Description
Chair: Shirley Ho
"Generative modeling of high-dimensional phenomena such as natural images has gone through a remarkable experimental revolution over the past years, spearheaded by diffusion-based models [Sohl-Dickstein et al~15, Song & Ermon, 20].
In this talk, I will describe two open questions raised by these models that I haven't been able to resolve yet: (i) what is the class of high-dimensional densities that can be provably learnt by diffusion models that other models cannot, and (ii) how can we provably leverage time-dependent scores to regularise inverse problems. I will describe joint work with Etienne Lempereur, Florentin Guth, Stephane Mallat, Carles Domingo, Jaume de Dios, Jiequn Han and Maarten de Hopp. "
