denoising oracles via tilted transport.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Provable posterior sampling with denoising oracles via tilted transport

Reference 37

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Observation 6a29e374-c25d-4f1a-98d8-ae89676950a9 · outbound

This paper cites A Survey on Diffusion Models for Inverse Problems.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A Survey on Diffusion Models for Inverse Problems

Reference 38

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Observation 74ed7f42-1c97-44f6-b1b0-bbffc27658bb · outbound

This paper cites ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

Reference 39

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Observation d7bd5856-2827-4994-b964-fb3164ac62d6 · outbound

This paper cites Solving Inverse Problems in Medical Imaging with Score-Based Generative Models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Reference 40

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Observation 17e8d10a-c944-47a6-9881-484b5beb0dd7 · outbound

This paper cites Tweedie Moment Projected Diffusions For Inverse Problems.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Tweedie Moment Projected Diffusions For Inverse Problems

Reference 41

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Observation 5ddbcee9-9d66-41b8-af36-f53fc4ba188e · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

Reference 42

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Observation 149d128f-e4a0-4d82-83e1-099d478c23c4 · outbound

This paper cites Denoising diffusion restora- tion models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Denoising diffusion restora- tion models

Reference 43

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Observation ca177778-d1f5-4ae7-9448-c1b9e9080611 · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Solving linear inverse problems provably via posterior sampling with latent diffusion models

Reference 44

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Observation a20cacdd-7c8f-4749-9632-12aa143bbd5e · outbound

This paper cites Plug-and-play split gibbs sampler: embedding deep generative priors in bayesian inference.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Plug-and-play split gibbs sampler: embedding deep generative priors in bayesian inference

Reference 45

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Observation 39776355-7863-470a-bbc1-fe08fb9f2fc7 · outbound

This paper cites Inversebench: Benchmarking plug-and-play diffusion priors for inverse problems in physical sciences.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Inversebench: Benchmarking plug-and-play diffusion priors for inverse problems in physical sciences

Reference 46

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This paper cites Split-and-augmented gibbs sam- pler—application to large-scale inference problems.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Split-and-augmented gibbs sam- pler—application to large-scale inference problems

Reference 47

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Observation 03051859-90ad-4e9c-9c5d-ca6569cd99e8 · outbound

This paper cites The split gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach The split gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution

Reference 48

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Observation c1083a72-cd11-4c56-9d81-7215816603b7 · outbound

This paper cites Solving linear-gaussian bayesian inverse problems with decoupled diffusion sequential monte carlo.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Solving linear-gaussian bayesian inverse problems with decoupled diffusion sequential monte carlo

Reference 49

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Observation 91f3e69d-5e59-42df-855b-0350820b3b3b · outbound

This paper cites Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts

Reference 50

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Observation 59e22e83-ae91-48e4-9f1b-b447dd5c80cb · outbound

This paper cites Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.849193Z

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Observation da3e911f-2f26-4d84-a7eb-79a21f4474ff · outbound

This paper cites LEAPS: A discrete neural sampler via locally equivariant networks.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach LEAPS: A discrete neural sampler via locally equivariant networks

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Observation 41ed4d40-c8c3-40e1-bf79-611566f348e8 · outbound

This paper cites Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.856762Z

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Observation 3f22b99f-0acf-4cb9-b52c-4a234be84a19 · outbound

This paper cites Monte Carlo strategies in scientific computing, volume 75.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Monte Carlo strategies in scientific computing, volume 75

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A sequential particle filter method for static models

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Sequential monte carlo samplers

Reference 56

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A tutorial on particle filtering and smoothing: Fifteen years later

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Mean field simulation for monte carlo integration

Reference 58

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Feynman-Kac formulae: genealogical and interacting particle systems with applications

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Observation 1a1cea1a-20dd-4321-a0c6-9b3c04f7b4ae · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A style-based generator architecture for generative adversarial networks

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Observation ef0b4797-c539-4770-97d5-1f287e113cce · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Imagenet: A large- scale hierarchical image database

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Observation 6429ab48-bd9e-44b1-807c-3027e64beda7 · outbound

This paper cites Inverse problems: a bayesian perspective.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Inverse problems: a bayesian perspective

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Observation 8be52dee-5421-4fba-8548-1959be2b93ea · outbound

This paper cites Renormalizing Diffusion Models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Renormalizing Diffusion Models

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Observation f7d671d9-c9d0-4708-9d01-ce78c3cf15a5 · outbound

This paper cites Diffusion models learn distributions generated by complex Langevin dynamics.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Diffusion models learn distributions generated by complex Langevin dynamics

Reference 64

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Observation d97bb954-d24d-42cb-85b0-8597ddd78c95 · outbound

This paper cites Quantum State Generation with Structure-Preserving Diffusion Model.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Quantum State Generation with Structure-Preserving Diffusion Model

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Observation 2ba86b55-4a04-4c7f-9c19-ca6530fab75e · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

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Observation d20e89f3-d00a-44e7-9780-9a73dd39fd0e · outbound

This paper cites Diffusion models in de novo drug design.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Diffusion models in de novo drug design

Reference 67

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Observation dc42dfa0-e31b-4b05-90ac-030bfd288765 · outbound

This paper cites Crystal structure determination from powder diffraction patterns with generative machine learning.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Crystal structure determination from powder diffraction patterns with generative machine learning

Reference 68

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Observation 9e441d78-de88-4ac2-9ace-dfc0a18eb715 · outbound

This paper cites Protein generation with evolutionary diffusion: sequence is all you need.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Protein generation with evolutionary diffusion: sequence is all you need

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Observation 392886c3-784a-4858-98ed-d2532654e6b8 · outbound

This paper cites De novo design of protein structure and function with rfdiffusion.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach De novo design of protein structure and function with rfdiffusion

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Observation c51324dc-5e27-427d-894f-0f23c2dc88bf · outbound

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

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach High-resolution image synthesis with latent diffusion models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.917939Z

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Observation b2d40e00-0aed-4e81-9b10-6ad3521c232d · outbound

This paper cites Tutorial on diffusion models for imaging and vision.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Tutorial on diffusion models for imaging and vision

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Observation 5618bd4f-06a7-460f-8ca0-9bd5b22e90c9 · outbound

This paper cites Diffusion-lm improves controllable text generation.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Diffusion-lm improves controllable text generation

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Observation 4c3ce1e3-f4a6-45dd-a1aa-4f085d658ceb · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Elucidating the design space of diffusion-based generative models

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Observation ffef78ce-5d84-430f-b32d-524626faa8a6 · outbound

This paper cites Reverse-time diffusion equation models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Reverse-time diffusion equation models

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Observation 7c7f08bc-e24c-45a1-84e7-58a2dbf00c05 · outbound

This paper cites Estimation of non-normalized statistical models by score matching.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Estimation of non-normalized statistical models by score matching

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Observation 56dfb50c-ea4e-43ee-a7d5-e7d67f0be606 · outbound

This paper cites A connection between score matching and denoising autoencoders.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A connection between score matching and denoising autoencoders

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Observation 081d9589-6873-4601-8c4c-71ceef57b20a · outbound

This paper cites Evaluating the design space of diffusion-based generative models.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Evaluating the design space of diffusion-based generative models

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Resolution: verified exact; local_arxiv, observed 2026-08-07T10:54:51.827115Z

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Observation 36d67d16-52e7-47a3-962d-bf212e789757 · outbound

This paper cites The weighted particle method for convection-diffusion equations.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach The weighted particle method for convection-diffusion equations

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Observation 7c39d8a6-8fb7-4db0-be42-498d36b59371 · outbound

This paper cites A deterministic approximation of diffusion equations using particles.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A deterministic approximation of diffusion equations using particles

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Observation c16d0fe5-4fb3-4981-8071-3f290bddb459 · outbound

This paper cites A stochastic weighted particle method for the boltzmann equation.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A stochastic weighted particle method for the boltzmann equation

Reference 81

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Observation 6f1fd8d3-ec9e-4435-82c0-e7c73ac40735 · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A stochastic particle method for the mckean-vlasov and the burgers equation

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.952988Z

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Observation cb5f173d-4788-454c-82ee-986a531b6086 · outbound

This paper cites A stochastic particle method with random weights for the computation of statistical solutions of mckean-vlasov equations.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A stochastic particle method with random weights for the computation of statistical solutions of mckean-vlasov equations

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.956337Z

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Observation 466274f0-6c83-47d0-94e2-7a27403d40a7 · outbound

This paper cites An analysis of particle methods.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach An analysis of particle methods

Reference 84

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Observation 6bafbd21-3c27-430d-a8ed-66fe92c4372b · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A practical guide to deterministic particle methods

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Observation b1635b88-9180-43b7-81a8-697e522b1e31 · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Langevin diffusions and metropolis-hastings algorithms

Reference 86

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Observation 42654a1d-019e-48f3-8ef2-749bf2ada435 · outbound

This paper cites Annealed importance sampling.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Annealed importance sampling

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.968831Z

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Observation f48231b2-fbe8-4e33-b7e6-ab1ed8a8dbe9 · outbound

This paper cites Accelerating Langevin Sampling with Birth-death.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Accelerating Langevin Sampling with Birth-death

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Observation ad196133-5e43-4127-b972-d6ea213f7d3b · outbound

This paper cites Accelerate Langevin Sampling with Birth-Death Process and Exploration Component.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Accelerate Langevin Sampling with Birth-Death Process and Exploration Component

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Resolution: verified exact; local_arxiv, observed 2026-08-07T10:54:51.800076Z

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Observation 2b01a494-3d0c-4382-8ded-3748897794e1 · outbound

This paper cites Ensemble-Based Annealed Importance Sampling.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Ensemble-Based Annealed Importance Sampling

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Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.978833Z

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Observation 5eeefef7-5d75-427d-8c2b-5e4641c6eba9 · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Ensemble markov chain monte carlo with teleporting walkers

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Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.982053Z

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Observation fcca040b-3db3-425a-bc74-c674cb2397ba · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach The probability flow ode is provably fast

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:49.985322Z

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Classifier-Free Guidance is a Predictor-Corrector

Reference 93

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Observation a066383e-a808-4e91-b18a-79b30b1fec13 · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Diffusion models beat gans on image synthesis

Reference 94

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Observation 09881df4-a0bd-4b62-ab66-06f0e09feb54 · outbound

This paper cites A dual algorithm for the solution of nonlinear variational problems via finite element approximation.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A dual algorithm for the solution of nonlinear variational problems via finite element approximation

Reference 95

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Observation 1fcb32b4-832f-4965-866f-6bb559b5f64b · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach A new alternating minimization algorithm for total variation image reconstruction

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Observation 2e9338e1-fc3c-49fd-b375-36ddfbc9537d · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Distributed optimization and statistical learning via the alternating direction method of multipliers

Reference 97

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Observation a8fd8cef-b998-41da-8421-0d7664c3fe67 · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Deep admm-net for compressive sensing mri.Advances in neural information processing systems, 29, 2016

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Observation af63b909-1916-47d4-91e3-894ee4e36b6a · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Plug-and-play admm for image restoration: Fixed-point convergence and applications

Reference 99

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Observation f4f35cc0-e4c4-48a9-87e3-4aae1f3f0b2c · outbound

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Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Plug-and-play methods provably converge with properly trained denoisers

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An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse Problems Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:25:54.738922Z

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ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Reference 9

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Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

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Provable Diffusion Posterior Sampling for Bayesian Inversion Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

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Observation a1dfbca6-728b-40fd-b9f4-045f21b29428 · inbound

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How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models? Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

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Resolution: verified exact; arxiv_id, observed 2026-05-16T07:57:33.082156Z

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Consistency Regularised Gradient Flows for Inverse Problems Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Reference 54

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Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:32:45.705590Z

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Observation fab8f5e8-5cca-4721-afd0-e9b681409da9 · inbound

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Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

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Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T01:13:18.612835Z

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Observation e82b8153-4761-4e78-9395-b7e51217ec17 · inbound

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SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

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Resolution: verified exact; arxiv_id, observed 2026-05-20T07:53:09.657372Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3902c712-1bd4-42c0-9ba3-d411dcf995ba · inbound

SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate cites this paper.

SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:24:59.743795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd4e0595-c342-46db-9a2a-e5b3cbb9a478 · inbound

Provable diffusion-based posterior sampling for linear inverse problems via DDIM cites this paper.

Provable diffusion-based posterior sampling for linear inverse problems via DDIM Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T12:52:51.947309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.