Paper Citation Record · LEDGER

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective

As of 21 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2508.16643.

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

pith.paper-citation-record.v1
2508.16643 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:24:36.013732Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:53:49.780277Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T09:03:16.195560Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 982c7017-72a2-47ca-b461-2014795c8899 · outbound

This paper cites Deep learning day: Generative modeling.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Deep learning day: Generative modeling

Reference 1

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Observation 43befff6-5bb8-4698-9d8c-fc17c910c8a8 · outbound

This paper cites The promise and peril of generative ai.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective The promise and peril of generative ai

Reference 2

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Observation 1a013c4a-7168-4680-849b-50e4a644dead · outbound

This paper cites Art and the science of generative ai.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Art and the science of generative ai

Reference 3

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Observation 7cef4bc8-58b3-4786-85c1-f9b74f991db4 · outbound

This paper cites Learning deep generative models.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Learning deep generative models

Reference 4

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Observation 3397e076-44ed-4b17-8ea4-950da318a90a · outbound

This paper cites A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

Reference 5

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Observation 464bb508-4427-4687-bb60-09bba8e615f1 · outbound

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Unresolved cited work

Reference 6

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Observation 33ca752d-974e-476d-9fbc-4bfbb4dcc396 · outbound

This paper cites Latent dirichlet allocation.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Latent dirichlet allocation

Reference 7

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Observation a6738c74-d8c0-4e43-8d7e-0dfdb3cd56ca · outbound

This paper cites Autoencoders and their applications in machine learning: a survey.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Autoencoders and their applications in machine learning: a survey

Reference 8

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This paper cites Auto-encoding variational bayes, 2022.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Auto-encoding variational bayes, 2022

Reference 9

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Observation 270bd25c-73a6-4cc0-a0bd-8238be315397 · outbound

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

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Normalizing flows: An introduction and review of current methods

Reference 10

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Observation 1696c5f6-888a-4619-9b22-1e2eb984ae80 · outbound

This paper cites Flow Matching for Generative Modeling.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Flow Matching for Generative Modeling

Reference 11

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Observation aabcb463-8f43-4e2d-99d1-2522cbf09c12 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Deep unsupervised learning using nonequilibrium thermodynamics

Reference 12

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Observation d8175491-51f3-4894-920e-41d8ca0d0fd1 · outbound

This paper cites Generative adversarial nets.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Generative adversarial nets

Reference 13

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Observation b57a2151-3877-458b-96fd-f4d3ec642cc0 · outbound

This paper cites A review on kalman filter models.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective A review on kalman filter models

Reference 14

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Observation 824f2da1-5d44-4892-8ba8-8395185c1d4c · outbound

This paper cites Item Response Theory: Principles and Applications.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Item Response Theory: Principles and Applications

Reference 15

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Observation 022dfdb9-277d-4db7-a48a-572578a9036a · outbound

This paper cites Hidden Markov models and applications.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Hidden Markov models and applications

Reference 16

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Observation b2368cf2-f5fe-4368-8985-616170ade9a8 · outbound

This paper cites Latent class analysis: a guide to best practice.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Latent class analysis: a guide to best practice

Reference 17

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Observation 2b0b342b-1092-49b2-a408-36cd5b8cece3 · outbound

This paper cites Cs229 lecture notes.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Cs229 lecture notes

Reference 18

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Observation 82bb92eb-3736-4233-b75b-d93e59242814 · outbound

This paper cites Deep Learning, volume 1.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Deep Learning, volume 1

Reference 19

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Observation 6d872de4-374c-492f-8e39-5b3a37ca69c5 · outbound

This paper cites Item Response Theory: Parameter Estimation Techniques.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Item Response Theory: Parameter Estimation Techniques

Reference 20

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Observation 1a40908e-a628-4df1-8781-f3daf14acd13 · outbound

This paper cites Probabilistic principal component analysis.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Probabilistic principal component analysis

Reference 21

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Observation 274a2cd7-787c-4148-8130-779b5ce5f027 · outbound

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Unresolved cited work

Reference 22

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Observation 910e250e-af4d-498a-9e2f-c5aced4dec4e · outbound

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Unresolved cited work

Reference 23

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This paper cites Principal component analysis.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Principal component analysis

Reference 24

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Observation 3f520336-171d-4be3-8f8f-04eadec440ec · outbound

This paper cites Maximum likelihood from incomplete data via the em algorithm.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Maximum likelihood from incomplete data via the em algorithm

Reference 25

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This paper cites Latent class analysis, volume 64.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Latent class analysis, volume 64

Reference 26

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Observation ecac11e0-e757-4378-8785-24e9dd1c4f30 · outbound

This paper cites Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:24:36.374551Z

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This paper cites Blei, Alp Kucukelbir, and Jon D.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Blei, Alp Kucukelbir, and Jon D

Reference 28

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Observation 2da95552-f9af-4fa3-bf3d-05892fbf3674 · outbound

This paper cites Topic modeling using latent dirichlet allocation: A survey.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Topic modeling using latent dirichlet allocation: A survey

Reference 29

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Observation 6d92b8c1-1a2e-4230-b5a8-2ab4065ab373 · outbound

This paper cites A systematic review of hidden markov models and their applications.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective A systematic review of hidden markov models and their applications

Reference 30

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Observation 8c65338b-f09f-4763-9a39-ea3092c52276 · outbound

This paper cites Markov decision processes: discrete stochastic dynamic programming.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Markov decision processes: discrete stochastic dynamic programming

Reference 31

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Industrial applications of the kalman filter: A review

Reference 32

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This paper cites A survey on variational autoencoders in recommender systems.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective A survey on variational autoencoders in recommender systems

Reference 33

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Observation 58ab084c-508c-42cc-b4f0-8d71de24a272 · outbound

This paper cites Normalizing flows for probabilistic modeling and inference.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Normalizing flows for probabilistic modeling and inference

Reference 34

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Variational inference with normalizing flows, 2016

Reference 35

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Density estimation using real nvp, 2017

Reference 36

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Denoising diffusion probabilistic models, 2020

Reference 37

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This paper cites A very preliminary analysis of DALL-E 2.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective A very preliminary analysis of DALL-E 2

Reference 38

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Stable diffusion 3.5

Reference 39

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

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Diffusion models: A comprehensive survey of methods and applications

Reference 40

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This paper cites Conditional image generation with pixelcnn decoders, 2016.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Conditional image generation with pixelcnn decoders, 2016

Reference 41

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This paper cites Wavenet: A generative model for raw audio, 2016.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Wavenet: A generative model for raw audio, 2016

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:24:36.198790Z

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Unresolved cited work

Reference 43

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This paper cites Hybrid variational autoencoder for time series forecasting.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Hybrid variational autoencoder for time series forecasting

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:24:36.170508Z

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This paper cites A style-based generator architecture for generative adversarial networks, 2019.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective A style-based generator architecture for generative adversarial networks, 2019

Reference 45

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From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective E2gan: Efficient training of efficient gans for image-to-image translation, 2024

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:24:36.149096Z

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Observation b77f9458-53c7-47aa-b124-19d8a6582a57 · outbound

This paper cites Generative adversarial networks for image super-resolution: A survey, 2024.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Generative adversarial networks for image super-resolution: A survey, 2024

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:24:36.135625Z

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Observation a3c97dbb-5b88-43a3-8c1d-43e6352d4bf1 · outbound

This paper cites Wasserstein gan, 2017.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Wasserstein gan, 2017

Reference 48

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Observation b489a9b9-0881-4daa-9acf-04062c358925 · outbound

This paper cites Diffusion-gan: Training gans with diffusion, 2023.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Diffusion-gan: Training gans with diffusion, 2023

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:24:36.114679Z

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Observation 8faa7c6f-713f-4c9b-8c26-485edbe33577 · outbound

This paper cites Seriesgan: Time series generation via adversarial and autoregressive learning, 2024.

From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective Seriesgan: Time series generation via adversarial and autoregressive learning, 2024

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:24:36.101366Z

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Pith citing papers

Observation f86015fd-f539-4edb-a169-102b1283749b · inbound

The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer cites this paper.

The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T09:03:16.197073Z

Source-reported events for the cited work

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