Davide Evangelista

Orcid: 0000-0001-6261-7717

According to our database1, Davide Evangelista authored at least 16 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

2021
2022
2023
2024
2025
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Legend:

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In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2025
To Be or Not to Be Stable, That Is the Question: Understanding Neural Networks for Inverse Problems.
SIAM J. Sci. Comput., 2025

2024
Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications.
PhD thesis, 2024

Deep Guess acceleration for explainable image reconstruction in sparse-view CT.
CoRR, 2024

LIP-CAR: contrast agent reduction by a deep learned inverse problem.
CoRR, 2024

Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging.
CoRR, 2024

2023
Image embedding for denoising generative models.
Artif. Intell. Rev., December, 2023

Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators.
J. Imaging, 2023

A data-dependent regularization method based on the graph Laplacian.
CoRR, 2023

RISING: A new framework for model-based few-view CT image reconstruction with deep learning.
Comput. Medical Imaging Graph., 2023

Graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet.
Proceedings of the Scale Space and Variational Methods in Computer Vision, 2023

Robust Non-convex Model-Based Approach for Deep Learning-Based Image Processing.
Proceedings of the Numerical Computations: Theory and Algorithms, 2023

2022
RISING a new framework for few-view tomographic image reconstruction with deep learning.
CoRR, 2022

2021
A Survey on Variational Autoencoders from a Green AI Perspective.
SN Comput. Sci., 2021

A Green Prospective for Learned Post-Processing in Sparse-View Tomographic Reconstruction.
J. Imaging, 2021

A survey on Variational Autoencoders from a GreenAI perspective.
CoRR, 2021

Dissecting FLOPs Along Input Dimensions for GreenAI Cost Estimations.
Proceedings of the Machine Learning, Optimization, and Data Science, 2021


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