7 papers
Statistical Convergence of Spherical First Hitting Diffusion Models
Simon Bienewald, Lukas Trottner
Denoising diffusion models have evolved into a state-of-the-art method for tasks in various fields, such as denoising and generation of images, text generation, or generation of sy…
Reflected diffusion models adapt to low-dimensional data
Asbjørn Holk, Claudia Strauch, Lukas Trottner
While the mathematical foundations of score-based generative models are increasingly well understood for unconstrained Euclidean spaces, many practical applications involve data re…
In situ Learning-Based Spin Engineering of Pulsed Dynamic Nuclear Polarization
Filip V. Jensen, José P. Carvalho, Nino Wili +6
Pulsed Dynamic Nuclear Polarization (DNP) is currently receiving substantial interest as a means to enhance the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonan…
Statistical guarantees for denoising reflected diffusion models
Asbjørn Holk, Claudia Strauch, Lukas Trottner
In recent years, denoising diffusion models have become a crucial area of research due to their abundance in the rapidly expanding field of generative AI. While recent statistical…
Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions
Sören Christensen, Jan Kallsen, Claudia Strauch +1
We introduce a new class of generative diffusion models that, unlike conventional denoising diffusion models, achieve a time-homogeneous structure for both the noising and denoisin…
Model-free filtering in high dimensions via projection and score-based diffusions
Sören Christensen, Jan Kallsen, Claudia Strauch +1
We consider the problem of recovering a latent signal from its noisy observation . The unknown law of , and in particular its support , are ac…