| 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 | 53 | 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 | 51 | 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 | 37 | 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 | 22 | 2022 |
| Generalized Normalizing Flows via Markov Chains P Hagemann, J Hertrich, G Steidl Elements in Non-local Data Interactions: Foundations and Applications, 2023 | 21 | 2023 |
| 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 | 20 | 2021 |
| 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 | 19* | 2023 |
| Inertial stochastic PALM and applications in machine learning J Hertrich, G Steidl Sampling Theory, Signal Processing, and Data Analysis 20, 1-33, 2022 | 16* | 2022 |
| 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 | 15 | 2023 |
| Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels F Altekrüger, J Hertrich, G Steidl International Conference on Machine Learning (ICML) 2023, 2023 | 14* | 2023 |
| Wasserstein patch prior for image superresolution J Hertrich, A Houdard, C Redenbach IEEE Transactions on Computational Imaging 8, 693-704, 2022 | 14 | 2022 |
| Wasserstein steepest descent flows of discrepancies with Riesz kernels J Hertrich, M Gräf, R Beinert, G Steidl Journal of Mathematical Analysis and Applications 531 (1), 127829, 2024 | 10 | 2024 |
| Generative sliced MMD flows with Riesz kernels J Hertrich, C Wald, F Altekrüger, P Hagemann International Conference on Learning Representations (ICLR) 2024, 2024 | 8 | 2024 |
| 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 | 7 | 2024 |
| Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line J Hertrich, R Beinert, M Gräf, G Steidl International Conference on Scale Space and Variational Methods in Computer …, 2023 | 7 | 2023 |
| Manifold learning by mixture models of vaes for inverse problems GS Alberti, J Hertrich, M Santacesaria, S Sciutto arXiv preprint arXiv:2303.15244, 2023 | 5 | 2023 |
| 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 (9), 1173-1194, 2020 | 4 | 2020 |
| Sparse Mixture Models inspired by ANOVA Decompositions J Hertrich, FA Ba, G Steidl Electronic Transactions on Numerical Analysis 55, 142-168, 2021 | 3 | 2021 |
| Fast kernel summation in high dimensions via slicing and Fourier transforms J Hertrich arXiv preprint arXiv:2401.08260, 2024 | 2 | 2024 |
| Proximal residual flows for bayesian inverse problems J Hertrich International Conference on Scale Space and Variational Methods in Computer …, 2023 | 2 | 2023 |
Gabriele SteidlTU Berlin在 math.tu-berlin.de 的电子邮件经过验证
