Deep Learning

Mobility-based prediction of SARS-CoV-2 spreading

This paper analyzes the effectiveness of containment measures for SARS-CoV-2, using mobility data to gauge their impact. A deep learning model predicts virus spread scenarios in Italy, showing how these measures help flatten the infection curve and estimating the time required for their noticeable effects.

Lorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni, Duccio Fanelli

Training of sparse and dense deep neural networks: Fewer parameters, same performance

This study presents a variant of spectral learning for deep neural networks, where adjusting two sets of eigenvalues for each layer mapping significantly enhances network performance with fewer trainable parameters. This method, inspired by homeostatic plasticity, offers a computationally efficient alternative to conventional training, achieving comparable results with a simpler parameter setup. It also enables the creation of sparser networks with impressive classification abilities.

Lorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni, Timoteo Carletti, Marco Ciavarella, Duccio Fanelli

Training of sparse and dense deep neural networks: Fewer parameters, same performance