Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-04T00:31:37.678997Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2511.00870.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-04T00:31:37.678997Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
44 of 44 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 8c57f7f5-4817-48d9-ab3e-e5cab46f4538 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Unresolved cited work
Reference 1
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Observation f01658ee-4d07-4fa7-9b91-36c2cb4e0f90 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Bayesian imaging with data-driven priors encoded by neural networks,
Reference 2
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Observation 747f9722-f783-4e5a-bb30-6b2d1f26284d · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Bayesian imaging using Plug & Play priors: When Langevin meets Tweedie,
Reference 3
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Observation 166c1c50-1835-4415-8831-b7f7835ea033 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Regularization by denois- ing: Bayesian model and Langevin-within-split Gibbs sampling,
Reference 4
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Observation 5b622a3b-7a2b-444d-a485-72d06177ea83 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Scalable Bayesian uncertainty quantification with data- driven priors for radio interferometric imaging,
Reference 5
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Observation c59c6636-31e1-4a23-bb05-1402f0139798 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Playing with duality: an overview of recent primal-dual approaches for solving large-scale optimization problems,
Reference 6
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Observation ca79ca77-d8db-4e34-a7da-a6051a44ad0e · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Non-log-concave and nonsmooth sampling via Langevin Monte Carlo algorithms,
Reference 7
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Observation a0b10b08-3153-4394-aa7e-e319968c2e62 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Analysis of Primal-Dual Langevin Algorithms
Reference 8
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Observation 1c9f9820-acd5-484a-bcd5-5fad71f42696 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Nonlinear image recovery with half-quadratic regularization,
Reference 9
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Observation 2f2b2154-f205-4e00-9ccc-a5eaaec4a9bd · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Asymptotically exact data augmentation: Models, properties, and algorithms,
Reference 10
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Observation abfaef90-e05b-4c79-abc1-12db9b6e2a09 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems A distributed split- Gibbs sampler with hypergraph structure for high-dimensional inverse problems,
Reference 11
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Observation 2f5fdd7d-814b-4c62-9a49-59179a225081 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Stochastic proximal Langevin algorithm: Potential splitting and nonasymptotic rates,
Reference 12
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Observation c03dfbd2-2557-4191-8abe-de1a55fa8dc6 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems The SPMD model: Past, present and future,
Reference 13
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Observation 939a791c-3006-4d48-a2b1-b874192ab926 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Deep learning techniques for inverse problems in imaging,
Reference 14
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Observation 9c2e6089-187d-4510-8dfa-2aa96eb56e09 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Plug- and-Play priors for model based reconstruction,
Reference 15
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Observation a7b855ee-3ad9-4bfa-9fdd-ab1bd5c6ec43 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Plug-and-Play image restoration with deep denoiser prior,
Reference 16
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Observation 68c4681f-14cb-4ed1-ba23-2651b02c191f · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems NF-ULA: Normalizing flow-based unadjusted Langevin algorithm for imaging inverse problems,
Reference 17
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Observation c47849c9-2a98-4f7f-92d7-9d0edfc1280b · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Plug-and-Play split Gibbs sampler: Embedding deep generative priors in Bayesian inference,
Reference 18
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Observation 8232ece1-b676-4db0-8bb0-d749caded8a9 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Principled probabilistic imaging using diffusion models as Plug-and-Play priors,
Reference 19
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Observation 3b8912c6-5718-423f-a120-30fca02852c1 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems The little book of deep learning,
Reference 20
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Observation 4237771d-a907-4662-9432-f4469eab1029 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Normalizing flows: An introduction and review of current methods,
Reference 21
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Observation c1a2d885-fbe9-4c97-9f4d-63a61edf3664 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems A Survey on Diffusion Models for Inverse Problems
Reference 22
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Observation bd0115d1-630f-4af4-87be-7d6179ab8b71 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,
Reference 23
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Observation 3bc0ab40-82e6-4a23-8757-f39862f56c1c · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Dual forward- backward unfolded network for flexible Plug-and-Play,
Reference 24
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Observation 0e2d5fe6-87ef-4da4-b620-7a9fe5ad3931 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Unfolded proximal neural networks for robust image Gaussian denoising,
Reference 25
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Observation 3bbb29ed-6696-4af7-9c6c-db5ffaeda4ea · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Multi-GPU distributed PnP-ULA for high-dimensional imaging inverse problems,
Reference 26
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Observation ee6b8648-42a1-4cc4-87ce-7dfb297615b3 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,
Reference 27
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Observation bb7b5ec9-c21b-462e-9fd8-1a73d61041c6 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Proximit ´e et dualit ´e dans un espace hilbertien,
Reference 28
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Observation 6cf94bf6-1fd9-4b0c-a708-5c45d02156df · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems The science of computing,
Reference 29
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Observation 9fadbce0-d6a9-4897-bdcc-70c7ce0244ab · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Efficient Bayesian compu- tation by proximal Markov chain Monte Carlo: When Langevin meets Moreau,
Reference 30
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Observation 9df2a5ca-03e2-4803-85b6-3a8b3855e066 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Nonlinear total variation based noise removal algorithms,
Reference 31
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Observation 848369f7-6c55-46f3-8ea0-daf04491c7ac · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Dualization of signal recovery problems,
Reference 32
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Observation fbd64eef-30f7-454b-ab81-4e93abf166a4 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Proximal splitting methods in signal processing,
Reference 33
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Observation 4717e601-1fe6-42a0-8ff7-ddda41193f5e · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Vetterli, J
Reference 34
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Observation 1fd9b2e8-ad26-4fb5-8c98-6a39cfa22aa7 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Scaling painting style transfer,
Reference 35
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Observation e9be532e-ab86-4ce6-976d-069a97adc5a2 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Split-and-augmented Gibbs sampler–Application to large-scale inference problems,
Reference 36
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Observation 36ee1e9f-8e28-43e9-8aa3-624b091f4780 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Accelerating proximal Markov chain Monte Carlo by using an explicit stabilized method,
Reference 37
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Observation 9fe428fd-b1b8-41b9-afba-e3af97f05fc7 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems ImageNet large scale visual recognition challenge,
Reference 38
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Observation 30fb84f6-ee19-42ce-be74-4a95a0036bc4 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Adam: A Method for Stochastic Optimization
Reference 39
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Observation 1a2cbde0-7954-4104-846d-12764386808e · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Learning maximally monotone operators for image recovery,
Reference 40
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Observation b1c7cc31-048e-4a68-ae6f-b6a1e5abea35 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems NTIRE 2017 challenge on single image super-resolution: Dataset and study,
Reference 41
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Observation db181272-f0dd-4313-ad1f-d87e1c7a06d9 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Image quality assessment: From error visibility to structural similarity,
Reference 42
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Observation da421bda-896a-4c15-b623-3fc8675e92a3 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Mean squared error: Love it or leave it? A new look at signal fidelity measures,
Reference 43
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Observation 981a0e18-e04c-4508-be87-f2ebf5469704 · outbound
A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems Available: https://ieeexplore.ieee.org/abstract/document/ 9454311
Reference 2021
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No inbound Pith citation observations are available.