31 citations · 133 across the 23 of their papers we have counts for
9 papers · 1 filter
Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models
Fabio Merizzi, Andrea Asperti, Stefano Colamonaco
The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years it has shown significant utility acr…
Precipitation nowcasting with generative diffusion models
Andrea Asperti, Fabio Merizzi, Alberto Paparella +3
In recent years traditional numerical methods for accurate weather prediction have been increasingly challenged by deep learning methods. Numerous historical datasets used for shor…
Comparing the latent space of generative models
Andrea Asperti, Valerio Tonelli
Different encodings of datapoints in the latent space of latent-vector generative models may result in more or less effective and disentangled characterizations of the different ex…
MicroRacer: a didactic environment for Deep Reinforcement Learning
Andrea Asperti, Marco Del Brutto
MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been…
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…