output
20102024
most citedThe reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study

179 citations

22 papers

cs.CV2024★ 6 cited

Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision

Kuinan Hou, Marco Zorzi, Alberto Testolin

Humans share with many animal species the ability to perceive and approximately represent the number of objects in visual scenes. This ability improves throughout childhood, sugges…

cond-mat.str-el2023★ 18 cited

Lindblad master equation approach to the dissipative quench dynamics of planar superconductors

Andrea Nava, Carmine Antonio Perroni, Reinhold Egger +2

We employ the Lindblad master equation method to study the nonequilibrium dynamics following a parametric quench in the Hamiltonian of an open, two-dimensional superconducting syst…

cs.NE2023

Investigating the generative dynamics of energy-based neural networks

Lorenzo Tausani, Alberto Testolin, Marco Zorzi

Generative neural networks can produce data samples according to the statistical properties of their training distribution. This feature can be used to test modern computational ne…

cond-mat.mes-hall2023★ 2 cited

Spin-Hall Current and Nonlocal Transport in Ferromagnet-Free Multi-band models for SrTiO3-Based Nanodevices in the presence of impurities

Domenico Giuliano, Andrea Nava, Carmine Antonio Perroni +3

We compute the spin-Hall conductance in a multiband model describing the two-dimensional electron gas formed at a LaAlO3/SrTiO3 interface in the presence of a finite concentration…

cond-mat.mtrl-sci2023★ 5 cited

Effect of confinement and Coulomb interactions on the electronic structure of (111) LaAlO/SrTiO interface

Mattia Trama, Vittorio Cataudella, Carmine Antonio Perroni +2

A tight binding supercell approach is used for the calculation of the electronic structure of the (111) LaAlO/SrTiO interface. The confinement potential at the interface is…

cs.LG2022★ 25 cited

A developmental approach for training deep belief networks

Matteo Zambra, Alberto Testolin, Marco Zorzi

Deep belief networks (DBNs) are stochastic neural networks that can extract rich internal representations of the environment from the sensory data. DBNs had a catalytic effect in t…