Fast and Flexible Generative Modeling with Free-Form Flows

Abstract: Generative models have achieved remarkable quality and success in a variety of machine learning applications, promising to become the standard paradigm for regression. However, each predominant approach comes with drawbacks in terms of inference speed, sample quality, training stability, or flexibility. In this talk, I will propose Free-Form Flows, a new generative model that offers fast data generation at high quality and flexibility. I will guide you through the fundamentals and showcase a variety of scientific applications.

Bio: Felix Draxler is a Postdoctoral Researcher at the University of California, Irvine. His research focuses on the fundamentals of generative models, with the goal of making them not only accurate but also fast and versatile. He received his PhD in 2024 from Heidelberg University, Germany.