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cs.LG2025
TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows
Moshe Eliasof, Eldad Haber, Carola-Bibiane Schönlieb
We introduce TANGO -- a dynamical systems inspired framework for graph representation learning that governs node feature evolution through a learned energy landscape and its associ…
cs.LG2025
Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields
Md Shahriar Rahim Siddiqui, Moshe Eliasof, Eldad Haber
Flow matching casts sample generation as learning a continuous-time velocity field that transports noise to data. Existing flow matching networks typically predict each point's vel…
cs.LG2025
Towards Efficient Training of Graph Neural Networks: A Multiscale Approach
Eshed Gal, Moshe Eliasof, Carola-Bibiane Schönlieb +3
Graph Neural Networks (GNNs) have become powerful tools for learning from graph-structured data, finding applications across diverse domains. However, as graph sizes and connectivi…