Paper Citation Record · LEDGER

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study

As of 14 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2505.22841.

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

pith.paper-citation-record.v1
2505.22841 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:05:46.193531Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T05:54:23.709309Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:18:37.950110Z

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f1c8fc52-df36-4a53-bfad-29a97f7547bf · outbound

This paper cites Matrix algebra, volume 1.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Matrix algebra, volume 1

Reference 1

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Observation 3d741a22-3574-41b6-9d57-6d74fa131959 · outbound

This paper cites Losing dimensions: Geometric memorization in generative diffusion, 2024.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Losing dimensions: Geometric memorization in generative diffusion, 2024

Reference 2

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This paper cites Anderson.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Anderson

Reference 3

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This paper cites Kovachki, Assad Oberai, and Andrew M.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Kovachki, Assad Oberai, and Andrew M

Reference 4

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This paper cites Advanced mathematical methods for scientists and engineers I: Asymptotic methods and perturbation theory.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Advanced mathematical methods for scientists and engineers I: Asymptotic methods and perturbation theory

Reference 5

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This paper cites Dynamical regimes of diffusion models.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Dynamical regimes of diffusion models

Reference 6

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This paper cites Shallow diffusion networks provably learn hidden low-dimensional structure.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Shallow diffusion networks provably learn hidden low-dimensional structure

Reference 7

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This paper cites Swarm gradient dynamics for global optimization: the mean-field limit case.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Swarm gradient dynamics for global optimization: the mean-field limit case

Reference 8

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This paper cites Extracting training data from diffusion models.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Extracting training data from diffusion models

Reference 9

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Observation 6a2971a2-3de0-4fcc-876e-e850313dc8e6 · outbound

This paper cites Towards memorization-free diffusion models, 2024.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Towards memorization-free diffusion models, 2024

Reference 10

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Observation ca8372ca-e14e-41db-b5ee-2cb05954f4fe · outbound

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

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Improved analysis of score-based generative modeling: user-friendly bounds under minimal smoothness assumptions

Reference 11

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Observation 958af38c-c3cd-46fd-be09-8628ec70fb74 · outbound

This paper cites Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data

Reference 12

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This paper cites On the interpolation effect of score smoothing, 2025.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study On the interpolation effect of score smoothing, 2025

Reference 13

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Observation 85bf3479-2dd2-4184-a783-6aeec631ee62 · outbound

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Unresolved cited work

Reference 14

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This paper cites Ambient diffusion: Learning clean distributions from corrupted data.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Ambient diffusion: Learning clean distributions from corrupted data

Reference 15

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Observation b39d3f66-aaf4-4132-874b-b2d84bbaaf58 · outbound

This paper cites Analysis of diffusion models for manifold data, 2025.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Analysis of diffusion models for manifold data, 2025

Reference 16

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Observation fa2752a6-47e8-4755-b4dd-a669d74a8f14 · outbound

This paper cites On memorization in diffusion models.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study On memorization in diffusion models

Reference 17

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Observation 763b295c-0105-4355-a127-15eb88c5d4c8 · outbound

This paper cites Linear Methods for Regression, pages 43–99.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Linear Methods for Regression, pages 43–99

Reference 18

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This paper cites Classifier-free diffusion guidance, 2022.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Classifier-free diffusion guidance, 2022

Reference 19

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This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Neural tangent kernel: Convergence and generalization in neural networks

Reference 20

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This paper cites The variational formulation of the fokker– planck equation.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study The variational formulation of the fokker– planck equation

Reference 21

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This paper cites Generalization in diffusion models arises from geometry-adaptive harmonic representation.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Generalization in diffusion models arises from geometry-adaptive harmonic representation

Reference 22

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Observation 65022b9b-0fec-4a12-aecc-dfa17c7fb22e · outbound

This paper cites An analytic theory of creativity in convolutional diffusion models, 2024.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study An analytic theory of creativity in convolutional diffusion models, 2024

Reference 23

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This paper cites Wide neural networks of any depth evolve as linear models under gradient descent.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Wide neural networks of any depth evolve as linear models under gradient descent

Reference 24

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study On the generalization properties of diffusion models

Reference 25

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This paper cites Understanding generalizability of diffusion models requires rethinking the hidden gaussian structure.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Understanding generalizability of diffusion models requires rethinking the hidden gaussian structure

Reference 26

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This paper cites Understanding diffusion models: A unified perspective, 2022.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Understanding diffusion models: A unified perspective, 2022

Reference 27

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This paper cites Accelerating diffusion models via early stop of the diffusion process, 2022.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Accelerating diffusion models via early stop of the diffusion process, 2022

Reference 28

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Unresolved cited work

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Unresolved cited work

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study U-net: Convolutional networks for biomedical image segmentation

Reference 31

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Closed-form diffusion models, 2025

Reference 32

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Deep unsupervised learning using nonequilibrium thermodynamics

Reference 33

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This paper cites Diffusion art or digital forgery? investigating data replication in diffusion models.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Diffusion art or digital forgery? investigating data replication in diffusion models

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Understanding and Mitigating Copying in Diffusion Models

Reference 35

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Score-based generative modeling through stochastic differential equations

Reference 36

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study An analysis of the noise schedule for score-based generative models, 2025

Reference 37

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Regularization can make diffusion models more efficient, 2025

Reference 38

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This paper cites On memorization in probabilistic deep generative models.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study On memorization in probabilistic deep generative models

Reference 39

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This paper cites Manifolds, random matrices and spectral gaps: The geometric phases of generative diffusion.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Manifolds, random matrices and spectral gaps: The geometric phases of generative diffusion

Reference 40

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Otto calculus, pages 421–433

Reference 41

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study A connection between score matching and denoising autoencoders

Reference 42

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Optimal score estimation via empirical bayes smoothing, 2024

Reference 43

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This paper cites On the generalization of diffusion model, 2023.

Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study On the generalization of diffusion model, 2023

Reference 44

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Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study Φ(1) N (t, x) Φ(0) N (t, x) − mt(x) # , √ N

Reference 45

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Pith citing papers

Observation 55702467-afd3-4775-96d8-f5df94dca12a · inbound

Training-Free Generative Sampling via Moment-Matched Score Smoothing cites this paper.

Training-Free Generative Sampling via Moment-Matched Score Smoothing Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study

Reference 32

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Observation fcf745ff-75e8-4b96-9767-cdc36a03812d · inbound

Smoothed-KL Reweighting: A Principled Account and Matching Rule for SNR-Based Diffusion Training cites this paper.

Smoothed-KL Reweighting: A Principled Account and Matching Rule for SNR-Based Diffusion Training Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study

Reference 6

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