Paul Hagemann

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 19

Top Topics

Image Reconstruction

Diffusion Models

Top Venues

Mach. Learn. Sci. Technol.

Publications

Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann
Generative Sliced MMD Flows with Riesz Kernels. ICLR (2024)
Jannis Chemseddine, Paul Hagemann, Christian Wald, Gabriele Steidl
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching. CoRR (2024)
Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl
Mixed Noise and Posterior Estimation with Conditional DeepGEM. CoRR (2024)
Paul Hagemann, Johannes Hertrich, Fabian Altekrüger, Robert Beinert, Jannis Chemseddine, Gabriele Steidl
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel. ICLR (2024)
Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl
Mixed noise and posterior estimation with conditional deepGEM. Mach. Learn. Sci. Technol. 5 (3) (2024)
Fabian Altekrüger, Paul Hagemann, Gabriele Steidl
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems. CoRR (2023)
Fabian Altekrüger, Paul Hagemann, Gabriele Steidl
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems. Trans. Mach. Learn. Res. 2023 (2023)
Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann
Generative Sliced MMD Flows with Riesz Kernels. CoRR (2023)
Jannis Chemseddine, Paul Hagemann, Christian Wald
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances. CoRR (2023)
Paul Hagemann, Johannes Hertrich, Fabian Altekrüger, Robert Beinert, Jannis Chemseddine, Gabriele Steidl
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel. CoRR (2023)
Paul Hagemann, Lars Ruthotto, Gabriele Steidl, Nicole Tianjiao Yang
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation. CoRR (2023)
Moritz Piening, Fabian Altekrüger, Johannes Hertrich, Paul Hagemann, Andrea Walther, Gabriele Steidl
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction. CoRR (2023)
Paul Hagemann, Johannes Hertrich, Gabriele Steidl
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint. SIAM/ASA J. Uncertain. Quantification 10 (1) (2022)
Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization. CoRR (2022)
Anna Andrle, Nando Farchmin, Paul Hagemann, Sebastian Heidenreich, Victor Soltwisch, Gabriele Steidl
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence. CoRR (2021)
Paul Hagemann, Johannes Hertrich, Gabriele Steidl
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint. CoRR (2021)
Johannes Hertrich, Paul Hagemann, Gabriele Steidl
A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains. CoRR (2021)
Anna Andrle, Nando Farchmin, Paul Hagemann, Sebastian Heidenreich, Victor Soltwisch, Gabriele Steidl
Invertible Neural Networks Versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence. SSVM (2021)
Paul Hagemann, Sebastian Neumayer
Stabilizing Invertible Neural Networks Using Mixture Models. CoRR (2020)