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Paul Hagemann
Paul Hagemann
PhD Student, TU Berlin
Bestätigte E-Mail-Adresse bei math.tu-berlin.de
Titel
Zitiert von
Zitiert von
Jahr
Stabilizing invertible neural networks using mixture models
P Hagemann, S Neumayer
Inverse Problems 37 (8), 085002, 2021
382021
Stochastic normalizing flows for inverse problems: a Markov Chains viewpoint
P Hagemann, J Hertrich, G Steidl
SIAM/ASA Journal on Uncertainty Quantification 10 (3), 1162-1190, 2022
372022
Generalized normalizing flows via Markov chains
PL Hagemann, J Hertrich, G Steidl
Elements in Non-local Data Interactions: Foundations and Applications, 2023
202023
PatchNR: learning from very few images by patch normalizing flow regularization
F Altekrüger, A Denker, P Hagemann, J Hertrich, P Maass, G Steidl
Inverse Problems 39 (6), 064006, 2023
18*2023
Invertible neural networks versus MCMC for posterior reconstruction in grazing incidence X-ray fluorescence
A Andrle, N Farchmin, P Hagemann, S Heidenreich, V Soltwisch, G Steidl
International Conference on Scale Space and Variational Methods in Computer …, 2021
182021
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation
P Hagemann, S Mildenberger, L Ruthotto, G Steidl, NT Yang
arXiv preprint arXiv:2303.04772, 2023
122023
Posterior sampling based on gradient flows of the MMD with negative distance kernel
P Hagemann, J Hertrich, F Altekrüger, R Beinert, J Chemseddine, G Steidl
arXiv preprint arXiv:2310.03054, 2023
72023
Generative sliced MMD flows with Riesz kernels
J Hertrich, C Wald, F Altekrüger, P Hagemann
arXiv preprint arXiv:2305.11463, 2023
72023
Conditional generative models are provably robust: Pointwise guarantees for bayesian inverse problems
F Altekrüger, P Hagemann, G Steidl
arXiv preprint arXiv:2303.15845, 2023
72023
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
J Chemseddine, P Hagemann, C Wald, G Steidl
arXiv preprint arXiv:2403.18705, 2024
2*2024
Mixed Noise and Posterior Estimation with Conditional DeepGEM
P Hagemann, J Hertrich, M Casfor, S Heidenreich, G Steidl
arXiv preprint arXiv:2402.02964, 2024
2024
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction
M Piening, F Altekrüger, J Hertrich, P Hagemann, A Walther, G Steidl
arXiv preprint arXiv:2312.16611, 2023
2023
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