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From the 1 of 16 linked papers with an AI index.

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20242026
most citedA short review on the maximum clique problem algorithms with classical, AI, and quantum methods

1 citations · 1 across the 7 of their papers we have counts for

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q-bio.NC2024

Learning in Wilson-Cowan model for metapopulation

Raffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi +4

The Wilson-Cowan model for metapopulation, a Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various…

quant-ph2024

Charging a quantum spin network towards Heisenberg-limited precision

Beatrice Donelli, Stefano Gherardini, Raffaele Marino +2

We present a cooperative protocol to charge quantum spin networks up to the highest-energy configuration, in terms of the network's magnetization. The charging protocol leverages s…

cs.LG2024

Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise

Lorenzo Chicchi, Duccio Fanelli, Diego Febbe +4

The Continuous-Variable Firing Rate (CVFR) model, widely used in neuroscience to describe the intertangled dynamics of excitatory biological neurons, is here trained and tested as…

quant-ph2024

Collective preparation of large quantum registers with high fidelity

Lorenzo Buffoni, Michele Campisi

We report on the preparation of a large quantum register of 5612 qubits, with the unprecedented high global fidelity of . This was achieved by applying an improved…

cs.LG2024

Engineered Ordinary Differential Equations as Classification Algorithm (EODECA): thorough characterization and testing

Raffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi +2

EODECA (Engineered Ordinary Differential Equations as Classification Algorithm) is a novel approach at the intersection of machine learning and dynamical systems theory, presenting…

cond-mat.dis-nn2024

Stable Attractors for Neural networks classification via Ordinary Differential Equations (SA-nODE)

Raffaele Marino, Lorenzo Giambagli, Lorenzo Chicchi +2

A novel approach for supervised classification is presented which sits at the intersection of machine learning and dynamical systems theory. At variance with other methodologies th…