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Paper Citation Record · LEDGER

Denoising Milky Way stellar survey data with normalizing flow models

As of 20 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2505.16553.

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

pith.paper-citation-record.v1
2505.16553 v1

Coverage vector

measured 49 of 49 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:07:28.433217Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation 5b7b082d-ed83-4dd6-ad8e-48d7435a56df · outbound

This paper cites W., Leistedt B., Price-Whelan A.

Denoising Milky Way stellar survey data with normalizing flow models W., Leistedt B., Price-Whelan A

Reference 1

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Observation d6dbb014-993a-4b00-b464-40caf8325337 · outbound

This paper cites an unresolved cited work.

Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 2

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Observation 3d4cd65e-da5e-4192-b962-1f343c154a5e · outbound

This paper cites an unresolved cited work.

Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 3

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Observation 80d52d5f-547b-4a71-94d9-c7bc2406c99b · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 4

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Observation 90be4bf3-1546-4a61-8e35-c9b321ad53a6 · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 5

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Observation b09a3046-21aa-45f5-bf6f-4dccbf94feee · outbound

This paper cites W., Roweis S.

Denoising Milky Way stellar survey data with normalizing flow models W., Roweis S

Reference 6

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Observation be7b9725-c9f7-41ee-95dd-9dd5ac54f2e7 · outbound

This paper cites J., Hall P., 1988, Journal of the American Statistical Association, 83, 1184.

Denoising Milky Way stellar survey data with normalizing flow models J., Hall P., 1988, Journal of the American Statistical Association, 83, 1184

Reference 7

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Observation a317560d-8a40-4c41-81ec-7d8c1ef99cc1 · outbound

This paper cites Unsupervised Learning for Stellar Spectra with Deep Normalizing Flows.

Denoising Milky Way stellar survey data with normalizing flow models Unsupervised Learning for Stellar Spectra with Deep Normalizing Flows

Reference 8

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Observation 70854b94-e4e5-4895-ba27-e3da514a78e6 · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 9

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Observation d96ba803-6bb9-4955-8320-68952362b05b · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 10

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Observation a27820c9-cc33-4b8e-87a1-f883a7c0c279 · outbound

This paper cites Density estimation with heteroscedastic error.

Denoising Milky Way stellar survey data with normalizing flow models Density estimation with heteroscedastic error

Reference 11

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Observation 00e5ba8a-42cf-4432-8fb3-3bf219544836 · outbound

This paper cites A., Bovy J., Myers A.

Denoising Milky Way stellar survey data with normalizing flow models A., Bovy J., Myers A

Reference 12

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Observation db094afa-a931-491a-b721-88025cb63612 · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Denoising Milky Way stellar survey data with normalizing flow models NICE: Non-linear Independent Components Estimation

Reference 13

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Observation ea4b9b9b-b2eb-4ded-8123-ae2973823b8d · outbound

This paper cites Density Deconvolution with Normalizing Flows.

Denoising Milky Way stellar survey data with normalizing flow models Density Deconvolution with Normalizing Flows

Reference 14

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Observation 538bd072-771b-487a-8c35-3c653a8b087c · outbound

This paper cites Neural Spline Flows.

Denoising Milky Way stellar survey data with normalizing flow models Neural Spline Flows

Reference 15

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Observation 81a78127-bdc1-4dae-8234-79a65a2f3b10 · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 16

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Observation 0ab5a2ae-133f-47bd-b273-45114700ec8c · outbound

This paper cites J., Buddelmeijer H., Trager S.

Denoising Milky Way stellar survey data with normalizing flow models J., Buddelmeijer H., Trager S

Reference 17

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Observation 595f31a4-bbc1-4d2e-9549-e07af1aeaa97 · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 18

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Observation f06e367f-0d77-49d1-8304-bef05c1c337d · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 19

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Observation 5cf21b5f-0595-4d9f-b65a-987ad08cafe2 · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 20

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Observation 01bc7cb7-cad3-4413-85f5-22734e94397f · outbound

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

Reference 21

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Observation 763171fd-af6f-4819-9cee-4363a357a42e · outbound

This paper cites FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.

Denoising Milky Way stellar survey data with normalizing flow models FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Reference 22

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Observation de4e66cb-dbeb-45a9-ab5e-38c7ab5aa634 · outbound

This paper cites Deep Potential: Recovering the gravitational potential from a snapshot of phase space.

Denoising Milky Way stellar survey data with normalizing flow models Deep Potential: Recovering the gravitational potential from a snapshot of phase space

Reference 23

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Observation 27b251f8-ae1b-4448-92d0-0e50b18af3d9 · outbound

This paper cites M., Ting Y.-S., Kamdar H., 2023, @doi [ ] 10.3847/1538-4357/aca3a7 , https://ui.adsabs.harvard.edu/abs/2023ApJ...942...26G 942, 26.

Denoising Milky Way stellar survey data with normalizing flow models M., Ting Y.-S., Kamdar H., 2023, @doi [ ] 10.3847/1538-4357/aca3a7 , https://ui.adsabs.harvard.edu/abs/2023ApJ...942...26G 942, 26

Reference 24

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Observation c85f435c-d11f-4636-8499-589ed30c2a77 · outbound

This paper cites R., et al., 2020, @doi [ ] 10.1038/s41586-020-2649-2 , https://ui.adsabs.harvard.edu/abs/2020Natur.585..357H 585, 357.

Denoising Milky Way stellar survey data with normalizing flow models R., et al., 2020, @doi [ ] 10.1038/s41586-020-2649-2 , https://ui.adsabs.harvard.edu/abs/2020Natur.585..357H 585, 357

Reference 25

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Observation 4442093c-e786-46db-a41c-bdf17aef3c54 · outbound

This paper cites D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , https://ui.adsabs.harvard.edu/abs/2007CSE.....9...90H 9, 90.

Denoising Milky Way stellar survey data with normalizing flow models D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , https://ui.adsabs.harvard.edu/abs/2007CSE.....9...90H 9, 90

Reference 26

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Observation 54316028-70dc-444c-aa02-23c59c5b71e5 · outbound

This paper cites M., Ghosh S., 2024, @doi [ ] 10.1093/mnras/stae011 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.52712284K 527, 12284.

Denoising Milky Way stellar survey data with normalizing flow models M., Ghosh S., 2024, @doi [ ] 10.1093/mnras/stae011 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.52712284K 527, 12284

Reference 27

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Observation 3d7e2e3b-5290-44ce-aeda-82f9bf047038 · outbound

This paper cites Extreme Deconvolution Reimagined: Conditional Densities via Neural Networks and an Application in Quasar Classification.

Denoising Milky Way stellar survey data with normalizing flow models Extreme Deconvolution Reimagined: Conditional Densities via Neural Networks and an Application in Quasar Classification

Reference 28

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Observation 6b13454e-b8c2-44ef-93c8-9b7b04f2ab6e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Denoising Milky Way stellar survey data with normalizing flow models Adam: A Method for Stochastic Optimization

Reference 29

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Observation 0d01b7bf-93bb-4fe1-8eeb-34d063819755 · outbound

This paper cites Glow: Generative Flow with Invertible 1x1 Convolutions.

Denoising Milky Way stellar survey data with normalizing flow models Glow: Generative Flow with Invertible 1x1 Convolutions

Reference 30

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Observation 11ea8659-c7c6-4bc5-8b88-9a008d813d6f · outbound

This paper cites Auto-Encoding Variational Bayes.

Denoising Milky Way stellar survey data with normalizing flow models Auto-Encoding Variational Bayes

Reference 31

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Observation 0fee0f62-a687-408a-b3b0-aaf1dde97898 · outbound

This paper cites M., Kraus A.

Denoising Milky Way stellar survey data with normalizing flow models M., Kraus A

Reference 32

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Observation ba03ff57-2f8d-4481-baaa-fdd77790bb00 · outbound

This paper cites emPDF: Inferring the Milky Way mass with data-driven distribution function in phase space.

Denoising Milky Way stellar survey data with normalizing flow models emPDF: Inferring the Milky Way mass with data-driven distribution function in phase space

Reference 33

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Observation d85d8c81-cd4e-433e-9b87-20a3343ef4ce · outbound

This paper cites H., Putney E., Buckley M.

Denoising Milky Way stellar survey data with normalizing flow models H., Putney E., Buckley M

Reference 34

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Observation d8edb884-95ab-40b1-b5e4-01da8c81af89 · outbound

This paper cites Gaussianization Flows.

Denoising Milky Way stellar survey data with normalizing flow models Gaussianization Flows

Reference 35

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Observation da84998f-b619-4ad5-b885-e93136dfaacb · outbound

This paper cites C., Figueras F., Roca-F \`a brega S., Luri X., 2019, @doi [ ] 10.1051/0004-6361/201935105 , https://ui.adsabs.harvard.edu/abs/2019A&A...624L...1M 624, L1.

Denoising Milky Way stellar survey data with normalizing flow models C., Figueras F., Roca-F \`a brega S., Luri X., 2019, @doi [ ] 10.1051/0004-6361/201935105 , https://ui.adsabs.harvard.edu/abs/2019A&A...624L...1M 624, L1

Reference 36

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Observation b6b45c2f-4c8e-4800-8619-01747012ee8d · outbound

This paper cites Neural Importance Sampling.

Denoising Milky Way stellar survey data with normalizing flow models Neural Importance Sampling

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Denoising Milky Way stellar survey data with normalizing flow models Masked Autoregressive Flow for Density Estimation

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This paper cites Normalizing Flows for Probabilistic Modeling and Inference.

Denoising Milky Way stellar survey data with normalizing flow models Normalizing Flows for Probabilistic Modeling and Inference

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Denoising Milky Way stellar survey data with normalizing flow models PyTorch: An Imperative Style, High-Performance Deep Learning Library

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

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Denoising Milky Way stellar survey data with normalizing flow models Scalable Extreme Deconvolution

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

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Denoising Milky Way stellar survey data with normalizing flow models Unresolved cited work

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This paper cites Deep transformation models: Tackling complex regression problems with neural network based transformation models.

Denoising Milky Way stellar survey data with normalizing flow models Deep transformation models: Tackling complex regression problems with neural network based transformation models

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This paper cites GalacticFlow: Learning a Generalized Representation of Galaxies with Normalizing Flows.

Denoising Milky Way stellar survey data with normalizing flow models GalacticFlow: Learning a Generalized Representation of Galaxies with Normalizing Flows

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Denoising Milky Way stellar survey data with normalizing flow models write newline

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Observation 55525600-ace1-4d6d-837d-600d39a4df0a · inbound

Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows cites this paper.

Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows Denoising Milky Way stellar survey data with normalizing flow models

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