Active Diffusion-Based Inference for Ill-Posed Inverse Problems under Incomplete Priors

arXiv:2608.27080 · stat.ML, cs.AI, cs.LG · Submitted 2026-08-27 · Read on arXiv

stat.ML, cs.AI, cs.LG

Submitted: 2026-08-27

Updated: 2026-08-27

Terminology

• Inverse Problem — 37× in this paper · explained in 2 episodes
• Active Learning — 21× in this paper · explained in 6 episodes
• Generative Model — 9× in this paper · explained in 2 episodes
• Generative Models — 8× in this paper · explained in 2 episodes
• Wasserstein Distance — 8× in this paper · explained in 4 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Variational Autoencoder Inverse Mapper — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Invertible Neural Networks — 3× in this paper
• Normalizing Flow — 3× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Normalizing Flows — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Uncertainty Quantification — 2× in this paper · explained in 8 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Deep Inelastic Scattering — 1× in this paper
• Epistemic Uncertainty — 1× in this paper · explained in 9 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Forward modeling — 1× in this paper · explained in 2 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Particle Momentum Distribution — 1× in this paper
• Physics-Informed Constraints — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Diffusion Posterior Sampling for General Noisy Inverse Problems
• NICE: Non-linear Independent Components Estimation
• Do Deep Generative Models Know What They Don't Know?
• Score-Based Generative Modeling through Stochastic Differential Equations

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