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Paper Citation Record · LEDGER

Learning to Transport with Neural Networks

As of 16 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:1908.01394.

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

pith.paper-citation-record.v1
1908.01394 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:19:18.786072Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

21 of 21 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee60658c-b937-4d8a-b81a-9e3c0b718aba · outbound

This paper cites Towards Principled Methods for Training Generative Adversarial Networks.

Learning to Transport with Neural Networks Towards Principled Methods for Training Generative Adversarial Networks

Reference 1

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no resolver link, observed 2026-08-14T15:19:18.672117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 89409c92-0970-4571-bf1d-9a51f1005fbb · outbound

This paper cites Wasserstein GAN.

Learning to Transport with Neural Networks Wasserstein GAN

Reference 2

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no resolver link, observed 2026-08-14T15:19:18.678595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T15:19:18.678595Z digest=sha256:e91b9df43bce2ab4bd717788125202b9c1a78c55893163134b364655547329d9

Observation 546d688d-cd8a-4def-844e-eb415d4dd657 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Learning to Transport with Neural Networks Sinkhorn distances: Lightspeed computation of optimal transport

Reference 3

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raw_fallback, observed 2026-08-14T15:19:19.205564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.685109Z digest=sha256:a44c0cc3dd9b05176824bba0c252de669daa004ac64a8b115cd23e4f0fa68c4b

Observation 35dfba85-0e4d-4b14-810c-1e3bef1b39b3 · outbound

This paper cites Stochastic optimization for large-scale optimal transport.

Learning to Transport with Neural Networks Stochastic optimization for large-scale optimal transport

Reference 4

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.690721Z digest=sha256:d4affcb63a9036e41a67a72648ea1fd61dbc4e92d461ce19a197fbf25c646b00

Observation 816727dd-1ab0-4fcb-8fbd-9fb1a67fb652 · outbound

This paper cites Multiscale Strategies for Computing Optimal Transport.

Learning to Transport with Neural Networks Multiscale Strategies for Computing Optimal Transport

Reference 5

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local_arxiv, observed 2026-08-14T15:19:18.884393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.696775Z digest=sha256:87551ac4128396eb90ea093b6eaf8c37491aee66cc94fe1e214a29059dde9fb3

Observation 9c025b1a-0292-4e06-809e-9bd265411e54 · outbound

This paper cites Generative adversarial nets.

Learning to Transport with Neural Networks Generative adversarial nets

Reference 6

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source=arxiv_source observed=2026-08-14T15:19:18.702371Z digest=sha256:00e9f854c5826b9d7e6e4a2dc787d84bda66c1d75ee3e9e06336c289652b5cd5

Observation 7a96af76-4413-4af9-873b-013dd753569e · outbound

This paper cites Shunmuga Krishnan and Ramesh K.

Learning to Transport with Neural Networks Shunmuga Krishnan and Ramesh K

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.708077Z digest=sha256:ca8477e2bfd168c8880b41b2895fd961633c0b577c208956b5085de9808dfa44

Observation 8de5a998-614c-4bc6-953c-b27218f5a6f7 · outbound

This paper cites From word embeddings to document distances.

Learning to Transport with Neural Networks From word embeddings to document distances

Reference 8

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raw_fallback, observed 2026-08-14T15:19:19.127704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.712870Z digest=sha256:810d52f38f2a762f8ef3a489eb48fe2bf3162c4235273d0f295c56312cbba39e

Observation c3e8a602-5225-414a-9970-d0fc5383f834 · outbound

This paper cites Gradient Flows In Metric Spaces and in the Space of Probability Measures.

Learning to Transport with Neural Networks Gradient Flows In Metric Spaces and in the Space of Probability Measures

Reference 9

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raw_fallback, observed 2026-08-14T15:19:19.110395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.718554Z digest=sha256:3ec553127332604f87416c725561919e7269ab941ca8d265e2cc411f000b9227

Observation 9bbd5ead-473e-424a-9732-eb78c607021b · outbound

This paper cites Ricci curvature for metric-measure spaces via optimal transport.

Learning to Transport with Neural Networks Ricci curvature for metric-measure spaces via optimal transport

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-14T15:19:19.090477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.723594Z digest=sha256:d0622e98b4c581fb7e755350ab87bb1324a4f0386e573c7a4a97dca7e4bef9be

Observation 7fb1175e-0c8e-4a92-9b3c-a87c0d128413 · outbound

This paper cites Quasi experimentation at netflix.

Learning to Transport with Neural Networks Quasi experimentation at netflix

Reference 11

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raw_fallback, observed 2026-08-14T15:19:19.073137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.728942Z digest=sha256:e97a43836c662b5ce7e8095d0f09781a0db51f361739183850971ee9de2d6498

Observation 743d04c0-3363-47e4-87d1-1695c1137c54 · outbound

This paper cites Orlova, Noah Zimmerman, Stephen Meehan, Connor Meehan, Jeffrey Waters, Eliver E.

Learning to Transport with Neural Networks Orlova, Noah Zimmerman, Stephen Meehan, Connor Meehan, Jeffrey Waters, Eliver E

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.734881Z digest=sha256:6cce836928af405be091b7c259e3246b2e03da058ecdb9b1bba4e5c77063140d

Observation 5c2a4749-dc68-41c5-ae5e-8cf045a70809 · outbound

This paper cites Computational optimal transport.

Learning to Transport with Neural Networks Computational optimal transport

Reference 13

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raw_fallback, observed 2026-08-14T15:19:19.040451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.740372Z digest=sha256:17f94b14b6e7b3bbe1de18ea7f066c494576eaea47ff2a7e4b25185f681110f9

Observation 087c06a4-955e-4dc2-af3f-d512692d3826 · outbound

This paper cites Sinkhorn AutoEncoders.

Learning to Transport with Neural Networks Sinkhorn AutoEncoders

Reference 14

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no resolver link, observed 2026-08-14T15:19:18.745152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T15:19:18.745152Z digest=sha256:138b6b2c778fb0e533c7781f63b5dc479eab138cc2fdef93bb6d9009a6182dff

Observation b69f3cc7-b296-497d-8edb-3829af800d21 · outbound

This paper cites Optimal Transport for Applied Mathematicians.

Learning to Transport with Neural Networks Optimal Transport for Applied Mathematicians

Reference 15

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raw_fallback, observed 2026-08-14T15:19:19.017065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.752300Z digest=sha256:16d6942b9ea39db5bfaacbb8aa40eee365cdbf81a33a896e61317d1fa6e6ebae

Observation 20be39b5-7051-49ac-9706-f967dfd6f3f5 · outbound

This paper cites Large-scale optimal transport and mapping estimation.

Learning to Transport with Neural Networks Large-scale optimal transport and mapping estimation

Reference 16

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raw_fallback, observed 2026-08-14T15:19:18.999549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.758083Z digest=sha256:db63d1523c8eab37565a8d0affe938a84acbcd362acc14dea9b0d479a0646f68

Observation 9dfeb528-a6f0-422e-af9c-508ccc3d8444 · outbound

This paper cites Convolutional wasserstein distances: Efficient optimal transportation on geometric domains.

Learning to Transport with Neural Networks Convolutional wasserstein distances: Efficient optimal transportation on geometric domains

Reference 17

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raw_fallback, observed 2026-08-14T15:19:18.981275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.763739Z digest=sha256:47d25692f802fa8c43141757a95986dd04fbf56e0e128bed49f93da07ae5d6ab

Observation 611c40f9-3dc6-4dfa-8675-a3d7b6db44e9 · outbound

This paper cites Concerning nonnegative matrices and doubly stochastic matrices.

Learning to Transport with Neural Networks Concerning nonnegative matrices and doubly stochastic matrices

Reference 18

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raw_fallback, observed 2026-08-14T15:19:18.954425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.769214Z digest=sha256:6202fbeacf769aebde240758546324d86e87ea24a180f2c2dd01a1b0cd6308cc

Observation 6ff0385f-abdd-4bef-bff5-c5abbf7f6a23 · outbound

This paper cites an unresolved cited work.

Learning to Transport with Neural Networks Unresolved cited work

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-14T15:19:18.773815Z digest=sha256:dda8421bce40c6a57fe3af4c2608904749ee30dbdba7ca015486021428a4b14e

Observation 4e1bfcf6-f8c2-48c8-b372-427aa5763cc3 · outbound

This paper cites Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance.

Learning to Transport with Neural Networks Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T15:19:18.779429Z digest=sha256:998750ac908b1e542953472760a93fdf8b734df9ba37584b443dc61dbec0140e

Observation 90d72c47-459c-4683-9aec-231b2223b418 · outbound

This paper cites Energy-based Generative Adversarial Network.

Learning to Transport with Neural Networks Energy-based Generative Adversarial Network

Reference 21

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unresolved
no resolver link, observed 2026-08-14T15:19:18.786072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.