Paper Citation Record · LEDGER

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis

As of 10 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.00849.

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

pith.paper-citation-record.v1
2506.00849 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:06:40.736774Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 1710d7d4-c156-44d1-baf9-6390dd2086c1 · outbound

This paper cites Catoni-style.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Catoni-style

Reference 1

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Observation b779b45c-6437-4b91-ac79-05a93409f51e · outbound

This paper cites an unresolved cited work.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Unresolved cited work

Reference 3

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Observation 9d7ba069-9247-4a48-9b53-542dfec1ec52 · outbound

This paper cites beta-vae: Learning basic visual concepts with a constrained variational framework.ICLR (Poster), 3,.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis beta-vae: Learning basic visual concepts with a constrained variational framework.ICLR (Poster), 3,

Reference 8

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Observation cf9df51e-415c-4df8-99a8-9994e91de2e9 · outbound

This paper cites High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve

Reference 10

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Observation 9f6272d6-c062-4b6c-ad04-993bb9e5bba9 · outbound

This paper cites Learning dynamics in linear vae: Posterior collapse threshold, superfluous latent space pitfalls, and speedup with kl annealing.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Learning dynamics in linear vae: Posterior collapse threshold, superfluous latent space pitfalls, and speedup with kl annealing

Reference 11

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Observation 412970ca-260d-4ff7-bc33-b90a807ddbe8 · outbound

This paper cites Auto-Encoding Variational Bayes.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Auto-Encoding Variational Bayes

Reference 12

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Observation 6fcdbd84-5193-416d-94ed-4f6a0c352bb3 · outbound

This paper cites On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning

Reference 13

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Observation 4028a3b9-1e84-4784-84eb-d4d3d4acc85a · outbound

This paper cites Adversarial Autoencoders.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Adversarial Autoencoders

Reference 14

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This paper cites Dispersed exponential family mixture vaes for interpretable text generation.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Dispersed exponential family mixture vaes for interpretable text generation

Reference 17

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Observation a8457743-3d4c-483b-851c-f14a44c139e4 · outbound

This paper cites Denoising Diffusion Implicit Models.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Denoising Diffusion Implicit Models

Reference 18

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Observation be5002b6-99de-4c4f-8483-f54e49d73d8e · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Score-Based Generative Modeling through Stochastic Differential Equations

Reference 19

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Observation 3ecd7ba6-5d4c-42dd-9e67-0e575c90df05 · outbound

This paper cites A note on the evaluation of generative models.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis A note on the evaluation of generative models

Reference 20

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Observation a705d7cc-f7d5-48cd-81a1-9b80257cca7b · outbound

This paper cites Wasserstein Auto-Encoders.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Wasserstein Auto-Encoders

Reference 21

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Observation 2a612cd0-a6bf-4e37-b86d-61e22bc43da2 · outbound

This paper cites Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

Reference 22

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Observation 1ffaf879-1ae6-4977-b755-ea0aacd68b87 · outbound

This paper cites On the Generalization for Transfer Learning: An Information-Theoretic Analysis.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis On the Generalization for Transfer Learning: An Information-Theoretic Analysis

Reference 24

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Observation a0ab129c-4c0b-42a6-96fc-f2bad533e000 · outbound

This paper cites Information-theoretic analysis of generalization capability of learning algorithms.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Information-theoretic analysis of generalization capability of learning algorithms

Reference 25

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Observation f6472c4d-d4ee-436d-b8a4-f08eab4a9e80 · outbound

This paper cites Intersectional Unfairness Discovery.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Intersectional Unfairness Discovery

Reference 26

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Observation 969a02ad-6fb9-4146-946a-3e7e4733923f · outbound

This paper cites , m} Upper case letter (e.g.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis , m} Upper case letter (e.g

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:06:43.161340Z

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Observation 7e6c1777-5c72-418b-b76c-13cf1fcd220a · outbound

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Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Unresolved cited work

Reference 31

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Observation 7923c798-5bca-40af-8fc9-8d5a17259e4d · outbound

This paper cites By improving the generalization, we can decrease replicated generation to help address the privacy and copyright issues in generative models.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis By improving the generalization, we can decrease replicated generation to help address the privacy and copyright issues in generative models

Reference 32

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Observation 2900493b-0385-43a9-aa6d-3574a4174d03 · outbound

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Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Unresolved cited work

Reference 34

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Observation 1b75ba82-6f13-43f8-aded-ee813f4e562a · outbound

This paper cites 37 Published as a conference paper at ICLR 2025 Score-based diffusion modelsSong et al.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis 37 Published as a conference paper at ICLR 2025 Score-based diffusion modelsSong et al

Reference 35

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Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Unresolved cited work

Reference 36

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Observation 38da9fec-ce47-4f2e-aa30-a36c3de9fe3b · outbound

This paper cites Later on, the variational perspective of these models was studied in (Huang et al., 2021; Kingma et al., 2021; Franzese et al., 2023).

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Later on, the variational perspective of these models was studied in (Huang et al., 2021; Kingma et al., 2021; Franzese et al., 2023)

Reference 37

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Observation d705e9b5-3838-497a-b0db-6038bf201252 · outbound

This paper cites Convergence theory for diffusion modelsDe Bortoli et al.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Convergence theory for diffusion modelsDe Bortoli et al

Reference 38

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Observation 24b47556-f8d3-47f2-975b-6d55dfaeaffb · outbound

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Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Unresolved cited work

Reference 39

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Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis 5 and Fig

Reference 1000

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Observation 66ce7a5f-de6f-4d55-8589-f4c1c0e94dc4 · outbound

This paper cites VAE Approximation Error: ELBO and Exponential Families.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis VAE Approximation Error: ELBO and Exponential Families

Reference 1997

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Observation 3538826a-0e27-4b6b-a2eb-48320cb45710 · outbound

This paper cites Rate-Regularization and Generalization in VAEs.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Rate-Regularization and Generalization in VAEs

Reference 2002

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This paper cites Tighter Information-Theoretic Generalization Bounds from Supersamples.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Tighter Information-Theoretic Generalization Bounds from Supersamples

Reference 2011

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Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Unresolved cited work

Reference 2013

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Observation 61f56a63-0061-47a1-95fb-7352c4513c49 · outbound

This paper cites Pac-bayesian generalization bounds for adversarial generative models.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Pac-bayesian generalization bounds for adversarial generative models

Reference 2015

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Observation 906e0bd9-6132-40b5-bc56-5194d11c17ef · outbound

This paper cites Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve

Reference 2017

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Observation 86437540-2685-4648-8523-8af9c9467098 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

Reference 2019

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This paper cites Quantifying Memorization Across Neural Language Models.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Quantifying Memorization Across Neural Language Models

Reference 2020

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Observation 9f563c67-1081-4163-8edd-3a87084d3389 · outbound

This paper cites Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2021

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Observation 61f03126-ecbb-450c-846d-ab95b9a843ad · outbound

This paper cites Convergence of denoising diffusion models under the manifold hypothesis.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Convergence of denoising diffusion models under the manifold hypothesis

Reference 2022

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Observation 2866bb25-5458-4d0d-81e9-3fba20772932 · outbound

This paper cites Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:06:44.220391Z

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Observation d455ce45-8570-496d-ba65-54c463c81b2b · outbound

This paper cites Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:06:41.327358Z

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