E-LIB Suche - Ergebnisse für: Hertrich, Johannes

1

Mixed noise and posterior estimation with conditional deepG..:

Hagemann, Paul ; Hertrich, Johannes ; Casfor, Maren..
Machine Learning: Science and Technology. 5 (2024) 3 - p. 035001 , 2024

Link: https://doi.org/10.1088/..

2

Wasserstein steepest descent flows of discrepancies with Ri..:

Hertrich, Johannes ; Gräf, Manuel ; Beinert, Robert.
Journal of Mathematical Analysis and Applications. 531 (2024) 1 - p. 127829 , 2024

Link: https://doi.org/10.1016/..

3

Generalized normalizing flows via Markov chains

Cambridge elements. Elements in non-local data interactions : foundations and applications

Hagemann, Paul Lyonel ; Hertrich, Johannes ; Steidl, Gabriele , 2023

Link: https://doi.org/10.1017/..

4

Proximal Residual Flows for Bayesian Inverse Problems:

, In: Lecture Notes in Computer Science; Scale Space and Variational Methods in Computer Vision,

Hertrich, Johannes - p. 210-222 , 2023

Link: https://doi.org/10.1007/..

5

WPPNets and WPPFlows: The Power of Wasserstein Patch Priors..:

Altekrüger, Fabian ; Hertrich, Johannes
SIAM Journal on Imaging Sciences. 16 (2023) 3 - p. 1033-1067 , 2023

Link: https://doi.org/10.1137/..

6

PatchNR: learning from very few images by patch normalizing..:

Altekrüger, Fabian ; Denker, Alexander ; Hagemann, Paul...
Inverse Problems. 39 (2023) 6 - p. 064006 , 2023

Link: https://doi.org/10.1088/..

7

Wasserstein Gradient Flows of the Discrepancy with Distance..:

, In: Lecture Notes in Computer Science; Scale Space and Variational Methods in Computer Vision,

Hertrich, Johannes ; Beinert, Robert ; Gräf, Manuel. - p. 431-443 , 2023

Link: https://doi.org/10.1007/..

8

Wasserstein Patch Prior for Image Superresolution:

Hertrich, Johannes ; Houdard, Antoine ; Redenbach, Claudia
IEEE Transactions on Computational Imaging. 8 (2022) - p. 693-704 , 2022

Link: https://doi.org/10.1109/..

9

Inertial stochastic PALM and applications in machine learni..:

Hertrich, Johannes ; Steidl, Gabriele
Sampling Theory, Signal Processing, and Data Analysis. 20 (2022) 1 - p. , 2022

Link: https://doi.org/10.1007/..

10

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

Hagemann, Paul ; Hertrich, Johannes ; Steidl, Gabriele
SIAM/ASA Journal on Uncertainty Quantification. 10 (2022) 3 - p. 1162-1190 , 2022

Link: https://doi.org/10.1137/..

11

Correction to: Alternatives to the EM algorithm for ML esti..:

Hasannasab, Marzieh ; Hertrich, Johannes ; Laus, Friederike.
Numerical Algorithms. 88 (2021) 1 - p. 521-522 , 2021

Link: https://doi.org/10.1007/..

12

Sparse mixture models inspired by ANOVA decompositions:

Hertrich, Johannes ; Ba, Fatima Antarou ; Steidl, Gabriele
ETNA - Electronic Transactions on Numerical Analysis. 55 (2021) - p. 142-168 , 2021

Link: https://doi.org/10.1553/..

13

Correction to: Parseval Proximal Neural Networks:

Hasannasab, Marzieh ; Hertrich, Johannes ; Neumayer, Sebastian...
Journal of Fourier Analysis and Applications. 27 (2021) 3 - p. , 2021

Link: https://doi.org/10.1007/..

14

Convolutional proximal neural networks and Plug-and-Play al..:

Hertrich, Johannes ; Neumayer, Sebastian ; Steidl, Gabriele
Linear Algebra and its Applications. 631 (2021) - p. 203-234 , 2021

Link: https://doi.org/10.1016/..

15

Minimal Lipschitz and ∞-harmonic extensions of vector-value..:

Bačák, Miroslav ; Hertrich, Johannes ; Neumayer, Sebastian.
Information and Inference: A Journal of the IMA. 9 (2020) 4 - p. 935-959 , 2020

Link: https://doi.org/10.1093/..