Hugging Face Trending Papers
A Lagrangian View of Flow Matching
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arXiv Computer Vision
Sep 2
A Lagrangian View of Flow Matching
arXiv:2609.00198v1 Announce Type: new Abstract: Modern explicit-time generative models, such as Flow Matching [Lipman et al., 2023] and Rectified Flow [Liu et al., 2023], are typically derived top-do...
Hugging Face Trending Papers
Jul 5
Constrained Flow Matching via Lagrangian Dual Flows
Flow matching is a powerful tool for generative modeling, but emerging applications in robotics, planning, and physics require inference-time constraints on generated outputs. Such constraints are often complex and highly nonlinear.
arXiv Machine Learning
Aug 10
Limit Points of Reflow with Minibatch Optimal Transport
arXiv:2608. 07042v1 Announce Type: cross Abstract: Rectified flows, also called flow matching or stochastic interpolants, are generative models that learn a time-dependent vector field steering a probability curve between two probability distributions, usually referred to as latent and target distributions.
arXiv Machine Learning
Jul 7
Constrained Flow Matching via Lagrangian Dual Flows
arXiv:2607. 04513v1 Announce Type: cross Abstract: Flow matching is a powerful tool for generative modeling, but emerging applications in robotics, planning, and physics require inference-time constraints on generated outputs.
arXiv Machine Learning
Aug 4
Beckmann Transport Models: From Autonomous Flows to One-Step Maps
arXiv:2608. 01692v1 Announce Type: new Abstract: We propose an instantiation of flow matching that relies on a time-independent velocity field (an \emph{autonomous flow}) to exactly map between two distributions, so long as the target is singular, i.
arXiv AI
Jul 7
Efficient Flow Matching for Sparse-View CT Reconstruction
arXiv:2603. 00205v2 Announce Type: replace-cross Abstract: Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed inverse problems.