Paper Citation Record · LEDGER

Deep generative models as the probability transformation functions

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2506.17171.

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

pith.paper-citation-record.v1
2506.17171 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:15:18.075016Z

measured 38 of 38 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:18:01.525748Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T13:18:03.850017Z

Reference resolution

37 of 37 outbound references displayed

verified exact0
verified fuzzy19
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f898d1d6-918b-4e7e-9daf-a3763eba0968 · outbound

This paper cites Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models.

Deep generative models as the probability transformation functions Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.896759Z

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Observation d49bcc0b-5d24-4f10-83f1-08201430c184 · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications.

Deep generative models as the probability transformation functions Diffusion models: A comprehensive survey of methods and applications

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.632622Z

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.

Observation eaeca57f-20dd-47f0-930d-08925f94f5cd · outbound

This paper cites An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications.

Deep generative models as the probability transformation functions An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.908584Z

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Observation b5d5fbcd-39f7-48f7-95de-10561ba77dda · outbound

This paper cites Flow Matching for Generative Modeling.

Deep generative models as the probability transformation functions Flow Matching for Generative Modeling

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.914380Z

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Observation 03503533-5a0c-4f00-a076-dfd598265b7a · outbound

This paper cites NIPS 2016 Tutorial: Generative Adversarial Networks.

Deep generative models as the probability transformation functions NIPS 2016 Tutorial: Generative Adversarial Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.920033Z

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Observation 28e8b86c-58cf-4082-add5-893880f81bdb · outbound

This paper cites Masked autoencoders are scalable vision learners.

Deep generative models as the probability transformation functions Masked autoencoders are scalable vision learners

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.925244Z

Source-reported events for the cited work

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Observation 8c5df6a6-c034-41fd-ab83-20dd2ed35bb5 · outbound

This paper cites Auto-encoding variational bayes, 2013.

Deep generative models as the probability transformation functions Auto-encoding variational bayes, 2013

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.930831Z

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Observation ee592d03-b433-4f5f-81b9-61aaaad1dd18 · outbound

This paper cites Neural discrete representation learning.

Deep generative models as the probability transformation functions Neural discrete representation learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.595473Z

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.

Observation 601e9445-7ab7-4369-a91c-5245f02b3f6a · outbound

This paper cites High-resolution image synthesis with latent diffusion models, 2021, 2021.

Deep generative models as the probability transformation functions High-resolution image synthesis with latent diffusion models, 2021, 2021

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.578506Z

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.

Observation 2f2a8982-b84b-4020-8bb3-2a52905c00c3 · outbound

This paper cites Improving language understanding by generative pre-training.

Deep generative models as the probability transformation functions Improving language understanding by generative pre-training

Reference 10

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Observation 9f8d68c7-daf7-40de-aaa8-f85e595c2dd0 · outbound

This paper cites GPT-4 Technical Report.

Deep generative models as the probability transformation functions GPT-4 Technical Report

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.949065Z

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Observation f1cdbc67-89e0-4de0-9a66-7fea0c40a183 · outbound

This paper cites Language models are few-shot learners.

Deep generative models as the probability transformation functions Language models are few-shot learners

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.953737Z

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Observation cae418fc-a0e4-4f27-a3c7-d890066b72ef · outbound

This paper cites The Llama 3 Herd of Models.

Deep generative models as the probability transformation functions The Llama 3 Herd of Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.958493Z

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Observation 5bd2e238-021c-4241-bdf4-16ba65da0292 · outbound

This paper cites Scaling Laws for Autoregressive Generative Modeling.

Deep generative models as the probability transformation functions Scaling Laws for Autoregressive Generative Modeling

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:17.963561Z

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Observation 3dac3ca1-d2ac-4b72-ab8e-f89a169f8e20 · outbound

This paper cites An empirical analysis of compute-optimal large language model training.

Deep generative models as the probability transformation functions An empirical analysis of compute-optimal large language model training

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.541553Z

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.

Observation ec699984-a89b-4b45-8c68-cd996b666c17 · outbound

This paper cites Scaling data-constrained language models.

Deep generative models as the probability transformation functions Scaling data-constrained language models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.525549Z

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.

Observation 567f21c4-db55-456b-9e74-8ffc0b2b3998 · outbound

This paper cites Autoregressive models in vision: A survey.

Deep generative models as the probability transformation functions Autoregressive models in vision: A survey

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.508368Z

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.

Observation 0a6108a7-b5bf-40b7-b2a2-3fec1fa02b46 · outbound

This paper cites Conditional image generation with pixelcnn decoders.

Deep generative models as the probability transformation functions Conditional image generation with pixelcnn decoders

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.492059Z

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

Observation 83c4f130-4937-47ff-8bd4-c5a6a03df88f · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Deep generative models as the probability transformation functions Taming transformers for high-resolution image synthesis

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.475992Z

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.

Observation c9eb320f-058a-4f28-b863-d66eedf681e3 · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.

Deep generative models as the probability transformation functions Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.458175Z

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.

Observation 87f29696-0a54-4704-ad78-2e9beb40d01d · outbound

This paper cites Generative adversarial nets.

Deep generative models as the probability transformation functions Generative adversarial nets

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.441821Z

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.

Observation 3a33ecb0-ae21-4188-a2b7-cfe7e0cc0353 · outbound

This paper cites Generative adversarial network applications in industry 4.0: A review.

Deep generative models as the probability transformation functions Generative adversarial network applications in industry 4.0: A review

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.424575Z

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.

Observation 09e2fd44-cde5-4743-9285-a1f41ad0502c · outbound

This paper cites Generative adversarial networks (gans): introduction, taxonomy, variants, limitations, and applications.

Deep generative models as the probability transformation functions Generative adversarial networks (gans): introduction, taxonomy, variants, limitations, and applications

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.406876Z

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.

Observation 34c35246-bf11-45d3-85e6-c67a8292fed5 · outbound

This paper cites Ten years of generative adversarial nets (gans): a survey of the state-of-the-art.

Deep generative models as the probability transformation functions Ten years of generative adversarial nets (gans): a survey of the state-of-the-art

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.391094Z

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

Observation af61a9e8-f077-4af7-b811-7cf9f8dd8b51 · outbound

This paper cites Generative Adversarial Networks for Malware Detection: a Survey.

Deep generative models as the probability transformation functions Generative Adversarial Networks for Malware Detection: a Survey

Reference 25

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Observation 2d13c164-916f-4bde-a895-c6a772bfadb6 · outbound

This paper cites Normalizing flows: An introduction and review of current methods.

Deep generative models as the probability transformation functions Normalizing flows: An introduction and review of current methods

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:18.020666Z

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Observation 07e8d04f-20c7-4b9d-82eb-9a917e68e747 · outbound

This paper cites Variational inference with normalizing flows.

Deep generative models as the probability transformation functions Variational inference with normalizing flows

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:18.026270Z

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Observation 858efec9-6feb-4fae-9a82-b5649ffb1d7e · outbound

This paper cites Density estimation using Real NVP.

Deep generative models as the probability transformation functions Density estimation using Real NVP

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:18.030982Z

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Observation ec433f6d-6f2c-4587-8776-056005177b90 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Deep generative models as the probability transformation functions Deep unsupervised learning using nonequilibrium thermodynamics

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.356207Z

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

Observation dbf6517b-a69b-4b19-a367-a249d850cfdf · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Deep generative models as the probability transformation functions Generative modeling by estimating gradients of the data distribution

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.340197Z

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.

Observation 3462a284-8ca9-4c75-92e5-ad19e8d1547d · outbound

This paper cites Denoising diffusion probabilistic models.

Deep generative models as the probability transformation functions Denoising diffusion probabilistic models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:18.047072Z

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Observation 96d1b9a5-ee94-4649-af24-58e815339168 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Deep generative models as the probability transformation functions Diffusion models beat gans on image synthesis

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:18.051668Z

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Observation 361aec18-7a07-4c3c-a596-12ea97378a06 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Deep generative models as the probability transformation functions Elucidating the design space of diffusion-based generative models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T19:15:18.056342Z

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Observation 4ddea9c4-b360-48c9-943a-c22b1c8e9341 · outbound

This paper cites Diffusion schrödinger bridge matching.

Deep generative models as the probability transformation functions Diffusion schrödinger bridge matching

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.292890Z

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.

Observation 2fe6f380-41dc-4dc6-b3ad-31c7d8bfddf7 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

Deep generative models as the probability transformation functions Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.276480Z

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.

Observation 634735ef-3ff0-4000-9378-b4ff895701d8 · outbound

This paper cites A new probability transformation approach of mass function.

Deep generative models as the probability transformation functions A new probability transformation approach of mass function

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.258553Z

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

Observation 2a2b9411-859c-4f2f-bcf3-b0582491efb9 · outbound

This paper cites On decoding strategies for neural text generators.

Deep generative models as the probability transformation functions On decoding strategies for neural text generators

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:15:18.242272Z

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

Pith citing papers

Observation 903a3f86-d7b9-4c42-a0f8-db2eca1a4f11 · inbound

FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator cites this paper.

FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator Deep generative models as the probability transformation functions

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:18:03.913924Z

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.