2 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Centroids Matching: an efficient Continual Learning approach operating in the embedding space
Jary Pomponi, Simone Scardapane, Aurelio Uncini
Catastrophic forgetting (CF) occurs when a neural network loses the information previously learned while training on a set of samples from a different distribution, i.e., a new tas…
Adaptive Propagation Graph Convolutional Network
Indro Spinelli, Simone Scardapane, Aurelio Uncini
Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertex-wise operations and message-passing exchanges…
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…
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…