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Quentin Bertrand

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#machine learning Preprint Sep 2026

Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching

Flow matching and diffusion can be trained to predict different quantities, most commonly the data $x_1$, the source noise $x_0$, or the velocity~$v$. Although theoretically equivalent, these can lead to substantially different performances. We identify two main drivers for these differences: the source--data signal-to...

Ségolène Martin, Anne Gagneux, Quentin Bertrand et al. · 0 citations

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