1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
How well do generative models solve inverse problems? A benchmark study
Patrick Krüger, Patrick Materne, Werner Krebs +1
Generative learning generates high dimensional data based on low dimensional conditions, also called prompts. Therefore, generative learning algorithms are eligible for solving (Ba…
math.NA2025★ 1 cited
Learning to Integrate
Oliver G. Ernst, Hanno Gottschalk, Toni Kowalewitz +1
This work deals with uncertainty quantification for a generic input distribution to some resource-intensive simulation, e.g., requiring the solution of a partial differential equat…
cs.LG2023
Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group
Patrick Krüger, Hanno Gottschalk
Convolutional neural networks revolutionized computer vision and natrual language processing. Their efficiency, as compared to fully connected neural networks, has its origin in th…