A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms
This manuscript provides a comprehensive review of the Maximum Clique Problem, a
computational problem that involves finding subsets of vertices in a graph that are all …
computational problem that involves finding subsets of vertices in a graph that are all …
Stable attractors for neural networks classification via ordinary differential equations (SA-nODE)
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 that …
machine learning and dynamical systems theory. At variance with other methodologies that …
Engineered ordinary differential equations as classification algorithm (eodeca): thorough characterization and testing
EODECA (Engineered Ordinary Differential Equations as Classification Algorithm) is a novel
approach at the intersection of machine learning and dynamical systems theory, presenting …
approach at the intersection of machine learning and dynamical systems theory, presenting …
Learning in Wilson-Cowan model for metapopulation
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 …
subcortical regions of the brain as connected nodes, with connections representing various …
Automatic Input Feature Relevance via Spectral Neural Networks
Working with high-dimensional data is a common practice, in the field of machine learning.
Identifying relevant input features is thus crucial, so as to obtain compact dataset more prone …
Identifying relevant input features is thus crucial, so as to obtain compact dataset more prone …
Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise
The Continuous-Variable Firing Rate (CVFR) model, widely used in neuroscience to
describe the intertangled dynamics of excitatory biological neurons, is here trained and …
describe the intertangled dynamics of excitatory biological neurons, is here trained and …