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Found 3 papers, 1 papers with code
A Note on the Convergence of Denoising Diffusion Probabilistic Models
Diffusion models are one of the most important families of deep generative models.
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
Since their inception, Variational Autoencoders (VAEs) have become central in machine learning.
PAC-Bayesian Generalization Bounds for Adversarial Generative Models
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models based on the Wasserstein distance and the total variation distance.
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