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2020 – today
- 2024
[j3]Paul Hagemann
, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl:
Mixed noise and posterior estimation with conditional deepGEM. Mach. Learn. Sci. Technol. 5(3): 35001 (2024)- [c3]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 - [c2]Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann:
Generative Sliced MMD Flows with Riesz Kernels. ICLR 2024 - [i14]Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl:
Mixed Noise and Posterior Estimation with Conditional DeepGEM. CoRR abs/2402.02964 (2024) - [i13]Jannis Chemseddine, Paul Hagemann, Christian Wald, Gabriele Steidl:
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching. CoRR abs/2403.18705 (2024) - [i12]Ségolène Martin, Anne Gagneux, Paul Hagemann, Gabriele Steidl:
PnP-Flow: Plug-and-Play Image Restoration with Flow Matching. CoRR abs/2410.02423 (2024) - 2023
- [j2]Fabian Altekrüger, Paul Hagemann, Gabriele Steidl:
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems. Trans. Mach. Learn. Res. 2023 (2023) - [i11]Paul Hagemann, Lars Ruthotto, Gabriele Steidl, Nicole Tianjiao Yang:
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation. CoRR abs/2303.04772 (2023) - [i10]Fabian Altekrüger, Paul Hagemann, Gabriele Steidl:
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems. CoRR abs/2303.15845 (2023) - [i9]Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann:
Generative Sliced MMD Flows with Riesz Kernels. CoRR abs/2305.11463 (2023) - [i8]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 abs/2310.03054 (2023) - [i7]Jannis Chemseddine, Paul Hagemann, Christian Wald:
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances. CoRR abs/2310.13433 (2023) - [i6]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 abs/2312.16611 (2023) - 2022
- [j1]Paul Hagemann, Johannes Hertrich, Gabriele Steidl:
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint. SIAM/ASA J. Uncertain. Quantification 10(1): 1162-1190 (2022) - [i5]Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl:
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization. CoRR abs/2205.12021 (2022) - 2021
- [c1]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: 528-539 - [i4]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 abs/2102.03189 (2021) - [i3]Paul Hagemann, Johannes Hertrich, Gabriele Steidl:
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint. CoRR abs/2109.11375 (2021) - [i2]Johannes Hertrich, Paul Hagemann, Gabriele Steidl:
A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains. CoRR abs/2111.12506 (2021) - 2020
- [i1]Paul Hagemann, Sebastian Neumayer:
Stabilizing Invertible Neural Networks Using Mixture Models. CoRR abs/2009.02994 (2020)
Coauthor Index
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