FB-GDM: Fully-Bayesian Guided Diffusion Models for High-Dimensional Linear Inverse Problems via Unsupervised Variational Inference

arXiv:2609.29216 · cs.LG, cs.AI, stat.ML · Submitted 2026-09-24 · Read on arXiv

cs.LG, cs.AI, stat.ML

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Diffusion Model — 27× in this paper · explained in 8 episodes
• Diffusion Models — 24× in this paper · explained in 6 episodes
• Inverse Problem — 23× in this paper · explained in 2 episodes
• Robustness — 12× in this paper · explained in 5 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• Bayesian Inference — 5× in this paper · explained in 5 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 3× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization

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