Davide Evangelista

Liwei Hu

Fabiana Zama

Neural Comput. Appl., February, 2025

Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling.

[BibT_eX]

[DOI]

Fabio Merizzi

Harilaos Loukos

CoRR, January, 2025

Adaptive Weighted Total Variation boosted by learning techniques in few-view tomographic imaging.

[BibT_eX]

[DOI]

Andrea Sebastiani

CoRR, January, 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

Language Models Are Implicitly Continuous.

[BibT_eX]

[DOI]

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