477 citations · 1.4k across the 57 of their papers we have counts for
8 papers · 1 filter
Combined Sparse Regularization for Nonlinear Adaptive Filters
Danilo Comminiello, Michele Scarpiniti, Simone Scardapane +2
Nonlinear adaptive filters often show some sparse behavior due to the fact that not all the coefficients are equally useful for the modeling of any nonlinearity. Recently, a class…
Distributed Training of Graph Convolutional Networks
Simone Scardapane, Indro Spinelli, Paolo Di Lorenzo
The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful…
Pseudo-Rehearsal for Continual Learning with Normalizing Flows
Jary Pomponi, Simone Scardapane, Aurelio Uncini
Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of…
Distributed Stochastic Nonconvex Optimization and Learning based on Successive Convex Approximation
Paolo Di Lorenzo, Simone Scardapane
We study distributed stochastic nonconvex optimization in multi-agent networks. We introduce a novel algorithmic framework for the distributed minimization of the sum of the expect…
Why should we add early exits to neural networks?
Simone Scardapane, Michele Scarpiniti, Enzo Baccarelli +1
Deep neural networks are generally designed as a stack of differentiable layers, in which a prediction is obtained only after running the full stack. Recently, some contributions h…
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
Jary Pomponi, Simone Scardapane, Aurelio Uncini
Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e.g., interpre…