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cs.LG2026
Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models
Rania Briq, Ohad Fried, Michael Kamp +1
Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples influence the generated image th…
cs.LG2026
Gefen: Optimized Stochastic Optimizer
Nadav Benedek, Tomer Koren, Ohad Fried
AdamW is a default optimizer for modern deep learning, but its first and second moment states add roughly two parameter-sized buffers to training memory, increasing the already sub…
cs.LG2026
Exploring and Exploiting Stability in Latent Flow Matching
Rania Briq, Michael Kamp, Ohad Fried +2
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. We characterize…