Paper Citation Record · LEDGER

Generalization through variance: how noise shapes inductive biases in diffusion models

As of 18 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 3 inbound Pith citation observations for arXiv:2504.12532.

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

pith.paper-citation-record.v1
2504.12532 v1

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measured 61 of 61 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

61 of 61 outbound references displayed

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External citation measurements

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Outbound references

Observation c8274703-abee-4bfa-bbc8-686694e7b9a2 · outbound

This paper cites write newline.

Generalization through variance: how noise shapes inductive biases in diffusion models write newline

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This paper cites On exact computation with an infinitely wide neural net.

Generalization through variance: how noise shapes inductive biases in diffusion models On exact computation with an infinitely wide neural net

Reference 2

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This paper cites On the inductive bias of neural tangent kernels.

Generalization through variance: how noise shapes inductive biases in diffusion models On the inductive bias of neural tangent kernels

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This paper cites Align your latents: High-resolution video synthesis with latent diffusion models.

Generalization through variance: how noise shapes inductive biases in diffusion models Align your latents: High-resolution video synthesis with latent diffusion models

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This paper cites Self-consistent dynamical field theory of kernel evolution in wide neural networks.

Generalization through variance: how noise shapes inductive biases in diffusion models Self-consistent dynamical field theory of kernel evolution in wide neural networks

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This paper cites Self-consistent dynamical field theory of kernel evolution in wide neural networks*.

Generalization through variance: how noise shapes inductive biases in diffusion models Self-consistent dynamical field theory of kernel evolution in wide neural networks*

Reference 6

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This paper cites Spectrum dependent learning curves in kernel regression and wide neural networks.

Generalization through variance: how noise shapes inductive biases in diffusion models Spectrum dependent learning curves in kernel regression and wide neural networks

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

Generalization through variance: how noise shapes inductive biases in diffusion models Convergence of denoising diffusion models under the manifold hypothesis

Reference 8

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This paper cites Diffusion schr\"odinger bridge with applications to score-based generative modeling.

Generalization through variance: how noise shapes inductive biases in diffusion models Diffusion schr\"odinger bridge with applications to score-based generative modeling

Reference 9

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This paper cites Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks.

Generalization through variance: how noise shapes inductive biases in diffusion models Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

Reference 10

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This paper cites Extracting training data from diffusion models.

Generalization through variance: how noise shapes inductive biases in diffusion models Extracting training data from diffusion models

Reference 11

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This paper cites Denoising likelihood score matching for conditional score-based data generation.

Generalization through variance: how noise shapes inductive biases in diffusion models Denoising likelihood score matching for conditional score-based data generation

Reference 12

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

Generalization through variance: how noise shapes inductive biases in diffusion models Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data

Reference 13

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Generalization through variance: how noise shapes inductive biases in diffusion models Unresolved cited work

Reference 14

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This paper cites The probability flow ode is provably fast.

Generalization through variance: how noise shapes inductive biases in diffusion models The probability flow ode is provably fast

Reference 15

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

Generalization through variance: how noise shapes inductive biases in diffusion models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 16

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This paper cites On lazy training in differentiable programming.

Generalization through variance: how noise shapes inductive biases in diffusion models On lazy training in differentiable programming

Reference 17

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Generalization through variance: how noise shapes inductive biases in diffusion models Group equivariant convolutional networks

Reference 18

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Generalization through variance: how noise shapes inductive biases in diffusion models Crisanti and H

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Generalization through variance: how noise shapes inductive biases in diffusion models Schoenberg, and Sandy Engelhardt

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Generalization through variance: how noise shapes inductive biases in diffusion models GENIE : Higher-order denoising diffusion solvers

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Generalization through variance: how noise shapes inductive biases in diffusion models Score-based generative modeling with critically-damped langevin diffusion

Reference 22

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Generalization through variance: how noise shapes inductive biases in diffusion models Disentangling feature and lazy training in deep neural networks

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Generalization through variance: how noise shapes inductive biases in diffusion models Neural network-based score estimation in diffusion models: Optimization and generalization

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Generalization through variance: how noise shapes inductive biases in diffusion models Denoising diffusion probabilistic models

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Generalization through variance: how noise shapes inductive biases in diffusion models Neural tangent kernel: Convergence and generalization in neural networks

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Generalization through variance: how noise shapes inductive biases in diffusion models Generalization in diffusion models arises from geometry-adaptive harmonic representations

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Generalization through variance: how noise shapes inductive biases in diffusion models An analytic theory of creativity in convolutional diffusion models

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Generalization through variance: how noise shapes inductive biases in diffusion models Elucidating the design space of diffusion-based generative models

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Generalization through variance: how noise shapes inductive biases in diffusion models Analyzing and improving the training dynamics of diffusion models, 2024

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Generalization through variance: how noise shapes inductive biases in diffusion models Path integrals in quantum mechanics, statistics, polymer physics, and financial markets

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Generalization through variance: how noise shapes inductive biases in diffusion models Pseudo Numerical Methods for Diffusion Models on Manifolds

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Generalization through variance: how noise shapes inductive biases in diffusion models Boomerang: Local sampling on image manifolds using diffusion models

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Generalization through variance: how noise shapes inductive biases in diffusion models Unresolved cited work

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Generalization through variance: how noise shapes inductive biases in diffusion models Dynamical mean-field theory for stochastic gradient descent in gaussian mixture classification

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Generalization through variance: how noise shapes inductive biases in diffusion models Towards a Mechanistic Explanation of Diffusion Model Generalization

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Generalization through variance: how noise shapes inductive biases in diffusion models On the spectral bias of neural networks

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Generalization through variance: how noise shapes inductive biases in diffusion models High-resolution image synthesis with latent diffusion models

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Generalization through variance: how noise shapes inductive biases in diffusion models A Theory of Neural Tangent Kernel Alignment and Its Influence on Training

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Generalization through variance: how noise shapes inductive biases in diffusion models On the generalization benefit of noise in stochastic gradient descent

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Generalization through variance: how noise shapes inductive biases in diffusion models Smith and Quoc V

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Generalization through variance: how noise shapes inductive biases in diffusion models Deep unsupervised learning using nonequilibrium thermodynamics

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Generalization through variance: how noise shapes inductive biases in diffusion models Diffusion art or digital forgery? investigating data replication in diffusion models

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Generalization through variance: how noise shapes inductive biases in diffusion models Understanding and mitigating copying in diffusion models

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Generalization through variance: how noise shapes inductive biases in diffusion models Generative modeling by estimating gradients of the data distribution

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Generalization through variance: how noise shapes inductive biases in diffusion models Sliced score matching: A scalable approach to density and score estimation

Reference 46

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Generalization through variance: how noise shapes inductive biases in diffusion models Score-based generative modeling through stochastic differential equations

Reference 47

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Generalization through variance: how noise shapes inductive biases in diffusion models Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L

Reference 48

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Generalization through variance: how noise shapes inductive biases in diffusion models A connection between score matching and denoising autoencoders

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Generalization through variance: how noise shapes inductive biases in diffusion models Kakade, and Boaz Barak

Reference 50

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Generalization through variance: how noise shapes inductive biases in diffusion models The unreasonable effectiveness of gaussian score approximation for diffusion models and its applications

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This paper cites Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later.

Generalization through variance: how noise shapes inductive biases in diffusion models Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later

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Generalization through variance: how noise shapes inductive biases in diffusion models Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, and Nathan Srebro

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Generalization through variance: how noise shapes inductive biases in diffusion models Jaakkola

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Generalization through variance: how noise shapes inductive biases in diffusion models Diffusion models: A comprehensive survey of methods and applications

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Generalization through variance: how noise shapes inductive biases in diffusion models Understanding deep learning requires rethinking generalization

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Generalization through variance: how noise shapes inductive biases in diffusion models The emergence of reproducibility and consistency in diffusion models

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Generalization through variance: how noise shapes inductive biases in diffusion models @esa (Ref

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Generalization through variance: how noise shapes inductive biases in diffusion models Unresolved cited work

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The two clocks and the innovation window: When and how generative models learn rules Generalization through variance: how noise shapes inductive biases in diffusion models

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Unified Audio Intelligence Without Regressing on Text Intelligence Generalization through variance: how noise shapes inductive biases in diffusion models

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Unified Audio Intelligence Without Regressing on Text Intelligence Generalization through variance: how noise shapes inductive biases in diffusion models

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