6 papers
Hierarchical Multiscale Structure-Function Coupling for Brain Connectome Integration
Jianwei Chen, Zhengyang Miao, Wenjie Cai +10
Integrating structural and functional connectomes remains challenging because their relationship is non-linear and organized over nested modular hierarchies. We propose a hierarchi…
Building Privacy-and-Security-Focused Federated Learning Infrastructure for Global Multi-Centre Healthcare Research
Fan Zhang, Daniel Kreuter, Javier Fernandez-Marques +10
Collaborative healthcare research across multiple institutions increasingly requires diverse clinical datasets, but cross-border data sharing is strictly constrained by privacy reg…
Trajectory Stitching for Solving Inverse Problems with Flow-Based Models
Alexander Denker, Moshe Eliasof, Zeljko Kereta +1
Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial latent code (noise), such that the flow ou…
Diffeomorphism-Equivariant Neural Networks
Josephine Elisabeth Oettinger, Zakhar Shumaylov, Johannes Bostelmann +2
Incorporating group symmetries via equivariance into neural networks has emerged as a robust approach for overcoming the efficiency and data demands of modern deep learning. While…
SpectraKAN: Conditioning Spectral Operators
Chun-Wun Cheng, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
Spectral neural operators, particularly Fourier Neural Operators (FNO), are a powerful framework for learning solution operators of partial differential equations (PDEs) due to the…
Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs
Fabrizio Frasca, Fabian Jogl, Moshe Eliasof +4
To develop a preliminary understanding towards Graph Foundation Models, we study the extent to which pretrained Graph Neural Networks can be applied across datasets, an effort requ…