Publications

2025

E. Loli Piccolomini, D. Evangelista, E. Morotti, Deep Guess acceleration for explainable image reconstruction in sparse-view CT Computational Medical Imaging and Graphics, 2025, https://doi.org/10.1016/j.compmedimag.2025.102530;
S. Marro, D. Evangelista, X.A. Huang, E. La Malfa, M. Lombardi, M.J. Wooldridge, Language Models Are Implicitly Continuous, Thirteenth International Conference on Learning Representations (ICLR2025), 2025, https://openreview.net/forum?id=SMK0f8JoK;
D. Evangelista, E. Morotti, J. Nagy, E. Loli Piccolomini, To be or not to be stable, that is the question - stability and accuracy trade-off in neural networks for inverse problems, SIAM Journal on Scientific Computing (SISC), 2025, https://doi.org/10.1137/23M1586872;
G.V. Spinelli, D. Evangelista, L. Hu, F. Zama, Neural network-based inversion of NMR dispersion profiles for enhanced analysis of food systems, Neural Computing and Applications, 2025, https://doi.org/10.1007/s00521-024-10859-y;

N. Dal Seno, D. Evangelista, E. Loli Piccolomini, M. Berti, Comparative analysis of conventional and machine learning techniques for rainfall threshold evaluation under complex geological conditions, Landslides, 2024, https://doi.org/10.1007/s10346-024-02367-w;
E. Morotti, F. Merizzi, D. Evangelista, P. Cascarano, Inpainting with style: forcing style coherence to image inpainting with deep image prior, Frontiers in Computer Science 6, 2025, https://doi.org/10.3389/fcomp.2024.1478233;
D. Evangelista, Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications, PhD Thesis, 2024;

A. Asperti, D. Evangelista, S. Marro, F. Merizzi, Image embedding for denoising generative models, Artificial Intelligence Review, 2023, https://doi.org/10.1007/s10462-023-10504-5;
D. Evangelista, E. Morotti, J. Nagy, E. Loli Piccolomini, Ambiguity in solving imaging inverse problems with deep learning based operators, Journal of Imaging, 2023, https://doi.org/10.3390/jimaging9070133;
E. Morotti, D. Evangelista, E. Loli Piccolomini, Increasing noise robustness of deep learning-based image processing with model-based approaches, Numerical Computations: Theory and Algorithms (NUMTA), 2023, https://doi.org/10.1007/978-3-031-81241-5_30;
D. Bianchi, M. Donatelli, D. Evangelista, W. Li, E. Loli Piccolomini, Graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet, International Conference on Scale Space and Variational Methods in Computer Vision, 2023, http://doi.org/10.1007/978-3-031-31975-4_14;

E. Morotti, D. Evangelista, E. Loli Piccolomini, RISING a new framework for few-view tomographic image reconstruction with deep learning, Computerized Medical Imaging and Graphics, 2022, https://doi.org/10.1016/j.compmedimag.2022.102156;

E. Morotti, D. Evangelista, E. Loli Piccolomini, A green prospective for learned post-processing in sparse-view tomographic reconstruction, Journal of Imaging, 2021, https://doi.org/10.3390/jimaging7080139;
A. Asperti, D. Evangelista, M. Marzolla, Dissecting FLOPs along input dimensions for GreenAI cost estimations, International Conference on Machine Learning, Optimization, and Data Science, 2021, https://doi.org/10.1007/978-3-030-95470-3_7;
A. Asperti, D. Evangelista, E. Loli Piccolomini, A survey on Variational Autoencoders from a GreenAI perspective, SN Computer Science, 1st March 2021, www.doi.org/10.1007/s42979-021-00702-9;

D. Bianchi, D. Evangelista, S. Aleotti, M. Donatelli, E. Loli Piccolomini, A data-dependent regularization method based on the graph Laplacian, submitted to SIAM Journal on Scientific Computing (SISC), arXiv preprint: https://arxiv.org/abs/2312.16936, 2024;
E. Morotti, D. Evangelista, A. Sebastiani, E. Loli Piccolomini, Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imaging, submitted to Inverse Problems, 2025;
D. Evangelista, E. Morotti, D. Bianchi, S.C. Serra, P. Luo, G. Valbusa, LIP-CAR: a learned inverse problem approach for medical imaging with contrast agent reduction, submitted to MICCAI2025 Conference, 2025;