6 papers
Approximating velocity fields with planted attractors via Neural-ODEs for classification purposes
Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni +3
In this work, Neural ODEs equipped with a curated collection of equilibrium points have been successfully employed for classification tasks. The planted attractors serve as indicat…
Exact Fixed-Point Constraints in Neural-ODEs with Provable Universality
Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni +3
We introduce a technique that enables Neural-ODEs to approximate arbitrary velocity fields with a priori planted fixed-points. Specifically, a recipe is given to explicitly accommo…
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…
Train Stochastic Non Linear Coupled ODEs to Classify and Generate
Stefano Gagliani, Feliciano Giuseppe Pacifico, Lorenzo Chicchi +4
A general class of dynamical systems which can be trained to operate in classification and generation modes are introduced. A procedure is proposed to plant asymptotic stationary a…
Spectral Architecture Search for Neural Network Models
Gianluca Peri, Lorenzo Chicchi, Duccio Fanelli +1
Architecture design and optimization are challenging problems in the field of artificial neural networks. Working in this context, we here present SPARCS (SPectral ARchiteCture Sea…
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…