Search Results for author: Matteo Santacesaria

Found 7 papers, 3 papers with code

Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?

no code implementations3 Mar 2024 Roberto Di Via, Matteo Santacesaria, Francesca Odone, Vito Paolo Pastore

Our findings can provide an indication for the development of robust landmark detection systems in medical images when no large annotated dataset is available.

Transfer Learning

Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems

no code implementations29 Jan 2024 Giovanni S. Alberti, Luca Ratti, Matteo Santacesaria, Silvia Sciutto

In inverse problems, it is widely recognized that the incorporation of a sparsity prior yields a regularization effect on the solution.

Dictionary Learning

Manifold Learning by Mixture Models of VAEs for Inverse Problems

1 code implementation27 Mar 2023 Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria, Silvia Sciutto

Representing a manifold of very high-dimensional data with generative models has been shown to be computationally efficient in practice.

Deblurring

Continuous Generative Neural Networks

1 code implementation29 May 2022 Giovanni S. Alberti, Matteo Santacesaria, Silvia Sciutto

In this work, we present and study Continuous Generative Neural Networks (CGNNs), namely, generative models in the continuous setting: the output of a CGNN belongs to an infinite-dimensional function space.

Deblurring Image Deblurring

Market Areas in General Equilibrium

no code implementations29 Oct 2021 Gianandrea Lanzara, Matteo Santacesaria

This paper proposes a spatial model with a realistic geography where a continuous distribution of agents (e. g., farmers) engages in economic interactions with one location from a finite set (e. g., cities).

Learning the optimal Tikhonov regularizer for inverse problems

1 code implementation NeurIPS 2021 Giovanni S. Alberti, Ernesto de Vito, Matti Lassas, Luca Ratti, Matteo Santacesaria

Then, we consider the problem of learning the regularizer from a finite training set in two different frameworks: one supervised, based on samples of both $x$ and $y$, and one unsupervised, based only on samples of $x$.

Deblurring Denoising +1

Neural networks for classification of strokes in electrical impedance tomography on a 3D head model

no code implementations5 Nov 2020 Valentina Candiani, Matteo Santacesaria

We employ two neural network architectures -- a fully connected and a convolutional one -- for the classification of hemorrhagic and ischemic strokes.

Anatomy

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