arXiv:2605. 00941v4 Announce Type: replace Abstract: Flow matching has become a leading framework for generative modeling, but quantifying the uncertainty of its samples remains an open problem.
By Jiarui Xing, Song Wang, Jian Wang
arXiv:2608.30081v1 Announce Type: new
Abstract: Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples inf...
By Rania Briq, Ohad Fried, Michael Kamp, Stefan Kesselheim
arXiv:2609.38547v1 Announce Type: cross
Abstract: Defining a weighted mean over probability measures under probability metrics is a central tool in probabilistic machine learning. Under the Wasserste...
By Eduardo Fernandes Montesuma
arXiv:2606. 04092v1 Announce Type: cross Abstract: Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution.
By Shimon Malnick, Matan Rusanovsky, Ohad Fried, Shai Avidan
The paper introduces FAMIS, a flow-based multiple importance sampling framework that learns a nonuniform mixture of normalizing flow proposals for rare‑event estimation. It does not need presampled failure data or prior knowledge of failure modes, instead adapting the mixture through sequential evaluations of the limit state function. The method employs a smooth rare‑event surrogate, a tempered target sequence, defensive exploration, Rao‑Blackwellized weight updates, and a Jensen‑Shannon repulsion term to promote diversity, achieving accurate failure probability estimates with fewer training samples and stable variance reduction in complex reliability problems.
By Sara Helal, Victor Elvira
arXiv:2605. 08398v2 Announce Type: replace Abstract: In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capacity shrinkage.
By Rania Briq, Michael Kamp, Ohad Fried, Sarel Cohen, Stefan Kesselheim