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Paper Citation Record · LEDGER

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models

As of 19 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:1908.11462.

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

pith.paper-citation-record.v1
1908.11462 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:20:11.700730Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:32:45.387057Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T10:32:45.436515Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact0
  • verified fuzzy17
  • unresolved13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6bd4b1d9-bead-4170-a7b0-fec4a021109e · outbound

This paper cites an unresolved cited work.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Unresolved cited work

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1f1fb842-410d-4950-b8ab-17730260a4c2 · outbound

This paper cites Gradient flows: in metric spaces and in the space of probability measures.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Gradient flows: in metric spaces and in the space of probability measures

Reference 2

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no resolver link, observed 2026-08-14T10:20:11.527381Z

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Unavailable: canonical work link unavailable.

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Observation a82d9818-2eb6-48c0-8dee-ed6d6c249a7a · outbound

This paper cites Wasserstein generative adversarial networks.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Wasserstein generative adversarial networks

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 75c97ae0-197d-4c6a-8509-cd50913c4b89 · outbound

This paper cites A computational fluid mechanics solution to the monge-kantorovich mass transfer problem.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models A computational fluid mechanics solution to the monge-kantorovich mass transfer problem

Reference 4

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Observation fcaf1d9d-2bd1-4b67-94a8-978885b9f9c2 · outbound

This paper cites Neural ordinary differential equations.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Neural ordinary differential equations

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6f4daf69-44cb-4658-961f-cf349125f875 · outbound

This paper cites Generative modeling using the sliced Wasserstein distance.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Generative modeling using the sliced Wasserstein distance

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 51a1f663-1de8-4243-8659-5faa09e4bf76 · outbound

This paper cites The geometry of optimal transportation.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models The geometry of optimal transportation

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ddc7b446-4552-4167-9f25-66926a7e7b8e · outbound

This paper cites On a formula for the L _2 Wasserstein metric between measures on Euclidean and Hilbert spaces.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models On a formula for the L _2 Wasserstein metric between measures on Euclidean and Hilbert spaces

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation da50cb0e-3e79-40ed-b1c7-cfa679d876a0 · outbound

This paper cites Generative adversarial nets.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Generative adversarial nets

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation cd62fa7b-94ed-4d0f-8367-407abe571828 · outbound

This paper cites Improved training of Wasserstein GANs.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Improved training of Wasserstein GANs

Reference 10

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6622c6e7-41dd-4719-a205-f4f3a5f6cedb · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Image-to-image translation with conditional adversarial networks

Reference 11

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T10:20:11.575785Z digest=sha256:f66be000eefa43afba46b39de0e3ed3919b93c3466090ae4c2f333b4bd40e09e

Observation 9628c454-61e7-44d0-8859-8fdb552abfb1 · outbound

This paper cites Principal component analysis.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Principal component analysis

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation d31db260-6e28-4830-b0ae-b7fba3baa62f · outbound

This paper cites Learning to discover cross-domain relations with generative adversarial networks.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Learning to discover cross-domain relations with generative adversarial networks

Reference 13

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e2863fd2-e47d-4599-b03e-df517b632e57 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Adam: A Method for Stochastic Optimization

Reference 14

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:20:11.594925Z digest=sha256:2242e73c0ef3be27e751ccb1c5318b50186d818cfc5d157069663aca6919efe2

Observation ff9fa17e-388b-47cb-b2c7-634097b0b694 · outbound

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

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Glow: Generative flow with invertible 1x1 convolutions

Reference 15

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation d9c545b1-45e5-4a7f-bfd8-bfc472bf7de1 · outbound

This paper cites MNIST handwritten digit database.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models MNIST handwritten digit database

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 3b88035c-6f91-4462-969b-1444eebacd5b · outbound

This paper cites Adversarial Computation of Optimal Transport Maps.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Adversarial Computation of Optimal Transport Maps

Reference 17

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Observation 87507dfb-cfd9-4056-8e4a-f5cf4373e397 · outbound

This paper cites Deep learning face attributes in the wild.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Deep learning face attributes in the wild

Reference 18

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Observation 25351f1f-b715-4ae5-8ce7-94ed5b75a6e0 · outbound

This paper cites Five lectures on optimal transportation: geometry, regularity and applications.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Five lectures on optimal transportation: geometry, regularity and applications

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation fe43e5ef-08f6-4bf9-aeb3-dc92d2e8f5e6 · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Spectral Normalization for Generative Adversarial Networks

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ff5eb50e-e8a8-40b9-80e9-39fc1e9b05fe · outbound

This paper cites Viscous fingering: an optimal bound on the growth rate of the mixing zone.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Viscous fingering: an optimal bound on the growth rate of the mixing zone

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T10:20:11.643230Z digest=sha256:a1c5dc9e5975e931ef8b746040f299d752a47ee572a8c3119d456c9870ea86fe

Observation 10fab530-5dbe-4751-bc7a-00a966dcb687 · outbound

This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 22

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source=arxiv_source observed=2026-08-14T10:20:11.647807Z digest=sha256:04ede210e3c7117e62e72e6108cef78aab9afdd8601393dbd5025e18f9009ac2

Observation 99de40e2-d7d3-4d63-acf4-bfdfafc65b22 · outbound

This paper cites Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations

Reference 23

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source=arxiv_source observed=2026-08-14T10:20:11.654541Z digest=sha256:4621620135a4383059bf6ab3b0051953014373c18abf3a5336204efbf7d81a1f

Observation 989e03bb-d071-4950-9869-813f2ebdc049 · outbound

This paper cites Variational Inference with Normalizing Flows.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Variational Inference with Normalizing Flows

Reference 24

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:20:11.659879Z digest=sha256:0d9e556f244025ea77aac7fed4e719b4f37f3baac9f49a3f20ec88ec09cf7fd7

Observation b233eb0c-ffec-47bd-ba56-799b8996b3b0 · outbound

This paper cites Improving GANs Using Optimal Transport.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Improving GANs Using Optimal Transport

Reference 25

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Unavailable: canonical work link unavailable.

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Observation 1f04b6dc-1ef8-4300-9bf4-5cfe43c562af · outbound

This paper cites \ Euclidean, metric, and Wasserstein \ gradient flows: an overview.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models \ Euclidean, metric, and Wasserstein \ gradient flows: an overview

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T10:20:11.673713Z digest=sha256:7b6d730f3fe64c7320a9dae42cc1089f1935a83b3fb7e090253811ecf0f3d931

Observation 5423646b-128b-43f0-abd0-b69efd6aeb88 · outbound

This paper cites Large-Scale Optimal Transport and Mapping Estimation.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Large-Scale Optimal Transport and Mapping Estimation

Reference 27

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no resolver link, observed 2026-08-14T10:20:11.681253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:20:11.681253Z digest=sha256:76e9b70497d4c9758af42399315c707277dbcc4f97bc5706c1d699a1531a7dd1

Observation 214961da-e798-4000-820c-9fe6b4114eb1 · outbound

This paper cites Scalable Unbalanced Optimal Transport using Generative Adversarial Networks.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Scalable Unbalanced Optimal Transport using Generative Adversarial Networks

Reference 28

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no resolver link, observed 2026-08-14T10:20:11.688058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5d1f98b9-5735-433a-b626-28163b0ba91c · outbound

This paper cites Dual GAN : Unsupervised dual learning for image-to-image translation.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Dual GAN : Unsupervised dual learning for image-to-image translation

Reference 29

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ba7fac76-b306-4cf7-b345-e8515bccadfa · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks.

Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models Unpaired image-to-image translation using cycle-consistent adversarial networks

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-14T10:20:11.907097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-14T10:20:11.700730Z digest=sha256:7271fbded96e2c9535ceae38d38c1983b18c6b81120018170081d32fc038bfef

Pith citing papers

Observation bc911147-f659-4cc7-88b0-571654711d21 · inbound

Optimal transport mapping via input convex neural networks cites this paper.

Optimal transport mapping via input convex neural networks Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models

Reference 20

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local_arxiv, observed 2026-08-14T10:32:45.443355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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