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Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks
Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn robust representations from large quantities of unlabeled data.
Learning Combinations of Activation Functions
In the last decade, an active area of research has been devoted to design novel activation functions that are able to help deep neural networks to converge, obtaining better performance.
Automated Pruning for Deep Neural Network Compression
In this work we present a method to improve the pruning step of the current state-of-the-art methodology to compress neural networks.
Dynamic Graph Convolutional Networks
Many different classification tasks need to manage structured data, which are usually modeled as graphs.
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