Paper Citation Record · LEDGER

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks

As of 14 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.05143.

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

pith.paper-citation-record.v1
2507.05143 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:42:58.006729Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy20
unresolved15
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e4865818-ff77-4081-ae03-b76175399010 · outbound

This paper cites Agresti , An Introduction to Categorical Data Analysis , Wiley, 3rd ed., 2018.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Agresti , An Introduction to Categorical Data Analysis , Wiley, 3rd ed., 2018

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.462799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 54371d9e-e21b-4868-a245-0b2742098c27 · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:42:58.452440Z

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Observation c31ad463-e0e1-4b98-97a4-26a50974461d · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 3

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Observation 64030cb9-851e-4216-814c-615e29b03083 · outbound

This paper cites Clement and W.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Clement and W

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.431528Z

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Observation bc149fd1-9dd3-4aba-80c7-57b6b33e2b69 · outbound

This paper cites Cuturi , Sinkhorn distances: lightspeed computation of optimal tra nsport, Adv.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Cuturi , Sinkhorn distances: lightspeed computation of optimal tra nsport, Adv

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.421117Z

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Observation 9dfd4d49-5d4b-4eb9-ad09-76616e7a85de · outbound

This paper cites Fournier and A.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Fournier and A

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.411208Z

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Observation 6cebe431-97e4-4b4c-bb4c-3d7f4e133bd2 · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 7

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Observation 76d9a7f5-a599-4eab-ab46-f0f33ac9cebe · outbound

This paper cites Gao and M.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Gao and M

Reference 8

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Observation 2f2fdb11-ef44-4e1f-ac76-a8268f4998d6 · outbound

This paper cites Ghahramani , Bayesian nonparametrics and the probabilistic approach to modelling, Phil.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Ghahramani , Bayesian nonparametrics and the probabilistic approach to modelling, Phil

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.380794Z

Source-reported events for the cited work

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Observation 97d84bae-e61c-4bb0-a403-7c493ab2e213 · outbound

This paper cites Ghahramani and M.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Ghahramani and M

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.370964Z

Source-reported events for the cited work

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Observation b4875787-43ae-4606-9669-3ea5c553e1f3 · outbound

This paper cites Hastie, R.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Hastie, R

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.360728Z

Source-reported events for the cited work

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Observation c8f09091-7995-4a96-aa9b-ca84be44affb · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 12

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Observation 8645e7b9-d034-4796-bd0d-c69a77091faf · outbound

This paper cites Jaruszewicz and T.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Jaruszewicz and T

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.338865Z

Source-reported events for the cited work

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Observation 4f86002d-44d8-4e8d-84ae-5b033cbc3dec · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 14

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Source-reported events for the cited work

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Observation b3129343-9d88-45b8-a3d3-05ccf36f2e8e · outbound

This paper cites Koller and N.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Koller and N

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.319312Z

Source-reported events for the cited work

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Observation 00030242-dce4-4e75-90ad-8557fc2a052d · outbound

This paper cites Lakshminarayanan, A.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Lakshminarayanan, A

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.309652Z

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Observation 6796a05d-0fab-4105-a405-7d398eb0b367 · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation 514f66e4-cf1d-4c2c-ad63-0f807f675610 · outbound

This paper cites MCF ADDEN, Conditional logit analysis of qualitative choice behavior , Front.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks MCF ADDEN, Conditional logit analysis of qualitative choice behavior , Front

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.289404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 36e2287d-2cb7-474b-831b-29a3e61fe6f4 · outbound

This paper cites Bayesian Neural Networks.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Bayesian Neural Networks

Reference 19

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Observation c160440e-dd47-4163-8304-dc65924e65e8 · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 20

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Observation 14efd2fc-65a4-4310-b416-a4eae253b803 · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:42:58.277789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4b4e9e04-db39-4cf0-872c-2baada33e92b · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:42:58.267899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 06dde41d-d438-4cff-91ec-e5799765e7d3 · outbound

This paper cites Pesarin and L.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Pesarin and L

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d0a631c7-1019-4de1-b798-e6da3d4cb05a · outbound

This paper cites Schiebinger, J.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Schiebinger, J

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3e1805c-9114-4920-a8fb-cdd19238eb0b · outbound

This paper cites Sensoy, L.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Sensoy, L

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.236935Z

Source-reported events for the cited work

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Observation 5407e097-6f1c-4efa-98d7-09c6713e1116 · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6587242d-1f35-49c2-9f1e-3b40bef720eb · outbound

This paper cites Solomon, F.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Solomon, F

Reference 27

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Observation ea56a090-b1aa-4f71-b717-d129f033083e · outbound

This paper cites Tian and K.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Tian and K

Reference 28

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Source-reported events for the cited work

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Observation a0285eae-2201-4ebb-8103-1334afb196f3 · outbound

This paper cites Villani et al.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Villani et al

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.197462Z

Source-reported events for the cited work

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Observation 1ca2ebbe-46b2-43b2-afe0-0d2fa26da8d6 · outbound

This paper cites W ang, J.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks W ang, J

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.103022Z

Source-reported events for the cited work

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Observation 18127125-a581-4342-9168-f18582d752b5 · outbound

This paper cites an unresolved cited work.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Unresolved cited work

Reference 31

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Source-reported events for the cited work

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Observation 482ecdc0-37b4-47a3-bc81-ae7f26a73cd7 · outbound

This paper cites Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations

Reference 32

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Source-reported events for the cited work

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Observation a82685c7-98b5-4785-94f7-f706b9594693 · outbound

This paper cites A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 713f8007-d2f3-4c14-90a7-caa7310f04ee · outbound

This paper cites A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T19:42:58.003414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8baf6479-3a21-47ad-b1fb-667a1daf67e0 · outbound

This paper cites Zheng, F.-Y.

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Zheng, F.-Y

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:42:58.082427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.