Davide Evangelista

According to our database, Davide Evangelista authored at least 11 papers between 2021 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2023

Image embedding for denoising generative models.

Andrea Asperti, Davide Evangelista, Samuele Marro, Fabio Merizzi

Artif. Intell. Rev., December, 2023

Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators.

Davide Evangelista, Elena Morotti, Elena Loli Piccolomini, James G. Nagy

J. Imaging, 2023

A data-dependent regularization method based on the graph Laplacian.

Davide Bianchi, Davide Evangelista, Stefano Aleotti, Marco Donatelli, Elena Loli Piccolomini, Wenbin Li

CoRR, 2023

RISING: A new framework for model-based few-view CT image reconstruction with deep learning.

Davide Evangelista, Elena Morotti, Elena Loli Piccolomini

Comput. Medical Imaging Graph., 2023

Graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet.

Davide Bianchi, Marco Donatelli, Davide Evangelista, Wenbin Li, Elena Loli Piccolomini

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2023

2022

To be or not to be stable, that is the question: understanding neural networks for inverse problems.

Davide Evangelista, James G. Nagy, Elena Morotti, Elena Loli Piccolomini

CoRR, 2022

RISING a new framework for few-view tomographic image reconstruction with deep learning.

Davide Evangelista, Elena Morotti, Elena Loli Piccolomini

CoRR, 2022

2021

A Survey on Variational Autoencoders from a Green AI Perspective.

Andrea Asperti, Davide Evangelista, Elena Loli Piccolomini

SN Comput. Sci., 2021

A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction.

Elena Morotti, Davide Evangelista, Elena Loli Piccolomini

J. Imaging, 2021

A survey on Variational Autoencoders from a GreenAI perspective.

Andrea Asperti, Davide Evangelista, Elena Loli Piccolomini

CoRR, 2021

Dissecting FLOPs Along Input Dimensions for GreenAI Cost Estimations.

Andrea Asperti, Davide Evangelista, Moreno Marzolla

Proceedings of the Machine Learning, Optimization, and Data Science, 2021