2 citations · 2 across the 9 of their papers we have counts for
10 papers
Generating Physically Consistent Molecules with Energy-Based Models
Christoph Griesbacher, Lea Bogensperger, Andreas Habring +1
Molecules in equilibrium follow a Boltzmann distribution, making the underlying energy landscape a physically grounded modeling objective. However, such landscapes are difficult to…
Time-Inhomogeneous Preconditioned Langevin Dynamics
Alexander Falk, Laurenz Nagler, Andreas Habring +1
Langevin sampling from distributions of the form faces two major challenges: (global) mode coverage and (local) mode exploration. The first challenge is…
Accelerated Sampling with the Inertial Langevin Algorithm
Alexander Falk, Andreas Habring, Christoph Griesbacher +1
We consider the \emph{inertial Langevin algorithm} (ILA), a simple momentum-based method for sampling from Gibbs distributions of the form . ILA augments…
Energy-based models for inverse imaging problems
Andreas Habring, Martin Holler, Thomas Pock +1
In this chapter we provide a thorough overview of the use of energy-based models (EBMs) in the context of inverse imaging problems. EBMs are probability distributions modeled via G…
Total Variation-Based Image Decomposition and Denoising for Microscopy Images
Marco Corrias, Giada Franceschi, Michele Riva +5
Experimentally acquired microscopy images are unavoidably affected by the presence of noise and other unwanted signals, which degrade their quality and might hide relevant features…
Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes [conference paper]
Andreas Habring, Alexander Falk, Thomas Pock
In this article we propose a novel method for sampling from Gibbs distributions of the form with a potential . In particular, inspired by diffusion m…
Diffusion at Absolute Zero: Langevin Sampling using Successive Moreau Envelopes [journal paper]
Andreas Habring, Alexander Falk, Martin Zach +1
We propose a method for sampling from Gibbs distributions of the form by considering a family of approximations of the target density which is…
Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction
Lukas Glaszner, Martin Zach
Diffusion model have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for uncond…
An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation
Lea Bogensperger, Matthias J. Ehrhardt, Thomas Pock +2
We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minim…
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms
Lea Bogensperger, Dominik Narnhofer, Alexander Falk +2
Medical image segmentation plays an important role in accurately identifying and isolating regions of interest within medical images. Generative approaches are particularly effecti…