Cardiac Mesh Flow generates 3D+t four-chamber cardiac meshes with anatomical correspondence and volume conditioning via one-step flow matching on multi-scale deformation fields.
International Conference on Learning Representations , year=
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Training and sampling in static scalar energy generative models are two instances of the same Lyapunov-driven density transport dynamics on Wasserstein space, differing only by initial condition, which yields a finite stopping criterion for Langevin sampling and additive composition rules that keep
RNA-FM is a flow-matching generative model that predicts genome-wide bulk RNA-seq expression from WSIs by learning a conditional velocity field, outperforming deterministic baselines.
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Cardiac Mesh Flow: One-Step Generation of 3D+t Cardiac Four-Chamber Meshes via Flow Matching
Cardiac Mesh Flow generates 3D+t four-chamber cardiac meshes with anatomical correspondence and volume conditioning via one-step flow matching on multi-scale deformation fields.
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Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective
Training and sampling in static scalar energy generative models are two instances of the same Lyapunov-driven density transport dynamics on Wasserstein space, differing only by initial condition, which yields a finite stopping criterion for Langevin sampling and additive composition rules that keep
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RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
RNA-FM is a flow-matching generative model that predicts genome-wide bulk RNA-seq expression from WSIs by learning a conditional velocity field, outperforming deterministic baselines.