| Parseval proximal neural networks M Hasannasab, J Hertrich, S Neumayer, G Plonka, S Setzer, G Steidl Journal of Fourier Analysis and Applications 26, 1-31, 2020 | 31 | 2020 |
| Convolutional proximal neural networks and plug-and-play algorithms J Hertrich, S Neumayer, G Steidl Linear Algebra and its Applications 631, 203-234, 2021 | 26 | 2021 |
| 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 | 13 | 2022 |
| PCA reduced Gaussian mixture models with applications in superresolution J Hertrich, DPL Nguyen, JF Aujol, D Bernard, Y Berthoumieu, A Saadaldin, ... Inverse Problems and Imaging 16 (2), 341-366, 2022 | 12 | 2022 |
| Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the student t distribution M Hasannasab, J Hertrich, F Laus, G Steidl Numerical Algorithms 87 (1), 77-118, 2021 | 8* | 2021 |
| Inertial stochastic PALM and applications in machine learning J Hertrich, G Steidl Sampling Theory, Signal Processing, and Data Analysis 20 (1), 4, 2022 | 7* | 2022 |
| Wasserstein patch prior for image superresolution J Hertrich, A Houdard, C Redenbach IEEE Transactions on Computational Imaging 8, 693-704, 2022 | 6 | 2022 |
| Variational models for color image correction inspired by visual perception and neuroscience T Batard, J Hertrich, G Steidl Journal of Mathematical Imaging and Vision 62, 1173-1194, 2020 | 5 | 2020 |
| PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization F Altekrüger, A Denker, P Hagemann, J Hertrich, P Maass, G Steidl arXiv preprint arXiv:2205.12021, 2022 | 3* | 2022 |
| WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution F Altekrüger, J Hertrich arXiv preprint arXiv:2201.08157, 2022 | 3* | 2022 |
| Generalized Normalizing Flows via Markov Chains P Hagemann, J Hertrich, G Steidl Elements in Non-local Data Interactions: Foundations and Applications, 2023 | 2 | 2023 |
| Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels J Hertrich, M Gräf, R Beinert, G Steidl arXiv preprint arXiv:2211.01804, 2022 | 2 | 2022 |
| Minimal Lipschitz and∞-harmonic extensions of vector-valued functions on finite graphs M Bačák, J Hertrich, S Neumayer, G Steidl Information and Inference: A Journal of the IMA 9 (4), 935-959, 2020 | 2 | 2020 |
| Infinity-Laplacians on scalar-and vector-valued functions and optimal Lipschitz extensions on graphs J Hertrich arXiv preprint arXiv:1910.13805, 2019 | 2 | 2019 |
| Sparse Mixture Models inspired by ANOVA Decompositions J Hertrich, FA Ba, G Steidl Electronic Transactions on Numerical Analysis 55, 142-168, 2021 | 1 | 2021 |
| Minimal Lipschitz extensions for vector-valued functions on finite graphs J Hertrich, M Bačák, S Neumayer, G Steidl Scale Space and Variational Methods in Computer Vision: 7th International …, 2019 | 1 | 2019 |
| Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels F Altekrüger, J Hertrich, G Steidl arXiv preprint arXiv:2301.11624, 2023 | | 2023 |
| Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line J Hertrich, R Beinert, M Gräf, G Steidl arXiv preprint arXiv:2301.04441, 2023 | | 2023 |
| Proximal Residual Flows for Bayesian Inverse Problems J Hertrich arXiv preprint arXiv:2211.17158, 2022 | | 2022 |
| Image super-resolution with PCA Reduced generalized Gaussian mixture models DPL Nguyen, J Hertrich, JF Aujol, Y Berthoumieu | | 2022 |
Sebastian NeumayerPostdoc, EPFL LausanneBestätigte E-Mail-Adresse bei epfl.ch
