arXiv:2510.12636v5 Announce Type: replace-cross Abstract: The default Gaussian latent in flow-based generative models poses challenges when learning certain distributions such as heavy-tailed ones. We introduce a general framework for learning data-adaptive parametric prior distributions (latent noise) using one-dimensional quantile functions, optimized via the Wasserstein distance between noise and data. The quantile-based prior parameterization naturally adapts to both heavy-tailed and compactly supported distributions and shortens transport paths. Numerical results on heavy-tailed weather a
The continuous evolution of generative AI models necessitates solutions for challenges like accurately representing diverse data distributions, driving research into adaptive noise processes.
Improving generative models' ability to handle complex and heavy-tailed data distributions enhances their applicability across various scientific and industrial domains, from finance to climate modeling.
The development of data-adaptive prior distributions using quantile functions suggests a more robust and efficient approach to generative modeling, potentially leading to more accurate and generalizable AI.
- · AI researchers
- · Generative AI developers
- · Industries using complex data (e.g., finance, weather)
- · Generative models reliant solely on Gaussian latents
Generative models become more effective at learning and producing data with non-Gaussian characteristics.
Enhanced generative model performance could accelerate drug discovery, material science, and climate simulation, where complex data distributions are common.
More robust generative models could lead to new forms of data synthesis and augmentation, reshaping data requirements for AI training and deployment.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG