Complex recurrent spectral network
This paper presents a novel approach to advancing artificial intelligence (AI) through the
development of the Complex Recurrent Spectral Network (ℂ-RSN), an innovative variant of …
development of the Complex Recurrent Spectral Network (ℂ-RSN), an innovative variant of …
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 …
Kernel Shape Renormalization In Bayesian Shallow Networks: a Gaussian Process Perspective
R Pacelli, L Giambagli… - 2024 IEEE Workshop on …, 2024 - ieeexplore.ieee.org
The Bayesian approach has proven to be a valuable tool for analytical inspection of neural
networks. Recent theoretical advances have led to the development of an effective statistical …
networks. Recent theoretical advances have led to the development of an effective statistical …