arXiv Machine Learning
Multi-Marginal Flow Matching with Adversarially Learnt Interpolants
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arXiv Machine Learning
Oct 23
Learning Domain Trajectories with Flow Matching for Gradual Domain Adaptation
The paper introduces FlowGDA, a flow‑matching framework for gradual domain adaptation that models adaptation as continuous transport on a statistical manifold. FlowGDA learns time‑dependent velocity fields to generate smooth domain trajectories, avoiding adversarial Min–Max optimization by using a simulation‑free regression objective. The method incorporates source‑anchored transport, semantic consistency loss, and a two‑stage optimization scheme to prevent semantic drift, achieving superior performance over state‑of‑the‑art techniques.
arXiv AI
Oct 9
Finsler Flow Matching: Dynamics-Aware Geodesic Interpolation for Single-Snapshot Trajectory Inference
The paper introduces Finsler Flow Matching (FFM), a method for learning continuous stochastic dynamics from discrete Markov transition graphs in single‑cell snapshot data. FFM constructs a Finsler geometry using first and second local moments, learns directed geodesics via neural networks, and employs these to build geometry‑aware stochastic paths that can be simulated without explicit score or flow matching. Benchmark tests show that FFM better recovers intermediate cellular states, especially when dynamics are directional or anisotropic.
arXiv Machine Learning
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Trajectory inference via Acceleration Matching
arXiv:2608. 03916v1 Announce Type: new Abstract: Trajectory inference is a fundamental problem in many scientific domains: given a collection of unpaired snapshots of observations at discrete time points, the goal is to generate smooth trajectories that best resemble and interpolate the data.
arXiv Machine Learning
Oct 2
A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions
The paper introduces Function Projection for Flow Matching (FP‑FM), an algorithm that conditions generative models directly on samples from a target distribution. FP‑FM learns basis functions spanning velocity fields for training distributions and adapts to new distributions via a simple least‑squares projection, enabling efficient sample generation without additional training at inference time. Variants of the method trade expressivity for computational cost by enriching coefficient calculations, and the approach shows markedly better precision and recall than baselines, especially on unseen distributions.
arXiv Machine Learning
Oct 15
Is Flow Matching Just Trajectory Replay for Sequential Data?
arXiv:2602.08318v3 Announce Type: replace-cross Abstract: Flow matching (FM) is increasingly used in scientific domains for time series generation and forecasting, where data often arise from underly...
Hugging Face Trending Papers
Oct 6
Co-Evolving Paths and Flows via Path-Flow Alignment
We study path-flow alignment as a unified training objective for flow matching. Instead of fixing the interpolation path and learning only the velocity field, we jointly train an endpoint-preserving p...