Paul Hagemann

According to our database, Paul Hagemann authored at least 14 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Mixed noise and posterior estimation with conditional deepGEM.

Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl

Mach. Learn. Sci. Technol., 2024

PnP-Flow: Plug-and-Play Image Restoration with Flow Matching.

Ségolène Martin, Anne Gagneux, Paul Hagemann, Gabriele Steidl

CoRR, 2024

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching.

Jannis Chemseddine, Paul Hagemann, Christian Wald, Gabriele Steidl

CoRR, 2024

Generative Sliced MMD Flows with Riesz Kernels.

Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel.

Paul Hagemann, Johannes Hertrich, Fabian Altekrüger, Robert Beinert, Jannis Chemseddine, Gabriele Steidl

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems.

Fabian Altekrüger, Paul Hagemann, Gabriele Steidl

Trans. Mach. Learn. Res., 2023

Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction.

Moritz Piening, Fabian Altekrüger, Johannes Hertrich, Paul Hagemann, Andrea Walther, Gabriele Steidl

CoRR, 2023

Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances.

Jannis Chemseddine, Paul Hagemann, Christian Wald

CoRR, 2023

Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation.

Paul Hagemann, Lars Ruthotto, Gabriele Steidl, Nicole Tianjiao Yang

CoRR, 2023

2022

Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint.

Paul Hagemann, Johannes Hertrich, Gabriele Steidl

SIAM/ASA J. Uncertain. Quantification, March, 2022

PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization.

Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl

CoRR, 2022

2021

A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains.

Johannes Hertrich, Paul Hagemann, Gabriele Steidl

CoRR, 2021

Invertible Neural Networks Versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence.

Anna Andrle, Nando Farchmin, Paul Hagemann, Sebastian Heidenreich, Victor Soltwisch, Gabriele Steidl

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2021

2020

Stabilizing Invertible Neural Networks Using Mixture Models.

Paul Hagemann, Sebastian Neumayer

CoRR, 2020