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20182026
most citedNew Trends in Quantum Machine Learning

34 citations · 59 across the 18 of their papers we have counts for

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6 papers · 1 filter

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…

cs.LG2024

Estimating Global Input Relevance and Enforcing Sparse Representations with a Scalable Spectral Neural Network Approach

Lorenzo Chicchi, Lorenzo Buffoni, Diego Febbe +3

In machine learning practice it is often useful to identify relevant input features. Isolating key input elements, ranked according their respective degree of relevance, can help t…

cs.LG2023

Complex Recurrent Spectral Network

Lorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni +2

This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network (-RSN), an innovative v…

cs.LG2023

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…

cs.LG2023

How a student becomes a teacher: learning and forgetting through Spectral methods

Lorenzo Giambagli, Lorenzo Buffoni, Lorenzo Chicchi +1

In theoretical ML, the teacher-student paradigm is often employed as an effective metaphor for real-life tuition. The above scheme proves particularly relevant when the student net…

cs.LG2020

Machine learning in spectral domain

Lorenzo Giambagli, Lorenzo Buffoni, Timoteo Carletti +2

Deep neural networks are usually trained in the space of the nodes, by adjusting the weights of existing links via suitable optimization protocols. We here propose a radically new…