2 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
Efficient Continual Learning in Neural Networks with Embedding Regularization
Jary Pomponi, Simone Scardapane, Vincenzo Lomonaco +1
Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architec…
Compressing deep quaternion neural networks with targeted regularization
Riccardo Vecchi, Simone Scardapane, Danilo Comminiello +1
In recent years, hyper-complex deep networks (such as complex-valued and quaternion-valued neural networks) have received a renewed interest in the literature. They find applicatio…
Efficient data augmentation using graph imputation neural networks
Indro Spinelli, Simone Scardapane, Michele Scarpiniti +1
Recently, data augmentation in the semi-supervised regime, where unlabeled data vastly outnumbers labeled data, has received a considerable attention. In this paper, we describe an…
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
Indro Spinelli, Simone Scardapane, Aurelio Uncini
Missing data imputation (MDI) is a fundamental problem in many scientific disciplines. Popular methods for MDI use global statistics computed from the entire data set (e.g., the fe…
On the Stability and Generalization of Learning with Kernel Activation Functions
Michele Cirillo, Simone Scardapane, Steven Van Vaerenbergh +1
In this brief we investigate the generalization properties of a recently-proposed class of non-parametric activation functions, the kernel activation functions (KAFs). KAFs introdu…
Widely Linear Kernels for Complex-Valued Kernel Activation Functions
Simone Scardapane, Steven Van Vaerenbergh, Danilo Comminiello +1
Complex-valued neural networks (CVNNs) have been shown to be powerful nonlinear approximators when the input data can be properly modeled in the complex domain. One of the major ch…