4 papers
Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics
Benjamin Sterling, Mónica F. Bugallo, Tom Tirer
Diffusion/score-based models have emerged as powerful generative models, capable of generating high-quality samples that mimic the training data distribution. However, it has been…
Defending Diffusion Models Against Membership Inference Attacks via Higher-Order Langevin Dynamics
Benjamin Sterling, Yousef El-Laham, Mónica F. Bugallo
Recent advances in generative artificial intelligence applications have raised new data security concerns. This paper focuses on defending diffusion models against membership infer…
Critically-Damped Higher-Order Langevin Dynamics for Generative Modeling
Benjamin Sterling, Chad Gueli, Mónica F. Bugallo
Denoising diffusion probabilistic models (DDPMs) represent an entirely new class of generative AI methods that have yet to be fully explored. They use Langevin dynamics, represente…
Critically Damped Third-Order Langevin Dynamics
Benjamin Sterling, Mónica F. Bugallo
While systems analysis has been studied for decades in the context of control theory, it has only been recently used to improve the convergence of Denoising Diffusion Probabilistic…