Dissecting FLOPs Along Input Dimensions for GreenAI Cost Estimations. - Andrea Asperti; Davide Evangelista; Moreno Marzolla (LOD, 2021)

A survey on Variational Autoencoders from a GreenAI perspective. - Andrea Asperti; Davide Evangelista; Elena Loli Piccolomini (CoRR, 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 Green AI Perspective. - Andrea Asperti; Davide Evangelista; Elena Loli Piccolomini (SN Comput. Sci., 2021)

RISING a new framework for few-view tomographic image reconstruction with deep learning. - Davide Evangelista; Elena Morotti; Elena Loli Piccolomini (CoRR, 2022)

Robust Non-convex Model-Based Approach for Deep Learning-Based Image Processing. - Elena Morotti; Davide Evangelista; Elena Loli Piccolomini (NUMTA (1), 2023)

Graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet. - Davide Bianchi; Marco Donatelli; Davide Evangelista; Wenbin Li 007; Elena Loli Piccolomini (SSVM, 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)

Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators. - Davide Evangelista; Elena Morotti; Elena Loli Piccolomini; James G. Nagy (J. Imaging, 2023)

Image embedding for denoising generative models. - Andrea Asperti; Davide Evangelista; Samuele Marro; Fabio Merizzi (Artif. Intell. Rev., 2023)

Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging. - Elena Morotti; Davide Evangelista; Andrea Sebastiani; Elena Loli Piccolomini (CoRR, 2024)

LIP-CAR: contrast agent reduction by a deep learned inverse problem. - Davide Bianchi; Sonia Colombo Serra; Davide Evangelista; Pengpeng Luo; Elena Morotti; Giovanni Valbusa (CoRR, 2024)

Inpainting with style: forcing style coherence to image inpainting with deep image prior. - Elena Morotti; Fabio Merizzi; Davide Evangelista; Pasquale Cascarano (Frontiers Comput. Sci., 2024)

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications. - Davide Evangelista (2024)

Language Models Are Implicitly Continuous. - Samuele Marro; Davide Evangelista; X. Angelo Huang; Emanuele La Malfa; Michele Lombardi 001; Michael J. Wooldridge (ICLR, 2025)

Deep Guess acceleration for explainable image reconstruction in sparse-view CT. - Elena Loli Piccolomini; Davide Evangelista; Elena Morotti (Comput. Medical Imaging Graph., 2025)

A Data-Dependent Regularization Method Based on the Graph Laplacian. - Davide Bianchi; Davide Evangelista; Stefano Aleotti; Marco Donatelli; Elena Loli Piccolomini; Wenbin Li 007 (SIAM J. Sci. Comput., 2025)

To Be or Not to Be Stable, That Is the Question: Understanding Neural Networks for Inverse Problems. - Davide Evangelista; Elena Loli Piccolomini; Elena Morotti; James G. Nagy (SIAM J. Sci. Comput., 2025)

Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imaging. - Elena Morotti; Davide Evangelista; Andrea Sebastiani; Elena Loli Piccolomini (CoRR, 2025)

Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling. - Fabio Merizzi; Davide Evangelista; Harilaos Loukos (CoRR, 2025)

Neural network-based inversion of NMR dispersion profiles for enhanced analysis of food systems. - Giovanni Vito Spinelli; Davide Evangelista; Liwei Hu; Fabiana Zama (Neural Comput. Appl., 2025)