Seminario del Departamento de Probabilidad y Estadística

Resumen

To address the safety-critical risk of unrealistic artifacts produced by deep learning image reconstruction models we introduce a model-agnostic framework that combines multiscale representations with conformalized quantile regression to reliably quantify and detect hallucinations.

Ponente

Inés Rosellón, Doctoral Researcher, relAI