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Fabian Altekrüger
Fabian Altekrüger
HU Berlin
在 hu-berlin.de 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
21*2023
WPPNets and WPPFlows: The power of Wasserstein patch priors for superresolution
F Altekrüger, J Hertrich
SIAM Journal on Imaging Sciences 16 (3), 1033-1067, 2023
162023
Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels
F Altekrüger, J Hertrich, G Steidl
International Conference on Machine Learning (ICML) 2023, 2023
15*2023
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
International Conference on Learning Representations (ICLR) 2024, 2024
102024
Conditional generative models are provably robust: Pointwise guarantees for bayesian inverse problems
F Altekrüger, P Hagemann, G Steidl
Transactions on Machine Learning Research (TMLR), 2023
102023
Generative sliced MMD flows with Riesz kernels
J Hertrich, C Wald, F Altekrüger, P Hagemann
International Conference on Learning Representations (ICLR) 2024, 2024
92024
Learning regularization parameter-maps for variational image reconstruction using deep neural networks and algorithm unrolling
A Kofler, F Altekrüger, F Antarou Ba, C Kolbitsch, E Papoutsellis, D Schote, ...
SIAM Journal on Imaging Sciences 16 (4), 2202-2246, 2023
92023
Stability of Data-Dependent Ridge-Regularization for Inverse Problems
S Neumayer, F Altekrüger
arXiv preprint arXiv:2406.12289, 2024
12024
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
12023
Generative Modeling via Wasserstein Gradient flows of Maximum Mean Discrepancies
P Hagemann, F Altekrüger, R Beinert, J Chemseddine, M Gräf, ...
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