14 citations · 33 across the 8 of their papers we have counts for
9 papers
Vector-Valued Reproducing Kernel Banach Spaces for Neural Networks and Operators
Sven Dummer, Tjeerd Jan Heeringa, José A. Iglesias
Recently, there has been growing interest in characterizing the function spaces underlying neural networks. While shallow and deep scalar-valued neural networks have been linked to…
RONOM: Reduced-Order Neural Operator Modeling
Sven Dummer, Dongwei Ye, Christoph Brune
Time-dependent partial differential equations are ubiquitous in physics-based modeling, but they remain computationally intensive in many-query scenarios, such as real-time forecas…
Joint Manifold Learning and Optimal Transport for Dynamic Imaging
Sven Dummer, Puru Vaish, Christoph Brune
Dynamic imaging is critical for understanding and visualizing dynamic biological processes in medicine and cell biology. These applications often encounter the challenge of a limit…
Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging
Simone Garzia, Patryk Rygiel, Sven Dummer +3
Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. Howe…
Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
Ioana Mazilu, Shunxin Wang, Sven Dummer +3
Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane,…
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations
Sven Dummer, Nicola Strisciuglio, Christoph Brune
Diffeomorphic registration frameworks such as Large Deformation Diffeomorphic Metric Mapping (LDDMM) are used in computer graphics and the medical domain for atlas building, statis…
Generative modeling of living cells with SO(3)-equivariant implicit neural representations
David Wiesner, Julian Suk, Sven Dummer +4
Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limite…
Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators
Yannik Wotte, Sven Dummer, Nicolò Botteghi +3
It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these pe…
Implicit Neural Representations for Generative Modeling of Living Cell Shapes
David Wiesner, Julian Suk, Sven Dummer +2
Methods allowing the synthesis of realistic cell shapes could help generate training data sets to improve cell tracking and segmentation in biomedical images. Deep generative model…