Invited Talk
in
Workshop: Deep Generative Model in Machine Learning: Theory, Principle and Efficacy （2nd Workshop） Mon, Apr 27, 2026 • 3:50 PM – 4:20 PM -03

Jannis Chemseddine — Generative Flows from 1D Processes and Adapting Noise to Data

Abstract

The aim of the talk is twofold. First, interesting noising processes besides Brownian motion are examined: the physics-inspired Kac process and the MMD gradient flow, leading to compactly supported measure curves and a better regularity of the flow matching velocity field. Second, a "quantile toolbox" for building generative models is presented: a unifying theory and a practical toolkit that turns latent noise selection into a data-driven design element.

Video

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