Denoising diffusion learns pair-wise input statistics at linear sample complexity and fourth-order cumulants only at cubic complexity, unless latent variables are correlated.
& Klivans, A.Learning Mixtures of Gaussians Using the DDPM Objective2023
2 Pith papers cite this work. Polarity classification is still indexing.
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Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
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A theory of learning data statistics in diffusion models, from easy to hard
Denoising diffusion learns pair-wise input statistics at linear sample complexity and fourth-order cumulants only at cubic complexity, unless latent variables are correlated.
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Statistical Properties of Training & Generalization
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.