20 citations · 37 across the 15 of their papers we have counts for
3 papers · 1 filter
On the flow matching interpretability
Francesco Pivi, Simone Gazza, Davide Evangelista +2
Generative models based on flow matching have demonstrated remarkable success in various domains, yet they suffer from a fundamental limitation: the lack of interpretability in the…
Dissecting FLOPs along input dimensions for GreenAI cost estimations
Andrea Asperti, Davide Evangelista, Moreno Marzolla
The term GreenAI refers to a novel approach to Deep Learning, that is more aware of the ecological impact and the computational efficiency of its methods. The promoters of GreenAI…
A survey on Variational Autoencoders from a GreenAI perspective
A. Asperti, D. Evangelista, E. Loli Piccolomini
Variational AutoEncoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks to ef…