Paper Citation Record · LEDGER

An invertible generative model for forward and inverse problems

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2509.03910.

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

pith.paper-citation-record.v1
2509.03910 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:39:20.957090Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T05:25:44.696924Z

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

45 of 45 outbound references displayed

verified exact0
verified fuzzy34
unresolved11
parse uncertain0
malformed identifier0
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External citation measurements

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

Outbound references

Observation 271a477b-3ef6-4daf-ac82-d421b412df2d · outbound

This paper cites Deep Bayesian Inversion.

An invertible generative model for forward and inverse problems Deep Bayesian Inversion

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:18.867155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93b2d595-2f8a-4400-b9d6-27d7a8dd2e84 · outbound

This paper cites Learned primal-dual reconstruction.

An invertible generative model for forward and inverse problems Learned primal-dual reconstruction

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:22.080249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 84672eda-7659-4311-862a-16aecf41f82e · outbound

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

An invertible generative model for forward and inverse problems Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:19.095698Z

Source-reported events for the cited work

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Observation 0c322a2f-e927-4457-b3f7-f948e2ba4518 · outbound

This paper cites Point spread function approximation of high-rank hessians with locally supported nonneg- ative integral kernels.

An invertible generative model for forward and inverse problems Point spread function approximation of high-rank hessians with locally supported nonneg- ative integral kernels

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:22.065235Z

Source-reported events for the cited work

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Observation 7c34e910-4515-40e4-964e-c845ba15cb2e · outbound

This paper cites Analyzing inverse problems with invertible neural networks.

An invertible generative model for forward and inverse problems Analyzing inverse problems with invertible neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:22.048459Z

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Observation d8eda946-a594-4901-b297-8414d4b5105d · outbound

This paper cites Solving inverse problems using data-driven models.

An invertible generative model for forward and inverse problems Solving inverse problems using data-driven models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:19.286066Z

Source-reported events for the cited work

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Observation e02a1693-f810-4cf6-8efe-645a2641b1eb · outbound

This paper cites CAFLOW: Conditional autoregressive flows.

An invertible generative model for forward and inverse problems CAFLOW: Conditional autoregressive flows

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:22.020887Z

Source-reported events for the cited work

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Observation a13647ec-37e8-4fca-8f17-89765e2cb1cb · outbound

This paper cites Conditional Image Generation with Score-Based Diffusion Models.

An invertible generative model for forward and inverse problems Conditional Image Generation with Score-Based Diffusion Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:19.408738Z

Source-reported events for the cited work

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Observation 2a98eabd-fa96-407d-98ed-6e322771e5c8 · outbound

This paper cites Invertible residual networks.

An invertible generative model for forward and inverse problems Invertible residual networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:22.004952Z

Source-reported events for the cited work

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Observation daf72712-8f29-43a2-b385-095ae36c7e76 · outbound

This paper cites The promises and pit- falls of stochastic gradient langevin dynamics.

An invertible generative model for forward and inverse problems The promises and pit- falls of stochastic gradient langevin dynamics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.989161Z

Source-reported events for the cited work

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Observation 88637034-480d-4431-9b36-865cdf2a83e3 · outbound

This paper cites Un- supervised approaches based on optimal transport and convex analysis for inverse problems in imaging.

An invertible generative model for forward and inverse problems Un- supervised approaches based on optimal transport and convex analysis for inverse problems in imaging

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.974122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2e5b6944-7a32-4496-99f8-3a4c640b431e · outbound

This paper cites an unresolved cited work.

An invertible generative model for forward and inverse problems Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:39:21.958524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d7f36444-2454-477b-bedd-e50172a27c3f · outbound

This paper cites Density estimation using real NVP.

An invertible generative model for forward and inverse problems Density estimation using real NVP

Reference 13

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Source-reported events for the cited work

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Observation 5b928e54-4fb1-44cf-aeb8-d0bfe996b122 · outbound

This paper cites Deep equilibrium architec- tures for inverse problems in imaging.

An invertible generative model for forward and inverse problems Deep equilibrium architec- tures for inverse problems in imaging

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.931746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64858be3-c64d-4a86-8806-c10a8201d175 · outbound

This paper cites Generative adver- sarial networks.

An invertible generative model for forward and inverse problems Generative adver- sarial networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.916369Z

Source-reported events for the cited work

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Observation c76e87e3-3556-4599-9558-97ffcfa936f3 · outbound

This paper cites Image restoration.

An invertible generative model for forward and inverse problems Image restoration

Reference 16

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Source-reported events for the cited work

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Observation 9d146683-26ce-48a9-9ce6-b62e9c0d4f2f · outbound

This paper cites Stochastic normal- izing flows for inverse problems: A markov chains viewpoint.

An invertible generative model for forward and inverse problems Stochastic normal- izing flows for inverse problems: A markov chains viewpoint

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.890204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4949175-6337-4536-966b-537bf0f4f632 · outbound

This paper cites Computed tomography: algorithms, insight, and just enough theory.

An invertible generative model for forward and inverse problems Computed tomography: algorithms, insight, and just enough theory

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.876041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e435910-3881-4b38-9304-06f5923a162a · outbound

This paper cites Universal approximation property of invertible neural networks.

An invertible generative model for forward and inverse problems Universal approximation property of invertible neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.861607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c1eed529-8021-463e-b696-c6ea98396433 · outbound

This paper cites Deep convolutional neural network for inverse problems in imaging.

An invertible generative model for forward and inverse problems Deep convolutional neural network for inverse problems in imaging

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.846255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cb985fcf-d025-4280-9525-768d57f466c4 · outbound

This paper cites A plug-and-play priors approach for solving nonlinear imaging inverse problems.

An invertible generative model for forward and inverse problems A plug-and-play priors approach for solving nonlinear imaging inverse problems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.531735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 39d948b7-0dc5-46bd-a1e3-9cfe118028b9 · outbound

This paper cites Auto-Encoding Variational Bayes.

An invertible generative model for forward and inverse problems Auto-Encoding Variational Bayes

Reference 22

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Source-reported events for the cited work

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Observation 4570d25e-13d6-4e87-98c6-69aa9bff9a18 · outbound

This paper cites Glow: Generative flow with invertible 1x1 convolutions.

An invertible generative model for forward and inverse problems Glow: Generative flow with invertible 1x1 convolutions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.422179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4c5cff4-a5cc-4ed9-a2f6-8f0fdb454184 · outbound

This paper cites Improved variational inference with inverse autoregressive flow.

An invertible generative model for forward and inverse problems Improved variational inference with inverse autoregressive flow

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.390722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 74f010bb-414e-4f77-be95-18651cfb56a5 · outbound

This paper cites Prince, and Marcus A.

An invertible generative model for forward and inverse problems Prince, and Marcus A

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.368028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5a5dda8c-7bc4-4619-ab44-b17c00b11031 · outbound

This paper cites Sr- flow: Learning the super-resolution space with normalizing flow.

An invertible generative model for forward and inverse problems Sr- flow: Learning the super-resolution space with normalizing flow

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.353646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d2357b37-6aa3-4ce7-9929-a1f005a15d1e · outbound

This paper cites Sam- pling via measure transport: An introduction.

An invertible generative model for forward and inverse problems Sam- pling via measure transport: An introduction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.339330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd963f54-e8f0-41ba-904e-41aedaf8aee8 · outbound

This paper cites Conditional Generative Adversarial Nets.

An invertible generative model for forward and inverse problems Conditional Generative Adversarial Nets

Reference 28

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Source-reported events for the cited work

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Observation 50ea6d59-b028-41f8-aa08-69745b6cc7bb · outbound

This paper cites VISCOS flows: Variational schur conditional sampling with normalizing flows, 2022.

An invertible generative model for forward and inverse problems VISCOS flows: Variational schur conditional sampling with normalizing flows, 2022

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.325323Z

Source-reported events for the cited work

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Observation c37cae4d-d396-4302-9cb8-0c18a4ba2e5a · outbound

This paper cites Algorithm unrolling: Inter- pretable, efficient deep learning for signal and image processing.

An invertible generative model for forward and inverse problems Algorithm unrolling: Inter- pretable, efficient deep learning for signal and image processing

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.311993Z

Source-reported events for the cited work

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Observation e1a71fb0-33ba-40d0-bf86-240094060778 · outbound

This paper cites Monotone Parameterization Toolbkit (MParT),.

An invertible generative model for forward and inverse problems Monotone Parameterization Toolbkit (MParT),

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.297807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3cff8d42-2edd-4b46-8387-5078a34a61ba · outbound

This paper cites End-to-end reconstruction meets data-driven regularization for inverse problems.

An invertible generative model for forward and inverse problems End-to-end reconstruction meets data-driven regularization for inverse problems

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.283681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 771782f2-b916-457c-8b71-afa014017c91 · outbound

This paper cites Aspire: iterative amortized posterior inference for bayesian inverse problems.

An invertible generative model for forward and inverse problems Aspire: iterative amortized posterior inference for bayesian inverse problems

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.269649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ea4b1b87-5a1e-4113-bcd3-f40946662a93 · outbound

This paper cites Sinkhorn autoencoders.

An invertible generative model for forward and inverse problems Sinkhorn autoencoders

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.255471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 43cf4938-2600-4295-987e-3b064b1fa066 · outbound

This paper cites Radev, Ulf K.

An invertible generative model for forward and inverse problems Radev, Ulf K

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.241323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a6987553-1f56-4635-9280-c061fe130999 · outbound

This paper cites Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ull- rich K¨ othe, and Paul-Christian B¨ urkner.

An invertible generative model for forward and inverse problems Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ull- rich K¨ othe, and Paul-Christian B¨ urkner

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.227366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd328777-4e91-4ed8-b2cd-fde8e41946e0 · outbound

This paper cites Autore- gressive denoising diffusion models for multivariate probabilistic time series forecasting.

An invertible generative model for forward and inverse problems Autore- gressive denoising diffusion models for multivariate probabilistic time series forecasting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.212393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d9e961a8-9c63-409d-a66d-81fa4b10602a · outbound

This paper cites The little engine that could: Regularization by denoising (red).

An invertible generative model for forward and inverse problems The little engine that could: Regularization by denoising (red)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.197248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c90b0ec8-5065-4597-b75c-ccdd2abfbf8e · outbound

This paper cites Variational methods in imaging , volume 167.

An invertible generative model for forward and inverse problems Variational methods in imaging , volume 167

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.181343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 532605e4-07c9-4280-ab47-85dac96f1d26 · outbound

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

An invertible generative model for forward and inverse problems Score-Based Generative Modeling through Stochastic Differential Equations

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:20.936298Z

Source-reported events for the cited work

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Observation 41c673ad-3c04-47ce-998b-b13dd1744145 · outbound

This paper cites Inverse Problem Theory and Methods for Model Parameter Estimation.

An invertible generative model for forward and inverse problems Inverse Problem Theory and Methods for Model Parameter Estimation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.165453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c95d0b8b-75e2-44e0-8d8b-720d6fff3b20 · outbound

This paper cites The reversible simulator – a data-driven approach for solving forward and inverse problems.

An invertible generative model for forward and inverse problems The reversible simulator – a data-driven approach for solving forward and inverse problems

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.150948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ad15df52-a908-4dc2-a88f-58250e44c59f · outbound

This paper cites Learning Likelihoods with Conditional Normalizing Flows.

An invertible generative model for forward and inverse problems Learning Likelihoods with Conditional Normalizing Flows

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:20.948799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b57d978b-77d9-4935-8d25-7f01afc16dee · outbound

This paper cites Stochastic normalizing flows.

An invertible generative model for forward and inverse problems Stochastic normalizing flows

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.136308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87e1fc50-cd9b-4471-bda5-14dbf8ef9fc6 · outbound

This paper cites Image reconstruction by domain-transform manifold learning.

An invertible generative model for forward and inverse problems Image reconstruction by domain-transform manifold learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:39:21.121603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 1609082f-4682-4623-8140-1c04c06bc57c · inbound

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems cites this paper.

A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems An invertible generative model for forward and inverse problems

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-08-11T02:23:40.723014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.