Paper Citation Record · LEDGER

Copula & Marginal Flows: Disentangling the Marginal from its Joint

As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 3 inbound Pith citation observations for arXiv:1907.03361.

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

pith.paper-citation-record.v1
1907.03361 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T01:09:19.410189Z

measured 27 of 27 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:43:37.921541Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

24 of 24 outbound references displayed

verified exact4
verified fuzzy18
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

1
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation cf7870fe-3a69-4da2-a5f8-96cace6d6afb · outbound

This paper cites Pair-Copula Constructions of Multiple Dependence.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Pair-Copula Constructions of Multiple Dependence

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.803187Z

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.

Observation 6632adbe-b4ee-41a4-94e2-0434a50f6031 · outbound

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

Copula & Marginal Flows: Disentangling the Marginal from its Joint Towards Principled Methods for Training Generative Adversarial Networks

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-25T01:10:09.898994Z

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.

Observation ef99752b-455c-436e-adb8-81e579f55471 · outbound

This paper cites Size-Noise Tradeoffs in Generative Networks.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Size-Noise Tradeoffs in Generative Networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.807066Z

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.

Observation 2e4d60d7-4a61-49c4-a643-5284b91ce7ad · outbound

This paper cites an unresolved cited work.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-25T01:10:10.858486Z

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.

Observation baa84daf-e479-401a-9bde-9f3ab1b7cb3e · outbound

This paper cites Vines - A new graphical model for dependent random variables.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Vines - A new graphical model for dependent random variables

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.815000Z

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.

Observation a1fdb218-570c-44b6-9dd0-28c73b373999 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-25T01:10:09.905820Z

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.

Observation e7ce534b-0c72-4d4b-bd08-48a7d08486ee · outbound

This paper cites Extreme value theory: an introduction.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Extreme value theory: an introduction

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.862118Z

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.

Observation 0a400b84-267d-4693-b524-0b1b4d110642 · outbound

This paper cites NICE: Non-linear Independent Compo- nents Estimation.

Copula & Marginal Flows: Disentangling the Marginal from its Joint NICE: Non-linear Independent Compo- nents Estimation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.865812Z

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.

Observation f5108a40-1090-4dc8-a43c-c548a942cdf1 · outbound

This paper cites Density estimation using Real NVP.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Density estimation using Real NVP

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-25T01:10:09.890767Z

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.

Observation 3c4e3d89-cc1f-44f3-99fa-b43f929064c9 · outbound

This paper cites Copula Bayesian Networks.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Copula Bayesian Networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.850821Z

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.

Observation 2a3cf330-6bc3-4244-a04e-39a99fcc0b22 · outbound

This paper cites Practical Extreme Value Modelling of Hydrological Floods and Droughts: A Case Study.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Practical Extreme Value Modelling of Hydrological Floods and Droughts: A Case Study

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.819083Z

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.

Observation 51ad67a1-5595-4f40-bd1f-4d543170783b · outbound

This paper cites Maxout Networks.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Maxout Networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.869439Z

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.

Observation 24ee1121-5357-49f2-a99d-abd0efd8284e · outbound

This paper cites Generative Adversarial Nets.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Generative Adversarial Nets

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.840276Z

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.

Observation 9ae5c189-8987-4740-8164-a3579aa58c19 · outbound

This paper cites Delving Deep into Rectiﬁers: Surpassing Human-Level Performance on ImageNet Classiﬁcation.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Delving Deep into Rectiﬁers: Surpassing Human-Level Performance on ImageNet Classiﬁcation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.843628Z

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.

Observation 6a792809-e0eb-4b9e-90c6-3be10a826eb4 · outbound

This paper cites Approximation capabilities of multilayer feedforward networks.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Approximation capabilities of multilayer feedforward networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.847112Z

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.

Observation 31b1d689-9933-4d55-b608-0f273a2dc898 · outbound

This paper cites Neural Autoregressive Flows.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Neural Autoregressive Flows

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-25T01:10:09.877204Z

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.

Observation 62c1979b-91d1-4f56-a8c2-be60295e00c3 · outbound

This paper cites Monte Carlo Methods and Models in Finance and Insurance.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Monte Carlo Methods and Models in Finance and Insurance

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.825961Z

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.

Observation 8d136d2e-375c-4cc1-9c91-0dc5ae936253 · outbound

This paper cites Introduction to Vine Copulas.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Introduction to Vine Copulas

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.829487Z

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.

Observation bae3e53a-c84d-4b31-b55e-230326916856 · outbound

This paper cites Efﬁcient BackProp.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Efﬁcient BackProp

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.822536Z

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.

Observation e443ef53-ab89-4835-98a7-437f47067f8a · outbound

This paper cites Which Training Methods for GANs do actually Converge?.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Which Training Methods for GANs do actually Converge?

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T01:10:09.883865Z

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.

Observation 400e14d9-9b70-41da-969f-12935b0a79b5 · outbound

This paper cites Rectiﬁed Linear Units Improve Restricted Boltzmann Machines.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Rectiﬁed Linear Units Improve Restricted Boltzmann Machines

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.811110Z

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.

Observation 60deff81-a325-4a24-991e-f40ce90aa716 · outbound

This paper cites The Double Pareto-Lognormal Distribution—A New Parametric Model for Size Distributions.

Copula & Marginal Flows: Disentangling the Marginal from its Joint The Double Pareto-Lognormal Distribution—A New Parametric Model for Size Distributions

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.836840Z

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.

Observation 738da61e-8ee7-497a-a034-2f360b67af12 · outbound

This paper cites Variational Inference with Normalizing Flows.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Variational Inference with Normalizing Flows

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.833115Z

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.

Observation b70a0630-a585-4cf1-a481-7c4de3557628 · outbound

This paper cites Survival Probabilities Based on Pareto Claim Distributions.

Copula & Marginal Flows: Disentangling the Marginal from its Joint Survival Probabilities Based on Pareto Claim Distributions

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T01:10:10.855055Z

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.

Pith citing papers

Observation f5c4be9e-1512-4c3e-9c51-bd68050d8fe4 · inbound

On the Statistical Capacity of Deep Generative Models cites this paper.

On the Statistical Capacity of Deep Generative Models Copula & Marginal Flows: Disentangling the Marginal from its Joint

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T20:43:37.921541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a435ce05-01dd-444c-8ebc-1a82bce309be · inbound

Extrapolation in Statistical Learning with Extreme Value Theory cites this paper.

Extrapolation in Statistical Learning with Extreme Value Theory Copula & Marginal Flows: Disentangling the Marginal from its Joint

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:31:09.422189Z

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.

Observation fb6ee02f-15f5-4b92-bf73-0b316b33b9e1 · inbound

Valid and Expressive Copulas for Irregular Multivariate Time Series cites this paper.

Valid and Expressive Copulas for Irregular Multivariate Time Series Copula & Marginal Flows: Disentangling the Marginal from its Joint

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-25T04:35:20.206430Z

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.