Davide Evangelista

Andrea Sebastiani

J. Sci. Comput., March, 2026

CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments.

[BibT_eX]

[DOI]

Isabella Marasco

Michele Colajanni

CoRR, March, 2026

The CompMath-MCQ Dataset: Are LLMs Ready for Higher-Level Math?

[BibT_eX]

[DOI]

CoRR, March, 2026

Controlling ensemble variance in diffusion models: an application for reanalyses downscaling.

[BibT_eX]

[DOI]

Fabio Merizzi

Harilaos Loukos

Neural Comput. Appl., February, 2026

A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography.

[BibT_eX]

[DOI]

Pasquale Cascarano

CoRR, February, 2026

2025

On the flow matching interpretability.

[BibT_eX]

[DOI]

CoRR, October, 2025

Neural network-based inversion of NMR dispersion profiles for enhanced analysis of food systems.

[BibT_eX]

[DOI]

Giovanni Vito Spinelli

Liwei Hu

Fabiana Zama

Neural Comput. Appl., February, 2025

To Be or Not to Be Stable, That Is the Question: Understanding Neural Networks for Inverse Problems.

[BibT_eX]

[DOI]

James G. Nagy

SIAM J. Sci. Comput., 2025

A Data-Dependent Regularization Method Based on the Graph Laplacian.

[BibT_eX]

[DOI]

Wenbin Li

SIAM J. Sci. Comput., 2025

Deep Guess acceleration for explainable image reconstruction in sparse-view CT.

[BibT_eX]

[DOI]

Comput. Medical Imaging Graph., 2025

LIP-CAR: A Learned Inverse Problem Approach for Medical Imaging with Contrast Agent Reduction.

[BibT_eX]

[DOI]

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2025, 2025

Language Models Are Implicitly Continuous.

[BibT_eX]

[DOI]

Samuele Marro

Xuanqiang Angelo Huang

Emanuele La Malfa

Michele Lombardi

Michael J. Wooldridge

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications.

[BibT_eX]

[DOI]

PhD thesis, 2024

Inpainting with style: forcing style coherence to image inpainting with deep image prior.

[BibT_eX]

[DOI]

Frontiers Comput. Sci., 2024

LIP-CAR: contrast agent reduction by a deep learned inverse problem.

[BibT_eX]

[DOI]

CoRR, 2024

Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging.

[BibT_eX]

[DOI]

Andrea Sebastiani

CoRR, 2024

2023

Image embedding for denoising generative models.

[BibT_eX]

[DOI]

Artif. Intell. Rev., December, 2023

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

[BibT_eX]

[DOI]

James G. Nagy

J. Imaging, 2023

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

[BibT_eX]

[DOI]

Comput. Medical Imaging Graph., 2023

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

[BibT_eX]

[DOI]

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

Robust Non-convex Model-Based Approach for Deep Learning-Based Image Processing.

[BibT_eX]

[DOI]

Proceedings of the Numerical Computations: Theory and Algorithms, 2023

2022

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

[BibT_eX]

[DOI]

CoRR, 2022

2021

A Survey on Variational Autoencoders from a Green AI Perspective.

[BibT_eX]

[DOI]

Andrea Asperti

SN Comput. Sci., 2021

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

[BibT_eX]

[DOI]

J. Imaging, 2021

A survey on Variational Autoencoders from a GreenAI perspective.

[BibT_eX]

[DOI]

Andrea Asperti

CoRR, 2021

Dissecting FLOPs Along Input Dimensions for GreenAI Cost Estimations.

[BibT_eX]

[DOI]

Andrea Asperti