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Found 5 papers, 1 papers with code
Recurrent Spectral Network (RSN): shaping the basin of attraction of a discrete map to reach automated classification
A novel strategy to automated classification is introduced which exploits a fully trained dynamical system to steer items belonging to different categories toward distinct asymptotic attractors.
On the training of sparse and dense deep neural networks: less parameters, same performance
Deep neural networks can be trained in reciprocal space, by acting on the eigenvalues and eigenvectors of suitable transfer operators in direct space.
Synchronization dynamics in non-normal networks: the trade-off for optimality
Using this method, it has been shown that for a class of models, synchronization in strongly directed networks is robust to external perturbations.
Adaptation and Self-Organizing Systems Statistical Mechanics Pattern Formation and Solitons
Machine learning in spectral domain
Interestingly, spectral learning limited to the eigenvalues returns a distribution of the predicted weights which is close to that obtained when training the neural network in direct space, with no restrictions on the parameters to be tuned.
Comparison of Discrete Choice Models and Artificial Neural Networks in Presence of Missing Variables
The outcomes of those experiments highlight the fact that artificial neural networks outperforms the discrete choice models, except when the distribution of the classes in the training data is highly unbalanced.
