26 citations · 58 across the 8 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching
Ségolène Martin, Anne Gagneux, Quentin Bertrand +2
Flow matching and diffusion can be trained to predict different quantities, most commonly the data , the source noise , or the velocity~. Although theoretically equiva…
cs.LG2025
On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity
Quentin Bertrand, Anne Gagneux, Mathurin Massias +1
Modern deep generative models can now produce high-quality synthetic samples that are often indistinguishable from real training data. A growing body of research aims to understand…