332 citations · 860 across the 23 of their papers we have counts for
26 papers
SVD-NO: Learning PDE Solution Operators with SVD Integral Kernels
Noam Koren, Ralf J. J. Mackenbach, Ruud J. G. van Sloun +2
Neural operators have emerged as a promising paradigm for learning solution operators of partial differential equa- tions (PDEs) directly from data. Existing methods, such as those…
Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation
Radhika Dua, Young Joon, Kwon +9
Radiological imaging is central to diagnosis, treatment planning, and clinical decision-making. Vision-language foundation models have spurred interest in automated radiology repor…
ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment
Tomer Borreda, Daniel Freedman, Or Litany
We present ReHub, a novel graph transformer architecture that achieves linear complexity through an efficient reassignment technique between nodes and virtual nodes. Graph transfor…
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
Omer Belhasin, Yaniv Romano, Daniel Freedman +2
Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable value…
Semi-Equivariant Conditional Normalizing Flows
Eyal Rozenberg, Daniel Freedman
We study the problem of learning conditional distributions of the form , where and are two 3D graphs, using continuous normalizing flows. We derive a se…
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems
Gilad Kutiel, Regev Cohen, Michael Elad +1
Estimating uncertainty in image-to-image networks is an important task, particularly as such networks are being increasingly deployed in the biological and medical imaging realms.…